Modeling disturbance-based tree mortality is becoming increasingly important in the discussion of how to adapt forests to climate change and to preserve their ecosystem services and mitigate the risk of economic losses. In this study, we fitted species-specific interval-censored Accelerated Failure Time models for five major tree species to derive the influence of climate, soil, silvicultural measures, stand and tree characteristics on survival times. We coded all disturbance-based mortality causes as events and analyzed 473,501 individual trees distributed across 2248 long-term (1929–2014) forest growth and yield plots in southwestern Germany. We observed different survival probabilities among tree species with Douglas-fir having the lowest survival probability at age 100 years, followed by Norway spruce and Silver fir. Contrastingly, beech and oak had survival probabilities above 0.98 at age 100 years. Most important factor influencing these survival times was climate. Higher summer temperature shortens the survival time of beech, Silver fir and oak, while Norway spruce suffers more from warmer and wetter winters. Beside climatic factors, base saturation showed a significant positive relationship to survival time for all investigated tree species, except for Norway spruce, which had shorter survival times with increasing cation exchange capacity of the soil. Additionally, short-term effects of destabilization after thinning were found. In conclusion, favoring broadleaved tree species, avoiding heavy thinning in older stands and limiting tree age reduce the probability of disturbance-based tree mortality. However, some of the effects found that cause-unspecific mortality modeling has limited potential to describe the mortality–climate change relation.

Forest growth and wood supply projections are increasingly used to estimate the future availability of woody biomass and the correlated effects on forests and climate. This research parameterizes an inventory-based business-as-usual wood supply scenario, with a focus on southwest Germany and the period 2002-2012 with a stratified prediction. First, the Classification and Regression Trees algorithm groups the inventory plots into strata with corresponding harvest probabilities. Second, Random Forest algorithms generate individual harvest probabilities for the plots of each stratum. Third, the plots with the highest individual probabilities are selected as harvested until the harvest probability of the stratum is fulfilled. Fourth, the harvested volume of these plots is predicted with a linear regression model trained on harvested plots only. To illustrate the pros and cons of this method, it is compared to a direct harvested volume prediction with linear regression, and a combination of logistic regression and linear regression. Direct harvested volume regression predicts comparable volume figures, but generates these volumes in a way that differs from business-as-usual. The logistic model achieves higher overall classification accuracies, but results in underestimations or overestimations of harvest shares for several subsets of the data. The stratified prediction method balances this shortcoming, and can be of general use for forest growth and timber supply projections from large-scale forest inventories.

Penalized splines have potential to decrease estimates of variance in forest inventories with a design-based population-level inference, and a model-based domain-level inference by decreasing the likelihood of a model misspecification. We provide examples with second-order (B2) B-splines and radial basis (RB) functions as extensions to a linear working model (WM). Bias was not prominent, yet greater with B2 and in particular with RB than with WM, and decreased with sample size. Important reductions in the variance of a population mean were achieved with both B2 and RB, but at the domain-level only with RB. The proposed regression estimator of variance generated estimates of variance being slightly smaller than the observed variance. A consistent and larger underestimation was seen with the popular difference estimator of variance. Study Implications: Forest inventories supported by light detection and range (LiDAR) data require—in the estimation phase—a model for linking LiDAR metrics to attributes of interest. Formulating a parametric model can be a challenge and unsatisfactory if the goodness of fit varies across the range of the attribute of interest. A semiparametric model provides more flexibility and lessens the chance of a model misspecification, albeit with the potential of overfitting. A penalty directed at reducing overfitting is required. A flexible semiparametric model is potentially also better suited for applications to small areas like stands than a parametric model. We demonstrate that important reductions in variance are indeed possible, but also that they depend on the form of the nonparametric part of the chosen model and the level of inference (population versus domains). With regard to practical application, reliable estimates of forest attributes at stand-level are of special interest within the scope of forest-management planning, as silvicultural treatments are always stand-oriented, at least with small-scale forestry under Central European conditions, and stand-related volume (basal area, tree density) belongs to the set of relevant parameters for management decisions regarding harvest and regeneration measures.

Background Increasingly, molecular measurements from multiple studies are pooled to identify risk scores, with only partial overlap of measurements available from different studies. Univariate analyses of such markers have routinely been performed in such settings using meta-analysis techniques in genome-wide association studies for identifying genetic risk scores. In contrast, multivariable techniques such as regularized regression, which might potentially be more powerful, are hampered by only partial overlap of available markers even when the pooling of individual level data is feasible for analysis. This cannot easily be addressed at a preprocessing level, as quality criteria in the different studies may result in differential availability of markers – even after imputation. Methods Motivated by data from the InterLymph Consortium on risk factors for non-Hodgkin lymphoma, which exhibits these challenges, we adapted a regularized regression approach, componentwise boosting, for dealing with partial overlap in SNPs. This synthesis regression approach is combined with resampling to determine stable sets of single nucleotide polymorphisms, which could feed into a genetic risk score. The proposed approach is contrasted with univariate analyses, an application of the lasso, and with an analysis that discards studies causing the partial overlap. The question of statistical significance is faced with an approach called stability selection. Results Using an excerpt of the data from the InterLymph Consortium on two specific subtypes of non-Hodgkin lymphoma, it is shown that componentwise boosting can take into account all applicable information from different SNPs, irrespective of whether they are covered by all investigated studies and for all individuals in the single studies. The results indicate increased power, even when studies that would be discarded in a complete case analysis only comprise a small proportion of individuals. Conclusions Given the observed gains in power, the proposed approach can be recommended more generally whenever there is only partial overlap of molecular measurements obtained from pooled studies and/or missing data in single studies. A corresponding software implementation is available upon request. Trial registration All involved studies have provided signed GWAS data submission certifications to the U.S. National Institute of Health and have been retrospectively registered. Electronic supplementary material The online version of this article (10.1186/s12881-019-0849-0) contains supplementary material, which is available to authorized users.