Unsuspected implications arising from assumptions in simulations: insights from recasting a forest growth model in system dynamics

Background:Familiarity with a simulation platform can seduce modellers into accepting untested assumptions for convenience of implementation.These assumptions may have consequences greater than commonly suspected,and it is important that modellers remain mindful of assumptions and remain diligent with sensitivity testing.Methods:Familiarity with a technique can lead to complacency,and alternative approaches and software can reveal untested assumptions.Visual modelling environments based on system dynamics may help to make critical assumptions more evident by offering an accessible visual overview and empowering a focus on representational rather than computational efficiency.This capacity is illustrated using a cohort-based forest growth model developed for mixed species forest.Results:The alternative model implementation revealed that untested assumptions in the original model could have substantial influence on simulated outcomes.Conclusions:An important implication is that modellers should remain conscious of all assumptions,consider alternative implementations that reveal assumptions more clearly,and conduct sensitivity tests to inform decisions.

An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

The Altitudinal Belts of Subalpine Virgin Forest on Mt.Gongga Simulated by a Succession Model

How to accurately simulate the distribution of forest species based upon their biological attributes has been a traditional biogeographical issue.Forest gap models are very useful tools for examining the dynamics of forest succession and revealing the species structure of vegetation.In the present study,the GFSM(Gongga Forest Succession Model) was developed and applied to simulate the distribution,composition and succession process of forests in 100 m elevation intervals.The results indicate that the simulated results of the tree species,quantities of the different types of trees,tree age and differences in DBH(diameter at breast height) composition were in line with the actual situation from 1400 to 3700 MASL(meters above sea level) on the eastern slope of Mt.Gongga.Moreover,the dominant species in the simulated results were the same as those in the surveyed database.Thus,the GFSM model can best simulate the features of forest dynamics and structure in the natural conditions of Mt.Gongga.The work provides a new approach to studying the structure and distribution characteristics of mountain ecosystems in varied elevations.Moreover,the results of this study suggest that the biogeochemistry mechanism model should be combined with the forestsuccession model to facilitate the ecological model in simulating the physical and chemical processes involved.

Growth models and site index table of natural Korean pine forests

According to the growth characteristics of natural Korean pine (Pinus Koraiensis) forests, 6 equations such as Chapman-Richards equation, Logistic equation, Power equation, and so on were selected to ftt for the growth modeIs for Korean pine forest. The growth models were developed based on 208 random trees and 24o dominant trees. Results show that the Chapman-Richards equation is the best model for estimating tree height by age and DBH, while the Parabola equation is fittest for predicting DBH by age or estimating age from DBH. The site index table of Korean pine forest was compiled by using the proportional method with the Chapman-Richards equation as the guide curve and vaIidated by accuracy test.

Studies on stand dynamic growth model for larch in Jilin in China

The stand growth and yield dynamic models for Larch in Jilin Province were developed based on the forest growth theories with the forest continuous inventory data. The results indicated that the developed models had high precision, and they could be used for the updating data of inventory of planning and designing and optimal decision of forest management.

Ecosystem service multifunctionality of mixed conifer-broad-leaved forests under climate change and forest management based on matrix growth modelling

Climate change and forest management are recognized as pivotal factors influencing forest ecosystem services and thus multifunctionality.However,the magnitude and the relative importance of climate change and forest management effects on the multifunctionality remain unclear,especially for natural mixed forests.In this study,our objective is to address this gap by utilizing simulations of climate-sensitive transition matrix growth models based on national forest inventory plot data.We evaluated the effects of seven management scenarios(combinations of various cutting methods and intensities)on the future provision of ecosystem services and multifunctionality in mixed conifer-broad-leaved forests in northeastern China,under four climate scenarios(SSP1-2.6,SSP2-4.5,SSP5-8.5,and constant climate).Provisioning,regulating,cultural,and supporting services were described by timber production,carbon storage,carbon sequestration,tree species diversity,deadwood volume,and the number of large living trees.Our findings indicated that timber production was significantly influenced by management scenarios,while tree species diversity,deadwood volume,and large living trees were impacted by both climate and management separately.Carbon storage and sequestration were notably influenced by both management and the interaction of climate and management.These findings emphasized the profound impact of forest management on ecosystem services,outweighing that of climate scenarios alone.We found no single management scenario maximized all six ecosystem service indicators.The upper story thinning by 5%intensity with 5-year interval(UST5)management strategy emerged with the highest multifunctionality,surpassing the lowest values by more than 20%across all climate scenarios.In conclusion,our results underlined the potential of climate-sensitive transition matrix growth models as a decision support tool and provided recommendations for long-term strategies for multifunctional forest management under future climate change context.Ecosystem services and multifunctionality of forests could be enhanced by implementing appropriate management measures amidst a changing climate.

The carbon fluxes in different successional stages:modelling the dynamics of tropical montane forests in South Ecuador

Background： Tropical forests play an important role in the global carbon（C） cycle.However, tropical montane forests have been studied less than tropical lowland forests, and their role in carbon storage is not well understood.Montane forests are highly endangered due to logging, land-use and climate change.Our objective was to analyse how the carbon balance changes during forest succession.Methods： In this study, we used a method to estimate local carbon balances that combined forest inventory data with process-based forest models.We utilised such a forest model to study the carbon balance of a tropical montane forest in South Ecuador, comparing two topographical slope positions（ravines and lower slopes vs upper slopes and ridges）.Results： The simulation results showed that the forest acts as a carbon sink with a maximum net ecosystem exchange（NEE） of 9.3 Mg C？（ha？yr）-1during its early successional stage（0–100 years）.In the late successional stage, the simulated NEE fluctuated around zero and had a variation of 0.77 Mg C？（ha？yr）–1.The simulated variability of the NEE was within the range of the field data.We discovered several forest attributes（e.g., basal area or the relative amount of pioneer trees） that can serve as predictors for NEE for young forest stands（0–100 years） but not for those in the late successional stage（500–1,000 years）.In case of young forest stands these correlations are high, especially between stand basal area and NEE.Conclusion： In this study, we used an Ecuadorian study site as an example of how to successfully link a forest model with forest inventory data, for estimating stem-diameter distributions, biomass and aboveground net primary productivity.To conclude, this study shows that process-based forest models can be used to investigate the carbon balance of tropical montane forests.With this model it is possible to find hidden relationships between forest attributes and forest carbon fluxes.These relationships promote a better understanding of the role of tropical montane forests in the context of global carbon cycle, which in future will become more relevant to a society under global change.

Analysis of the inflection points of height-diameter models

The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.

Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA

Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.

Effects of Industrial Relocation on Chinese Regional Economic Growth Disparities: Based on System Dynamics Modeling

The economic growth of China has led to increasing growth disparities between regions. Such disparities are uncontrolled and are severely negative symptoms in the process of economic development. On the basis of system dynamics(SD) modeling and the relationship between industrial relocation and regional economic growth, we construct a model of the interrelationship between the two aforementioned phenomena. The model is an effective and creative exploration for examining effects of industrial relocation on Chinese regional economic growth disparities. The SD model is employed in this study to build an inter-regional labor migration SD model, an inter-regional capital migration SD model, an intra-industry SD model, an intra-regional population SD model, and an intra-regional SD model which are based on realities in labor and capital flow from the view of industrial relocation. VENSIM software is utilized to perform a system simulation based on the data of the eastern, middle, and western regions from 2000 to 2010. Results show that industrial relocation gradually narrows the relative disparity in GDP among the three regions. Moreover, the absolute one is enlarged continuously. The absolute and relative disparities in per capita GDP among eastern, middle, and western regions generally exhibit decreasing trends.

A system dynamics model for billion trees tsunami afforestation project of Khyber Pakhtunkhwa in Pakistan:Model application to afforestation activities

As part of the global effort to plant billion trees,an afforestation project is launched in Pakistan in Khyber Pakhtunkhwa(KP)province to conserve existing forests and to increase area under forest cover.The present study is designed to build a Systems'model by incorporating major activities of the Billion Tree Tsunami Afforestation Project(BTTAP)with special focus on afforestation activities to estimate the growth in forest area of KP.Availability of complete dataset was a challenge.To fix the model,the raw data taken from the project office has been utilized.Planning Commission Form 1-Phase I&II helped us with additional information.We relied on the data available for one and half period of the project as rest of the data is subject to the completion of the project.Our results show that the project target to enhance area under forest differs from the target to afforest area under the project.The system dynamics'model projection shows that the forest area of KP would be 23.59 million hectares at the end of the BTTA project,thus having an increase of 3.29%instead of 2%that has been initially proposed.However,the results show that the progress to meet the target in some afforestation classes is slow as compared to other categories.Farm forestry,plantation on communal lands and owners'plantation need special focus of the authority.Deforestation would affect 0.02 million hectares area of the project.The model under study may be used as a reference model that can be replicated to other areas where billion tree campaigns are going on.

Model-Based Forecasts of North American Forest Growth: A Review

In the context of climate change, planning for forest management goals becomes more complicated. Possible changes in precipitation, temperature, and CO2 can affect tree growth substantially and potential effects differ by species and region. However, integration of potential forest growth responses to these factors can be achieved using models. Because of the need to understand the range of forest growth forecasts and the strengths and limitations of different modeling approaches, I summarized results from 25 studies of forecasted forest responses over coming decades. Some models used statistical relationships between tree rings and climate to forecast growth responses to future climate, some simulated net photosynthesis of a standard forest canopy, and many used tree or stand growth models at various levels of mechanistic detail. In general, models that included CO2 responses predicted enhanced forest growth by 2100 across most of the commercial timberland areas of the US and Canada. For modest warming, most models showed growth enhancement in most regions. For hotter scenarios, many models and regions showed even more growth enhancement, but some regions such as the Southwest, mountain West, and southwestern Canada were predicted to experience drought stress and increased fire incidence, although projections in these regions were variable. Young stands, angiosperms, and early-successional species were predicted to exhibit the most positive responses. As a result, commercial harvest ages might be accelerated by several years, depending on species. Some simulations for the Midwest and Northeast US predicted a doubling or more of net primary productivity although other studies show a lesser response. Model runs that did not include mechanisms of CO2 fertilization showed positive growth responses in only limited cases and generally showed growth declines. There also was some evidence indicating the potential spread of forest into woodland at shrub or prairie ecotones.

Rebirth after death: forest succession dynamics in response to climate change on Gongga Mountain, Southwest China

Global climate change is having long-term impacts on the geographic distribution of forest species. However, the response of vertical belts of mountain forests to climate change is still little known. The vertical distribution of forest vegetation(vertical vegetation belt) on Gongga Mountain in Southwest China has been monitored for 30 years. The forest alternation of the vertical vegetation belt under different climate conditions was simulated by using a mathematical model GFSM(the Gongga Forest Succession Model). Three possible Intergovernmental Panel on Climate Change(IPCC) climate scenarios(increase of air temperature and precipitation by 1.8℃/5%, 2.8℃/10% and 3.4℃/15% for B_1, A_1B and A_2 scenarios, respectively) were chosen to reflect lower, medium and higher changes of global climate. The vertical belts of mountainous vegetation will shift upward by approximately 300 m, 500 m and 600 m in the B_1, A_1B and A_2 scenarios, respectively, according to the simulated results. Thus, the alpine tree-line will move to a higher altitude. The simulation also demonstrated that, in a changing climate, the shift in the vegetation community will be a slow and extended process characterized by two main phases. During the initial phase, trees of the forest community degrade or die, owing to an inability to adapt to a warmer climate. This results in modest environment for the introduction of opportunistic species, consequently, the vegetation with new dominant tree species becomes predominant in the space vacated by the dead trees at the expense of previously dominated original trees as the succession succeed and climate change advance. Hence, the global climate change would dramatically change forest communities and tree species in mountainous regions because that the new forest community can grow only through the death of the original tree. Results indicated that climate change will cause the change of distribution and composition of forest communities on Gongga Mountain, and this change may enhance as the intensity of climate change increases. As a result, the alternation of death and rebirth would finally result in intensive landscape changes, and may strongly affect the eco-environment of mountainous regions.

Impact of Wavelength-Routed Network Physical Topology on Blocking Probability Using a Dynamic Traffic Growth Model

We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.

Machine learning models for stroke detection by observing the eye-movement features under five-color visual stimuli in traditional Chinese medicine

Objective:To develop a novel diagnostic modality to identify and diagnose stroke in daily life scenarios for improving the therapeutic effects and prognoses of patients.Methods:In this study,16 stroke patients and 24 age-matched healthy participants as controls were recruited for comparative analysis.Leveraging a portable eye-tracking device and integrating traditional Chinese medicine theory with modern color psychology principles,we recorded the eye movement signals and calculated eye movement features.Meanwhile,the stroke recognition models based on eye movement features were further trained by using random forest(RF),k-nearest neighbors(KNN),decision tree(DT),gradient boosting classifier(GBC),XGBoost,and CatBoost.Results:The stroke group and the healthy group showed significant differences in some eye movement features(P<.05).The models trained based on eye movement characteristics had good performances in recognizing stroke individuals,with accuracies ranging from 77.40%to 88.45%.Under the red stimulus,the eye movement model trained by RF became the best machine learning model with a recall of 84.65%,a precision of 86.48%,a F1 score of 85.47%.Among the six algorithms,RF and CatBoost performed better in classification.Conclusion:This study pioneers the application of traditional Chinese medicine's five-color stimuli to visual observation tasks.On the basis of the combined design,the eye-movement models can accurately identify stroke,and the developed high-performance models may be used in daily life scenarios.

DYNAMIC PREDICION OF FOREST FUEL LOADS BY GREY VERHULST MODEL

The variation of fuel loads after a fire for three forest types, phododendron -Larix gmetinii forest, herb--Larix gmelinii forest and herb--Betula plalyphlla forest , in the northern forest area of Daxing’anling region was discussed. The dynamic models were developed by gray theory for estimating the fuels loads of arbor- shrub, herbs’ grass, litter, and semi-decomposed litter, inflamma ble fuel and the total fuels in each forest type. After a fire, the inflammabIe fuel loads in phododendron-- Larix gmelinii forest and in the herb- - Betula platyphlla fores was estimated at 10.958 t/hm2and 10.473 t/hm2 respectively’ by 13 years later. and that was 12.297 t/hm 2 in herb--Larix gmeliniiforest by 7 years later.. It was predicated that a big fire may occur after 10 years based on inflammable fuel biomass accumulated.

Existence and Uniqueness of Almost Periodic Solution for a Mathematical Model of Tumor Growth

This article is concerned with a mathematical model of tumor growth governed by 2nd order diffusion equation . The source of mitotic inhibitor is almost periodic and time-dependent within the tissue. The system is set up with the initial condition C(r, 0) = C0(r) and Robin type inhomogeneous boundary condition . Under certain conditions we show that there exists a unique solution for this model which is almost periodic.

Drivers of spatial structure in thinned forests

Background:As is widely known,an increasing number of forest areas were managed to preserve and enhance the health of forest ecosystems.However,previous research on forest management has often overlooked the importance of structure-based.Aims:Our objectives were to define the direction of structure-based forest management.Subsequently,we investigated the relationships between forest structure and the regeneration,growth,and mortality of trees under different thinning treatments.Ultimately,the drivers of forest structural change were explored.Methods:On the basis of 92 sites selected from northeastern China,with different recovery time (from 1 to 15years) and different thinning intensities (0–59.9%) since the last thinning.Principal component analysis (PCA)identified relationships among factors determining forest spatial structure.The structural equation model (SEM)was used to analyze the driving factors behind the changes in forest spatial structure after thinning.Results:Light thinning (0–20%trees removed) promoted forest regeneration,and heavy thinning (over 35% of trees removed) facilitated forest growth.However,only moderate thinning (20%–35%trees removed) created a reasonable spatial structure.While dead trees were clustered,and they were hardly affected by thinning intensity.Additionally,thinning intensity,recovery time,and altitude indirectly improve the spatial structure of the forest by influencing diameter at breast height (DBH) and canopy area.Conclusion:Creating larger DBH and canopy area through thinning will promote the formation of complex forest structures,which cultivates healthy and stable forests.

The effects of data aggregation on long-term projections of forest stands development

Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.

DYNAMIC MODELS OF MOISTURE CONTENT OF FOREST FUELS

The fuel moisture content is an integral part of any workable fire danger-rating system. This paper presented dynamic models for estimating 1-h, 10-h. 100-h and 1000-h timelag fuels, that were developed by multiple regression and stepwise variable selection of statistics. The variables include both meteorological factors and moisture contents observed prior to the day correspondingly. The analysis revealed that the fuel moisture content are correlated positively with the precipitation of 24 hours prior to the observation time, and negatiyely with air temperature at observing height of 1.5 meter in forest.