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13 Pág., Forest stand and environmental factors influence soil organic carbon (SOC) storage, but little is known about their relative impacts in different soil layers. Moreover, how environmental factors modulate the impact of stand factors, particularly species mixing, on SOC storage, is largely unexplored. In this study, conducted in 21 forest triplets (two monocultures of different species and their mixture on the same site) distributed in Europe, we tested the hypothesis that stand factors (functional identity and diversity) have stronger effects on topsoil (FF + 0–10 cm) C storage than environmental factors (climatic water availability, clay + silt content, oxalate-extractable Al–Alox) but that the opposite occurs in the subsoil (10–40 cm). We also tested the hypothesis that functional diversity improves SOC storage under high climatic water availability, clay + silt contents, and Alox. We characterized functional identity as the basal area proportion of broadleaved species (beech and/or oak), and functional diversity as the product of broadleaved and conifer (pine) proportions. The results show that functional identity was the main driver of topsoil C storage, while climatic water availability had the largest control on subsoil C storage. Functional diversity decreased topsoil C storage under increasing climatic water availability, but the opposite was observed in the subsoil. Functional diversity effects on topsoil C increased with increasing clay + silt content, while its effects on subsoil C were negative at increasing Alox content. This suggests that functional diversity effect on SOC storage changes along gradients in environmental factors and the direction of effects depends on soil depth., The European Union and the National Fund for Scientific Research (Fonds de la recherche scientifique) of Belgium supported this research as part the ERA-Net SUMFOREST project REFORM–Mixed species forest management. Lowering risk, increasing resilience (www.reform-mixing.eu). The lead author was further supported by Université catholique de Louvain for covid-19-related delays of his PhD project. Some of the triplets were selected, established and measured with funding from the EuMIXFOR FP1206 Cost Action project. The Polish State Forests Enterprise also supported one of the Polish co-authors (Grant No: OR.271.3.15.2017). The Orléans site, OPTMix was installed thanks to ONF (National Forest Service, France), belongs to research infrastructure ANAEE-F; it is also included in the SOERE TEMPO, ZAL (LTSER Zone Atelier Loire) and the GIS Coop network.

Heterogeneity of structure can increase mechanical stability, stress resistance and resilience, biodiversity and many other functions and services of forest stands. That is why many silvicultural measures aim at enhancing structural diversity. However, the effectiveness and potential of structuring may depend on the site conditions. Here, we revealed how the stand structure is determined by site quality and results from site-dependent partitioning of growth and mortality among the trees. We based our study on 90 mature, even-aged, fully stocked monocultures of Scots pine (Pinus sylvestris L.) sampled in 21 countries along a productivity gradient across Europe. A mini-simulation study further analyzed the site-dependency of the interplay between growth and mortality and the resulting stand structure. The overarching hypothesis was that the stand structure changes with site quality and results from the site-dependent asymmetry of competition and mortality. First, we show that Scots pine stands structure across Europe become more homogeneous with increasing site quality. The coefficient of variation and Gini coefficient of stem diameter and tree height continuously decreased, whereas Stand Density Index and stand basal area increased with site index. Second, we reveal a site-dependency of the growth distribution among the trees and the mortality. With increasing site index, the asymmetry of both competition and growth distribution increased and suggested, at first glance, an increase in stand heterogeneity. However, with increasing site index, mortality eliminates mainly small instead of all-sized trees, cancels the size variation and reduces the structural heterogeneity.Third, we modelled the site-dependent interplay between growth partitioning and mortality. By scenario runs for different site conditions, we can show how the site-dependent structure at the stand level emerges from the asymmetric competition and mortality at the tree level and how the interplay changes with increasing site quality across Europe. Our most interesting finding was that the growth partitioning became more asymmetric and structuring with increasing site quality, but that the mortality eliminated predominantly small trees, reduced their size variation and thus reversed the impact of site quality on the structure. Finally, the reverse effects of mode of growth partitioning and mortality on the stand structure resulted in the highest size variation on poor sites and decreased structural heterogeneity with increasing site quality. Since our results indicate where heterogeneous structures need silviculture interventions and where they emerge naturally, we conclude that these findings may improve system understanding and modelling and guide forest management aiming at structurally rich forests., This study was supported by the ERA-Net COFUND programme SUMFOREST, with the national funding organization Federal Ministry of Nutrition and Agriculture (REFORM, grant #2816ERA02S). The project has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No952314 and under the Marie Skłodowska-Curie grant agreement No 778322, and the 2017 - 2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND programme, with the national funding organization Federal Ministry of Education and Research (grant #16LC1805B). The German Science Foundation funded the project “Structure and growth of mixed Scots pine/European beech stands compared with pure stands analyzed along an ecological gradient through Europe” (grant #PR 292/15-1). H.P. and E.U. were additionally supported by the Bavarian Ministry of Nutrition, Agriculture and Forestry through the projects “Pine-spruce mixed stands in Bavaria” (grant #7831-20339-2012) and “Maintenance of the long term experimental plots in Bavaria (W07, #7831-26625-2017). M.d.R. and R.R.-P. were supported by the projects CLU-2019-01 - iuFOR Institute Unit of Excellence” of the University of Valladolid and VA183P20–SMART, funded by the Junta de Castilla and Leon (Spain) and co-financed by the European Union (ERDF “Europe drives our growth”). J.C. was supported by the National Agency of Agricultural Research (Project No. QK21020307). K.B. was supported by the Polish Government MNiSW 2018–2021 Matching Fund No. 117/H2020/2018. M.M. was supported by the Estonian Research Council grant (PRG1586) and Estonian University of Life Sciences projects number P180024MIME and P200029MIME. R.S. was supported by the Slovak Research and Development Agency project No. APVV- 19-0035.

Digitization can help the forest industry to improve cost efficiency and to reduce possible environmental impacts. In the context of this study, models were implemented using the example of windthrow processing, which enables a capacity planning for carrying out timber harvesting. For capacity planning, it is necessary to estimate the time required by the harvesting systems. For this purpose, existing productivity models were analyzed, the models were validated and adjusted, and the time required for each harvesting system and calamity area was calculated using stand and terrain parameters. Depending on the scenario and the preferred harvesting system, the time for harvesting the timber in an almost 200-hectare windthrow area in a case study region in Carinthia (Austria) varied. The harvesting with cable yarder and tractor takes about 26,000 machine hours and 86,000 man-hours. Harvesting operations with cable yarder and harvester-forwarder has proven to be the most productive with a duration of around 20,000 machine hours and 70,000 man-hours. Depending on the scenario, in windthrow areas, forest workers are needed for 28 to 42 min to fell, delimb, buck and extract 1 m3 of timber to the forest landing.

Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage. Storm-felled trees provide a surplus of breeding material for bark beetles, often resulting in calamities in the subsequent years. Thus, the timely removal of fallen trees is regarded as a good management practice that requires strategic planning of salvage harvesting. Precise information on the number of stems and their location and orientation are needed for the efficient planning of strip roads and/or cable yarding lines. An accurate assessment of these data using conventional field-based methods is very difficult and time-consuming; remote sensing techniques may be a cost-efficient alternative. In this research, a methodology for the automatic detection of fallen stems from aerial RGB images is presented. The presented methodology was based on a line segment detection algorithm and proved to be robust regarding image quality. It was shown that the method can detect frequency, position, spatial distribution and orientation of fallen stems with high accuracy, while stem lengths were systematically underestimated. The methodology can be used for the optimized planning of salvage harvesting in the future and may thus help to reduce consequential bark beetle calamities after storm events.

14 Pág., While the impacts of forest management options on carbon (C) storage are well documented, the way they affect C distribution among ecosystem components remains poorly investigated. Yet, partitioning of total forest C stocks, particularly between aboveground woody biomass and the soil, greatly impacts the stability of C stocks against disturbances in forest ecosystems. This study assessed the impact of species composition and stand density on C storage in aboveground woody biomass (stem + branches), coarse roots, and soil, and their partitioning in pure and mixed forests in Europe. We used 21 triplets (5 beech-oak, 8 pine-beech, 8 pine-oak mixed stands, and their respective monocultures at the same sites) in seven European countries. We computed biomass C stocks from total stand inventories and species-specific allometric equations, and soil organic C data down to 40 cm depth. On average, the broadleaved species stored more C in aboveground woody biomass than soil, while C storage in pine was equally distributed between both components. Stand density had a strong effect on C storage in tree woody biomass but not in the soil. After controlling for stand basal area, the mixed stands had, on average, similar total C stocks (in aboveground woody biomass + coarse roots + soil) to the most performing monocultures. Although species composition and stand density affect total C stocks and its partitioning between aboveground woody biomass and soil, a large part of variability in soil C storage was unrelated to stand characteristics., The European Union and the National Fund for Scientific Research (Fonds de la recherche scientifique) of Belgium supported this research as part the ERA-Net SUMFOREST project REFORM–Mixed species forest management. Lowering risk, increasing resilience (www.reform-mixing.eu). The lead author was further supported by Université catholique de Louvain for covid-19 related delays of his PhD project. Some of the triplets were selected, established and measured with funding from the EuMIXFOR FP1206 Cost Action project. The Polish State Forests Enterprise also supported one of the Polish co-authors (Grant No: OR.271.3.15.2017). The Orléans site, OPTMix was installed thanks to ONF (National Forest Service, France), belongs to research infrastructure ANAEE-F; it is also included in the SOERE TEMPO, ZAL (LTSER Zone Atelier Loire) and the GIS Coop network.

The estimation of single tree and complete stand information is one of the central tasks of forest inventory. In recent years, automatic algorithms have been successfully developed for the detection and measurement of trees with laser scanning technology. Nevertheless, most of the forest inventories are nowadays carried out with manual tree measurements using traditional instruments. This is due to the high investment costs for modern laser scanner equipment and, in particular, the time-consuming and incomplete nature of data acquisition with stationary terrestrial laser scanners. Traditionally, forest inventory data are collected through manual surveys with calipers or tapes. Practically, this is both labor and time-consuming. In 2020, Apple implemented a Light Detection and Ranging (LiDAR) sensor in the new Apple iPad Pro (4th Gen) and iPhone Pro 12. Since then, access to LiDAR-generated 3D point clouds has become possible with consumer-level devices. In this study, an Apple iPad Pro was tested to produce 3D point clouds, and its performance was compared with a personal laser scanning (PLS) approach to estimate individual tree parameters in different forest types and structures. Reference data were obtained by traditional measurements on 21 circular forest inventory sample plots with a 7 m radius. The tree mapping with the iPad showed a detection rate of 97.3% compared to 99.5% with the PLS scans for trees with a lower diameter at a breast height (dbh) threshold of 10 cm. The root mean square error (RMSE) of the best dbh measurement out of five different dbh modeling approaches was 3.13 cm with the iPad and 1.59 cm with PLS. The data acquisition time with the iPad was approximately 7.51 min per sample plot; this is twice as long as that with PLS but 2.5 times shorter than that with traditional forest inventory equipment. In conclusion, the proposed forest inventory with the iPad is generally feasible and achieves accurate and precise stem counts and dbh measurements with efficient labor effort compared to traditional approaches. Along with future technological developments, it is expected that other consumer-level handheld devices with integrated laser scanners will also be developed beyond the iPad, which will serve as an accurate and cost-efficient alternative solution to the approved but relatively expensive TLS and PLS systems. Such a development would be mandatory to broadly establish digital technology and fully automated routines in forest inventory practice. Finally, high-level progress is generally expected for the broader scientific community in forest ecosystem monitoring, as the collection of highly precise 3D point cloud data is no longer hindered by financial burdens.

Tree growth is expected to be responsive to climatic drivers across a spectrum of temporal scales, ranging from yearly growth to daily water use and photosynthesis. Automatic dendrometers offer the potential to provide continuous high-resolution measurements of tree radius changes. The signal recorded contains three components: (1) a long-term seasonal growth component, (2) a mid-term component representing swelling after rainfall and subsequent drying and (3) daily cycles of water-uptake related to tree transpiration. For 91 trees at 4 sites (Picea abies: 58, Pinus cembra: 17, Fagus sylvatica: 14, Pinus sylvestris: 2) monitored in Austria between 2012 and 2015, we simultaneously modelled these three processes using a hierarchical nonlinear mixed-effects model represented by two logistic growth curves. The focus was on the mid-term and daily component, and therefore long-term growth that is typically modelled by including tree size, competition or site variables was represented by random effects only. Both mid-term and short-term components were species-specific. In general, P. cembra and F. sylvatica were less sensitive to climate variables than P. abies. For all species, the mid-term component was best represented using a 14-day moving average difference between rainfall and potential evapotranspiration, a 24-h moving average of precipitation and its 1–3 days lags, a 24-h moving average temperature and its 1–3 days lags. The daily cycles of water uptake were best related to hourly humidity and its 3-h lag, and interactions with the 14-day moving average difference between rainfall and potential evapotranspiration accounted for attenuating cycles after rainy events and increasing cycles in dry periods.

Penalized regression splines and distributed lag models were used to evaluate the effects of species mixing on productivity and climate-related resistance via tree-ring width measurements from sample cores. Data were collected in Lower Austria from sample plots arranged in a triplet design. Triplets were established for sessile oak [Quercus petraea (Matt.) Liebl.] and Scots pine (Pinus sylvestris L.), European beech (Fagus sylvatica L.) and Norway spruce [Picea abies (L.) H. Karst.], and European beech and European larch (Larix decidua Mill.). Mixing shortened the temporal range of time-lagged climate effects for beech, spruce, and larch, but only slightly changed the effects for oak and pine. Beech and spruce as well as beech and larch exhibited contrasting climate responses, which were consequently reversed by mixing. Single-tree productivity was reduced by between − 15% and − 28% in both the mixed oak–pine and beech–spruce stands but only slightly reduced in the mixed beech–larch stands. Measures of climate sensitivity and resistance were derived by model predictions of conditional expectations for simulated climate sequences. The relative climate sensitivity was, respectively, reduced by between − 16 and − 39 percentage points in both the beech–spruce and beech–larch mixed stands. The relative climate sensitivity of pine increased through mixing, but remained unaffected for oak. Mixing increased the resistance in both the beech–larch and the beech–spruce mixed stand. In the mixed oak–pine stand, resistance of pine was decreased and remained unchanged for oak.

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal changes of LAI were assessed throughout Ardabil Province—a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine (GEE) was tested against traditional ENVI measures to provide LAI estimations. Moreover, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI, at large areas in a short time, and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including the Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Moreover, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured in June and July 2020, in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various plant functional types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at a province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), respectively, in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70) ecoregions, and in July, the Bilesavar ecoregion, respectively, with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r > 0.83 and R2 > 0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r = 0.56 and R2 = 0.31). On average, all three examined LAI measures tended to underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.

A spatial regression model framework is presented to predict growing stock volume loss due to storm Adrian which caused heavy forest damage in the upper Gail valley in Carinthia, Austria, in October 2018. Model parameters were estimated using growing stock volume measured with a terrestrial laser scanner on 62 sample plots distributed across five sub-regions. Predictor variables were derived from high resolution vegetation height measurements collected during an airborne laser scanning campaign. Non-spatial and spatial candidate models were proposed and assessed based on fit to observed data and out-of-sample prediction. Spatial Gaussian processes associated model intercepts and regression coefficients were used to capture spatial dependence. Results show a spatially-varying coefficient model, which allowed the intercept and regression coefficients to vary spatially, yielded the best fit and prediction. Two approaches were considered for prediction over blowdown areas: 1) an areal approach that viewed each blowdown as a single prediction unit indexed by its centroid; and 2) a block approach where each blowdown was partitioned into smaller prediction units to better align with sample plots’ spatial support. Joint prediction was used to acknowledge spatial dependence among block units. Results demonstrated the block approach is preferable as it mitigated change-of-support issues encountered in the areal approach. Despite the small sample size, predictions for 55% of the total 564 blowdown areas, accounting for 93% of the total loss, had a coefficient of variation less than 25%. Key advantages of the proposed regression framework and chosen Bayesian inferential paradigm, were the ability to quantify uncertainty in spatial covariance parameters, propagate parameter uncertainty through to prediction, and provide statistically valid prediction point and interval estimates for individual blowdowns and collections of blowdowns at the sub-region and region scale via posterior predictive distribution summaries.

15 Pág. Ecological Modelling, Tree species mixing of oak (Quercus petraea (Matt.) Liebl./Quercus robur L.) and pine (Pinus sylvestris L.) has been shown to have positive effects on ecosystem service provision. From a management perspective, however, it is still uncertain which thinning regime provides the highest possible productivity of mixed oak–pine forests in the long term. Because of a lack of empirical studies dealing with thinning and species mixing effects on oak–pine forests, we simulated forest growth in order to test which thinning type and intensity may provide the highest productivity in the long-term. To achieve this, we simulated the growth of pure and mixed stands of oak and pine for 100 years in 23 triplets located on an ecological gradient across Europe. For this purpose, we applied four different growth simulators and compared their results: the distance-independent single-tree simulator PROGNAUS, the distance-dependent single-tree simulator SILVA, the gap model ForCEEPS, and the process-based simulator 3D-CMCC-FEM. We investigated the effects of species mixing and thinning from the upper (thinning from above) and lower tail (thinning from below) of the diameter distribution by reducing the stand basal area to 50 and 80% of the maximum basal area. We compared simulated results of the relative volume productivity of mixed versus pure stands and of thinned versus unthinned stands to empirical results previously obtained on the same set of triplets. Simulated relative volume productivity ranged between 61 and 156%, although extremes of 10% and of 300% could be observed. We found the relative volume productivity to be influenced by stand age, but not by stand density, except for PROGNAUS. Relative volume productivity did not increase with the site water supply of the triplet location. Highest long-term productivity for oak, pine and oak–pine stands can be expected in consequence of thinning from above, but the effect of thinning intensity differed between simulators. Thinning effects were positively affected by stand density, but not by stand age, except for thinning from above predicted by PROGNAUS. Predicted thinning effects showed good approximation of results from thinning experiments for oak, but not for pine stands. We hypothesize the results might be caused by the insufficient simulator representation of climate and its interaction with other site variables and stand structure. Further work is needed to reduce the revealed limitations of the existing growth models, as we currently see no alternative to such kind of studies and simulators., The authors thank the European Union for funding the project “Mixed species forest management. Lowering risk, increasing resilience (REFORM)” under the framework of Sumforest ERA-NET. The authors from Austria also thank the Austrian Federal Ministry for Sustainability and Tourism for supporting the establishment of the Austrian triplet plots and for covering the work expenses of Markus Engel within the project ”Forstwirtschaft mit Mischwäldern – geringes Risiko, hohe Widerstandskraft – REFORM” under the grant number 101199. All contributors thank their national funding institutions to establish, measure and analyze data from the triplets. Maude Toïgo and Xavier Morin thank François de Coligny and Nicolas Beudez for their help in the development of the ForCEEPS model. Antonio Tomao, Carlo Trotta and Anna Barbati thank Alessio Collalti for his support and suggestions about the simulations with the 3D-CMCC-FEM model.

Mixed forests are suggested as a strategic adaptation of forest management to climate change. Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) are tree species of high economic and ecological value for European forestry. Both species coexist naturally in a large part of their distributions but there is a lack of knowledge on the ecological functioning of mixtures of these species and how to manage such stands. This paper analyses these species’ intra- and inter-specific competition, including size-symmetric vs. size-asymmetric competition, and explore the effect of weather conditions on tree growth and competition. We studied basal area growth at tree level for Scots pine and Norway spruce in mixed versus pure stands in 22 triplets of fully-stocked plots along a broad range of ecological conditions across Europe. Stand inventory and increment cores provided insights into how species mixing modifies tree growth compared with neighbouring pure stands. Five different competition indices, weather variables and their interactions were included and checked in basal area growth models using a linear mixed model approach. Interspecific size-asymmetric competition strongly influenced growth for both tree species, and was modulated by weather conditions. However, species height stratification in mixed stands resulted in a greater tree basal area growth of Scots pine (10.5 cm2 year−1) than in pure stands (9.3 cm2 year−1), as this species occupies the upper canopy layer. Scots pine growth depended on temperature and drought, whereas Norway spruce growth was influenced only by drought. Interspecific site-asymmetric competition increased in cold winters for Scots pine, and decreased after a drought year for Norway spruce. Although mixtures of these species may reduce tree size for Norway spruce, our results suggest that this could be offset by faster growth in Scots pine. How inter-specific competition and weather conditions alter tree growth may have strong implications for the management of Scots pine-Norway spruce mixtures along the rotation period into the ongoing climate change scenario.

Centro de Investigación Forestal (CIFOR), Mixed-species stands have been found to be more productive than would be expected from the performance of their component species in monocultures due to facilitation and complementarity between species, although these interactions depend on the combination of species present. Our study focuses on monospecific and mixed-species stands of Scots pine and Norway spruce using 20 triplets established in nine countries along a climatic gradient across Europe. Differences in mean tree and stand characteristics, productivity and stand structure were assessed. Basal area increment in mixed stands was 8% higher than expected while volume increment was only 2% greater. Scots pine trees growing in mixed-species stands showed 11% larger quadratic mean diameter, 7% larger dominant diameter, 17% higher basal area and 25% higher stand volume than trees growing in monospecific stands. Norway spruce showed only a non-significant tendency to lower mean values of diameters, heights, basal area, as well standing volume in mixtures than monocultures. Stand structure indices differed between mixed stands and monocultures of Scots pine showing a greater stratification in mixed-species stands. Furthermore, the studied morphological traits showed little variability for trees growing in monospecific stands, except for diameter at breast height, crown length and crown length ratio. For trees growing in mixed stands, all the morphological traits of the trees were identified as different. Some of these morphological traits were associated with relative productivity. Nevertheless, relative productivity in mixed-species stands was not related to site conditions., We thank national funders of REFORM project (Spanish Ministry of Science and Innovation: PCIN2017-026; Research Council of Lithuania: S-SUMFOREST-17-1) and those funders for the support to non-participants in the REFORM project (Denmark: Contract between Danish Ministry of Environment and Food and Department of Geosciences and Natural Resource Management of UCPH; Estonia: Estonian University of Life Sciences projects number P180024MIME and P200029MIME; Poland: EU CARE4C project (GA 778322) supported by the Polish Government MNiSW2018-2021 matching fund (W117/H2020/2018); Slovakia: APVV-15-0265) that allowed the establishment and measurement of the triplets. A grant for a research stay was received by RR-P from the CARE4C project (Carbon smart forestry under climate change, H2020-MSCA-RISE-2017)., 13 Pág.

Centro de Investigación Forestal (CIFOR), Despite growing evidence that diverse forests play an important role in ecosystem functioning, ensuring the provision of different ecosystem services, whether such diversity improves their response to drought events remains unclear. In this study, we use a large tree-ring database from thirty case studies across nine European countries and eleven species, covering from Mediterranean to hemiboreal forests, to test if the growth response to site specific drought events that occurred between 1975 and 2015 varied between mixed and monospecific stands. In particular, we quantify how stands resist those specific drought events and recover after them, thus analyzing their resilience. For each drought event and forest stand we calculated resistance, recovery, resilience and relative resilience and we related the variation in these indices between monospecific and mixed stands with type of admixture, tree species identity, site aridity gradient, stand basal area and stand age. We found a large variability among case studies, even for those that share similar species composition and have similar climates. On average, mixed stands showed higher resistance, resilience and relative resilience to drought events than monospecific stands. However, the beneficial effect of mixtures could not be generalized, being greatly modulated by the type of admixture and tree species identity, and depending on site water supply and stand characteristics, such as basal area and age. The increase in resilience in mixtures compared with monocultures was greater on the conifer-broadleaved admixtures, and to a lesser extent in the broadleaved-broadleaved combinations. The observed response patterns to drought largely varied among the eleven studied species, thus revealing the importance of functional traits for understanding a species’ response to drought across its distribution range. Along the site aridity gradient, resilience and relative resilience to drought increased in drier sites for both monospecific and mixed stands, with an observed trend towards higher resilience in mixed stands in the drier and hotter sites. Our results confirm the complexity of the relationships found of resistance, recovery, resilience and relative resilience with drought when comparing pure vs mixed stands., The study was supported by the ERA-Net SUMFOREST project REFORM “Mixed species forest management. Lowering risk, increasing resilience” (PCIN2017-026) and Marie Sklodowska Curie Actions-RISE , CARE4C . Special thankW117/H2020/2018s to Nuria Simón and Guillermo Madrigal for their assistance building up the common database. All coauthors thank their national funding institutions for supporting the establishment, measure and analysis of core data in the studied plots. Research at the Polish case studies was additionally supported by the Ministry of Science and Higher Education of the Republic of Poland (No W117/H2020/2018 )., 15 Pág.

In forest inventory, trees are usually measured using handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory has been redesigned since modern laser scanner technology became available. Laser scanners generate massive data in the form of 3D point clouds. We have developed a novel methodology to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data were collected by two different laser scanning systems: (1) A mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK) and (2) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contain digital terrain models (DTMs), field measurements as reference data (ground-truth), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.

The use of new and modern sensors in forest inventory has become increasingly efficient. Nevertheless, the majority of forest inventory data are still collected manually, as part of field surveys. The reason for this is the sometimes time-consuming and incomplete data acquisition with static terrestrial laser scanning (TLS). The use of personal laser scanning (PLS) can reduce these disadvantages. In this study, we assess a new personal laser scanner and compare it with a TLS approach for the estimation of tree position and diameter in a wide range of forest types and structures. Traditionally collected forest inventory data are used as reference. A new density-based algorithm for position finding and diameter estimation is developed. In addition, several methods for diameter fitting are compared. For circular sample plots with a maximum radius of 20 m and lower diameter at breast height (dbh) threshold of 5 cm, tree mapping showed a detection of 96% for PLS and 78.5% for TLS. Using plot radii of 20 m, 15 m, and 10 m, as well as a lower dbh threshold of 10 cm, the respective detection rates for PLS were 98.76%, 98.95%, and 99.48%, while those for TLS were considerably lower (86.32%, 93.81%, and 98.35%, respectively), especially for larger sample plots. The root mean square error (RMSE) of the best dbh measurement was 2.32 cm (12.01%) for PLS and 2.55 cm (13.19%) for TLS. The highest precision of PLS and TLS, in terms of bias, were 0.21 cm (1.09%) and −0.74 cm (−3.83%), respectively. The data acquisition time for PLS took approximately 10.96 min per sample plot, 4.7 times faster than that for TLS. We conclude that the proposed PLS method is capable of efficient data capture and can detect the largest number of trees with a sufficient dbh accuracy.

A novel methodological framework is presented for climate-sensitive modeling of annual radial stem increment using year-ring width time series. The approach is based on a generalized additive model with penalized regression splines together with a distributed time lag model taking into account smooth nonlinear effects of a series of monthly temperature and precipitation values, as well as their interactions. Climate effects are also assumed to vary smoothly with time lag. The model framework enables both the detrending of the individual time series and the regression modeling to be performed simultaneously in a single model step. The approach is applied to year-ring width time series of Norway spruce (Picea abies (L.) H. Karst.) trees in Tyrol, Austria. The marginal response curves show that tree growth is mainly promoted by high temperatures in late spring and early summer and by precipitation in fall and winter. Summer drought does not have a negative influence on the current year’s radial increment; however, when it is associated with high temperatures, it lowers the increment in the subsequent growth period. Higher winter precipitation in conjunction with lower temperatures has a positive effect. A significant non-climate related long-term growth trend is demonstrated, probably reflecting NOx and SO2 emission trends in Austria.

Radial tree stem growth of Norway spruce (Picea abies (L.) H. Karst.), European beech (Fagus sylvatica L.), Scots pine (Pinus sylvestris L.), and stone pine (Pinus cembra L.) was monitored from 2012 to 2015 across sites in Austria with high-resolution dendrometers. Seasonal cumulative diameter increment was modeled using a hierarchical nonlinear mixed-effects model framework based on a logistic growth curve. In the dry and warm year 2015, the average annual diameter increment of 0.30 cm decreased by 50% on lower elevation sites and by 10% on higher elevation sites. In the cool and moist year 2014, Norway spruce achieved a higher annual diameter increment than European beech, whereas the opposite occurred in the dry and warm years 2013 and 2015. In the mixed beech–spruce stand, beech’s consumptive water-use strategy has obviously caused intensified stress for spruce in these drought periods. On higher elevation sites, Norway spruce reacted more sensitively to climate fluctuation compared with stone pine, but overall reactions were only weak. Productivity varied strongly depending on the social status of the tree, with dominant and intermediate trees suffering more from drought. As warming and drought lowers increment rates on lower elevation sites and as trees on higher elevation sites react less flexibly, productivity losses are expected for Austrian forests due to climate warming.

Tree species mixing has been widely promoted as a promising silvicultural tool for reducing drought stress. However, so far only a limited number of species combinations have been studied in detail, revealing inconsistent results. In this study, we analysed the effect of mixing Scots pine and oak (pedunculate oak and sessile oak) trees on their drought response along a comprehensive ecological gradient across Europe. The objective was to improve our knowledge of general drought response patterns of two fundamental European tree species in mixed versus monospecific stands. We focused on three null hypotheses: () tree drought response does not differ between Scots pine and oak, () tree drought response of Scots pine and oak is not affected by stand composition (mixture versus monoculture) and () tree drought response of Scots pine and oak in mixtures and monocultures is not modified by tree size or site conditions. To test the hypotheses, we analysed increment cores of Scots pine and oak, sampled in mixed and monospecific stands, covering a wide range of site conditions. We investigated resistance (the ability to maintain growth levels during drought), recovery (the ability to restore a level of growth after drought) and resilience (the capacity to recover to pre-drought growth levels), involving site-specific drought events that occurred between 1976 and 2015. In monocultures, oak showed a higher resistance and resilience than Scots pine, while recovery was lower. Scots pine in mixed stands exhibited a higher resistance, but also a lower recovery compared with Scots pine in monocultures. Mixing increased the resistance and resilience of oak. Ecological factors such as tree size, site water supply and site fertility were found to have significant effects on the drought response. In the case of Scots pine, resistance was increased by tree size, while recovery was lowered. Resistance of oak increased with site water supply. The observed mixing effect on the tree drought response of Scots pine and oak was in some cases modified by the site conditions studied. Positive mixing effects in terms of resistance and resilience of oak increased with site water supply, while the opposite was found regarding recovery. In contrast, site fertility lessened the positive mixing effect on the resistance of Scots pine. We hypothesise that the observed positive mixing effects under drought mainly result from water- and/or light-related species interactions that improve resource availability and uptake according to temporal and spatial variations in environmental conditions. This work was supported by the European Union as part of the ERA-Net SUMFOREST project REFORM – Mixed species forest management. Lowering risk, increasing resilience (2816ERA02S, PCIN2017-026) and the Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 778322. All contributors thank their national funding institutions for supporting the establishment, mensuration and analysis of the studied triplets. The first author wants to thank the German Federal Ministry of Food and Agriculture (BMEL) for financial support through the Federal Office for Agriculture and Food (BLE) (grant number 2816ERA02S), as well as the Bayerische Staatsforsten (BaySF) and Landesbetrieb Forst Brandenburg for providing suitable research sites. Research on the Lithuanian triplets (LT 1, LT 2) was made possible by the national funding institution Research Council of Lithuania (LMTLT) (agreement number S-SUMFOREST-17-1). The French site FR 1 belongs to the OPTMix experimental site (https://optmix.irstea.fr), which is supported annually by Ecofor, Allenvi, and the French national research infrastructure ANAEE-F. A special thank is due to Peter Biber for supporting the statistical analysis.

13 Pág. Instituto de Ciencias Forestales (ICIFOR), Mixed forests are usually associated with higher aboveground carbon storage compared to the corresponding monocultures but information on the impact of tree species mixing on soil organic carbon (SOC) is still limited. Yet, maximizing SOC storage is crucial for ecosystem C sequestration and many other ecosystem services. This study used a triplet approach (ie. two-species mixed stand and respective pure stands at the same site) to assess the impact of tree species identity and mixing on SOC storage in eight pine-oak, eight pine-beech and five beech-oak triplets in Europe. We sampled the forest floor (FF) and 0–40 cm in the mineral soil per 10 cm interval. For each triplet type, we fitted basal area (BA) proportion of one component species (for species identity) and a BA-based plot-level True Shannon Diversity index (for species mixing) as explanatory variables for SOC stocks in linear mixed effects models, which included stone content and plot BA as covariates, and site as a random intercept. Considering the total soil depth (FF + 0–40 cm), species identity effect on SOC stocks was only significant for pine-beech and pine-oak triplets but explained more variability in SOC stocks than species mixing across triplet types. Species mixing effect was not significant for any triplet type in the total soil depth. While species identity consistently drove SOC storage in the topsoil layers across triplet types, species mixing explained more variability in SOC stocks in the deeper soil layers except for pine-oak triplets. The results showed that species identity is a stronger driver of SOC storage than species mixing. While tree species identity effect was strongly related to a conifers vs broadleaves signature, the drivers behind mixing effects remained elusive. The results suggest that targeted selection of tree species could better enhance SOC storage in European forests than a mere increase in species richness., The European Union and the National Fund for Scientific Research (Fonds de la recherche scientifique) of Belgium supported this research as part the ERA-Net SUMFOREST project REFORM – Mixed species forest management. Lowering risk, increasing resilience (www.reform-mixing.eu). Some triplets were selected, established and measured with funding from the EuMIXFOR FP1206 Cost Action project. The Polish State Forests Enterprise also supported one of the Polish co-authors (Grant No: OR.271.3.15.2017). We are grateful to Karine Henin for her key role in the laboratory analyses of the samples. We appreciate the technical assistance by Benjamin Willems for soil sampling across Europe. We would like to thank Thomas Pérot for the dendrometric measurements in the Orléans sites. Finally, we are debtful to the two anonymous reviewers who contributed to improving a previous version of this manuscript.

A novel methodological framework is presented for climate-sensitive modeling of annual radial stem increments using tree-ring width time series. The approach is based on a hierarchical Bayes model together with a distributed time lag model that take into account the effects of a series of monthly temperature and precipitation values, as well as their interactions. By using a set of random walk priors, the hierarchical Bayes model allows both the detrending of the individual time series and the regression modeling to be performed simultaneously in a single model step. The approach was applied to comprehensive tree-ring width data from Austria collected on sample plots arranged in triplets representing different mixture types. Bayesian predictions revealed that European larch (Larix decidua Mill.), Norway spruce (Picea abies (L.) H. Karst.), and Scots pine (Pinus sylvestris L.) show positive climate-related growth trends throughout higher elevation sites in Tyrol, and these trends remain unchanged under a mixed-stand scenario. At the lower Austrian sites, Norway spruce was found to show a severely negative growth trend under both the pure- and mixed-stand scenario. The increment rates of European beech (Fagus sylvatica L.) were found to have a negative climate-related trend in pure stands, and the trend diminished through an admixture of spruce or larch. The trends of European larch and sessile oak (Quercus petraea (Matt.) Liebl.) showed stationary behavior, irrespective of the mixture scenario. Scots pine data showed a positive trend at the lower elevation sites under both the pure- and mixed-stand scenario. These findings indicate that species mixing does not lower the climate-related increment fluctuations of beech, oak, pine, and spruce at lower elevation sites.

A novel approach is presented to model the tree detection probability of terrestrial laser scanning (TLS) in forest inventory applications using a multi-scan mode. The traditional distance sampling framework is further extended to account for multiple scan positions at a single sample plot and to allow for an imperfect detection probability at distance r = 0. The novel methodology is tested with real world data, as well as in simulations. It is shown that the underlying detection model can be parameterized using only data from single scans. Hereby, it is possible to predict the detection probability also for different sample plot sizes and scanner position layouts in a multi-scan setting. Simulations showed that a minor discretization bias can occur if the sample size is small. The methodology enables a generalized optimization of the scanning layout in a multi-scan setting with respect to the detection probability and the sample plot area. This will increase the efficiency of multi-scan TLS-based forest inventories in the future.

Past failures of monocultures, caused by wind-throw or insect damages, and ongoing climate change currently strongly stimulate research into mixed-species stands. So far, the focus has mainly been on combinations of species with obvious complementary functional traits. However, for any generalization, a broad overview of the mixing reactions of functionally different tree species in different mixing proportions, patterns and under different site conditions is needed, including assemblages of species with rather similar demands on resources such as light. Here, we studied the growth of Scots pine and oak in mixed versus monospecific stands on 36 triplets located along a productivity gradient across Europe, reaching from Sweden to Spain and from France to Georgia. The set-up represents a wide variation in precipitation (456–1250 mm year−1), mean annual temperature (6.7–11.5 °C) and drought index by de Martonne (21–63 mm °C−1). Stand inventories and increment cores of trees stemming from 40- to 132-year-old, fully stocked stands on 0.04–0.94-ha-sized plots provided insight into how species mixing modifies stand growth and structure compared with neighbouring monospecific stands. On average, the standing stem volume was 436 and 360 m3 ha−1 in the monocultures of Scots pine and oak, respectively, and 418 m3 ha−1 in the mixed stands. The corresponding periodical annual volume increment amounted to 10.5 and 9.1 m3 ha−1 year−1 in the monocultures and 10.5 m3 ha−1 year−1 in the mixed stands. Scots pine showed a 10% larger quadratic mean diameter (p p p p p p p

In this study, height–diameter relations were modeled using two different mixed model types for imputation of missing heights from longitudinal data. Model Type A had a hierarchical structure of sample plot-specific and measurement occasion-specific random effects. In Model Type B, a possible temporal variance was modeled by a sample plot-specific linear time trend. Furthermore, various calibration strategies of random effects were performed on past and current data, and a combination of both. The performance of the mixed models was compared on independent data using bias and root mean square error (RMSE). The results showed that Model Type A achieved the highest precision (lowest RMSE), if sample plot-specific random effects were predicted from old data and measurement occasion-specific ones were predicted from new data. In comparison, however, Model Type B had a higher RMSE, and lower bias. Model performance was almost unaffected from the usage of past or current data for the prediction of random effects. Results revealed that a certain calibration strategy should be simultaneously applied to all random effects from the same hierarchy level. Otherwise, predictions would become imprecise and a serious bias may result. In comparison with traditional uniform height curves, the novel mixed model approach performed slightly better.

The rapid re-establishment of forests following large disturbances is being seen as one option to increase the contribution of forests to climate change mitigation. The temporary inclusion of pioneer trees as nurse crops on disturbed sites can facilitate the establishment of target tree species and may additionally benefit productivity and soil fertility. In this study we compared productivity and nutrient cycling between stands of oak target species ( Quercus robur and Quercus petraea ) that were established with and without widely spaced Betula ssp. or Populus ssp. nurse crops. Simulation results for a full rotation of oaks (180 years) indicated that both types of forests, with and without nurse crops, have a comparable total productivity. However, stands with nurse crops supplied 59–96 Mg ha −1 harvestable biomass after 20 years, whereas the first harvest of biomass from stands without nurse crops would occur at least 30 years later. Nutrient element costs associated with the removal of Betula ssp. wood were low compared to Populus ssp. Also, nurse crop stands had up to 2.5 times larger pools of exchangeable base cations in top mineral soils (0–30 cm) compared to mono-specific oak stands. The high soil cation pools may have resulted from reduced leaching under nurse crops or the increased recycling of cations, also from deeper soil depth, via litter fall and fine-root turnover. Our results show that forest reestablishment with pioneer tree species may be a suitable tool for the rapid recovery of forest productivity and mitigation potential following disturbances while simultaneously helping to maintain or increase soil fertility.

This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic measurements of tree diameters. This further development of the algorithm reduced the proportion of falsely detected tree locations by about 64%. The algorithms were tested in different settings with respect to the number and spatial alignment of scanner positions and under manifold forest conditions, covering different age classes and a mixture of scenarios, and representing a broad gradient of structural complexity. For circular sample plots with a maximum radius of 20 m, the tree mapping algorithm showed a detection rate of 82.4% with seven scanner positions at the vertices of a hexagon plus the center coordinates, and 68.3% with four scanner positions aligned in a triangle plus the center. Detection rates were significantly increased with smaller maximum radii. Thus, with a maximum radius of 10 m, the hexagon setting yielded a detection rate of 90.5% and the triangle 92%. Other alignments of scanner positions were also tested, but proved to be either unfavorable or too labor-intensive. The commission rates were on average less than 3%. The root mean square error (RMSE) of the dbh (diameter at breast height) measurement was between 2.66 cm and 4.18 cm for the hexagon and between 3.0 cm and 4.7 cm for the triangle design. The robustness of the algorithm was also demonstrated via tests by means of an international benchmark dataset. It has been shown that the number of stems per hectare had a significant impact on the detection rate.

An approach is presented to predict the effects climate change may have on mortality of forest trees. Mortality is modeled using long-term observations from the Pan-European Programme for Intensive and Continuous Monitoring of Forest Ecosystems plots, retrospective climate data and frailty models having a parametric baseline hazard function. The linear predictor is modeled by B-spline regression techniques to allow for nonlinear cause-and-effect curves. Spatio-temporal predictions of mortality of four major tree species in the German state of Baden-Wurttemberg were derived in terms of unconditional hazard ratios and based on climate projection data. According to the model, marginal risk of tree death for 100 year old Norway spruce trees will be doubled until 2100. Hazard rates of common beech will be halved in low elevation areas and will be reduced by 25% in higher elevations until 2100. Hazard rates of silver fir will be less affected by a changing climate and will increase by at least 25% and by a maximum of 100% in mountainous regions. Scots pines hazard rates will be halved on higher elevation sites and will increase on lower elevation sites.

A methodological framework is provided for the quantification of climate change effects on site index. Spatio-temporal predictions of site index are derived for six major tree species in the German state of Baden-Wurttemberg using simplified universal kriging (UK) based on large data sets from forest inventories and a climate sensitive site-index model. It is shown by a simulation study that, with the underlying large sample size, residual kriging using ordinary least squares (OLS) estimates of the mean function leads to an approximately unbiased spatial predictor. Moreover, the simulated coverage probabilities of resulting prediction intervals are quite close to the required level. B-spline regression techniques are applied to model nonlinear cause-and-effect curves for estimating site indexes at existing inventory plots dependent on retrospective climate covariates. The spatially structured error is modeled by exponential covariance functions. The mean model is then applied to downscaled climate projection data to spatially predict the relative changes of site index under perturbed climate conditions. Applying climate projections of an existing regional climate model based on IPCC emission scenarios A1B and A2, it is found that site index of all tree species would be decreased in lowland areas, and may increase in mountainous regions. Silver fir and common oak stands would also show increased site indexes in mountainous regions, but further extended to lower elevation levels. Site conditions in the Alpine foothills may remain highly productive for growth of Norway spruce, Baden-Wurttemberg’s most dominant tree species. Whereas site index of common beech and Douglas-fir may decrease to almost the same relative amount and on nearly the same sites as Norway spruce, site index of Scots pine may be less affected by future climate change.

Summary In a mixed temperate forest landscape in southwestern Germany, multiple remote sensing variables from aerial orthoimages, Thematic Mapper data and small footprint light detection and ranging (LiDAR) were used for plot-level nonparametric predictions of the total volume and biomass using three distance measures of Euclidean, Mahalanobis and Most Similar Neighbour as well as a regression tree-based classifier (Random Forest). The performances of nearest neighbour (NN) approaches were examined by means of relative bias and root mean squared error. The original highdimensional dataset was pruned using an evolutionary genetic algorithm search with a NN classification scenario, as well as by a stepwise selection. The genetic algorithm (GA)-selected variables showed improved performance when applying Euclidean and Mahalanobis distances for predictions, whereas the Most Similar Neighbour and Random Forests worked more precise with the full dataset. The GA search proved to be unstable in multiple runs because of intercorrelations among the high-dimensional predictors. The selected datasets are dominated by LiDAR height metrics. Furthermore, The LiDAR-based metrics showed major relevance in predicting both response variables examined here. The Random Forest proved to be superior to the other examined NN methods, which was eventually used for a wallto-wall mapping of predictions on a grid of 20 × 20 m spatial resolution.

The three nonparametric k nearest neighbour (kNN) approaches, most similar neighbour inference (MSN), random forests (RF) and random forests based on conditional inference trees (CF) were compared for spatial predictions of standing timber volume with respect to tree species compositions and for predictions of stem number distributions over diameter classes. Various metrics derived from airborne laser scanning (ALS) data and the characteristics of tree species composition obtained from coarse stand level ground surveys were applied as auxiliary variables. Due to the results of iterative variable selections, only the ALS data proved to be a relevant predictor variable set. The three applied NN approaches were tested in terms of bias and root mean squared difference (RMSD) at the plot level and standard errors at the stand level. Spatial correlations were considered in the statistical models. While CF and MSN performed almost similarly well, large biases were observed for RF. The obtained results suggest that biases in the RF predictions were caused by inherent problems of the RF approach. Maps for Norway spruce and European beech timber volume were exemplarily created. The RMSD values of CF at the plot level for total volume and the species-specific volumes for European beech, Norway spruce, European silver fir and Douglas fir were 32.8, 80.5, 99.0, 137.0 and 261.1%. These RMSD values were smaller than the standard deviation, although Douglas fir volume did not belong to the actual response variables. All three non-parametric approaches were also capable of predicting diameter distributions. The standard errors of the nearest neighbour predictions on the stand level were generally smaller than the standard error of the sample plot inventory. In addition, the employed model-based approach allowed kNN predictions of means and standard errors for stands without sample plots.

In k-tree sampling, also referred to as point-to-tree distance sampling, the k nearest trees are measured. The problem associated with k-tree sampling is its lack of unbiased density estimators. The presented density estimator based on point pattern reconstruction remedies that shortcoming. It requires the coordinates of all k trees. These coordinates are translated into a simulation window where they remain unchanged. Empirical cumulative distribution functions of intertree and location-to-tree distances estimated from the sample plots are set as target characteristics. Using the idea of simulated annealing, an optimal new tree pattern is constructed in the simulation window outside the k-tree samples. The reconstruction of the point pattern minimizes the contrast between the empirical cumulative distribution functions and their analogs for the simulated pattern. The density estimator is simply the tree density of the optimum pattern in the simulation window. The performance of the reconstruction-based density estimator is assessed for k = 6 and k = 4 based on systematic sampling grids regarding its potential application in forest inventories. Simulations are carried out using real stem maps (covering different stand densities and different types of spatial point patterns, such as regular, clustered, and random) as well as completely random patterns. The new density estimator proves to be empirically superior in terms of bias and root mean squared error compared with commonly used estimators. The reconstruction-based density estimator has biases smaller than 2%.

This article asks whether international law is moving towards a more unified constitutional order or whether differentiated types of constitutional processes are emerging. We study the sequencing and ratification pace of 32 ‘quasi-constitutional’ international agreements containing procedural guidelines for inter-state relations and fundamental human rights provisions for individuals drawn up between 1945 and 2007. We do so in a comparative and quantitative fashion applying sophisticated statistical tools, namely event history techniques combined with counting processes. On the basis of our multi-treaty framework, the findings do not lend support to a unified and quick process of global constitutionalisation. Rather, they provide evidence for the idea of a ‘multi-speed globe’ of differentiated constitutionalisation. We also make a first attempt to study antecedents to global constitutionalisation. Our findings show that processes of global constitutionalisation vary across regime types and world regions (while there is no effect for new and old states).

A non-linear hierarchical mixed model approach is used to describe height growth of Norway spruce from longitudinal measurements. The parameter variation in the model was divided into unknown random effects, fixed effects and covariate-dependent effects in order to model tree height growth. The values for fixed effect parameters and the variance–covariance matrix of random effects were estimated. Covariates could only explain up to 10% of parameter variability. Height curves were calibrated by means of BLUPs for the unknown random effects using prior height measurements and evaluated using a separate dataset. The resulting curves had a small error variance and plausible shapes.

Mapping of exact tree positions can be regarded as a crucial task of field work associated with forest monitoring, especially on intensive research plots. We propose a two-stage density clustering approach for the automatic mapping of tree positions, and an algorithm for automatic tree diameter estimates based on terrestrial laser-scanning (TLS) point cloud data sampled under limited sighting conditions. We show that our novel approach is able to detect tree positions in a mixed and vertically structured stand with an overall accuracy of 91.6%, and with omission- and commission error of only 5.7% and 2.7% respectively. Moreover, we were able to reproduce the stand’s diameter in breast height (DBH) distribution, and to estimate single trees DBH with a mean average deviation of ±2.90 cm compared with tape measurements as reference.