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• We explored the potential of bi-temporal ALS data for site index estimation. • Species-specific site index models were developed using NFI and ALS data. • ALS estimated height and height increment were key predictors of site index. • Validation checks indicate that the functions seem suitable for their purpose. • Nationwide site index maps are provided at a resolution of 12.5 × 12.5 m. Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even-aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper-parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 – 2.11 m, and the relative RMSE was less than 10 % (7.68 – 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden. [ABSTRACT FROM AUTHOR]

Unsustainable exploitation of tropical forest resources is raising worldwide concern. In Papua New Guinea (PNG) timber harvesting has been identified as a major contributor to deforestation and forest degradation but its impact on biodiversity is still poorly understood. In this study we investigated the effect of selective logging on tree taxonomic composition, structure and diversity of PNG forests. We used data from 101 one-hectare permanent sample plots (PSPs) belonging to two vegetation types: low altitude forests on plains and fans (type P) and low altitude forests on uplands (type H). We used multivariate techniques to test for significant differences in species composition between plots of different vegetation types and disturbance regimes, identifying the tree taxa to which these differences could be ascribed. ANOVA was used to test for differences between logged-over and unlogged forest PSPs with respect to biodiversity (richness, Shannon’s diversity, Pielou’s evenness) and stand structure (stem density, basal area - BA). Temporal trends of forest features were analyzed using linear regression. Significant differences in taxonomic composition were found between logged-over and unlogged plots of the H type (p = 0.04). No differences were found in richness, diversity and evenness between logged-over and unlogged forest plots, while stem density was higher in the latter (421 ± 153 stems ha-1). Greater BA was found in unlogged forests (30.28 ± 4.45 m2 ha-1) of the H type when compared to the logged-over stands (15.52 ± 4.04 m2 ha-1). We detected positive trends in richness (0.55 ± 0.19 taxa ha-1 yr-1) and diversity after logging. Furthermore, H type forest exhibited positive trends in stem density (9 ± 1 stems ha-1 yr-1) and BA (0.42 ± 0.06 m2 ha-1 yr-1) with elapsed time since harvesting. Our analysis highlights some significant effects of logging activities on biodiversity and structure of PNG forests. Additionally, forests exhibited a significant recovery with respect to richness, diversity and stand structure. These preliminary results will be compared with data collected by the forthcoming National Forest Inventory in order to assess and monitor the effects of human activities and ecological factors on PNG forest biodiversity and develop appropriate conservation measures and sustainable management strategies.

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons.

Aim of study: Agroforestry systems of Castanea sativa have specific forest structures, which are different from other ecosystems of sweet chestnut. They have provided several ecosystems services (ES) to local inhabitants for centuries including relevant pastoral use. However on present times, have isolated distribution ranges and declining trends. The chestnut trees are their main components but occur at low densities. They are cultivated by using different treatments to improve specific features and maximize different types of production. Area of study: North-western of Iberian Peninsula. Material and methods: We used a large database (>750 field plots) to classify C. sativa dominated-stands into different ecosystems typology (including traditional agroforestry systems), and to assess their most relevant ES. We used field data to define their spatial distribution and discriminant analysis to determine the classification accuracy. Finally we also defined a set of qualitative and quantitative ES indicators for different groups to compare different trends. Main results: We successfully classified these ecosystems and found that the traditional agroforestry systems are of major importance in providing ES, as food provision or cultural services, but showed isolated distribution patterns. Moreover, other types of chestnut-dominated ecosystems, supply important ES such as biomass provision and climate regulation. Research highlights: The relevance of the C. sativa agroforestry systems from ES point of view was pointed out in this work, but also their declining dynamic. Further analysis, based on temporal trends, could help to a better understanding of their status and to define conservation and management strategies.

Aims: Plant responses to disturbances and environmental variation can manifest in communities as compositional nestedness (i.e., one community is a subset of another) and/or turnover (two communities represent different compositional gradient spaces). Yet, different sampling designs can artificially give an illusion of such compositional differences among two datasets, making it problematic to harmonize them in multi‐species analysis. We test the prediction that sampling differences which increase beta‐diversity components (nestedness and turnover) among two vegetation datasets will decrease their exchangeability. Location: Boreal forests of Tanana River region, interior Alaska, USA. Methods: We develop novel methods for comparing compositional variation among two datasets in nonmetric multidimensional scaling (NMDS) ordination. Resampled NMDS establishes internal sampling variability for each dataset independently, and reciprocal NMDS determines external exchangeability when the two are mutually exchanged. We first compare simulated data with specified beta‐diversity differences, then evaluate two forest inventories based on local vs regional sampling designs in Alaska's boreal forests. Results: As simulated species turnover and nestedness increased, internal sampling variability remained essentially constant, but external exchangeability progressively declined. Species turnover (not nestedness) had the larger negative effect on exchangeability. Among the boreal forest inventories, internal sampling variability was relatively similar, and exchangeability was weakly moderate, but the regional inventory exhibited much better fit to broad‐scale environment. Species turnover (not nestedness) contributed the majority of beta‐diversity differences among the two forest inventories, suggesting that strong environmental gradients were unequally represented. Conclusions: Species turnover alters multivariate outcomes more drastically than species nestedness. Therefore, combining two vegetation datasets may be inadvisable when turnover prevails. Instead, a multi‐scale perspective, with separate but complementary forest inventory analyses, can portray local and regional variation at appropriate scales. Our method is tractable for assessing exchangeability of potentially inconsistent sampling designs, like those that are common in synthesis studies and long‐term ecological monitoring. Combining two community datasets in one ordination can be risky if sampling designs differ. We present "reciprocal NMDS" methods and software to estimate multivariate data exchangeability. From simulations and real vegetation communities of boreal Alaska, poor exchangeability arises when species turnover prevails over nestedness. Long‐term monitoring programs can benefit from evaluating exchangeability when sampling designs change over time. [ABSTRACT FROM AUTHOR]

Graphical abstract Modulation in basal area growth with climate for Quercus petraea according to three IPCC scenarios for the Orleans Forest area. Black dots correspond to IPCC historical climate values, colored dots correspond to modulation in basal area growth for three IPCC climate projections. The dashed line corresponds to smoothed model extrapolations for scenario RCP 8.5. Highlights • Large-scale NFI data provide growth models including silvicultural effects. • Tree rings data provide models for annual modulation of growth by climate. • Coupling both models allowed to develop climate-dependent stand growth models. Abstract Climate change has swept away the former general principles of long-term stability in forest productivity. New types of models are needed to predict growth and to plan forest management under future climate conditions. These models must remain robust for silvicultural practices and variations in climate. In this study, we present a new type of model development to achieve these goals. Our study focused on pure and mixed stands of Quercus petraea and Pinus sylvestris in central France. We used National Forest Inventory (NFI) data: respectively, 525 and 548 pure plots of Quercus petraea and Pinus sylvestris , and 68 plots of mixed species. We also used 108 tree cores from an experimental site of the same species. The cores cover the period from 1971 to 2013, making a total of 4572 individual annual increments. We coupled two types of models. One was developed with NFI data (transversal data). This model takes into account mean diameter and stand density effects on stand growth. It includes a set of biophysical factors accounting for stand fertility. The other one was developed with the data from tree cores (longitudinal data), and provides a climate modulation thanks to the correlation between ring width and yearly climate. The model with tree core data reveals the influence of December to July rainfalls on yearly variability in stand growth for Quercus petraea and of May to August rainfalls for Pinus sylvestris. We obtained a coupled model that allowed us to project growth up to 2100 for all the different IPCC scenarios but one; the model was outside its area of validity beyond 2060 for the RCP 8.5 scenario. [ABSTRACT FROM AUTHOR]

This study attempts to review the various biodiversity related parameters envisaged in the current national forest inventory (NFI) of India in comparison to those used worldwide as indicators of forest biodiversity. The study reveals that a good amount of biodiversity related parameters are available, covering the structural attribute of biodiversity, tree species composition, and other important functional attributes like deadwood and litter. In spite of this timber-based approach, a number of variables are available to talk on biodiversity aspects of Indian forests. However, the comparison with the parameters found in literature shows that the scope of further improvement does exist in current NFI’s ability to contribute towards a documentation and management of biodiversity. Our gap analysis indicates that enhancing the capacity of the current NFI to generate overall biodiversity information urgently required for safeguarding and conserving the natural wealth of India is strongly needed. [ABSTRACT FROM AUTHOR]

Context Afforestation and reforestation programs might have noticeable effect on carbon stock. An integrated assessment of the forest carbon density in mountain regions is vital to evaluate the contribution of planted forests to carbon sequestration. Aims We compared the carbon densities and carbon stocks between natural and planted forests in the Lüliang Mountains region where large-scale afforestation and reforestation programs have been implemented. The introduced peashrubs (Caragana spp.), poplars (Populus spp.), black locust (Robinia pseudoacacia), and native Chinese pine (Pinus tabulaeformis) were the four most common species in planted forests. In contrast, the deciduous oaks (Quercus spp.), Asia white birch (Betula platyphylla), wild poplar (Populus davidiana), and Chinese pine (Pinus tabulaeformis) dominated in natural forests. Methods Based on the forest inventory data of 3768 sample plots, we estimated the values of carbon densities and carbon stocks of natural and planted forests, and analyzed the spatial patterns of carbon densities and the effects of various factors on carbon densities using semivariogram analysis and nested analysis of variance (nested ANOVA), respectively. Results The carbon density was 123.7 and 119.7 Mg ha−1 for natural and planted forests respectively. Natural and planted forests accounted for 54.8% and 45.2% of the total carbon stock over the whole region, respectively. The biomass carbon density (the above- and belowground biomass plus dead wood and litter biomass carbon density) was greater in natural forests than in planted forests (22.5 versus 13.2 Mg ha−1). The higher (lower) spatial carbon density variability of natural (planted) forests was featured with a much smaller (larger) range value of 32.7 km (102.0 km) within which a strong (moderate) spatial autocorrelation could be observed. Stand age, stand density, annual mean temperature, and annual precipitation had statistically significant effects on the carbon density of all forests in the region. Conclusion No significant difference was detected in the carbon densities between natural and planted forests, and planted forests have made a substantial contribution to the total carbon stock of the region due to the implementation of large-scale afforestation and reforestation programs. The spatial patterns of carbon densities were clearly different between natural and planted forests. Stand age, stand density, temperature, and precipitation were important factors influencing forest carbon density over the mountain region. [ABSTRACT FROM AUTHOR]

Copyright of Gozdarski Vestnik is the property of Federation of Forestry Associations of Slovenia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Questions: We aim for a better understanding of the different modes of intra-and inter-specific competition in two-and three-species mixed-forests. How can the effect of different modes of competitive interactions be detected and integrated into individual tree growth models? Are species interactions in spruce--fir--beech forests more associated with size-symmetric or size-asymmetric competition? Do competitive interactions between two of these species change from two-to three-species mixtures? Location: Temperate mixed-species forests in Central Europe (Switzerland). Methods: We used data from the Swiss National Forest Inventory to fit basal area increment models at the individual tree level, including the effect of ecological site conditions and indices of size-symmetric and size-asymmetric competition. Interaction terms between species-specific competition indices were used to disentangle significant differences in species interactions from two-to three-species mixtures. Results: The growth of spruce and fir was positively affected by increasing proportions of the other species in spruce--fir mixtures, but negative effects were detected with increasing presence of beech. We found that competitive interactions for spruce and fir were more related to size-symmetric competition, indicating that species interactions might be more associated with competition for below-ground resources. Under constant amounts of stand basal area, the growth of beech clearly benefited from the increasing admixture of spruce and fir. For this species, patterns of size-symmetric and size-asymmetric competitive interactions were similar, indicating that beech is a strong self-competitor for both above-ground and below-ground resources. Only for silver fir and beech, we found significant changes in species interactions from two-to three-species mixtures, but these were not as prominent as the effects due to differences between intra-and inter-specific competition. Conclusions: Species interactions in spruce--fir--beech, or other mixed forests, can be characterized depending on the mode of competition, allowing interpretations of whether they occur mainly above or below ground level. Our outcomes illustrate that species-specific competition indices can be integrated in individual tree growth functions to express the different modes of competition between species, and highlight the importance of considering the symmetry of competition alongside competitive interactions in models aimed at depicting growth in mixed-species forests. [ABSTRACT FROM AUTHOR]

The National Forest Inventory of Finland (NFI) produces national- and regional-level statistics for sustainability assessment and strategical-level decision making. So far, the regional-level statistics are based on a systematic sampling design with geographical stratification. Auxiliary information such as remote sensing is not used for design or estimation at the regional level, but it is used at the small-area level, i.e., for municipality-level results. To improve the cost efficiency of the NFI, possibilities for using auxiliary data in both the design and estimation are of interest. We assessed the improvements obtainable by using an interpreted satellite image - the multisource NFI result from a previous NFI - as auxiliary information in the design phase. The results show that even though the multisource NFI map is not very accurate, significant improvements in efficiency can be obtained by using either the local pivotal method (LPM) or stratification. LPM improves efficiency by matching the sample distribution to population distribution. These results encourage us to further investigate ( i) what would be the improvement with more accurate auxiliary information, for example, laser scanning data, and ( ii) how LPM fits in a real-life situation where part of the plots are permanent and it would be used to select only the temporary plots. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large-area forest inventories are often undertaken following a stratified random or systematic design. Yet the strata rarely correspond to the reporting areas of interest (domains) over which the country wants to report specific variables. The process is exemplified by a country aiming to use national forest inventory data to obtain average biomass estimates per forest type for GHGI international reporting, where activity data (areas of land use or land use changes) and emission factors (carbon coefficients) are typically compiled from disparate sources and estimated using different sampling schemes. This study aims to provide a decision tree for the choice of the estimator to be used in forest surveys to draw conclusions about population sub-groups created after (and independently of) the sample selection. This manuscript describes two unbiased estimators that can be used to estimate reporting-strata means, regardless of the sampling design adopted, and extends the result to the common situation in which the reporting-strata are spatially explicit, where a nested group estimator outperforms in terms of both bias and precision other more traditional estimators. From this estimator, an optimal sample allocation scheme is also derived. [ABSTRACT FROM AUTHOR]

Abstract: Mixed species forests can often be more productive and deliver higher levels of ecosystem services and functions than monocultures. However, complementarity effects for any given tree species are difficult to generalize because they can vary greatly along gradients of climatic conditions and resource availability. Identifying the conditions where species diversity can positively influence productivity is crucial. To date, few studies have examined how growth complementarity across species and mixture types is modulated by stand and environmental factors, and fewer have considered more than one or two factors. We investigated how complementarity effects for several major Central European tree species change with climatic and edaphic conditions, and with stand structural characteristics, including species composition. We used data from the Swiss National Forest Inventory, which is based on 3,231 plots of pure and mixed stands (19 mixture types) across a broad environmental gradient, to test (i) how mixing effects change depending on the identity of the admixed species and (ii) if complementarity consistently increases when environmental conditions become harsher. The magnitude, whether positive or negative, of complementarity increased with increasing stand density and stand developmental stage, but no general pattern could be identified across mixture types. Complementarity for many species increased as drought intensity and temperature increased, but not for all species and mixture types. While soil conditions, nitrogen and site topography influenced complementarity for many species, there was no general pattern (increases and decreases were observed). Synthesis. Our study indicates that complementarity varies strongly with stand density and stand development as well as with topographic, climatic and soil conditions. This emphasizes the need to account for site‐dependent conditions when exploring mixture effects in relation to forest productivity. We found that under certain conditions (i.e. increasing drought, higher temperature), mixed forests can promote individual tree growth in Central European temperate forests. However, careful assessments depending on the species composing the stands are required under changing resource availability as well as under different levels of stand density and development. [ABSTRACT FROM AUTHOR]

Abstract: Assessing how species productivity in mixtures is influenced by species shade tolerance (ST) and phylogeny would be helpful to improve our general understanding of the relationship between tree species diversity and productivity in forests. We investigated the effects of differences in ST and phylogenetic distances (PDs) between pairs of species on the productivity of Quercus petraea growing in 18 mixtures in lowland temperate forests. We calculated the mixture effect as the difference in productivity of Q. petraea in mixed vs. pure stands. Our analyses were based on data from seven annual campaigns of the French National Forest Inventory covering 1,573 plots. The mixture effect on Q. petraea productivity increased when the ST of the companion species decreased. Compared to its productivity in pure stands, Q. petraea productivity in mixed coniferous stands varied from −14.6% up to +39.6% as the ST of the companion species inversely varied from highest to lowest. With broadleaved companion species, the mixture effect varied from −10% up to +13.9% with decreasing ST. We found no effect of PD between Q. petraea and the companion species on the mixture effect. Synthesis. Our results confirm that shade tolerance is an important driver of the diversity effect on productivity at species level in temperate forests and that phylogenetic distance is not a relevant proxy for species functional dissimilarity. [ABSTRACT FROM AUTHOR]

Spatially explicit wall-to-wall forest-attributes information is critically important for designing management strategies resilient to climate-induced uncertainties. Multivariate estimation methods that link forest attributes and auxiliary variables at full-information locations can be used to estimate the forest attributes for locations with only auxiliary variables information. However, trade-offs between estimation accuracies versus logical consistency among estimated attributes may occur. This is particularly likely for macroscales (i.e., ≥1 Mha) with large forest-attributes variances and wide spacing between full-information locations. We examined these trade-offs for ∼390 Mha of Canada's boreal zone using variable-space nearest-neighbours imputation versus two modelling methods (i.e., a system of simultaneous nonlinear models and kriging with external drift). We found logical consistency among estimated forest attributes (i.e., crown closure, average height and age, volume per hectare, species percentages) using ( i) k ≤ 2 nearest neighbours or ( ii) careful model selection for the modelling methods. Of these logically consistent methods, kriging with external drift was the most accurate, but implementing this for a macroscale is computationally more difficult. This extra cost is justified given the importance of assessing strategies under expected climate changes in Canada's boreal forest and in other forest regions. [ABSTRACT FROM AUTHOR]

A new strategic inventory across all publicly managed forests has been installed in Victoria. For the preceding two decades, Victoria’s forest inventory has been compiled from various data sources from across the state, an approach that doesn’t allow information about the nature and rate of changes to the resource to be estimated, or for projections or forecasts to be made. To address these limitations, the Victorian Forest Monitoring Program (VFMP) was established, to monitor Victoria’s progress in meeting a commitment towards sustainable forest management, and to satisfy requirements for state and national reporting. The purpose of the new program is to ‘assess and monitor the extent, state and sustainable development of Victoria’s public forests in a timely and accurate manner.’ The VFMP consists of a plot-based system of permanent observational units located on a state-wide grid. A combination of ground-based and aerial photo plots and remotely sensed data are used to capture a set of basic attributes that are used to derive indicators of sustainability. To meet the monitoring needs a remeasurement framework has been developed to guide the change estimation procedures. The program was designed and implemented between 2009 and 2015 and used to generate data and information for the most recent Victoria’s State of the Forest Report. The first annual change estimate is due to be completed in 2017. A summary of implementation and proposed maintenance is presented and discussed in this paper. [ABSTRACT FROM AUTHOR]

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009–2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k -nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10–20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher. [ABSTRACT FROM AUTHOR]

Using Korean National Forest Inventory (NFI) data, our study developed a model to estimate stand mean diameter at breast height (DBH) reflecting the influence of site and climate factors on forest growth for the major tree species in South Korea. A DBH estimation model was developed using stand-level variables (stand age, site index and number of trees per hectare) as independent factors. The spatial autocorrelation of residuals of the model was identified using semi-variogram analysis for each tree species. Further, a residual model, in which residuals were estimated by climatic factors (mean temperature, sum temperature in the growing season and precipitation), was developed assuming that the spatial autocorrelation of residuals reflects the differences in regional climatic conditions. Linear regression analysis showed that residuals of all tree species were significantly correlated with temperature and precipitation. The DBH and residual models were integrated to estimate the current DBH under different climatic factors (temperature and precipitation) and stand-level variables. This model had high reliability (R² = 0.74-0.79), and no obvious dependencies or patterns in residuals were noted. Our results indicated that temperature increases caused by climate change would negatively affect the DBH estimate of coniferous trees, but not of oak species. [ABSTRACT FROM AUTHOR]

We used data from representatively sampled trees to identify key drivers of tree growth for central European tree species. Nonlinear mixed models were fitted to individual-tree basal area increments (BAI) from the Swiss national forest inventory. Data from 1983 to 2006 were used for model fitting and data from 2009 to 2013 for model evaluation. We considered 23 potential explanatory variables specifying individual-tree characteristics, site and stand conditions, management, climate, and nitrogen deposition. Model selection was processed separately for Picea abies, Abies alba, Pinus sp., Larix sp., other conifers, Fagus sylvatica, Quercus sp., Fraxinus sp./Acer sp., and other broadleaves. The selected models explained 56-70% of the BAI variance in the model fitting dataset and 21-64% in the evaluation dataset. While some variables were relevant for all species, the combination of further variables differed among the species, reflecting their physiological properties. In general, BAI was positively related to DBH and temperature and negatively related to basal area of larger trees, stand density, mean DBH of the 100 thickest trees per ha, slope, and soil pH. For most species, harvesting had a positive effect on BAI. In general, nitrogen deposition was positively related to BAI, except for spruce and fir, for which the inverse effect was found. Increasing drought reduced BAI for most species, except for pine and oak. These BAI models incorporate many influencing factors while representing large spatial extents, making them useful for both nationwide scenario analyses and deepening the understanding of the main drivers modulating tree growth throughout central Europe. [ABSTRACT FROM AUTHOR]

We used national scenario analyses to examine the effects of fertilization, use of improved regeneration material and ditch network maintenance (DNM), both separately and simultaneously, on timber production of Finnish forests under the current climate. We also analyzed how the area of artificial regeneration, forest fertilization, and DNM developed in different management and harvesting intensity scenarios. The initial data were obtained from the 11th National Forest Inventory of Finland, excluding protected forests. Four sets of even-flow harvesting scenarios with annual timber harvest targets of 60, 70, 80, and 90 million m3 were developed for 90-year simulation period. Use of improved material in artificial forest regeneration was assumed to result in 10% higher diameter and height increment compared to naturally regenerated seedlings. Sub-xeric pine-dominated and mesic spruce-dominated sites were fertilized, and 40% of drained peatlands were maintenance-ditched when they fulfilled a set of predetermined criteria for temperature sum, stand basal area, and mean tree diameter. As a result, when fertilization, improved regeneration material, and DNM were all used, the mean annual volume increment over the 90-year simulation period increased by 3.4-5.4 million m3 depending on harvesting intensity. The maximum sustainable harvest of timber would be almost 80 million m3 yr−1. The simulated fertilization area was about four times larger than the presently fertilized area, and the simulated DNM area was about the same as the current. Fertilization gave the largest additional 90-year volume increment and the DNM the smallest when they were used separately. The use of improved regeneration material gave the largest additional volume increment in southern Finland and fertilization in central and northern Finland. [ABSTRACT FROM AUTHOR]

Aims Forest vegetation variability may be explained by the complex interplay among several spatial structuring factors, including climate and topography. We modelled the spatial variability of forest vegetation assemblages and significant environmental variables along a complex environmental gradient or coenocline to produce a detailed cartographic database portraying the distribution of forests along it. Methods We combined an analysis of ordination coenoclines with kriging over 772 field data plots from the third Spanish National Forest Inventory in an Atlantic--Mediterranean transitional area (northern Spain). Important Findings The best fitted empirical semivariogram revealed a strong spatial structure of forest species composition along the complex environmental gradient considered (the climatic--topographic gradient from north to south). The steady and gradual increase of semivariance with a marked lag distance indicates a gradual turnover of forest assemblages according to the climatic--topographic variations (regional or local). Two changes in the slope of the semivariogram suggest the existence of two different scales of spatial variation. The interpolation map by Kriging of forest vegetation assemblages along the main coenocline shows a clear spatial distribution pattern of trees and shrubs in accordance with the spatial variation of significant environmental variables. We concluded that the multivariate geostatistical approach is a suitable technique for spatial analysis of forest systems employing data from national forest inventories based on a regular network of field plots. The development of an assortment of maps describing changes in vegetation assemblages and variation in environmental variables is expected to be a suitable tool for an integrated forest management and planning. [ABSTRACT FROM AUTHOR]