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 ten 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.

The role of values in climate-related decision-making is a prominent theme of climate communication research. The present study examines whether forest professionals are more driven by values than scientists are, and if this results in value polarization. A questionnaire was designed to elicit and assess the values assigned to expected effects of climate change by forest professionals and scientists working on forests and climate change in Europe. The countries involved covered a north-to-south and west-to-east gradient across Europe, representing a wide range of bio-climatic conditions and a mix of economic–social–political structures. We show that European forest professionals and scientists do not exhibit polarized expectations about the values of specific impacts of climate change on forests in their countries. In fact, few differences between forest professionals and scientists were found. However, there are interesting differences in the expected values of forest professionals with regard to climate change impacts across European countries. In Northern European countries, the aggregated values of the expected effects are more neutral than they are in Southern Europe, where they are more negative. Expectations about impacts on timber production, economic returns, and regulatory ecosystem services are mostly negative, while expectations about biodiversity and energy production are mostly positive.

Health Psychology ResearchFew studies have explored the impacts of COVID-19 and lockdown on the mental health of undergraduate nursing students. This study aimed: a) to explore perceived stress among undergraduate nursing students in Portugal and Spain during the COVID-19 pandemic outbreak; and b) to analyze several COVID-19 related factors and psychological issues that may be associated with perceived stress. A cross-sectional study was conducted with a convenience sample of Portuguese and Spanish undergraduate nursing students (n=1075). The data gathered included demographic information, questions about COVID-19 related factors, and psychological issues. Data were analyzed using multiple logistic regression with a degree of significance at p<0.05. High perceived stress scores were found in 558 participants (51.9%). Students with high perceived stress most likely had a COVID-19 diagnosis in their household; their household income had been affected by the COVID-19 pandemic; experienced difficulty falling asleep or sleeping all night; consumed junk food in excess; neglected their appearance; felt headaches, stomach aches, and back pain; and lacked the patience or desire to exercise. Additionally, high perceived stress was negatively associated with life satisfaction. The results provide evidence that infectious diseases, such as COVID-19, may significantly influence mental health. Further research should explore the long-term psychological effects of the COVID-19 pandemic among nursing students.

Non-destructive testing (NDT) techniques play an important role in the characterization and diagnosis of historic buildings, keeping in mind their conservation and possible rehabilitation. This paper presents a new approach that merges building information modeling (BIM) with environment geospatial data obtained by several non-destructive techniques, namely terrestrial laser scanning, ground-penetrating radar, infrared thermography, and the automatic classification of pathologies based on RGB (red, green, blue) imaging acquired with an unmanned aircraft system (UAS). This approach was applied to the inspection of the Monastery of Batalha in Leiria, Portugal, a UNESCO World Heritage Site. To assess the capabilities of each technique, different parts of the monastery were examined, namely (i) part of its west façade, including a few protruding buttresses, and (ii) the masonry vaults of the Church (nave, right-hand aisle, and transept) and the Founder’s Chapel. After describing the employed techniques, a discussion of the optimization, treatment and integration of the acquired data through the BIM approach is presented. This work intends to contribute to the application of BIM in the field of cultural heritage, aiming at its future use in different activities such as facility management, support in the restoration and rehabilitation process, and research.

Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.

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