Variation in Carbon Content among the Major Tree Species in Hemiboreal Forests in Latvia

This study was designed to estimate the variation in non-volatile carbon (C) content in different above- and belowground tree parts (stem, living branches, dead branches, stumps, coarse roots and small roots) and to develop country-specific weighted mean C content values for the major tree species in hemiboreal forests in Latvia: Norway spruce (Picea abies (L.) H. Karst.), Scots pine (Pinus sylvestris L.), birch spp. (Betula spp.) and European aspen (Populus tremula L.). In total, 372 sample trees from 124 forest stands were selected and destructively sampled. As the tree samples were pre-treated by oven-drying before elemental analysis, the results of this study represent the non-volatile C fraction. Our findings indicate a significant variation in C content among the tree parts and studied species with a range of 504.6 ± 3.4 g·kg−1 (European aspen, coarse roots) to 550.6 ± 2.4 g·kg−1 (Scots pine, dead branches). The weighted mean C content values for whole trees ranged from 509.0 ± 1.6 g·kg−1 for European aspen to 533.2 ± 1.6 g·kg−1 for Scots pine. Only in Norway spruce was the whole tree C content significantly influenced by tree age and size. Our analysis revealed that the use of the Intergovernmental Panel on Climate Change (IPCC) default C content values recommended for temperate and boreal ecological zones leads to a 5.1% underestimation of C stock in living tree biomass in Latvia’s forests. Thus, the country-specific weighted mean C content values for major tree species we provide may improve the accuracy of National Greenhouse Gas Inventory estimates.

Availability and Structure of Coarse Woody Debris in Hemiboreal Mature to Old-Growth Aspen Stands and Its Implications for Forest Carbon Pool

European aspen deadwood is extensively studied as a habitat for saproxylic species, while less is known of its dynamics and role in carbon sequestration. We studied unmanaged mature (41–60 years), moderately overmature (61–80 years), overmature (81–100 years), and old-growth (101–140 years) and managed mature and moderately overmature aspen stands on fertile mineral soils. In unmanaged stands, marginal mean CWD volume was from 67.3 ± 12.1 m3 ha−1 in moderately overmature to 92.4 ± 5.1 m3 ha−1 in old-growth stands, with corresponding marginal mean CWD carbon pool 8.2 ± 1.6 t ha−1 and 12.5 ± 0.7 t ha−1, respectively. Both CWD volume and its carbon pool had substantial yet non-significant differences (all p > 0.05) among the age groups. High CWD volume was present in most stands, by at least two-thirds of plots comprising more than 20 m3 ha−1, and about half of CWD was larger than 30 cm in diameter. Changes in CWD species composition toward a higher proportion of deciduous deadwood in old-growth stands, together with a high volume of recently dead trees, suggest early senescence of the dominant aspen cohort.

Identification of genetic markers and wood properties that predict wood biorefinery potential in aspen bioenergy feedstock (Populus tremula).

Wood represents the majority of the biomass on lands, and it constitutes a renewable source of biofuels and other bioproducts. However, wood is recalcitrant to bioconversion, meaning that feedstocks must be improved. We investigated the properties of wood that affect bioconversion, as well as the underlying genetics, to help identify superior biorefinery tree feedstocks. We recorded as many as 65 wood-related and growth traits in a population of European aspen natural genotypes. These traits included three growth and field performance traits, 20 traits for wood chemical composition, 17 traits for wood anatomy and structure, and 25 wood saccharification traits as indicators of bioconversion potential. We used statistical modelling to determine which wood traits best predict bioconversion yield traits. This way, we identified a core set of wood properties that predict bioprocessing traits. Several of these predictor traits showed high broad-sense heritability, suggesting potential for genetic improvement of feedstocks. Finally, we performed genome-wide association study (GWAS) to identify genetic markers for yield traits or for wood traits that predict yield. GWAS revealed only a few genetic markers for saccharification yield traits, but many more SNPs were associated with wood chemical composition traits, including predictors traits for saccharification. Among them, 16 genetic markers associated specifically with lignin chemical composition were situated in and around two genes which had not previously been associated with lignin. Our approach allowed linking aspen wood bioprocessing yield to wood properties and the underlying genetics, including the discovery of two new potential regulator genes for wood chemical composition.

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.

Detecting a keystone species European aspen in boreal forests with airborne hyperspectral, LiDAR and UAV data with machine learning methods

<p>Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. Different proxy variables indicating species richness and quality of the sites are essential for efficient detecting and monitoring forest biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree species with a high importance in maintaining biodiversity in boreal forests. Large aspen trees host hundreds of species, many of them classified as threatened. However, accurate fine-scale spatial data on aspen occurrence remains scarce and incomprehensive.</p><p> </p><p>We studied detection of aspen using different remote sensing techniques in Evo, southern Finland. Our study area of 83 km<sup>2</sup> contains both managed and protected southern boreal forests characterized by Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), whereas European aspen has a relatively sparse and scattered occurrence in the area. We collected high-resolution airborne hyperspectral and airborne laser scanning data covering the whole study area and ultra-high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors from selected parts of the area. We tested the discrimination of aspen from other species at tree level using different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks).</p><p> </p><p>Airborne hyperspectral and lidar data gave excellent results with machine learning and deep learning classification methods The highest classification accuracies for aspen varied between 91-92% (F1-score). The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm) (Viinikka et al. 2020; Mäyrä et al 2021). Aspen detection using RGB and multispectral data also gave good results (highest F1-score of aspen = 87%) (Kuzmin et al 2021). Different remote sensing data enabled production of a spatially explicit map of aspen occurrence in the study area. Information on aspen occurrence and abundance can significantly contribute to biodiversity management and conservation efforts in boreal forests. Our results can be further utilized in upscaling efforts aiming at aspen detection over larger geographical areas using satellite images.</p>

Adaptive introgression and standing genetic variation, two facilitators of adaptation to high latitudes in European aspen (Populus tremula L.)

Understanding local adaptation in plants from a genomic perspective has become a key research area given the ongoing climate challenge and the concomitant requirement to conserve genetic resources. Perennial plants, such as forest trees, are good models to study local adaptation given their wide geographic distribution, largely outcrossing mating systems and demographic histories. We evaluated signatures of local adaptation in European aspen (Populus tremula) across Europe by means of whole genome re-sequencing of a collection of 411 individual trees. We dissected admixture patterns between aspen lineages and observed a strong genomic mosaicism in Scandinavian trees, evidencing different colonization trajectories into the peninsula from Russia, Central and Western Europe. As a consequence of the secondary contacts between populations after the last glacial maximum (LGM), we detected an adaptive introgression event in a genome region of ∼500kb in chromosome 10, harboring a large-effect locus that has previously been shown to contribute to adaptation to the short growing seasons characteristic of northern Scandinavia. Demographic simulations and ancestry inference suggest an Eastern origin - probably Russian - of the adaptive Nordic allele which nowadays is present in a homozygous state at the north of Scandinavia. The strength of introgression and positive selection signatures in this region is a unique feature in the genome. Furthermore, we detected signals of balancing selection, shared across regional populations, that highlight the importance of standing variation as a primary source of alleles that facilitate local adaptation. Our results therefore emphasize the importance of migration-selection balance underlying the genetic architecture of key adaptive quantitative traits.

Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds

Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen ( tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests ( L., [L.] Karst., spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.PopulusPinus sylvestrisPicea abiesBetula

Long-term effects of prescribed burning, tree retention, and browsing on deciduous tree recruitment in European boreal forests

Silvicultural practices, effective fire suppression, and increased browser densities have profoundly altered structural diversity in boreal forests. Prescribed burning and retention forestry may counteract losses in structural diversity in managed forests, by maintaining higher deciduous admixture. We constructed an experiment on 18 sites with three types of timber harvesting (uncut, cut with retention, and clearcut) and burned half these sites. Subsequently, we established a herbivore treatment with three compartments (unfenced, fenced excluding moose (Alces alces), and fenced excluding moose and hares (Lepus spp.)). In these compartments, we planted rowan (Sorbus aucuparia), European aspen (Populus tremula), and silver birch (Betula pendula) seedlings, and monitored these for 17 years. Birch and rowan mortality were lower on cut and burned sites, with retention further enhancing birch survival on these sites. Retention without burning did not lower seedling mortality of any tree species. While browsing resulted in greater mortality on cut sites, burning appeared to greatly reduce browsing on birch and rowan. On mature uncut sites, seedlings of all tree species exhibited high mortality. Our findings show that deciduous tree recruitment can be improved through prescribed burning, particularly for birch and rowan, and that browsing impacts on deciduous trees depend on forest age.

Analysis of adsorption of heavy metals from water solutions by wood of selected domestic species using X-Ray Fluorescence (XRF)

Analysis of adsorption of heavy metals from water solutions by wood of selected domestic species using X-Ray Fluorescence (XRF).The aim of this study was to analyze the absorption of three specific heavy metals from model water solution by wood species from domestic Polish forests. This paper focuses on XRF method to determine metal content in samples. European aspen (Populus tremula L.) and Black locust (Robinia pseudoacacia L.) have been chosen. Firstly, shavings were prepared and soaked with standard solutions of lead (II) nitrate, cadmium nitrate and mercury (II) chloride for 7 days. Then, the material was dried and reduced to ashes using muffle furnace. Finally, content of absorbed metal was marked (XRF) and analyzed with a view to initial contents of metal ions in standard solutions. It was established, that the higher the concentration of mercury in standard solution is, the higher impulse counts is obtained for European aspen. This reversal of the dependence is noticeable for second analyzed species.