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Questions: We aim for a better understanding of the different modes of intra- and inter-specific competition in two- and three-species mixed-forests. How can the effect of different modes of competitive interactions be detected and integrated into individual tree growth models? Are species interactions in spruce–fir–beech forests more associated with size-symmetric or size-asymmetric competition? Do competitive interactions between two of these species change from two- to three-species mixtures? Location: Temperate mixed-species forests in Central Europe (Switzerland). Methods: We used data from the Swiss National Forest Inventory to fit basal area increment models at the individual tree level, including the effect of ecological site conditions and indices of size-symmetric and size-asymmetric competition. Interaction terms between species-specific competition indices were used to disentangle significant differences in species interactions from two- to three-species mixtures. Results: The growth of spruce and fir was positively affected by increasing proportions of the other species in spruce–fir mixtures, but negative effects were detected with increasing presence of beech. We found that competitive interactions for spruce and fir were more related to size-symmetric competition, indicating that species interactions might be more associated with competition for below-ground resources. Under constant amounts of stand basal area, the growth of beech clearly benefited from the increasing admixture of spruce and fir. For this species, patterns of size-symmetric and size-asymmetric competitive interactions were similar, indicating that beech is a strong self-competitor for both above-ground and below-ground resources. Only for silver fir and beech, we found significant changes in species interactions from two- to three-species mixtures, but these were not as prominent as the effects due to differences between intra- and inter-specific competition. Conclusions: Species interactions in spruce–fir–beech, or other mixed forests, can be characterized depending on the mode of competition, allowing interpretations of whether they occur mainly above or below ground level. Our outcomes illustrate that species-specific competition indices can be integrated in individual tree growth functions to express the different modes of competition between species, and highlight the importance of considering the symmetry of competition alongside competitive interactions in models aimed at depicting growth in mixed-species forests.

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

Study objectivesTLD-1 is a novel pegylated liposomal doxorubicin (PLD) formulation aiming to optimise the PLD efficacy-toxicity ratio. We aimed to characterise TLD-1’s population pharmacokinetics using non-compartmental analysis and nonlinear mixed-effects modelling.MethodsThe PK of TLD-1 was analysed by performing a non-compartmental analysis of longitudinal doxorubicin plasma concentration measurements obtained from a clinical trial in 30 patients with advanced solid tumours across a 4.5-fold dose range. Furthermore, a joint parent-metabolite PK model of doxorubicinentrapped, doxorubicinfree, and metabolite doxorubicinol was developed. Interindividual and interoccasion variability around the typical PK parameters and potential covariates to explain parts of this variability were explored.ResultsMedians ± standard deviations of dose-normalised doxorubicinentrapped+free Cmax and AUC0−∞ were 0.342 ± 0.134 mg/L and 40.1 ± 18.9 mg·h/L, respectively. The median half-life (95 h) was 23.5 h longer than the half-life of currently marketed PLD. The novel joint parent-metabolite model comprised a one-compartment model with linear release (doxorubicinentrapped), a two-compartment model with linear elimination (doxorubicinfree), and a one-compartment model with linear elimination for doxorubicinol. Body surface area on the volumes of distribution for free doxorubicin was the only significant covariate.ConclusionThe population PK of TLD-1, including its release and main metabolite, were successfully characterised using non-compartmental and compartmental analyses. Based on its long half-life, TLD-1 presents a promising candidate for further clinical development. The PK characteristics form the basis to investigate TLD-1 exposure-response (i.e., clinical efficacy) and exposure-toxicity relationships in the future. Once such relationships have been established, the developed population PK model can be further used in model-informed precision dosing strategies.Clinical trial registrationClinicalTrials.gov–NCT03387917–January 2, 2018

Tree age (AGE) and stocking degree (P) strongly influence tree shape, but their effects have been neglected in most tree profile equations. In addition, data used to build traditional tree profile equations usually do not meet the statistical requirements of independence and identical distribution of observations. Therefore, our main objectives were to present a method to improve taper equations with measurements easily collected in tree inventories (age, stocking degree) and also improve the statistical accuracy of those equations by selecting parameters with a more rigorous way than what is traditionally being done. We evaluated the effects of incorporating age and stocking degree as regressors in tree profile equations selected among 30 candidate foundation equations and parameterized with data from 1858 Larix gmelinii (Rupr.) trees growing in the northern China. We used nonlinear mixed-effects models to minimize statistical problems present when building traditional tree profile equations: lack of independence and identical distribution of observations, random effects related to individual trees. Equations incorporating age and stocking degree significantly improved their accuracy. When the equation parameters were estimated with mixed-effects models containing exponential variance functions and accounting for non-independence of observations from the same tree, diameters at any height along the tree bole were more accurately estimated. We demonstrate a new methodology to build more accurate tree profile equations that could support better economic valorization of timber and improve calculations of carbon flows in forests, not only for natural L. gmelinii forest but for other species growing in dense natural stands around the globe.

The stand basal area, closely related to age, site quality, and stand density, is an important factor for predicting forest growth and yield. The accurate estimation of site quality is especially a key component in the stand basal area model. We utilized sample plots with Picea asperata Mast. as the dominant species in the multi-period National Forest Inventory (NFI) dataset to establish a site index (SI) model including climate effects through the difference form of theoretical growth equations and mixed-effects models. We combined the SI calculated from the SI model, stand age, and stand density index to construct a basal area growth model for Picea asperata Mast. stands. The results show that the Korf model is the best SI base model for Picea asperata Mast. The mean temperatures in summer and winter precipitation were used as the fixed parameters to construct a nonlinear model. Ultimately, elevation, origin, and region, as random effects, were incorporated into the mixed-effects model. The coefficients (R2) of determination of the base model, the nonlinear model including climate, and the nonlinear mixed-effects model are 0.869, 0.899, and 0.921, with root-mean-square errors (RMSEs) of 1.320, 1.315, and 1.301, respectively. Among the basal area models, the Richards model has higher precision. And the basal area model including an SI incorporating climatic factors had a higher determination coefficient (R2) of 0.918 than that of the model including an SI without considering climatic effects. The mixed-effects model incorporating climatic and topographic factors shows a better fitting performance of SI, resulting in a higher precision of the basal area model. This indicates that in the development of forest growth models, both biophysical and climatic factors should be comprehensively considered.

Competition among free branches in the tree canopy is an important factor influencing branch length growth. Therefore, there is a need to quantify this competition and to understand the impact of the regression technique on the predictive accuracy of the growth of free branch length (GFBL) model in a Larix principis-rupprechtii plantation. This study focused on an L. principis-rupprechtii plantation in Saihanba Mechanized Forest Farm. Five competition indices based on 2176-branch data points from 76 trees were used to quantify the branch competition, and three regression techniques (nonlinear least squares (NLS), nonlinear mixed-effects model (NLME), and nonlinear quantile regression (NQR)) were used to construct the GFBL model including the branch competition index. The results showed that the Chapman–Richards growth function, including the diameter at breast height (DBH) and depth of branch into crown (DINC), was the optimal equation for describing the GFBL in the studied L. principis-rupprechtii plantation. The branch competition index (CI) was found to be optimal for quantifying the branch competition when used with the maximum value parameter (a0) of the Chapman–Richards growth function. The three parameter estimation methods were compared, and the NLME, which included the CI, was found to have the highest predictive accuracy. The results of this study can act as a reference for improving the management, assessing the management effectiveness, and enhancing the quality of L. principis-rupprechtii plantations.

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm−2 to 25.313 t·hm−2. We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed-effects model framework, we introduce subject- and biomarker-specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.

Increased fire activity due to climate change may impact the successional dynamics of boreal forests, with important consequences for caribou habitat. Early successional forests have been shown to support lower quantities of caribou forage lichens, but geographic variation in, and controls on, the rates of lichen recovery has been largely unexplored. In this study, we sampled across a broad region in northwestern Canada to compare lichen biomass accumulation in ecoprovinces, including the Saskatchewan Boreal Shield, the Northwest Territories Taiga Shield, and Northwest Territories Taiga Plains, divided into North and South. We focused on the most valuable Cladonia species for boreal and barren‐ground caribou: Cladonia mitis and C. arbuscula, C. rangiferina and C. stygia, and C. stellaris and C. uncialis. We developed new allometric equations to estimate lichen biomass from field measurements of lichen cover and height; allometries were consistent among ecoprovinces, suggesting generalizability. We then used estimates of lichen biomass to quantify patterns of lichen recovery in different stand types, ecoprovinces, and with time following stand‐replacing fire. We used a hurdle model to account both for the heterogeneous nature of lichen presence (zero inflation) and for the range of abundance in stands where lichen was present. The first component of the hurdle model, a generalized linear model, identified stand age, stand type, and ecoprovince as significant predictors of lichen presence. With a logistic growth model, a measure of lichen recovery (time to 50% asymptotic value) varied from 28 to 73 yr, dependent on stand type and ecoprovince. The combined predictions of the hurdle model suggest the most rapid recovery of lichen biomass across our study region occurred in jack pine in the Boreal Shield (30 yr), while stands located in the Taiga Plains (North and South) required a longer recovery period (approximately 75 yr). These results provide a basis for estimating future caribou habitat that encompasses some of the large variation in fire effects on lichen abundance and vegetation types across the range of boreal and barren‐ground caribou in North America.

Accurate estimates of tree bole volume are fundamental to sustainable forest management. Total inside and outside bark and merchantable volume equations were developed for 25 major commercial tree species grown in natural stands in eastern and central Canada and the northeastern United States. Data used to develop these equations was collected from 9647 trees sampled from natural stands across the study area. The number of trees sampled varied among species. Jack pine (Pinus banksiana Lamb.) had the most observations (1648 trees) and American basswood (Tilia americana) and red oak (Quercus rubra L.) had the fewest (28 trees each). Two mathematically consistent volume equations (dimensionally compatible and combined variable) were fitted to inside and outside bark and merchantable tree volume data from these tree species. The final volume equation was selected based on fit statistics, predictive accuracy, and logical consistency. Its predictive accuracy was compared with a volume equation previously developed by Honer. Both (total and merchantable) volume equations were fitted using a nonlinear mixed-effects modelling approach. However, random effects were significant for total volumes for only four tree species. A weight (power function) was used to address heteroscedasticity in the data. The modified form of the dimensionally compatible volume equation outperformed the combined variable volume equation in terms of fit statistics and predictive accuracy and was selected as the total inside and outside bark and merchantable volume equations for all tree species. This equation produced logically consistent estimates of total and merchantable volumes and was more accurate than that previously developed by Honer to estimate volumes for most of the tree species used in this study. This new equation can be used to estimate total inside and outside bark and merchantable volumes of major commercial tree species in eastern and central Canada and the northeastern United States.