Tree Growth Prediction Research Articles

Stochastic Optimization of the Management Schedule of Korean Pine Plantations

Korean pine is one of the most important tree species in northeastern China, where Korean pine plantations produce timber and edible seeds. Often, seeds create more income than timber. Predicting the timber and cone yields of alternative management schedules of the plantations involves uncertainty because the future climatic conditions for tree growth and cone production are unknown. This study developed a simulation model that generates stochastic variation around the predictions of tree growth and cone yield models, allowing the forest manager to seek cutting schedules that maximize the expected amounts of timber or cones, or the expected economic profit, under uncertain future states of nature. Stochastic analysis also facilitates management optimizations for different risk attitudes. The differential evolution algorithm and the developed stochastic simulation model were used to optimize the management of planted Korean pine. Timber and cone yields of a management schedule were calculated under 100 different scenarios for tree growth and cone production. When the growth and cone yield scenarios were stationary (no temporal trends), the optimal management schedules were similar to those of deterministic optimization. The benefits of stochastic optimization increased when it was assumed that the tree growth scenarios may contain climate-change-induced trends. Non-stationary growth variation led to shorter optimal rotation lengths than stationary growth variation. Increasing risk tolerance shortened optimal rotations.

Using high-resolution images to analyze the importance of crown size and competition for the growth of tropical trees

The influence of canopy structure on tropical tree growth has been scantly studied because of the difficulties making field measurements in these dense multi-layered ecosystems. The recent advent of unmanned aerial vehicles (UAVs), has made it easier to collect canopy data, so offering a way to gain a better understanding of forest productivity and thereby improve forest management. In this study, we assessed tree growth prediction using UAV-derived crown measurements as an alternative for field data.Four experimental 9 ha plots were sampled in two forest sites, Yoko in the Democratic Republic of the Congo and Loundoungou in the Republic of Congo. Field inventories were made between 2015 and 2020. For each tree, we computed the diameter increment (DBHI) using censuses and diameter-based competition indices (diameter-based CIs) using the first census. High-resolution orthoimages and digital surface models were acquired with UAVs in 2016 and 2018 in the two sites. They gave estimates of crown characteristics (size, relative elevation, shape) and crown-based competition indices (crown-based CIs). Co-recorded UAV and field measurements were obtained for 1558 trees. The diameter increment of these trees was then modelled using supervised component generalized linear regression, and 20 % of trees were kept for cross-validation.Combined field and UAV data predicted tree DBHI twice better than either taken separately. Diameter at breast height (DBH) and crown area (CA) were found to be complementary predictors. Crown-based CIs significantly improved predictions of models already containing DBH and CA. Adding diameter-based CIs to models containing DBH, CA, and crown-based CIs only marginally improved growth predictions, showing that tree competition can be well-described with UAV data. The model calibrated at one site predicted the growth at the other site well, suggesting that a general model could be devised for multiple sites. Growth variance was better explained in the site (Yoko) where the crown density was higher and the crown smaller. Further data are now needed from multiple sites with ranging stand structures and compositions to build a general model.

Development of regional height growth model for Scots pine using repeated airborne laser scanning data

The rapid development of remote sensing technologies is creating unprecedented opportunities for monitoring and inventorying forest ecosystems. One advantage of remote sensing data is that it can be used to monitor and measure tree growth in near real-time, providing extremely useful data for growth modelling. This study used Aerial Laser Scanning (ALS) data from 14,920 Scots pine stands for the Katowice Regional Directorate of State Forests in southwestern Poland. We tested the possibility of calibrating a regional height growth model for Scots pine for a study area covering 754 thousands of hectares of forests. The model was validated with models developed for Scots pine using the traditional approach based on field data. Our results show that the model calibrated using remote sensing data does not differ significantly from the model calibrated using traditional field measurements from stem analysis. What is more, using a model developed from ALS data gives even better accuracy in modelling height growth than a traditional model calibrated with ground data. Our results are promising for the application of repeated ALS data to the development of regional height growth models, allowing long-term prediction of tree growth under current climatic conditions.

Do Distance-Dependent Competition Indices Contribute to Improve Diameter and Total Height Tree Growth Prediction in Juvenile Cork Oak Plantations?

Competition indices may improve tree growth modelling in high-density stands, found often in new cork oak plantations. Distance-dependent competition indices have hardly been considered for juvenile cork oak plantations since existing models were developed for low-density mature stands. This study aims at inspecting the potential of including distance-dependent competition indices into diameter at breast height (d) and total height (h) growth models for Quercus suber L., comparing several distance-dependent and distance-independent competition indices. Annual d and h growth were modelled with linear and non-linear growth functions, formulated as difference equations. Base models were initially fitted considering parameter estimates depending only on site index (S) and/or stand density (N). They were refitted, testing the significance of adding each competition index to the model parameters. Selected models included the best-performing distance-dependent or -independent competition indices as additional predictors. Best base d and h growth models showed a modelling efficiency (ef) of ef = 0.9833 and ef = 0.9900, respectively. Adding a distance-dependent competition index slightly improved growth models, to an ef = 0.9851 for d and ef = 0.9902 for h. Best distance-dependent competition indices slightly overperformed distance-independent ones in diameter growth models. Neither S nor N were included on best fitted models. If inter-tree competition is present in juvenile undebarked cork oak plantations, it does not yet strongly impact individual tree growth, which may diminish the importance of using, at this stage, more complex spatially explicit competition indices on predicting individual tree growth.

Silver birch shows nonlinear responses to moisture availability and temperature in the eastern Baltic Sea region

Silver birch (Betula pendula Roth.) is a widespread species with a high potential for aiding sustainability and multifunctionality of European forests, as evidenced in Finland and the Baltics. However, under increasing relevance of climate change for tree growth, the meteorological sensitivity of the species is largely unknown, presuming it to be weather tolerant (low sensitivity). Considering local adaptations of populations of widespread species, climatic changes are subjecting trees to extreme conditions, thus testing their adaptability. Accordingly, information on the plasticity (variability) of responses across a gradient of meteorological conditions is crucial for reliable predictions of tree growth. Tree-ring width network was established to assess the plasticity of growth responses of silver birch to meteorological conditions across the eastern Baltic climatic gradient. Time series analysis in combination with generalized additive modelling were applied to assess responses of birch from 21 naturally regenerated conventionally managed stands scattered from southern Finland to northern Germany. Despite the presumed tolerance, explicit meteorological sensitivity of silver birch was estimated. A gradient of local linear weather-growth relationships was estimated, as growth limitation shifted from temperature during the dormancy to water availability during vegetation period in southern Finland and northern Germany, respectively. However, these relationships were nonstationary, as the effect of summer water shortage was intensifying and sensitivity to it has likely been subjected to local adaptation. The regional generalization revealed presence of stationary, yet nonlinear and plastic growth responses, implying disproportional effects of climatic changes. Such responses also explained the nonstationarities, as the local climates shifted along the regional gradient. At the regional scale, summer water shortage was the main driver of increment, while winter conditions had a secondary role; temperature of the preceding vegetation season also had an effect on increment. Accordingly, increased variability of increment of silver birch is expected under changing climate; still, sensitivity and plasticity of increment can be considered as an adaptation to shifting environments.

Transplanting larch trees into warmer areas increases the photosynthesis and its temperature sensitivity.

To investigate the effects of climate warming on photosynthesis, Dahurian larch (Larix gmelinii Rupr.) trees from four sites (spanning ~ 5.5° in latitude and~4 °C of warming) within the geographic range in China were transplanted into a common garden close to the warmer border in 2004. Throughout the growing season of 2018, the CO2- and temperature-response curves of the photosynthesis in the common garden and at the original sites were measured. It was discovered that warming treatment considerably increased the maximum net photosynthetic rate (Amax) by 23.4-35.3% depending on the sites, signifying that warming upregulated Amax with respect to the degree of warming. At 25 °C, warming enhanced the maximum Rubisco carboxylation rate (Vcmax), maximum electron transport rate (Jmax), and mass-based leaf nitrogen concentration (Nmass). The climate warming effect (CWE) on Amax was positively associated with the CWEs on Vcmax, Jmax and Nmass, which indicated that warming promoted Amax primarily via increasing carboxylation and photosynthetic electron transport rates and leaf nitrogen supply. The CWE in optimal photosynthetic temperature (Topt) was significant for the trees from the northern sites rather than the southern sites; however, the effect vanished for the trees transplanted to the common garden; this implied that Topt exhibited limited local thermal acclimation. Nevertheless, warming narrowed the temperature-response curve, the effect of which was positively associated with the warming magnitude. These findings implied that trees transplanted into warmer areas changed the photosynthetic optimum temperature and sensitivity. In summary, our results deepen the understanding of the underlying mechanisms of intraspecific responses of photosynthesis to temperature changes, including which of the modeling would improve the prediction of tree growth and forest carbon cycling under climate warming.

Tree organ growth and carbon allocation dynamics impact the magnitude and δ13C signal of stem and soil CO2 fluxes.

Incomplete knowledge of carbon (C) allocation dynamics in trees hinders accurate modeling and future predictions of tree growth. We studied C allocation dynamics in a mature Pinus sylvestris L. dominated forest with a novel analytical approach, allowing the first comparison of: (i) magnitude and δ13C of shoot, stem and soil CO2 fluxes (Ashoot, Rstem and Rsoil), (ii) concentration and δ13C of compound-specific and/or bulk non-structural carbohydrates (NSCs) in phloem and roots and (iii) growth of stem and fine roots. Results showed a significant effect of phloem NSC concentrations on tracheid growth, and both variables significantly impacted Rstem. Also, concentrations of root NSCs, especially starch, had a significant effect on fine root growth, although no effect of root NSC concentrations or root growth was detected on Rsoil. Time series analysis between δ13C of Ashoot and δ13C of Rstem or δ13C of Rsoil revealed strengthened C allocation to stem or roots under high C demands. Furthermore, we detected a significant correlation between δ13C of Rstem and δ13C of phloem sucrose and glucose, but not for starch or water-soluble carbohydrates. Our results indicate the need to include C allocation dynamics into tree growth models. We recommend using compound-specific concentration and δ13C analysis to reveal C allocation processes that may not be detected by the conventional approach that utilizes bulk organic matter.

Turgor-limited predictions of tree growth, height and metabolic scaling over tree lifespans.

Increasing evidence suggests that tree growth is sink-limited by environmental and internal controls rather than by carbon availability. However, the mechanisms underlying sink-limitations are not fully understood and thus not represented in large-scale vegetation models. We develop a simple, analytically solved, mechanistic, turgor-driven growth model (TDGM) and a phloem transport model (PTM) to explore the mechanics of phloem transport and evaluate three hypotheses. First, phloem transport must be explicitly considered to accurately predict turgor distributions and thus growth. Second, turgor-limitations can explain growth-scaling with size (metabolic scaling). Third, turgor can explain realistic growth rates and increments. We show that mechanistic, sink-limited growth schemes based on plant turgor limitations are feasible for large-scale model implementations with minimal computational demands. Our PTM predicted nearly uniform sugar concentrations along the phloem transport path regardless of phloem conductance, stem water potential gradients and the strength of sink-demands contrary to our first hypothesis, suggesting that phloem transport is not limited generally by phloem transport capacity per se but rather by carbon demand for growth and respiration. These results enabled TDGM implementation without explicit coupling to the PTM, further simplifying computation. We test the TDGM by comparing predictions of whole-tree growth rate to well-established observations (site indices) and allometric theory. Our simple TDGM predicts realistic tree heights, growth rates and metabolic scaling over decadal to centurial timescales, suggesting that tree growth is generally sink and turgor limited. Like observed trees, our TDGM captures tree-size- and resource-based deviations from the classical ¾ power-law metabolic scaling for which turgor is responsible.

Modelling Tree Growth in Monospecific Forests from Forest Inventory Data

The prediction of tree growth is key to further understand the carbon sink role of forests and the short-term forest capacity on climate change mitigation. In this work, we used large-scale data available from three consecutive forest inventories in a Euro-Mediterranean region and the Bertalanffy–Chapman–Richards equation to model up to a decade’s tree size variation in monospecific forests in the growing stages. We showed that a tree-level fitting with ordinary differential equations can be used to forecast tree diameter growth across time and space as function of environmental characteristics and initial size. This modelling approximation was applied at different aggregation levels to monospecific regions with forest inventories to predict trends in aboveground tree biomass stocks. Furthermore, we showed that this model accurately forecasts tree growth temporal dynamics as a function of size and environmental conditions. Further research to provide longer term prediction forest stock dynamics in a wide variety of forests should model regeneration and mortality processes and biotic interactions.

Evaluation of Individual Distance-Independent Diameter Growth Models for Japanese Cedar (Cryptomeria japonica) Trees under Multiple Thinning Treatments

Understanding the tree growth process is essential for sustainable forest management. Future yields are affected by various forest management regimes such as thinning; therefore, accurate predictions of tree growth are needed under various thinning intensities. This study compared the accuracy of individual-level distance-independent diameter growth models constructed for different thinning intensities (thinning intensity-dependent multiple models: TDM model) against the model designed to include all thinning intensities (thinning intensity-independent single model: TIS model) to understand how model accuracy is affected by thinning intensity. We used long-term permanent plot data of Japanese cedar (Cryptomeria japonica) stands in Japan, which was gathered from four plots where thinning was conducted at different thinning intensities: (1) intensive (41% and 38% of trees removed at 25 and 37 years old, respectively), (2) moderate (38% and 34%), (3) light (32% and 34%), and (4) no thinning. First, we specified high interpretability distance-independent competition indices, and we compared the model accuracy both in TDM and TIS models. The results show that the relative spacing index was the best competition index both in TDM and TIS models across all thinning intensities, and the differences in the RMSE (Root mean square error) and rRMSE (relative RMSE) in both TDM and TIS models were 0.001–0.01 cm and 0.2–2%, respectively. In the TIS model, rRMSE varied with thinning intensity; the rRMSE was the lowest for moderate thinning intensity (45.8%) and the highest for no thinning (59.4%). In addition, bias values were negative for the TIS model for all thinning intensities. These results suggest that the TIS model could express diameter growth regardless of thinning intensities. However, the rRMSE had varied with thinning intensity and bias had negative values in the TIS model. Therefore, more model improvements are required for accurate predictions of long-term growth of actual Japanese cedar stands.

Contrasting strategies of xylem formation between black spruce and balsam fir in Quebec, Canada.

Present-day global warming is occurring faster at higher elevations. Although there is much information regarding the divergent responses of tree growth to climate change, the altitudinal patterns of species-specific xylogenesis remains poorly understood. We investigated the xylogenesis of balsam fir (Abies balsamea Mill.) and black spruce (Picea mariana Mill. B.S.P.) at two elevations in Quebec (Canada). The number of enlarging and mature cells of the developing tree ring were counted on microcores collected weekly between 2011 and 2014. At the lower site, the growth pattern and duration of xylogenesis were similar between species. No difference in responses to temperature and solar radiation between species was observed. At the higher site, however, cell production was higher and lasted longer in balsam fir than black spruce. Furthermore, the xylem growth of balsam fir had a stronger response to temperature and solar radiation than black spruce. These findings demonstrate the contrasting strategies of wood formation of the two species, with black spruce being more conservative than balsam fir. Our study provides evidence that sympatric species can have species-specific growth dynamics and site-specific responses to the local environment. Predictions of tree growth under a changing environment require the incorporation of species-specific growth strategies.

경복궁 경회루 권역의 식생경관원형과 개선에 관한 연구

This study was conducted to calculate the optimum height of trees, estimating a model for the prediction of tree growth for the landscape improvement of the Gyeonghoeru area. For the verification of measures for management, this study conducted a photographic survey of the Gyeonghoeru area and used the Pressler

The possibility of predicting the collision of trees with construction investments

The authors of this article propose that organic architecture is based on a broadly understood prediction of the impact a constructed building will have on the condition of adjacent trees. One of the key issues is deciding if a tree may be left near a constructed facility based on an assessment of the impact the structure will have on tree health and its survival chances. This article presents selected aspects of predicting collisions of buildings with trees based on tree growth prediction. This material presents a case study: predicting the collision of Betula pendula and Tilia cordata with a building development, erected closer than 2.5 m from their tree trunks.

Needle longevity of balsam fir is increased by defoliation by spruce budworm

Retention of 10–16-year-old balsam fir needles increased significantly with cumulative spruce budworm defoliation. Mean needle longevity on defoliated 50-year-old fir was almost double that for undefoliated 25-year-old fir. Conifers experiencing environmental change affecting photosynthetic capacity have been observed to compensate by adjusting foliage longevity and increasing retention of old foliage. Defoliation by spruce budworm [Choristoneura fumiferana (Clem.)] is one such change, therefore, it was hypothesized that conifers, such as balsam fir [Abies balsamea (L.) Mill.] may increase foliage life spans to compensate for losses of photosynthetic capacity to defoliation. Understanding foliage longevity is a key component of predicting foliage complement, which is the main driver of effects of defoliation on forest growth and productivity. Defoliation and needle loss were assessed on 16 age classes of foliage on mid-crown branches sampled from 134 mature balsam fir trees near Amqui and Causapscal, Quebec, and related to needle age and cumulative defoliation (summed annual defoliation from 2012 to 2016). A general linear mixed model fitted to the needle survivorship data accounted for 68% of the total deviance. The model interaction term of cumulative defoliation with foliage age indicated that for foliage ages less than about 9 years, increased cumulative defoliation resulted in lower needle survival, possibly because of backfeeding of spruce budworm on 5+-year-old foliage, while in the older age classes, needle survival increased with increasing cumulative defoliation. For the oldest, 11–16-year-old foliage age classes, 10–16% more foliage per age class was retained under severe defoliation than under light defoliation. As a result, median needle age increased from 9.5 to 10.4 years as cumulative defoliation increased from 0 to 500%. Mean needle longevity of 9.5 years for 50-year-old balsam fir with low defoliation observed in this study was considerably higher than the mean of 5.5 years previously observed for 22- to 27-year-old fir in Cape Breton, Nova Scotia. Such differences in needle longevity and retention would cause considerable differences in predictions of tree growth using foliage-based stand growth models.

Fusing tree‐ring and forest inventory data to infer influences on tree growth

AbstractBetter understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources, tree‐ring and forest inventory data. A Bayesian hierarchical model was used to gain inference on the effects of many factors on tree growth—individual tree size, climate, biophysical conditions, stand‐level competitive environment, tree‐level canopy status, and forest management treatments—using both diameter at breast height (dbh) and tree‐ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. This model was applied to a data set of ~130 increment cores and ~500 repeat measurements of dbh at a single site in the Jemez Mountains of north‐central New Mexico,USA. The tree‐ring data serve as the only source of information on how annual growth responds to climate variation, whereas both data types inform non‐climatic effects on growth. Inferences from the model included positive effects on growth of seasonal precipitation, wetness index, and height ratio, and negative effects of dbh, seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model were confirmed by a dendroclimatic analysis. Combining the two data sources substantially reduced uncertainty about non‐climate fixed effects on radial increments. This demonstrates that forest inventory data measured on many trees, combined with tree‐ring data developed for a small number of trees, can be used to quantify and parse multiple influences on absolute tree growth. We highlight the kinds of research questions that can be addressed by combining the high‐resolution information on climate effects contained in tree rings with the rich tree‐ and stand‐level information found in forest inventories, including projection of tree growth under future climate scenarios, carbon accounting, and investigation of management actions aimed at increasing forest resilience.

Introducing a Non-Stationary Matrix Model for Stand-Level Optimization, an Even-Aged Pine (Pinus Sylvestris L.) Stand in Finland

In general, matrix models are commonly applied to predict tree growth for size-structured tree populations, whereas empirical–statistical models are designed to predict tree growth based on a vast amount of field observations. From the theoretical point of view, matrix models can be considered to be more generic since their dependency on ad hoc growth conditions is far less prevalent than that of empirical–statistical models. On the other hand, the main pitfall of matrix models is their inability to include variation among the individuals within a size class, occasionally resulting in less accurate predictions of tree growth compared to empirical–statistical models. Thus, the relevant question is whether a matrix model can capture essential tree-growth dynamics/characteristics so that the model produces accurate stand projections which can further be applied in practical decision-making. Such a dynamic characteristic in our model is the basal area of trees, which causes nonlinearity in time. Therefore, our matrix model is a nonlinear model. The empirical data for models was based on 20 sample plots representing 8360 tree records. Further, according to the model, stand projections were produced for three Scots pine (Pinus sylvestris L.) sapling stands (age of 25 years, stand density fluctuating from 850 to 1400 stems ha - 1 ). Then, (even-aged) stand management was optimized by applying sequential quadratic programming (SQP) among those growth predictions. The objective function of the optimization task was to maximize the net present value (NPV) of the ongoing rotation. The stands were located in Northern Ostrobothnia, Finland, on nutrient-poor soil type. The results indicated that initial stand density had an effect on optimal solutions—optimal stand management varied with respect to thinnings (timing and intensity) as well as to optimal rotation. Further, an increasing discount rate shortened considerably the optimal rotation period, and relaxing the minimum thinning removal to 30 m 3 ha - 1 resulted in an increase both in number of thinnings and in the maximum net present value.

Monitoring intra-annual dynamics of wood formation with microcores and dendrometers in Picea abies at two different altitudes.

Seasonal analyses of cambial cell production and day-by-day stem radial increment can help to elucidate how climate modulates wood formation in conifers. Intra-annual dynamics of wood formation were determined with microcores and dendrometers and related to climatic signals in Norway spruce (Picea abies (L.) Karst.). The seasonal dynamics of these processes were observed at two sites of different altitude, Savignano (650 m a.s.l.) and Lavazè (1800 m a.s.l.) in the Italian Alps. Seasonal dynamics of cambial activity were found to be site specific, indicating that the phenology of cambial cell production is highly variable and plastic with altitude. There was a site-specific trend in the number of cells in the wall thickening phase, with the maximum cell production in early July (DOY 186) at Savignano and in mid-July (DOY 200) at Lavazè. The formation of mature cells showed similar trends at the two sites, although different numbers of cells and timing of cell differentiation were visible in the model shapes; at the end of ring formation in 2010, the number of cells was four times higher at Savignano (106.5 cells) than at Lavazè (26.5 cells). At low altitudes, microcores and dendrometers described the radial growth patterns comparably, though the dendrometer function underlined the higher upper asymptote of maximum growth in comparison with the cell production function. In contrast, at high altitude, these functions exhibited different trends. The best model was obtained by fitting functions of the Gompertz model to the experimental data. By combining radial growth and cambial activity indices we defined a model system able to synchronize these processes. Processes of adaptation of the pattern of xylogenesis occurred, enabling P. abies to occupy sites with contrasting climatic conditions. The use of daily climatic variables in combination with plant functional traits obtained by sensors and/or destructive sampling could provide a suitable tool to better investigate the effect of disturbances on response strategies in trees and, consequently, contribute to improving our prediction of tree growth and species resilience based on climate scenarios.

Can plot edge bias be reduced by means of airborne laser scanning?

ABSTRACTThis paper presents new methods for plot edge bias (PEB) correction of individual tree competition indices (CIs) using airborne laser scanning (ALS) as auxiliary information in multivariate ratio and multivariate regression models. I applied the methods to 557 plots from the Norwegian National Forest Inventory located in Hedmark County in South East Norway and compared them to the benchmarks methods linear expansion and simulated annealing by studying the individual tree growth predictions before and after PEB correction. Generally, the simulated annealing method seemed to improve growth prediction most and overall the existing methods performed better than those based on ALS. All PEB-correction methods tested gave minor changes in growth predictions and the absolute change in percent in adj. R2 and Akaike's Information Criterion (AIC) was numerically small, with an average percentage change for the tested CIs between 1.8 for adj. R2 and 0.6 for AIC of the observed values in the original data. The small differences in the results between the original data and the PEB-corrected data show that on average PEB remains a minor problem for individual tree growth prediction. Whether the results are valid for forests with significantly different spatial composition than that of this database remains to be confirmed.

A new probabilistic canopy dynamics model (SLCD) that is suitable for evergreen and deciduous forest ecosystems

There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment.The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R2=0.898 adjustment, R2=0.698 validation). Litterfall production was correctly simulated (R2=0.562, R2=0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, LAImax was not accurately simulated in the beech forest, and further improvement is required.Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions.

Models of the longitudinal distribution of ring area as a function of tree and stand attributes for four major Canadian conifers

It is widely accepted that ring area increment generally increases from the tree apex to the crown base and is more-or-less constant below the crown base (Pressler’s law), but few quantitative models of this distribution have been developed. The aim of this study was to develop a model of ring area increment using easily obtained crown features and other tree or stand characteristics in order to further the understanding and prediction of tree growth, form, and wood quality. The models were fit to stem analysis observations from white spruce, black spruce, balsam fir, and lodgepole pine. In the final model, which includes tree crown and stand variables, ring area increment within the crown region was slightly curvilinear, the slope of ring area increment below the crown was non-zero, and the effect of butt swell was appreciable up-to a relative height of 0.10. The high accuracy of the mixed effects model suggests that the three-component model form is appropriate for describing ring area profiles, whereas some tree-to-tree variation remains unexplained. The tree and stand variables used in these models can be easily measured in the field or obtained from remote sensing techniques.