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Deadwood in forests is becoming an important topic globally for forest management and carbon markets. While deadwood volume is now routinely assessed by forest inventories and can be even monitored using remote sensing, data about deadwood carbon and density depending on the extent of decay is scarce. We propose a simple concept for estimating deadwood carbon using data from a pilot study in selected coniferous and broadleaf forests covering different climate zones in Austria. We measured deadwood volume and decay classes and calculated deadwood density and carbon fraction from destructive field samples. We found that decay reduces deadwood density significantly, while carbon fraction increases moderately with decay. Partitioning the explained variation confirmed that size and site parameters are more important for explaining deadwood carbon fraction than for deadwood density. These patterns were consistent for coniferous and broadleaf tree species. We also show how to estimate deadwood carbon stocks using standard forest inventory measurements or country statistics, without information on decay classes. We found differences in decay classes of standing versus lying deadwood, and our proposed method allows considering this in large-scale deadwood carbon assessments. We highlight research gaps and missing deadwood information, to provide more accurate carbon assessments of deadwood. [ABSTRACT FROM AUTHOR]

Ethiopian mixed forests have multipurpose benefits and services. However, there is a gap of information in the entire Ethiopian highlands on forest growth needed for implementation of sustainable forest management and provision of ecosystem services. Therefore, main aim of this study was to identify main factors deriving tree growth in forest and woodland in Amhara Regional State of Ethiopia and to provide an individual tree basal area increment (BAI) model based on increment cores. The data came from 170 sampling plots in study area and cover 51 tree species. For each tree species, diameter at breast height (DBH), height, increment core measurement, tree location, and distance from subject tree measurements were recorded. Because of large number of trees species, it is difficult to develop growth functions for each individual tree species. Thus, classification of species groups provided a framework to organize information that differs between vegetation types. Our classification resulted in three groups. For each tree species group, an individual tree BAI model was calibrated. The tree species group of BAI differs by DBH class. The response to BAI depends on competitive condition of a tree. This growth model can be used to predict tree growth for Ethiopian mixed-species forests. [ABSTRACT FROM AUTHOR]

In Europe, Norway spruce forests are facing challenges due to climate change. One possible adaptive option is to enhance the resilience of forests by thinning. Usually, thinning requires two working steps: (i) tree marking by a forest manager and (ii) the harvesting operation. Since the typical tree selection procedure by forest managers is expensive and time consuming, the integration of the tree selection process in the harvesting operation is of high interest. In this paper, we examined four tree selection methods on 21 experimental plots located within eight Norway spruce stands and assessed their impact on the future stand development. The future stand development by tree selection methods was examined by means of a simulation study using the tree growth model MOSES (MOdeling Stand rESponse). The selection methods were (i) tree selection by forest managers, (ii) tree selection by the harvester operator as part of a fully mechanized harvesting system. Since we used the tree growth model MOSES, we additionally employed (iii) a random tree selection process implemented in MOSES, and (iv) a control simulation assuming no thinning. The Norway spruce stands are located in Lower Austria. The results show that 70% of the trees selected by the forest managers were identical to those chosen by the harvester operator and thus no significant differences in the key stand parameters after a 50-year simulation is detectable. Our study suggests that the tree selection for thinning by a trained harvester operator is a cost-efficient and fast method. [ABSTRACT FROM AUTHOR]

The management of non-native tree species in European forests has a long history, but the information on the current number and geographic distribution of these species in European forests is incomplete and scattered across various datasets. This study aims to perform an inventory of the most frequent non-native tree species growing in European forests and analyse their current extent, geographic distribution and geographic origin. Our results show that at least 150 non-native tree species are currently growing in European forests and provenance trials. The genera represented by largest number of species are Eucalyptus, Pinus, Acacia and Abies. Species growing at the largest areas are Robinia pseudoacacia (2.44 million ha), Eucalyptus globulus (1.46 million ha), Picea sitchensis (1.16 million ha) and Pseudotsuga menziesii (0.83 million ha). In total, non-native tree species in Europe are found in an area of approximately 8.54 million ha, or 4.0% of the European forest area, and the five most abundant species account for up to 77% of this area. The largest number of these 150 species were introduced from North America (71), followed by Asia (45) and Australia (20). North American species occupy by far the largest area. [ABSTRACT FROM AUTHOR]

We assessed the impact of anthropogenic activities such as selective felling and resource extraction on plant diversity and forest structure in the broad-leaved forests of Bhutan. The forest area was grouped into three zones according to human influence: settlement-agriculture, semi-disturbed, and natural forest. A total of 140 plant species were identified. Maximum species richness of trees (8 species/plot) was recorded in natural forests and least in the settlement-agriculture zone (3 species/plot). Shannon (1.73 ± 0.62) and Simpson diversity (5.49 ± 2.97) indices were highest for the natural forest zone as compared to the semi-disturbed (0.92 ± 0.74, 2.70 ± 2.05) and settlement-agriculture zones (0.74 ± 0.74, 2.24 ± 2.13). The density and basal area of trees decreased from 373 N ha−1and 37.9 m2 ha−1in the natural forest zone to 114 N ha−1and 5.7 m2 ha−1in the settlement-agriculture zone. The diameter distribution revealed a lack of commercial tree species in the higher dbh classes from the settlement-agriculture and semi-disturbed zones due to selective felling. Increasing accessibility and anthropogenic activities caused the reduction in biodiversity of the watershed. Selective felling and resource extraction created gaps which were colonized by non-timber species and this may change the forest structure. [ABSTRACT FROM AUTHOR]

Large-scale ecosystem models are important tools for carbon assessment at national scales. Many of these models are not initialised with known field data from any particular time, but simulate the growth of each stand from its estimated germination year up to the present or future. The models will overestimate current-day standing volume or biomass unless historic stand management (biomass removal due to thinning) is taken into account. The full management history of each stand is rarely known, and must be somehow estimated. One possibility is to build statistical thinning models based on data in a National Forest Inventory, which could then be integrated into the ecosystem models. If the harvesting model is constructed using only variables that are also used within the ecosystem model, then the management impacts can be included in the ecosystem model for the entire simulated life of the stand. In the case of most flux dynamics models, this precludes the use of the tree-level data that harvesting models have traditionally relied on. In this article, we develop a novel means to interrogate a subset of the Austrian National Forest Inventory based on deriving probability density functions for particular combinations of stand and site variables. We determine the parameters of a probabilistic model to estimate historic patterns of timber removals and validate it against inventory estimates. Our procedure can establish supportable estimates of historic management regimes suitable as input data for subsequent modelling of national-scale forest carbon stocks, sources and sinks. [ABSTRACT FROM AUTHOR]