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.

Impacts of COVID-19 on tourism and management response from Banff National Park,Canada

The COVID-19 pandemic posed challenges to the tourism sector globally.We investigated changes in visitor demographics,satisfaction level,and its determinants pre-and peri-COVID-19.Data were collected using questionnaire surveys in 2019 and 2021 within Banff National Park(BNP).The data analyses were based on a sample size of 1183 respondents by conducting factor analysis,correlation analysis and stepwise regression analysis.Results highlight that there were fewer international visitors and more local and domestic visitors during the pandemic.Park attributes were evaluated at a higher satisfaction level peri-COVID-19.The quality of the Park facilities and services were the most important satisfaction determinants pre-and peri-COVID-19,and all the Park COVID-19 measures and actions received positive experience from visitors.This research fills this knowledge gap by developing a better understanding in the change of visitor demographics and satisfaction level in BNP under the context of the pandemic.It also provides implication for both scholars and practitioners to understand the impacts of the pandemic on Park visitation.The study can provide insights for utilizing the pandemic as a transformative strength and for mitigating its negative impact on tourism industry.

Functional composition of tall-statured trees underpins aboveground biomass in tropical forests

The influences of trait diversity(i.e.,the niche complementarity effect)and functional composition(i.e.,the mass ratio effect)on aboveground biomass(AGB)is a highly debated topic in forest ecology.Therefore,further studies are needed to explore these mechanisms in unstudied forest ecosystems to enhance our understanding,and to provide guidelines for specific forest management.Here,we hypothesized that functional composition would drive AGB better than trait diversity and stem size inequality in the(sub-)tropical forests of Nepal.Using data from 101 forest plots,we tested 25 structural equation models(SEMs)to link elevation,stem DBH inequality,trait diversity(i.e.,trait richness,evenness,dispersion and divergence),functional composition[i.e.,community-weighted of maximum height mean(CWM of Hmax),specific leaf area(CWM of SLA),leaf dry matter content(CWM of LDMC),and wood density(CWM of WD)]and AGB.The best-fitted SEMs indicated that CWM of Hmax promoted AGB while overruling the impacts of trait diversity indices on AGB.However,low trait diversity indices were linked with higher AGB while overruling the effects of CWM of SLA,LDMC and WD on AGB.In addition,AGB decreased with increasing elevation,whereas stem size inequality did not influence AGB.Our results suggest that divergent species’functional strategies could shape AGB along an altitudinal gradient in tropical forests.We argue that forest management practices should include plant functional traits in the management plan for the co-benefits of biodiversity conservation and carbon sequestration that underpins human wellbeing.

Population distribution and threats to sustainable management of selected non-timber forest products in tropical lowland rainforests of south western Nigeria

Uncontrolled harvesting of non-timber forest products （NTFPs） poses a serious risk of extermination to several of these species in Nigeria. Yet, there is a paucity of information on the distribution, population status and sustainable management of NTFPs in most of the tropical lowland rainforests. We, therefore, assessed the population, distribution and threats to sustainable management of NTFPs within the tropical lowland rainforests of Omo and Shasha Forest Reserves, south western Nigeria. Data were obtained through inventory surveys on five top priority species including： bush mango （Irvingia gabonensis （Aubry-Lecomte ex O’Rorke） Baill）, African walnut （Tetracarpidium conophorum （Mull. Arg.） Hutch. ＆ Dalziel syn. Plukenetia conophora）, chew-stick （Massularia acuminata （G. Don） Bullock）, fever bark （Annickia chlorantha Setten ＆ P.J.Maas syn. Enantia chloranta） and bush pepper （Piper guineense Schumach. ＆ Thonn.）. Purposive and stratified random sampling techniques were used for the inventory. Each forest reserve was stratified into three, viz： less disturbed natural forest （for areas that have been rested for at least ten years）, recently disturbed natural forest （for areas that have suffered one form of human perturbation or the other in the last five years）, and plantation forest （for areas carrying forest plantation）. Data were collected from eighteen 10 m × 500 m belt transects located in the above strata. The species were generally fewer in both plantation and recently disturbed natural forest than the less disturbed natural forest, suggesting that forest disturbances （habitat modification） for other uses may have an effect on the occurrence and densities of the NTFPs. Exceptions to this trend were found for P. guineense and T. conophorum, which were fairly common in both plantation and recently disturbed natural forest. Among three tree NTFP species （i.e. I. gabonensis, M. acuminata and A. chlorantha）, only I. gabonensis showed a significant difference in overall DBH size classes for both reserves （t=？2.404; df =21; p=0.026）. Three tree NTFP species in both reserves further showed differences from the regular patterns of distribution of trees. The fairly regular reverse J-shaped size class distribution observed for M. acuminata in the study sites, however, suggests a recuperating population. In general, destructive harvesting of species, logging operations, low population size, narrow distribution ranges and habitat degradation are the major threats to the population of NTFPs in the study area. The implications of our findings for sustainable management of NTFPs in the study area are discussed and recommendations are made for a feasible approach towards enhancing the status of the species.

Using satellite-derived land surface temperatures to clarify the spatiotemporal warming trends of the Alborz Mountains in northern Iran

The Alborz Mountains are some of the highest in Iran,and they play an important role in controlling the climate of the country’s northern regions.The land surface temperature(LST)is an important variable that affects the ecosystem of this area.This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500-4000 m above sea level.MODIS data were extracted for the period of 2000-2021,and the Mann-Kendall nonparametric test was applied to evaluating the changes in the LST.The results indicated a significant increasing trend for the monthly average LST in May-August along the southern aspect.Both the northern and southern aspects showed decreasing trends for the monthly average LST in October,November,and March and an increasing trend in other months.At all elevations,the average decadal change in the monthly average LST was more severe along the southern aspect(0.60°C)than along the northern aspect(0.37°C).The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months.At the same elevation,the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months.With increasing elevation,the lapse rate between the northern and southern aspects disappeared.Climate change was concluded to greatly decrease the difference in LST at different elevations for April-July.

Use of models in large-area forest surveys： comparing model-assisted, model-based and hybrid estimation

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role： model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design- based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data.We review studies on large-area forest surveys based on model-assisted, model- based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

Comparison of estimators of variance for forest inventories with systematic sampling-results from artificial populations

Background:Large area forest inventories often use regular grids(with a single random start)of sample locations to ensure a uniform sampling intensity across the space of the surveyed populations.A design-unbiased estimator of variance does not exist for this design.Oftentimes,a quasi-default estimator applicable to simple random sampling(SRS)is used,even if it carries with it the likely risk of overestimating the variance by a practically important margin.To better exploit the precision of systematic sampling we assess the performance of five estimators of variance,including the quasi default.In this study,simulated systematic sampling was applied to artificial populations with contrasting covariance structures and with or without linear trends.We compared the results obtained with the SRS,Matern’s,successive difference replication,Ripley’s,and D’Orazio’s variance estimators.Results:The variances obtained with the four alternatives to the SRS estimator of variance were strongly correlated,and in all study settings consistently closer to the target design variance than the estimator for SRS.The latter always produced the greatest overestimation.In populations with a near zero spatial autocorrelation,all estimators,performed equally,and delivered estimates close to the actual design variance.Conclusion:Without a linear trend,the SDR and DOR estimators were best with variance estimates more narrowly distributed around the benchmark;yet in terms of the least average absolute deviation,Matern’s estimator held a narrow lead.With a strong or moderate linear trend,Matern’s estimator is choice.In large populations,and a low sampling intensity,the performance of the investigated estimators becomes more similar.

Delineation of homogeneous forest patches using combination of field measurements and LiDAR point clouds as a reliable reference for evaluation of low resolution global satellite data

Backgrounds： There are many satellite systems acquiring environmental data on the world. Acquired global remote sensing datasets require ground reference data in order to calibrate them and assess their quality. Regarding calibration and validation of these datasets with broad geographical extents, it is essential to register zones which might be considered as Homogeneous Patches （HPs）. Such patches enable an optimal calibration of satellite data/sensors, and what is more important is an analysis of components which significantly influence electro-magnetic signals registered by satellite sensors. Methods： We proposed two structurally different methods to identify HPs： predefined thresholding-based one （static one）, and statistical thresholding-based technique （dynamic one）. In the first method, 3 different thresholds were used： 5%, 10%, and 20%. Next, it was aimed to assess how delineated HPs were spatially matched to satellite data with coarse spatial resolution. Selected cell sizes were 25, 50, 100, 250, and 500 m. The number of particular grid cells which almost entirely fell into registered HPs was counted （leaving 2% cell area tolerance level）. This procedure was executed separately for each variant and selected structural variables, as well as for their intersection parts. Results： The results of this investigation revealed that ALS data might have the potential in the identification of HPs of forest stands. We showed that different ALS based variables and thresholds of HPs definition influenced areas which can be treated as similar and homogeneous. We proved that integration of more than one structural variable limits size of the HPs, in contrast, visual interpretation revealed that inside such patches vegetation structure is more constant. Conclusions： We concluded that ALS data can be used as a potential source of data to ＂enlarge＂ small ground sample plots and to be used for evaluation and calibration of remotely sensed datasets provided by global systems with coarse spatial resolutions.

An Investigation of the Factors That Motivated Illegal Settlements in the Mau Forest, Kenya

The Mau Forest has in the recent past elicited serious political and environmental debates regarding its conservation status, as the forest is fast dwindling and the repercussions felt widely across the country. The forest, regarded as the largest indigenous montane forest in east Africa, has been hard hit by land-use changes mainly extensive and ill-planned human settlements. To save the forest, the government has resorted to forced evictions of the settlers. We sought to understand the drivers and causes for the observed illegal settlements in the Mau Forest. To collect data, we conducted focus group discussions and administered household questionnaires on evictees in the South-West and Eastern Mau. Data were analyzed using descriptive and inferential statistics. The results of the binary logistic regression model indicate that Poverty (p = 0.000), Agricultural production (p = 0.000) and Land Given by Government (p = 0.018) contributed significantly to the prediction of people’s motivation of settling in the Mau Forest. In conclusion, population pressure, laxity in forest law enforcement and insecure land tenure and politics were identified as some of the factors that motivated the observed rise in illegal settlements in Mau Forest. Such information on the factors that led to the illegal settlements in Mau Forest would be useful for forest conservation policy makers and managers. It will be a basis upon which interventions can be undertaken to enhance sustainable forest management in Kenya and beyond.

Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time-and how this affects applications

Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking.However,wall-to-wall information typically relies on model-based prediction,and several features of model-based prediction should be understood before extensively relying on this type of information.One such feature is that model-based predictors can be considered both unbiased and biased at the same time,which has important implications in several areas of application.In this discussion paper,we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary data.From this point of view,model-based predictors are typically unbiased.Secondly,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered biased,and sometimes severely so.We suggest distinguishing between conventional model-bias,defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted,and design-bias of model-based estimators,defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted.We show that model-based estimators(or predictors)are typically design-biased,and that there is a trend in the design-bias from overestimating small true values to underestimating large true values.Further,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend.We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.

Evaluation and scenario simulation for forest ecological security in China

Continuously growing populations and rapid economic development have led to the excessive use of forest resources,and the forest ecosystem is threatened.In response,forest ecological security(FES)has attracted attention.In this study,an integrated dynamic simulation model was constructed using the system dynamic method,and it was used to evaluate the FES in China from 1999 to 2014.A scenario analysis was then used to evaluate the changes in the FES under five forestry policy scenarios for the 2015–2050 period,including the baseline,afforestation policy,harvesting policies,management policy,investment policy,and a policy mix.The results showed that the evaluation values of the FES increased during the period from 1999 to 2002,the period from 2004 to 2010 and the year 2014,and they decreased in 2003 and during the period from 2011 to 2013.During the 2015–2050 simulation period,the FES improved continuously.In particular,China would enter a new stage when the economic systems,social systems and ecosystems were in harmony after 2040.To improve the FES and the current status of the FES,a scenario analysis showed the most suitable scenario to be Scenario 5 from 2015 to 2020 and Scenario 2 from 2021 to 2050.To relieve pressure,the most suitable scenario would be Scenario 5 from 2015 to 2040 and from 2046 to 2050,and the most suitable scenario would be Scenario 4 for 2041–2045.A policy mix(Scenario 5)would be most efficient under current conditions,while the effects of all the benefits of the forestry policies would weaken over the long term.The integrated method can be regarded as a decision support tool to help policy makers understand FES and promulgate a reasonable forestry policy.

Influence of soil microorganisms and physicochemical properties on plant diversity in an arid desert of Western China

Soil microorganisms and physicochemical properties are considered the two most influencing factors for maintaining plant diversity.However,the operational mechanisms and which factor is the most influential manipulator remain poorly understood.In this study,we examine the collaborative influences of soil physicochemical properties(i.e.,soil water,soil organic matter(SOM),salinity,total phosphorus and nitrogen,pH,soil bulk density and fine root biomass)and soil microorganisms(fungi and bacteria)on plant diversity across two types of tree patches dominated by big and small trees(big trees:height≥7 m and DBH≥60 cm;small trees:height≤4.5 m and DBH≤20 cm)in an arid desert region.Tree patch is consists of a single tree or group of trees and their accompanying shrubs and herbs.It was hypothesized that soil physicochemical properties and microorganisms affect plant diversity but their influence differ.The results show that plant and soil microbial diversity increased with increasing distances from big trees.SOM,salinity,fine root biomass,soil water,total phosphorus and total nitrogen contents decreased with increasing distance from big trees,while pH and soil bulk density did not change.Plant and soil microbial diversity were higher in areas close to big trees compared with small trees,whereas soil physicochemical properties were opposite.The average contribution of soil physicochemical properties(12.2%-13.5%)to plant diversity was higher than microbial diversity(4.8%-6.7%).Salinity had the largest negative affect on plant diversity(24.7%-27.4%).This study suggests that soil fungi constrain plant diversity while bacteria improve it in tree patches.Soil physicochemical properties are the most important factor modulating plant diversity in arid desert tree patches.

Methods of modeling relative growth rate

Background： Analysing and modelling plant growth is an important interdisciplinary field of plant science. The use of relative growth rates, involving the analysis of plant growth relative to plant size, has more or less independently emerged in different research groups and at different times and has provided powerful tools for assessing the growth performance and growth efficiency of plants and plant populations. In this paper, we explore how these isolated methods can be combined to form a consistent methodology for modelling relative growth rates. Methods： We review and combine existing methods of analysing and modelling relative growth rates and apply a combination of methods to Sitka spruce （Piceo sitchensis （Bong.） Carr.） stem-analysis data from North Wales （UK） and British Douglas fir （Pseudotsugd menziesii （Mirb.） Franco） yield table data. Results： The results indicate that, by combining the approaches of different plant-growth analysis laboratories and using them simultaneously, we can advance and standardise the concept of relative plant growth. Particularly the growth multiplier plays an important role in modelling relative growth rates. Another useful technique has been the recent introduction of size-standardised relative growth rates. Conclusions： Modelling relative growth rates mainly serves two purposes, 1） an improved analysis of growth performance and efficiency and 2） the prediction of future or past growth rates. This makes the concept of relative growth ideally suited to growth reconstruction as required in dendrochronology, climate change and forest decline research and for interdisciplinary research projects beyond the realm of plant science.

Climate change impacts and forest adaptation in the Asia–Pacific region: from regional experts' perspectives

Expert opinions have been used in a variety of fields to identify relevant issues and courses of action. This study surveys experts in forestry and climate change from the Asia–Pacific region to gauge their perspectives on the impacts of climate change and on the challenges faced by forest adaptation in the region, and explores recommendations and initiatives for adapting forests to climate change. There was consensus regarding the impacts of climate change on forest ecosystems and on economic sectors such as agriculture and forestry. Respondents also indicated a lack of public awareness and policy and legislation as challenges to addressing climate change. However, the results indicate differences in opinion between regions on the negative impacts of climate change and in satisfaction with actions taken to address climate change,highlighting the need for locally specific policies and research. The study presents specific recommendations to address issues of most concern, based on subregion and professional affiliation throughout the Asia–Pacific region.The results can be used to improve policy and forest management throughout the region. This research will also provide valuable suggestions on how to apply research findings and management recommendations outside of the AP region. The conclusions should be communicated relative to the level of the research and the target audience,ensuring that scientific findings and management recommendations are effectively communicated to ensure successful implementation of forest adaptation strategies.

Comparison of carbon-stock changes, eddycovariance carbon fluxes and model estimates in coastal Douglas-fir stands in British Columbia

Background： The global network of eddy-covariance （EC） flux-towers has improved the understanding of the terrestrial carbon （C） cycle, however, the network has a relatively limited spatial extent compared to forest inventory data and plots. Developing methods to use inventory-based and EC flux measurements together with modeling approaches is necessary evaluate forest C dynamics across broad spatial extents. Methods： Changes in C stock change （AC） were computed based on repeated measurements of forest inventory plots and compared with separate measurements of cumulative net ecosystem productivity （~NEP） over four years （2003 - 2006） for Douglas-fir （Pseudotsuga menzies# var menziesil} dominated regeneration （HDF00）, juvenile （HDF88 and HDF90） and near-rotation （DF49） aged stands （6, 18, 20, 57 years old in 2006, respectively） in coastal British Columbia. AC was determined from forest inventory plot data alone, and in a hybrid approach using inventory data along with litter fall data and published decay equations to determine the change in detrital pools. These AC-based estimates were then compared with Y_NEP measured at an eddy-covariance flux-tower （EC-flux） and modelled by the Carbon Budget Model - Canadian Forest Sector （CBM-CFS3） using historic forest inventory and forest disturbance data. Footprint analysis was used with remote sensing, soils and topography data to evaluate how well the inventory plots represented the range of stand conditions within the area of the flux-tower footprint and to spatially scale the plot data to the area of the EC-flux and model based estimates, Results： The closest convergence among methods was for the juvenile stands while the largest divergences were for the regenerating clearcut, followed by the near-rotation stand. At the regenerating clearcut, footprint weighting of CBM-CFS3 TNEP increased convergence with EC flux Z_NEP, but not for AC. While spatial scaling and footprint weighting did not increase convergence for AC, they did provide confidence that the sample plots represented site conditions as measured by the EC tower. Conclusions： Methods to use inventory and EC flux measurements together with modeling approaches are necessary to understand forest C dynamics across broad spatial extents. Each approach has advantages and limitations that need to be considered for investigations at varying spatial and temporal scales.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

Selected physical and chemical properties of mechanically extracted neem seed oil sourced as a preservative for ligno-cellulose in south-western Nigeria

Most of the seeds produced by neem （Azadirachta indica A. Juss） trees in Nigeria are currently underutilized. Hence, relevant literature provides only limited information conceming many of the seed oils from this country, especially where it concems the potential applications of these oils as preservatives for ligno-cellulose against bio-deterioration. Using standard procedures therefore, this study was carried out to evaluate and document selected physical and chemical properties of neem seed oil （NSO）, mechanically extracted using a cold press at 31.03 N-mm^-2 pressure and a room temperature of 25 ± 2℃. The results show that oil yield was 38.42% with a specific gravity of 0.91 ± 0.01. The amount of acid was 18.24 ± 1.31 mg KOH.g^-1 and that of iodine 93.12 ± 2.01 g-100 g^- 1, while saponification and peroxide values were 172.88 ± 2.06 and 1.42 ± 0.04 mg·g^-1 respectively. The implication of the values obtained, particularly those for the chemical properties, as they concern the potential application of NSO as a preservative for ligno-eellulose, is likely that it may be useful in this regard since the values may support some of the documented anti-microbial properties of the oil, although other physical and chemical properties that may affect this potential are recommended for investigations. Conclusions and other recommendations follow in line with the results of the study.