A method and system for tending coniferous forests in arid mountainous areas

Abstract

This invention discloses a method and system for tending coniferous forests in arid mountainous areas. The tending method includes: obtaining regional evaluation information of the target tree species; obtaining the regeneration probability value corresponding to each point within the potential distribution range, and combining it with seedling density to obtain the effective regeneration range, including high-altitude and low-altitude areas; obtaining the nutrient relative limitation index and altitude asymmetry index of different altitude areas within the effective regeneration range, and based on these indices, diagnosing the differences in nutrient limitation types among different altitude areas to obtain nutrient diagnosis results; and obtaining tending strategies for the effective regeneration range based on the regional evaluation information, the effective regeneration range, and the nutrient diagnosis results. The tending strategies include assisted migration and nutrient addition measures for corresponding high-altitude areas, and disturbance control and soil function restoration measures for corresponding low-altitude areas. This invention achieves low-cost and high-efficiency sustainable forest management.

Description

Technical Field

[0001] This invention belongs to the technical field of forest ecological restoration and sustainable management, specifically relating to a method and system for tending coniferous forests in arid mountainous areas. Background Technology

[0002] Mountain coniferous forests in arid regions are important water conservation forests, playing a crucial ecological barrier role. However, under the dual pressures of climate change and human disturbance, the natural regeneration and restoration of these forests face severe challenges.

[0003] In related technologies, the definition of forest regeneration extent largely relies on experience to determine the influence range of mother trees, failing to comprehensively consider the differences in seed dispersal dynamics and seed physiological activity caused by altitude gradients. This leads to blurred regeneration boundaries at high altitudes, a high misjudgment rate of seedling retention areas at low altitudes, and insufficient spatial precision in implementing tending measures. Simultaneously, nutrient limitations at different altitudes exhibit significant asymmetry, and current fertilization management often employs a homogenization strategy, ignoring altitude-specific differentiation patterns, resulting in low nutrient utilization efficiency and the risk of secondary soil salinization.

[0004] In other words, most related technologies focus on single factors (such as seed treatment or soil improvement), which restricts the precise implementation of tending measures and easily leads to the risk of secondary soil salinization. Therefore, there is an urgent need to provide a tending method and system for coniferous forests in arid mountainous areas. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for tending coniferous forests in arid mountainous areas, in order to overcome the shortcomings of the prior art.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] An embodiment of the present invention provides a method for tending coniferous forests in arid mountainous areas, comprising the following steps:

[0008] Obtain regional evaluation information for the target tree species, including the potential distribution range and multiple suitability levels of the target tree species;

[0009] Obtain an effective update probability prediction model; take the distance to the nearest healthy mother tree, annual seed yield of the mother tree, slope aspect, altitude and litter thickness corresponding to the potential distribution range as input variables of the effective update probability prediction model, obtain the update probability value corresponding to each point in the potential distribution range, and combine it with seedling density to obtain the effective update range including high altitude area and low altitude area.

[0010] Obtain the relative nutrient limitation index and altitude asymmetry index for different altitude regions within the effective update range, and diagnose the differences in nutrient limitation types in different altitude regions based on these indices to obtain nutrient diagnosis results.

[0011] Based on the regional evaluation information, the effective regeneration range, and the nutrient diagnosis results, a tending strategy for the effective regeneration range is obtained; the tending strategy includes assisted migration and nutrient addition measures for high-altitude areas, and disturbance control and soil function restoration measures for low-altitude areas.

[0012] In a preferred embodiment, the regional evaluation information is obtained in the following manner:

[0013] Based on historical distribution data and regional environmental data of the target tree species, a species distribution model is used to predict the potential distribution range of the target tree species under current and future climate scenarios, and the potential distribution range is divided into multiple suitability levels; the potential distribution range and its corresponding multiple suitability levels are used as regional evaluation information.

[0014] The species distribution model is a maximum entropy model; the regional environmental data includes climate data, topographic data, and soil data; and the suitability levels include unsuitable, low-suitable, moderately suitable, and high-suitable.

[0015] In a preferred embodiment, the effective update probability prediction model is obtained by: setting up fixed survey plots along the elevation gradient within the potential distribution range, and collecting plot data; and constructing the effective update probability prediction model based on the plot data.

[0016] The valid update range is determined using the following method:

[0017] For high-altitude areas, starting from the mother tree and moving downhill, the area covered by the location where the update probability value is lower than the preset probability threshold for the first N consecutive occurrences is defined as the effective update range; N is a positive integer not less than 3.

[0018] For low-altitude areas, patches with an update probability value not less than a preset probability threshold and a seedling density greater than a preset density threshold are defined as valid update ranges.

[0019] In a preferred embodiment, the relative nutrient limitation index and the altitude asymmetry index are obtained in the following manner:

[0020] Within the effective update range, leaf and root zone soil samples were collected from sample trees at different altitudes and forest ages; leaf nutrient content and soil physicochemical properties were measured; and the relative nutrient limitation index and altitude asymmetry index were calculated based on the leaf nutrient content.

[0021] The leaf nutrient content includes the total carbon, total nitrogen, and total phosphorus content of the leaves; the soil physicochemical properties include soil organic carbon, total nitrogen, available nitrogen, available phosphorus, pH value, and water content.

[0022] In a preferred embodiment, the measures for assisted migration and nutrient addition in high-altitude areas include: protecting healthy mother trees and applying slow-release compound fertilizer in areas with high suitability levels in the regional evaluation information, and replanting cold-resistant container seedlings in potential suitable areas above the current treeline.

[0023] Disturbance control and soil function restoration measures for low-altitude areas include: setting up physical protection facilities based on natural expansion distances, and applying organic materials and inoculating microbial agents on degraded patches within the protection area.

[0024] In a preferred embodiment, the reference distance for setting up physical protective facilities is not less than their natural expansion distance, wherein the value of the natural expansion distance is 20-30 meters upward expansion from the upper forest line or 15-30 meters downward expansion from the lower forest edge.

[0025] In a preferred embodiment, the method further includes step S5: periodically monitoring using a monitoring network deployed within the effective update range to obtain monitoring feedback data, wherein the monitoring feedback data is used to adjust the nurturing strategy.

[0026] In a preferred embodiment, the step of obtaining nutrient diagnosis results based on the differences in nutrient limitation types in different altitude regions includes: when the leaf nitrogen-phosphorus ratio is not greater than a first preset ratio, it is determined to be nitrogen limitation; when the leaf nitrogen-phosphorus ratio is greater than the first preset ratio and less than a second preset ratio, it is determined to be both nitrogen and phosphorus limitation; when the leaf nitrogen-phosphorus ratio is not less than the second preset ratio, it is determined to be phosphorus limitation; and the second preset ratio is greater than the first preset ratio.

[0027] One embodiment of the present invention provides a tending system for coniferous forests in arid mountainous areas, comprising:

[0028] The information acquisition module is used to acquire regional evaluation information of the target tree species, including the potential distribution range and multiple suitability levels of the target tree species.

[0029] The update range acquisition module is used to acquire an effective update probability prediction model. The distance to the nearest healthy mother tree, the annual seed yield of the mother tree, the slope aspect, the altitude, and the thickness of the litter corresponding to the potential distribution range are used as input variables of the effective update probability prediction model to acquire the update probability value corresponding to each point in the potential distribution range. Combined with the seedling density, the effective update range including high-altitude and low-altitude areas is acquired.

[0030] The nutrient diagnosis module is used to obtain the relative nutrient limitation index and altitude asymmetry index of different altitude regions within the effective update range, and to diagnose the differences in nutrient limitation types in different altitude regions based on these indices, thereby obtaining nutrient diagnosis results.

[0031] The strategy acquisition module is used to acquire the tending strategy for the effective update range based on the regional evaluation information, the effective update range, and the nutrient diagnosis results; the tending strategy includes auxiliary migration and nutrient addition measures for high-altitude areas, and disturbance control and soil function restoration measures for low-altitude areas.

[0032] Compared with existing technologies, the advantages of this invention lie in its ability to comprehensively apply remote sensing, plot surveys, and model simulations to establish a quantitative identification method for regeneration range based on a regeneration probability prediction model. It also proposes differentiated definition standards for different regeneration mechanisms at high and low altitudes, significantly improving the spatial accuracy of tending measures. Simultaneously, it organically integrates distribution prediction, regeneration diagnosis, nutrient regulation, physical protection, and dynamic monitoring, establishing a long-term monitoring network covering regeneration, soil, leaves, and climate. This forms a complete management closed loop from diagnosis to implementation and evaluation, achieving multi-factor collaborative management and enhancing the system's sustainability and adaptability. Furthermore, the core parameters, such as the proposed fence deployment distance limit and regeneration probability prediction equation, are derived from field observations and statistical analysis. The parameters are easy to obtain and calculate, making them suitable for practical applications in grassroots forestry units. The overall implementation plan has controllable costs and good prospects for widespread application. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of the characteristic curve of the subjects in the Qinghai spruce forest predicted by the MaxEnt model in one embodiment of this application;

[0035] Figure 2 This is a schematic diagram illustrating the potential distribution range of Qinghai spruce forests under current climatic conditions in one embodiment of this application;

[0036] Figure 3 This is a schematic diagram illustrating the statistical data of different suitable habitat areas for Qinghai spruce in one embodiment of this application;

[0037] Figure 4 This is a schematic diagram illustrating the dynamic change process of the upper treeline of Qinghai spruce in one embodiment of this application;

[0038] Figure 5 This is a schematic diagram illustrating the relationship between tree growth in the treeline and temperature and precipitation in one embodiment of this application;

[0039] Figure 6 This is a schematic diagram of the spatial distribution pattern of the understory population of Qinghai spruce in the Qilian Mountains according to one embodiment of this application;

[0040] Figure 7 This is a schematic diagram of the dynamic change process of tree regeneration at the edge of the Qinghai spruce forest under a certain embodiment of this application;

[0041] Figure 8 This is a schematic diagram illustrating the relationship between the age and diameter at breast height (DBH) of Qinghai spruce forests in one embodiment of this application;

[0042] Figure 9 This is a schematic diagram illustrating the variation of nutrient content in the leaves of *Picea qinghaiensis* at the upper and lower forest edges with forest age, according to one embodiment of this application.

[0043] Figure 10 This is a schematic diagram of the correlation coefficients between nutrient indicators and environmental factors of Qinghai spruce leaves at the upper and lower forest edges in one embodiment of this application;

[0044] Figure 11 This is a visual schematic diagram of the strong correlation between nutrient content factors in the leaves of Qinghai spruce at the upper and lower forest edges in one embodiment of this application;

[0045] Figure 12 This is a schematic diagram showing the correlation coefficients between nutrient indicators and environmental factors of Qinghai spruce leaves at the lower forest edge (2700 meters) in one embodiment of this application;

[0046] Figure 13 This is a schematic diagram showing the correlation coefficients between nutrient indicators and environmental factors of Qinghai spruce leaves at the upper edge of the forest (3200 meters) in one embodiment of this application;

[0047] Figure 14 This is a schematic diagram of the regression model coefficients for nutrient content in leaves of Qinghai spruce at the lower edge of the forest (2700 meters) in one embodiment of this application;

[0048] Figure 15 This is a schematic diagram of the regression model coefficients for nutrient content in leaves of Qinghai spruce at the upper edge of the forest (3200 meters) in one embodiment of this application;

[0049] Figure 16 This is a flowchart illustrating a method for tending coniferous forests in arid mountainous areas according to one embodiment of this application.

[0050] Figure 17 This is a comparison table of the changes in soil physicochemical properties and environmental factors of Qinghai spruce of different ages at the upper and lower forest edges in one embodiment of this application;

[0051] Figure 18 This is a comparison table used in one embodiment of this application to display the basic information of sample trees selected from sample plots of different age groups along the upper forest line and the lower forest edge. Detailed Implementation

[0052] The invention will be more fully understood through the following detailed description, which should be read in conjunction with the accompanying drawings. Detailed embodiments of the invention are disclosed herein; however, it should be understood that the disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms. Therefore, the specific functional details disclosed herein should not be construed as limiting, but rather as the basis for the claims and as intended to teach those skilled in the art to employ the representative basis of the invention in different ways in any suitable detailed embodiment.

[0053] This application provides a method for tending coniferous forests in arid mountainous areas. Specifically, it uses natural Qinghai spruce forests at different altitudinal gradients in the Qilian Mountains and the Dayekou watershed of the central section of a typical arid region in Northwest my country as examples. Based on remote sensing interpretation, plot surveys, location observations, and model simulations, it identifies the potential distribution areas and key driving factors of pure Qinghai spruce coniferous forests in the Qilian Mountains, elucidates the regeneration characteristics and changing patterns of Qinghai spruce forests, reveals the asymmetric mechanism of nutrient limitation on Qinghai spruce growth, and finally, combined with dynamic monitoring and evaluation, proposes altitudinal differentiated management strategies, providing scientific and technological support for the protection and sustainable management of pure coniferous forests in arid mountainous areas.

[0054] Specifically, this type of forest ecosystem exhibits typical fragility, and its regeneration and restoration have long faced systemic technical bottlenecks. Firstly, natural regeneration in this type of forest is generally difficult. Constrained by factors such as seed scarcity and harsh microhabitats, the survival rate of seedlings after establishment is often less than 5%, resulting in patchy or discontinuous regeneration patterns and weak self-sustaining capacity of the stands. Simultaneously, there is significant uncertainty in the spatial delineation of the regeneration range: traditional methods relying on experience to determine the influence range of mother trees fail to comprehensively consider the seed dispersal dynamics (such as wind and gravity) and differences in seed physiological activity caused by altitude gradients, leading to blurred regeneration boundaries at high altitudes and a high rate of misjudgment of seedling retention areas at low altitudes, thus hindering the precise implementation of tending measures.

[0055] Furthermore, nutrient limitations at different altitudes exhibit a significant asymmetry. In low-altitude areas, soil nitrogen and phosphorus availability is restricted by carbon limitation and water stress, while in high-altitude areas, low temperatures significantly inhibit the rate of organic matter mineralization, resulting in a lack of available nutrients, while potassium, calcium, and magnesium ion leaching is severe. Current fertilization management often adopts a homogenization strategy, ignoring these altitude-specific patterns, resulting in nutrient utilization efficiency of less than 30% and a high risk of secondary soil salinization. Regarding physical protection, the deployment of fencing and other facilities lacks scientific basis: grazing disturbance is severe at low altitudes, but fixed-radius fencing does not consider topographical shielding effects, seedling density gradients, and animal behavior patterns, leading to low protection efficiency.

[0056] Current technologies mostly focus on single factors (such as seed treatment or soil improvement), lacking systematic solutions that integrate regeneration potential diagnosis, precise definition of regeneration range, altitude-specific matching of nutrient supply and demand, and spatial optimization of protective measures. Therefore, there is an urgent need to develop comprehensive restoration technologies targeting the multi-dimensional limiting factors in the regeneration process of monoculture coniferous forests in arid mountainous areas, in order to enhance the self-sustaining capacity and ecological function of monoculture coniferous forests in arid mountainous areas.

[0057] The technical solution provided in this application addresses the pressing issues of natural regeneration, ecological protection, and restoration of pure coniferous forests in arid mountainous areas. It combines integrated remote sensing and ground surveys to accurately quantify the regeneration potential range and nutrient limitation thresholds at different altitudes, enabling the implementation of differentiated nutrient supplementation and physical protection strategies. This significantly improves the regeneration success rate and ecosystem stability, ultimately achieving low-cost, high-efficiency sustainable forest management. The method will be described first, followed by a description of the system.

[0058] Example 1

[0059] See Figure 16 This embodiment provides a method for tending coniferous forests in arid mountainous areas. This method integrates regional assessment, regeneration potential diagnosis, and nutrient regulation to address the issues of insufficient spatial precision in tending measures and lack of targeted nutrient management in related technologies. The method specifically includes the following steps:

[0060] Step S101: Obtain regional evaluation information of the target tree species, including the potential distribution range and multiple suitability levels of the target tree species.

[0061] Specifically, this step aims to clarify the targets for tending at a macro scale. By obtaining the potential distribution range of the target tree species (such as Qinghai spruce), the geographical boundaries of tending operations can be delineated, avoiding the waste of resources in unsuitable areas. The classification of suitability levels provides a spatial basis for the development of subsequent differentiated measures. For example, highly suitable areas are often the focus of provenance conservation, while less suitable areas may require more intensive human intervention. It should be understood that obtaining regional assessment information can rely on various methods such as historical distribution data, remote sensing image interpretation, and environmental factor modeling.

[0062] Step S102: Obtain the effective update probability prediction model; take the distance to the nearest healthy mother tree, the annual seed yield of the mother tree, the slope aspect, the altitude, and the thickness of the litter corresponding to the potential distribution range as the input variables of the effective update probability prediction model, obtain the update probability value corresponding to each point in the potential distribution range, and combine it with the seedling density to obtain the effective update range including high-altitude areas and low-altitude areas.

[0063] Traditional tending methods often rely on experience to determine the influence radius of the mother tree, neglecting the differences in seed dispersal dynamics (such as wind and gravity) at different altitude gradients. This embodiment constructs an effective renewal probability prediction model, quantifying biological factors such as mother tree distance and seed yield, along with environmental factors such as slope aspect, altitude, and litter thickness, as input variables to calculate the renewal probability value at each point in space. This not only avoids the problem of blurred renewal boundaries in high-altitude areas but also accurately identifies patchy renewal areas affected by disturbance in low-altitude regions by introducing a seedling density index.

[0064] Step S103: Obtain the relative nutrient limitation index and altitude asymmetry index for different altitude regions within the effective update range, and diagnose the differences in nutrient limitation types in different altitude regions based on these indices to obtain nutrient diagnosis results.

[0065] This can be understood as follows: Coniferous forests in arid mountainous areas face drastically different nutrient limiting mechanisms at different altitudes. High-altitude areas are often inhibited by low temperatures, leading to slow organic matter mineralization, while low-altitude areas may be affected by water stress or soil physicochemical properties. By calculating the relative nutrient limiting index, the degree of deficiency for specific nutrient elements (such as nitrogen and phosphorus) can be quantified; while the altitude asymmetry index reveals the differentiated characteristics of this limitation across different altitude gradients. Based on the diagnostic results of these two indices, it is possible to accurately determine whether high altitudes are characterized by nitrogen limitation or low altitudes by phosphorus limitation, thus providing a scientific basis for differentiated fertilization and avoiding the problem of low nutrient utilization efficiency caused by traditional uniform management.

[0066] Step S104: Based on the regional evaluation information, the effective renewal range, and the nutrient diagnosis results, obtain the tending strategy for the effective renewal range; the tending strategy includes assisted migration and nutrient addition measures for high-altitude areas, and disturbance control and soil function restoration measures for low-altitude areas.

[0067] For high-altitude areas, since climate-driven factors are the dominant influence on treeline shifts, often manifesting as nitrogen limitation, measures such as assisted migration (e.g., replanting cold-resistant container seedlings above the treeline) and nutrient supplementation (e.g., applying slow-release fertilizers) are adopted to adapt to climate change trends and alleviate nutrient bottlenecks. For low-altitude areas, disturbance (e.g., grazing) and soil function degradation are the main limiting factors. Therefore, measures such as disturbance control (e.g., fencing) and soil function restoration (e.g., applying organic materials and inoculating microorganisms) are adopted to eliminate external disturbances and improve the soil microenvironment.

[0068] The technical solution provided in this embodiment introduces an effective renewal probability prediction model, incorporating key biophysical factors such as mother tree distance, seed yield, slope aspect, altitude, and litter thickness into a unified quantitative framework. Differentiated renewal boundary judgment criteria are set for high and low altitude regions. This overcomes the shortcomings of traditional experience-based judgments that fail to comprehensively consider differences in seed dispersal dynamics and seed physiological activity caused by altitude gradients, effectively solving the technical problems of ambiguous renewal boundaries at high altitudes and high misjudgment rates in low-altitude seedling retention areas.

[0069] By combining the relative nutrient limitation index and the altitude asymmetry index for diagnosis, the differences in nutrient limitation types in different altitude regions can be accurately identified, thereby achieving altitude-differentiated matching of nutrient management.

[0070] By organically integrating potential distribution prediction, renewal potential diagnosis, nutrient limitation identification and differentiated tending measures, this approach overcomes the fragmented limitations of existing technologies that often focus on a single factor (such as seed treatment or soil improvement).

[0071] In one embodiment, the regional evaluation information is obtained through the following method:

[0072] Based on historical distribution data and regional environmental data of the target tree species, a species distribution model is used to predict the potential distribution range of the target tree species under current and future climate scenarios, and the potential distribution range is divided into multiple suitability levels; the potential distribution range and its corresponding multiple suitability levels are used as regional evaluation information.

[0073] The species distribution model is a maximum entropy model; the regional environmental data includes climate data, topographic data, and soil data; and the suitability levels include unsuitable, low-suitable, moderately suitable, and high-suitable.

[0074] This paper takes the Qinghai spruce evergreen coniferous forest in the Qilian Mountains, a typical mountainous area in the arid northwest of my country, as an example. Through the acquisition of Qinghai spruce distribution points, the construction of climate data, and the accumulation of meteorological, hydrological, and soil data over many years in the Dayekou watershed of the central Qilian Mountains, based on climate suitability assessment, the paper uses the maximum entropy model to screen the dominant factors affecting the geographical distribution of Qinghai spruce, gives its potential geographical distribution range, and predicts the changes in its potential distribution area under the background of future climate change. The paper comprehensively determines the potential suitable areas, aiming to provide a scientific basis for the management of Qinghai spruce, regional ecological restoration, and response to climate change.

[0075] To evaluate the model's predictive accuracy, this embodiment introduces receiver operating characteristic (ROC) curves for validation. The simulation results of the maximum entropy (MaxEnt) model are evaluated using ROC curves. Generally, the area under the curve (AUC) ranges from 0 to 1.0; the closer the AUC value is to 1, the higher the predictive accuracy. This application obtains a binary distribution image of species distribution (suitable habitat and unsuitable habitat) by querying the actual distribution areas described in floras and field survey results, selecting the 5th percentile mean of 0.3 as the training threshold. The current and future potential distribution areas of Qinghai spruce are divided into four levels: unsuitable (0-0.3), poorly suitable (0.3-0.5), moderately suitable (0.5-0.7), and highly suitable (0.7-1).

[0076] The ROC curves for predicting the current potential distribution of *Picea acutissima* based on the MaxEnt model show an average area under the curve (AUC) of 0.97 for both the training and test sets, indicating that the model is statistically significant in predicting the potential distribution of *Picea acutissima* (see [link to article]). Figure 1 Under current climatic conditions, the potential distribution of Qinghai spruce forests in the Qilian Mountains, predicted based on the MaxEnt model, is as follows: Figure 2 As shown. The highly suitable area is mainly located in the central and eastern parts of the Qilian Mountains, covering an area of ​​208.63 km². The medium and low suitable areas are distributed around the highly suitable area, mainly in eastern Qinghai. The existing forest distribution in the three suitable areas accounts for 36%, 32%, and 55% of the predicted area of ​​each suitable area, respectively (see...). Figure 3 ).

[0077] Furthermore, atmospheric circulation models can be used to drive maximum entropy models to predict potential distribution changes under future climate scenarios (such as the 2050s and 2070s), thus providing a scientific basis for developing forward-looking adaptive nurturing strategies (such as assisted migration). As an example, the MaxEnt model, constructed using four RCP scenarios driven by the GCMs-CCMS4 atmospheric circulation model, simulated the future potential distribution of Qinghai spruce forests in two time periods (2050s and 2070s). The results showed that the spatial pattern and potential suitable distribution area of ​​Qinghai spruce forests under climate change scenarios are basically similar to those under current climate conditions. However, the potential distribution area of ​​Qinghai spruce exhibits different trends under different RCP pathways. Comparing the areas of each suitability level within the potential distribution area reveals that although the total distribution area of ​​Qinghai spruce in the 2050s and 2070s does not change significantly (within 5%) compared to the present under different RCP warming scenarios, the potential distribution area changes significantly between different suitability levels (Table 1). Among them, the potential distribution area of ​​Qinghai spruce forests in the low and moderate suitability zones is significantly affected by future climate scenarios. The distribution area in the low and moderate suitability zones shows an increasing trend in the 2050s and 2070s, while the area in the high suitability zone shows a decreasing trend to varying degrees. With higher emission scenarios, the uncertainty of climate change increases, and the transition between different suitability zone levels becomes more frequent.

[0078] Table 1. Percentage change in potential distribution of Qinghai spruce at different suitability levels compared to current area.

[0079] In one embodiment, the effective update probability prediction model is obtained by: setting up fixed survey plots along the elevation gradient within the potential distribution range, and collecting plot data; and constructing the effective update probability prediction model based on the plot data.

[0080] The valid update range is determined using the following method:

[0081] For high-altitude areas, starting from the mother tree and moving downhill, the area covered by the location where the update probability value is lower than the preset probability threshold for the first N consecutive occurrences is defined as the effective update range; N is a positive integer not less than 3.

[0082] For low-altitude areas, patches with an update probability value not less than a preset probability threshold and a seedling density greater than a preset density threshold are defined as valid update ranges.

[0083] As an example, the Dayekou watershed in the central Qilian Mountains, a typical arid region in Northwest my country, was selected as the main study area. Considering the distribution of dominant species in this watershed at altitudes of 2700-3200m and the resulting distinct upper and lower forest edges, different survey plots were established along the altitudinal gradient. Specifically, fixed survey plots of 30m×100 / 150m were set at the upper forest edge, 30m×50m at the lower forest edge, and 30m×30m plots were set every 100m in the intermediate altitude range. Simultaneously, five 5m×5m update quadrats and three soil profile excavations were conducted within the fixed survey plots along the altitudinal gradient. Furthermore, geographical information (slope, aspect, altitude, etc.), the relative position of each tree within the quadrat, and tree ring information were obtained for each plot. The upper and lower forest edges are mainly assessed by reconstructing tree settlement processes over the past 100 years, especially the last 50 years, through tree ring inversion. Specifically, this is reflected in the upward shift of the upper forest line and the downward expansion of the lower forest edge. For fixed-elevation plots in the middle range, regeneration potential is comprehensively evaluated by establishing fixed regeneration quadrats, supplemented by environmental factors such as soil nutrients, temperature, humidity, light, and litter characteristics.

[0084] Studies on the upper edge of the forest have found that the treeline of Qinghai spruce in the Dayekou watershed of the Qilian Mountains has risen by 20-30 meters in the past 100 years, and the spatial pattern of the forest shows a trend of transformation from random distribution to clustered distribution (see [link to study]). Figure 4 The significant uplift of the treeline in arid mountain coniferous forests over the past century is a conclusive signal of climate change-driven ecosystem evolution. Specifically, the average temperature from June to September and the minimum temperature in June show a significant positive correlation with treeline growth and seedling regeneration, but precipitation does not show a significant correlation with these factors (see [link to relevant documentation]). Figure 5Based on the above findings, new requirements have been placed on forest topline management. First, we need to shift from static management to dynamic adaptive management, reassessing and adjusting the boundaries of nature reserves, incorporating squeezed alpine ecosystems into core protection, and establishing ecological corridors to facilitate species migration. Second, the upward shift of the treeline profoundly impacts the crucial "water tower" function in arid regions; forest expansion may alter snowmelt and watershed runoff, thus necessitating strengthened hydrological monitoring and adjustments to water resource planning accordingly. Simultaneously, the newly formed treeline front also presents new challenges, creating a continuous flammable zone, increasing fire risk in high-altitude areas, and potentially becoming a new channel for pest and disease spread, requiring fire prevention and disease control systems to extend upwards. This process is also a double-edged sword; while providing new habitats for some species, it more seriously threatens the biodiversity of alpine endemic species with nowhere else to retreat, forcing management strategies to prioritize these vulnerable species. Finally, this conclusion also guides the future direction of afforestation practices, requiring the adoption of more forward-looking "climate-smart" strategies, revising afforestation plans, and selecting seed sources adapted to future climates. In conclusion, changes in the forest line require managers to break down departmental barriers, adopt cross-sectoral collaboration and a long-term perspective to carry out comprehensive watershed management, and proactively maintain the ecological security and service functions of this key area.

[0085] Based on the survey results of fixed plots distributed at intermediate altitudes, we analyzed the distribution pattern of regenerating seedlings / saplings using the variance-to-mean ratio, clustering index, Morisita index, and crowding index (NCI) to determine the spatial distribution pattern of regenerating seedlings / saplings. Furthermore, combining the heterogeneity of population structure and the survey data on the spatial pattern of seedlings / saplings, we compared the differences in the spatial pattern and regeneration characteristics of seedlings / saplings, exploring the potential impact of micro-topography and surrounding growth environment on the spatial pattern of seedlings / saplings. Finally, we used binary logistic regression and structural equation modeling to establish the effective regeneration probability (PRP) for different altitude zones. Prediction equation:

[0086]

[0087] In the formula: : Distance to the nearest healthy mother tree (m); Annual seed yield of mother tree (seeds per tree⁻¹); Slope aspect index (south slope = 1, east / west slope = 2, north slope = 3); : Altitude (m); Thickness of fallen debris (cm); For parameters specific to altitude zones, the maximum likelihood estimation is used for fitting.

[0088] It should be understood that the selection of these independent variables—distance from the nearest healthy mother tree reflecting the distance attenuation effect of seed dispersal, annual seed yield of the mother tree representing seed source supply capacity, aspect index and altitude comprehensively reflecting hydrothermal conditions and light distribution, and litter thickness characterizing the microhabitat's hindering or promoting effect on seedling establishment—allows the model to quantify the regeneration potential of each location under current environmental conditions.

[0089] As an example, when A value ≥0.6 is defined as the valid update range. The update range for higher elevation gradients is... The first three consecutive times the distance to the farthest mother tree is less than 0.6 (usually downhill); the update range for lower elevation gradients is... 0.6 and seedling density > 5 plants / m -2 plaques.

[0090] In one embodiment, the relative nutrient limitation index and the altitude asymmetry index are obtained by:

[0091] Within the effective update range, leaf and root zone soil samples were collected from sample trees at different altitudes and forest ages; leaf nutrient content and soil physicochemical properties were measured; the relative nutrient limitation index and altitude asymmetry index were calculated based on the leaf nutrient content; the leaf nutrient content included total carbon, total nitrogen, and total phosphorus content; the soil physicochemical properties included soil organic carbon, total nitrogen, available nitrogen, available phosphorus, pH value, and water content.

[0092] The nutrient diagnosis results obtained based on the differences in nutrient limitation types in different altitude regions include: when the leaf nitrogen-phosphorus ratio is not greater than a first preset ratio, it is determined to be nitrogen limitation; when the leaf nitrogen-phosphorus ratio is greater than the first preset ratio but less than a second preset ratio, it is determined to be both nitrogen and phosphorus limitation; when the leaf nitrogen-phosphorus ratio is not less than the second preset ratio, it is determined to be phosphorus limitation; and the second preset ratio is greater than the first preset ratio.

[0093] As an example, we focus on the leaf nutrient characteristics of pure coniferous forests in arid mountainous areas.

[0094] Based on the characteristics of soil environmental factors, there are significant differences in soil physicochemical properties and environmental factors between the upper treeline (3200m) and the lower forest edge (2700m) of Qinghai spruce (Table 3). Soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN content), and soil moisture content are significantly higher in the upper treeline than in the lower forest edge, but soil bulk density is significantly lower. However, the available phosphorus (AP) content does not differ much between the two altitudes. The pH value in the upper forest edge is more alkaline, while the pH value in the lower forest edge is more neutral. Soil organic carbon and total nitrogen contents differ significantly among different age groups in both the upper and lower forest edges. Available nitrogen differs significantly among different age groups in the lower forest edge, while total phosphorus (TP) differs significantly among different age groups in the upper forest edge. Furthermore, the hydrothermal conditions differ significantly between the upper and lower treelines. The annual average temperature in the upper treeline is significantly lower than that in the lower forest edge, but the precipitation is the opposite. These differences in hydrothermal conditions lead to significant differences in the growing season between the upper and lower treelines, with the growing season length in the upper treeline being significantly shorter than that in the lower forest edge (P<0.01).

[0095] See Table 3. Figure 17 In the table, different lowercase letters indicate significant differences between different age groups at the upper and lower forest edges, while different uppercase letters indicate significant differences between the same age group at the upper and lower forest edges. SOC, TN, TP, AN, AP, SWC, SBD, SP, pH, LGR, MT, and MP represent soil organic carbon, total soil nitrogen, total soil phosphorus, available soil nitrogen, available soil phosphorus, soil moisture content, soil bulk density, soil porosity, soil pH, growing season length, annual mean temperature, and annual mean precipitation, respectively.

[0096] Regarding the variation of leaf nutrients with altitude and forest age, overall, the total nitrogen (N) and total phosphorus (P) contents of leaves at both the upper and lower distribution limits of *Picea qinghaiensis* showed a decreasing trend with increasing forest age, but the decrease in total phosphorus at the lower forest edge was significantly greater than that at the upper forest line (Table 4). Two-way ANOVA showed that forest age had a significant impact on the C, N, and P contents and their stoichiometric ratios in leaves, while altitude only had a significant impact on C, P, and the N:P ratio. However, the interaction effect between forest age and altitude had no significant impact on leaves (Table 5). Leaf carbon (C) content showed little variation among different altitudes and forest ages, remaining relatively stable between 420-490 g / kg. The C / N, C / P, and N / P ratios all increased with increasing forest age, but the increase in the C / P ratio at the lower forest edge was significantly greater than that at the upper forest edge (P<0.01) (see Table 4 and Table 5). Figure 9 ).

[0097] Table 4. Statistical characteristics of nutrient content in leaves of Qinghai spruce at different ages at the upper and lower forest edges.

[0098]

[0099] Wherein, C, N, P, C / N, C / P, and N / P represent leaf organic carbon, total nitrogen, total phosphorus, carbon-nitrogen ratio, carbon-phosphorus ratio, and nitrogen-phosphorus ratio, respectively.

[0100] Table 5. Effects of altitude and stand age on carbon, nitrogen, phosphorus and their ecostoichiometry in Qinghai spruce leaves.

[0101]

[0102] As another example, building on the previous example, we focus on the asymmetric characteristics of leaf nutrient content and the calculation of the asymmetric index of altitude.

[0103] Given the asymmetric characteristics of nutrient content in leaves at the upper and lower forest edges, the leaf N / P ratio is a key indicator for determining the type of nutrient limitation in plants. This application found that the evolution path of nutrient limitation differs between the upper and lower forest edges with varying forest age. At the upper forest line, the average leaf N / P ratio for all age groups ranged from 7.9 to 9.6, significantly lower than 10, indicating persistent nitrogen (N) limitation. However, at the lower forest edge, the leaf N / P ratio exhibited a dynamic change with forest age. The average N / P ratios for young and middle-aged forests were 8.9 and 8.6, respectively, indicating N limitation. However, upon entering the near-mature and mature forest stages, the N / P ratios increased to 10.8 and 12.4, respectively, entering the range of combined N and P limitation. This asymmetric difference was particularly pronounced in the near-mature (III) and mature (IV) forest stages, with the N / P ratio at the lower forest edge significantly higher than that at the upper forest line (P<0.01).

[0104] The process of calculating the altitudinal asymmetry index includes: measuring the nitrogen, phosphorus, potassium (NPK) and Ca and Mg concentrations of leaves in fixed plots at different developmental stages at the upper and lower forest edges, and calculating the relative nutrient limitation index (NPK). ):

[0105]

[0106] In the formula This is the actual concentration measured on the leaves. Reference values ​​for nutrient concentration in the leaves of healthy adult trees (established by altitude zone). When < 0.15 and available soil nutrients < critical value (AN < 30 mg•kg) -1 AP < 5 mg•kg -1 AK < 80 mg / kg -1 Ca < 500 mg•kg -1 Or Mg < 80 mg•kg -1 When ), the element is determined to be the first limiting factor.

[0107] The formula for calculating the Altitude Asymmetry Index (ASI) is as follows:

[0108]

[0109] in and The values ​​represent the average limiting indices for low altitude (<2500m) and high altitude (>3200m), respectively. An ASI > 0.2 indicates significant altitude-specific differences in the limiting factors.

[0110] As another example, based on the previous example, we will explore the driving mechanism of leaf nutrient limitation.

[0111] Based on the aforementioned relative nutrient limiting index, the driving mechanism of nutrient limitation in leaves at the upper and lower forest edges of *Picea qinghaiensis* was further explored. The study found that the forest distribution boundary (altitude) was the most significant factor affecting the organic carbon content of *Picea qinghaiensis* leaves (r=0.72), followed by total soil nitrogen (r=0.71), organic carbon (r=0.71), pH (r=0.61), available nitrogen (r=0.59), and soil moisture content (r=0.56). The visualization of these strongly correlated factors is shown below. Figure 11 As shown. However, the relationships between leaf total nitrogen, total phosphorus, CN ratio, CP ratio, NP ratio and soil properties were not significant (see [reference]). Figure 10 pH was the strongest correlation factor for total nitrogen (r=0.200), total phosphorus (r=0.329), CP (r=-0.224), and NP ratio (r=-0.278) in leaves, while soil bulk density was the strongest correlation factor for CN ratio (r=-0.316) in leaves.

[0112] Therefore, further correlation analysis using altitude as a factor revealed that total phosphorus, organic carbon, pH, and soil moisture content were the main factors affecting the nutrient content of Qinghai spruce leaves in the lower forest edge. The N / P ratio of leaves in the lower forest edge showed a weak positive correlation with soil AN content (r=0.28, P>0.05), while showing a significant negative correlation with soil moisture content (r=-0.36, P<0.05), indicating that water stress may exacerbate N limitation in this region (see [link to relevant documentation]). Figure 12 Soil total phosphorus, organic carbon, bulk density, and dissolved nitrogen are the main factors affecting the nutrient content of leaves at the upper edge of Qinghai spruce forests. The N / P ratio of leaves at the upper edge of the forests showed a weak positive correlation with soil AN content (r=0.13, P>0.05) (see [reference]). Figure 13 Furthermore, the N / P ratio of leaves at the lower forest edge was significantly positively correlated with the length of the growing season (r=0.29, P<0.05), but the ratio at the upper forest edge was weakly negatively correlated with the length of the growing season (r=-0.12, P>0.05).

[0113] Soil properties and growing season length are key factors influencing the stoichiometric characteristics of Qinghai spruce leaves in the forest edge. Regression models showed that increased soil pH significantly promoted the accumulation of total nitrogen and total phosphorus in leaves (p<0.1), while increased soil organic carbon content significantly decreased total nitrogen and tended to decrease total phosphorus, but increased the nitrogen-to-phosphorus ratio (NP). A longer growing season also significantly increased leaf NP. Structural equation modeling further validated the positive driving effect of soil organic carbon and growing season length on leaf NP, and the negative effect of soil moisture content (p<0.05) (see Table 6 and...). Figure 14 Overall, the models had limited explanatory power (adjusted R² mostly below 0.25), suggesting the existence of other important regulatory factors. However, the nutrient balance of plants in the lower forest edge may be shifting towards phosphorus limitation, and is profoundly regulated by soil carbon pool and water conditions. However, neither the multiple regression model nor the structural equation model established for the upper forest edge could effectively explain the changes in leaf stoichiometry; the regression models for the four leaf indices were all insignificant (p>0.1), with extremely low explanatory power (adjusted R² negative or close to 0); the structural equation model also failed to fit (CFI=0), and no significant influence pathways were found (see Table 6 and...). Figure 15 Path analysis showed that the soil carbon and nitrogen pools at the upper forest edge had a synergistic promoting effect on leaf organic carbon accumulation. Specifically, both soil organic carbon and total nitrogen content had a significant positive impact on leaf organic carbon, which was in stark contrast to the inhibitory relationship observed at the lower forest edge (Table 7).

[0114] Table 6. Summary of significant results of regression and structural equation model (SEM) models of nutrient content and environmental factors in Qinghai spruce leaves at the upper and lower forest edges.

[0115]

[0116] Table 7. Equations and statistics of pathway analysis models for nutrient and environmental factors in Qinghai spruce leaves at the upper and lower forest edges.

[0117]

[0118] In summary, a key ecological shift exists in plant nutrient utilization strategies along the altitudinal gradient. This shift occurs from a complex regulation of soil nutrient availability and growing season length at the lower forest edge (lower altitudes) to a synergistic control by climate stress and basic soil fertility at the upper forest edge (higher altitudes). At the lower forest edge, soil properties (organic carbon, pH, water content) and growing season length partially explain changes in leaf nitrogen, phosphorus, and the nitrogen-to-phosphorus ratio (NP). Increased soil organic carbon content significantly inhibits leaf nitrogen and phosphorus uptake but increases the leaf NP ratio, suggesting a possible shift in nutrient limitation type. However, at the upper forest edge, this relationship is fundamentally reversed: soil organic carbon and total nitrogen synergistically promote leaf organic carbon accumulation, while all models attempting to explain leaf nitrogen, phosphorus, and NP ratios fail (p>0.05). This suggests that at higher altitudes, harsh abiotic environments (such as low temperatures) may have surpassed soil nutrient availability to become the key factor dominating plant leaf traits, with basic soil fertility (carbon and nitrogen pools) now primarily supporting plant carbon fixation processes.

[0119] In one embodiment, the assisted migration and nutrient addition measures for high-altitude areas include: protecting healthy mother trees and applying slow-release compound fertilizer in areas with high suitability levels in the regional evaluation information, and replanting cold-resistant container seedlings in potential suitable areas above the current treeline.

[0120] Disturbance control and soil function restoration measures for low-altitude areas include: setting up physical protective facilities based on the natural expansion distance, and applying organic materials and inoculating microbial agents on degraded patches within the protected area. The baseline distance for setting up physical protective facilities shall not be less than the natural expansion distance, which ranges from 20-30 meters upward from the upper treeline to 15-30 meters downward from the lower forest edge.

[0121] As an example, climate-driven adaptation and support strategies in high-altitude areas (>3200m) include:

[0122] In the upper forest edge area, soil organic carbon and total nitrogen contents are high, but the mineralization rate is inhibited by low temperatures, resulting in insufficient available nitrogen supply (leaf N / P < 10), and the explanatory power of soil nutrient properties for leaf nutrient content is low. These results suggest that the harsh abiotic environment (such as low temperature) in this region has outpaced soil nutrient availability. Forest conservation needs to adapt to climate-driven patterns, strengthen seed source protection and artificial migration assistance, and weaken local soil engineering. Therefore, based on the main findings of climate-dominated suitability in "potential distribution area determination" and temperature-driven treeline shifting in "regeneration potential assessment," the following targeted measures are formulated:

[0123] It can also be used for climate suitability protection and parent tree reinforcement. Within the high suitability zone determined by the MaxEnt model (e.g., RCP4.5 / 6.0 scenarios), it can be used to protect and renew parent trees. Healthy mother trees within the core effective regeneration range identified by the model are given priority protection and cultivation. General soil improvement is downplayed, and instead, targeted climate-adaptive nutrient supplementation is implemented in the root zone of the mother trees. Slow-release compound fertilizer is applied after the snow melt in spring to enhance the physiological vitality and seed yield of the mother trees under short growing season and temperature restrictions, thereby consolidating their function as a source of natural regeneration driven by climate.

[0124] Furthermore, boundary anchoring and assisted migration can be updated. Based on the upward trend and magnitude revealed by the dynamic changes in the treeline, a potential suitable area outside the model-predicted updated boundary (e.g., 20-30 meters above the current treeline) can be selected as an auxiliary area for climate-driven treeline upward movement. Cold-resistant seed sources can be selected and 3-5 year old container seedlings can be replanted to form an "artificially promoted transition zone." This measure can transform theoretically potential suitable areas into actual population distribution areas, accelerate the adaptive migration of the treeline to climate warming, and form new seed dispersal sources.

[0125] Disturbance control and soil function restoration strategies in low-altitude areas (<2700m) include:

[0126] In the lower forest edge area, water stress is significant, and soil organic carbon inhibits nitrogen and phosphorus absorption by leaves. An increased N / P ratio (>10) in mature forest leaves indicates a phosphorus limitation trend. Simultaneously, human disturbance such as grazing is a key external factor limiting regeneration. Addressing the two core contradictions revealed by the research—"grazing disturbance dominates regeneration limitation" and "soil function inhibits nutrient availability"—the core management strategy for this area focuses on limiting disturbance and restoring soil function, strengthening precise physical protection and soil ecological restoration. Therefore, by precisely deploying physical protection to ensure basic space for seedling survival and by restoring the damaged soil ecosystem through biological and ecological means, the following targeted measures are formulated to fundamentally improve the system's nutrient supply capacity and seedling adaptability:

[0127] Precise fencing based on evidence of forest expansion. To effectively protect the natural expansion potential of forest patches to the lower edge, fencing must strictly avoid the traditional practice of "close to the forest edge." This application found that over the past 50 years, the lower forest edge has shifted downwards by an average of 15-30 meters under conditions without severe obstruction. Therefore, the baseline distance (L) for fencing should be set at least 30 meters from the current forest edge. However, the regeneration dynamics of the lower forest edge are significantly affected by human disturbances such as grazing, and a "disturbance-topography" correction model (D = L + k × (I - S)) needs to be introduced for dynamic adjustment. In this model, the disturbance intensity (I) is quantified based on livestock manure density and path frequency from transect surveys; topographic shielding (S) is assessed based on slope and shrub cover. For high-disturbance-weak-shielding areas identified by the model (such as the periphery of enriched patches with seedling density > 5 plants / m²), the fencing distance should be appropriately increased or auxiliary protective facilities should be added for focused protection to maximize conservation efficiency.

[0128] Ecosystem management for soil function restoration: Addressing the issue identified in the leaf nutrient-driven mechanism of the understory edge—that "increased soil organic carbon content is negatively correlated with leaf nitrogen and phosphorus absorption, and the significantly increased nitrogen-to-phosphorus ratio (N:P) in mature forests indicates a phosphorus-limiting trend"—the management focus in this area must shift from simple fertilizer application to deep-seated biological and biochemical restoration of soil functions. Specific management measures include: within the fenced area, implementing "organic matter enhancement and microbial inoculation" for degraded patches (low or poor SOC content). In autumn, well-rotted local shrub and grass residues are applied, followed by inoculation with a compound functional microbial agent (such as arbuscular mycorrhizal fungi and phosphorus-solubilizing microorganisms) selected from healthy forest stands. This aims to improve soil organic matter quality, promote aggregate formation, establish a symbiotic microbial network, break the biochemical inhibition of nutrient availability under high organic carbon conditions, fundamentally improve the soil's nutrient buffering and supply capacity, reverse its inhibitory effect, gradually alleviate the phosphorus-limiting trend, and promote long-term nutrient balance in seedlings.

[0129] Regarding the establishment of physical protective facilities based on natural expansion distances, in one example, a study based on the spatial pattern of the lower forest edge found that the average treeline has shifted downwards by 15–30 meters over the past 50 years (see [reference]). Figure 6 , Figure 7Furthermore, the downward movement distance is limited by grazing intensity, topography, and regeneration boundary conditions. This conclusion suggests that the setting of fences (physical protection facilities) during the natural forest closure and protection process should fully consider the possible "expansion distance" of forest patches, and fences should not be set up close to the forest edge, otherwise, the regeneration seedlings outside the fence will be disturbed by grazing and other factors, limiting the expansion of forest patches. Closure fences should be set at least outside the lower limit of forest patch expansion. For closure fences in the upper forest edge area, they should be at least 40m away from the upper forest line. For forest patches in the lower forest edge with sparse shrubs (coverage <30%), flat terrain (slope less than 10 degrees), and far away from valleys, rivers, roads, etc. (>30m), the closure fences should be at least 15m away from the lower forest edge. At the same time, some artificially cultivated seedlings should be appropriately replanted or naturally regenerated seedlings should be managed within the fenced "buffer zone". This technique ensures the effective range of normal regeneration of natural forest patches, which not only helps to ensure the closure effect, but also helps to maintain the biodiversity and eco-hydrological functions of the forest edge area. However, in the early stages of setting up natural forest enclosure fences, it is necessary to investigate and analyze the topographic features, community structure, and disturbance conditions of forest patches within the specific enclosure area, and to provide specific fence setting distances and configuration patterns. In arid areas, the forest edges in mountainous areas are subject to greater human disturbance, mainly due to grazing. Therefore, in the specific process of setting up enclosure fences, factors such as grazing intensity and grassland compensation for herders must also be comprehensively considered.

[0130] As another example, among the fixed survey plots established in the above example, 37 fixed plots were selected, distributed along the upper and lower forest edges. Based on previous survey data (2022-2023), we screened plots with different average diameter at breast height (DBH). Referring to the age group classification standards for the genus *Picea* in the Chinese Forestry Industry Standard (LY / T2908-2017), and striving to ensure that the average DBH of the plots along the upper and lower forest edges was consistent as much as possible, and that the standard deviation of individual DBH within each plot was minimized, we finally selected eight forest stands (four in the upper and four in the lower) at different growth and development stages (juvenile I, middle-aged II, near-mature III, and mature IV) for subsequent analysis in this study. Considering that differences in the number of individuals among different forest stands might affect the comparison of analytical results, we further screened the sample trees within each plot. Specifically, we averaged the diameter at breast height (DBH) of all individuals in the same age group belonging to the upper forest line and lower forest edge plots to obtain the average DBH for each age group; then, we selected 10 individuals in each plot that were closest to the average DBH as sample trees for that age group (Table 2).

[0131] See Table 2. Figure 18 This is used to display basic information about the selected sample trees from different age groups in the sample plots along the upper forest line and lower forest edge. Among them, UFL and LFE represent the upper forest edge (3200 meters) and the lower forest edge (2700 meters), respectively; DBH represents the diameter at breast height (DBH).

[0132] Within fixed sample plots selected at the upper and lower forest edges, core samples were obtained using growth cones. Individual ages were determined through cross-dating, establishing a correlation between age and diameter at breast height (DBH) (see [reference]). Figure 8 This was used to determine the age of other individuals. Simultaneously, leaves were collected from 10 poplar trees at different developmental stages along the upper and lower forest edges, with diameters at breast height (DBH) close to the average DBH of the sample plot (for analysis of total carbon, total nitrogen, and total phosphorus in the leaves). Soil samples were also collected around the sample trees (for analysis of soil organic carbon, nitrogen, phosphorus, and soil moisture). Phenological observations of the sample trees and monitoring and recording of geographical environmental factors were also conducted.

[0133] In one embodiment, the method further includes step S105: periodically monitoring using a monitoring network deployed within the effective update range to obtain monitoring feedback data, wherein the monitoring feedback data is used to adjust the nurturing strategy.

[0134] As an example, to ensure the sustainability and adaptability of the technical system provided in this application, a data-driven dynamic monitoring and management system linked to the aforementioned research results is established, and an altitude-differentiated management strategy is formulated and implemented accordingly. The dynamic monitoring and evaluation system specifically includes:

[0135] The aim is to verify the effectiveness of management measures and, based on monitoring feedback data, periodically optimize potential distribution areas, regeneration ranges, and nutrient management strategies. Specifically, permanent monitoring plots are systematically deployed within suitable potential distribution areas and effective regeneration ranges, covering both treatment and control areas, to conduct the following core monitoring:

[0136] Update dynamic monitoring: Every August and September, the density, height, diameter at root, and growth status of seedlings / saplings in the sample plots are investigated. The core evaluation indicator, "renewal success rate" (number of seedlings ≥3 years old / initial number of seedlings × 100%), is calculated, and a renewal success rate of ≥40% is used as a direct test of the effectiveness of fence protection and boundary support measures and as a constraint for calibrating the renewal model.

[0137] Soil and leaf nutrient monitoring: Systematic sampling is conducted every 5 years to measure key factors such as soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), pH, and water content. Simultaneously, leaf samples from dominant trees are collected to analyze carbon, nitrogen, and phosphorus content and the nitrogen-to-phosphorus ratio (N:P). The effectiveness of differentiated fertilization strategies in alleviating nutrient limitation at specific altitudes (such as phosphorus limitation trends at low altitudes and nitrogen limitation at high altitudes) is quantitatively evaluated by calculating the "nutrient limitation mitigation rate" [(NRI_min in the management area − NRI_min in the control area) / NRI_min in the control area × 100%], with a target of ≥50%.

[0138] Environmental and climate response monitoring: Based on continuous monitoring of environmental factors such as soil temperature, moisture, and air temperature and humidity within fixed plots, combined with tree rings and re-measurement of fixed plots, the spatial pattern changes of population regeneration are continuously tracked, and correlation analysis is performed with climate data such as temperature to verify and revise the population dynamic regeneration prediction model.

[0139] The aforementioned monitoring data is periodically fed back into predictive models and management decisions, achieving a closed-loop iteration of "monitoring-evaluation-optimization" to improve the adaptability and long-term effectiveness of management. Specifically, this is reflected in: using updated data to optimize... Model parameters; nutrient management pathways are validated using soil-leaf nutrient relationship data; treeline change data is used to reinforce the constraints of the MaxEnt model on real-world distribution boundaries. Finally, a comprehensive evaluation of the "ecological benefit-cost ratio" drives the periodic optimization of management strategies.

[0140] The technical solution provided in this embodiment comprehensively applies remote sensing, plot surveys, and model simulations to break through the traditional empirical methods of determining regeneration range and uniform management. It establishes a quantitative identification method for regeneration range based on a regeneration probability prediction model and proposes differentiated definition standards for different regeneration mechanisms at high and low altitudes, significantly improving the spatial accuracy of tending measures. Simultaneously, it organically integrates distribution prediction, regeneration diagnosis, nutrient regulation, physical protection, and dynamic monitoring, establishing a long-term monitoring network covering regeneration, soil, leaves, and climate. This forms a complete management closed loop from diagnosis to implementation to evaluation, achieving multi-factor collaborative management and enhancing the system's sustainability and adaptability. Furthermore, the proposed core parameters, such as fence placement distance limits and regeneration probability prediction equations, are derived from field observations and statistical analysis. These parameters are easy to obtain and calculate, making them suitable for practical applications in grassroots forestry units. The overall implementation plan has controllable costs and good prospects for widespread application.

[0141] Example 2

[0142] This embodiment provides a tending system for coniferous forests in arid mountainous areas, including:

[0143] The information acquisition module is used to acquire regional evaluation information of the target tree species, including the potential distribution range and multiple suitability levels of the target tree species.

[0144] The update range acquisition module is used to acquire an effective update probability prediction model. The distance to the nearest healthy mother tree, the annual seed yield of the mother tree, the slope aspect, the altitude, and the thickness of the litter corresponding to the potential distribution range are used as input variables of the effective update probability prediction model to acquire the update probability value corresponding to each point in the potential distribution range. Combined with the seedling density, the effective update range including high-altitude and low-altitude areas is acquired.

[0145] The nutrient diagnosis module is used to obtain the relative nutrient limitation index and altitude asymmetry index of different altitude regions within the effective update range, and to diagnose the differences in nutrient limitation types in different altitude regions based on these indices, thereby obtaining nutrient diagnosis results.

[0146] The strategy acquisition module is used to acquire the tending strategy for the effective update range based on the regional evaluation information, the effective update range, and the nutrient diagnosis results; the tending strategy includes auxiliary migration and nutrient addition measures for high-altitude areas, and disturbance control and soil function restoration measures for low-altitude areas.

[0147] In one embodiment, it also includes:

[0148] The feedback adjustment module is used to periodically monitor the area using the monitoring network deployed within the effective update range and obtain monitoring feedback data, which is used to adjust the nurturing strategy.

[0149] Although the invention has been described with reference to illustrative embodiments, those skilled in the art will understand that various other changes, omissions, and / or additions can be made without departing from the spirit and scope of the invention, and that elements of the described embodiments can be substituted with substantially equivalents. Furthermore, many modifications can be made without departing from the scope of the invention to adapt particular situations or materials to the teachings of the invention. Therefore, this document is not intended to limit the invention to the specific embodiments disclosed for carrying out the invention, but rather to include all embodiments falling within the scope of the appended claims.

Claims

1. A method for tending a mountainous coniferous forest in a dry region, characterized by, Includes the following steps: Obtain regional evaluation information for the target tree species, including the potential distribution range and multiple suitability levels of the target tree species; Obtain an effective update probability prediction model; take the distance to the nearest healthy mother tree, annual seed yield of the mother tree, slope aspect, altitude and litter thickness corresponding to the potential distribution range as input variables of the effective update probability prediction model, obtain the update probability value corresponding to each point in the potential distribution range, and combine it with seedling density to obtain the effective update range including high altitude area and low altitude area. Obtain the relative nutrient limitation index and altitude asymmetry index for different altitude regions within the effective update range, and diagnose the differences in nutrient limitation types in different altitude regions based on these indices to obtain nutrient diagnosis results. Based on the regional evaluation information, the effective update range, and the nutrient diagnosis results, a nurturing strategy for the effective update range is obtained; The nurturing strategies include assisted migration and nutrient addition measures for high-altitude areas, and disturbance control and soil function restoration measures for low-altitude areas.

2. The rearing method according to claim 1, characterized by, The regional evaluation information is obtained through the following methods: Based on historical distribution data and regional environmental data of the target tree species, a species distribution model is used to predict the potential distribution range of the target tree species under current and future climate scenarios, and the potential distribution range is divided into multiple suitability levels; the potential distribution range and its corresponding multiple suitability levels are used as regional evaluation information. The species distribution model is a maximum entropy model; the regional environmental data includes climate data, topographic data, and soil data; and the suitability levels include unsuitable, low-suitable, moderately suitable, and high-suitable.

3. The rearing method according to claim 1, characterized by, The effective update probability prediction model is obtained by: setting up fixed survey plots along the elevation gradient within the potential distribution range, and collecting plot data; and constructing the effective update probability prediction model based on the plot data. The valid update range is determined using the following method: For high-altitude areas, starting from the mother tree and moving downhill, the area covered by the location where the update probability value is lower than the preset probability threshold for the first N consecutive occurrences is defined as the effective update range; N is a positive integer not less than 3. For low-altitude areas, patches with an update probability value not less than a preset probability threshold and a seedling density greater than a preset density threshold are defined as valid update ranges.

4. The rearing method according to claim 1, characterized by, The relative nutrient limitation index and the altitude asymmetry index are obtained through the following methods: Within the effective update range, leaf and root zone soil samples were collected from sample trees at different altitudes and forest ages; leaf nutrient content and soil physicochemical properties were measured; and the relative nutrient limitation index and altitude asymmetry index were calculated based on the leaf nutrient content. The leaf nutrient content includes the total carbon, total nitrogen, and total phosphorus content of the leaves; the soil physicochemical properties include soil organic carbon, total nitrogen, available nitrogen, available phosphorus, pH value, and water content.

5. The nurturing method according to claim 4, characterized in that, The nutrient diagnosis results obtained based on the differences in nutrient limitation types in different altitude regions include: when the leaf nitrogen-phosphorus ratio is not greater than a first preset ratio, it is determined to be nitrogen limitation; when the leaf nitrogen-phosphorus ratio is greater than the first preset ratio and less than a second preset ratio, it is determined to be both nitrogen and phosphorus limitation; when the leaf nitrogen-phosphorus ratio is not less than the second preset ratio, it is determined to be phosphorus limitation; and the second preset ratio is greater than the first preset ratio.

6. The nurturing method according to claim 1, characterized in that, Assisted migration and nutrient addition measures for high-altitude areas include: protecting healthy mother trees and applying slow-release compound fertilizer in areas with high suitability levels in the regional evaluation information, and replanting cold-resistant container seedlings in potential suitable areas above the current treeline; Disturbance control and soil function restoration measures for low-altitude areas include: setting up physical protection facilities based on natural expansion distances, and applying organic materials and inoculating microbial agents on degraded patches within the protection area.

7. The nurturing method according to claim 6, characterized in that, The baseline distance for setting up physical protective facilities shall not be less than their natural expansion distance, which shall be in the range of 20-30 meters upward expansion of the upper forest line or 15-30 meters downward expansion of the lower forest edge.

8. The nurturing method according to claim 1, characterized in that, The method further includes step S5: periodically monitoring using the monitoring network deployed within the effective update range to obtain monitoring feedback data, which is used to adjust the nurturing strategy.

9. A tending system for coniferous forests in arid mountainous areas, characterized in that, include: The information acquisition module is used to acquire regional evaluation information of the target tree species, including the potential distribution range and multiple suitability levels of the target tree species. The update range acquisition module is used to acquire an effective update probability prediction model. The distance to the nearest healthy mother tree, the annual seed yield of the mother tree, the slope aspect, the altitude, and the thickness of the litter corresponding to the potential distribution range are used as input variables of the effective update probability prediction model to acquire the update probability value corresponding to each point in the potential distribution range. Combined with the seedling density, the effective update range including high-altitude and low-altitude areas is acquired. The nutrient diagnosis module is used to obtain the relative nutrient limitation index and altitude asymmetry index of different altitude regions within the effective update range, and to diagnose the differences in nutrient limitation types in different altitude regions based on these indices, thereby obtaining nutrient diagnosis results. The strategy acquisition module is used to acquire the nurturing strategy for the effective update range based on the regional evaluation information, the effective update range, and the nutrient diagnosis results. The nurturing strategies include assisted migration and nutrient addition measures for high-altitude areas, and disturbance control and soil function restoration measures for low-altitude areas.

10. The nurturing system according to claim 9, characterized in that, Also includes: The feedback adjustment module is used to periodically monitor the area using the monitoring network deployed within the effective update range and obtain monitoring feedback data, which is used to adjust the nurturing strategy.

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