Tree-ring and remote sensing analyses uncover the role played by elevation on European beech sensitivity to late spring frost.

Extreme climate events such as late spring frosts (LSFs) negatively affect productivity and tree growth in temperate beech forests. However, detailed information on how these forests recover after such events are still missing. We investigated how LSFs affected forest cover and radial growth in European beech (Fagus sylvatica L.) populations located at different elevations at four sites in the Italian Apennines, where LSFs have been recorded. We combined tree-ring and remote-sensing data to analyse the sensitivity and recovery capacity of beech populations to LSFs. Using daily temperature records, we reconstructed LSF events and assessed legacy effects on growth. We also evaluated the role played by elevation and stand structure as modulators of LSFs impacts. Finally, using satellite images we computed Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and LAI (Leaf Area Index) to evaluate the post-LSF canopy recovery. The growth reduction in LSF-affected trees ranged from 36 % to 84 %. We detected a negative impact of LSF on growth only during the LSF year, with growth recovery occurring within 1-2 years after the event. LSF-affected stands featured low vegetation indices until late June, i.e. on average 75 days after the frost events. We did not find a clear relationship between beech forest elevation and occurrence of LSFs defoliations. Our results indicate a high recovery capacity of common beech and no legacy effects of LSFs.

Biogeographic variability in wildfire severity and post-fire vegetation recovery across the European forests via remote sensing-derived spectral metrics.

Wildfires have large-scale and profound effects on forest ecosystems, and they force burned forest areas toward a wide range of post-fire successional trajectories from simple reduction of ecosystem functions to transitions to other stable non-forest states. Fire disturbances represent a key driver of changes in forest structure and composition due to post-fire succession processes, thus contributing to modify ecosystem resilience to subsequent disturbances. Here, we aimed to provide useful insights into wildfire severity and post-fire recovery processes at the European continental scale, contributing to improved description and interpretation of large-scale wildfire spatial patterns and their effects on forest ecosystems in the context of climate change. We analyzed fire severity and short-term post-fire vegetation recovery patterns across the European forests between 2004 and 2015 using Corine Land Cover Forest classes and bioregions, based on MODIS-derived spectral metrics of the relativized burn ratio (RBR), normalized difference vegetation index (NDVI) and relative recovery indicator (RRI). The RBR-based fire severity showed geographic differences and interannual variability in the Boreal bioregion compared to that in other biogeographic regions. The NBR-based RRI showed a slower post-fire vegetation recovery rate with respect to the NDVI, highlighting the differential sensitivities of the analyzed remote sensing-spectral metrics. Moreover, the RRI showed a significant decreasing trend during the observation period, suggesting a growing lag in post-fire vegetation recovery across European forests.

The impact of drought spells on forests depends on site conditions: The case of 2017 summer heat wave in southern Europe.

A major component of climate change is an increase in temperature and precipitation variability. Over the last few decades, an increase in the frequency of extremely warm temperatures and drought severity has been observed across Europe. These warmer and drier conditions may reduce productivity and trigger compositional shifts in forest communities. However, we still lack a robust, biogeographical characterization of the negative impacts of climate extremes, such as droughts on forests. In this context, we investigated the impact of the 2017 summer drought on European forests. The normalized difference vegetation index (NDVI) was used as a proxy of forest productivity and was related to the standardized precipitation evapotranspiration index, which accounts for the temperature effects of the climate water balance. The spatial pattern of NDVI reduction in 2017 was largely driven by the extremely warm summer for parts of the central and eastern Mediterranean Basin (Italian and Balkan Peninsulas). The vulnerability to the 2017 summer drought was heterogeneously distributed over Europe, and topographic factors buffered some of the negative impacts. Mediterranean forests dominated by oak species were the most negatively impacted, whereas Pinus pinaster was the most resilient species. The impact of drought on the NDVI decreased at high elevations and mainly on east and north-east facing slopes. We illustrate how an adequate characterization of the coupling between climate conditions and forest productivity (NDVI) allows the determination of the most vulnerable areas to drought. This approach could be widely used for other extreme climate events and when considering other spatially resolved proxies of forest growth and health.

The sensitivity of the forest carbon budget shifts across processes along with stand development and climate change.

The 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.

Shaping the role of 'fast' and 'slow' drivers of change in forest-shrubland socio-ecological systems.

The temporal speeds and spatial scales at which ecosystem processes operate are often at odds with the scale and speed at which natural resources such as soil, water and vegetation are managed those. Scale mismatches often occur as a result of the time-lag between policy development, implementation and observable changes in natural capital in particular. In this study, we analyse some of the transformations that can occur in complex forest-shrubland socio-ecological systems undergoing biophysical and socioeconomic change. We use a Multiway Factor Analysis (MFA) applied to a representative set of variables to assess changes in components of natural, economic and social capitals over time. Our results indicate similarities among variables and spatial units (i.e. municipalities) which allows us to rank the variables used to describe the SES according to their rapidity of change. The novelty of the proposed framework lies in the fact that the assessment of rapidity-to-change, based on the MFA, takes into account the multivariate relationships among the system's variables, identifying the net rate of change for the whole system, and the relative impact that individual variables exert on the system itself. The aim of this study was to assess the influence of fast and slow variables on the evolution of socio-economic systems based on simplified multivariate procedures applicable to vastly different socio-economic contexts and conditions. This study also contributes to quantitative analysis methods for long-established socio-ecological systems, which may help in designing more effective, and sustainable land management strategies in environmentally sensitive areas.

A global assessment of forest surface albedo and its relationships with climate and atmospheric nitrogen deposition.

We present a global assessment of the relationships between the short-wave surface albedo of forests, derived from the MODIS satellite instrument product at 0.5° spatial resolution, with simulated atmospheric nitrogen deposition rates (Ndep ), and climatic variables (mean annual temperature Tm and total annual precipitation P), compiled at the same spatial resolution. The analysis was performed on the following five forest plant functional types (PFTs): evergreen needle-leaf forests (ENF); evergreen broad-leaf forests (EBF); deciduous needle-leaf forests (DNF); deciduous broad-leaf forests (DBF); and mixed-forests (MF). Generalized additive models (GAMs) were applied in the exploratory analysis to assess the functional nature of short-wave surface albedo relations to environmental variables. The analysis showed evident correlations of albedo with environmental predictors when data were pooled across PFTs: Tm and Ndep displayed a positive relationship with forest albedo, while a negative relationship was detected with P. These correlations are primarily due to surface albedo differences between conifer and broad-leaf species, and different species geographical distributions. However, the analysis performed within individual PFTs, strengthened by attempts to select 'pure' pixels in terms of species composition, showed significant correlations with annual precipitation and nitrogen deposition, pointing toward the potential effect of environmental variables on forest surface albedo at the ecosystem level. Overall, our global assessment emphasizes the importance of elucidating the ecological mechanisms that link environmental conditions and forest canopy properties for an improved parameterization of surface albedo in climate models.