Restoration of Vegetation Greenness and Possible Changes in Mature Forest Communities in Two Forests Damaged by the Vaia Storm in Northern Italy

Giupponi, Luca; Leoni, Valeria; Pedrali, Davide; Giorgi, Annamaria

doi:10.3390/plants12061369

Cited by 3 publications

(1 citation statement)

References 63 publications

(76 reference statements)

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“…Additionally, the presence of past outbreaks serves as a benchmark for anticipating future occurrences. Age, height, diameter, and density of trees within the plots offer insights into the maturity and health of the forest [20][21][22], influencing the susceptibility to moth infestations. Furthermore, factors such as volume and area of selected forest plots provide quantitative measures of forest coverage [23], influencing the scale of potential outbreaks.…”

Section: Data Collection Methods and Analysismentioning

confidence: 99%

Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling

Malashin,

Masich,

Tynchenko

et al. 2024

Forests

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This study presents an approach to forecast outbreaks of Dendrolimus sibiricus, a significant pest affecting taiga ecosystems. Leveraging comprehensive datasets encompassing climatic variables and forest attributes from 15,000 taiga parcels in the Krasnoyarsk Krai region, we employ genetic programming-based predictive modeling. Our methodology utilizes Random Forest algorithm to develop robust forecasting model through integrated data analysis techniques. By optimizing hyperparameters within the predictive model, we achieved heightened accuracy, reaching a maximum precision of 0.9941 in forecasting pest outbreaks up to one year in advance.

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Section: Data Collection Methods and Analysismentioning

confidence: 99%

Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling

Malashin,

Masich,

Tynchenko

et al. 2024

Forests

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Phytochemical profiling of red raspberry (Rubus idaeus L.) honey and investigation of compounds related to its pollen occurrence

Leoni,

Panseri,

Giupponi

et al. 2024

J Sci Food Agric

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BACKGROUNDRed raspberry (Rubus idaeus L.) is an important nectar source for honey production in some specific habitats as well as an important crop, so the definition of the features of this kind of honey is noteworthy. However, due to its rarity on the market, red raspberry honey is poorly characterized. The aim of this work was the phytochemical characterization of honey containing red raspberry from different geographical origins, through melissopalynological analyses concurrently to untargeted metabolomics achieved with different chromatographic techniques coupled to mass spectroscopy (Solid‐Phase Micro‐Extraction (SPME)/Gas Chromatography/Mass Spectrometry (SPME‐GC‐MS) High‐Performance Liquid Chromatography/Mass Spectrometry Orbitrap (HPLC‐Orbitrap).RESULTSOnly 4 out of the 12 samples involved in the study contained raspberry pollen as dominant pollen, although these honeys did not group in the hierarchical cluster analysis (HCA) neither in the classical multidimensional scaling (MDS) analyses used for data evaluation. The first result was the detection of mislabelling in two samples, that contained raspberry pollen only as minor or important minor pollen. Of the 188 compounds identified by HPLC‐Orbitrap and of the 260 identified by SPME‐GC‐MS, 87 and 31 compounds were present in all samples, respectively. The structurally related compounds nicotinaldehyde and nicotinamide, nicotinic acid and nicotinyl alcohol, were present in 100% of the samples and correlated with R. idaeus pollen count (r>0.60, Pearson's correlation analysis).CONCLUSIONThis study reveals important aspects about the characterization of red raspberry honey and could give new insights on bee diet and preferences, since niacin compounds resulted interestingly related to the presence of red raspberry pollen.This article is protected by copyright. All rights reserved.

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Collectively Remembering Environmental Disasters: The Vaia Storm as a Case Study

Menegatto,

Freschi,

Bulfon

et al. 2024

Sustainability

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This study investigated the relatively unexplored topic of the collective memory of environmental disasters from a psychosocial perspective. To achieve this, we conducted an exploratory case study on the Vaia storm, which hit the Italian Alps in October 2018, causing significant social and ecological damage. We carried out thirteen in-depth semi-structured interviews with members of the enunciatory community of Vaia as follows: groups of people who either experienced the disaster firsthand, studied it, or had a particular interest in it. Through a thematic analysis of the interview transcripts, the four following key themes emerged that illustrate the forms taken by the collective memory of the disaster: (1) a sensory dimension; (2) an emotional dimension; (3) a narrative dimension; and (4) a material dimension. Our findings suggest a non-anthropocentric, hybrid understanding of collective memory, aligning with emerging concepts in the psychosocial literature on the emotional impacts of environmental disasters. We also advance potential future research questions at the intersection of memory studies, resilience, and sustainability and stress the practical implications of collective memory in fostering sustainable practices and enhancing climate resilience.

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Restoration of Vegetation Greenness and Possible Changes in Mature Forest Communities in Two Forests Damaged by the Vaia Storm in Northern Italy

Cited by 3 publications

References 63 publications

Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling

Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling

Phytochemical profiling of red raspberry (Rubus idaeus L.) honey and investigation of compounds related to its pollen occurrence

Collectively Remembering Environmental Disasters: The Vaia Storm as a Case Study

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