scholarly journals Individual tree traits shape insect and disease damage on oak in a climate‐matching tree diversity experiment

2019 ◽  
Vol 9 (15) ◽  
pp. 8524-8540 ◽  
Author(s):  
Elsa Field ◽  
Karsten Schönrogge ◽  
Nadia Barsoum ◽  
Andrew Hector ◽  
Melanie Gibbs
Keyword(s):  
Tree Diversity ◽  
Individual Tree ◽  
Climate Matching ◽  
Disease Damage ◽  
Tree Traits

scholarly journals Neighbourhood-based evidence of tree diversity promotion by beech in an old-growth deciduousconiferous mixed forest (Eastern Carpathians)

2021 ◽  
Vol 64 (1) ◽  
pp. 13-30
Author(s):  
Dan Gafta ◽  
Annik Schnitzler ◽  
Déborah Closset-Kopp ◽  
Vasile Cristea
Keyword(s):  
Tree Species ◽  
Species Coexistence ◽  
Mixed Forest ◽  
Spatial Segregation ◽  
Tree Diversity ◽  
Point Pattern ◽  
Old Growth ◽  
Individual Tree ◽  
Eastern Carpathians ◽  
The Individual

Neighbourhood models are useful tools for understanding the role of positive and negative interactions in maintaining the tree species diversity in mixed forests. Under such a presumption, we aimed at testing several hypotheses concerning the mechanisms of autogenic species coexistence in an old-growth, beech-fir-spruce stand, which is part of the Slătioara forest reserve (Eastern Carpathians). Univariate/bivariate spatial point pattern analyses, the individual tree species-area relationship, the species mingling analysis and generalised linear mixed models of neighbour interference were applied on data concerning the position and allometry of all saplings and trees occurring within a 0.24 ha plot. The monospecific distribution of either beech or spruce saplings did not support the spatial segregation hypothesis. There was no evidence of conspecific negative distance dependence, as no spatial segregation was detected between the saplings and trees of any species. Within 4 m-neighbourhood, the beech saplings appeared as diversity accumulators, which might be indicative of indirect facilitation (e.g., herd protection hypothesis). At tree stage, none of the three species showed either accumulator or repeller patterns in their neighbourhood with respect to sapling species richness. Signals of positive and negative interspecific association were found in tree-sized beech (at scales of 10 to 20 m) and spruce (at scales of 4 to 17 m), respectively. The former, highly interspersed pattern is in accordance with the hypothesis of positive complementary effects, whereas the latter, poorly intermingled pattern is probably linked to the unexpected, positive neighbouring effect of spruce trees on the stem growth of their conspecific saplings. Such self-favouring process might be due to a facilitative below-ground mechanism. Conversely, the beech saplings were suppressed through interference from the neighbouring conspecific trees. The beech appears to be the key promoter of tree species coexistence in the study forest stand, in contrast to the low interspersion of spruce in the overstorey leading to lower local tree diversity.

scholarly journals Plot diversity differentially affects the chemical composition of leaves, roots and root exudates in four subtropical tree species

2020 ◽  
Author(s):  
Alexander Weinhold ◽  
Stefanie Döll ◽  
Min Liu ◽  
Andreas Schedl ◽  
Xingliang Xu ◽  
...  
Keyword(s):  
Root Exudates ◽  
Tree Species ◽  
Plant Competition ◽  
Tree Diversity ◽  
Chemical Diversity ◽  
Individual Plant ◽  
List Type ◽  
Individual Tree ◽  
Main Compound ◽  
Bef China

ABSTRACTPlants produce thousands of compounds, collectively called the metabolome, which mediate interactions with other organisms. The metabolome of an individual plant may change according to the number and nature of these interactions. We tested the hypothesis that tree diversity level affects the metabolome of four subtropical tree species in a biodiversity ecosystem-functioning experiment, BEF-China. We postulated that the chemical diversity of leaves, roots and root exudates increases with tree diversity. We expected the strength of this diversity effect to differ among leaf, root and root exudates samples. Considering their role in plant competition, we expected to find the strongest effects in root exudates.In an ecometabolomics approach, roots, root exudates and leaves of four tree species (Cinnamomum camphora, Cyclobalanopsis glauca, Daphniphyllum oldhamii, Schima superba) were sampled from selected plots in BEF-China. Samples were extracted and analysed using Liquid Chromatography-Time of Flight-Mass Spectrometry. The exudate metabolomes were normalized over their non-purgeable organic carbon level. Multivariate analyses were applied to identify the effect of both neighbouring (local) trees and plot diversity on tree metabolomes. The species and sample specific metabolites were assigned to major compound classes using the ClassyFire tool, whereas m/z features related to diversity effects were annotated manually.Individual tree species showed distinct leaf, root and root exudate metabolomes. The main compound class in leaves were the flavonoids, whereas carboxylic acids, prenol lipids and specific alkaloids were most prominent in root exudates and roots. Overall plot diversity had a stronger effect on metabolome profiles than the diversity of local, directly neighbouring trees. Leaf metabolomes responded more often to tree diversity level than exudates, whereas root metabolomes varied the least. We found not overall correlation between metabolite richness or diversity and tree diversity.Synthesis: Classification of metabolites supported initial ecological interpretation of differences among species and organs. Particularly the metabolomes of leaves and root exudates respond to differences in tree diversity. These responses were neither linear nor uniform and individual metabolites showed different dynamics. More controlled interaction experiments are needed to dissect the causes and consequences of the observed shifts in plant metabolomes.

Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

2017 ◽  
Vol 168 (3) ◽  
pp. 127-133
Author(s):  
Matthew Parkan
Keyword(s):  
Cross Sections ◽  
Forest Edge ◽  
Airborne Lidar ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Small Areas ◽  
Interactive 3D ◽  
The Individual ◽  
Airborne Lidar Data ◽  
Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

scholarly journals Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

2019 ◽  
Vol 11 (22) ◽  
pp. 2614 ◽  
Author(s):  
Nina Amiri ◽  
Peter Krzystek ◽  
Marco Heurich ◽  
Andrew Skidmore
Keyword(s):  
Tree Species ◽  
Feature Selection Method ◽  
Silver Fir ◽  
Maximum Point ◽  
Individual Tree ◽  
Dead Trees ◽  
Multi Wavelength ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
532 Nm

Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

scholarly journals Tree diversity and functional leaf traits drive herbivore‐associated microbiomes in subtropical China

2021 ◽  
Author(s):  
Yi Li ◽  
Douglas Chesters ◽  
Ming‐Qiang Wang ◽  
Tesfaye Wubet ◽  
Andreas Schuldt ◽  
...  
Keyword(s):  
Leaf Traits ◽  
Tree Diversity ◽  
Subtropical China
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