Growth functions describe the change in size of an individual or population with time. The selection of appropriate growth functions for tree and stand modeling is an important aspect in the development of growth and yield models. Here we present information on the forms and characteristics of the more commonly-used growth functions for modeling forest development. When fitted to data, a number of these functions will give essentially equivalent results within the range of the observations used for estimating the equation's coefficients. However, their behavior when extrapolated may be quite different depending on the underlying mathematical properties involved. Hence, understanding these properties is helpful to modelers to determine which candidate functions to consider for specific applications.Unless the data available for modeling cover a very small range of time, there are certain properties that a growth function should exhibit to be consistent with the principles of biological growth (Fig. 6.1): (i) The curve is often limited by the value zero at a specific beginning (t D 0 or t D t 0 ), depending if the variable that is being modeled starts at t D 0, as is the case for the great majority of the tree and stand variables, or later on, as happens with tree diameter at breast height or stand basal area; (ii) The curve generally should exhibit a maximum value usually achieved at an older age (existence of an asymptote); (iii) The slope of the curve should increase with increasing growth rate in the initial phase and decrease in the final stages (show an inflection point).At this point it is important to understand the concepts of growth and yield. Growth is the increase in size of an individual or population per unit of time (for instance volume growth in m 3 ha 1 year 1 ) while yield is the size of the tree or population at a certain point in time (for instance total volume at age 50

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Knowledge of factors that trigger human response to climate change is crucial for effective climate change policy communication. Climate change has been claimed to have low salience as a risk issue because it cannot be directly experienced. Still, personal factors such as strength of belief in local effects of climate change have been shown to correlate strongly with responses to climate change and there is a growing literature on the hypothesis that personal experience of climate change (and/or its effects) explains responses to climate change. Here we provide, using survey data from 845 private forest owners operating in a wide range of bio-climatic as well as economic-social-political structures in a latitudinal gradient across Europe, the first evidence that the personal strength of belief and perception of local effects of climate change, highly significantly explain human responses to climate change. A logistic regression model was fitted to the two variables, estimating expected probabilities ranging from 0.07 (SD ±0.01) to 0.81 (SD ±0.03) for self-reported adaptive measures taken. Adding socio-demographic variables improved the fit, estimating expected probabilities ranging from 0.022 (SD ±0.008) to 0.91 (SD ±0.02). We conclude that to explain and predict adaptation to climate change, the combination of personal experience and belief must be considered.

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Recent studies projecting future climate change impacts on forests mainly consider either the effects of climate change on productivity or on disturbances. However, productivity and disturbances are intrinsically linked because 1) disturbances directly affect forest productivity (e.g. via a reduction in leaf area, growing stock or resource-use efficiency), and 2) disturbance susceptibility is often coupled to a certain development phase of the forest with productivity determining the time a forest is in this specific phase of susceptibility. The objective of this paper is to provide an overview of forest productivity changes in different forest regions in Europe under climate change, and partition these changes into effects induced by climate change alone and by climate change and disturbances. We present projections of climate change impacts on forest productivity from state-of-the-art forest models that dynamically simulate forest productivity and the effects of the main European disturbance agents (fire, storm, insects), driven by the same climate scenario in seven forest case studies along a large climatic gradient throughout Europe. Our study shows that, in most cases, including disturbances in the simulations exaggerate ongoing productivity declines or cancel out productivity gains in response to climate change. In fewer cases, disturbances also increase productivity or buffer climate-change induced productivity losses, e.g. because low severity fires can alleviate resource competition and increase fertilization. Even though our results cannot simply be extrapolated to other types of forests and disturbances, we argue that it is necessary to interpret climate change-induced productivity and disturbance changes jointly to capture the full range of climate change impacts on forests and to plan adaptation measures.

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High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R 2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86-87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015-2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool.