Permanent Sample Plot Data Research Articles

Models for simulating the temporal development of black cottonwood (Populus balsamifera L. ssp. trichocarpa (Torr. & Gray ex Hook.) Brayshaw) plantations in Iceland

Black cottonwood (Populus balsamifera ssp. trichocarpa) was initially introduced to Iceland in 1944 from the Kenai Peninsula in Alaska and has been widely planted in shelterbelts and afforestation projects since the 1980s. There is currently much interest in increasing the planting of black cottonwood, especially in carbon sequestration projects, because of its rapid growth at an early age. Growth models simulate the growth of a forest over time and are important tools for forest managers, researchers, and policymakers. This study presents, for the first time, site index, individual-tree diameter increment and tree height models for even-aged black cottonwood stands in Iceland. The data were collected from Icelandic national forest inventory (NFI) plots and from three plots from a network of permanent sample plots (PSP). The NFI data were collected during 2005–2022, and the PSP data were collected between 2009 and 2022. The model of McDill and Amateis was selected for predicting site index and dominant height development, and the model of Schumacher was selected for predicting tree height. For diameter increment modelling, an optimization-based modelling approach was found to be more suitable than non-linear regression analysis. The models developed in this study can be used in forestry practice and in optimization studies for thinned black cottonwood stands. The models produced simulation results that corresponded to measured stand development.

Predicting net growth rates in boreal forests using Landsat time series and permanent sample plot data

Abstract Increasing temperature and changes in water dynamics are bringing uncertainty regarding the future productivity of boreal forests, even in the absence of stand-replacing disturbances. There is accumulating evidence that water deficits caused by warmer summer temperatures are linked to decreases in the growth rate of boreal tree species in some regions. In this context, it is essential to provide forest professionals with a means of monitoring net forest growth rates in undisturbed areas and at the scale of a management unit in order to determine where and when changes in growth are taking place. This is challenging using conventional forest inventory approaches. In this study, we use Landsat time series and data from permanent sample plots (PSP) to develop spatially explicit estimates of annual net basal area growth at a 30-m spatial resolution for a forest management unit in Canada. An ordinary least square regression model was developed using data from 120 PSPs and validated on an independent set of 60 PSPs, with R2 values of 0.61 and 0.58, respectively. Applying the model over a 586 607-ha study area revealed considerable temporal and spatial variability in the predicted growth rates and their evolution through time. There was an overall decline in predicted growth rates over time, with this trend corroborated by the PSP data and attributed to the ageing demographics of the forests in the study area. This variability was related to forest development stage, species composition, and structural attributes derived from light detection and ranging (LiDAR). The information generated by the suggested approach can help to improve yield predictions, optimize rotation lengths, and allow for the identification of target areas where silvicultural interventions aimed at maintaining or enhancing growth could be conducted.

Fusing tree-ring and permanent sample plot data to model influences of climate and thinning on tree growth in larch plantations in northeast China

An improved understanding of the effects of thinning and climate on tree growth is essential to adopting adaptive forest management strategies under climate change. In this study, we developed a climate-sensitive individual-tree growth model for Changbai larch (Larix olgensis) in northeast China by fusing tree-ring and permanent sample plot data. We assessed the impacts of tree age, competition, site condition, thinning, and climate on radial growth using the mixed-effects modeling approach. Results showed that the radial growth rate of Changbai larch decreased with increasing cambial age and competition intensity. Radial growth was negatively associated with summer water deficit and positively associated with spring growing degree-days. Radial growth also responded positively to mean annual precipitation of the previous year. In addition, a significant positive effect of thinning on radial growth was observed. The magnitude of thinning effects was affected by thinning intensity and time elapsed since the thinning. Hierarchical partitioning analysis showed that cambial age was the most important factor affecting growth (relative contribution 35.96%), followed by competition (31.42%), climate (19.24%), thinning (8.40%), and site condition (4.98%). Compared to unthinned plots, moderate- and high-intensity thinning significantly enhanced the radial growth under unfavorable climates, indicating that thinning has great potential to mitigate the negative effects of future climate. Disentangling different sources of variations in ring width will help advance our understanding of the factors driving radial growth and reduce the uncertainty in forest management decisions.

Enhancing predictive precision of dominant height projection equations for eucalypts by incorporating rainfall and temperature terms

Short-rotation forest stands are sensitive to extreme climate conditions during their growth period, which presents a challenge to managing forests and modelling forest growth in a constantly changing climate. We developed climate-sensitive dominant height models for the Eucalyptus grandis × Eucalyptus urophylla hybrid (GU) in South Africa. In addition, dominant height growth under three future climate scenarios was investigated. The Chapman– Richards and Gompertz models, modified by within-rotation and long-term climate data, were used to model dominant height. Model testing using independent permanent sample plot data showed that the Gompertz model modified by within-rotation bioclimatic data performed better than the other models. The climate-modified Gompertz model was used to project height growth for eucalypt stands under three future climate scenarios; ‘No change’, ‘RCP4.5’ and ‘RCP8.5’, for two periods: 2050 (years 2040 to 2060); and 2070 (years 2061 to 2080). Climate change might decelerate dominant height growth in the study area, therefore forest management plans need to be adapted accordingly.

Height-diameter relationships and stem volume equations in young and middle-aged forest stands of Ukraine

Height-diameter (h-d) relationships in forest stands are commonly used in various scientific and practical forestry applications. Accurate h-d models combined with tree stem volume equations are recognised to be effective in growing stock volume estimation. The purpose of the study was threefold: 1) development of a set of mathematical models of the h-d relationship in young and middle-aged forest stands for ten forest-forming species in Ukraine; 2) modelling stem volume in above mentioned forest stands; 3) comparison of established mathematical models with corresponding ones for premature, mature, and overmature forest stands. The study was based on permanent and temporal sample plots data (about 600) established in forest stands during 1950s-2020s within the most forested regions of Ukraine (Polissia, Forest-Steppe, Carpathians). In total, about 10,000 sample trees were measured on the sample plots to accurately estimate their stem volume outside bark. The h-d models demonstrated very similar relationships between stem heights and diameters for most of our species except for spruce and fir in mountain Carpathian forests where the steeper h-d curves were obtained. The study revealed that birch and hornbeam tree stems had the lowest volumes among surveyed species. The results also indicated that tree species tend to have higher volumes (up to 7% for coniferous, and up 10% for aspen and birch forests) in young and middle-aged forest sands than in older ones. For the other species, a statistically significant difference between stem volumes of trees of different ages was not observed. The developed mathematical models can complement the corresponding models for older groups of forest stands since they revealed an important aspect of relationships between the key tree stem parameters. These models are also applicable for a more precise stem volume estimation during thinning operations in the young and middle-aged forests in Ukraine

Using hybrid modelling to predict basal area and evaluate effects of climate change on growth of Norway spruce and Scots pine stands

ABSTRACT When modelling forest growth, capturing the effects of climate change is needed for reliable long-term predictions and management choices. This remains a challenge because commonly used mensurational forest growth and yield models, relying on inventory data, cannot account for climate change effects. We developed hybrid physiological/mensurational basal area growth and yield models, which combine physiological response to climatic conditions and empirical relations. We included climate and site effects by replacing time with light sums of photosynthetically active radiation and modifying the latter with monthly soil water, vapour pressure deficit, temperature, and frost days. When parameterised with permanent sample plot data for Scots pine and Norway spruce across Sweden, the hybrid models could reproduce observations well, although with no increase in precision compared with time-based mensurational models. When considering different climate scenarios, a significant impact on productivity from climate change emerged. For example, a 2 °C warming enhanced Scots pine production by up to 14% in regions where temperatures were originally cooler and soil water deficit was low (i.e. northwest Sweden), but depressed it, up to 9%, elsewhere. Hence, climate-sensitive models that take local variations into account are necessary for accurate predictions and sustainable forest management.

Long-term dynamics of stand structure and regeneration in high-stocked selection fir-beech forest stand: Croatian Dinarides case study

In recent decades, changes in stand structure in Central European fir-beech forests, such as accumulation of large-diameter firs, fir dieback, and poor regeneration, have been well documented. Besides environmental factors, light harvesting was suggested as one of the main drivers of the negative structural dynamics of these forests in Croatia. This study applied the MOSES 3.0 stand simulator on permanent sample plot data to reconstruct stand development over the past 20 years and simulate long-term projections of selection stand structure and regeneration with respect to theoretical values, using three management regimes differing by the applied harvesting intensity (traditionally applied, theoretical intensity, and no management). Sample plot data from three sets of detailed measurements (1992, 2002, and 2012) were used for validation of the simulator, and eleven 10-year cycles of management were then simulated under the above management scenarios. Results showed a positive influence of harvest intensity on stand regeneration and the achievement of a targeted selection structure in the long term. Two management scenarios predicted a decrease in stand volume (34% and 40%, respectively), an increase in the initial percentage (28%) of beech and maple (58% and 75%, respectively), and the achievement of optimal stand regeneration of 11-13 recruited trees per hectare annually (60% firs). No management scenario could achieve old-growth structure (accumulation of standing stock, large trees, and deadwood). The theoretical intensity scenario was evaluated as the better approach to be applied in this type of forest stands in Croatia.

Estimating crown width in degraded forest: A two-level nonlinear mixed-effects crown width model for Dacrydium pierrei and Podocarpus imbricatus in tropical China

Tropical forest degradation makes a major contribution to greenhouse gas emission. Crown width (CW) is one of the important predictors in forest growth and yield models that provide basic data for assessment of forest degradation. Precise method of estimating tree crown for two tropical tree species (Dacrydium pierrei Hickel and Podocarpus imbricatus Bl), which are the major species in the degraded coniferous mixed forests in the tropical China, is necessary. These forests play a pivotal role in maintaining ecosystem functions, but are under the threat of severe degradation in recent years, and none of the studies has provided focus to these forests. We developed a nonlinear mixed-effects CW model using the permanent sample plot data acquired from D. pierrei and P. imbricatus forests. A number of tree- and stand-level variables were evaluated for their potential contribution to the CW variations, and included only highly significant ones in the model. The random effects at the levels of both sample plots and stands with different site quality class (blocks) were included in the CW model through mixed-effect modeling, and resulting model is therefore a two-level nonlinear mixed-effects model. Leave-one-out cross-validation was applied to evaluate the models. Among several predictor variables, diameter at breast height (DBH), height-to-DBH ratio (HDR), and height to crown base (HCB) contributed relatively highly to the CW variations. Dummy variable was introduced into the model to differentiate CW variations of two tree species. Correlations of CW and predictor variables significantly decreased when random effects at both the block and sample plot levels were included. We calibrated the nonlinear mixed effects CW model following the empirical best linear unbiased prediction theory, using four strategies of selecting CW trees per sample plot (largest, medium-sized, smallest trees and randomly selected trees) and fifteen sample sizes (one to fifteen trees). The prediction accuracy increased with increasing number of trees per sample plot, except the smallest trees, but the largest increase occurred with three largest trees used in calibration. This article emphasized more on modeling methodology, which can be applied to construct CW models for any forest elsewhere including degraded forest in the tropics.

Conditional inference trees in the assessment of tree mortality rates in the transitional mixed forests of Atlantic Canada.

Long-term predictions of forest dynamics, including forecasts of tree growth and mortality, are central to sustainable forest-management planning. Although often difficult to evaluate, tree mortality rates under different abiotic and biotic conditions are vital in defining the long-term dynamics of forest ecosystems. In this study, we have modeled tree mortality rates using conditional inference trees (CTREE) and multi-year permanent sample plot data sourced from an inventory with coverage of New Brunswick (NB), Canada. The final CTREE mortality model was based on four tree- and three stand-level terms together with two climatic terms. The correlation coefficient (R2) between observed and predicted mortality rates was 0.67. High cumulative annual growing degree-days (GDD) was found to lead to increased mortality in 18 tree species, including Betula papyrifera, Picea mariana, Acer saccharum, and Larix laricina. In another ten species, including Abies balsamea, Tsuga canadensis, Fraxinus americana, and Fagus grandifolia, mortality rates tended to be higher in areas with high incident solar radiation. High amounts of precipitation in NB’s humid maritime climate were also found to contribute to heightened tree mortality. The relationship between high GDD, solar radiation, and high mortality rates was particularly strong when precipitation was also low. This would suggest that although excessive soil water can contribute to heightened tree mortality by reducing the supply of air to the roots, occasional drought in NB can also contribute to increased mortality events. These results would have significant implications when considered alongside regional climate projections which generally entail both components of warming and increased precipitation.

Hybrid height growth and survival model for juvenile Eucalyptus globoidea (Blakely) and E. bosistoana (F. Muell) in New Zealand

Juvenile plantation forest dynamics are complex but understanding and predicting early growth and survival enables effective management. Information provided by traditional growth and yield models is an essential input in decision-making processes for managing planted forests. They are generally fitted using permanent sample plot data, thus they are robust and simple. However, the introduction of explanatory factors affecting tree development (e.g. wetness index, morphometric protection index etc.) not only to improve the quality of predictions but also to add useful information underpinning forest management decisions (e.g. species choice, silvicultural practice etc.). In this study, three different hybrid growth and yield approaches for modelling height growth and survival were tested, culminating in models being developed for Eucalyptus globoidea and E. bosistoana by integrating climatic, edaphic and topographic factors. Permanent sample plot data (n = 84) were used to develop these models. The results showed that inclusion of topographic and climatic factors alongside potentially usable light sum (PULS) slightly improved both height and survival prediction of both species compared to time-based models (TA). The ranking and alignment of coefficient of determination for height models were PULS = 0.79 > 0.77 > TA = 0.75 and survival models were PULS = −0.47 > TA = −0.45, respectively. Both of the species were site-sensitive, especially to soil moisture and wind speed. This sensitivity also indicated the optimal requirements for height growth and survival for these species. All three modelling approaches can be applied for juvenile Eucalyptus plantations in any given site-specific case, based on available information and management demands. The models developed in this study can help forest managers make better decisions for new plantation establishment and management.

Mixed Regional Shifts in Conifer Productivity under 21st-Century Climate Projections in Canada’s Northeastern Boreal Forest

Models of forest growth and yield (G&Y) are a key component in long-term strategic forest management plans. Models leveraging the industry-standard “empirical” approach to G&Y are frequently underpinned by an assumption of historical consistency in climatic growing conditions. This assumption is problematic as forest managers look to obtain reliable growth predictions under the changing climate of the 21st century. Consequently, there is a pressing need for G&Y modelling approaches that can be more robustly applied under the influence of climate change. In this study we utilized an established forest gap model (JABOWA-3) to simulate G&Y between 2020 and 2100 under Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 in the Canadian province of Newfoundland and Labrador (NL). Simulations were completed using the province’s permanent sample plot data and surface-fitted climatic datasets. Through model validation, we found simulated basal area (BA) aligned with observed BA for the major conifer species components of NL’s forests, including black spruce [Picea mariana (Mill.) Britton et al.] and balsam fir [Abies balsamea (L.) Mill]. Model validation was not as robust for the less abundant species components of NL (e.g., Acer rubrum L. 1753, Populus tremuloides Michx., and Picea glauca (Moench) Voss). Our simulations generally indicate that projected climatic changes may modestly increase black spruce and balsam fir productivity in the more northerly growing environments within NL. In contrast, we found productivity of these same species to only be maintained, and in some instances even decline, toward NL’s southerly extents. These generalizations are moderated by species, RCP, and geographic parameters. Growth modifiers were also prepared to render empirical G&Y projections more robust for use under periods of climate change.

Assessing the influence of climate on the growth rate of boreal tree species in northeastern Canada through long-term permanent sample plot data sets

Climate has a considerable influence on tree growth. Forest managers benefit from the empirical study of the historic relationship between climatic variables and tree growth to support forest management frameworks that are to be applied under scenarios of climate change. Through this research, we have utilized long-term permanent sample plot records, historic climate data sets, and linear mixed modelling techniques to evaluate the historic influence of climatic variables on the growth rates of major boreal tree species in Newfoundland and Labrador, Canada. For the commercially significant spruce and fir forests of the province, we found growing degree-days (GDD) to negatively correlate with tree productivity in warmer regions, such as much of Newfoundland (±1350 GDD), but positively correlate with growth in cooler regions, such as those in Labrador (±750 GDD). With respect to precipitation, environmental moisture was not on average a limiting factor to species productivity in the province. These dynamics have implications for the productivity of the spruce–fir forests of the study area when considered alongside contemporary climate projections for the region, which generally entail both a warmer and wetter growing environment.

The millennium shift: Investigating the relationship between environment and growth trends of Norway spruce and Scots pine in northern Europe

For boreal forests in colder climates, changes in environmental conditions are hypothesised to substantially affect ecosystem processes. In this study, trends of top height growth of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst) were analysed using permanent sample plot data from more than 300 long-term experimental sites distributed from temperate zones to the boreal forest conditions in Sweden. By regression analyses, the effects of temperature-sum and precipitation-sum on top height growth were assessed in the period 1986–2018. A significant upward temporal trend in height growth was observed for both species, with the trend more pronounced after the millennium shift. The magnitude of the annual relative height growth after the millennium was about 16.92% and 9.54% higher than expected, respectively for Scots pine and Norway spruce. A potential climate response on height growth was found for both species with temperature-sum positively correlated with top height growth. No significant effect of precipitation-sum on height growth was observed for either species. Our results suggest improved growing conditions and forest sites became more productive in response to increasing temperature in the northern temperate and boreal regions. The increasing growth trends may offer shorter rotation periods and increased forest value for Norway spruce and Scots pine, coupled with contributions of boreal forests to the emerging bio-economy and the regulation of global atmospheric carbon.

Comprehensive yield model for plantation teak in Panama

The purpose of this study was to prepare a comprehensive, computerized teak ( L.f) plantation yield model system that can be used to describe the forest dynamics, predict growth and yield and support forest planning and decision-making. Extensive individual tree and permanent sample plot data were used to develop tree-level volume models, taper curve models and stand-level yield models for teak plantations in Panama. Tree volume models were satisfactorily validated against independent measurement data and other published models. Tree height as input parameter improved the stem volume model marginally. Stand level yield models produced comparable harvest volumes with models published in the literature. Stand level volume product outputs were found like actual harvests with an exception that the models marginally underestimate the share of logs in very large diameter classes. The kind of comprehensive model developed in this study and implemented in an easy to use software package provides a very powerful decision support tool. Optimal forest management regimes can be found by simulating different planting densities, thinning regimes and final harvest ages. Forest practitioners can apply growth and yield models in the appropriate stand level inventory data and perform long term harvest scheduling at property level or even at an entire timberland portfolio level. Harvest schedules can be optimized using the applicable financial parameters (silviculture costs, harvesting costs, wood prices and discount rates) and constraints (market size and operational capacity).Tectona grandis

Climatic change only stimulated growth for trees under weak competition in central boreal forests

Abstract Global change ecologists have often used trees under weak competition (e.g. dominant/codominant trees) to examine relationships between climatic change and tree growth. Scaling up these results to a forest relies on the assumption that the climatic change–tree growth relationship is not affected by tree‐level competition. Using permanent sample plot data from the central Canadian boreal region where warming did not result in water deficit, we tested the above‐mentioned assumption by looking at whether the relationship between climatic change and tree growth varied with tree‐level competition, which was quantified using a modified Hegyi competition index. We found that tree growth increased over time for trees under weak competition, but decreased for those under strong competition. The divergent temporal trends among trees under different levels of competition led to a non‐significant change in growth for our study plots. Growth increased with regional warming, atmospheric [CO2] and water availability for trees under weak competition, but not for those under strong competition. Synthesis. Our results suggest that upscaling the growth responses of dominant/codominant trees to climate change to a forest or a region can lead to biased estimates. Tree‐level competition should be taken into account when expressing climatic change and tree growth relationships.

Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China

Forest aboveground biomass (AGB) estimation modeling based on remote sensing is an important method for large-scale biomass estimation; the accuracy of the estimation models has been a topic of broad and current interest. In this study, we used permanent sample plot data and Landsat 8 Operational Land Imager (OLI) images of western Hunan. Remote-sensing-based models were developed for different vegetation types, and different crown density classes were incorporated. The linear model, linear dummy variable model, and linear mixed-effects model were used to determine the most effective and accurate method for remote-sensing-based AGB estimation. The results show that the adjusted coefficient of determination (R2adj) and root mean square error (RMSE) of the linear dummy model and linear mixed-effects model were significantly better than those of the linear model; the R2adj increased more than 0.16 and the RMSE decreased more than 2.12 for each vegetation type, and the F-test also showed significant differences between the linear model and linear dummy variable model and between the linear model and linear mixed-effects model. The accuracies of the AGB estimations of the linear dummy variable model and the linear mixed-effects model were significantly better than those of linear model in the thin and dense crown density classes. There were no significant differences in the AGB estimation performance between the linear dummy variable model and linear mixed-effects model; these two models were more flexible and more suitable than the linear model for remote-sensing-based AGB estimation. The results of this study provide a new approach for solving the low-accuracy estimations of linear models.

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration. Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation. Southwest China is characterized by complex topographic features and forest canopy structures, complicating methods for mapping aboveground biomass and its dynamics. The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics. This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM time-series images. This method was formulated by comparing two parametric methods: Linear Regression for Multiple Independent Variables (MLR), and Partial Least Square Regression (PLSR); and two nonparametric methods: Random Forest (RF) and Gradient Boost Regression Tree (GBRT) based on the state of forest aboveground biomass and change models. The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la, Yunnan, China. Landsat images and national forest inventory data were acquired for 1987, 1992, 1997, 2002 and 2007. The results show that: (1) correlation and homogeneity texture measures were able to characterize forest canopy structures, aboveground biomass and its dynamics; (2) GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR; (3) GBRT was the most reliable approach in the estimation of aboveground biomass and its changes; and, (4) the aboveground biomass change models showed a promising improvement of prediction accuracy. This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.

Survival functions for boreal tree species in northwestern North America

Logistic survival probability models were developed for seven tree species in northwestern North America using Permanent Sample Plot (PSP) data from: six Canadian provincial and territorial governments, the government of Alaska (USA), and four forestry companies; for a total of 1,250,257 trees within 11,673PSPs. The survival probability of: white/Engelmann spruce (Picea glauca (Moench) Voss/Picea engelmannii Parry ex Engelm.), black spruce (Picea mariana (Mill.) BSP.), lodgepole pine (Pinus contorta Douglas ex Loudon), jack pine (Pinus banksiana Lamb.), trembling aspen (Populus tremuloides Michx.), balsam poplar (Populus balsamifera L.), and balsam fir (Abies balsamea (L.) Mill.) was modeled using tree size (dbh), competition estimates (basal area of the larger trees by species group), climate normal, tagging limits and the time elapsed between consecutive measurements. Survival increased nonlinearly with tree size and the effect of competition on tree survival was related to the shade-tolerance of the species and to stand composition, with shade intolerant conifer species (i.e. lodgepole and jack pine) being more negatively affected by competition compared to shade intolerant deciduous species (trembling aspen and balsam poplar) and shade tolerant spruce species (white and black spruce). Competition from larger spruce (Picea spp.), fir (Abies spp.) and deciduous species (e.g. Populus spp. and Betula spp.) had stronger influences on survival than pine species (Pinus spp.). Intraspecific competition also had significant effects on survival of the majority of the species. Climate Moisture Index provided better results than other climate variables for most species and survival probability increased with increasing values of CMI (i.e. relatively cooler and wetter climate), while for pine species survival probability decreased with increasing CMI and showed higher levels of survival on warmer and drier sites for the range of conditions included in our data. The predictive equations developed in this study could be used to improve the predictive ability of existing growth and yield models such as the Mixedwood Growth Model.

Windthrow Dynamics in Boreal Ontario: A Simulation of the Vulnerability of Several Stand Types across a Range of Wind Speeds

In Boreal North America, management approaches inspired by the variability in natural disturbances are expected to produce more resilient forests. Wind storms are recurrent within Boreal Ontario. The objective of this study was to simulate wind damage for common Boreal forest types for regular as well as extreme wind speeds. The ForestGALES_BC windthrow prediction model was used for these simulations. Input tree-level data were derived from permanent sample plot (PSP) data provided by the Ontario Ministry of Natural Resources. PSPs were assigned to one of nine stand types: Balsam fir-, Jack pine-, Black spruce-, and hardwood-dominated stands, and, Jack pine-, spruce-, conifer-, hardwood-, and Red and White pine-mixed species stands. Morphological and biomechanical parameters for the major tree species were obtained from the literature. At 5 m/s, predicted windthrow ranged from 0 to 20%, with damage increasing to 2 to 90% for winds of 20 m/s and to 10 to 100% for winds of 40 m/s. Windthrow varied by forest stand type, with lower vulnerability within hardwoods. This is the first study to provide such broad simulations of windthrow vulnerability data for Boreal North America, and we believe this will benefit policy decisions regarding risk management and forest planning.

Population structure and spatial pattern analysis of Quercus aquifolioides on Sejila Mountain, Tibet, China

Understanding population structure provides basic ecological data related to species and ecosystems. Our objective was to understand the mechanisms involved in the maintenance of Quercus aquifolioides populations. Using a 1 ha permanent sample plot data for Q. aquifolioides on Sejila Mountain, Tibet Autonomous Region (Tibet), China, we analyzed the population structure of Q. aquifolioides by combining data for diameter class, static life table and survival curve. Simultaneously, the spatial distribution of Q. aquifolioides was studied using Ripley’s L Function in point pattern analysis. The results showed: (1) Individuals in Q. aquifolioides populations were mainly aggregated in the youngest age classes, that accounted for 94.3% of the individuals; the older age classes had much smaller populations. Although the youngest age classes (Classes I and II) had fewer individuals than Class III, the total number of individuals in classes I and II was also greater than in classes IV to IX. In terms of tree height, few saplings, more medium-sized saplings and few large-sized trees were found. The diameter class structure of Q. aquifolioides populations formed an atypical ‘pyramid’ type; the population was expanding, but growth was limited, tending toward a stable population. (2) Mortality of Q. aquifolioides increased continuously with age; life expectancy decreased over time, and the survivorship curve was close to a Deevey I curve. (3) The spatial distribution pattern of Q. aquifolioides varied widely across different developmental stages. Saplings and medium-sized tree showed aggregated distributions at the scales of 0–33 m and 0–29 m, respectively. The aggregation intensities of saplings and medium-sized trees at small scales were significantly stronger than that of large-sized trees. However, large-sized trees showed a random distribution at most scales. (4) No correlation was observed among saplings, medium- and large-sized trees at small scales, while a significant and negative association was observed as the scale increased. Strong competition was found among saplings, medium- and large-sized trees, while no significant association was observed between medium- and large-sized trees at all scales. Biotic interactions and local ecological characteristics influenced the spatial distribution pattern of Q. aquifolioides populations most strongly.