conditions. Trend-adaptive decision making takes into account expert predictions of the most likely climate change scenarios and its impacts on forest, e.g., projection of future forest conditions under the most likely representative concentration pathway (RCP) scenario. The decisions also consider the presently observed climate and forest conditions, but are not based on the belief that the past repeats itself. Thus, they look forward and react to beliefs about the trends. However, when making a management decision, the uncertainty characterizing the situation is not fully taken into account, and the decision-making process is not designed to include learning. We return to this type of decision making in our discussion of simulation-optimization studies. Finally, in forward-looking adaptive decision making the state of the climate and the forest, as well as recent and ongoing climate change are observed, but instead of formulating expectations in the form of a single trend or scenario, the uncertainty inherent in the predictions of climate change and particularly in the likely impacts is acknowledged. Therefore, a spectrum of outcomes is considered, and most importantly, a repeated evaluation process is used to make new projections in the future based on improved observations that have the ability to modify decisions at future points in time according to observed changes. Already when evaluating current decision alternatives, the possibility of future adjustments is taken into account. Thus, decision making is dynamic in this mode and fully adaptive; forward-looking decision makers redesign AFM strategies taking full advantage of the information available on climate change and from monitoring impacts on forests. We use this case to illustrate a way on how beliefs can be systematically updated.

<figure><img src="image_32.png" /><figcaption>Figure 1: Decision-making types under climate change (Source: Yousefpour et al., 2017b)</figcaption></figure>

Nonaadaptive (determinist) decisions may chose different forest strategies and thereby risk achieving the expected outcomes. As shown in Figure 2 and from economic point of view, all decision-making types are valid if their benefits exceeds the costs of adaptation (Yousefpour et al., 2017a). Robust decisions impose the highest costs, however, the greatest safety under worst
conditions. Therefore, they are especially relevant for highly valued ecosystems. Low value forests or forests that are not expected to be considerably affected by climate change and risks may rely on reactive adaptations and react as the risk is realized. Proactive adaptation is conditional on the expected future conditions and subjective to the beliefs of decision-makers about future conditions. As the figure 2 shows, it is valid for a range of ecosystems subject to a moderate level of climate change impacts. As stated above, behaviors of decision-makers play a major role in deciding upon adaptive decisions. The higher the risk aversion coefficient of them, the higher the chances for proactive and robust decisions under climate change.

<figure><img src="image_33.png" /><figcaption>Figure 2: Pertinence of adaptive forest management strategies under risk and uncertainty of climate change and subject to the value of ecosystem and risk attitude (aversion) of decision-makers (sources: Yousefpour et al., 2017a)</figcaption></figure>

Regarding the risk attitude (aversion) and the effects on adaptive behaviors, Deng et al. (2017) found that the individual experience (know how) and valid evidences affect the final adaptive behaviors more than positive perceptions about climate change and water saving urgency. In line with this study, Brunette et al., 2020 studied the adaptive behavior in action of forestry professionals in Southwestern Germany and eastern France and found that exactly the risk aversion attitude of professionals hinders their adaptive action. This has been a surprise outcome but correlating risk and uncertainty attitude of respondents to the set of questions about their
behaviors revealed that the perceived risk and uncertainty of changing business as usual strategy
is higher than climate change and its impacts for professionals.

The behavioral studies in forest decision process are rare but emerging and show the great value of such understanding in supporting decision-makers. The main outcomes of the behavioral studies agree that valid and transferable evidences are crucial to persuade forestry professionals to proactively adapt to future forecasted conditions. However, the behaviors may differ after realizing risk and the urgent need for alternative and cost efficient strategies. Hereby, the integration of behavioral factors in supporting the final adaptive decisions is suitable to generate optimal and if needed robust strategies under multi-risk and uncertainty.

In summary of this report, adaptive forest management is a comprehensive decision processes and asks for true integration of multi-risk and forest objectives. For this, understanding forest processes and their responses to future global change is essential to build a realistic build of future forest conditions, process-based models may be a good choice to build such economic expectations as well. There are novel decision-making approaches that forestry can borrow from other disciplines such as robust decision-making and adopt it for forest decision-making purposes. Such robust approach can be used for direct policy search and realize climate smart forest decisions. Moreover as highlighted in the study of Petr et al. (2020) there are differences between forest decision support tools in the way the present many sources of uncertainty and the way decision-makers need to integrate them in the decision-making process. Equipping forest decision support systems with modules quantifying uncertainty and integrating multi-risk relevant to local conditions is the crucial step to provide essential knowledge base for forest decision-makers.

References

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