Stem exclusion and mortality in unmanaged subalpine forests of the Swiss Alps

Understanding the causes and consequences of spatiotemporal structural development in forest ecosystems is an important goal of basic and applied ecological research. Most existing knowledge about the sequence and timing of distinct structural stages following stand origin in unmanaged (not actively managed in >50years) forests has been derived from forests in North America, which are characterized by particular topographic, climatic, biotic and other environmental factors. Thus, the effects on structural development remain poorly understood for many other forest systems, such as the dense, unmanaged, subalpine Norway spruce forests of the Swiss Alps. Over the past century, land abandonment and reductions in active forest management have led to a substantial increase in the density of these forests types. Consequently, many stands are entering the stem exclusion stage and are currently characterized by associated self-thinning mortality. However, the environmental influences on the rate of this structural development as well as this structural stage itself have not yet been examined. We studied stem exclusion processes based on forest inventory data (National Swiss Forest Inventory; NFI) over three survey periods (1983-1985, 1993-1995 and 2004-2006) using repeated measures statistics. To complement these analyses, we also collected and analysed 3,700 increment cores from 20 field plots within dense subalpine Norway spruce forests dispersed across the Swiss Alps. Over the past decades, basal area (BA) has generally increased, particularly on N-facing and steeper slopes, and within 300m of potential treeline. The number of dead trees was higher on N-facing compared with S-facing slopes, but the BA of dead wood was higher on S-facing slopes. Tree ring analysis confirmed important differences in growth patterns between N- and S-facing slopes and verified the results of the NFI analysis. This study provides a detailed example of how environmental heterogeneity and management history can influence the spatiotemporal structural development of forest ecosystem

Identifying Tree-Related Microhabitats in TLS Point Clouds Using Machine Learning

Tree-related microhabitats (TreMs) play an important role in maintaining forest biodiversity and have recently received more attention in ecosystem conservation, forest management and research. However, TreMs have until now only been assessed by experts during field surveys, which are time-consuming and difficult to reproduce. In this study, we evaluate the potential of close-range terrestrial laser scanning (TLS) for semi-automated identification of different TreMs (bark, bark pockets, cavities, fungi, ivy and mosses) in dense TLS point clouds using machine learning algorithms, including deep learning. To classify the TreMs, we applied: (1) the Random Forest (RF) classifier, incorporating frequently used local geometric features and two additional self-developed orientation features, and (2) a deep Convolutional Neural Network (CNN) trained using rasterized multiview orthographic projections (MVOPs) containing top view, front view and side view of the point’s local 3D neighborhood. The results confirmed that using local geometric features is beneficial for identifying the six groups of TreMs in dense tree-stem point clouds, but the rasterized MVOPs are even more suitable. Whereas the overall accuracy of the RF was 70%, that of the deep CNN was substantially higher (83%). This study reveals that close-range TLS is promising for the semi-automated identification of TreMs for forest monitoring purposes, in particular when applying deep learning techniques

Trade-offs between ecosystem service provision and the predisposition to disturbances : a NFI-based scenario analysis

Background Scenario analyses that evaluate management effects on the long-term provision and sustainability of forest ecosystem services and biodiversity (ESB) also need to account for disturbances. The objectives of this study were to reveal potential trade-offs and synergies between ESB provision and disturbance predisposition at the scale of a whole country. Methods The empirical scenario model MASSIMO was used to simulate forest development and management from years 2016 to 2106 on 5086 sample plots of the Swiss National Forest Inventory (NFI). We included a business-as-usual (BAU) scenario and four scenarios of increased timber harvesting. Model output was evaluated with indicators for 1) ESB provision including a) timber production, b) old-growth forest characteristics as biodiversity proxies and c) protection against rockfall and avalanches and 2) for a) storm and b) bark beetle predisposition. Results The predisposition indicators corresponded well (AUC: 0.71–0.86) to storm and insect (mostly bark beetle) damage observations in logistic regression models. Increased timber production was generally accompanied with decreased predisposition (storm: >−11%, beetle: >−37%, depending on region and scenario), except for a scenario that promoted conifers where beetle predisposition increased (e.g. + 61% in the Southern Alps). Decreased disturbance predisposition and decreases in old-growth forest indicators in scenarios of increased timber production revealed a trade-off situation. In contrast, growing stock increased under BAU management along with a reduction in conifer proportions, resulting in a reduction of beetle predisposition that in turn was accompanied by increasing old-growth forest indicators. Disturbance predisposition was elevated in NFI plots with high avalanche and rockfall protection value. Conclusions By evaluating ESB and disturbance predisposition based on single-tree data at a national scale we bridged a gap between detailed, stand-scale assessments and broader inventory-based approaches at the national scale. We discuss the limitations of the indicator framework and advocate for future amendments that include climate-sensitive forest development and disturbance modelling to strengthen decision making in national forest policy making.peerReviewe