KR102392131B1 - Food-web network analysis-based ecosystem prediction evaluation system and operation method thereof - Google Patents

Abstract

An ecosystem prediction evaluation system using food web network analysis, and an operating method thereof are disclosed. A method for predicting changes in a food web network of an ecosystem prediction evaluation system according to an embodiment may include the steps of: receiving food web data for a specific ecosystem; analyzing the structure of a food web network included in the food web data; performing an extinction simulation on the basis of the food web network and calculating sustainability of the ecosystem on the basis of a result of the simulation; selecting an extinction scenario for at least one species among species included in the ecosystem; and generating change prediction information of the food web network included in the food web data on the basis of the selected extinction scenario. Interactions between species are not excluded, but are to be included in the scope of analysis.

Description

Ecosystem prediction evaluation system through food web network analysis and its operation method {FOOD-WEB NETWORK ANALYSIS-BASED ECOSYSTEM PREDICTION EVALUATION SYSTEM AND OPERATION METHOD THEREOF}

The present invention relates to a technology for analyzing, predicting, and evaluating an ecosystem, and more particularly, to a technology for analyzing, predicting, and evaluating changes in the structure and sustainability of an ecosystem by performing a food web network analysis.

As a conventional technique for analyzing an ecosystem structure, there is a community structure analysis technique. In community analysis, the structure of an ecosystem is described based on indicators for various communities. Community indices are mainly calculated using data such as the number and density of emerging species, and examples of community indices include species abundance index, species diversity index, uniformity index, and dominance index. The health evaluation program developed by the Environmental Agency of the United States and Europe evaluates the health of the ecosystem by actively utilizing the results of such an analysis of the biological community structure. In Korea, based on the evaluation protocols of the United States and Europe, the health evaluation of various ecosystems in Korea is being carried out by using the results of the analysis of the community indicators and the community structure.

In the conventional technique of biological community structure analysis, the analysis is performed on the premise that if the target environment is rich in species, the environment is good and the health of the ecosystem is high. Under the prior art, the sustainability of the ecosystem is also indirectly explained under this premise. Cases of direct prediction or analysis of the sustainability of the ecosystem are extremely rare due to problems such as low access to related technologies and data processing technology.

Recently, as the importance of sustainable development has gradually emerged, the importance of the concept of ecological sustainability is also increasing at home and abroad. The concept of ecosystem sustainability is a concept that includes stability, robustness, and resilience of the ecosystem, and domestic ecological research has demonstrated the sustainability of ecosystems qualitatively and indirectly through conventional community structure analysis techniques.

Accordingly, the need to analyze and predict the structure and sustainability of the ecosystem based on a scientific and direct approach by improving the qualitative and indirect methods of the prior art is emphasized. In addition, as interactions between species in the community structure of living organisms in an ecosystem are known to be significantly involved in enhancing the stability of the ecosystem, the need to explain the structure of the ecosystem based on the interactions between species is also emphasized.

Korean Patent Registration No. 10-0710445

Various embodiments of the present invention are intended to be included in the scope of analysis without excluding interactions between biological species.

Various embodiments of the present invention have a purpose to explain the sustainability of the ecosystem using a quantitative and direct method instead of a qualitative and indirect method.

Various embodiments of the present invention have an object of analyzing and predicting the diversity and complexity of the food web network, the morphological characteristics of the network, and the robustness of the network.

Various embodiments of the present invention aim to build an ecosystem prediction and evaluation system focusing on the interaction between various biological classifications.

Various embodiments of the present invention aim to process the processes from the food web network structure to extinction simulation, sustainability evaluation, and prediction of changes in the food web structure for each vulnerable species scenario.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

According to one of various embodiments of the present invention for solving the above problems, in the method for the ecosystem prediction evaluation system predicting changes in the food web network for a specific ecosystem, the step of receiving food web data for a specific ecosystem ; analyzing the structure of the food web network included in the food web data; performing an extinction simulation based on the food web network and calculating the sustainability of the ecosystem based on a result of the simulation; selecting an extinction scenario for at least one species from among the species included in the ecosystem; and generating change prediction information of the food web network included in the food web data based on the selected extinction scenario.

The step of receiving the food web data for a specific ecosystem may include: performing class identification of the food web data and determining whether the identified class is a food web network type; and when the class of the food web data is not in the form of a food web, converting the class of the food web data into the form of a food web network.

The step of analyzing the structure of the food web network included in the food web data is to perform diversity analysis, complexity analysis and structural characteristic analysis, wherein the diversity analysis is performed based on the number of species present in the food web network, , the complexity analysis is based on the number of interaction links present in the food web network, the link density calculated as the average number of interaction links per species, and the interaction that actually exists compared to the total number of interactions that can be expressed in the food web network. The structural characterization is performed based on the number of links, the linkage, and the structural characterization is the proportion of species that have food but no predators, the proportion of species that do not have food, the proportion of species that have both food and predators, and the It can be characterized in that it is performed based on the average of the total length of the existing food chain, the proportion of species having prey of two or more trophic levels, the average number of prey per predator, or the average number of predators per prey.

In the step of performing the extinction simulation and calculating sustainability based on the simulation result, the species included in the food web network are removed in random order, and a specific proportion of all species are removed until extinction. It may be characterized in that a simulation of a method of calculating the number of users is performed a plurality of times, and sustainability is calculated based on an average value of the simulation results.

In the step of performing extinction simulation and calculating sustainability based on the simulation results, the species included in the food web network are removed in the order of the highest degree of connection and removed until a specific proportion of all species become extinct. It may be characterized in that a simulation of a method of calculating the number of species is performed a plurality of times and sustainability is calculated based on an average value of the simulation results.

The step of selecting the extinction scenario comprises performing a network node centrality index analysis on the food web network to determine the species with the highest centrality as the keystone and selecting a scenario in which the species determined as the keystone becomes extinct. can

The selecting of the extinction scenario may include selecting a scenario in which a specific type of climate change is assumed and a vulnerable species related to the hypothesized climate change becomes extinct.

The selecting of the extinction scenario may include selecting a scenario in which a specific type of pollutant exposure is assumed and a vulnerable species related to the assumed pollutant exposure is extinct.

The step of calculating the change prediction information of the food web network includes receiving the number of cores from the researcher terminal, generating clusters having the same number of cores as the inputted number of cores, dividing and allocating the food web network data to individual clusters, and assigning a plurality of It may be characterized in that change prediction information is calculated through food web network analysis through parallel processing in the cluster.

The step of performing the extinction simulation includes receiving the number of cores from the researcher terminal, creating clusters of the same number as the received cores, dividing and allocating food web network data to individual clusters, and performing parallel processing in a plurality of clusters. It can be characterized by performing extinction simulation through.

According to one of various embodiments of the present invention for solving the above-described problems, a data collection unit for receiving food web data for a specific ecosystem; a network structure analysis unit for analyzing the structure of the food web network included in the food web data; a sustainability calculation unit for performing an extinction simulation based on the food web network and calculating the sustainability of an ecosystem based on a result of the simulation; an extinction scenario selection unit that selects an extinction scenario for at least one species from among the species included in the ecosystem; and a network change prediction unit for generating change prediction information of the food web network included in the food web data based on the selected extinction scenario, an ecosystem prediction evaluation system may be disclosed.

According to an embodiment of the present invention, it may become possible to analyze and evaluate the structure of an ecosystem based on the complexity and sustainability of the food web network.

An embodiment of the present invention may calculate the robustness of the system against the extinction of the ecosystem, and based on this, it is possible to calculate a quantitative prediction value for the sustainability of the system.

An embodiment of the present invention provides a system for predicting changes in the structure of the food web before and after extinction, and can analyze the effect on the structure of the food web when a specific species is removed from the ecosystem in an extinction scenario.

Embodiments of the present invention can be used to predict changes in ecosystem sustainability for each extinction scenario under various disturbance environments.

Embodiments of the present invention may be applicable to food web network structure analysis and sustainability evaluation of various ecosystems, such as lakes, estuaries, coasts, forests, land, and marine ecosystems.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a schematic diagram of an environment in which an ecosystem prediction evaluation system according to an embodiment of the present invention operates.
2 is a flowchart for explaining how an ecosystem prediction evaluation system according to an embodiment of the present invention operates.
3 is a diagram specifically illustrating how the ecosystem prediction evaluation system according to an embodiment of the present invention operates.
4 is a flowchart illustrating a method of processing food web data by an ecosystem prediction evaluation system according to an embodiment of the present invention.
5 and 6 are diagrams for explaining a method of calculating the robustness of a food web network by using a specific method of extinction simulation by the ecosystem prediction evaluation system according to an embodiment of the present invention.
7 and 8 are diagrams for explaining how the ecosystem prediction evaluation system of the present invention processes and analyzes a large amount of food web network data.
9 is a block diagram schematically showing the configuration of an ecosystem prediction evaluation system according to an embodiment of the present invention.
10 shows data obtained by analyzing the structure of a food web network for a specific ecosystem by the ecosystem prediction evaluation system according to an embodiment of the present invention.
11 shows data obtained by analyzing the structure of a food web network for various ecosystems by the ecosystem prediction evaluation system according to an embodiment of the present invention.
12 is a diagram illustrating data in which the ecosystem prediction evaluation system according to an embodiment of the present invention predicts a structural change of a food web network based on an extinction scenario.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

In the entire specification, when a part "includes" a certain element, this means that other elements may be further included, rather than excluding other elements, unless otherwise stated. In addition, terms such as "...unit" and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software, or a combination of hardware and software. .

The ecosystem analyzed, predicted, and evaluated through the system disclosed in the present invention may be composed of various ecosystems such as rivers, lakes, estuaries, coasts, forests, land, and oceans.

Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings.

1 is a schematic diagram of an environment in which an ecosystem prediction evaluation system 100 according to an embodiment of the present invention operates.

Referring to FIG. 1 , it is a schematic diagram of an environment in which an ecosystem prediction evaluation system 100 according to an embodiment of the present invention operates.

Referring to FIG. 1 , the ecosystem prediction evaluation system 100 according to an embodiment of the present invention may operate in an environment in which the researcher terminal 200 and the external server 300 are connected and communicated.

According to an embodiment, the ecosystem prediction evaluation system 100 may analyze the structure of a food web network (food web) based on data accumulated for a specific ecosystem, and through the analysis, diversity, complexity, and other structural factors of the ecosystem Characteristics can be analyzed and evaluated.

The ecosystem prediction evaluation system 100 may evaluate the sustainability of an ecosystem by performing an extinction simulation on the assumption that a specific species becomes extinct.

Extinction of certain species within a food web network can lead to secondary extinction of other species through the nutritional cascade effect. Secondary extinction of species is affected by the interaction structure between species, and if a specific species does not have any interaction relationship, secondary extinction of the species may occur. Specifically, a species that is a predator within a food web network may become extinct due to the absence of a food source if it does not have a prey species that interacts with the species. As such, when a specific prey species becomes extinct, a predator that consumes only the prey may become secondary to extinction due to the loss of a food source. On the other hand, even if a specific prey species becomes extinct, a predator that eats the prey species and another prey species together can supply food through other prey, so that it may not become a secondary extinction. Massive secondary extinctions within ecosystems can lead to the collapse of entire biota.

The robustness of the food web network against secondary extinction can be used as an important indicator to evaluate the sustainability of the ecosystem. Robustness can be calculated as defined as the proportion of species removed until a specific proportion of all species present in an ecosystem become extinct. It can be defined and calculated as the ratio of species. In this case, the robustness of the ecosystem can be expressed as a value of 0 or more and 0.5 or less.

The ecosystem prediction evaluation system 100 may select one of various extinction scenarios in which at least one or more species among the species included in the ecosystem become extinct, and predict a change in the food web network based on the selected extinction scenario. Extinction scenarios include extinction of specific keystone species, climate change, pollutant exposure, introduction of ecosystem disturbance species, introduction of alien species, total extinction of top predators, 50% extinction of top predators, total extinction of intermediate predators, extinction of specific species, etc. It may be configured in various forms.

The researcher terminal 200 is a terminal controlled by a researcher who manages or uses the ecosystem prediction evaluation system 100, and the researcher uses a web browser or a program such as an application installed on the researcher terminal 200 to evaluate the ecosystem prediction evaluation system ( 100) to perform ecosystem analysis in any way you want.

According to an embodiment, the researcher may determine which method to use to perform the extinction simulation through the researcher terminal 200 and determine which extinction scenario to select to calculate change prediction information of the food web network. can In addition, the researcher may designate the number of cores of the parallel processing process to be used in the process of the ecosystem prediction evaluation system 100 operating.

The researcher terminal 200 may include a memory for storing information necessary for operation, a central processing unit such as a CPU for performing various operations necessary for operation, an input/output device, and the like.

The researcher terminal 200 is a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a PMP (Portable Multimedia Player), a tablet PC, etc. all kinds of handheld-based that can be connected to the web server through a network. It may include a wireless communication device, and may be one of digital devices equipped with memory means such as a personal computer (eg, a desktop computer, a notebook computer, etc.), a workstation, a web pad, and equipped with a microprocessor to have computing power. may be

The external server 300 is a server that provides data necessary for the smooth operation of the ecosystem prediction evaluation system 100 and may be configured as an external information providing system or database. For example, the ecosystem prediction evaluation system 100 may receive food web data for a specific ecosystem from the external server 300 .

According to an embodiment of the present invention, the communication network used by each entity operating within the operating environment of the system shown in FIG. 1 to communicate can be configured regardless of its communication mode, such as wired and wireless, for example For example, it may be implemented in various communication networks, such as a local area network (LAN), a metropolitan area network (MAN), and a wide area network (WAN). Preferably, the communication network according to an embodiment of the present invention may be a known World Wide Web (WWW) or the like.

2 is a flowchart for explaining how the ecosystem prediction evaluation system 100 according to an embodiment of the present invention operates.

Referring to FIG. 2 , the ecosystem prediction evaluation system 100 may collect food web data for a specific ecosystem ( S210 ). Data collection of the ecosystem prediction evaluation system 100 may be made through an information providing system or database outside the system.

The ecosystem prediction evaluation system 100 may check the class of the food web data in the data collection process and determine whether the identified class is in the form of a food web network, and the class of the food web data is the food web If it is not in the form of a network, the class of food web data can be converted into the form of a food web network.

For example, analysis may be performed when the class is a network type classified into a network, matrix, etc., and when class conversion is not possible in such a network type, it may be excluded from the analysis target.

The ecosystem prediction evaluation system 100 may analyze the structure of the food web network included in the collected food web data ( S220 ).

Structural analysis of food web networks can be classified into diversity analysis, complexity analysis and structural characterization analysis. Diversity analysis may be performed based on the number of species (S) present in the food web network to be analyzed. Complexity analysis is based on the number of links of interactions in the food web network (L), the link density (LD), calculated as the average number of interaction links per species, and the actual existence compared to the total number of interactions that can be expressed in the food web network. It can be performed based on the connectivity (C) calculated as the number of interactive links. Structural characterization analyzes the proportion of species that have food but no predators (Top), the proportion of species that do not have food (Basal), the proportion of species that have both food and predators (Inter), and exist in the food web network. Based on the average total length of the food chain (MCL), the proportion of species that have prey in two or more trophic levels (O), the average number of prey per predator (Gen) or the average number of predators per prey (Vul) can be performed with

Thereafter, the ecosystem prediction evaluation system 100 may perform an extinction simulation (S230) and calculate the sustainability of the ecosystem based on the result of the extinction simulation (S240).

The extinction simulation of a species can be performed by employing one of a variety of methods. According to an embodiment, one method that the extinction simulation of species can adopt is to remove species included in the food web network in a random order so that a certain proportion (eg, 50%) of all species is It may be to perform a simulation of a method of calculating the number of species to be removed until extinction multiple times and to calculate sustainability based on the average value of the simulation results.

For example, when one randomly selected species goes extinct, other species that interact with the corresponding species may become extinct together. As described above, the sustainability of the ecosystem can be calculated based on the number of species that are randomly removed until 50% of species included in the food web become extinct.

According to another embodiment, one method that the extinction simulation of species can adopt is to remove species included in the food web network in the order of the highest degree of connection, so that a specific proportion of all species (eg, 50% ) may be to perform a simulation of a method that calculates the number of species to be removed until extinction, and to calculate sustainability based on the average value of the simulation results.

The degree of connection may mean the number of nodes of a specific species connected to other species in the food web network. If the species selected in the order of the highest degree of connection in the food web network become extinct, other species interacting with the species may also become secondary extinction. As described above, the sustainability of the ecosystem can be calculated based on the number of species that are sequentially removed until 50% of the species included in the food web become extinct.

The ecosystem prediction evaluation system 100 may then select an extinction scenario for at least one or more species ( S250 ).

Species existing within an ecosystem may have different sensitivities to external disturbances including changes in environmental conditions. Species with high vulnerability and relatively low vulnerability exist depending on the type of disturbance. When disturbance occurs, the species with high vulnerability to the disturbance has a high risk of extinction and the species with low vulnerability to the disturbance The species is at low risk of extinction.

The ecosystem prediction evaluation system 100 may select an extinction scenario including various disturbances (S250), and generate information on prediction of changes in the food web network based on the selected extinction scenario (S260).

The extinction scenario according to an embodiment may consist of a scenario in which a keystone species that plays a central role in an ecosystem becomes extinct. In the case of keystone species, the impact of extinction of the species on the ecosystem is relatively higher than that of other species, and the ecosystem prediction evaluation system 100 performs network node centrality index analysis to select the species with the highest centrality. It can be determined by the stone species.

The extinction scenario according to an embodiment may be configured as a scenario in which a vulnerable species becomes extinct through climate change. Climate change changes various environmental factors such as temperature, precipitation, and humidity of the ecosystem, and such changes in environmental factors can cause extinction of specific species vulnerable to the environmental factors. For example, an increase in temperature due to climate change may cause the extinction of species vulnerable to high temperatures, and a sudden change in precipitation patterns may cause the extinction of species that live in a stable precipitation environment.

The extinction scenario according to an embodiment may consist of a scenario in which a specific type of pollutant is exposed to an ecosystem and a vulnerable species becomes extinct. Species existing in an ecosystem may have different sensitivities depending on the intensity of pollution and types of pollutants. For example, a specific species can live in an environment with a relatively high level of pollution with respect to pollutant A, but other species may become extinct if the level of pollution with respect to the pollutant is high.

The extinction scenario selected by the ecosystem prediction evaluation system 100 is, in addition to the extinction scenarios described above, introduction of ecosystem disturbance species, introduction of alien species, total extinction of top predators, 50% extinction of top predators, total extinction of intermediate predators, and specific species. It can be composed of various forms, such as extinction.

In addition, the ecosystem prediction evaluation system 100 selects various scenarios that can affect the ecosystem, such as an ecosystem restoration scenario and a species introduction scenario, instead of the extinction scenario, and predicts changes in the food web network based on the selected scenario. can

3 is a diagram specifically showing how the ecosystem prediction evaluation system 100 according to an embodiment of the present invention operates.

Referring to FIG. 3 , the ecosystem change prediction of the ecosystem prediction evaluation system 100 may be classified into a food web network structure analysis, sustainability calculation through extinction simulation, and a food web network change prediction stage based on an extinction scenario. It can be implemented through simulation.

The structural analysis stage of the food web network includes the data preparation stage of collecting food web data about the ecosystem and preparing the data in the form of a network by checking the data class, and analyzing the structure of the food web network to analyze diversity, complexity, and structural It may be divided into an analysis step in which a characteristic analysis is performed.

In the step of calculating sustainability through extinction simulation, the ecosystem prediction evaluation system 100 selects one of various types of simulation methods, and assumes that the species included in the food web network become extinct one by one according to the order determined by the method and removes them. This can proceed in a way that calculates the robustness of an ecosystem based on the number of species removed until a certain percentage of all species (eg 50%) become extinct. The ecosystem prediction evaluation system 100 may calculate sustainability based on an average value of robustness calculated by performing such a simulation a plurality of times.

The step of predicting changes in the food web network based on the extinction scenario may be performed by selecting one of various extinction scenarios and generating information on predicting changes in the food web network by species that become extinct according to the scenario.

In the computer simulation step, when the researcher inputs the basic set values necessary to perform the ecosystem prediction evaluation using the researcher terminal 200 into the ecosystem prediction evaluation system 100 as input values, the above-described structure analysis of the food web network, extinction The steps of calculating sustainability through simulation and predicting changes in the food web network based on extinction scenarios can be simulated and the results are outputted.

4 is a flowchart illustrating a method of processing food web data by the ecosystem prediction evaluation system 100 according to an embodiment of the present invention.

Referring to FIG. 4 , the ecosystem prediction evaluation system 100 checks the class of the data after receiving the food web data, and if the class is in the form of a food web network that can be used for analysis on the system, it can be used for analysis as it is, Although the class is not in the form of a food web network that can be directly used for analysis, if it has the form of a network, it can be converted into the form of a food web network that can be used for analysis. On the other hand, if it is not in the form of a food web network and is not in any other network form, the corresponding data may be classified as unusable data.

5 and 6 are diagrams for explaining a method in which the ecosystem prediction evaluation system 100 according to an embodiment of the present invention calculates the robustness of a food web network by using a specific method of extinction simulation.

5 shows a method in which a method in which random species is removed until 50% of all species present in the food web network become extinct, and in FIG. 6 , 50 of all species present in the food web network Shows how the method is utilized in which % are removed sequentially, starting with the most connected species, until extinction.

In FIG. 5 , one species is randomly selected and removed for each step, and accordingly, a species connected to the corresponding species may become secondary to extinction. In FIG. 6 , in each step, the species with the highest degree of connection are sequentially selected and removed, and when a large number of species with the highest degree of connection exist, one species is randomly selected and removed from among the species with the highest degree of connection. can be

The ecosystem prediction evaluation system 100 may calculate the average value of the simulation result as the robustness of the corresponding ecosystem by performing the extinction simulation as in FIGS. 5 and 6 a plurality of times, and the sustainability of the ecosystem will be calculated based on the calculated robustness. can

7 and 8 are diagrams for explaining how the ecosystem prediction evaluation system 100 of the present invention processes and analyzes a large amount of food web network data.

Referring to FIG. 7 , when analyzing the structure of a plurality of food web networks, the ecosystem prediction evaluation system 100 may quickly process the structure analysis through parallel processing. The ecosystem prediction evaluation system 100 may determine the number of cores of a processor performing parallel processing by a setting of a researcher, and accordingly, analysis of a plurality of food web networks may be simultaneously performed.

In detail, when data for a plurality of food web networks are input, a number of clusters corresponding to a specified number of cores is generated, and food web network data may be divided and allocated to each of the generated clusters. In each cluster, network structure analysis on food web network data can be simultaneously performed, and when all clusters are analyzed, the food web network structure analysis results can be integrated into one data set.

Referring to FIG. 8 , the ecosystem prediction evaluation system 100 may analyze the extinction simulation through parallel processing in a manner similar to that of FIG. 7 in evaluating the sustainability of the food web network.

The ecosystem prediction evaluation system 100 may determine the number of cores of a processor performing parallel processing by designation of a researcher, and accordingly extinction simulation for a plurality of food web networks may be simultaneously performed.

In detail, when data for a plurality of food web networks are input, a number of clusters corresponding to a specified number of cores is generated, and food web network data may be divided and allocated to each of the generated clusters. In each cluster, extinction simulations for food web network data can be performed simultaneously, and when simulations of all clusters are finished, the resulting data on the sustainability of the ecosystem, which is the result of the simulation, can be integrated into one data set.

9 is a block diagram schematically showing the configuration of the ecosystem prediction evaluation system 100 according to an embodiment of the present invention.

Referring to FIG. 9 , the ecosystem prediction evaluation system 100 includes a data collection unit 110 , a network structure analysis unit 120 , a sustainability calculation unit 130 , an extinction scenario selection unit 140 , and a network change prediction unit ( 150 ), the communication unit 160 , the storage unit 170 , and the control unit 180 may be configured.

The data collection unit 110 may receive food web data for a specific ecosystem, and the data may be collected from an information system or database external to the ecosystem prediction evaluation system 100 .

The data collection unit 110 may check the class of the collected data, and may convert the class into the form of a food web network that can be analyzed.

The network structure analysis unit 120 may perform a structure analysis of the food web network included in the food web data.

The network structure analysis unit 120 may perform diversity analysis, complexity analysis, and structural characteristic analysis in analyzing the food web network as described above, and each analysis may be performed based on the various parameters described above.

The sustainability calculator 130 may perform an extinction simulation based on the food web network and calculate the sustainability of the ecosystem based on the result of the simulation.

The sustainability calculator 130 may select one of various methods and perform extinction simulation based on the selected method. The extinction simulation can be repeated a predetermined number of times by the researcher's selection, and the robustness of the ecosystem can be calculated through the average value of the simulation results, and the sustainability of the ecosystem can be calculated based on the calculated robustness.

The extinction scenario selection unit 140 may select an extinction scenario in which at least one species among the species included in the ecosystem becomes extinct.

Extinction scenarios may consist of extinction of keystone species, specific types of climate change, exposure to specific types of pollutants, etc., and species vulnerable to disturbance included in the extinction scenario may become extinct.

The network change prediction unit 150 may generate change prediction information of the food web network based on the extinction scenario selected by the extinction scenario selection unit 140 .

Among the species included in the food web network, a species vulnerable to disturbance included in the selected extinction scenario may become extinct, and a species that interacts with the species in the food web network may face secondary extinction as a result.

The communication unit 160 enables the ecosystem prediction evaluation system 100 to communicate with the researcher terminal 200 and the external server 300 . The communication network used by the communication unit 160 to perform communication may be configured regardless of its communication mode, such as wired and wireless, for example, a local area network (LAN), a metropolitan area network (MAN). It may be implemented in various communication networks, such as an area network) and a wide area network (WAN).

The storage unit 170 serves to store information that is collected, generated, and processed in various components in the ecosystem prediction evaluation system 100 . That is, the storage unit 170 may store the collected food web-related data, an algorithm for a method for calculating sustainability, information on various extinction scenarios, and the like. The storage unit 170 may include, for example, a memory, a cache, a buffer, and the like, and may be composed of software, firmware, hardware, or a combination of at least two or more thereof.

The control unit 180 includes a data collection unit 110 , a network structure analysis unit 120 , a sustainability calculation unit 130 , an extinction scenario selection unit 140 , a network change prediction unit 150 , a communication unit 160 , and storage. It may serve to control the data flow between the units 170 . That is, the control unit 180 according to an embodiment of the present invention includes the data collection unit 110 , the network structure analysis unit 120 , the sustainability calculation unit 130 , the extinction scenario selection unit 140 , and the network change prediction unit. 150 , the communication unit 160 , and the storage unit 170 may be controlled to perform their own unique roles.

In FIG. 9 , the data collection unit 110 , the network structure analysis unit 120 , the sustainability calculation unit 130 , the extinction scenario selection unit 140 , and the network change prediction unit 150 classify the control unit 180 functionally. Since it is one configuration, it may be configured to be integrated as one control unit 180 .

10 shows data obtained by analyzing the structure of a food web network for a specific ecosystem by the ecosystem prediction evaluation system 100 according to an embodiment of the present invention.

Referring to FIG. 10 , a food web network for a specific ecosystem is displayed, and a part of a structural analysis result for the food web network is displayed. In FIG. 10, S is the number of species existing in the corresponding food web network, L is the number of interaction links in the food web network, and C is the actual interaction with respect to the total number of interaction links that can be expressed in the food web network. The ratio of the number of working links, MCL, may mean the average length of all food chains existing in the food web network.

11 shows data obtained by analyzing the structure of a food web network for various ecosystems by the ecosystem prediction evaluation system 100 according to an embodiment of the present invention.

11 is a food web network structure analysis result for a plurality of urban rivers and a plurality of natural rivers.

Diversity analysis was performed based on the number of species (S) in the food web network to be analyzed. Complexity analysis is based on the number of links of interactions in the food web network (L), the link density (LD), calculated as the average number of interaction links per species, and the actual existence compared to the total number of interactions that can be expressed in the food web network. It was performed based on the connectivity (C) calculated as the number of interactive links. Structural characterization analyzes the proportion of species that have food but no predators (Top), the proportion of species that do not have food (Basal), the proportion of species that have both food and predators (Inter), and exist in the food web network. was performed based on the average of the total food chain length (MCL) and the average number of prey per predator (Gen).

Looking at the analysis results, in the case of urban rivers, the diversity and complexity of the food web network tends to increase over time, and in the case of natural rivers, the diversity and complexity of the food web network tends to increase but decrease. has a In addition, connectivity (C), one of the parameters of the complexity of urban and natural streams, tends to become closer to each other over time. It can also be observed that other structural characteristics of food web networks often change in opposite directions in the case of urban and natural streams. It can be observed that the robustness of urban and natural rivers increases with time, and the robustness of urban rivers was observed to be close to the maximum value of 0.5 despite relatively low species diversity compared to natural rivers. This may mean that there are almost no secondary extinction species because the species of urban rivers actively interact with each other so that even if one species goes extinct, only that species can survive.

12 is a diagram illustrating data obtained by predicting a structural change of a food web network based on an extinction scenario by the ecosystem prediction evaluation system 100 according to an embodiment of the present invention.

12 , changes in the structure of the food web network according to three extinction scenarios are displayed for a specific ecosystem: extinction of keystone species, extinction of vulnerable species due to climate change, and extinction of sensitive species due to the spread of pollutants. there is.

As such, according to various embodiments of the present invention, various analyzes of the food web network of the ecosystem are possible, the sustainability of the ecosystem can be calculated through extinction simulation, and the ecosystem under various disturbance environments through the selection of various extinction scenarios change can be predicted.

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains know that the present invention may be embodied in other specific forms without changing the technical spirit or essential features thereof. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: Ecosystem Prediction Evaluation System
200: researcher terminal
300: external server

Claims (11)

In the method of the ecosystem prediction evaluation system predicting changes in the food web network for a specific ecosystem,
receiving food web data for a specific ecosystem;
analyzing the structure of the food web network included in the food web data;
performing an extinction simulation based on the food web network and calculating the sustainability of the ecosystem based on the simulation result;
selecting an extinction scenario for at least one species from among the species included in the ecosystem; and
A method for predicting changes in a food web network of an ecosystem prediction evaluation system, comprising the step of generating change prediction information of the food web network included in the food web data based on the selected extinction scenario.
According to claim 1,
The step of receiving food web data for a specific ecosystem is:
performing class check of the food web data and determining whether the identified class is in the form of a food web network; and
The method of predicting a change in a food web network of an ecosystem prediction evaluation system, further comprising the step of converting the class of the food web data into the form of a food web when the class of the food web data is not in the form of a food web.
According to claim 1,
The step of analyzing the structure of the food web network included in the food web data includes:
to perform diversity analysis, complexity analysis and structural characterization;
The diversity analysis is performed based on the number of species present in the food web network,
The complexity analysis is the number of interaction links present in the food web network, the link density calculated as the average number of interaction links per species, and the number of interaction links actually existing compared to the total number of interactions that can be expressed in the food web network. It is done on the basis of connectivity,
The structural characterization analysis includes the proportion of species having food but no predators, the proportion of species without food, the proportion of species having both food and predators, the average of the total length of the food chain in the food web, A food web network change prediction method of an ecosystem prediction evaluation system, characterized in that it is performed based on the ratio of species having food of two or more trophic levels, the average number of prey per predator or the average number of predators per prey.
According to claim 1,
The steps of performing extinction simulation and calculating sustainability based on the simulation results are:
By removing the species included in the food web network in random order, the simulation is performed multiple times to calculate the number of species to be removed until a specific proportion of all species become extinct, and based on the average value of the simulation results A food web network change prediction method of an ecosystem prediction evaluation system, characterized in that it calculates sustainability.
According to claim 1,
The steps of performing extinction simulation and calculating sustainability based on the simulation results are:
By removing the species included in the food web network in the order of the highest degree of connection, the simulation is performed multiple times and the average value of the simulation results is calculated by calculating the number of species to be removed until a specific proportion of all species becomes extinct. A food web network change prediction method of an ecosystem prediction evaluation system, characterized in that it calculates sustainability based on it.
According to claim 1,
The step of selecting the extinction scenario comprises:
A food web network of an ecosystem prediction evaluation system, characterized in that by performing network node centrality index analysis on the food web network, the species with the highest centrality is determined as a keystone, and a scenario in which the species determined as the keystone becomes extinct How to predict change.
According to claim 1,
The step of selecting the extinction scenario comprises:
A method for predicting changes in a food web network of an ecosystem prediction and evaluation system, characterized in that a specific type of climate change is assumed and a scenario in which a vulnerable species related to the hypothesized climate change is extinct is selected.
According to claim 1,
The step of selecting the extinction scenario comprises:
A method for predicting changes in a food web network of an ecosystem prediction evaluation system, characterized in that a specific type of pollutant exposure is assumed and a scenario of extinction of a vulnerable species related to the assumed pollutant exposure is selected.
According to claim 1,
Calculating the change prediction information of the food web network comprises:
After receiving the number of cores from the researcher terminal, creating clusters with the same number of cores as the input, dividing and assigning food web network data to individual clusters, predicting changes through food web network analysis through parallel processing in multiple clusters A food web network change prediction method of an ecosystem prediction evaluation system, characterized in that the information is calculated.
According to claim 1,
The step of performing the extinction simulation is,
After receiving the number of cores from the researcher terminal, creating clusters with the same number of cores as received, dividing and assigning food web network data to individual clusters, and performing extinction simulation through parallel processing in a plurality of clusters A method for predicting changes in the food web network of an ecosystem prediction evaluation system.
In the ecosystem prediction evaluation system for predicting changes in the food web network for a specific ecosystem,
a data collection unit that receives food web data for a specific ecosystem;
a network structure analysis unit for analyzing the structure of the food web network included in the food web data;
a sustainability calculation unit for performing an extinction simulation based on the food web network and calculating the sustainability of an ecosystem based on a result of the simulation;
an extinction scenario selection unit that selects an extinction scenario for at least one species from among the species included in the ecosystem; and
and a network change prediction unit generating change prediction information of the food web network included in the food web data based on the selected extinction scenario.

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