ALBERT

Description: Trenčiansky M, Lieskovský M, Merganič J, Šulek R ANALYSIS AND EVALUATION OF THE IMPACT OF STAND AGE ON THE OCCURRENCE AND METAMORPHOSIS OF RED HEARTWOOD Abstract : The red heartwood of beech is responsible for decreasing the market value of the most important deciduous tree species of central Europe. The aims of this study were: (i) to verify the hypothesis that stand age affects the occurrence and metamorphosis of red heartwood in beech; and (ii) to quantify the economic loss due the sale price reduction of timber affected by red heartwood. Seven even-aged beech stands of different age (87, 100, 105, 110, 115, 132, and 145 years) were selected in Slovakia, and 213 trees were cut into 961 pieces of assortments which were evaluated for the presence, form and extension of red heartwood. The economic loss caused by red heartwood was determined as the difference in price between the actual and the potential quality grades of assortments. The results confirmed that stand age significantly influence the occurrence, development, and metamorphosis of red heartwood. The average loss in timber sale price caused by red heartwood varied between 0.76 and 28.04 € m-3, depending on age and form of red heartwood, with more severe losses in stands older than 110 years. To reduce the incidence of beech red heartwood in Central Europe, a reduction of the rotation period should be considered, as well as the adoption of suitable silvicultural practices in aged beech stands. Keywords : Red Heartwood, Economics of Beech, Timber Quality, Timber Prices iForest 10 (3): 605-610 (2017) - doi: 10.3832/ifor2116-010 http://iforest.sisef.org/contents/?id=ifor2116-010

Description: Beliefs, expectations and values are often assumed to drive decisions about climate change adaptation. We tested hypotheses based on this assumption using survey responses from 508 European forest professionals in 10 countries. We used the survey results to identify communication needs and the decision strategies at play, and to develop guidelines on adequate communications about climate change adaptation. We observed polarization in the positive and negative values associated with climate change impacts accepted by survey respondents. We identified a mechanism creating the polarization that we call the 'blocked belief' effect. We found that polarized values did not correlate with decisions about climate change adaptation. Strong belief in the local impacts of climate change on the forest was, however, a prerequisite of decision-making favoring adaptation. Decision-making in favor of adaptation to climate change also correlated with net values of expected specific impacts on the forest and generally increased with the absolute value of these in the absence of "tipping point" behavior. Tipping point behavior occurs when adaptation is not pursued in spite of the strongly negative or positive net value of expected climate change impacts. We observed negative and positive tipping point behavior, mainly in SW Europe and N-NE Europe, respectively. In addition we found that advice on effective adaptation may inhibit adaptation when the receiver is aware of effective adaptation measures unless it is balanced with information explaining how climate change leads to negative impacts. Forest professionals with weak expectations of impacts require communications on climate change and its impacts on forests before any advice on adaptation measures can be effective. We develop evidence-based guidelines on communications using a new methodology which includes Bayesian machine learning modeling of the equivalent of an expected utility function for the adaptation decision problem.

Description: Carbon allocation plays a key role in ecosystem dynamics and plant adaptation to changing environmental conditions. Hence, proper description of this process in vegetation models is crucial for the simulations of the impact of climate change on carbon cycling in forests. Here we review how carbon allocation modelling is currently implemented in 31 contrasting models to identify the main gaps compared with our theoretical and empirical understanding of carbon allocation. A hybrid approach based on combining several principles and/or types of carbon allocation modelling prevailed in the examined models, while physiologically more sophisticated approaches were used less often than empirical ones. The analysis revealed that, although the number of carbon allocation studies over the past 10 years has substantially increased, some background processes are still insufficiently understood and some issues in models are frequently poorly represented, oversimplified or even omitted. Hence, current challenges for carbon allocation modelling in forest ecosystems are (i) to overcome remaining limits in process understanding, particularly regarding the impact of disturbances on carbon allocation, accumulation and utilization of nonstructural carbohydrates, and carbon use by symbionts, and (ii) to implement existing knowledge of carbon allocation into defence, regeneration and improved resource uptake in order to better account for changing environmental conditions.

Description: Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely-used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models’ performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapor pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

Description: Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10–40% per century under current climate and 20–170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics.

Description: Tree regeneration is a key process in forest dynamics, particularly in the context of forest resilience and climate change. Models are pivotal for assessing long-term forest dynamics, and they have been in use for more than 50 years. However, an assessment of their ability to accurately represent tree regeneration is lacking. We assess how well current models capture the overall abundance, species composition, and mortality of tree regeneration. Using 15 models built to capture long-term forest dynamics at the stand, landscape, and global levels, we simulate tree regeneration at 200 sites representing large environmental gradients across Central Europe. The results are evaluated against comprehensive data from unmanaged forests. Most of the models overestimate regeneration levels, which is only compensated in some models by high simulated mortality rates in the early stages of individual trees dynamics. Simulated species diversity of regeneration matches the observed ranges. Models simulating higher species diversity at the stand level do not feature higher regeneration diversity. The effect of light availability on regeneration levels is captured better than the effect of temperature and soil moisture, but patterns are not consistent across models. Increasing complexity in the tree regeneration modules of the models is not related to higher accuracy of simulated tree regeneration. Furthermore, individual model design is more important than scale (stand, landscape, global) and approach (empirical, process-based) for accurately capturing tree regeneration. Despite considerable mismatches between simulation results and data, it is remarkable that most models capture the essential features of the highly complex process of tree regeneration, while not having been parameterized with such data. We conclude that much can be gained by evaluating and refining the modeling of regeneration processes. This has the potential to render long-term projections of forest dynamics under changing environmental conditions that are much more robust.