A method and apparatus for quantitatively predicting the spatiotemporal distribution changes of vole populations

Abstract

This invention provides a quantitative prediction method for the spatiotemporal distribution changes of vole populations, belonging to the field of species distribution prediction in the ecological environment. The method includes: determining vole habitats based on their activity range; dividing the study area of ​​the vole habitat into an unstructured grid based on triangular units using a hydrodynamic model and a two-dimensional sediment model; constructing a vole habitat quality assessment model, a vole subpopulation migration model, a vole population size change model, and a vole stored energy change model; coupling these models for calculation, and predicting the distribution changes of the vole population based on the calculation results. This invention, through the coupled calculation of different models, can accurately predict the distribution range of vole based on the calculation results, which is of great significance for effectively controlling vole outbreaks.

Description

Technical Field

[0001] This invention belongs to the field of species distribution prediction technology in the ecological environment, specifically relating to a quantitative prediction method and device for the spatiotemporal distribution changes of field mouse populations. Background Technology

[0002] The area surrounding Dongting Lake is flat and rich in water resources, making it an important grain-producing region in my country. However, the region is frequently plagued by rodent infestations, primarily caused by the Oriental field mouse. Population changes in the Oriental field mouse are mainly influenced by factors such as the growth and reproduction characteristics of the mouse population, habitat area and changes, initial population size, changes in the composition of the mouse's diet, changes in the population of its main predators, mouse migration, and human interference in rodent control efforts.

[0003] To control the reproduction of field mice, a population dynamics model of field mice has been proposed and coupled with a one-dimensional mixed river network unsteady water and sediment model around Dongting Lake. Supported by survey data on field mouse density and migration behavior, and regional DEM data, this study quantitatively investigates the changes in field mouse habitat, population size, and spatial distribution under the influence of different factors (focusing on changes in water and sediment conditions caused by reservoir regulation and reclamation of riverbanks), providing a scientific basis for effective rodent control. Research on field mice in the Dongting Lake riverbanks has a certain foundation, mainly including individual development, reproductive processes and their main influencing factors, field mouse population surveys, habitat changes, food chain composition and changes, migration processes, rodent population disaster mechanisms, and loss investigations. Research methods primarily include indoor observation, individual dissection, field observation and surveys, and statistical and dynamic model simulations.

[0004] Studies and surveys have fully demonstrated that the Oriental field mouse infestation in Dongting Lake is severe. The development of the mouse population is closely related to individual characteristics, habitat distribution and changes. Previous studies have a certain research foundation on the growth, reproduction and habitat of Oriental field mice in the Dongting Lake area, but there are few studies on population dynamic models, so it is impossible to make an accurate prediction of changes in the distribution of the field mouse population. Summary of the Invention

[0005] To address the problem that existing technologies lack research on the population dynamics of the Oriental vole, making it difficult to quantitatively analyze the mechanisms by which water and sediment conditions and geomorphological changes affect the population dynamics of the Oriental vole, and thus unable to propose corresponding water and sediment regulation measures, this invention provides a quantitative prediction method and apparatus for the spatiotemporal distribution changes of the vole population.

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

[0007] A quantitative prediction method for the spatiotemporal distribution changes of vole populations includes the following steps:

[0008] The habitat of voles was determined based on their activity range, and the habitat was divided into an unstructured grid based on triangular units.

[0009] Habitat environmental change parameters and vole density were selected as reference factors, and the spatiotemporal variation results of each reference factor were calculated. Based on the spatiotemporal variation results of each reference factor, a vole habitat quality assessment model was established using the geometric mean method.

[0010] The voles in each grid cell of the unstructured grid are grouped according to age and sex. Based on the grouping results, a vole subpopulation migration model with age and sex structure is constructed. A vole population change model is constructed based on the number of vole subpopulations, the mortality rate of subpopulations, and the population control equation in the grid cells of the unstructured grid.

[0011] The energy input, energy output, migration energy consumption rate, growth energy consumption rate, pregnancy additional energy consumption rate, and rearing additional energy consumption rate of voles in the grid cells of the unstructured grid are solved. Based on the solution results and the energy budget accounting equation, a model of the change in voles' stored energy in each time step is constructed.

[0012] The vole habitat quality assessment model, vole subpopulation migration model, vole population size change model, and vole stored energy change model were coupled to obtain vole population change values ​​in different regions of the unstructured grid. Based on the vole population change values, the distribution change of the vole population was predicted.

[0013] Preferably, the quality of vole habitat is scored using a vole habitat quality assessment model, specifically as follows:

[0014]

[0015] Where, Let be the vole habitat quality score at time t in the ie-th grid cell of the unstructured grid, and n' represent the number of influencing factors. ie SPLantT represents the scoring for different soil types. ie Indicates the score assigned to vegetation type. Indicates land use scoring, Indicates the score for water body inundation. The score represents the correlation between field mouse density and other factors. Scores are assigned based on the depth of water accumulation in the habitat. Indicates the temperature score of the habitat. The score is assigned based on soil moisture content. This indicates the predator score within a habitat unit.

[0016] Preferably, the expression for the field mouse population migration model is:

[0017]

[0018]

[0019] in, These represent the grid coordinates of the specific positions of the kag-th age group and jgd-th gender group at positions x and y at time step n+1, respectively. U represents the grid coordinates of the specific positions of the kag age group and jgd sex group at positions x and y at time step n, respectively. flow V flow Um represents the water flow velocity in the x and y directions, respectively. ie,jgd,kag U is the maximum migration speed of the subpopulation in the kag age group and jgd sex group, θ is the angle between the destination unit and the source unit; α is the speed correction coefficient, |ΔSc| is the difference between the source habitat and the destination migration unit, and U Max,jgd,kag For the maximum migration speed, D x and D y denoted as x and y, respectively, where n is the previous time step, * represents n+1 / 2 time steps, n+1 represents the current time step, Δt represents the time step size, and R is the random time constant.

[0020] Preferably, the expression for the field mouse population change model is:

[0021] When the field mouse is older than 20 days, the calculation formula is:

[0022]

[0023] in, This represents the number of subpopulations in the jgd and kag groups within the ie unit at time step n+1. Pmv represents the number of subpopulations living in the jgd and kag groups of unit ie at time step n. ie,jgd,kag,nm Dsk represents the number of all grid cells adjacent to or bordering ie within a time step of n to n+1, where Num(ie,jgd,kag) is the subpopulation of voles of the same age and sex that migrated to cell ie; ie,jgd,kag Dmv represents the mortality rate of the jgd and kag subpopulations in the static stack region of the ie unit. ie,jgd,kag,n The mortality rate of the jgd and kag group voles entering the target unit ie from other adjacent or bordering units;

[0024] When the field mice are under 20 days old, the number of field mice that have migrated here is not considered. The calculation formula is:

[0025]

[0026] in, Rpreg represents the total number of mature female Oriental voles during steps n and n+1.kag It is the pregnancy rate of voles, T Duration It is the gestation period of the field mouse, B kag It is the average number of pups per litter; R igd It is the sex ratio of newborn fetuses;

[0027] When the field mice are older than 10 days but younger than 20 days, excluding those that have migrated, the calculation formula is:

[0028]

[0029] Preferably, the expression for the field mouse energy storage change model is:

[0030]

[0031] Where, EIn represents the energy assignment at time n+1, where JGD represents the sex and KAG represents the age group of voles residing in cell ie, respectively. ie,jgd,kag Mb represents the energy replenishment obtained by the field mouse within a time step Δt. ie,jgd,kag The energy rate of basal metabolic consumption of vole subpopulations of jgd sex and kag age group residing in unit ie, EMov ie,jgd,kag EAdd represents the energy consumed by vole subpopulations of the jgd sex and kag age group residing within the ie unit during migration within a time step. ie,jgd,kag The additional energy consumed by the jgd sex and kag age group of voles living in unit ie within the time step for digging burrows and raising offspring is represented by Δt, which represents the time step.

[0032] This invention also provides a quantitative prediction device for the spatiotemporal distribution changes of vole populations, comprising:

[0033] The habitat delineation module is used to determine the habitat of voles based on their activity range and to divide the habitat into an unstructured grid based on triangular units.

[0034] The first model construction module is used to select habitat environmental change parameters and vole density as reference factors, calculate the spatiotemporal variation results of each reference factor, and establish a vole habitat quality evaluation model based on the spatiotemporal variation results of each reference factor using the geometric mean method.

[0035] The second model building module is used to group the voles in each grid cell of the unstructured grid according to age and sex, and to build a vole subpopulation migration model with age and sex structure based on the grouping results; and to build a vole population change model based on the number of vole subpopulations, the mortality rate of subpopulations and the population control equation in the grid cells of the unstructured grid.

[0036] The third model building module is used to solve for the energy input, energy output, migration energy consumption rate, growth energy consumption rate, pregnancy additional energy consumption rate, and rearing additional energy consumption rate of voles in the grid cells of the unstructured grid. Based on the solution results and the energy budget accounting equation, a model of the change in voles' stored energy in each time step is constructed.

[0037] The prediction module is used to perform coupled calculations on the vole habitat quality assessment model, vole subpopulation migration model, vole population size change model, and vole stored energy change model to obtain vole population change values ​​in different regions of the unstructured grid, and predict the distribution changes of the vole population based on the vole population change values.

[0038] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores computer execution instructions, and the processor executes the computer execution instructions stored in the memory to implement the steps of the quantitative prediction method for the spatiotemporal distribution change of vole populations as described above.

[0039] The present invention also provides a computer-readable storage medium for storing computer-executable instructions, which, when executed by a processor, are used to implement the steps of the quantitative prediction method for the spatiotemporal distribution changes of vole populations as described above.

[0040] The quantitative prediction method for the spatiotemporal distribution changes of vole populations provided by this invention has the following beneficial effects:

[0041] This invention first divides the vole habitat into an unstructured grid to facilitate model construction. Based on this, and combining habitat environmental change parameters, vole density, vole age, sex, and the energy consumption rate of voles within the unstructured grid cells, three models are constructed: a vole habitat quality assessment model, a vole population change model, and a vole stored energy change model. These models combine habitat environmental change parameters and individual vole characteristics, allowing for the acquisition of dynamic values ​​such as vole population changes. By coupling these four models on the unstructured grid according to prediction requirements, the distribution range of voles can be accurately predicted based on the calculation results, which is of great significance for effectively controlling vole outbreaks. Attached Figure Description

[0042] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. 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.

[0043] Figure 1This is a flowchart of a quantitative prediction method for the spatiotemporal distribution changes of vole populations proposed in this invention;

[0044] Figure 2 Map showing the field survey route around Dongting Lake;

[0045] Figure 3 This is a schematic diagram of the model principle framework of the prediction method in Embodiment 1 of the present invention;

[0046] Figure 4 This is a schematic diagram of the overall model calculation region in Embodiment 1 of the method of the present invention; wherein, Figure 4 (a) is the computational region of the model area, and (b) is a local unstructured mesh distribution map;

[0047] Figure 5 This is a soil distribution map within the model calculation area of ​​Embodiment 1 of the present invention; Figure 5 (a) is a map of soil distribution collection and interpretation within the model area, and (b) is a map of vegetation distribution collection and interpretation.

[0048] Figure 6 This is a graph showing the body weight of Oriental voles of different ages and their migration speed in water and land in Example 1 of the method of the present invention; wherein, Figure 6 (a) is the weight distribution curve of Oriental voles of different ages, and (b) is the migration speed distribution curve of Oriental voles of different ages in water and land.

[0049] Figure 7 This is a graph showing the energy consumption of a field mouse population in Example 1 of the method of the present invention;

[0050] Figure 8 The image shows the test results of the model of Embodiment 1 of the method of the present invention in Yueyang Lake beach and farmland habitat;

[0051] Figure 9 This is a graph showing the verification results of the Oriental field mouse density at four sites for the model of Embodiment 1 of the method of the present invention;

[0052] Figure 10 This is a map showing the predicted results of flood inundation, soil water content, habitat distribution, and density distribution in the calculated area as of January 1, 2007, in Example 1 of the method of the present invention.

[0053] Figure 11 This is a map showing the predicted results of flood inundation, soil moisture content, habitat fraction, and field mouse density distribution in the calculated area on June 1, 2007, according to Example 1 of the method of the present invention.

[0054] Figure 12 This is a map showing the predicted results of flood inundation, soil moisture content, habitat fraction, and field mouse density distribution in the calculated area as of July 31, 2007, in Example 1 of the method of the present invention.

[0055] Figure 13 This is a map showing the predicted results of flood inundation, soil water content, habitat distribution, and field mouse density distribution in the calculated area on November 8, 2007, according to Embodiment 1 of the method of the present invention.

[0056] Figure 14 This is a graph showing the calculated results of changes in environmental factors and the Oriental vole population in the Dongting Lake area from 1990 to 2011, according to Example 1 of the method of the present invention. Figure 14 (a) is a graph showing the changes in the average temperature and rainfall at 14 stations in the calculation area; Figure 14 (b) is the daily average measured water level change curve of Chenglingji; Figure 14 (c) is a graph showing the change in environmental carrying capacity of the Oriental field mouse in the Dongting Lake area. Figure 14 (d) is a graph showing the population changes of field mice in the calculated area and outside the dike.

[0057] Figure 15 This is a distribution map showing the age structure of field mice populations in the lake beach and surrounding farmland outside the dike, calculated using a graphical model from 1991 to 2011, as per Embodiment 1 of the method of the present invention. Detailed Implementation

[0058] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0059] This invention provides a quantitative prediction method for the spatiotemporal distribution changes of vole populations, such as... Figure 1 As shown, it includes the following steps:

[0060] S1. Determine the habitat of the field mouse based on its activity range. The main habitats of the field mouse include wetlands in the lake area, beaches and farmland areas outside the lake dike.

[0061] S2. Divide the vole habitat into an unstructured grid based on triangular units, and effectively distinguish lakes, islands, dikes and their farmland boundaries.

[0062] S3. Select habitat soil type, vegetation type, land use type, rainfall, flood inundation amount, soil moisture content and vole density as reference factors. Calculate the spatiotemporal variation results of each reference factor by coupling a distributed hydrological model, a hydrodynamic model and a biological population model. Based on the spatiotemporal variation results of each reference factor, establish a vole habitat quality evaluation model using the geometric mean method.

[0063] S4. Group the voles in each grid cell of the unstructured grid according to age and sex. Combine the grouping results with mass conservation, energy balance conservation, migration direction judgment, and key parameters of biological reproduction and behavior survey data to construct a vole subpopulation migration model with age and sex structure. At the same time, in the solution process, effectively combine the water flow velocity, water depth, water flow turbulence coefficient calculated by the regional hydrodynamic model and the habitat quality score calculated by the habitat quality model to make migration decisions and judgments. Construct a vole population change model based on the number of vole subpopulations, the mortality rate of subpopulations, and the population control equation in the grid cells of the unstructured grid.

[0064] S5. Solve for the energy input, energy output, migration energy consumption rate, growth energy consumption rate, pregnancy additional energy consumption rate, and rearing additional energy consumption rate of voles in the grid cells of the unstructured grid. Based on the solution results and the energy budget accounting equation, construct a model of the change in voles' stored energy in each time step.

[0065] S6. Perform different coupled calculations on the vole habitat quality assessment model, vole subpopulation migration model, vole population size change model, and vole stored energy change model to obtain the vole density in different regions of the unstructured grid. Based on the vole density in different regions of the unstructured grid, predict the distribution changes of the vole population.

[0066] Example 1

[0067] Below, taking Dongting Lake as an example, the quantitative prediction method for the spatiotemporal distribution changes of field vole populations proposed in this invention is used to predict the spatiotemporal distribution changes of the Oriental field vole population around Dongting Lake. A dynamic evaluation model for multi-factor field vole habitat quality scoring in the Dongting Lake area, field vole population and migration models for different ages and sexes, a distributed hydrological model of the lake wetland, and a two-dimensional hydrodynamic model of the lake area based on the SHEFE model were established. These models were coupled to varying degrees, and the changes in the Oriental field vole population in Dongting Lake over many years were reconstructed through model calculations.

[0068] A framework for the Oriental vole model was established, and key parameters required for the population model were obtained through historical data collection and field surveys. Population changes in the Oriental vole are closely related to flooding, population density changes, reservoir management, changes in natural predators, and vole migration behavior. The main mechanisms, influencing factors, and the model procedures to be used or established in this study are detailed below. Figure 3 A quarterly survey (monthly during peak field mouse density) is conducted around East Dongting Lake (survey area see [reference]). Figure 2The study included field surveys and population changes of the Oriental vole, as well as the extraction of vole parameters. Historical data from field surveys and indoor domestication of the Oriental vole were collected through visits to the Oriental vole indoor breeding laboratory at the Agricultural Research Institute. Based on remote sensing images, software such as ArcGIS was used to interpret and extract data on soil, vegetation, and land use in the study area (see...). Figure 5 Based on relevant literature, surveys, and experimental data, the maximum habitat density, birth rate, mortality rate, and habitat quality assessment score of the Oriental vole under different soil and vegetation conditions were determined, as detailed in Tables 1 and 2 below. Through analysis of field sampling, indoor domestication data, and relevant literature, parameters such as growth curve, reproductive parameters, mortality rate, and migration speed of the Oriental vole were summarized (see...). Figure 6 ), and actually used for the calibration of the necessary parameters of the model.

[0069] Table 1. Basic life parameters of Oriental field mice under different soil conditions.

[0070]

[0071] Table 2. Life and reproductive parameters of Oriental vole under different land use patterns.

[0072]

[0073] The main steps in obtaining experimental field mice in this invention include: (1) setting traps: placing mousetraps in different habitats (mainly farmland ridges, ditches, lake beaches, and at the foot of the Dongting Lake dike) (1 trap every 7 steps, 100 samples, to observe the trapping rate); placing traps at 4-5 pm and collecting them the next morning, calculating the trapping rate (number of traps / number of traps collected); (2) sealing and disinfecting the trapped field mice and traps with ether; and classifying the captured mice; (3) dissecting the field mice to identify their sex, whether they are adults, pregnancy rate, body length and tail length, weight and net weight, and then taking out the leg muscles, stomach and tail of the field mice and putting them into bottles containing alcohol and formaldehyde respectively, to be analyzed in the laboratory and compared with the food in the stomach under a microscope.

[0074] The hydrodynamic and sediment two-dimensional model constructed in this invention adopts an unstructured triangular mesh system, which is generated using Gambit software. Due to the highly complex boundary and large area of ​​Dongting Lake, the entire computational domain exceeds 4000 km². 2 Therefore, the calculation uses an unstructured mesh based on triangular elements, such as... Figure 4As shown, to facilitate accurate fitting of inner and outer boundaries and flexible control of local computational grid density and overall grid number, the length of the generated grid edges is 100-300m in the main channel of the lake area, 60-150m in the small channels and their tail sections, 400m in the lake beach area, and 500-600m in the outer area of ​​the dike. To avoid large spurious diffusion in the calculation, the transition between different grid scales is made as natural and smooth as possible, and the two sets of grids completely overlap and correspond on the dike boundary of the lake area. The total number of grids in the entire regional model is: more than 62,363 grid nodes and 123,673 cells. Considering that the field mouse problem is a two-dimensional problem, but considering that the field mouse sex, age, and migration grouping must be performed in each grid cell, the storage amount is specifically 123,673 × 3 × 2 × 24. To improve the calculation speed, only 3 grid nodes are taken in the vertical direction. At the same time, in order to reduce the storage amount, the three-dimensional model and the field mouse population model are processed by data connection, rather than directly connected by code for calculation. The one-dimensional model is also connected in this way.

[0075] 1) Model for assessing the quality of vole habitat.

[0076] Referring to the habitat quality assessment method, the habitat quality score of the i-th grid cell at time t is... like Figure 5 and Figure 6 As shown, the main influencing factors include soil, vegetation, land use type, flood inundation volume, inundation depth, field mouse density, food, temperature, land surface runoff depth, soil moisture content, and predator density, etc., and their expression is:

[0077]

[0078] Where, Let be the quality score of vole habitat at time t in the ie-th grid cell of the unstructured grid, and n' be the number of influencing factors (n=9). ie SPLantT represents the scoring for different soil types. ie Indicates the score assigned to vegetation type. Indicates land use scoring, Indicates the score for water body inundation. The score represents the correlation between field mouse density and other factors. Scores are assigned based on the depth of water accumulation in the habitat. Indicates the temperature score of the habitat. The score is assigned based on soil moisture content. The score represents the predator score within the habitat unit. Based on the survey and analysis of the distribution and density of field vole nests, the suitability scores for different soil types are different in the model, such as silt near water bodies, gray sandy soil on lake shores, muddy soil in paddy fields, humus soil in marshes, and yellow-purple mud on hillsides. The soil suitability scores are determined by Table 1. The vegetation suitability score is mainly determined by vegetation height, the edibility of the oriental vole, and its water inundation. The relevant parameters are determined by Table 2.

[0079] The habitat quality score driven by the inundation state of the Dongting Lake wetlands is mainly determined by the following formula:

[0080]

[0081] The change in habitat quality caused by inundation is represented by Dmax, which is the maximum water depth that limits the total submersion of different vegetation types. It is assumed to be equal to half the maximum trunk height of the vegetation. For different vegetation types: Populus, Phragmites australis, Carextristachya, a mixture of Phragmites australis and Carextristachya, and sandy beaches with a small amount of mixed vegetation and aquatic plants, the values ​​are 2.0m, 0.6m, 0.2m, 0.4m, 0.1m, and 0.08m, respectively. Zs represents the water surface height at the center of the grid cell, and its value is mainly calculated using a one-dimensional or two-dimensional model. Zb represents the topographic height (i.e., the topographic elevation of the habitat cell center) at the center of the grid cell for riverbeds, lakebeds, or sandy beaches. Since the density dynamics of voles have a certain impact on habitat quality, especially when the actual vole density is greater than the carrying capacity of the habitat, insufficient food and burrow resources will limit the voles' survival and development, and the habitat quality and corresponding score will decline. The expression is as follows:

[0082]

[0083] Where, D ie Let represent the environmental carrying capacity density and actual density of voles in the i-th grid cell at time t, respectively. The fraction related to surface waterlogging is:

[0084] Among them, S Max S and S represent the maximum surface runoff depth and surface runoff depth, respectively. Considering the land vegetation and its micro-topography, voles have the ability to build nests with grass balls made of vegetation, and they are generally unlikely to be completely submerged. Therefore, the minimum value is taken as 0.3.

[0085] The fraction involving temperature is:

[0086] T represents habitat temperature, determined by spatial interpolation of downloaded Chinese meteorological data (CMDSS, http: / / cdc.cma.gov.cn / ). High temperatures and solar radiation may affect the migration intensity and energy expenditure of voles during migration. Soil moisture content fraction is:

[0087]

[0088] WU, ML, and WM represent the dynamic tension water content (mm) of the upper, middle, and lower layers, respectively. Their values ​​are primarily calculated using the Xin'anjiang hydrological model. When the soil is directly submerged by water flow, these values ​​reach saturation within a finite time. If there is a high amount of free soil moisture, the nests of Oriental field mice will be submerged and collapse. The mice may then migrate to other computational units to rebuild their nests, or choose to nest in sedges and reeds. Increased soil moisture content in the habitat will increase the mortality rate of the field mice, as expressed by the formula.

[0089]

[0090] Among them, D ie,jgd,kag 、D Aquatic_Veg represents the dynamic mortality rate of the jgd sex group and the kag age group within the ie-th grid cell, and the mortality rate of voles in the wetland, respectively.

[0091] Due to the lack of data on natural enemies of voles, and for simplicity, while also considering the capture and poisoning of voles migrating into farmland by humans, the predator score for farmland in the single Oriental vole population calculation model is directly set to 0.3, while the score for lakeshores, which are less affected by predators and relatively safer for voles, is 0.9. For the established multi-population model, the solution is obtained by generalizing the predator population size, food chain relationships, and predator food requirements after applying certain parameters and processes.

[0092] 2) Individual Cohorts Based Model (ICBM) model with age and sex structure for vole population migration.

[0093] If we need to calculate the short-term migration behavior of voles, the formula is as follows:

[0094]

[0095] In the formula, These are the grid coordinates of the specific positions of the kag-th age group and jgd-th gender group at positions x and y at time step n+1. U represents the grid coordinates of the specific positions of the kag age group and jgd gender group at positions x and y at time step n. flow Vflow The water flow velocities in the x and y directions are calculated using a two-dimensional hydrodynamic model. When the oriental vole migrates to a drier habitat, the effects of water flow velocity, wind speed, etc., disappear, and Um... ie,jgd,kag is the maximum migration speed of the subpopulation group at age kag and sex jgd, and its value is related to the age and sex of the vole and the type of its habitat; θ is the angle between the destination cell and the source cell; α is the speed correction coefficient, whose correction value is closely related to the difference in mass fraction between the two grid cells involved, and its expression is α=Max(|ΔSc|.U Max,jgd,kag ,0.2), the mass difference between the source habitat and the target migration site unit |ΔSc|, U Max,jgd,kag Maximum migration speed, D x and D y Let be the turbulence coefficients in the x and y directions, n be the previous time step, * represent n+1 / 2 time steps, n+1 represent the current time step, and Δt represent the time step size. R is the random time constant [-1.0, 1.0]. Since there are 96 possible moving vole subgroups and stacked subgroups in each grid cell, the computational cost of the model would be very high if performed over a long period. Therefore, the model needs to be simplified, considering that the time required for a vole to migrate between two adjacent cells is usually within one time step (i.e., Δx << Um). ie,jgd,kag Therefore, under simplified calculation conditions, it is assumed that the migrating field mouse population can successfully reach the target migration unit, and the migration success rate is determined based on the remaining energy of the field mice. It is assumed that the migration success rate has a certain functional relationship with the remaining energy of the field mice before migration (field mouse reserve energy - energy required for migration). This invention assumes that the energy values ​​of the local sex and age-grouped field mouse populations follow a normal distribution. Then, by solving the cumulative probability using the probability table of the probability distribution, and based on the remaining energy values ​​of the field mouse subpopulation dynamically calculated by the model, the migration success rate of the field mice to be migrated can be solved.

[0096] (1) Selection of the number of migratory habitat units.

[0097] In the absence of an emergency, such as when habitat units are not flooded, some voles will choose to remain in the same computing unit, while the rest will migrate to the optimal habitat unit. For special cases, it is assumed that: (I) due to their weaker migratory ability, all offspring voles under 20 days old remain in their original habitat; (II) all mother voles under 20 days old remain in their original habitat; (III) if there is remaining habitat, pregnant voles will remain in their original habitat; (IV) if there is still remaining habitat, sub-adult voles will remain in their original habitat. In an emergency, voles older than 20 years will try to escape their original habitat and migrate to a safer habitat.

[0098] (2) A method for habitat selection based on habitat fraction difference, maximum density and energy control.

[0099] Assuming that Oriental voles older than 20 days have the ability to judge or follow other groups of older Oriental voles to migrate based on specific circumstances, and can migrate to adjacent superior candidate grid cells within a time step, when judging adjacent superior candidate grid cells, it is necessary to judge in real time whether the vole density of these grid cells has been saturated. If the density has been saturated, the vole group will migrate to other candidate grid cells.

[0100] 3) Population change model of Oriental vole.

[0101] In the next time step, the subpopulation size of Oriental voles grouped by specific age and sex was determined primarily by the following method. When the voles were older than 20 days, the calculation method was as follows:

[0102]

[0103] Where, This represents the number of subpopulations in the jgd and kag groups within the ie unit at time step n+1. Pmv represents the number of subpopulations living in the jgd and kag groups of unit ie at time step n. ie,jgd,kag,nm This represents a subgroup of voles that may migrate from cell ie to cell ie within time steps n to n+1, consisting of Num(ie, jgd, kag) adjacent grid cells, where nm is the index of the adjacent cell. ie,jgd,kag For the stack area in the IE unit (i.e., the population size of jgd sex and kag age group voles that remain in the original IE computing unit without migration), Dsk ie,jgd,kag Dmv represents the mortality rate of the unmigrated jgd and kag subpopulations within the ie unit. ie,jgd,kag,n The mortality rate of the jgd and kag groups of field mice entering the target unit ie from other units.

[0104] When the field mice are less than 20 days old, the number of field mice that have migrated here is not considered: (1) When the field mice are less than 10 days old, the mortality rate of the field mice born in this habitat is directly calculated to the age of the field mice grouped with this age, and then the results are accumulated. The calculation formula is as follows:

[0105]

[0106] The formula represents the total number of newly born field mice from time n to time n+1. for:

[0107]

[0108] Where, Rpreg represents the total number of mature female Oriental voles during steps n and n+1. kag For the pregnancy rate of voles, T Duration It's the gestation period for voles, Rpreg kag It is the pregnancy rate of voles, B kag It is the average number of pups per litter; R igd This refers to the sex ratio of newborn fetuses. (Tg during the gestation period of voles) Duration The pregnancy period is 20 days. Based on the survey, the pregnancy rate of voles is about 35%, therefore the pregnancy rate... B kag = Average number of pups per litter; R igd = Sex ratio of newborn offspring (♀(number of female mice) / ((number of female mice)♂+(number of male mice)♀)) (According to the survey, this value is taken as 60%).

[0109] When the field mice are older than 10 days but younger than 20 days, excluding those that have migrated, the calculation formula is:

[0110]

[0111] However, at this point, the newborn pups of eligible female voles are stored separately by sex and accumulated in the reserved stacks of each grid cell. Assuming a mortality rate of 0, the oldest voles in each grid cell die after reaching 480 days. Then, they are recruited together into the youngest age group, and a new survival time calculation begins. This ensures consistency in the model's calculation of vole ages.

[0112] (1) Mortality rate.

[0113] The mortality rate of Oriental voles is mainly related to the age of the voles, habitat type, and density of voles and predators, and the values ​​are determined by Table 2. The age of the largest age group is determined according to the literature (Zhang and Wang, 1998). When the age of the voles is greater than 480 days, all voles in this age group die and new recruited juvenile voles enter the group. Even if there are a few Oriental voles older than this age, their ecological function can be ignored. Therefore, this treatment method is reasonable.

[0114] (2) Solving the succession of the population stack.

[0115] Stack updates and the recruitment of newborn voles are crucial for population resolution. This invention demonstrates through trial calculations that different recruitment and update methods have a certain degree of "fluctuation" impact on the computational results, while death "removal" has a smaller impact on fluctuation. The smaller the time step of the selected population sub-stage (ΔT = 20d), the smaller the fluctuation in stack update computation; however, a small time step in the population sub-stage undoubtedly increases the computational and storage requirements significantly. To comprehensively consider fluctuation and efficient computation, this paper adopts a two-stage update approach for the newly born sub-population, which undoubtedly reduces fluctuation. The specific steps of the algorithm designed in this invention are as follows:

[0116] (1) For voles born between 0 and 10 days old, the population was directly calculated continuously in the birth habitat to the youngest vole population.

[0117] (2) Field mice born between 10 and 20 days old are directly stored in a fixed stack. After the oldest subpopulation reaches old age and dies, they are directly updated into that subpopulation and their age is set to 0 days. This achieves continuous population calculation. In effect, the age of the 0-10 day old population increases by 0-10 days, while the age of the 10-20 day old field mice decreases by 0-10 days, and the sum of the two is exactly 0. This update method can realize continuous succession calculation of continuous populations.

[0118] (3) Merge voles of the same age and sex in the same unit.

[0119] When using a two-dimensional model for detailed calculations, the coordinates of each vole subpopulation at each step may not be located at the center of the calculation unit. When there are two or more vole subpopulations of the same age and sex in the same unit, they need to be merged before further calculations. The main components to be merged are the coordinates of the vole subpopulations, the number of subpopulations, and the bioenergy carried by the subpopulations. The calculation formulas for the X and Y coordinates are given below:

[0120]

[0121] in, The x-coordinates represent the jgd gender and kag age group subgroups that migrate into and are in the original ie unit at time t+1, respectively, and the meanings of the other y-coordinates are similar.

[0122] The calculation assumes that the coordinates of the non-migrating field mouse subgroups are located at the center of the calculation cell.

[0123] (4) Calculation of bioenergetics in rodents and its equilibrium model.

[0124] Every biological individual needs energy for maintenance, growth, reproduction, daily movement, nest building, burrowing, escaping predators, and foraging. Among the three rodent species, only the Oriental field vole mainly inhabits lakeshores that are easily flooded. In order to escape rising floodwaters, it needs to undertake a relatively high-intensity migration from June to August each year. The other two rodent species migrate less frequently and have the opportunity to rest freely. They consume a lot of biological energy during the migration to avoid floods. The density of field voles is very high during the migration, and food along the way is somewhat limited. In order to meet the energy supply requirements for migration, the Oriental field vole sometimes feeds on the carcasses of its own kind that have died from exhaustion during the migration. Furthermore, during the pregnancy and birthing periods of Oriental voles, they need to forage more to replenish their bioenergy. Their own stored bioenergy is crucial for migration and feeding. Therefore, to avoid excessive storage and calculation, appropriate simplifications were made in the calculations: 1) Only the energy flow of Oriental voles was considered; 2) Additional energy expenditure mainly considered the energy consumption of migration and the energy consumption during female pregnancy (10-30%) and offspring nursing (45-200%); 3) A link was established between their own bioenergy reserves and their migration success probability; 4) Other populations and other energy expenditures were simplified accordingly. Figure 7 As shown, based on a survey of relevant literature, the main energy allocation and proportions of vole populations are analyzed.

[0125] 1) Energy input Ein.

[0126] The energy input of the Oriental field mouse (Striped Field Mouse) is closely related to the species and vegetation distribution of its habitat (its maximum value is approximately 10-20% of the habitat productivity, Gefeng et al., 2008, Table 6-20-3, pp. 388). Here, this invention uses the NDVI value of the habitat as the input: Ein = f(NDVI). t The energy of brown rats is closely related to their production methods and crop biomass in farmland.

[0127] 2) Energy output Eout.

[0128] Basal metabolic rate (BMR): Based on the animal's own body weight, its energy requirement is: Mb(kJ) = 70W b Kleiber (1961) set b = 0.75 in calculating the basic metabolism (Mb) of animals.

[0129] 3) Migration energy consumption rate Emov: The forces required for a vole to wade and swim, and its energy consumption rate, are determined using the following equation:

[0130] E sw =R GW .Δt+F D ·L;

[0131] in:

[0132] R GW =(M M -M f g;

[0133]

[0134]

[0135] F G The effective gravity of a field mouse in water, M M M f These represent the weight of the field mouse and its underwater weight (based on its body volume), respectively. g is the acceleration due to gravity, and R... GW The kinetic metabolic rate (J / s) required to prevent a vole from sinking, F D The drag force of the water current on the field mouse d M The effective diameter of the field mouse facing the water surface when swimming, U f Given the water flow velocity, ρ (water flow density), and μ (water flow viscosity), the energy consumption rate of the field mouse in the water is: E LD =R GL .Δt+Max(DZ)·M M g, where L is the migration distance of the field mouse.

[0136] 4) Growth energy consumption rate: During the growth period, a portion of the energy consumed by voles is mainly used to meet the energy required for the growth of body tissues.

[0137] 5) Pregnancy-related energy expenditure: 30% of the vole's basal metabolic rate (BMR).

[0138] 6) Additional energy consumption rate for raising: This is 40-100% of the basal energy consumption rate (BMR) of voles, and its value is closely related to the number of litters raised.

[0139] Field mice store energy E stk and threshold E sk0 However, its own stored bioenergy is less than the threshold E. sk0 At this time, field mice need to go out to find and eat food. Due to the lack of actual measurement data, the storage threshold E of field mice is assumed in the calculation process. sk0 E is equal to the basal metabolic rate of a field mouse in one day: sk0 = 1.0 × 86400 × Mb, meaning that assuming a field mouse can survive for one day at rest with this energy reserve, when the dynamic energy reserve of the field mouse falls below the threshold, the field mouse needs to replenish its energy in a timely manner to increase its energy reserves. Due to the limitation of the bioenergy storage capacity of the mouse's stomach and body, its maximum value is the basic metabolic rate E of the field mouse over 2 days. skmax = 2.0 × 86400 × Mb.

[0140] The model for the energy storage process of the Oriental field mouse within each time step is as follows:

[0141]

[0142] in, EIn represents the energy assignment at time n+1 and n for the jgd sex and kag age group voles residing in cell ie, respectively. ie,jgd,kag Mb represents the energy replenishment obtained by the field mouse within a time step Δt. ie,jgd,kag The energy rate of basal metabolic consumption of vole subpopulations of jgd sex and kag age group residing in unit ie, EMov ie,jgd,kag EAdd represents the energy consumed by vole subpopulations of the jgd sex and kag age group residing within the ie unit during migration within a time step. ie,jgd,kag The additional energy consumed by the jgd sex and kag age group of voles living in unit ie within the time step for digging burrows and raising offspring is represented by Δt, which represents the time step.

[0143] To verify the rationality of the model parameters and the accuracy of the calculations, this invention uses the measured density of field mice in Yueyang Lake beaches and farmland from 1991 to 1995 to test the model. The test chart ( Figure 8 As shown in the figure, during the dry season of 1993-1995, the density of Oriental field mice living on the lake shore was relatively high, reaching about 0.2 Ind. / m2, while the density of field mice in farmland was very low during the same period. When the flood came, the lake shore was submerged, and some Oriental field mice crossed the lake embankment and successfully migrated to the farmland, causing the density of Oriental field mice in the farmland area to increase rapidly, reaching 0.1-0.15 Ind. / m2. As the flood receded, the field mice in this area returned to the central area of ​​the lake shore (or were poisoned by local farmers, resulting in a decrease in population density). The figure shows that the model calculation value and the measured value match well on the lake shore, while the calculation error in the farmland is relatively poor. This may be closely related to the microhabitat of the farmland. In the farmland area, the grid resolution of the model is smaller, and the movement of field mice is mainly along the inner side of the farmland or the ridges for resting and migration, which may differ from the average density of the model's habitat. However, the calculation accuracy of the model can reach about 40%. Considering the strong heterogeneity of the habitat and the large number of biological parameters, the calculation is reasonable.

[0144] like Figure 9The model was primarily validated in Chunfeng, Chapanzhou, and Baizhouzi in Yueyang. These habitat units were largely reclaimed after 1998, and especially after 2009, the measured field mouse density in these areas approached zero, a significant difference from the calculated values. However, in 2009, the model's calculated values ​​matched the measured values ​​relatively well. This is likely closely related to effective management of the reclaimed areas, the establishment of rodent-proof walls, simplification of the model's migration patterns, and parameter settings. This is because human rodent control has a significant impact on the reclaimed areas, and the current model does not yet consider the active control, disturbance, and poisoning of field mice by human migration.

[0145] (1) Model calculation of the 2007 Dongting Lake field mouse disaster.

[0146] The 2007 disaster was the largest Oriental field mouse infestation in history. Nine characteristic variables were used in the calculation to describe the dynamic quality of the Oriental field mouse habitat in Dongting Lake. Two key characteristic variables were the inundation factor and soil water content. The former is closely related to the migration and escape of Oriental field mice, while the latter has a direct impact on nest conditions. Excessive soil moisture content can cause nest collapse. Therefore, these two characteristic variables, along with the model-calculated habitat comprehensive evaluation score and the spatiotemporal variation of the calculated field mouse density, were highlighted (see...). Figures 10-13 From this, we can see that:

[0147] In 2005 and 2006, due to two consecutive years without significant rainfall and upstream flooding, coupled with the Three Gorges Dam's impoundment reaching 156 meters in September and October 2006, the water level and inundation in the Dongting Lake area were significantly lower than normal. The low soil moisture content on the lakeshores provided ample breeding time and a rapid reproduction rate for the Oriental field mouse on the riverbanks before the 2007 floods, resulting in a large population base. Figure 10 d and Figure 14After July 28, 2007, due to heavy rainfall in the middle reaches of the Yangtze River and the pre-flood release of water from the Three Gorges Dam, the grasslands in the Dongting Lake area were rapidly submerged, and the habitat quality of the grasslands declined rapidly (below 0.4). At the same time, the habitat quality score in the farmland area could reach above 0.6. As a result, a large number of Oriental field mice from the lake beaches migrated towards the farmland. During the migration, the field mice ate various crops and bark along the way, which led to a large-scale field mouse infestation. During migration, many older and weaker sub-adults die from exhaustion due to flooding and long-distance migration. Furthermore, local farmers hunt and kill large numbers of voles near lake embankments and field edges to prevent them from invading farmland, leading to a rapid decline in the vole population. Calculations show that only about 1 / 8 of the lake voles successfully migrate to the outer lake embankments, farmland, high banks, and hills to survive. In their target habitats, the Oriental vole also shares and competes with local populations of striped field mice and brown rats. The Oriental vole's high reproductive rate (r-reproduction strategy) prevents its population from declining to extinction, which is closely related to the hydrological processes of the region. Figure 12 (d). After the flood season, due to the exposure of the lake beaches, their habitat quality is high, and a large number of Oriental voles that have migrated out will return to the optimal habitat on the lake beaches to begin a new breeding cycle. Figure 13 c), and then the Oriental Field Mouse begins a new cycle.

[0148] (2) Numerical calculation of field mouse population change.

[0149] Like other small rodents, the Oriental vole of Dongting Lake reproduces rapidly but has a short lifespan. Its population changes are of the r-selection type. Before its natural predators were largely eliminated or hunted by humans, its population changes were mainly controlled by regional rainfall, wetland inundation hydrological processes, and predator populations. After the large-scale elimination of natural predators through broad-spectrum poisoning, its population changes are mainly controlled by wetland hydrological processes. In addition, the construction of rodent-proof dikes, reclamation of farmland in the lake area, evolution of sandbars, and numerous water conservancy projects have also had a significant impact on the Oriental vole. This section uses the mathematical model established in this invention to calculate the population change process of the Oriental vole in the Dongting Lake area from 1991 to 2011. Figure 14(c) During this period, it can be assumed that the populations of the main natural enemies of the Oriental vole (owls, weasels, snakes, etc.) have decreased to a very small number, and their ecological function of controlling the Oriental vole population has been basically lost. Therefore, a vole population model was used and coupled with an interval hydrological, hydrodynamic, and habitat dynamic evaluation model for calculation. The calculated values ​​are consistent with the population numbers in 1993, 1995, 2005, 2007, and 2010 in the actual reported severe vole infestations (1993, 1995, 2005, 2007, and 2010). However, the model calculated values ​​for 2002 and 2005 were larger, and actual measurements showed no major vole infestations. The 2005 lake shore survey showed a relatively high field mouse density. However, due to the relatively low flood levels during the 2005 Yangtze River flood season and low rainfall in the lake shore, the field mouse population base was large, but the absence of major floods prevented the mice from migrating out of the lake shore and causing a field mouse infestation. Therefore, the model calculation results were relatively reasonable and consistent with the actual situation. The 2002 lake shore field mouse density survey showed that the field mouse density was indeed very low that year, and the model calculations for that year did have some errors. However, considering the temperature, rainfall, and hydrological processes of 2001-2002, there were indeed no major precipitation events, floods, or extreme temperatures. The low field mouse density may be due to inaccurate parameters, population collapse, interference and competition from other populations, or poisoning, but further confirmation and research are needed. The model calculation results show that when the field mouse population in the calculation area reaches 2.0 × 10⁸ individuals, the field mouse population is prone to migrating out and causing a disaster during floods in the flood season. The model accurately calculated the catastrophic outbreak of Oriental field mice in Dongting Lake in 2007, with a peak value of 8.0 × 10⁸ individuals. According to estimates from relevant departments at the time, the maximum value was 20 × 10⁸ individuals. The main reasons for the underestimation of the calculated value may be: (I) the estimated uniform distribution of lake beach field mice (5 Ind. / m²) at the time of calculation did not match the actual situation; (II) the actual lake beach area around Dongting Lake was larger than the area simulated by the model, so the actual field mouse population may be larger than the calculated result; (III) in addition, the actual birth rate and death rate of field mice were greater than the values ​​taken by the model. Meanwhile, the graph shows that the environmental carrying capacity of voles in the model's calculation area is approximately 8.0 × 10⁸ ind during the dry season and less than 100 million during the flood season (this is related to the size of the flood). The Oriental vole population undergoes changes under the stress of environmental carrying capacity. This can be seen from the graph showing the number of vole births and deaths calculated by the model. During floods, a large number of voles die, with the number of deaths exceeding the number of births, leading to an increase in the Oriental vole population. The opposite occurs during the dry season. The population change of farmland voles is mainly determined by the migration of Oriental voles, with a peak of approximately 0.3 × 10⁸ ind. After the flood season, the vole population declines rapidly, with only a very small number of Oriental voles overwintering in farmland areas.

[0150] (3) Simulation calculation of changes in sex and age structure of vole population.

[0151] Field surveys show that the average sex ratio of oriental vole pups at birth is 6:4. However, because female voles expend a significant amount of additional energy and outdoor exposure during pregnancy and raising offspring, they sometimes lose optimal migration opportunities due to being caught up with their pups while escaping floods. Furthermore, female voles of the same age do not migrate as quickly or persistently as their male counterparts. Therefore, the probability of female oriental vole successfully escaping floodwaters is relatively lower than that of males. Consequently, the proportion of females drops drastically after a flood, reaching only about 26% during the 1998 floods. However, during the dry season, the proportion can recover to about 50% thanks to the higher birth rate, showing a strong correlation with hydrological processes. It can be concluded that the sex ratio of oriental vole is influenced and stressed by hydrological processes. After a major flood, only a portion of sub-adult and adult voles (about 1 / 8 of the vole population) can successfully migrate to farmland. They also face habitat displacement from farmland brown rats and striped field mice, as well as various human defenses and hunting. Therefore, it can be considered that, without effective natural predator control, the flood process in the lake area controls the population changes of the Oriental vole. Large spatial rainfall will reduce the birth rate and increase the mortality rate of the Oriental vole. The alternating flood and dry season hydrological process is important for maintaining the population and sex changes of the Oriental vole and is the main factor of external environmental stress.

[0152] (4) Simulation calculation of age structure changes in vole population.

[0153] The model divides Oriental voles into 24 age groups using 20-day age segments. The model can calculate the changes in the age structure of voles over time (resolution 30 min) in different habitat units. The calculated distribution results of the age structure of voles in the inner lakeside beach area and the outer farmland area are shown below. Figure 15As shown, the proportion of young Oriental voles in the Dongting Lake beach area is relatively large, reaching over 33%, while the proportion of older voles is less than 0.1%. This indicates that only a very small number of voles die from old age and exhaustion, and the population structure is a typical pyramidal distribution structure. This structure is characterized by a high birth rate and rapid population growth. Conversely, the age structure of the vole population outside the lake embankment is significantly different from that in the lake area. The main age groups are concentrated in the sub-adult and adult stages, with relatively few older and young voles (<25%). Its age structure is more diverse and less stable than that of the lake beach area. This mainly indicates that the changes in the vole population structure in the lake beach area are primarily caused by vole migration, rather than by succession occurring in a closed system. This structure is a diamond-shaped distribution structure. Meanwhile, in the farmland area outside the dike, the age structure of Oriental vole is predominantly juvenile before July, which is a result of the vole's own development living outside the dike. After July, the age structure undergoes a rapid change, mainly due to migration. However, after 2-3 months, a bimodal age structure (with a larger proportion of Oriental vole in both age groups) will appear. This is mainly due to the combined influence of the vole that did not migrate back to the lake shore and its high reproductive rate. As can be seen from the figure, the age structure of Oriental vole is mainly determined by its high reproductive rate, mortality rate, and migration process. The dynamic process of lake shore wetland inundation will undoubtedly drive the age structure of vole in different habitats to a large extent and further affect the population process of Oriental vole.

[0154] The study focuses on the population changes of Oriental vole in the Dongting Lake area and uses numerical simulation as the main research method. The main results are as follows: (1) The habitat soil, vegetation, land use, rainfall, flood inundation, soil moisture content and vole density are selected as reference factors. By coupling distributed hydrology, one-dimensional and two-dimensional hydrodynamic models, and using the geometric mean method, a habitat quality evaluation model for Oriental vole in the Dongting Lake area is established. (2) Through field investigation and sampling, relevant historical data are collected, key parameters such as growth, reproduction and migration of Oriental vole are analyzed, and a population change and migration model of Oriental vole in the Dongting Lake area is established. (3) The habitat quality, population change, sex and age structure changes of Oriental vole in Dongting Lake from 1991 to 2011 are reconstructed. The calculation results can reflect several outbreaks of Oriental vole disasters, such as the Oriental vole catastrophic disaster in 2007. (4) The specific calculation of the model reveals one of the main driving forces of population, migration, sex and age structure adjustment of Oriental vole during the Dongting Lake wetland inundation process. Further research has led to the preliminary establishment of models for the population, community succession, and energy flow of key organisms at different food chain levels closely related to the Oriental vole in the Dongting Lake habitat, including owls, weasels, snakes, striped field mice, Oriental vole, and brown rats. Basic calculation formulas have been derived. By combining the grid-distributed hydrological model of the Yangtze River basin and the hydrodynamic model of the middle reaches of the Yangtze River established in this embodiment, which couples the scheduling process of all large reservoirs in the Yangtze River basin, further research can quantitatively calculate the ecological succession and energy flow process of rodent populations, mainly Oriental vole and their main natural enemies in the Dongting Lake area. This will better reveal the impact of the scheduling process of large reservoir groups (including the Three Gorges Reservoir and the four large reservoir groups of Dongting Lake) on wetland changes, Oriental vole and striped field mouse populations, sex and age structure, and energy flow changes in the Dongting Lake area. This study is of great value in deepening our understanding of the interconnected processes and mutual influences between reservoir group scheduling, hydrological and water environment changes, habitat quality changes, bioenergy flow, migration, population, community changes and succession, and also plays a positive role in the understanding and effective control of rodent infestations in the Dongting Lake area.

[0155] This invention establishes a coupled river network water and sediment model, a vole habitat-dependent vegetation model, and a vole population dynamics model system to quantitatively study the impact of changes in the area of ​​lake islands and shoals, reservoir scheduling, and lake reclamation on vole population changes in the Dongting Lake area. It is not only highly innovative in research methods, but its research results are also of great significance for effectively controlling vole outbreaks.

[0156] Based on the same inventive concept, this invention also provides a quantitative prediction device for the spatiotemporal distribution changes of vole populations, comprising:

[0157] The habitat delineation module is used to determine the habitat of voles based on their activity range and to divide the habitat into an unstructured grid based on triangular units.

[0158] The first model construction module is used to select habitat environmental change parameters and vole density as reference factors, calculate the spatiotemporal variation results of each reference factor, and establish a vole habitat quality evaluation model based on the spatiotemporal variation results of each reference factor using the geometric mean method.

[0159] The second model construction module is used to group the voles in each grid cell of the unstructured grid according to age and sex, and construct a vole subpopulation migration model with age and sex structure based on the grouping results; and construct a vole population change model based on the number of vole subpopulations, the mortality rate of subpopulations, and the population control equation in the grid cells of the unstructured grid.

[0160] The third model construction module is used to solve for the energy input, energy output, migration energy consumption rate, growth energy consumption rate, pregnancy additional energy consumption rate, and rearing additional energy consumption rate of voles in the grid cells of the unstructured grid. Based on the solution results and the energy budget accounting equation, a model of the change in voles' stored energy within each time step is constructed.

[0161] The prediction module is used to perform coupled calculations on the vole habitat quality assessment model, vole subpopulation migration model, vole population size change model, and vole stored energy change model to obtain vole population change values ​​in different regions of the unstructured grid, and predict the distribution changes of the vole population based on the vole population change values.

[0162] Each module in the aforementioned quantitative prediction device for the spatiotemporal distribution changes of vole populations can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0163] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in the embodiment of the method for quantitatively predicting the spatiotemporal distribution changes of vole populations. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0164] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions, on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium can be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in the method embodiment for quantitatively predicting the spatiotemporal distribution changes of vole populations. Specific implementation methods can be found in the method embodiment, and will not be repeated here.

[0165] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A quantitative prediction method for the spatiotemporal distribution changes of vole populations, characterized in that, Includes the following steps: The habitat of voles was determined based on their activity range, and the habitat was divided into an unstructured grid based on triangular units. Using habitat environmental change parameters and vole density as reference factors, a vole habitat quality assessment model was established using the geometric mean method. The voles in each grid cell of the unstructured grid are grouped according to age and sex. Based on the grouping results, a vole subpopulation migration model with age and sex structure is constructed. A vole population change model is constructed based on the number of vole subpopulations, the mortality rate of subpopulations, and the population control equation in the grid cells of the unstructured grid. The energy input, energy output, migration energy consumption rate, growth energy consumption rate, pregnancy additional energy consumption rate, and rearing additional energy consumption rate of voles in the grid cells of the unstructured grid are solved. Based on the solution results and the energy budget accounting equation, a model of the change in voles' stored energy in each time step is constructed. The vole habitat quality assessment model, vole subpopulation migration model, vole population size change model, and vole stored energy change model were coupled to obtain vole population change values ​​in different regions of the unstructured grid. Based on the vole population change values, the distribution change of the vole population was predicted.

2. The method for quantitatively predicting the spatiotemporal distribution changes of vole populations according to claim 1, characterized in that, The quality of vole habitats was scored using a vole habitat quality assessment model, specifically as follows: In the formula, Let be the vole habitat quality score at time t in the ie-th grid cell of the unstructured grid, and n' represent the number of influencing factors. ie SPLantT represents the scoring for different soil types. ie Indicates the score assigned to vegetation type. Indicates land use scoring, Indicates the score for water submersion. The score represents the correlation between field mouse density and other factors. Scoring is assigned based on the depth of water accumulation in the habitat. Indicates the temperature score of the habitat. The score is assigned based on soil moisture content. This indicates the predator score assigned within a habitat unit.

3. The method for quantitatively predicting the spatiotemporal distribution changes of vole populations according to claim 2, characterized in that, The expression for the field mouse population migration model is: in, These represent the grid coordinates of the specific positions of the kag-th age group and jgd-th gender group at positions x and y at time step n+1, respectively. U represents the grid coordinates of the specific positions of the kag age group and jgd sex group at positions x and y at time step n, respectively. flow V flow Um represents the water flow velocity in the x and y directions, respectively. ie,jgd,kag U is the maximum migration velocity of the subpopulation in the kag age group and jgd sex group, θ is the angle between the destination unit and the source unit; α is the velocity correction coefficient, U Max,jgd,kag For the maximum migration speed, D x and D y These are the turbulence coefficients in the x and y directions, respectively, where n is the previous time step, and * represents n+. 1 / 2 time steps: n+1 represents the current time step, Δt represents the time step size, and R is a random time constant.

4. The method for quantitatively predicting the spatiotemporal distribution changes of vole populations according to claim 3, characterized in that, The expression for the model of vole population change is: When the field mouse is older than 20 days old, the calculation formula is as follows: in, This represents the number of subpopulations in the jgd and kag groups within the ie unit at time step n+1. Pmv represents the number of subpopulations living in the jgd and kag groups of unit ie at time step n. ie,jgd,kag,nm Dsk represents the number of all grid cells adjacent to or bordering ie within a time step of n to n+1, where Num(ie,jgd,kag) is the subpopulation of voles of the same age and sex that migrated to cell ie; ie,jgd,kag Dmv represents the mortality rate of the jgd and kag subpopulations in the static stack region of the ie unit. ie,jgd,kag,n The mortality rate of the jgd and kag group voles entering the target unit ie from other adjacent or bordering units; When the field mice are under 20 days old, the number of field mice that have migrated here is not considered. The calculation formula is: in, Rpreg represents the total number of mature female Oriental voles during steps n and n+1. kag It is the pregnancy rate of voles, B kag It is the average number of pups per litter; R igd It is the sex ratio of newborn fetuses; When the field mice are older than 10 days but younger than 20 days, excluding those that have migrated, the calculation formula is:

5. The method for quantitatively predicting the spatiotemporal distribution changes of vole populations according to claim 4, characterized in that, The expression for the field mouse energy storage change model is: In the formula, EIn represents the energy assignment at time n+1, where JGD represents the sex and KAG represents the age group of voles residing in cell ie, respectively. ie,jgd,kag Mb represents the energy replenishment obtained by the field mouse within a time step Δt. ie,jgd,kag The energy rate of basal metabolic consumption of vole subpopulations of jgd sex and kag age group residing in unit ie, EMov ie,jgd,kag EAdd represents the energy consumed by vole subpopulations of the jgd sex and kag age group residing within the ie unit during migration within a time step. ie,jgd,kag The additional energy Δt represents the time step consumed by the jgd sex and kag age group of vole subpopulations residing in unit ie as they dig burrows and raise their young within the time step.

6. A quantitative prediction device for the spatiotemporal distribution changes of vole populations, characterized in that, include: The habitat delineation module is used to determine the habitat of voles based on their activity range and to divide the habitat into an unstructured grid based on triangular units. The first model construction module is used to select habitat environmental change parameters and vole density as reference factors, calculate the spatiotemporal variation results of each reference factor, and establish a vole habitat quality evaluation model based on the spatiotemporal variation results of each reference factor using the geometric mean method. The second model building module is used to group the voles in each grid cell of the unstructured grid according to age and sex, and to build a vole subpopulation migration model with age and sex structure based on the grouping results; and to build a vole population change model based on the number of vole subpopulations, the mortality rate of subpopulations and the population control equation in the grid cells of the unstructured grid. The third model building module is used to solve for the energy input, energy output, migration energy consumption rate, growth energy consumption rate, pregnancy additional energy consumption rate, and rearing additional energy consumption rate of voles in the grid cells of the unstructured grid. Based on the solution results and the energy budget accounting equation, a model of the change in voles' stored energy in each time step is constructed. The prediction module is used to perform coupled calculations on the vole habitat quality assessment model, vole subpopulation migration model, vole population size change model, and vole stored energy change model to obtain vole population change values ​​in different regions of the unstructured grid, and predict the distribution changes of the vole population based on the vole population change values.

7. A computer device, comprising a memory and a processor, characterized in that, The memory stores computer execution instructions, and the processor executes the computer execution instructions stored in the memory to implement the steps of the quantitative prediction method for the spatiotemporal distribution change of vole population as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions, which, when executed by a processor, are used to implement the steps of the quantitative prediction method for the spatiotemporal distribution change of vole populations as described in any one of claims 1-5.

Images