A method of selecting priority species and species sets for protection in conjunction with food web stability assessment

Abstract

This invention relates to a method for selecting priority protected species and species sets by combining food web stability assessment, comprising: acquiring species data, determining predator-prey relationships between species, and calculating trophic levels and food source ratios among species, thereby constructing a food web model; and screening priority protected species based on the directed, weighted food web model, including keystone species, umbrella species, and rare umbrella species. This invention overcomes the limitations of inconsistent screening standards and time-consuming methods for priority protected species, accurately screening priority protected species and species sets, thereby achieving a "point-to-line, line-to-area" protection effect and significantly improving the effectiveness of wildlife conservation.

Description

Technical Field

[0001] This invention relates to the fields of conservation biology and ecology, and in particular to a method for selecting priority conservation species and species sets by combining food web stability assessment. Background Technology

[0002] By scientifically and accurately identifying priority species for protection in an ecosystem, one can achieve significant returns with minimal investment.

[0003] Umbrella species and keystone species are often considered priority conservation species, a strategy seen as a "shortcut" to biodiversity conservation. Umbrella species are those whose protection benefits their symbiotic species and the ecosystems in which their habitats are located. Keystone species are those with significant ecological impact, whose impact on ecosystems is disproportionate to their abundance and biomass. While these approaches offer a "shortcut" to biodiversity conservation, current methods for selecting umbrella and keystone species still face challenges such as inconsistent selection criteria, subjective and time-consuming selection methods, and a lack of systematic and complementary approaches in choosing priority conservation sets. For example, directly classifying the giant panda as an umbrella species often leads to confusion between flagship and umbrella species concepts. Existing technological solutions combining infrared cameras and remote sensing have confirmed that current nature reserve systems designed with the giant panda as an umbrella species do not adequately cover the key landscapes of several species. In other words, the research suggests that the conservation effect of the giant panda as an umbrella species has been overestimated, and that the giant panda is more suitable as a flagship species than an umbrella species. Therefore, it is a common problem to use flagship varieties and umbrella varieties interchangeably, and it is even more necessary to clarify the differences between the two.

[0004] Therefore, there is an urgent need for a new method that takes a holistic and global perspective to accurately select priority protected species and priority protected species sets, represented by umbrella species and key species. Summary of the Invention

[0005] To address the problems of the existing technologies, the present invention aims to provide a method for selecting priority protected species and species sets by combining food web stability assessment. From the perspective of food webs, while ensuring food web stability, the present invention develops a standardized process for constructing directed, weighted food webs with multi-source data input. Existing technologies are limited by difficulties in field sampling, and the number of species that can be included in the food web framework determined by applying stable isotope values ​​and the MixSIAR model is limited. The present invention, however, relies on the established multi-source data fusion analysis topological "large food web" (number of species > 100) construction technology. Through multi-source data collection, it constructs a predator-prey relationship matrix, a predator-prey relationship feature matrix, and a species typical trait matrix. By fitting predator-prey correlation traits, it determines the predator-prey relationship between candidate predators and prey and quantitatively determines the predation ratio between species, thereby constructing a directed, weighted "large food web" (number of species > 100). On the other hand, this invention, from the perspective of food webs, pioneers a new method for screening umbrella species and key species, breaking through the limitations of previous screening methods that were not uniform in terms of screening standards and were time-consuming and laborious. It can accurately screen priority protected species and species sets, thereby significantly improving the benefits of wildlife conservation.

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

[0007] A method for selecting priority conservation species and species sets by combining food web stability assessment includes:

[0008] Acquire species data, use the species data to determine predation relationships, trophic level characteristics and food source ratios among species, and construct a food web model;

[0009] Priority species for protection are selected based on the food web model. These priority species include key species, umbrella species, and rare umbrella species.

[0010] Optionally, determining the predator-prey relationships between the species includes:

[0011] Establish predator-prey matrix, predator-prey feature matrix, and all species feature-species matrix respectively:

[0012] A = (a ij ) i×j

[0013] P=(p ik ) i×k

[0014] T=(t kj ) k×j

[0015] Where i represents the predator, j represents the prey, k represents the species trait, and A is the predator-prey matrix, (a ij ) i×j This represents the primitive predator-prey relationship between species, where P is the predator-prey feature matrix, (p ik ) i×k Let T be the predator's predator trait preference, and T be the species trait-species matrix. kj ) k×j These are the inherent characteristics of a species.

[0016] Using the predator-prey feature matrix and the all-species feature-species matrix, obtain the fourth matrix:

[0017] M = P × T = (m' ij ) i×j

[0018] Where P is the predator-prey feature matrix, T is the all-species feature-species matrix, (m' ij ) i×j This represents the probability of a predatory relationship occurring between species.

[0019] The fourth matrix is ​​compared with the predator-prey matrix to determine the minimum threshold for the occurrence of a predator-prey relationship. Based on the minimum threshold, the predator's predation preference for species characteristics is mapped to each species to obtain the fifth matrix.

[0020]

[0021] Where θ represents the minimum threshold at which a predator-prey relationship occurs, (a' ij ) i×j This refers to the ultimate predator-prey relationship between species.

[0022] Optionally, determining the food source ratio among the species includes:

[0023] Calculate the average probability of the predation relationship between the species occurring under the prediction of species trait characteristics:

[0024]

[0025] in, f represents the average probability that predator i occurs at predator node j. ij The probability of a predator-prey relationship occurring under a single feature is predicted, i.e., m' under the corresponding species feature. ij The value, n, represents the number of selected species characteristics;

[0026] The relative frequency of predator-prey relationships between predators and all prey is determined using the average probability, and is used as the food source ratio:

[0027]

[0028] Among them, F ij This represents the ratio of food sources for predator i to prey species j. Let be the average probability of a predator-prey relationship occurring between predator i and prey k.

[0029] Optionally, determining trophic level characteristics between species includes:

[0030] Tr i =1+∑Tr j ×F ij

[0031] Among them, Tr i Tr represents the trophic level of predator i. j For the trophic level of prey j, F ij Let be the food source ratio of predator i at predator node j.

[0032] Optionally, selecting the key species based on the food web model includes:

[0033] The inter-species food source ratio is used as the weighting of the links within the food web model, and the importance of each species in the food web model is ranked using centrality indicators, including: degree centrality, compactness centrality, betweenness centrality, and PageRank centrality.

[0034] Species in the food web model are removed in descending order or randomly according to the centrality index. During the removal of species, the stability of the food web model is calculated. Based on the stability, the optimal centrality index is determined. Based on the ranking of species according to the optimal centrality index, the key species are determined.

[0035] Optionally, calculating the stability of the food web model includes:

[0036] The extent of damage to the food web model after an attack is determined by using the relative size of the largest connected subgraph:

[0037]

[0038] Where N' is the number of species in the largest connected subgraph after the network is attacked, and N is the total number of species in the initial network when the network is not attacked.

[0039] The network global efficiency is used to measure the information transmission efficiency between different species in the food web model:

[0040]

[0041] In the formula, E(G) represents the global network efficiency of network G, n is the total number of species, and d ij This represents the shortest path between species.

[0042] Optionally, the umbrella-protecting species selected according to the food web model include:

[0043] Obtain the species home range area, and use the species home range area to update the umbrella protection strength index:

[0044]

[0045] Among them, TL i For the trophic level of food web node i, K i S is the number of nodes adjacent to node i. i The sum of the weights of all edges connected to node i, where α is a fixed value of 0.5, W(TL) represents the trophic level weight, and W(H) represents the home area weight.

[0046] Using the updated umbrella strength index, umbrella species were selected in the food web model, with the species ranking highest in the umbrella strength index being designated as umbrella species.

[0047] Optionally, obtaining the home range area of ​​the species includes:

[0048]

[0049] In the formula, H i X is the home range area index of species i. i Let X be the home range area of ​​species i. min and X max These represent the minimum and maximum home range areas for all species across all regions of the food web, respectively.

[0050] Optionally, the rare umbrella species selected according to the food web model include:

[0051] Based on the updated umbrella protection strength index, and by incorporating the species protection level, a rare umbrella protection species strength index is obtained:

[0052] RUSS(i)=K i (1-α) ×S i α ×(TL i ×W(TL)+PEI i ×W(PEI)+H i ×W(H))

[0053] In the formula, W(PEI) is the weight of the protection effectiveness index;

[0054] Rare umbrella species are selected in the food web model using the rare umbrella species strength index, and the species with the highest rare umbrella species strength index is selected as the rare umbrella species.

[0055] The beneficial effects of this invention are as follows:

[0056] This invention constructs a food web model using a predator-prey matrix based on existing data of different species; assesses food web stability through sequence removal; and identifies "key species" of the food web by combining complex network centrality indices, "umbrella species" by using the "umbrella strength" index, and rare "umbrella species" by using the rare "umbrella strength" index. This invention overcomes the problems of difficult data collection, inconsistent screening criteria for priority protected species, and time-consuming and laborious screening methods in previous studies.

[0057] This invention calculates the food source ratio between species and constructs a food web using a matrix method, improves the existing "umbrella strength" index, and proposes a rare "umbrella strength" index and algorithm. This provides a more scientific basis for the selection of priority protection species and helps to improve the scientificity and effectiveness of biodiversity conservation work while saving conservation funds. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be 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.

[0059] Figure 1 This is a flowchart illustrating a method for selecting priority protected species and species sets based on food web stability assessment, according to an embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram illustrating the food source ratios among species in a Himalayan forest ecosystem, as described in an embodiment of the present invention.

[0061] Figure 3 This is a schematic diagram of the trophic levels of various species in the Himalayan forest ecosystem, as described in an embodiment of the present invention.

[0062] Figure 4 This is a schematic diagram of a food web in a Himalayan forest ecosystem, according to an embodiment of the present invention.

[0063] Figure 5 This invention illustrates the impact of topology-based sequence removal on the stability of food webs in Himalayan forest ecosystems. Figure 5 (a) represents the stability under the relative size of the largest connected subgraph. Figure 5 (b) Stability under global network efficiency;

[0064] Figure 6 This is a schematic diagram of the species umbrella intensity results of the Himalayan forest ecosystem according to an embodiment of the present invention;

[0065] Figure 7 This is a schematic diagram illustrating the protection intensity of rare species in the Himalayan forest ecosystem, as described in an embodiment of the present invention. Detailed Implementation

[0066] 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.

[0067] This embodiment discloses a method for selecting priority protected species and species sets by combining food web stability assessment, including: acquiring species data, using the species data to determine the predator-prey relationships, trophic level characteristics and food source ratios among species, and establishing a food web model; selecting priority protected species based on the food web model, including: key species, umbrella species and rare umbrella species.

[0068] Specifically, this embodiment discloses a method for selecting priority protected species and species sets by combining food web stability assessment, including: constructing a directed, weighted "large food web" (number of species > 100) based on existing species data; and selecting priority protected species and species sets based on the constructed food web.

[0069] Furthermore, data needed to construct food webs are collected through experiments, books, literature, and other means, including predator-prey relationships between species, species traits, species home range, and species conservation status.

[0070] Further, determining the predator-prey relationships between species includes: establishing a predator-prey matrix, a predator-prey feature matrix, and a feature-species matrix for all species; using the predator-prey feature matrix and the feature-species matrix for all species, a fourth matrix is ​​obtained. The fourth matrix is ​​compared with the predator-prey matrix to determine the minimum threshold for the occurrence of a predator-prey relationship; based on the minimum threshold, the predator's predation preference for species features is mapped to each species, resulting in a fifth matrix.

[0071] Specifically, a predator-prey matrix is ​​constructed, and predator-prey relationships are predicted:

[0072] 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. The matrix is ​​constructed accordingly, and the specific process is as follows:

[0073] First, construct the predator-prey matrix A, which has dimensions i×j, as shown in the following formula:

[0074] A = (a ij ) i×j

[0075] In the formula, i represents the predator, and j represents the prey. The nutritional interaction between predator i and prey j is represented by the formula a. ij Using 1 or 0 (1 if predator i preys j, 0 otherwise), matrix A represents the recorded predator-prey relationships.

[0076] The second step is to construct the predator-prey feature matrix P, which has dimensions i×k, as shown in the following formula:

[0077] P=(p ik ) i×k

[0078] 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.

[0079] The third step is to construct a species feature-species matrix T with dimensions k×j, as shown in the following formula:

[0080] T=(t kj ) k×j

[0081] 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).

[0082] 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:

[0083] M = P × T = (m' ij ) i×j

[0084]

[0085] In the formula, θ represents the minimum threshold for the occurrence of a predator-prey relationship, which is derived from the comparison of matrix M and matrix A, m' ij With a ij The minimum overlap is the value of θ; for each species characteristic, a matrix A′ can be obtained. Predator-prey relationships that occur under each species characteristic are selected, and obviously unreasonable predator-prey relationships are removed, thus constructing a food web.

[0086] Further, determining the food source ratio between species includes: calculating the average probability of predation relationships between species occurring under the prediction of species trait characteristics; and determining the relative frequency of predation relationships between predators and all prey using the average probability as the food source ratio.

[0087] Specifically, the food source ratio between species is accurately calculated based on the probability of predator-prey relationships occurring in the aforementioned matrix. First, the average probability of predator-prey relationships occurring under the predicted species traits is calculated using the following formula:

[0088]

[0089] In the formula, f represents the average probability that predator i occurs at predator node j. ij Predict the probability of a predator-prey relationship occurring under a single feature (values ​​are m' for the corresponding species feature). ij (value), where n is the number of selected species characteristics.

[0090] Calculate the relative frequency of predator-prey relationships between predator i and all prey, and use this as the food source ratio, as shown in the following formula:

[0091]

[0092] In the formula, F ij This represents the ratio of predator i to the food source of prey species j. Let be the average probability of a predator-prey relationship occurring between predator i and prey k.

[0093] Accurate calculation of trophic levels for species is based on the food source ratios between species, using the following formula:

[0094] Tr i =1+∑Tr j ×F ij

[0095] In the formula, Tr i Tr represents the trophic level of predator i. j For the trophic level of prey j, F ijGiven the food source ratio of predator i at predator node j, this invention sets the primary producer trophic level to 1.

[0096] Furthermore, a 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 to build a quantitative and weighted food web.

[0097] Food web theory provides a natural framework for understanding complex interactions among species, revealing important ecological processes such as material cycling and energy flow, and comprehending the stability mechanisms of ecosystems against disturbances, thus laying a solid theoretical foundation for conservation practices. However, in practice, food webs contain a large number of species with complex relationships, making direct application difficult. An effective approach is to replace food webs with networks, where species are nodes and connections represent predator-prey relationships. By identifying key nodes crucial to network integrity and stability, we can accurately screen out umbrella species and keystone species with "point-to-line and line-to-area" significance, which is vital for improving the effectiveness of biodiversity conservation.

[0098] Furthermore, the selection of key species based on the food web model includes: using the food source ratio among species as the weighting of links within the food web model, and ranking the importance of each species in the food web model using centrality indicators; centrality indicators include: degree centrality, compactness centrality, betweenness centrality, and PageRank centrality; removing species from the food web model in descending order or randomly according to the centrality indicators, and calculating the stability of the food web model during the removal process, determining the optimal centrality indicator based on the stability, and identifying key species based on the ranking of species according to the optimal centrality indicator.

[0099] Furthermore, calculating the stability of the food web model includes: using the relative size of the largest connected subgraph to determine the extent of damage to the food web model after an attack, and using the global efficiency of the network to measure the efficiency of information transmission between different species in the food web model.

[0100] Specifically, we accurately select priority protection species—key species—combined with food web stability indicators. Based on the constructed food web model, we treat the food source ratio among species as a weighted factor of links within the food web. We use four representative centrality indicators—degree centrality, compactness centrality, betweenness centrality, and PageRank centrality—to rank the importance of each species in the food web. We further verify the effectiveness of the centrality indicators through topology-based sequence removal experiments to accurately identify key species in the food web.

[0101] Topological removal: Sequence removal is performed using topological removal. Removal methods are mainly divided into random removal and deterministic removal. Random removal mainly simulates the random disappearance of species in nature, while deterministic removal mainly removes certain species in a targeted manner based on their importance. Based on the calculation results of the above four centralities, the species in the food web are sorted, and five schemes are used to determine the priority of species to be deleted: (1) Remove species in descending order of degree centrality; (2) Remove species in descending order of betweenness centrality; (3) Remove species in descending order of density centrality; (4) Remove species in descending order of PageRank centrality; (5) Remove species randomly. During the process of gradually removing species, the network stability changes are observed. Each sequence removal is repeated until no secondary extinction occurs before starting the next round of sequence removal. When a species loses all its resources, it is considered to have experienced secondary extinction. Primary producers do not experience secondary extinction.

[0102] The following two metrics are used to indicate food web stability during topology sequence removal:

[0103] 1) Relative size of the largest connected subgraph:

[0104] The relative size of the largest connected subgraph refers to the ability of a network to maintain connections between remaining species when it is disrupted. It is used to indicate the extent of damage to the network after an attack and is calculated using the following formula:

[0105]

[0106] Where N' is the number of species in the largest connected subgraph after the network is attacked, and N is the total number of species in the initial network when the network is not attacked.

[0107] 2) Global network efficiency:

[0108] Since global efficiency reflects the overall connectivity of a network and measures the efficiency of information transmission between different species within the network, this embodiment uses global efficiency to indicate changes in food web stability during species removal. The formula is as follows:

[0109]

[0110] In the formula, E(G) represents the global network efficiency of network G, n is the total number of species, and d ij This represents the shortest path between species.

[0111] Furthermore, the selection of umbrella species based on the food web model includes: obtaining the species home range area, updating the umbrella strength index using the species home range area, and selecting umbrella species in the food web model using the updated umbrella strength index.

[0112] Specifically, to accurately select priority protected species—"umbrella species"—this invention innovatively integrates species homerange characteristics into the traditional "umbrella species strength" index, updating the umbrella strength index to more accurately identify "umbrella species." The specific formula is as follows:

[0113] The species home range area is normalized by converting the original data into data within a specific range [0-1] to eliminate the influence of dimensions and orders of magnitude, resulting in the species home range area index, as shown in the following formula:

[0114]

[0115] In the formula, H i X is the home range area index of species i. i Let X be the home range area of ​​species i. min and X max These represent the minimum and maximum home range areas for all species across all regions of the food web, respectively.

[0116] The updated formula for the "Umbrella Protection Strength" index is as follows:

[0117]

[0118] In the formula, TL i For the trophic level of food web node i, K i S is the number (degree) of nodes adjacent to node i. i The sum of the weights of all edges connected to node i (strong) (%) is given, with α taking an empirical value of 0.5. W(TL) represents the trophic level weight, and W(H) represents the home area weight. The weights of each indicator are calculated based on the CRITIC weight calculation method.

[0119] Using the updated umbrella strength index, umbrella species are selected in the food web model. The species with the highest umbrella strength index ranking is the most suitable species to serve as an umbrella species.

[0120] Furthermore, the selection of rare umbrella species based on the food web model includes: on the basis of the updated umbrella protection strength index, integrating the species protection level to obtain the rare umbrella species strength index, and using the rare umbrella species strength index to select rare umbrella species in the food web model.

[0121] Specifically, this invention aims to accurately select priority protected species—rare "umbrella species." Building upon the "umbrella strength" index, it incorporates the concept of species protection level (including Chinese national key protected wild animals, IUCN Red List animals, and CITES Appendix animals), proposing for the first time a rare "umbrella strength" index for quantitatively identifying rare "umbrella species." The proposal quantifies species protection level (including Chinese national key protected wild animals, IUCN Red List animals, and CITES Appendix animals) into a Protective Effect Index (PEI).

[0122] The formula for the protection effectiveness index is as follows:

[0123] PEI i =S CHINAi ×S IUCNi ×S CITESi

[0124] In the formula, S CHINAi S IUCNi S CITESi These are the average weighted protection levels of all species in a food web with species i as the apex predator, representing the protection levels of Chinese national key protected wild animals, IUCN Red List animals, and CITES Appendix animals. The formula for the rare "umbrella strength" index is as follows:

[0125]

[0126] In the formula, W(PEI) is the weight of the protection effectiveness index, and the weight of each index is calculated using the CRITIC weight calculation method.

[0127] 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.

[0128] like Figure 1 As shown in the example, this embodiment uses the selection of priority protection species and species sets for food webs in Himalayan forest ecosystems as an illustration. It can be understood that the ecosystem food web evaluation and optimization method of the present invention can also be applied to other ecosystems.

[0129] Step 1: For the Himalayan region, collect multi-source data needed to construct the food web through experiments, books, literature, and other means, including predator-prey relationships between species, species phenotypic characteristics, species home range, and species conservation status. The basic information collected is shown in Table 1.

[0130] Table 1. Species information in the forest ecosystem of the Himalayas

[0131]

[0132]

[0133]

[0134] Step 2: Construct a predator-prey matrix and predict predator-prey relationships:

[0135] To predict predator-prey relationships between species, a matrix-matrix method is used. To create the matrix, all feature variables are converted to binary format. For classification features such as order, family, and diet, data (0 or 1) are directly assigned based on the presence / absence of each classification feature. For the quantitative feature of species body weight, mammals are classified into three levels according to the Chinese terrestrial mammal size standard: small, medium, and large mammals. Based on this, the invention further subdivides mammals in each size level into large, medium, and small categories based on the upper and lower limits of body size for all species. Finally, based on body weight, Chinese terrestrial mammals are divided into 9 categories, then converted to binary variables, and a matrix is ​​constructed accordingly. The specific process is as follows:

[0136] The first step is to establish the predator-prey matrix A, which has dimensions i×j, as shown in the following formula:

[0137] A = (a ij ) i×j

[0138] In the formula, i represents the total number of predators, and j represents the total number of prey. The nutritional interaction between predator i and prey j is represented by a. ij Using 1 or 0 (1 if predator i preys j, 0 otherwise), matrix A represents the recorded predator-prey relationships.

[0139] The second step is to construct the predator-prey feature matrix P, which has dimensions i×k, as shown in the following formula:

[0140] P=(p ik ) i×k

[0141] 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.

[0142] The third step is to construct the species characteristic-species matrix T, which has dimensions k×j, as shown in the following formula:

[0143] T=(tkj ) k×j

[0144] 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).

[0145] 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:

[0146] M = P × T = (m' ij ) i×j

[0147]

[0148] In the formula, θ represents the minimum threshold for the occurrence of a predator-prey relationship, which is derived from the comparison of matrix M and matrix A, m' ij With a ij The minimum overlap is the value of θ; for each species characteristic, a matrix A′ can be obtained. Predator-prey relationships that occur under each species characteristic are selected, and obviously unreasonable predator-prey relationships are removed, thus constructing a food web.

[0149] 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, using the following formula:

[0150]

[0151] In the formula, f represents the average probability of predator i preying on prey species j. ij Predict the probability of a predator-prey relationship occurring under a single characteristic (i.e., order, family, trophic level, body weight) (the value is m' under the corresponding species characteristic). ij (value), where n is the number of selected species characteristics.

[0152] Calculate the relative frequency of predator-prey relationships among all predators of predator i, and use this to calculate the food source ratios among species in the Himalayan forest ecosystem, such as... Figure 2 The formula is as follows:

[0153]

[0154] In the formula, 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.

[0155] Step 4: Accurately calculate the trophic level of the species. The trophic level is calculated based on the food source ratio between species, using the following formula:

[0156] Tr i =1+∑Tr j ×F ij

[0157] In the formula, Tr i Tr represents the trophic level of predator i. j For the trophic level of prey j, F ij Given the food source ratio of predator i at predation node j, this invention sets the trophic level of primary producers to 1, obtaining the trophic levels of each species in the food web of the Himalayan forest ecosystem, such as... Figure 3 Among them, the overall trophic level range for each consumer is 2.00-4.55, with the tiger having the highest trophic level (4.55).

[0158] Step 5: Food web model construction. A quantitative, weighted food web is constructed by combining predation relationships among species, trophic level characteristics of each node, and food source ratios. Figure 4 .

[0159] Step Six: Key Species Selection Based on Centrality Indicators. The process of identifying key species using centrality indicators often involves the concept of shortest paths. This embodiment references the shortest path concept summarized by Dijkstra (1959) and Barrat et al. (2007), using the foraging ratio between species as the link weight to calculate the shortest path between species, as shown in the following formula:

[0160] dw(i,j)=min(1 / w ih +……+1 / w hi )

[0161] In the formula, dw(i,j) refers to the shortest distance from species i to species j, and w ih The weight of species i is used as the link weight in this embodiment, which is the inter-species feeding ratio.

[0162] Degree centrality is the most direct indicator of species centrality in a network, reflecting a species' ability to influence other species. The higher the degree centrality of a species, the more important that species is in the network. It is calculated using the following formula:

[0163]

[0164] Among them, D i For the degree centrality of species i, K iThe degree of a species is represented by n, and the number of species in the food web is represented by n.

[0165] Betweenness centrality is an indicator used to quantitatively analyze a species' ability to control information exchange within a network. A higher betweenness centrality indicates a stronger ability to control information exchange within the network and a more critical role in maintaining network stability. It is calculated using the following formula:

[0166]

[0167] Among them, B i For the betweenness centrality of species i, σ vj For all shortest paths between species v and species j, σ vj (i) represents the number of times species i is passed through the shortest path between species pair v and j.

[0168] Density centrality is an indicator of a species' dominance in transmitting information within a network. A higher density centrality indicates that a species can propagate network information to other species more quickly; in other words, it is more central to the network and therefore more important. It is calculated using the following formula:

[0169]

[0170] Among them, C i For the compactness centrality of species i, d ij Let be the shortest path length between species i and species j.

[0171] PageRank centrality, proposed by one of Google's founders, is a metric used to measure the importance of a webpage on the internet. This algorithm is primarily based on the hyperlink relationships between webpages; in other words, a webpage's importance depends not only on the number of other webpages linking to it, but also on the importance of those linking pages. In short, PageRank posits that a webpage's importance is determined by the quantity and quality of other webpages pointing to it, calculated using the following formula:

[0172]

[0173] Where PR(i) is the PageRank centrality of species i, j is a species connected to species i, and k is the PageRank centrality of species i. j,out The out-degree of species j is λ, which is the damping coefficient, taken as an empirical value of 0.85.

[0174] According to the results of the centrality index, as shown in Table 2, the leopard has the highest degree centrality (0.714) and tightness centrality (0.726), the clouded leopard has the highest betweenness centrality (0.008), and the tiger has the highest PageRank centrality (0.263).

[0175] Table 2. Results of species centrality indicators in Himalayan forest ecosystems.

[0176]

[0177]

[0178] By examining the impact of topological removal on food web stability, the most suitable centrality index was selected: topological removal was used for sequential removal. Removal methods were mainly divided into random removal and deterministic removal. Random removal simulated the random disappearance of species in nature, while deterministic removal targeted the removal of certain species based on their importance. Based on the calculation results of the above four centralities, the species in the food web were ranked, and five schemes were used to determine the priority of species to be deleted: (1) removing species in descending order of degree centrality; (2) removing species in descending order of betweenness centrality; (3) removing species in descending order of density centrality; (4) removing species in descending order of PageRank centrality; and (5) randomly removing species. The changes in network stability were observed during the gradual removal of species. Each sequential removal step was repeated until no secondary extinction occurred before starting the next round of sequential removal. When a species loses all its resources, it is considered to have experienced secondary extinction; primary producers do not experience secondary extinction.

[0179] The following two metrics are used to indicate food web stability during topology sequence removal:

[0180] 1) Relative size of the largest connected subgraph:

[0181] The relative size of the largest connected subgraph refers to the ability of a network to maintain connections between remaining species when it is disrupted. It is used to indicate the extent of damage to the network after an attack and is calculated using the following formula:

[0182]

[0183] N' represents the number of species in the largest connected subgraph after the network is attacked, and N represents the total number of species in the initial network when the network is not attacked.

[0184] 2) Global network efficiency:

[0185] Since global efficiency reflects the overall connectivity of a network and measures the efficiency of information transmission between different species within the network, this embodiment uses global efficiency to indicate changes in food web stability during species removal. The formula is as follows:

[0186]

[0187] In the formula, E(G) represents the global network efficiency of network G, n is the total number of species, and d ij This represents the shortest path between species.

[0188] Results of topology-based sequence removal on the stability of food webs in Himalayan forest ecosystems, such as Figure 5 (a)-(b) Sequence removal based on degree centrality and PageRank centrality causes the food web to collapse the fastest. Therefore, from the perspective of food web stability, degree centrality and PageRank centrality results can more accurately represent the keystone species ranking and are more valuable for reference. Based on the degree centrality and PageRank centrality results, we believe that leopards and tigers are more suitable as keystone species in this food web.

[0189] Figure 5 The impact of topology-based sequence removal on food web stability in Himalayan forest ecosystems, where DC represents degree centrality, CC represents compactness centrality, BC represents betweenness centrality, and PR represents PageRank centrality.

[0190] Step 7: Quantitative selection of umbrella species. Based on food webs, species with different spatial needs are connected through predator-prey relationships. Umbrella species are quantitatively determined using the umbrella strength index, which integrates four factors: the number (degree) of inter-species connections, the strength (strong) of connections, the trophic level (TL), and the species' home range area. The formula is as follows:

[0191]

[0192] In the formula, H i X is the home range area index of species i. i Let X be the home range area of ​​species i. min and X max These represent the minimum and maximum home range areas for all species across all regions of the food web, respectively.

[0193] The updated formula for the "Umbrella Protection Strength" index is as follows:

[0194]

[0195] In the formula, TL i Let represent the trophic level of node i in the food web, Ki be the number of adjacent nodes (degrees) of node i, Si be the sum of the weights of all edges connected to node i (strength) (%), and α be an empirical value of 0.5. W(TL) represents the trophic level weight, and W(H) represents the home area weight. The weights of each indicator are calculated based on the CRITIC weight calculation method.

[0196] The umbrella species in the food web were determined based on the calculation results of the umbrella strength index, such as... Figure 6The results showed that the leopard had the highest umbrella strength index (14.32), followed by the tiger (12.95), making them suitable as candidate "umbrella species" for forest ecosystems in the Himalayas.

[0197] Step 8: Quantitatively identify rare "umbrella species" using the "Rare Umbrella Strength" index. The proposal quantifies the conservation status of species (including nationally protected wild animals in China, animals on the IUCN Red List, and animals listed in CITES Appendices) into a Protective Effect Index (PEI).

[0198] The formula for the protection effectiveness index is as follows:

[0199] PEI i =S CHINAi ×S IUCNi ×S CITESi

[0200] In the formula, S CHINAi S IUCNi S CITESi These are the average weights of the conservation status of all species in the food web with species i as the apex predator, the average weights of the conservation status of animals under national key protection in China, the average weights of the conservation status of animals on the IUCN Red List, and the average weights of the conservation status of animals in the CITES Appendix (Table 3).

[0201] Table 3. Weighting of Wildlife Conservation Levels

[0202]

[0203] The formula for the rare "umbrella protection strength" index is as follows:

[0204]

[0205] In the formula, W(PEI) represents the weight of the protection effectiveness index, and the weights of each index are calculated using the CRITIC weighting method.

[0206] Rare "umbrella-protected" species in food webs were identified based on the umbrella protection strength index calculation results, such as... Figure 7 The results showed that the leopard had the highest rare umbrella protection strength index (14.60), followed by the tiger (12.54), making them suitable as candidate rare umbrella species for the forest ecosystem in the Himalayas.

[0207] Based on the above results, we believe that the priority species for conservation in the Himalayan forest ecosystem are leopards and tigers.

[0208] This invention develops a standardized process for constructing directed, weighted food webs using multi-source data input. Due to limitations such as difficulties in field sampling, the number of species that can be included in a food web framework determined by applying stable isotope values ​​combined with the MixSIAR model is limited. This invention establishes a topological "large food web" construction technology based on multi-source data fusion analysis. Through multi-source data collection, a predator-prey relationship matrix, a predator-prey characteristic matrix, and a species typical trait matrix are constructed. By fitting predator-prey correlation traits, the predator-prey relationship between candidate predators is determined, and the predation ratio between species is quantitatively determined, thereby constructing a directed, weighted "large food web" (number of species > 100).

[0209] This invention focuses on the hot topic of umbrella species selection methods and potential assessment in the field of biodiversity conservation. Traditional methods for selecting umbrella species mainly rely on qualitative selection or assessment using extensive survey data within the species' habitat. For example, Seddon and Leech suggest that conservation planners should follow seven criteria when selecting umbrella species. Sattler et al. argue that species selected as umbrella species are usually so-called "flagship species," but these methods are characterized by low precision, high cost, and significant limitations. Therefore, there is an urgent need for new methods to accurately select umbrella species to focus on key species, maximize the umbrella effect, and enhance conservation benefits. Food web theory is a classic ecological theory that reflects interspecific relationships and serves as a channel for the flow of matter and energy. From the perspective of food webs, based on the crucial predator-prey relationships between species and the core framework of directed weighted food webs, combined with network centrality theory, this invention updates the "Umbrella Species Strength" index, which integrates four aspects: the number of inter-species connections, connection strength, trophic level, and home range area, to measure the umbrella effect of species. Furthermore, based on the "Umbrella Strength" index, it innovatively incorporates the concept of species protection level (including national key protected wild animals in China, animals on the IUCN Red List, and animals listed in CITES appendices), proposing for the first time a rare "Umbrella Strength" index to quantitatively identify rare "umbrella species."

[0210] 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 selecting priority conservation species and species sets by combining food web stability assessment, characterized in that, include: Acquire species data, use the species data to determine predation relationships, trophic level characteristics and food source ratios among species, and construct a food web model; Priority protected species were selected based on the food web model, including: key species, umbrella species, and rare umbrella species. Based on the food web model, the following umbrella-protecting species were selected: Obtain the species home range area, and use the species home range area to update the umbrella protection strength index: in, For the trophic level of food web node i, Let be the number of nodes adjacent to node i, Si be the sum of the weights of all edges connected to node i, and α be a fixed value of 0.

5. Represents trophic level weight. Indicates the weight of the home area. is the home range area index for species i; Using the updated umbrella strength index, umbrella species were selected in the food web model, and the species with the highest umbrella strength index was selected as the umbrella species. Based on the food web model, the following rare umbrella-shaped species were selected for protection: Based on the updated umbrella protection strength index, and by incorporating the species protection level, a rare umbrella protection species strength index is obtained: RUSS(i) = K i (1−α) ×S i α × (TL i ×W(TL)+PEI i ×W(PEI)+H i ×W(H)) In the formula, W(PEI) To protect the weight of the effect index; Rare umbrella species are selected in the food web model using the rare umbrella species strength index, and the species with the highest rare umbrella species strength index is selected as the rare umbrella species.

2. The method for selecting priority conservation species and species sets by combining food web stability assessment according to claim 1, characterized in that, Determining the predator-prey relationships between the species includes: Establish predator-prey matrix, predator-prey feature matrix, and all species feature-species matrix respectively: Where i represents the predator, j represents the prey, k represents the species trait, and A is the predator-prey matrix. This represents the primitive predator-prey relationship between species, where P is the predator-prey feature matrix. Let T be the predator's predatory trait preference, and T be the species trait-species matrix. These are the inherent characteristics of a species. Using the predator-prey feature matrix and the all-species feature-species matrix, obtain the fourth matrix: in, For predator-prey feature matrix, For all species characteristics-species matrix, This represents the probability of a predatory relationship occurring between species. The fourth matrix is ​​compared with the predator-prey matrix to determine the minimum threshold for the occurrence of a predator-prey relationship. Based on the minimum threshold, the predator's predation preference for species characteristics is mapped to each species to obtain the fifth matrix. in, This represents the minimum threshold at which a predator-prey relationship occurs. This refers to the ultimate predator-prey relationship between species.

3. The method for selecting priority conservation species and species sets by combining food web stability assessment according to claim 1, characterized in that, Determining trophic level characteristics between species includes: in, This indicates the trophic level of predator i. For the trophic level of prey j, Let be the food source ratio of predator i at predator node j.

4. The method for selecting priority conservation species and species sets by combining food web stability assessment according to claim 1, characterized in that, The key species selected based on the food web model include: The inter-species food source ratio is used as the weighting of the links within the food web model, and the importance of each species in the food web model is ranked using centrality indicators, including: degree centrality, compactness centrality, betweenness centrality, and PageRank centrality. Species in the food web model are removed in descending order or randomly according to the centrality index. During the removal of species, the stability of the food web model is calculated. Based on the stability, the optimal centrality index is determined. Based on the ranking of species according to the optimal centrality index, the key species are determined.

5. The method for selecting priority conservation species and species sets by combining food web stability assessment according to claim 4, characterized in that, Calculating the stability of the food web model includes: The extent of damage to the food web model after an attack is determined by using the relative size of the largest connected subgraph: in, Let be the number of species in the largest connected subgraph after the network has been attacked. The total number of species in the initial network when the network has not been attacked; The network global efficiency is used to measure the information transmission efficiency between different species in the food web model: In the formula, E(G) represents the global network efficiency of network G. The total number of species, This represents the shortest path between species.

6. The method for selecting priority conservation species and species sets by combining food web stability assessment according to claim 1, characterized in that, Obtaining the home range area of ​​the species includes: In the formula, is the home range area index for species i. Let be the home range area of ​​species i. and These represent the minimum and maximum home range areas for all species across all regions of the food web, respectively.

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