Significant increase in natural disturbance impacts on European forests since 1950

Over the last decades, the natural disturbance is increasingly putting pressure on European forests. Shifts in disturbance regimes may compromise forest functioning and the continuous provisioning of ecosystem services to society, including their climate change mitigation potential. Although forests are central to many European policies, we lack the long-term empirical data needed for thoroughly understanding disturbance dynamics, modeling them, and developing adaptive management strategies. Here, we present a unique database of >170,000 records of ground-based natural disturbance observations in European forests from 1950 to 2019. Reported data confirm a significant increase in forest disturbance in 34 European countries, causing on an average of 43.8 million m³ of disturbed timber volume per year over the 70-year study period. This value is likely a conservative estimate due to under-reporting, especially of small-scale disturbances. We used machine learning techniques for assessing the magnitude of unreported disturbances, which are estimated to be between 8.6 and 18.3 million m³/year. In the last 20 years, disturbances on average accounted for 16% of the mean annual harvest in Europe. Wind was the most important disturbance agent over the study period (46% of total damage), followed by fire (24%) and bark beetles (17%). Bark beetle disturbance doubled its share of the total damage in the last 20 years. Forest disturbances can profoundly impact ecosystem services (e.g., climate change mitigation), affect regional forest resource provisioning and consequently disrupt long-term management planning objectives and timber markets. We conclude that adaptation to changing disturbance regimes must be placed at the core of the European forest management and policy debate. Furthermore, a coherent and homogeneous monitoring system of natural disturbances is urgently needed in Europe, to better observe and respond to the ongoing changes in forest disturbance regimes.     

The unexpected long period of elevated CH4 emissions from an inundated fen meadow ended only with the occurrence of cattail (Typha latifolia)

Drainage and agricultural use transform natural peatlands from a net carbon (C) sink to a net C source. Rewetting of peatlands, despite of high methane (CH₄) emissions, holds the potential to mitigate climate change by greatly reducing CO₂ emissions. However, the time span for this transition is unknown because most studies are limited to a few years. Especially, nonpermanent open water areas often created after rewetting, are highly productive. Here, we present 14 consecutive years of CH₄ flux measurements following rewetting of a formerly long-term drained peatland in the Peene valley. Measurements were made at two rewetted sites (non-inundated vs. inundated) using manual chambers. During the study period, significant differences in measured CH₄ emissions occurred. In general, these differences overlapped with stages of ecosystem transition from a cultivated grassland to a polytrophic lake dominated by emergent helophytes, but could also be additionally explained by other variables. This transition started with a rapid vegetation shift from dying cultivated grasses to open water floating and submerged hydrophytes and significantly increased CH₄ emissions. Since 2008, helophytes have gradually spread from the shoreline into the open water area, especially in drier years. This process was periodically delayed by exceptional inundation and eventually resulted in the inundated site being covered by emergent helophytes. While the period between 2009 and 2015 showed exceptionally high CH₄ emissions, these decreased significantly after cattail and other emergent helophytes became dominant at the inundated site. Therefore, CH₄ emissions declined only after 10 years of transition following rewetting, potentially reaching a new steady state. Overall, this study highlights the importance of an integrative approach to understand the shallow lakes CH₄ biogeochemistry, encompassing the entire area with its mosaic of different vegetation forms. This should be ideally done through a study design including proper measurement site allocation as well as long-term measurements.     

Woody plant encroachment in granite barrens on the Frontenac Arch,eastern Ontario,Canada

Aims

Woody plant encroachment is a widespread phenomenon affecting treeless or sparsely treed habitats. We aimed to determine the extent and timing of tree and shrub encroachment into rock barrens of eastern Ontario over the last century, and to assess implications for their ongoing management.

Location

Queen's University Biological Station in the Frontenac Arch ecoregion.

Methods

We quantified the extent of change in woody vegetation in 290 rock barrens using aerial photography from 1925, 1965, and 2008. Composition and structure of woody plant communities in 10 barrens was subsequently quantified in the field using plot-based sampling. Cores or cross-sections were obtained from individuals >1.5 m height and dendrochronological techniques were used to determine their age and identify temporal patterns of any woody encroachment.

Results

Aerial photography indicated that the mean proportion of woody plant cover in barrens increased 22.5% from 1925 to 2008. Dendroecological analysis supported this. Few trees were present prior to 1900 and most established since 1960. Fraxinus americana, Juniperus virginiana, and Juniperus communis were the most common woody species colonizing the barrens. Remnants of large Pinus strobus stumps with extensive charring were found in 90% of the sampled barrens at a mean density of 22.6 stumps ha⁻¹.

Conclusions

Rock barrens on the Frontenac Arch have changed substantially over the past century; gradually being colonized by trees and shrubs and losing their distinctly open character. Active management — including prescribed fire and mechanical thinning — may be necessary if there is a desire to maintain these barrens and the rare species they support as components of the region's biodiversity. However, identification of a reference state for restoration is complicated by the fact that the structure and composition of these habitats were undoubtedly altered by European land clearance in the 19th century, and that some of these areas likely existed as pine woodlands before that.     

Longitudinal growth of head circumference in Punjabi infants in Chandigarh (India)

One hundred and fifty four (86 male and 68 female) Punjabi infants residing in Chandigarh (India) were longitudinally measured for head circumference at monthly age intervals during first year of life. The general pattern of growth of head circumference was characterised by initial sharp rise followed by slow gain during second half of infancy. Beyond birth male infants, possessed higher and statistically significant mean values than their female counterparts. The pattern-wise similarity between growth curves plotted for Punjabi and Western infants, may be attributed to protective effects of breast feeding. Head circumference velocity showed rapid deceleration immediately after birth up to about 4 months, thereafter, it declined slowly. Sex differences in monthly growth rates were found to be statistically significant (p<0.05) at a few of the age intervals during first year of life.     

Latent group detection in functional partially linear regression models

In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.     

Variable selection in nonlinear function-on-scalar regression

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model. The proposed method provides a unified and flexible framework for variable selection in FOSR, allowing nonlinear effects of the covariates. Numerical analysis using simulation study illustrates the advantages of the proposed method over existing variable selection methods in FOSR even when the underlying covariate effects are all linear. The proposed procedure is demonstrated on accelerometer data from the 2003–2004 cohorts of the National Health and Nutrition Examination Survey (NHANES) in understanding the association between diurnal patterns of physical activity and demographic, lifestyle, and health characteristics of the participants.     

Multikink quantile regression for longitudinal data with application to progesterone data analysis

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly.     

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.