An anomalous event of drought and heat occurred in central Italy during the summer of 2017. Based on the SPI (Standardized Precipitation Index) and data from the European Space Agency, this event started in November 2016 and was characterized by a strong reduction of precipitation and soil moisture, especially in lowland areas with Mediterranean climate. The aim of this case report were to describe the impact of this event on representative forest communities in central Italy, to analyze the different responses of deciduous and evergreen tree and shrub species in contrasting environmental conditions and to assess their subsequent capacity of recovery or, if not, mortality. Trees suffered severe impacts consisting of widespread crown defoliation, leaf desiccation, crown dieback and whole tree mortality. Deciduous tree species (Fagus sylvatica, Quercus pubescens, Quercus cerris) shed their leaves during the summer, but apical buds and twigs were preserved. This allowed these species to produce new shoots in the following year (2018) and to restore the canopy closure of the stands. Mediterranean evergreen broadleaves, such as Quercus ilex and Phillyrea latifolia suffered of total or partial crown desiccation with wilting leaves and branch dieback. These species partially resprouted in 2018 from axillary and latent buds. The case presented here is discussed within the wider context of the impacts of climate change on Mediterranean forests. Future research directions should include an effective forest monitoring system that combines terrestrial and remote sensing surveys, ad hoc field climate change experiments and silvicultural trials from the perspective of proactive management for the adaptation of forests to future climatic conditions. © Copyright © 2019 Pollastrini, Puletti, Selvi, Iacopetti and Bussotti.

Question: In functional biogeography studies, generalizable patterns in the relationship between plant traits and the environment have yet to emerge. Local drivers (i.e., soil, land use, vegetation structure) can increase our understanding of the trait–environment relationship. What is the role of climate and local drivers in shaping abundance-weighted trait patterns of forest understories at biogeographic scales?. Location: Italian forests. Methods: We selected 201 sites that are statistically representative for the heterogeneity of Italian forests across three biogeographic regions (alpine, continental, and mediterranean). Understorey vegetation was recorded for each site on an area of 400 m², together with 25 environmental variables related to climate, soil, land use and forest structure. Specific leaf area (SLA), plant height (H) and seed mass (SM) were obtained from databases. Community-weighted mean (CWM) values were calculated. Variance partitioning was used to identify the relative role of groups of environmental variables on the CWM of traits. Generalized Additive Models were used to assess the relationship between traits and single variables. Results: Climate alone and climate–soil interactions explained the largest proportion of the variation of all the traits (13.7% to 22.8%). Temperature-related factors as well as soil N and P availability were the climatic and edaphic explanatory variables most correlated to trait variation. Forest structure and land use accounted for a smaller percentage of the variation in traits. Land-use factors alone were important in explaining only SLA variation. Conclusions: While climate plays a major role in trait–environment relationships in forest understories, our results highlighted the need to integrate at least soil properties as local drivers of trait variation in broad scale functional biogeography studies of these systems. © 2019 International Association for Vegetation Science

The study of plant trait-environment links is rarely focused on traits that inform on space occupancy and resprouting (both affecting plant persistence), especially in forest understories. Traits that can effectively capture such key functions are associated with clonality and bud banks. We hypothesized that: 1) climate is the main driver of clonal and bud bank traits, 2) traits related to space occupancy (e.g., greater lateral spread) are more important in more mesic, richer soils forests, and 3) traits related to resprouting ability (e.g., larger bud bank) are more important in more intensively and recently managed forests. We addressed these hypotheses by analysing a unique dataset that is statistically representative of Italian forests heterogeneity and includes three biogeographic regions (Alpine, Continental, Mediterranean). We recorded data for sixteen climatic, soil and management variables. We calculated community weighted mean (CWM) values of seven clonal and bud bank traits for the forest understory vegetation. We used i) redundancy analysis to assess trait-environment relations, and ii) variance partitioning analyses to identifying the relative role of different groups of abiotic variables on CWM variation of all traits combined together, as well as clonal and bud bank traits taken separately. Climate alone had a pervasive effect in determining patterns of clonal and bud bank traits in Italian forest understories, mainly related to the effects of temperature extremes and seasonality. Unexpectedly, soil and management factors alone showed marginal effects on clonal and bud bank traits. However, soil features influenced trait patterns when joined with climate. Our results confirmed that, at the biogeographic scale, climate played a lion-share role in determining persistence-related traits of forest-floor plants. At the local-scale, other interplaying factors (e.g., management, soil variables) may come into play in shaping patterns of the studied plant traits. This study stressed the importance of examining functional trait patterns along complex environmental gradients. © 2019 Elsevier GmbH

The lack of multi-dimensional data is one of the major gaps which limit the knowledge and the assessment possibilities of European forests. Nowadays, the most extensive and complete data on the European forest statuses are given by National Forest Inventories (NFIs) which provide information about the extent of forest’s resources and their composition and structure. Traditionally, NFIs collect data related to trees, with a limited consideration of other habitat components, such as ground vegetation. This information which goes beyond the mere arboreal component is instead essential for a more complete forest biodiversity assessment. This paper is aimed at introducing the ICP Forests LI-BioDiv database which resulted from BioSoil Forest Biodiversity, a large collaborative European project. This database is organized as a multi-dimensional forest geodatabase that contains forest structure and vegetation records collected in 19 European countries in the period of 2005–2008. The data were acquired from 3311 geocoded plots where several different types of data were gathered: stand-level general information, tree-level data, deadwood, canopy closure and floristic composition. This paper is structured in order to: (1) give a clear overview of the raw data available in the database and to (2) present an elaboration of raw data to calculate simple plot-level forest variables (biomass, deadwood volume, alpha diversity). On the basis of the results we achieved, the LI-BioDiv database appears useful mainly for research purposes aimed at studying cross-relationships between multiple forest variables and not for an operative use for monitoring and assessing European forest. In particular, we hope that this contribution can stimulate scientists to carry out cross-analysis of the database for defining future forest biodiversity indicators that could be introduced into the field protocols of the NFIs in Europe. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Key message: ICP Forests relies on a representative pan-European network based on a 16 × 16 km grid-net covering around 6000 plots. Dead wood volumes for 3243 plots, related to 19 European Countries, are presented in this data paper as a result of harmonised sampling procedure, and under compliance with FAIR Data Principles. Dataset access is at https://zenodo.org/record/1467784. Associated metadata are available athttps://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/a27d2a8f-1a2d-4a1c-b932-86ec5f4bd8a6(link to geo-network provided after acceptance). Context: ICP-Forests dataset represents unique opportunity for the assessment of forest resources sustainability and biodiversity in Europe because it monitors the status of forests under a coordinated Pan-European umbrella by standardised methods. Aims: The main goal of this paper is to provide standardized estimates of deadwood volume at European scale for a broader use among forest scientists. Methods: After quality checks, calculations of deadwood volumes distinguished by deadwood types (standing and lying dead trees, snags, coarse woody debris, stumps) have been performed. The obtained plot level data have been joined to available forest stand information (namely: forest type, forest management, and stand age) over 3,243 plots among Europe. Results: The database provides a basis for the evaluation of combined relationships between deadwood volume and forest type, deadwood type, decay status, forest management, and stand age classes at European level. Conclusion: Deadwood volume and quality is recognized as one of the most important source of information for forest biodiversity. Here, first results of a systematic and standardized European survey scheme for assessing deadwood volume are presented. This ICP Forests datasets analysis represents the base for further analysis and relationships. © 2019, INRA and Springer-Verlag France SAS, part of Springer Nature.

Mixed forests are particularly interesting for forest structure and diversity analyses, as higher complexity and diversity can be expected in these forests compared to pure ones. Integrating different approaches in the analyses of structure and diversity in these forests can provide complementary information on non-spatial, spatial and functional diversity patterns. The study aimed at evaluating the spatio-temporal dynamics in forest structure and diversity in a semi-natural mixed oak-hornbeam floodplain forest. All standing trees were mapped and inventoried in 1995, 2005 and 2016 in three 1-ha mixed forest stands, with different soil moisture regime (xeric, mesic, moist conditions). Traditional, non-spatial structure and diversity measures were coupled with spatially-explicit and functional diversity measures. Results indicated that the three stands showed limited variation in stand structure and similar non-spatial diversity attributes, despite the different species composition. Only the extension to spatial and functional analyses was able to reveal more pronounced differences of diversity patterns, as higher complexity, species mingling, and functional tree complementarity was observed in the moister stand. These findings support use of spatially-explicit measurements in traditional inventory measurement protocols to allow more refined analysis of diversity patterns. On the other hand, functional diversity can be easily implemented in diversity analyses, as it requires species abundance information (which is traditionally collected in forest inventory) and species-specific tree traits which can be inferred from literature. © 2019 Elsevier Ltd

Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 "forest" points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km ² in Tuscany, central Italy. © 2019 by the authors.

Global climate change is expected to result in more frequent and intense drought events, especially during the warm season. In such perspective, it is crucial to assess the forest stands vulnerability to extreme climatic events, such as drought, even for Mediterranean forest tree species, commonly considered resistant to dry spell. To test the capability of multitemporal imagery derived by Sentinel-2 (S2) in detecting the impacts of extreme drought events on forest health assessed as crown dieback, some forest stands in Tuscany (central Italy) were analyzed. Vegetation indices (VIs) and ancillary digital terrain model-derived data have been collected in 118 observational samples distributed along an ecological gradient. VIs detected a reduction of trees of photosynthetic activity in August 2017. S2 data have allowed the observation of the different response strategies of the tree species considered in this study to the extreme climatic event that occurred. The case study presented shows that S2 can be applied for monitoring climate-related processes providing a synthetic overview of forest conditions at regional scale. © 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).

Several studies reveal that there is a strong interconnection between climate change and biodiversity. Indeed, estimating plant biodiversity is an important issue under forest ecosystem monitoring, which allows the evaluation of carbon storage and sequestration capacity. To this end, a two-phase strategy, suitably compatible with the most adopted sampling designs in large-scale forest inventories, is proposed. In the first phase, tessellation stratified sampling is performed by partitioning the study area into a grid of quadrats and by randomly selecting a point in each quadrat. The first-phase points are classified as forest or nonforest using remotely sensed imagery. In the second phase, a sample of points is selected from those classified as forest by means of simple random sampling without replacement. The second-phase points constitute the centers of circular plots that are visited in the field to record plant species (usually trees) and their abundance. Estimators of abundance and diversity and estimators of their variances are presented. The proposed strategy is applied in a forest area from Central Italy, as a case study. With respect to the sampling effort, the resulting estimates of relative standard errors are satisfactory, especially those regarding the overall total and diversity index estimators. The proposed statistical approach represents a suitable reference for integrated forest inventory frameworks effectively supporting biodiversity monitoring and assessment. © 2018 John Wiley & Sons, Ltd.

Accurate inventory and mapping of olive (Olea europaea L.) tree attributes represents a central issue to support the olive production system. With reference to the cultivation, there is a high heterogeneity and complexity in the cultivation of olive trees, which is reflected in the large variability in olive grove surfaces. This poses some challenge in accurately estimating olive tree attributes via traditional inventory approaches, as commonly adopted in national forest inventory. From a methodological point of view, the complexity and heterogeneity of olive tree groves can be comparable to the problem of accurately estimating tree outside forests (TOF) attributes. In this study, we tested whether a plot sampling approach formerly developed for TOF is suitable for estimating olive tree attributes at large scale. We tested this approach in a case study where the census of the olive crop area and the number of olive groves was conducted from photo-interpretation of high resolution aerial orthoimagery, used as benchmark to test the effectiveness of the plot sampling approach. The main result of this study is that the plot sampling method can be applied for estimating olive tree attributes. Our obtained RSEs were below 20%, with a limited sampling effort of about 6% of the studied population; the obtained RSEs were below 6% when increasing sampling up to about 21% the studied population. Using robust statistical procedures among countries, should allow obtaining harmonized and comparable information, which can increase the knowledge of olive geographical distribution and structure at its relevant Mediterranean scale. © 2019, Italian Society of Remote Sensing. All rights reserved.

The value of wood for different timber assortments can vary by a factor of ten, optimization of stems assortment is hence a key element in the wood products supply chain, particularly for plantations. ‘Taper functions’ are commonly used in other countries to tackle this issue. In Italy, this approach has not yet entered operational use. These functions are developed based on measures of stem diameters taken at different distances from the base. Such measurements are commonly taken felling the tree and using a tape meter and the tree calliper, clearly assuming some approximations. This research assesses the advantages, in terms of assortments evaluation, that can be obtained if the diameters at different heights are extracted adequately processing Terrestrial Laser Scanning (TLS) output. TLS data have been collected, in a poplar plantation, on 36 trees distributed on three stands with different plantation densities in Padana Plane, Italy. The estimated profiles display a very high variability with an average of 1.8 cm of lateral compression. The results from this study demonstrate the potential and feasibility of estimating bole eccentricity by TLS, providing preliminary tools that will hopefully favour the diffusion of taper functions in operational environments. © 2019, Italian Society of Remote Sensing. All rights reserved.

The value of wood for different timber assortments can vary by a factor of ten. Optimization of stem assortments is, hence, a key element in the wood products supply chain, particularly for plantations. 'Taper functions' are commonly used in other countries to tackle this issue. In Italy, this approach has not yet entered operational use. These functions are developed based on measures of stem diameters taken at different distances from the base. Such measurements are commonly taken felling the tree and using a tape meter and tree caliper, clearly assuming some approximations. This research assesses the advantages, in terms of assortments evaluation, that can be obtained if the diameters at different heights are extracted adequately to process terrestrial laser scanning (TLS) output. TLS data have been collected, in a poplar plantation, on 36 trees distributed on three stands with different plantation densities in Padana Plane, Italy. The estimated profiles display high variability with an average of 1.6 cm of lateral compression. The results from this study demonstrate the potential and feasibility of estimating bole eccentricity by TLS, providing preliminary tools that will hopefully favor the diffusion of taper functions in operational environments. © 2019 by the authors.