CN113806922A - Ecosystem development simulation method based on Ecospace model - Google Patents
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- 238000004088 simulation Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000011161 development Methods 0.000 title claims description 19
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- 125000000524 functional group Chemical group 0.000 claims abstract description 49
- 230000008859 change Effects 0.000 claims abstract description 24
- 238000011160 research Methods 0.000 claims abstract description 18
- 241000894007 species Species 0.000 claims abstract description 12
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Abstract
The invention relates to the technical field of ecological system research, and discloses an ecological system development simulation method based on an Ecospace model, which comprises the following steps: A) setting a plurality of collection points in an ecosystem of a research area, collecting samples of species of the collection points, and determining different functional groups, wherein the functional groups are composed of a single species or a plurality of related species; B) inputting the input parameters of each functional group and the current fishery fishing data into an Ecospace model, acquiring biomass change trend data of each functional group in a preset future time, grouping fishes with different food habits, and outputting a corresponding biomass change simulation diagram. By analyzing the change characteristics of the relative biomass of different fishes under the current fishing pressure, the influence on fishery resources under the existing fishing strength and man-made interference degree is judged, and a management strategy with scientific guiding significance is provided for fishery resource management.
Description
Technical Field
The invention relates to the technical field of ecological system research, in particular to an ecological system development simulation method based on an Ecospace model.
Background
Fishing is an important guarantee for the safety of human food. The Zhujiang river mouth aquatic products have abundant resources and bear huge fishing pressure, so that the nutrition level of the fisheries is reduced, the fisheries are changed to be small and low in age, and the influence on the local ecological system is important. To address these challenges facing the Zhujiang estuary fishery, local governments have implemented a series of fishery management policies, including setting up periods of no-fish, limiting fishing tools, encouraging fishermen to switch to the industry, and the like. Although the establishment of various fishery policies represented by fishing prohibition can recover the fishery resources at the Zhujiang river mouth to a certain extent, the fishing catching pressure in the non-fishing-prohibition period is higher than that in the past due to the increase of horsepower of a fishing boat and the high efficiency of the catching mode. The increase of fishing pressure in fishery seriously affects the composition of fish biological groups, and has important influence on the functional structure of an ecosystem, so that the change trend of the ecosystem is obviously changed. Researches show that the ecological system modeling can obviously improve the fishery management effect and has good predictability on fishery resource biomass.
At present, a method for evaluating and predicting an island ecosystem by using an Ecospace model is provided, for example, the Chinese patent with the publication number of CN110738385A discloses a method for evaluating and predicting the current situation of the island ecosystem based on the Ecospace model, but the method only analyzes the space change of each biological functional group, does not analyze fish biomass, cannot judge the influence of current fishery fishing on the fish biomass, and cannot provide a better management strategy for fishery resource management.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the ecoscope model-based ecosystem development simulation method for analyzing the biomass of the fishes is provided, and influences on fishery resources under the current fishery fishing pressure are simulated.
In order to solve the technical problem, the invention provides an ecosystem development simulation method based on an Ecospace model, which comprises the following steps:
A) setting a plurality of collection points in an ecosystem of a research area, collecting samples of species of the collection points, and determining different functional groups, wherein the functional groups are composed of a single species or a plurality of related species;
B) inputting the input parameters of each functional group and the current fishery fishing data into an Ecospace model, acquiring biomass change trend data of each functional group in a preset future time, grouping fishes with different food habits, and outputting a corresponding biomass change simulation diagram.
Preferably, the method further comprises the following steps:
C) according to the biological space distribution of different functional groups, dividing a research area into a plurality of habitat types, and obtaining a space distribution change simulation diagram of each functional group in a preset future time.
Preferably, in said step C),
the study area is divided into grids of 50 x 50m, a base map with complete spatial information is generated, and the maximum ecological accommodation capacity of each living being in each grid is defined according to the feeding habits and frequent emergence areas of each living being.
Preferably, in said step A),
when the research area is an island, the collection points are arranged along four different directions of a water area around the island, and different functional groups are determined according to the types of fishes.
Preferably, in said step A),
the functional group includes at least one group of debris.
Preferably, in the step a), the functional group includes fish, benthic organisms, producers, and debris.
Preferably, in the step a), the fish animals include carnivorous fish, omnivorous fish, clastic fish, filter-feeding fish and phytophagous fish.
Preferably, in said step a), the primary producers comprise phytoplankton, aquatic plants and epiphytic algae, and the benthos macrobiota comprises shrimps, crabs, insecta, polychaeta, oligochaeta, bichaeta and gastropoda.
Preferably, in the step B), the input parameters include biomass, a ratio of production to biomass, a ratio of consumption to biomass, ecological nutrition efficiency, a proportion of unassified food, and a fishing amount.
As a preferred scheme, in the step B), the current fishery fishing data includes fishery protection area geographic information, fishing prohibition period information, fishing mode information, and fishing pressure information.
Compared with the prior art, the ecosystem development simulation method based on the Ecospace model has the beneficial effects that:
the invention collects samples of species at different collection points, divides the functional groups, inputs the input parameters of each functional group and the current fishery fishing data into an Ecospace model to output a biomass change simulation diagram of fishes with different food habits within preset future time, judges the influence on fishery resources under the existing fishing strength and artificial interference degree by analyzing the change characteristics of the relative biomass of the different fishes under the current fishing pressure, and provides a management strategy with scientific guiding significance for fishery resource management.
Drawings
Fig. 1 is a block flow diagram of an ecosystem development simulation method based on an Ecospace model according to a preferred embodiment of the present invention.
FIG. 2 is a simulated graph of the change in relative biomass of carnivorous fish according to the preferred embodiment of the present invention.
FIG. 3 is a simulation of the relative biomass of omnivorous fish in accordance with a preferred embodiment of the invention.
FIG. 4 is a simulated graph of the variation of filter feeding fish relative biomass according to the preferred embodiment of the present invention.
FIG. 5 is a graph showing a simulation of the change in the relative biomass of the clastogenic fish in the preferred embodiment of the present invention.
FIG. 6 is a simulation of the relative biomass of herbivorous fish in accordance with a preferred embodiment of the invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The present application is described in further detail below with reference to specific examples, which should not be construed as limiting the scope of the invention as claimed.
As shown in fig. 1, the present invention provides an ecosystem development simulation method based on an Ecospace model, which includes the following steps:
A) setting a plurality of collection points in an ecosystem of a research area, collecting samples of species of the collection points, and determining different functional groups, wherein the functional groups are composed of a single species or a plurality of related species;
B) inputting the input parameters of each functional group and the current fishery fishing data into an Ecospace model, acquiring biomass change trend data of each functional group in a preset future time, grouping fishes with different food habits, and outputting a corresponding biomass change simulation diagram.
The ecological system development simulation method based on the technology comprises the steps of collecting samples of species at different collecting points, dividing function groups, inputting input parameters of the function groups and current fishery fishing data into an Ecospace model to output biomass change simulation graphs of fishes with different food habits within preset future time, judging the influence on fishery resources under the existing fishing strength and artificial interference degree by analyzing the change characteristics of the relative biomass of the different fishes under the current fishing pressure, and providing a management strategy with scientific guiding significance for fishery resource management.
In this embodiment, the method further includes a step C) of dividing the research area into a plurality of habitat types according to the biological spatial distribution of different functional groups, and obtaining a spatial distribution change simulation diagram of each functional group in a preset future time. And the change characteristics of each functional group in time and space are further analyzed, and the simulation quality of the model is improved.
In this embodiment, a large oyster sand island ecosystem is taken as a research object for further explanation, and an Ecospace model around a large oyster sand island is constructed to research the distribution situation and the energy flow process of organisms around the island and simulate and predict the change situation of fish biomass in each functional group in the next 15 years. It is understood that the ecosystem development simulation method of the present invention can also be applied to other ecosystems.
In the step A), the functional groups are single species or a plurality of related species, and according to the division method, the functional groups of the oyster sand island ecosystem comprise a fish functional group, a large benthonic animal functional group, a producer functional group, a zooplankton functional group and a debris functional group, wherein the total number of the functional groups is 30. Wherein the number of fish functional groups is 18, including 4 carnivorous fishes, 4 omnivorous fishes, 7 clastic predatory fishes, 2 filter-feeding fishes and 1 planting predatory fishes. The primary producers mainly comprise phytoplankton, aquatic plants and epiphytic algae, and the large benthos mainly comprises shrimps, crabs, insecta, polychaeta, oligochaeta, bichaeta and gastropoda.
In step B), according to the sampling result, the species, the functional group, the biomass (B) of the primary producer, the ratio of the production capacity to the biomass (P/B) and the ratio of the consumption to the biomass (Q/B) of the plurality of collection points are averaged to represent the average biomass composition of the large oyster sand island. The functional groups are arranged in the order of fish, benthos, producer and debris, and the interior of the functional groups is arranged according to the order of the nutritional level. The biomass, the ratio of the production quantity to the biomass, the ratio of the consumption quantity to the biomass, the ecological nutrition efficiency, the unassified food proportion and the specific numerical values of the fishery fishing quantity of each functional group are not detailed in detail, wherein the fishery fishing quantity is estimated according to the biomass collected on site and the research of the Zhujiang fishery resources.
The ecological system management strategy comprises information of fishery protection area position, range size, fishing forbidden period, fishing mode, fishing pressure and the like, and different situations can be set into different management strategies, so that fishery measurement is discussed. The Ecospace model predicts the change of the biomass of fishes in different functional groups through the flowing conditions of nutrient spaces among different nutrient levels of the food net, thereby achieving the purpose of evaluating the benefits of different fishery policies and providing reasonable suggestions for the selection of the fishery policies.
In addition, after the Ecospace model is constructed, step C) may be performed, and the research area is divided into representative habitat types by considering factors that may affect the spatial distribution of aquatic organisms, such as the substrate, water depth, salinity, and offshore distance of the research area. Firstly, dividing a research area into a grid base map of 50 multiplied by 50, wherein the actual length represented by each grid is 50 multiplied by 50m, secondly, drawing the land area of a large oyster sand island on a map, dividing the biological space distribution mainly by taking the water depth and the offshore distance as the standards, and importing the information into the base map, thereby generating the base map with complete spatial information. After the floor map is constructed, the maximum ecological containment capacity of each organism within each grid is defined in terms of the feeding habits and the ubiquitous areas of each organism. The active area for each living being is then determined by the probability that it appears in that area, dividing the active area into different depths and different offshore distances. The step can analyze the spatial distribution of each function group in the water area around the oyster sand island at present, after 5 years, after 10 years and after 15 years, can analyze the influence on the biodistribution of the coastal zone, further improves the simulation quality of the model, is the prior art, and is not detailed herein.
And step B), obtaining a trend simulation chart of the biomass of the fishes in each functional group changing along with time according to the output result of the model, and grouping the fishes with different food habits as units for respectively analyzing, as shown in figures 2 to 6. The abscissa of each plot is time (/ year) and the ordinate is relative biomass (based on the input biomass of the Ecospace model).
As shown in FIG. 2, the biomass of carnivorous brevicia carpio, red briggy goby and clarias leather was reduced in the previous 5 years, and the biomass was less than 0.100t/km2 in less than 5 years, while the biomass of erythroculter funiculorum of carnivorous fishes was reduced but was not less than 0.100t/km2 until 15 years. From the input parameters, the biomass of the erythroculter hainanensis is lower than that of the other three carnivorous fishes, but the catching amount of the erythroculter hainanensis only accounts for 12.5 percent of the biomass, and the catching amount of the other three fishes accounts for more than 40 percent of the biomass, so that the biomass of the three carnivorous fishes is reduced more quickly under the simulation of the current catching strength.
As shown in fig. 3, the biomass of omnivorous fishes also shows a trend of decreasing, wherein the relative biomass of crucian and bream is lower than 0.100t/km2 in about 5 th year, carp is lower than 0.100t/km2 in 7 th year, and squaliobarbus curriculus is stabilized at about 0.145t/km2 in 7 th year after a period of time of decreasing. Compared with crucian carp and bream, the biomass of carp is higher and is less stressed by top predators, so the biomass of carp is relatively slowly reduced, but the carp still cannot bear the current fishing pressure. The major food sources of squaliobarbus curriculus are phytoplankton and debris, the food is very abundant and easy to obtain, so the biomass is reduced less, and after the 5 th year, the relative biomass gradually tends to be stable due to the reduction of top predators.
As shown in FIG. 4, the biomass of silver carp and bighead carp is consistent, gradually reduced, and stabilized after 7 years, and the relative biomass is about 0.220t/km 2. The two methods have the same fishing amount, and the initial biomass of the bighead carp is higher than that of the silver carp, so the bighead carp biomass is higher when the stable state is achieved. As for filter-feeding fishes, the biomass of silver carps and bighead carps is closely related to the biomass of plankton, and the chart shows that the plankton is maintained at a lower level around the oyster island, which is also an important factor for restricting the biomass of the filter-feeding fishes.
As shown in FIG. 5, the biomass of the clastogenic fish eventually stabilized after a period of time had elapsed. Wherein the highest relative biomass of the ziza tilapia is 0.447t/km2, and the lowest relative biomass of the loach is 0.155t/km 2. As with filter-feeding fish, the biomass at which they reach steady state is related to the initial biomass, with the higher the initial biomass, the higher the biomass at which they reach steady state. In one aspect, reduced ecosystem size results in a reduced amount of debris flowing into the system, resulting in a reduced biomass of debris-feeding fish; on the other hand, the accumulation of contaminants in the sediment may be harmful to the fish, limiting their biomass.
As shown in FIG. 6, the grass carp relative biomass reached a minimum value of 0.083t/km2 at year 5, and then gradually increased back to a steady state, reaching 0.287t/km2, due to the decrease in the pressure for feeding. Unlike other fish species, grass carp has an abundant food source due to its abundance of aquatic plants along the riparian zone, so that its biomass can rise back after a period of decline and tends to be relatively stable.
As can be seen from the trend of biomass change of each functional group, the biomass of all fishes shows a trend of reduction. Particularly, carnivorous fishes and omnivorous fishes with relatively high nutritional grade have high biomass reduction rate, and part of the biomass reduction rate even disappears within 5-7 years, so that the biomass of the fishes is reduced due to the relatively high catching strength of the fishes, and the biomass of the fishes disappears due to the double reasons that the baits of the fishes are reduced due to artificial catching. For filter feeding, debris feeding and phytophagous fishes, although the food source is relatively stable, the biomass is still reduced by more than half due to the larger fishing pressure. At present, the fishing pressure born by the Zhujiang river mouth is large, which is very unfavorable for the growth and the propagation of various organisms, and the exhaustion of the fishery resources of the Zhujiang river mouth can be caused in the past.
In conclusion, by the ecosystem development simulation method provided by the embodiment of the invention, the biomass of all fishes can be seen to show a trend of reduction under the condition that the existing fishing strength is maintained unchanged. Particularly, carnivorous fishes and omnivorous fishes with relatively high nutritional grade have high biomass reduction rate, and part of the biomass reduction rate even disappears within 5-7 years, so that the biomass of the fishes is reduced due to the relatively high catching strength of the fishes, and the biomass of the fishes disappears due to the double reasons that the baits of the fishes are reduced due to artificial catching. For filter feeding, debris feeding and phytophagous fishes, although the food source is relatively stable, the biomass is still reduced by more than half due to the larger fishing pressure. The result shows that the current fishing strength has great influence on the ecological system of the oyster sand island, and exceeds the bearing range of the ecological system, so that the ecological system is gradually degraded, and a strategy with scientific guiding significance is provided for the resource management of the Zhujiang fishery or the restoration of the ecological system through fish manipulation.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.
Claims (10)
1. An ecosystem development simulation method based on an Ecospace model is characterized by comprising the following steps:
A) setting a plurality of collection points in an ecosystem of a research area, collecting samples of species of the collection points, and determining different functional groups, wherein the functional groups are composed of a single species or a plurality of related species;
B) inputting the input parameters of each functional group and the current fishery fishing data into an Ecospace model, acquiring biomass change trend data of each functional group in a preset future time, grouping fishes with different food habits, and outputting a corresponding biomass change simulation diagram.
2. The Ecospace model-based ecosystem development simulation method of claim 1, further comprising the steps of:
C) according to the biological space distribution of different functional groups, dividing a research area into a plurality of habitat types, and obtaining a space distribution change simulation diagram of each functional group in a preset future time.
3. The ecosystem development simulation method based on the Ecospace model according to claim 2, wherein, in the step C),
the study area is divided into grids of 50 x 50m, a base map with complete spatial information is generated, and the maximum ecological accommodation capacity of each living being in each grid is defined according to the feeding habits and frequent emergence areas of each living being.
4. The ecosystem development simulation method based on the Ecospace model according to claim 1, wherein, in the step A),
when the research area is an island, the collection points are arranged along four different directions of a water area around the island, and different functional groups are determined according to the types of fishes.
5. The ecosystem development simulation method based on the Ecospace model according to claim 4, wherein, in the step A),
the functional group includes at least one group of debris.
6. The Ecospace model-based ecosystem development simulation method according to claim 4, wherein in the step a), the functional groups include fish animals, benthic organisms, producers, and debris.
7. The Ecospace model-based ecosystem development simulation method according to claim 6, wherein in the step A), the fish animals include carnivorous fish, omnivorous fish, clastic fish, filter-feeding fish and phytophagous fish.
8. The Ecospace model-based ecosystem development simulation method according to claim 6, wherein in the step A), the primary producers include phytoplankton, aquatic plants and epiphytic algae, and the benthos includes shrimps, crabs, insects, hirsute, oligochaeta, bivalves and gastropoda.
9. The Ecospace model-based ecosystem development simulation method according to any one of claims 1 to 8, wherein in the step B), the input parameters include biomass, a production amount to biomass ratio, a consumption amount to biomass ratio, an ecological nutrition efficiency, an unassified food ratio and a fishery fishing amount.
10. The Ecospace model-based ecosystem development simulation method according to any one of claims 1 to 8, wherein in the step B), the current fishery fishing data includes fishery protected area geographic information, fishing prohibition period information, fishing mode information, and fishing pressure information.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109101707A (en) * | 2018-07-25 | 2018-12-28 | 广州资源环保科技股份有限公司 | A method of for simulating Shallow Lake Ecosystems model |
| CN110738385A (en) * | 2019-07-31 | 2020-01-31 | 暨南大学 | island ecosystem current situation assessment and development prediction method based on Ecospace model |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109101707A (en) * | 2018-07-25 | 2018-12-28 | 广州资源环保科技股份有限公司 | A method of for simulating Shallow Lake Ecosystems model |
| CN110738385A (en) * | 2019-07-31 | 2020-01-31 | 暨南大学 | island ecosystem current situation assessment and development prediction method based on Ecospace model |
Non-Patent Citations (3)
| Title |
|---|
| SAI WANG ET AL: ""Application of mass-balance modelling to assess the effects of ecological restoration on energy flows in a subtropical reservoir, China"", SCIENCE OF THE TOTAL ENVIRONMENT, vol. 664, pages 780 - 791 * |
| 刘玉等: "南海北部大陆架海洋生态系统Ecopath模型的应用与分析", 中山大学学报(自然科学版), vol. 46, no. 1, pages 123 - 127 * |
| 李娜等: ""Ecospace 模型及其在海洋保护区评估中的应用"", 世界科技研究与发展, vol. 30, no. 6, pages 723 - 727 * |
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