CN112597661A - Industrial forest productivity prediction method based on species distribution and productivity coupling - Google Patents
Industrial forest productivity prediction method based on species distribution and productivity coupling Download PDFInfo
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Abstract
The invention discloses an industrial forest productivity prediction method based on species distribution and productivity coupling. And then carrying out adaptive area simulation under a plurality of different-intensity climate change scenes in the future, and further carrying out grid calculation on the obtained global environment suitability map layer through a conversion equation between productivity and environment suitability. Finally, industrial timber forests are obtained in the world in suitable growing areas and production potential. The species distribution model of the invention combines published ground survey biomass data to predict the future forest productivity, not only realizes high-precision simulation of the global scale, but also can carry out future biomass distribution simulation on a large scale by combining the characteristics of future climate change, and has important significance for selection of industrial forest afforestation regions and maintenance or promotion of the future productivity.
Description
Technical Field
The invention belongs to the technical field of forest productivity prediction, and particularly relates to a future industrial forest productivity prediction method based on species distribution and productivity coupling.
Background
The shortage of wood is a global problem all the time, and the shortage of wood in China is particularly serious. At present, China is the first large wood import country and the second large wood consumption country in the world. In order to meet the production and living needs of people, China can only increase the import quantity to maintain wood supply. In recent years, the proportion of imported wood in China to the domestic wood consumption is increased from 27.9% in 2000 to 50.7% in 2014. Even if the import quantity of the wood is increased, the annual average gap of the wood in China still reaches 3 billionth of cubic meters. Under the background of wood shortage, the production maintenance or promotion problem of industrial woods is about the raw material guarantee supply of various industries in China.
In recent years, the problems of simulation prediction of future productivity and yield maintenance of industrial forests have become important issues due to the cutting of natural forests. Since 1990, the reduction of the area of the original forest by 8100 million hectares has brought about more serious ecological problems in some countries. Then, the countries such as brazil, australia, germany, usa, canada, south africa and the like have successively introduced natural forest protection engineering, established national parks or natural protection areas, and implemented cutting or restriction policies to strengthen the restriction of over-development of natural forests. On the other hand, the speed of afforestation of global artificial forests has started to slow down in the last 10 years. However, the demand of wood is still continuously increasing, so that the contradiction between supply and demand of wood is increasingly prominent.
Industrial forests (Industrial forest plants) are a very important component of the forest ecosystem. It provides important basic raw materials for human survival and development, such as wood, food, oil and the like. Meanwhile, it is one of the most abundant and stable carbon storage banks in nature, and plays an irreplaceable important role in maintaining ecological balance. According to The statistical results of The united nations Food and Agriculture Organization (FAO) Forest resource Development Service (Forest Resources Development Service), The artificial Forest provides 35% of The wood in The world with a specific gravity of 3.2% in The Forest, wherein The industrial materials for providing wood account for more than 60% of The artificial Forest, and The specific gravity is still increasing year by year. However, the production of industrial forests will also face significant challenges in the context of global climate change. Climate change has now become a significant global problem. Unprecedented global climate change has resulted in the transition of the habitability area of species and the extinction of local species, and is expected to have greater impact in the future. Some originally suitable habitat of forests may become no longer suitable for the original forest growth under future climatic conditions and affect the climatic suitability and productivity of existing forests. Thus, the habitability area and productivity of industrial forests face great uncertainty under global climate change.
The MaxEnt model is one of the most widely used species distribution models because of its high stability and predictability. The model is based on a machine learning algorithm, and completes the simulation of the environmental suitability of the forest by mainly utilizing the geographic coordinates of the appearance points of species, the environmental map-layer data such as climatic conditions and the like. At present, the model is widely applied to the fields of endangered animal and plant habitability area prediction, biological invasion prediction and the like under the climate change background, and has a good prediction effect. The model plays a great role in the field of protection and management of endangered animals and plants. No scholars have yet tried to predict forest productivity against a climate change background.
From the perspective of the impact of environmental variables on forest productivity, previous forest productivity prediction techniques typically only consider the impact of a single climate variable or soil on forest productivity. From the perspective of the scale of productivity simulation, existing productivity simulation techniques typically only simulate forest productivity in a small scale area in response to climate change. The prior simulation technology of the habitability area of the species is also focused on the response of endangered animals, plants and microorganisms to the climate change, and is less concerned with the response of industrial forests to the climate change.
Past species distribution models have only been used for simulation of forest habitability areas and have not been used to simulate forest productivity. Although conventional means such as ground survey and satellite remote sensing can realize large-scale biomass simulation and calculation, it is difficult to predict future productivity by combining with future climate change patterns. Although the ecosystem process model can realize the simulation of future forest productivity under climate change, the model needs to depend on a plurality of physiological indexes such as plant photosynthetic rate, leaf porosity and the like, so that the global large-scale simulation cannot be realized.
Disclosure of Invention
The technical problems solved by the invention are as follows: the invention provides an industrial timberland productivity prediction method which predicts future forest productivity by combining a species distribution model with published ground survey biomass data, realizes high-precision simulation of global scale, and can also simulate the future biomass distribution on large scale by combining the characteristics of future climate change.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an industrial forest productivity prediction method based on species distribution and productivity coupling comprises the following steps:
s1: modeling a current global survival area of the industrial forest, and simulating the environmental suitability of the industrial forest in a current climate scene by using the established model;
s2: extracting productivity data of each distribution point;
s3: establishing a coupling relation between the environment suitability simulation result and productivity data of the industrial forest at each distribution point to obtain a conversion equation between the environment suitability and the productivity of the industrial forest;
s4: carrying out simulation on suitable areas of a plurality of future climate scenes of the industrial forest under climate change to obtain the environment suitability of the industrial forest under the future climate scenes;
s5: and performing grid calculation on the environment suitability result of the simulated industrial forest under various future different climate change scenes by using an environment suitability-productivity conversion equation to obtain the potential productivity of the industrial forest under various future climate change scenes.
Preferably, in step S1, the current generation industrial forest distribution big data and the current generation related environment data are collected, the environment data includes climate variables and soil variables, and then a maximum entropy model is established, and the model outputs a result that each geographical grid on the large scale has an environment suitability value.
Preferably, in step S2, productivity data is collected and the productivity big data required for proper modeling is screened.
Preferably, the screening method of the productivity data comprises: (1) if no productivity data is contained, only the biomass data should include at least two measurements of biomass for calculating productivity (productivity is the number of years between two times the productivity data is subtracted and divided by the two times); (2) in studies involving inclusion of treatment, productivity data for control groups (no treatment) were used; (3) in studies using only different planting density treatments, productivity was averaged across all different planting density treatment groups; (4) if the study included productivity measurements for multiple forest ages for the same plot, the productivity values for all forest ages were averaged.
Preferably, in step S3, the data of environmental suitability for each grid point around the world output by the species distribution model is given To each piece of productivity data by the function of "Extract Values To Points" in the GIS, the productivity data and the corresponding data of environmental suitability are coupled by using a "basic trends" package (including 7 types of models) in R (statistical application software, hereinafter referred To as R), and the conversion equation between environmental suitability and productivity is obtained as
y＝ax+b
In the formula: y is productivity, x is environmental fitness, a is a slope coefficient, and b is intercept.
Preferably, in step S4, a maximum entropy model of a plurality of future climate scenes under climate change is established using the future climate change data and the large data of industrial forest distribution and soil data collected in step 1, so as to simulate the environmental suitability of the industrial forest of the plurality of future climate scenes under climate change.
Preferably, the latest sixth global atmospheric coupling model of IPCC is adopted as the climate change data, and future climate change scenes under two different intensity greenhouse gas emission intensities in two future times of 2041-.
Preferably, in steps S4 and S5, simulation of habitability areas under a plurality of future different intensity climate change scenes is performed, and then the obtained global environment suitability map layer is subjected to further grid calculation through an environment suitability-productivity conversion equation, so as to finally obtain the future habitability areas and production potential of the industrial forest in the world.
Preferably, the potential productivity grid map of future industrial forests obtained after grid calculation and the productivity grid map of the current generation are subjected to superposition analysis in ArcGIS to obtain intersection, union and complement to obtain the increase and decrease of the future productivity in which areas.
Has the advantages that: compared with the prior art, the invention has the following advantages:
the invention provides a method for predicting future industrial forest productivity under the climate change background.
(1) The invention can realize the high-precision productivity simulation of the current generation and the future on the national or global large scale by coupling the maximum entropy model MaxEnt and the large data of the industrial timber productivity, and has obvious advantages on the simulation scale compared with the prior art which can only realize the simulation of the current generation on the large scale or can only realize the simulation of the future on the small scale.
(2) Taking the poplar industrial timber forest as an example, the simulation by the method shows that the industrial timber forest has a remarkable positive correlation between the environmental suitability and the productivity, namely the higher the environmental suitability is, the higher the productivity of the industrial timber forest is.
(3) The invention tries to predict the future forest productivity by combining the species distribution model with published ground survey biomass data, not only realizes high-precision simulation of the global scale, but also can simulate the future biomass distribution on the large scale by combining the characteristics of future climate change, simulates the global potential survival area and the productivity of the global main industrial forest under the future climate change, and fills the gap of the field.
(4) The invention provides a method for predicting the future industrial forest productivity under the climate change background, and has higher reference value and operability for forestry management departments. The method can simulate the productivity variation trend of the industrial forest in advance under the climate change background, and can take more active forest management measures in advance in areas with lost productivity in the future; for areas where productivity will increase in the future, forestation plans should be laid out in advance, which has important significance for maintaining or improving productivity of industrial forestation in China.
Drawings
FIG. 1 is a schematic diagram of the technical scheme of the invention;
FIG. 2 is a schematic illustration of industrial timberline data obtained through the public big data platform at a global distribution point;
FIG. 3 is a plot of environmental fitness value versus productivity big data fit of the MaxEnt model output;
FIG. 4 is a global potential productivity map based on simulated industrial forests under contemporary climatic conditions;
FIG. 5 is a diagram illustrating the amount of global productivity variation of 2041-2060 year old industrial woods based on future SSP126 scenarios;
FIG. 6 is a graph illustrating the amount of change in global productivity of industrial forests in 2061-2080 years based on future SSP126 scenarios;
FIG. 7 is a diagram illustrating the amount of global productivity variation of industrial woods in 2041-2060 years based on the future SSP585 scenario;
FIG. 8 is a diagram illustrating the global productivity variation of industrial forests in 2061-2080 years based on future SSP585 scenario;
FIG. 9 is a statistical graph of the amount of change in productivity in future scenarios over the current generation.
Detailed Description
The present invention will be further illustrated by the following specific examples, which are carried out on the premise of the technical scheme of the present invention, and it should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention.
As shown in fig. 1, the method for predicting the productivity of an industrial forest based on species distribution and productivity coupling mainly comprises the following steps:
s1: modeling a global survival area where the industrial timbers are distributed in the current generation, and simulating the environmental suitability of the industrial timbers in the current climate scene by utilizing an established maximum entropy model (MaxEnt model);
before the model is established, data acquisition is firstly carried out, and modeling is carried out by using proper data. The data mainly comprises large industrial timber distribution data and contemporary related environmental data. Large data of industrial wood forest distribution: the method is obtained by consulting data published by global biodiversity platform (GBIF), Web of science, Hopkins, Chinese digital plant specimen museum and other platforms or published papers. The environmental data included climate and soil variables, and the soil data for MaxEnt model input is shown in table 2: the soil data is obtained from a world soil database (HWSD, https:// www, world clim. org/data/index. html), which contains 15 types of soil physicochemical property data (such as sand content, soil organic carbon, conductivity and other indexes) in the global range. The original resolution of the soil data was 30 arcsec, which was converted to 2.5 arcmin for matching with the environmental data. After the resolutions of all the environment data are unified, the normal operation of the maximum entropy model can be ensured. The climate variables selected for the MaxEnt model simulation input in this example are shown in table 3, and include 19 weather data indicators (including the highest, lowest and average values of temperature and precipitation). This data was downloaded by the WorldClim platform (https:// www.worldclim.org /).
TABLE 2 15 soil characteristic variables for MaxEnt model simulation
| Name of Chinese | Variable description in HWSD soil database |
| Surface soil gravel content | Topsoil Gravel Content |
| Sand content of surface soil | Topsoil Sand Fraction |
| Content of surface soil particles | Topsoil Silt Fraction |
| Clay content of surface soil | Topsoil Clay Fraction |
| Surface soil volume weight | Topsoil Bulk Density |
| Organic carbon content of surface soil | Topsoil Organic Carbon |
| Acidity and alkalinity of surface soil | Topsoil pH(H2O) |
| Cation exchange capacity of clay on surface | Topsoil cation exchange capacity of(clay) |
| Amount of cation exchange in the surface soil | Topsoil cation exchange capacity(soil) |
| Basic saturation of surface soil | Topsoil Base Saturation |
| Total amount of surface soil base exchanged | Topsoil TEB |
| Surface soil calcium carbonate content | Topsoil Calcium Carbonate |
| Surface soil calcium sulfate content | Topsoil Gypsum |
| Percent of surface soil exchangeable sodium salt | Topsoil Sodicity(ESP) |
| Conductivity of surface soil | Topsoil Salinity(Elco) |
TABLE 3 19 climate variables for MaxEnt model simulation
S2: extracting productivity data of each distribution point of the industrial timber forest;
large data of industrial forest productivity: to couple industrial forest productivity with the fitness results output by the MaxEnt model, productivity data collected from the latest peer review journal papers on the four platforms, Web of Science, Web of chinese knowledge, and google academic, will be used. The search keywords that are formulated include (species name or latin university name) and (biomass, distribution, occurrence, location, biomass, carbon reserve or productivity). After all relevant documents are downloaded, the title, abstract, result and conclusion of each productivity relevant document are checked, and the productivity big data are screened through certain standards.
The productivity big data required for proper modeling will be screened by the following criteria: (i) if the productivity data is not contained, only the biomass data, at least two measurements of biomass should be included for calculating the productivity; (ii) in studies involving inclusion of treatment measures, productivity data for the control group will be used; (iii) in studies using only different planting density treatments, the productivity of all different planting density treatment groups was averaged; (iv) if the study included productivity measurements for multiple forest ages of the same plot, the productivity values for all forest ages would also be averaged.
S3: establishing a coupling relation between the environment suitability simulation result and productivity data of the industrial forest at each distribution point to obtain a conversion equation between the environment suitability and the productivity of the industrial forest;
after the collection of the current distribution point data, the climate data and the soil data is finished, inputting the data into a MaxEnt model for modeling, simulating the suitability of the industrial forest under the current environmental variable condition of the world by adopting the MaxEnt model, establishing a coupling relation between the prediction result of the maximum entropy model (the environmental suitability value data of all grids) and the productivity value of the industrial forest at each point of the world by using tools such as GIS, R and the like, and specifically comprising the following steps of: the environmental suitability data of each grid point in the world output by the species distribution model is given to each productivity data through the function of 'value extraction to point' in the GIS, the productivity data and the corresponding environmental suitability data are coupled by using a 'basic trends' package in an R tool (statistical application software, hereinafter referred to as R), and the obtained conversion equation between the environmental suitability and the productivity is detailed in table 1:
table 1R 7 models contained in the "basic trends" package of the tool
| Serial number | Type of model |
| 1 | y＝a*x+b |
| 2 | y＝a*x62+b*x+c |
| 3 | y＝a*ln(x)+b |
| 4 | y＝a*exp(b*x) |
| 5 | y＝a*exp(b*x)+c |
| 6 | y＝a*x^b |
| 7 | y＝a*x^b+c |
In the model of table 1, y is productivity and x is environmental suitability. a. And b and c are formula coefficients, and the sizes of the formula coefficients are determined by the results after the environmental suitability data and the productivity data of different industrial forest species are coupled.
The results of the simulation using 7 models in table 1 show that the y ═ a × x + b model of code 1 has the highest precision, using populus as an example,
y＝12.582x+4.6122
in the formula: y is productivity and x is environmental suitability. 12.582 is the slope of the equation and 4.61 is the intercept of the equation. The magnitude of both values depends on the results of the coupling of environmental suitability data and productivity data for different industrial forest species.
S4: and performing the simulation of the suitable growing areas of a plurality of future climate scenes of the industrial forest under the climate change to obtain the environment suitability of the industrial forest under the future climate scenes.
The process of the step S4 is the same as that of the step S1, except that the climate data adopts climate data in a plurality of future different climate change scenes, and the industrial forest distribution big data and soil data collected in the step 1, and the maximum entropy models of the plurality of future climate scenes in the climate change are established, so as to simulate the environmental suitability of the industrial forests in the plurality of future climate scenes in the climate change. The latest sixth global atmospheric coupling model (CMIP6) of IPCC is selected for future climate change data, the coupling model has multiple versions of global multiple-country simulation, and BCC-CSM2-MR (simulation by Beijing climate center) climate model data is adopted. The future climate change scenes (SSP126 and SSP585) under two different intensity greenhouse gas emission intensities in two future times of 2041-2060 and 2061-2080 are selected. All climate factor data resolution was 2.5 arc minutes.
S5: grid calculation is carried out on the suitability result of the industrial forest obtained by simulation under various future different climate change scenes by utilizing an environment suitability-productivity conversion equation, and the potential productivity of the industrial forest under various future climate change scenes is obtained;
and (3) performing survival area simulation under a plurality of different intensity climate change scenes (SSP126 and SSP585) in the future (2041-2060 or 2061-2080), and performing grid calculation on the obtained global environment suitability map layer through a conversion equation between productivity and environment suitability to finally obtain the future global survival area and production potential of the industrial forest.
Four major industrial forests were analyzed for characteristics and mechanisms of provincial and productivity changes, both in the present generation and in the future, under climate change: and (3) carrying out superposition analysis on the future potential productivity grid map of the future industrial forest obtained after grid calculation and the current productivity grid map of the future industrial forest in ArcGIS to obtain intersection, union and complement to obtain increase and decrease of the future productivity in which areas. Under the climate change background, suggestions are made for the migration assistance and afforestation layout planning of the four main industrial forests in the current generation and the future in the world for reference. The method adopts a maximum entropy model (MaxEnt) to simulate the suitability of the industrial timber forest (poplar is taken as a case analysis in the invention) under the global modern climatic conditions, and establishes a coupling relation between the predicted environmental suitability and the productivity values of the industrial timber forest at various points in the world through tools such as GIS, R and the like to obtain a conversion equation between the productivity and the environmental suitability. Then, the simulation of the survival regions under a plurality of different intensity climate change scenes (SSP126 and SSP585) in the future (2041-. Finally, industrial timber forests are obtained in the world in suitable growing areas and production potential. Finally, the characteristics and mechanisms of the industrial forests in future niche areas and productivity changes are analyzed. And aiming management and afforestation planning suggestions are provided for industrial timber producing areas in different geographic areas of the world by combining the actual distribution situation of the modern industrial timber.
The invention tries to predict the future forest productivity by combining the species distribution model with published ground survey biomass data, thereby not only realizing high-precision simulation of the global scale, but also being capable of carrying out future biomass distribution simulation on a large scale by combining the characteristics of future climate change. The method provided by the invention provides theoretical guidance for the forestry management department to make large-scale afforestation plans, and the application of the result plays a positive role in guaranteeing the production supply of industrial timberwood in China. The method considers the influence of climate and soil variables on the industrial forest, utilizes the maximum entropy model to simulate the environmental suitability of the industrial forest, then utilizes the productivity big data to be coupled with the environmental suitability output by the maximum entropy model, and analyzes the influence of climate change on the potential suitability area and the productivity of the future industrial forest. And put forward future afforestation planning and select appropriate suggestions and appropriate management measures on site on the basis of analyzing the suitable growth area of the industrial forest and the future change trend of the productivity. The inventive result will help to maintain or increase productivity of industrial forests on a large scale against climate change. On the other hand, the pressure of excessive felling of the natural forest will also be relieved.
Taking poplar as an example, the test process and results of this embodiment are as follows:
through the steps described in S1 through S5, contemporary and future global production simulations of aspen industrial timber forest were obtained (fig. 2-9). The environment fitness value output by the environment fitness after the MaxEnt simulation is completed is usually between 0 and 1, the environment fitness value on a certain grid is 0 to 0.1, the grid is considered as a non-suitable area, 0.1 to 0.3 are low-suitable areas, 0.3 to 0.5 are medium-suitable areas, and 0.5 to 1 are high-suitable areas. The two environment suitability segmentation values of 0.3 and 0.5 are substituted into the environment suitability-productivity conversion equation to obtain two productivity value segmentation values (8.38 and 10.9).
Accordingly, an area where the productivity is less than 8.38 t/ha/year is defined as a low productivity area (light gray in fig. 4), an area where the productivity is 8.38 to 10.9 t/ha/year is defined as a medium productivity area (dark gray in fig. 4), and an area where the productivity is more than 10.9 t/ha/year is defined as a high productivity area (black in fig. 4). And then summing each grid of the high-yield area, the medium-yield area and the low-yield area in the GIS by using a grid calculator and dividing the sum by the number of the grids to respectively obtain the yield per unit area of the high-yield area, the medium-yield area and the low-yield area, namely the yield of the high-yield area is 11.73 tons/hectare/year (mainly located in the local area of the United states and the small part of North China), the yield of the medium-yield area is 9.44 tons/hectare/year (located in the periphery of the local area of the United states, the plain of North China, the middle Europe, the eastern Europe and the south of the Yangtze river), and the yield of the medium-yield area is 6.96 tons/hectare/year (located in the regions of the east Europe.
The present method successfully simulates the current generation (fig. 4) and future (fig. 5-8) productivity of poplar industrial woods on a global scale. The future productivity simulation result shows that the productivity of the poplar in China has a tendency of transferring to the north under a plurality of climate change scenes in the future. The productivity of the poplar industrial forest in the future of northeast, Shandong, Hunan, Zhejiang and the like of China may be reduced (the inclined part with gray in FIGS. 5-8 is a productivity reduction area of the poplar industrial forest in the future, and the deeper the gray is, the more severe the decline is), and the productivity in the areas of south Henan, Hebei, Shanxi and the like may be improved in the future (the part without gray inclined bar in FIGS. 5-8 is a productivity reduction area of the poplar industrial forest in the future, and the deeper the gray is, the more severe the decline is). In summary, the total productivity of the aspen americana industrial forest will be under future scenarios SSP226 and SSP585 of two greenhouse gas emission concentrations in 2041-.
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 decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (9)
1. An industrial forest productivity prediction method based on species distribution and productivity coupling is characterized by comprising the following steps:
s1: modeling a current global fitness area of the industrial forest, and simulating the environmental fitness of the industrial forest in a current climate scene by using the established model;
s2: extracting productivity data of each distribution point;
s3: establishing a coupling relation between the environment suitability simulation result and productivity data of the industrial forest at each distribution point to obtain a conversion equation between the environment suitability and the productivity of the industrial forest;
s4: carrying out simulation on the suitable growing areas of a plurality of future climate scenes of the industrial forest under the climate change to obtain the environment suitability of the industrial forest under the plurality of future climate scenes;
s5: and performing grid calculation on the environment suitability result of the simulated industrial forest under various future different climate change scenes by using an environment suitability-productivity conversion equation to obtain the potential productivity of the industrial forest under various future climate change scenes.
2. The method of claim 1, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: in step S1, the current generation industrial forest distribution big data and the current generation relevant environmental data are collected, the environmental data include climate variables and soil variables, and then the maximum entropy model is established, and the output result of the model is that each geogrid has an environmental suitability value on a large scale.
3. The method of claim 1, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: in step S2, productivity data is collected and screened for productivity big data needed for proper modeling.
4. The method of claim 3, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: the screening method of the productivity data comprises the following steps: (1) if no productivity data is contained, only biomass data should include at least two measurements of biomass for calculating productivity; (2) in studies involving inclusion of treatment measures, productivity data was used for control groups without treatment measures; (3) in studies using only different planting density treatments, productivity was averaged across all different planting density treatment groups; (4) if the study included productivity measurements for multiple forest ages for the same plot, the productivity values for all forest ages were averaged.
5. The method of claim 2, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: in step S3, the "value extraction to point" function in the GIS assigns the environmental suitability data of each grid point in the world output by the species distribution model to each piece of productivity data, establishes a coupling relationship between the productivity data and the corresponding environmental suitability data using the "basic trends" package in the R tool, and obtains the conversion equation between environmental suitability and productivity as
y＝ax+b
In the formula: y is productivity, x is environmental fitness, a is a slope coefficient, and b is intercept.
6. The method of claim 1, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: in step S4, a maximum entropy model of a plurality of future climate scenes under climate change is established by using the future climate change data and the large data of the industrial forest distribution and the soil data collected in step 1, and the environmental suitability of the industrial forest of the plurality of future climate scenes under climate change is simulated.
7. The method of claim 6, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: the latest sixth global atmosphere coupling model of the IPCC is adopted for the climate change data, and future climate change scenes under two different intensity greenhouse gas emission intensities in two future times of 2041-2060 and 2061-2080 are adopted for the future climate change scenes.
8. The method of claim 1, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: in the steps S4 and S5, the habitability area simulation under a plurality of different intensity climate change scenes in the future is firstly carried out, then the obtained global environment suitability map layer is subjected to further grid calculation through an environment suitability-productivity conversion equation, and finally the habitability area and the production potential of the industrial forest in the future in the world are obtained.
9. The method of claim 1, wherein the species distribution and productivity coupling-based method for predicting productivity of industrial forests comprises: and (3) carrying out superposition analysis on the future potential productivity grid map of the future industrial forest obtained after grid calculation and the current productivity grid map of the future industrial forest in ArcGIS to obtain intersection, union and complement to obtain increase and decrease of the future productivity in which areas.
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|---|---|---|---|---|
| CN113128871A (en) * | 2021-04-21 | 2021-07-16 | 中国林业科学研究院资源信息研究所 | Cooperative estimation method for distribution change and productivity of larch under climate change condition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104517030A (en) * | 2014-11-06 | 2015-04-15 | 北京师范大学 | Extraction method for artificial grassland growth suitable region |
| CN109767040A (en) * | 2019-01-15 | 2019-05-17 | 上海海洋大学 | Saury cental fishing ground prediction technique based on habitat suitability index |
| CN109919515A (en) * | 2019-03-25 | 2019-06-21 | 中国气象科学研究院 | Eco-Environmental Synthetic Analyses method and device |
-
2020
- 2020-12-30 CN CN202011612643.3A patent/CN112597661B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104517030A (en) * | 2014-11-06 | 2015-04-15 | 北京师范大学 | Extraction method for artificial grassland growth suitable region |
| CN109767040A (en) * | 2019-01-15 | 2019-05-17 | 上海海洋大学 | Saury cental fishing ground prediction technique based on habitat suitability index |
| CN109919515A (en) * | 2019-03-25 | 2019-06-21 | 中国气象科学研究院 | Eco-Environmental Synthetic Analyses method and device |
Non-Patent Citations (2)
| Title |
|---|
| YOAN FOURCADE 等: "Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias", 《PLOS ONE》 * |
| 刘丹: "基于分布适宜性和潜在生产力的定量适地适树研究", 《中国优秀博硕士学位论文全文数据库（博士） 农业科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113128871A (en) * | 2021-04-21 | 2021-07-16 | 中国林业科学研究院资源信息研究所 | Cooperative estimation method for distribution change and productivity of larch under climate change condition |
| CN113128871B (en) * | 2021-04-21 | 2023-10-20 | 中国林业科学研究院资源信息研究所 | Cooperative estimation method for larch distribution change and productivity under climate change condition |
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