27 research outputs found
Intrapopulation Genotypic Variation of Foliar Secondary Chemistry during Leaf Senescence and Litter Decomposition in Silver Birch (Betula pendula)
Abundant secondary metabolites, such as condensed tannins, and their interpopulation genotypic variation can remain through plant leaf senescence and affect litter decomposition. Whether the intrapopulation genotypic variation of a more diverse assortment of secondary metabolites equally persists through leaf senescence and litter decomposition is not well understood. We analyzed concentrations of intracellular phenolics, epicuticular flavonoid aglycones, epicuticular triterpenoids, condensed tannins, and lignin in green leaves, senescent leaves and partly decomposed litter of silver birch, Betula pendula. Broad-sense heritability (H-2) and coefficient of genotypic variation (CVG) were estimated for metabolites in senescent leaves and litter using 19 genotypes selected from a B. pendula population in southern Finland. We found that most of the secondary metabolites remained through senescence and decomposition and that their persistence was related to their chemical properties. Intrapopulation H-2 and CVG for intracellular phenolics, epicuticular flavonoid aglycones and condensed tannins were high and remarkably, increased from senescent leaves to decomposed litter. The rank of genotypes in metabolite concentrations was persistent through litter decomposition. Lignin was an exception, however, with a diminishing genotypic variation during decomposition, and the concentrations of lignin and condensed tannins had a negative genotypic correlation in the senescent leaves. Our results show that secondary metabolites and their intrapopulation genotypic variation can for the most part remain through leaf senescence and early decomposition, which is a prerequisite for initial litter quality to predict variation in litter decomposition rates. Persistent genotypic variation also opens an avenue for selection to impact litter decomposition in B. pendula populations through acting on their green foliage secondary chemistry. The negative genotypic correlations and diminishing heritability of lignin concentrations may, however, counteract this process.Peer reviewe
Intrapopulation genotypic variation in leaf litter chemistry does not control microbial abundance and litter mass loss in silver birch, Betula pendula
Background and aims Differences among plant genotypes can influence ecosystem functioning such as the rate of litter decomposition. Little is known, however, of the strength of genotypic links between litter quality, microbial abundance and litter decomposition within plant populations, or the likelihood that these processes are driven by natural selection. Methods We used 19 Betula pendula genotypes randomly selected from a local population in south-eastern Finland to establish a long-term, 35-month litter decomposition trial on forest ground. We analysed the effect of litter quality (N, phenolics and triterpenoids) of senescent leaves and decomposed litter on microbial abundance and litter mass loss. Results We found that while litter quality and mass loss both had significant genotypic variation, the genotypic variation among silver birch trees in the quantity of bacterial and fungal DNA was marginal. In addition, although the quantity of bacterial DNA at individual tree level was negatively associated with most secondary metabolites of litter and positively with litter N, litter chemistry was not genotypically linked to litter mass loss. Conclusions Contrary to our expectations, these results suggest that natural selection may have limited influence on overall microbial DNA and litter decomposition rate in B. pendula populations by reworking the genetically controlled foliage chemistry of these populations.Peer reviewe
Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks
During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend to focus on economically significant components of forests, thus leaving out many factors vital for forest biodiversity, such as the occurrence of species with low economical but high ecological values. Airborne hyperspectral imagery has shown significant potential for tree species classification, but the most common analysis methods, such as random forest and support vector machines, require manual feature engineering in order to utilize both spatial and spectral features, whereas deep learning methods are able to extract these features from the raw data. Our research focused on the classification of the major tree species Scots pine, Norway spruce and birch, together with an ecologically valuable keystone species, European aspen, which has a sparse and scattered occurrence in boreal forests. We compared the performance of three-dimensional convolutional neural networks (3D-CNNs) with the support vector machine, random forest, gradient boosting machine and artificial neural network in individual tree species classification from hyperspectral data with high spatial and spectral resolution. We collected hyperspectral and LiDAR data along with extensive ground reference data measurements of tree species from the 83 km2 study area located in the southern boreal zone in Finland. A LiDAR-derived canopy height model was used to match ground reference data to aerial imagery. The best performing 3D-CNN, utilizing 4 m image patches, was able to achieve an F1-score of 0.91 for aspen, an overall F1-score of 0.86 and an overall accuracy of 87%, while the lowest performing 3D-CNN utilizing 10 m image patches achieved an F1-score of 0.83 and an accuracy of 85%. In comparison, the support-vector machine achieved an F1-score of 0.82 and an accuracy of 82.4% and the artificial neural network achieved an F1-score of 0.82 and an accuracy of 81.7%. Compared to the reference models, 3D-CNNs were more efficient in distinguishing coniferous species from each other, with a concurrent high accuracy for aspen classification. Deep neural networks, being black box models, hide the information about how they reach their decision. We used both occlusion and saliency maps to interpret our models. Finally, we used the best performing 3D-CNN to produce a wall-to-wall tree species map for the full study area that can later be used as a reference prediction in, for instance, tree species mapping from multispectral satellite images. The improved tree species classification demonstrated by our study can benefit both sustainable forestry and biodiversity conservation.peerReviewe
A keystone species, European aspen (Populus tremula L.), in boreal forests : Ecological role, knowledge needs and mapping using remote sensing
European aspen (Populus tremula L.) is a keystone species in boreal forests that are dominated by coniferous tree species. Both living and dead aspen trees contribute significantly to the species diversity of forest landscapes. Thus, spatial and temporal continuity of aspen is a prerequisite for the long-term persistence of viable populations of numerous aspen-associated species. In this review, we collate existing knowledge on the ecological role of European aspen, assess the knowledge needs for aspen occurrence patterns and dynamics in boreal forests and discuss the potential of different remote sensing techniques in mapping aspen at various spatiotemporal scales. The role of aspen as a key ecological feature has received significant attention, and studies have recognised the negative effects of modern forest management methods and heavy browsing on aspen occurrence and regeneration. However, the spatial knowledge of occurrence, abundance and temporal dynamics of aspen is scarce and incomprehensive. The remote sensing studies reviewed here highlight particularly the potential of three-dimensional data derived from airborne laser scanning or photogrammetric point clouds and airborne imaging spectroscopy in mapping European aspen, quaking aspen (Populus tremuloides Michx.) and other Populus species. In addition to tree species discrimination, these methods can provide information on biophysical, biochemical properties and even genetic diversity of aspen trees. Major obstacles in aspen detection using remote sensing are the low proportion and scattered occurrence of European aspen in boreal forests and the overlap of spectral and/or structural properties of European aspen and quaking aspen with some other tree species. Furthermore, the suitability of remote sensing data for aspen mapping and monitoring depends on the geographical coverage of data, the availability of multitemporal data and the costs of data acquisition. Our review highlights that integration of ecological knowledge with spatiotemporal information acquired by remote sensing is key to understanding the current and future distribution patterns of aspen-related biodiversity.peerReviewe
Detecting european aspen (Populus tremula L.) in boreal forests using airborne hyperspectral and airborne laser scanning data
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. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455â2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiersâsupport vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724â727 nm) and shortwave infrared (1520â1564 nm and 1684â1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests
Developing a spatially explicit modelling and evaluation framework for integrated carbon sequestration and biodiversity conservation: application in southern Finland
The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (KokemĂ€enjoki basin, 27 024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq a-1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilised for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits.The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (KokemĂ€enjoki basin, 27,024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq aâ1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilized for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits.Peer reviewe
Spectral Reflectance in Silver Birch Genotypes from Three Provenances in Finland
The goal of this study was to investigate the variation in the leaf spectral reflectance and its association with other leaf traits from 12 genotypes among three provenances of origin (populations) in a common garden for Finnish silver birch trees in 2015 and 2016. The spectral reflectance was measured in the laboratory from the detached leaves in the wavelength range of visible and near-infrared (VNIR, 400–1000 nm) and shortwave infrared (SWIR, 1000–2500 nm). The variation among the provenance was initially visualized with principal component analysis (PCA) and a clear separation among the provenances was detected with the discriminant analysis of principal components (DAPC) and partial least squares discriminant analysis (PLS-DA) depicting a less strong variation among the genotypes within the provenances. Wavelengths contributing to the separation of the genotypes and provenances were identified from the contribution plot of DAPC and the red edge was strongly related to the differences. Chlorophyll content showed clear provenance variation and was associated with the separation among the genotypes and provenances in the DAPC space. The normalized difference vegetation index (NDVI705,750) and chlorophyll reflectance index (CRI) showed clear significance among the provenances, whereas NDVI670,780 showed no variation. The variation in the chlorophyll content and the CRI and red edge-based NDVI indices indicated seasonal variation as the chlorophyll content starts increasing in early June. The correlation of foliar chlorophyll content and the chlorophyll-related spectral indices for the discrimination of provenances and genotypes are reported for the first time in a naturally occurring tree species consecutively for two years
The joint adverse effects of aged nanoscale plastic debris and their co-occurring benzo[α]pyrene in freshwater mussel (Anodonta anatina)
: Although the presence of small-scale plastics, including nanoscale plastic debris (NPD, size <1 Όm), is expected in the environment, our understanding of their potential uptake and biodistribution in organisms is still limited. This mostly is because of the limitations in analytical techniques to characterize NPD in organisms' bodies. Moreover, it is still debatable whether aged NPD can sorb and transfer chemicals into organisms. Here, we apply iron oxide-doped polystyrene nanoparticles (Fe-PS NPs) of 270 nm size to quantify the uptake and biodistribution of NPD in freshwater mussels (Anodonta anatina). The Fe-PS NPs were, first, oxidized using heat-activated potassium persulfate treatments to produce NPD (aged particles). Then, the sorption of benzo[a]pyrene (B[α]P), as a model of organic chemicals, into the aged NPD was studied. Chemical oxidation (i.e. aging) significantly decreased the sorption of B[α]P into the particles over 5 days when compared to pristine particles. After 72-h of exposure, A. anatina accumulated NPD in the gills and digestive gland. When exposed to the mixture of NPD and B[α]P, the number of particles in the gills and digestive gland increased significantly compared to the mussels exposed to NPD alone. Moreover, the mixture of NPD and B[α]P increased the activity of Superoxide dismutase and Catalase enzymes in the exposed mussels when compared to the control and to the NPD alone. The present study provides evidence that aged NPD not only could accumulate and alter the toxicity profile of organic chemicals in aquatic organisms, but the chemicals also could facilitate the uptake of NPD (combined effects)
High Variation in Resource Allocation Strategies among 11 Indian Wheat (Triticum aestivum) Cultivars Growing in High Ozone Environment
Eleven local cultivars of wheat (Triticum aestivum) were chosen to study the effect of ambient ozone (O3) concentration in the Indo-Gangetic Plains (IGP) of India at two high-ozone experimental sites by using 300 ppm of Ethylenediurea (EDU) as a chemical protectant against O3. The O3 level was more than double the critical threshold reported for wheat grain production (AOT40 8.66 ppm h). EDU-grown plants had higher grain yield, biomass, stomatal conductance and photosynthesis, less lipid peroxidation, changes in superoxide dismutase and catalase activities, changes in content of oxidized and reduced glutathione compared to non-EDU plants, thus indicating the severity of O3 induced productivity loss. Based on the yield at two different growing sites, the cultivars could be addressed in four response groups: (a) generally well-adapted cultivars (above-average yield); (b) poorly-adapted (below-average yield); (c) adapted to low-yield environment (below-average yield); and (d) sensitive cultivars (adapted to high-yield environment). EDU responses were dependent on the cultivar, the developmental phase (vegetative, flowering and harvest) and the experimental site