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The link between biodiversity and ecosystem function can depend on environmental conditions. This contingency can impede our ability to predict how biodiversity‐ecosystem function (BEF) relationships will respond to future environmental change, causing a clear need to explore the processes underlying shifts in BEF relationships across large spatial scales and broad environmental gradients. We compiled a dataset on five functional traits (maximum height, wood density, specific leaf area [SLA], seed size, and xylem vulnerability to embolism [P50]), covering 78%–90% of the tree species in the National Forest Inventory from Italy, to test (i) how a water limitation gradient shapes the functional composition and diversity of forests, (ii) how functional composition and diversity of trees relate to forest annual increment via mass ratio and complementarity effects, and (iii) how the relationship between functional diversity and annual increment varies between Mediterranean and temperate climate regions. Functional composition varied with water limitation; tree communities tended to have more conservative traits in sites with higher levels of water limitation. The response of functional diversity differed among traits and climatic regions but among temperate forest plots, we found a consistent increase of functional diversity with water limitation. Tree diversity was positively associated with annual increment of Italian forests through a combination of mass ratio and niche complementarity effects, but the relative importance of these effects depended on the trait and range of climate considered. Specifically, niche complementarity effects were more strongly associated with annual increment in the Mediterranean compared to temperate forests. Synthesis: Overall, our results suggest that biodiversity mediates forest annual increment under water‐limited conditions by promoting beneficial interactions between species and complementarity in resource use. Our work highlights the importance of conserving functional diversity for future forest management to maintain forest annual increment under the expected increase in intensity and frequency of drought. [ABSTRACT FROM AUTHOR]

The future dynamics of forest species and ecosystems depend on the effects of climate change and are related to forest management strategies. The expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequencies, intensities, durations and timings of extreme events. The main aim of this work is to assess and describe the climatic requirements for Italian forest tree species. We used 7,272 field observations from Italian National Forest Inventory plots and average annual temperatures and precipitation as interpolated from raster maps with 1 km spatial resolution. On this basis we evaluated the current observed distributions of the 19 most important tree species in Italy with respect to potential climatic limits based on expert knowledge and the available literature. We found that only 46% of the observations fall within the potential joint temperature and precipitation limits as defined by expert knowledge. For precipitation alone, 70% of observations were within the potential limits, and for temperature alone, 80% of observations were within the potential limits. Similarity between current observed and potential limits differ from species-to-species with broadleaves in general more frequently distributed within the potential climatic limits than conifers. We found that ecological requirements and potential information should be revised for some species, particularly for the Pinus genus and more frequently for precipitation. The results of the study are particularly relevant given the threat of climate change effects for Italian forests which are broadly acknowledged to be a biodiversity hotspot. Further investigations should be aimed at modelling the effects of climate changes on Italian forests as a basis for development of mitigation and adaptation forest management strategies. [ABSTRACT FROM AUTHOR]

Within the Paris Agreement's Enhanced Transparency Framework, consistent data collections are the prerequisite for a successful reporting of GHG emissions. For such purposes, NFIs are usually the primary source of information, even if they are frequently not designed for producing estimations on a yearly basis and in the form of wall-to-wall high-resolution maps. In this framework, we present a new spatial model to produce yearly growing stock volume (GSV), above-ground biomass (AGB), and carbon stock wall-to-wall estimates. We tested the model in Italy for the period 2005–2018, obtaining a time-series of yearly maps at 23 m spatial resolution. Results were validated against the 2015 Italian NFI reaching an average RMSE% of 19% for aggregated areas. Results were also compared against data reported by the Italian GHG inventory, reaching an RMSE% of 28% and 20% for GSV and carbon stock respectively. We demonstrated that the modeling approach can be successfully used for setting up a forest monitoring system to meet the interests of governments in inventories of GHG emissions and private entities in carbon offset investments. • A spatial approach for multitemporal estimation of carbon stock is presented. • The approach is consistent with the IPCC's best guidance and practices. • The aboveground carbon stock of forests in Italy exceeded 566 million tons in 2018. • Results are consistent with official Greenhouse gasses and national forest inventories. [ABSTRACT FROM AUTHOR]

UNFCCC's “Cancun safeguards” (COP 16, 2010) provide a strong call for comprehensive steps to prevent harm to biodiversity from Reducing Emissions from Deforestation and forest Degradation (REDD+) activities and to support its conservation. However, as non-binding “principles” and due to their general wording, they are not operational in the present form. Additionally, the scientific literature on biodiversity monitoring for REDD+ is still very limited, particularly when it comes to REDD+ in tropical forests and at the national scale. Whereas some authors suggest that biodiversity integration can be achieved by means of standardised protocols and techniques, others consider that an effective monitoring of biodiversity in tropical forests at the national scale may be an impossible task to achieve in a cost-effective way. However, recent research offers some functional approaches to tackle the many challenges involved. This paper explores the perspectives and limits of developing and effectively incorporating appropriate biodiversity objectives and indicators in Papua New Guinea's multipurpose National Forest Inventory (PNG's NFI). The PNG's NFI is currently being designed under the UN-REDD programme as a key component of the National Forest Monitoring System that PNG is required to establish in order to participate in a future REDD+ mechanism. We conclude that the challenge cannot be effectively tackled only at the design stage of the NFI, as it needs to address a number of issues related to different stages of the REDD+ preparedness process:If biodiversity integration is carried out directly at the NFI stage, it will need to rely on proxies derived from indicators designed to monitor carbon stock change;At the planning stage, a carbon–biodiversity overlay map analysis would allow for a preliminary selection of areas of high biodiversity that could be threatened by REDD+ activities either directly or indirectly through “leakage”;During the implementation stage, the selection could be refined by identifying a sub-sample of sites where forests are undergoing the greatest changes;A comprehensive biodiversity monitoring programme involving field measurements of key species could only be designed once the priority areas have been clearly defined and limited in both number and size. [ABSTRACT FROM AUTHOR]

Highlights: [•] The Carbon Budget Model (CBM) was applied to Italian forests at the national scale. [•] Developed standing and net increment curves based on forest inventory data. [•] Reconstructed past forest age structure for 1995. [•] A novel approach was implemented for uneven-aged forests. [•] Italian forests were a C sink between 2000 and 2009. [•] Demonstrated the impacts of fire and harvest rates on forest C budget in 2020. [Copyright &y& Elsevier]

Abstract: Deadwood biomass is one of the five terrestrial carbon pools that are relevant for the estimation of carbon stocks and carbon stock changes under the UNFCCC and the Kyoto Protocol (IPCC Guidelines for National Greenhouse Gas Inventories). Statistics on this important ecosystem component are generally provided by national forest inventories: in the last few decades, national forest inventories in Europe have gradually included deadwood among the traditionally investigated components. However, deadwood is generally surveyed in terms of volume, which is then converted to biomass using basic density values. These are rarely specifically derived for the population under estimation; more often, values available from the literature and based on the findings of local studies are used instead. Based on a survey conducted by the Italian national forest inventory in 2008 and 2009 that specifically intended to more deeply investigate the carbon storage of forests, samples of deadwood were collected all over the country from the plots of the national forest inventory network and used to derive basic density values. This paper presents the basic densities obtained for groups of species (conifers, broadleaves), deadwood components (coarse woody debris, standing dead trees, stumps) and decay classes (using a five-class system). Fine woody debris was also surveyed, but in terms of weight and not volume. For such material, the ratio of dry weight to fresh weight for conversion to biomass is presented. In addition, the main aspects of the field protocol are discussed. Lastly, the use of less accurate, generalised basic density values is tested to evaluate its effects on deadwood biomass estimates. [Copyright &y& Elsevier]

Province-scale comparison between harvest wood products reported by ISTAT and forested areas reported by INFC. The future agreements on the LULUCF sector will probably implement the role of harvest wood products (HWP) for climate change mitigation. As highlighted by many Authors however, for Italy to quantify the amount of HWP will impose a number of issues mainly related to the inconsistency of the data provided by the National Institute for Statistic (ISTAT). New data collected by the National Forest and Carbon Inventory (INFC) will probably provide useful information. In the meantime, however, a comparison based on the forest area reported by INFC and data on HWP provided by ISTAT, both scaled at province level, could be proposed. The 2005 was assumed as reference year for both data sources. At this purpose, grouping the 103 Italian provinces in 9 groups mainly based on the 20 administrative regions, the Pearson coefficient of correlation was estimated, for each group, comparing the amount of harvest reported by ISTAT with (i) the forest area, (ii) the plantation area and (iii) the total forest area, including both plantations and forests. Possible outliers were identified estimating the harvest rates per hectare of forest and considering the interquantile range of these values. A linear model was finally applied between the total forest area and the total amount of harvest estimated at regional level. The results highlighted a strong correlation between the amount of harvest reported by ISTAT and the forest area reported by INFC for 13 out of 21 regions. For 5 regions, including Lombardia, Piemonte, Lazio, Umbria and Marche the amount of harvest was only correlated with the area of plantations. No correlation was detected for Toscana region. Based on the harvest rate per hectare, 5 provinces out of 103 were identified as outliers. The amount of harvest reported for 3 of these provinces (Cremona, Mantova and Pavia) was clearly referred to plantations. The analysis of the studentized residuals based on the application of the linear model, highlighted outliers values also for Piemonte region. As in the previous case, the amount of harvest reported for this region was probably partially referred to plantations. We could therefore speculate that for many regions data reported by national statistics as referred to the amount of harvest in forest, were statistically correlated with the forest area detected by INFC. However, for some important regions, such as Piemonte and Lombardia, they were clearly correlated with plantations. [ABSTRACT FROM AUTHOR]

• The study aims at constructing wall-to-wall estimates of forest growing stock (GSV). • We combine NFI plot data, remotely sensed and auxiliary variables. • We applied the methodology in Mediterranean Forest. • We create a wall-to-wall GSV forest map in a large test area. • The GSV map was used to produce model-based estimates of GSV at small scale. Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy. For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model. Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE % = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI. [ABSTRACT FROM AUTHOR]