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In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained. [ABSTRACT FROM AUTHOR]

Key message: This paper proposes a methodology that could be considered as a base for a harmonized protocol for stem-quality reporting in Europe while conducting National Forest Inventories, in order to cost-efficiently obtain a visual wood quality proxy. The importance of the variables selected, the limitations identified, and some improvements to the methodology are suggested. Forest areas with better wood quality, which in turn it would be useful for breeding programs, can be easily detected. Context: The establishment of harmonized standards or indicators that allow us to determine the quality of the wood present in a forest prior to its exploitation has long been demanded by the European forestry sector, although agreed methodologies for the evaluation of wood quality in standing trees, which is one of the sector's most urgent requirements, have not, as yet, been implemented. Aims: To develop a protocol that visually characterizes wood quality on standing trees in a cost-effective way for the National Forest Inventory (NFI). After some improvements, it can be considered as a base for a European harmonized protocol. Methods: In this article, we analyze the implementation, in the NFI, of a visual wood-quality assessment methodology in forests of Central Spain based on the different European standards as well as on research papers addressing this issue. Results: The silvicultural practices employed are of the utmost importance to obtain the best wood quality, regardless of the species. Several areas with higher wood quality were identified as well as areas most affected by specific pests in the studied region. The impact of the variables measured (e.g., branchiness, crookedness, maximum branch diameter) is discussed. Conclusion: It is feasible to estimate a proxy for wood quality on standing trees in the NFI. Furthermore, after studying the inventory data provided, several enhancements are proposed, not only to improve wood-quality estimates but also to optimize fieldwork costs. Harmonizing NFIs to assess and map European standing wood quality can be achieved. [ABSTRACT FROM AUTHOR]

Legacies of human land use have the potential to impact demographic responses to climate. However, few studies have investigated the interactive effects of land use legacies and climate change on tree demography. The demographic performance of rear edge populations in particular is an important determinant of a species' long-term persistence. In this study, we investigated whether human land use legacies affect demographic responses to climate and population isolation in rear edge European beech populations (Fagus sylvatica L.) at the temperate-Mediterranean transition zone in the NE Iberian Peninsula. We utilised data from the Spanish Forest Inventory and generalised linear mixed models to compare the potential interactions across four different demographic rates (tree growth, survival probability, new adult recruitment and sapling recruitment). We found that the demographic rates were affected by the combination of land use legacies, climate and population isolation in different ways, which could potentially lead to complex shifts in future population dynamics under climate change. We identified that intense historic management either magnified negative relationships between tree demography and climate or population isolation, or reduced demographic performance in favourable climates to levels observed in unfavourable climates. Through either form of interaction, we found that intense historic forest management had a negative impact on tree demography, which has the potential to compromise future carbon stocks and long-term population viability. Overall, we show that disentangling human and environmental factors can enable us to better understand heterogeneous demographic performance across the rear edge of species distributions., (© 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.)

Tree‐ring data has been widely used to inform about tree growth responses to drought at the individual scale, but less is known about how tree growth sensitivity to drought scales up driving changes in forest dynamics. Here, we related tree‐ring growth chronologies and stand‐level forest changes in basal area from two independent data sets to test if tree‐ring responses to drought match stand forest dynamics (stand basal area growth, ingrowth, and mortality). We assessed if tree growth and changes in forest basal area covary as a function of spatial scale and tree taxa (gymnosperm or angiosperm). To this end, we compared a tree‐ring network with stand data from the Spanish National Forest Inventory. We focused on the cumulative impact of drought on tree growth and demography in the period 1981–2005. Drought years were identified by the Standardized Precipitation Evapotranspiration Index, and their impacts on tree growth by quantifying tree‐ring width reductions. We hypothesized that forests with greater drought impacts on tree growth will also show reduced stand basal area growth and ingrowth and enhanced mortality. This is expected to occur in forests dominated by gymnosperms on drought‐prone regions. Cumulative growth reductions during dry years were higher in forests dominated by gymnosperms and presented a greater magnitude and spatial autocorrelation than for angiosperms. Cumulative drought‐induced tree growth reductions and changes in forest basal area were related, but initial stand density and basal area were the main factors driving changes in basal area. In drought‐prone gymnosperm forests, we observed that sites with greater growth reductions had lower stand basal area growth and greater mortality. Consequently, stand basal area, forest growth, and ingrowth in regions with large drought impacts was significantly lower than in regions less impacted by drought. Tree growth sensitivity to drought can be used as a predictor of gymnosperm demographic rates in terms of stand basal area growth and ingrowth at regional scales, but further studies may try to disentangle how initial stand density modulates such relationships. Drought‐induced growth reductions and their cumulative impacts have strong potential to be used as early‐warning indicators of regional forest vulnerability. [ABSTRACT FROM AUTHOR]

Questions: Non‐native tree species (NNT) may bring about economic benefits, but also threats to ecosystems, mostly if they show expansive trends. Location: A set of 12,000 permanent plots of the second (1986–1996), third (1997–2007) and fourth (2008–2017) Spanish Forest Inventory. Methods: We quantified changes over time (1986–2017) of the NNT present in forests of peninsular Spain and we assessed how NNT's traits, propagule pressure and human perception of NNT explain changes in distribution and demography of NNT. We quantified changes in four demographic parameters of every NNT: changes in the occupancy of species (number of plots where the species are present), annual changes in tree density and basal area, and tree growth. To explain the observed species trends, we selected functional traits related to the resource acquisition strategy, and key human drivers. Results: Most of the NNT expanded their occupancy in the study area and increased their density, basal area, and tree growth through time. Increases in tree density and growth were greater in NNT with greater tolerance for low water potentials, with low specific leaf area, and with high propagule pressure. Increases in basal area were greater with high height of the NNT. Conclusions: The overall increase in occupancy suggests that there is room for expansion of NNT in Spain. This knowledge will help to predict the dynamics of NNT already present in Spain and identify risks for forest biodiversity. [ABSTRACT FROM AUTHOR]

• Forest biomass and productivity are correlated to tree density and NDVI. • Species models differ on the effect of soil factors on biomass and productivity. • Aridity negatively affects forest biomass and productivity. • Projections of increasing aridity show a decrease on forest biomass and productivity. One of the main challenges under global warming is understanding and predicting the effects of increased aridity on the carbon sink role of forests, particularly in Mediterranean regions. Forest inventories monitor the real state of the forest at a high temporal and financial cost. Cloud computing tools and high spatio-temporal resolution datasets generate fast and low-cost remote sensing data. Our objective is to understand the underlying variables explaining carbon storage (aboveground biomass) and forest productivity of Mediterranean forests using remote and in field-based variables and predict expected future trends. Then, we quantify the potential effects of a hypothetical increase in aridity under climate change on aboveground biomass and forest productivity. We included remote sensing indices (NDVI), abiotic factors (climate, soil and topography) and biotic factors (forest structure) as key variables of forest biomass and productivity in a large and heterogeneous Mediterranean region (Andalusia, southern Spain). We used around 7000 forest plots from the second and the third Spanish National Forest Inventory (1995 and 2006) considering the eight most abundant species (Olea europaea, Pinus pinea, P. pinaster, P. halepensis, P. nigra, P. sylvestris, Quercus ilex subsp ballota, and Q. suber). The variance explained by the models ranged from 25% in Q. ilex forests to 65% in P. sylvestris forests. Aridity affected all-species and Quercus biomass and most productivity models. NDVI and tree density had a strong positive effect on forest biomass and productivity with a significant interaction effect in all-species models, whereas aridity had a negative effect on both. The predicted increase in aridity under future climate change scenarios could seriously reduce forest biomass by 18% and productivity by 16%. Our study suggests that aridity is a key factor determining forest biomass and productivity in Mediterranean forests, that could potentially lead to reductions of their carbon sink role. [Display omitted] [ABSTRACT FROM AUTHOR]

One approach to compensating for rapid climate change and protecting biodiversity is assisted migration (AM) of key tree species. However, tools for evaluating the sensitivity of target sites and identifying potential sources have not yet been developed. We used the National Forest Inventories of Spain and France to design scenarios for AM between and within both countries. We characterized sensitivity to climate change as the expected changes in volume and mortality of Pinus halepensis Miller and Pinus pinaster Aiton between the present and 2050. Target zones were selected from provenances with high sensitivity and seed zones from provenances with low sensitivity to climate change; the latter can be considered 'seed refugia' as the climate changes. Three plausible scenarios for translocation to the target zone were developed on the basis of volume simulations calibrated with different planting strategies: (1) seeds only from foreign provenances; (2) foreign provenances plus local seeds; and (3) only local seeds. The results for both species show that models based on foreign 'top-three' provenances always increased the standing volume of the target zone. Models run with only local seeds predicted increased volume for P. halepensis but not for P. pinaster. Our results suggest that volume and mortality trends are not always correlated with seed sources and targets, that projected provenances mortality do not follow always a southern-northern pattern and that seed refugia, if any, may be useful for compensating for the effects of climate change only in a subset of provenances. [ABSTRACT FROM AUTHOR]

Aim Biodiversity loss could reduce primary productivity and the carbon storage provided by forests; however, the mechanisms underpinning the effects of biodiversity on multiple ecosystem functions are not completely understood. Spanish forests are of particular interest because of the broad variation in environmental conditions and management history. We tested for the existence of a relationship between diversity effects and both carbon storage and tree productivity, and examined the relative importance of complementarity and selection mechanisms in a wide variety of forests, from cold deciduous Atlantic to xeric Mediterranean evergreen forests. Location Continental Spain. Methods We used c. 54,000 plots of the Spanish Forest Inventory and maximum likelihood techniques to quantify how climate, stand structure and diversity shape carbon storage and tree productivity. Diversity effects included both complementarity and selection mechanisms, measured respectively through functional diversity and functional identity measures. Results Diversity had a significant effect on both carbon storage and tree productivity, even when controlling for confounding factors of climate and stand structure. A consistent positive effect of functional diversity on carbon storage and tree productivity was observed in all seven forest types studied. This relationship was not linear, and the largest changes in carbon storage and tree productivity were observed at low levels of functional diversity. However, the importance of complementarity effects was not consistent with the productivity of different forest types. Selection effects were particularly important in deciduous and Mediterranean pine forests, but had very little effect on mountain pines. Main conclusions We found a generally positive effect of diversity on carbon storage and tree productivity, supported by both complementarity and selection mechanisms. Thus, both functionally diverse forests and functionally important species should be maintained to adequately preserve and promote key ecosystem functions such as carbon storage and tree productivity. [ABSTRACT FROM AUTHOR]

• Heterospecific tree density negatively affects non-native tree success. • Species and functional diversity decrease Eucalyptus and Pinus performance. • Colonization increases with propagule availability in the surrounding landscape. • High annual temperatures and sand content decrease Eucalyptus and Pinus success. • Management favours both adult and juvenile Pinus radiata trees. Non-native trees enhance services that are fundamental for human well-being. Yet, the extensive use of non-native trees has the potential of causing environmental and socio-economic harm. Eucalyptus and Pinus are the most widely distributed and extensively planted tree genera worldwide, because their rapid growth allows profitable production of timber and pulp. Their naturalization is causing severe effects on the environment, but the relative importance of underlying factors determining their demographic performance is not well known. Thus, our aim was to evaluate the relative importance of biotic, abiotic, and anthropogenic factors driving demographic changes of Eucalyptus and Pinus at the regional scale. We compiled environmental variables and demographic data for Eucalyptus globulus , Eucalyptus camaldulensis , and Pinus radiata across 6388 permanent forestland plots surveyed in the Spanish Forest Inventory (SFI). We used the second (1986–1996), third (1997–2007), and fourth (2008–2017) SFI datasets to quantify annual changes in basal area per plot between consecutive inventories (ΔBA; m2 ha−1 year−1). We also quantified the components of ΔBA: tree ingrowth (transitions from juvenile to adult trees), growth, and mortality. We evaluated juvenile recruitment with in-situ regeneration (No. juvenile trees ha−1) within plots already occupied by the focal species in the previous inventory, and with natural colonization of plots where the focal species was absent at the beginning of the time interval. We found that the structure of the biotic community was especially important to explain demographic performance of non-native trees growing in benign environments (E. globulus and P. radiata), whereas abiotic factors were particularly important regulating basal area increments of E. camaldulensis , which occurs in harsher environments. Basal area increments decreased with species and functional richness, heterospecific density, mean annual temperature, and increased with soil capacity to retain nutrients and water. Colonization of new plots increased with propagule availability in the surrounding landscape. Tree cutting was beneficial for P. radiata. Collectively, our results suggest that non-native trees perform better in forests with high propagule pressure, low biotic resistance, favourable abiotic conditions, and human management. [ABSTRACT FROM AUTHOR]

Aim Our study aimed to identify and explore the main factors that influence tree recruitment of multiple species at a regional scale across peninsular Spain, an understanding of which is essential for predicting future forest species composition in the face of ongoing environmental change. The study focused on the dynamics of the key transition phase from saplings to adult trees. Location The forests of peninsular Spain. Methods We used the extensive network of plots sampled in two consecutive Spanish national forest inventories (> 30,000 plots) to identify the factors that determine regeneration patterns of the 10 most abundant forest species of Spain at relatively large temporal ( c. 10 years) and spatial scales (across Spain): five coniferous species of Pinus (pines) and five broadleaved species of the genera Fagus and Quercus. We fitted separate generalized linear models for the pine species and the broadleaved species to assess the response of sapling abundance and ingrowth rate to the spatial variability of climate (temperature, water availability and recent warming), forest structure (tree density, understorey and overstorey canopy cover, and basal area change) and disturbances (previous forest logging, wildfires and grazing). Results Mean sapling abundance was four times higher for broadleaved species than for pines, while mean annual ingrowth was twice as high. Sapling abundance and ingrowth rate were mainly determined by stand structure, both in pines and broadleaved trees. The direct effects of disturbances and climate were comparatively smaller, and there was no detectable effect of recent warming. Main conclusions The higher values of ingrowth rate of broadleaved species can be explained by their ability to maintain a higher sapling bank due to their greater shade tolerance. This differential response of pines and broadleaved species to canopy closure suggests a probable increase in broadleaved species at the expense of pines. This transition could occur earlier in stands with faster canopy closure dynamics. Spatially explicit, mixed-species demographic models incorporating both the ingrowth and the tree mortality components are needed for predicting the composition of future forests. [ABSTRACT FROM AUTHOR]

Mediterranean semi-arid forest ecosystems are especially sensitive to external forcing. An understanding of the relationship between forest carbon (C) stock, and environmental conditions and forest structure enable prediction of the impacts of climate change on C stocks and help to define management strategies that maximize the value of forests for C mitigation. Based on the national forest inventory of Spain (1997-2008 with 70,912 plots), we estimated the forest C stock and spatial variability in Peninsular Spain and, we determined the extent to which the observed patterns of stand C stock can be explained by structural and species richness, climate and disturbances. Spain has an average stand C stock of 45.1 Mg C/ha. Total C stock in living biomass is 621 Tg C (7.8% of the C stock of European forests). The statistical models show that structural richness, which is driven by past land use and life forest history including age, development stage, management activities, and disturbance regime, is the main predictor of stand tree C stock with larger C stocks in structurally richer stands. Richness of broadleaf species has a positive effect on both conifer and broadleaf forests, whereas richness of conifer species shows no significant or even a negative effect on C stock. Climate variables have mainly an indirect effect through structural richness but a smaller direct predictive ability when all predictors are considered. To achieve a greater standing C stock, our results suggest promoting high structural richness by managing for uneven-aged stands and favoring broadleaf over conifer species. [ABSTRACT FROM AUTHOR]

Abstract: The influence of climate on forest stand composition, development and growth is undeniable. Many studies have tried to quantify the effect of climatic variables on forest growth and yield. These works become especially important because there is a need to predict the effects of climate change on the development of forest ecosystems. One of the ways of facing this problem is the inclusion of climatic variables into the classic empirical growth models. The work has a double objective: (i) to identify the indicators which best describe the effect of climate on Pinus halepensis growth and (ii) to quantify such effect in several scenarios of rainfall decrease which are likely to occur in the Mediterranean area. A growth mixed model for P. halepensis including climatic variables is presented in this work. Growth estimates are based on data from the Spanish National Forest Inventory (SNFI). The best results are obtained for the indices including rainfall, or rainfall and temperature together, with annual precipitation, precipitation effectiveness, Emberger’s index or free bioclimatic intensity standing out among them. The final model includes Emberger’s index, free bioclimatic intensity and interactions between competition and climate indices. The results obtained show that a rainfall decrease about 5% leads to a decrease in volume growth of 5.5–7.5% depending on site quality. [Copyright &y& Elsevier]

Plant functional traits are highly plastic to changes in climatic factors and nutrient availability. However, the intraspecific plant response to abiotic factors and the overall effect on tree growth and productivity is still under debate. We studied forest productivity for 30 Quercus ilex subsp. ballota forests in Spain along a broad climatic gradient of aridity (mean annual precipitation from 321 to 858 mm). We used linear mixed models to quantify the effect of climatic and edaphic (soil nutrients, topography, and texture) factors on tree functional traits (leaf and branch traits), and subsequently, the effect of such functional traits and abiotic factors on the relative growth rate (RGR) of adult trees. We used piecewise structural equation models (SEMs) to determine the causal effect of intrinsic and extrinsic factors on forest productivity. Our results showed that tree functional traits were mainly explained by climatic and edaphic factors. Functional traits and tree biomass explained forest biomass and RGR, respectively, which ultimately explained forest productivity. In conclusion, intraspecific variability of functional traits has a significant effect on plant biomass and growth, which ultimately may explain forest productivity in Quercus ilex forests., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

• National Forest Inventory (NFI) data were analysed. • Biomass equations were used for tree species and components, including roots. • Most C stocks were estimated using regional-specific equations and C-content data. • Assumption of a C-content of 50% led to overestimation of C stocks. • Increasing both forest and C-density (Mg C ha−1) increases live tree C stocks. Almost 50% of the land in Galicia (NW Spain) is covered by forest (minimum area of land of 1 ha with tree crown cover of more than 10%) and the region is the top timber–producing area in Spain. Regional changes in forest live tree biomass and carbon stocks were estimated from National Forest Inventory (NFI) data covering almost 40 years, i.e. between NFI-1 (1972) and NFI–4 (2009). Carbon stocks were estimated from biomass of different tree species and components, including roots. Biomass equations were applied to the tree stock data for different species and diameter classes. The biomass increased in all species or groups for the period considered. However, although the biomass of softwood species (mainly Pinus spp.) increased, because of the increase in the number of trees belonging to the largest diameter classes (DC), the balance of the number of small trees (DC < 25 cm) was negative. The estimated total tree dry biomass (above- and below-ground) in the region increased from 94.77 Tg (teragrams) to 217.09 Tg between the first (1972) and fourth NFI (2009). This is the equivalent of an increase in carbon stocks from 45.51 Tg to 102.83 Tg. Most of the carbon stocks were estimated by applying regional-specific biomass equations and C-content data for different tree species and components. Assuming a global carbon content of 50% led to overestimation of the total carbon stocks by between 4.1% and 5.6%. Although harvesting intensity in the region is one of the highest in Europe, the live tree C stocks have increased; this was attributed to the increase in forestland area as well as to the increased C density, i.e. from 40 Mg (megagrams) C ha−1 in NFI-1 to 72 Mg C ha−1 in NFI-4. [ABSTRACT FROM AUTHOR]

The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8–38.3%, 34.2–41.9% and 31.7–38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation. [ABSTRACT FROM AUTHOR]