• Energy Research

Purpose of Review: the elemental composition of organisms (the elementome) strongly determines their functional traits and their functioning. Global change presents signifcant potential impacts on forest elementomes of trees, soils, and soil microbes, infuencing primary production, nutrient cycling, and food-web dynamics in forest ecosystems. This review aims to summarize recent advancements in understanding the response of forest elementomes to global change and how we can help them adapt to new conditions through improved management practices. - Recent Findings: atmospheric CO2 enrichment, increased nitrogen (N) deposition, climate warming and droughts strongly infuence the elemental composition of trees, microbes and soils of forest ecosystems. Accounting for the composition and availability of essential elements such as N, phosphorus (P) and potassium (K) in the plant-soil system can largely improve projections of forest carbon(C) cycle, especially when simulating the capacity of globally increasing C fxation by the rising atmospheric CO2 concentration and N deposition. - Summary: global change infuences forest elementomes across various scales, with diverse spatiotemporal variation and underlying mechanisms. Future research should integrate multi-source information to enhance the monitoring of elementomes and facilitate the adaptation of forests to the new environmental conditions through forest management, particularly focusing on the interaction efects of the multiple facets of global change.

AbstractThe future trajectory of atmospheric CO2 concentration depends on the development of the terrestrial carbon sink, which in turn is influenced by forest dynamics under changing environmental conditions. An in‐depth understanding of model sensitivities and uncertainties in non‐steady‐state conditions is necessary for reliable and robust projections of forest development and under scenarios of global warming and CO2 enrichment. Here, we systematically assessed if a biogeochemical process‐based model (3D‐CMCC‐CNR), which embeds similarities with many other vegetation models, applied in simulating net primary productivity (NPP) and standing woody biomass (SWB), maintained a consistent sensitivity to its 55 input parameters through time, during forest ageing and structuring as well as under climate change scenarios. Overall, the model applied at three contrasting European forests showed low sensitivity to the majority of its parameters. Interestingly, model sensitivity to parameters varied through the course of >100 yr of simulations. In particular, the model showed a large responsiveness to the allometric parameters used for initialize forest carbon and nitrogen pools early in forest simulation (i.e., for NPP up to ~37%, 256 g C·m−2·yr−1 and for SWB up to ~90%, 65 Mg C/ha, when compared to standard simulation), with this sensitivity decreasing sharply during forest development. At medium to longer time scales, and under climate change scenarios, the model became increasingly more sensitive to additional and/or different parameters controlling biomass accumulation and autotrophic respiration (i.e., for NPP up to ~30%, 167 g C·m−2·yr−1 and for SWB up to ~24%, 64 Mg C/ha, when compared to standard simulation). Interestingly, model outputs were shown to be more sensitive to parameters and processes controlling stand development rather than to climate change (i.e., warming and changes in atmospheric CO2 concentration) itself although model sensitivities were generally higher under climate change scenarios. Our results suggest the need for sensitivity and uncertainty analyses that cover multiple temporal scales along forest developmental stages to better assess the potential of future forests to act as a global terrestrial carbon sink.

AbstractCarbon allocation plays a key role in ecosystem dynamics and plant adaptation to changing environmental conditions. Hence, proper description of this process in vegetation models is crucial for the simulations of the impact of climate change on carbon cycling in forests. Here we review how carbon allocation modelling is currently implemented in 31 contrasting models to identify the main gaps compared with our theoretical and empirical understanding of carbon allocation. A hybrid approach based on combining several principles and/or types of carbon allocation modelling prevailed in the examined models, while physiologically more sophisticated approaches were used less often than empirical ones. The analysis revealed that, although the number of carbon allocation studies over the past 10 years has substantially increased, some background processes are still insufficiently understood and some issues in models are frequently poorly represented, oversimplified or even omitted. Hence, current challenges for carbon allocation modelling in forest ecosystems are (i) to overcome remaining limits in process understanding, particularly regarding the impact of disturbances on carbon allocation, accumulation and utilization of nonstructural carbohydrates, and carbon use by symbionts, and (ii) to implement existing knowledge of carbon allocation into defence, regeneration and improved resource uptake in order to better account for changing environmental conditions.

AbstractForest conservation strategies and plans can be unsuccessful if the new habitat conditions determined by climate change are not considered. Our work aims at investigating the likelihood of future suitability, distribution and diversity for some common European forest species under the projected changes in climate, focusing on Southern Europe. We combine an Ensemble Platform for Species Distribution Models (SDMs) to five Global Circulation Models (GCMs) driven by two Representative Concentration Pathways (RCPs), to produce maps of future climate‐driven habitat suitability for ten categories of forest species and two time horizons. For each forest category and time horizon, ten maps of future distribution (5 GCMs by 2 RCPs) are thus combined in a single suitability map supplied with information about the “likelihood” adopting the IPCC terminology based on consensus among projections. Then, the statistical significance of spatially aggregated changes in forest composition at local and regional level is analyzed. Finally, we discuss the importance, among SDMs, that environmental predictors seem to have in influencing forest distribution. Future impacts of climate change appear to be diversified across forest categories. A strong change in forest regional distribution and local diversity is projected to take place, as some forest categories will find more suitable conditions in previously unsuitable locations, while for other categories the same new conditions will become less suited. A decrease in species diversity is projected in most of the area, with Alpine region showing the potentiality to become a refuge for species migration.

Forest ecosystems are characterized by high spatial heterogeneity, often related to complex composition and vertical structure which is a challenge in many process-based models. The need to expand process-based models (PBMs) to take in account such structural complexity led to development and testing of a new approach into Forest Ecosystem Models (FEMs), named 3D-CMCC-FEM, able to investigate carbon and water fluxes, including biomass pools and their partitioning, for complex multi-layer forests. 3D-CMCC FEM integrates several characteristics of the functional structural tree models and the robustness of the light use efficiency CLUE) approach to investigate forest growth patterns and yield processes. The modelling approach was tested by simulating the effects of competition for light and water, growth and yield of a two-layered deciduous forest dominated by Turkey Oak in central Italy for a period of eight years. The model outputs were validated against a series of independently measured data for the major biomass pools, the inter-annual stem increments and above-ground net primary productivity of the overstorey and understorey, respectively. The comparison of Leaf Area Index, Gross Primary Production, and evapotranspiration produced by the model against MODIS data showed agreement in results. In addition, the multi-layered model approach was evaluated against a series of simplified versions to determine whether the enhanced complexity of the model positively contributed to its predictive ability. The proposed model reduced the error in the estimates of forest productivity (e.g. NPP) and dynamics (e.g. growth, mortality) and indicates the importance of considering, as far as possible, the structural complexity in PBMs. (C) 2013 Elsevier B.V. All rights reserved.

In recent decades, droughts, deforestation and wildfires have become recurring phenomena that have heavily affected both human activities and natural ecosystems in Amazonia. The time needed for an ecosystem to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, little is known about the impacts of these disturbances, alone and synergistically, on forest recovery time and the resulting spatiotemporal patterns at the regional scale. In this study, we combined the 3-PG forest growth model, remote sensing and field derived equations, to map the Amazonia-wide (3 km of spatial resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our results indicate that AGB decreases by 4%, 19% and 46% in forests affected by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: (1) the recovery time of the forests takes longer in the southeastern part of the basin, and, (2) as droughts and wildfires become more frequent—since the intervals between the disturbances are getting shorter than the rate of forest regeneration—the long lasting damage they cause potentially results in a permanent and increasing carbon losses from these fragile ecosystems.