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

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.

Water availability is a major limiting factor in plant productivity and plays a key role in plant species distribution over a given area. New technologies, such as terahertz quantum cascade lasers (THz‐QCLs) have proven to be non‐invasive, effective, and accurate tools for measuring and monitoring leaf water content. This study explores the feasibility of using an advanced THz-QCL device for measuring the absolute leaf water content in Corylus avellana L., Laurus nobilis L., Ostrya carpinifolia Scop., Quercus ilex L., Quercus suber L., and Vitis vinifera L. (cv. Sangiovese). A recently proposed, simple spectroscopic technique was used, consisting in determining the transmission of the THz light beam through the leaf combined with a photographic measurement of the leaf area. A significant correlation was found between the product of the leaf optical depth (τ) and the leaf surface area (LA) with the leaf water mass (Mw) for all the studied species (Pearson’s r test, p ≤ 0.05). In all cases, the best fit regression line, in the graphs of τLA as a function of Mw, displayed R2 values always greater than 0.85. The method proposed can be combined with water stress indices of plants in order to gain a better understanding of the leaf water management processes or to indirectly monitor the kinetics of leaf invasion by pathogenic bacteria, possibly leading to the development of specific models to study and fight them. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.