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Volume 14
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Forests, Volume 14, Issue 1 (January 2023) – 165 articles

Cover Story (view full-size image): This case study aimed to evaluate the impact of natural defoliation produced by Clostera anastomosis L. (Lepidoptera: Notodontidae) on the growth and mortality of defoliated trees in a hybrid poplar short rotation culture (AF8 clone) in northeastern Romania. Thus, the attack of this pest led to the loss of about 70% of the above-ground biomass estimated to be accumulated in the year of defoliation and to the death of about 29% of the affected poplar trees. View this paper
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19 pages, 2969 KiB  
Article
Data Mining in the Analysis of Tree Harvester Performance Based on Automatically Collected Data
by Krzysztof Polowy and Marta Molińska-Glura
Forests 2023, 14(1), 165; https://doi.org/10.3390/f14010165 - 16 Jan 2023
Cited by 5 | Viewed by 3607
Abstract
Data recorded automatically by harvesters are a promising and potentially very useful source of information for scientific analyses. Most researchers have used StanForD files for this purpose, but these are troublesome to obtain and require some pre-processing. This study utilized a new source [...] Read more.
Data recorded automatically by harvesters are a promising and potentially very useful source of information for scientific analyses. Most researchers have used StanForD files for this purpose, but these are troublesome to obtain and require some pre-processing. This study utilized a new source of similar data: JDLink, a cloud-based service, run by the machine manufacturer, that stores data from sensors in real time. The vast amount of such data makes it hard to comprehend and handle efficiently. Data mining techniques assist in finding trends and patterns in such databases. Records from two mid-sized harvesters working in north-eastern Poland were analyzed using classical regression (linear and logarithmic), cluster analysis (dendrograms and k-means) and Principal Component Analysis (PCA). Linear regression showed that average tree size was the variable having the greatest effect on fuel consumption per cubic meter and productivity, whereas fuel consumption per hour was also dependent, e.g., on distance driven in a low gear or share of time with high engine load. Results of clustering and PCA were harder to interpret. Dendrograms showed most dissimilar variables: total volume harvested per day, total fuel consumption per day and share of work time on high revolutions per minute (RPMs). K-means clustering allowed us to identify periods when specific clusters of variables were more prominent. PCA results, despite explaining almost 90% of variance, were inconclusive between machines, and, therefore, need to be scrutinized in follow-up studies. Productivity values (avg. around 10 m3/h) and fuel consumption rates (13.21 L/h, 1.335 L/m3 on average) were similar to the results reported by other authors under comparable conditions. Some new measures obtained in this study include, e.g., distance driven in a low gear (around 7 km per day) or proportion of time when the engine was running on low, medium or high load (34%, 39% and 7%, respectively). The assumption of this study was to use data without supplementing from external sources, and with as little processing as possible, which limited the analytic methods to unsupervised learning. Extending the database in follow-up studies will facilitate the application of supervised learning techniques for modeling and prediction. Full article
(This article belongs to the Special Issue Forest Harvesting, Operations and Management)
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Figure 1
<p>Relationship between mean tree size (AvgT) and relative fuel consumption (Flm3) per cubic meter; each point represents a working day.</p> Full article ">Figure 2
<p>Dendrogram of variables for machine A.</p> Full article ">Figure 3
<p>Dendrogram of variables for machine B.</p> Full article ">Figure 4
<p>K-means separation of observations (workdays) into clusters and influence of variables—for both machines.</p> Full article ">Figure 5
<p>K-means separation of variables into clusters and their prevalence in consecutive workdays for machine A.</p> Full article ">Figure 6
<p>K-means separation of variables into clusters and their prevalence in consecutive workdays for machine B.</p> Full article ">Figure 7
<p>Loading plots of machine A (<b>a</b>) and machine B (<b>b</b>) for the first two principal components.</p> Full article ">
16 pages, 3611 KiB  
Article
Evaluation of the Community Land Model-Simulated Specific Leaf Area with Observations over China: Impacts on Modeled Gross Primary Productivity
by Yuanhao Zheng, Li Zhang, Pan Li, Xiaoli Ren, Honglin He, Yan Lv and Yuping Ma
Forests 2023, 14(1), 164; https://doi.org/10.3390/f14010164 - 16 Jan 2023
Cited by 1 | Viewed by 2867
Abstract
Specific leaf area (SLA) is a key leaf functional trait associated with the ability to acquire light. Substantial variations in SLA have not been well described in the community land model (CLM) and similar terrestrial biosphere models. How these SLA variations influence the [...] Read more.
Specific leaf area (SLA) is a key leaf functional trait associated with the ability to acquire light. Substantial variations in SLA have not been well described in the community land model (CLM) and similar terrestrial biosphere models. How these SLA variations influence the simulation of gross primary productivity (GPP) remains unclear. Here, we evaluated the mismatch in SLA between the CLM4.5 and observed data collected from China and quantified the impacts of SLA variation calculated from both observations and the default values across seven terrestrial biosphere models on modeled GPP using CLM4.5. The results showed that CLM4.5 tended to overestimate SLA values at the top and gradient of the canopy. The higher default SLA values could cause an underestimation of the modeled GPP by 5–161 g C m−2 yr−1 (1%–7%) for temperate needleleaf evergreen tree (NET), temperate broadleaf deciduous tree (BDT), and C3 grass and an overestimation by 50 g C m−2 yr−1 (2%) for temperate broadleaf evergreen tree (BET). Moreover, the observed SLA variation among species ranged from 21% to 59% for 14 plant functional types (PFTs), which was similar to the variation in default SLA values across models (9%–60%). These SLA variations would lead to greater changes in modeled GPP by 7%–19% for temperate NET and temperate BET than temperate BDT and C3 grass (2%–9%). Our study suggested that the interspecific variation in SLA and its responses to environmental factors should be involved in terrestrial biosphere models; otherwise, it would cause substantial bias in the prediction of ecosystem productivity. Full article
(This article belongs to the Special Issue Modelling Forest Ecosystems)
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Figure 1
<p>Distribution of observed specific leaf area (SLA) data in China.</p> Full article ">Figure 2
<p>The differences in specific leaf area (SLA) between observation data in China and default values in the CLM4.5 model. The error bar shows the standard deviation. 1, temperate NET; 2, boreal NET; 3, boreal NDT; 4, tropical BET; 5, temperate BET; 6, tropical BDT; 7, temperate BDT; 8, boreal BDT; 9, temperate BES; 10, temperate BDS; 11, boreal BDS; 12, C3 grass; 13, C4 grass; 14, rainfed crop.</p> Full article ">Figure 3
<p>The responses of specific leaf area (SLA) to light gradients for temperate broadleaf deciduous tree (BDT) (<b>a</b>), temperate needleleaf evergreen tree (NET) (<b>b</b>), and temperate broadleaf evergreen tree (BET) (<b>c</b>). More details about the species can be found in <a href="#app1-forests-14-00164" class="html-app">Table S2</a> [<a href="#B40-forests-14-00164" class="html-bibr">40</a>,<a href="#B41-forests-14-00164" class="html-bibr">41</a>,<a href="#B42-forests-14-00164" class="html-bibr">42</a>,<a href="#B43-forests-14-00164" class="html-bibr">43</a>].</p> Full article ">Figure 4
<p>The default SLA values in terrestrial biosphere models and the SLA mean observed values in China for different plant functional types. Box plots show the mean, 25th percentile, and 75th percentile of the observed SLA values. Abbreviations: NET, needleleaf evergreen tree; NDT, needleleaf deciduous tree; BET, broadleaf evergreen tree; BDT, broadleaf deciduous tree; BES, broadleaf evergreen shrub; BDS, broadleaf deciduous shrub.</p> Full article ">Figure 5
<p>The differences in annual gross primary productivity (GPP), mean RuBP-limited photosynthesis rate (Ac), mean leaf area index (LAI) between model experiment S1 (with default SLA values in the CLM4.5 model) and model experiment S2 (with mean SLA observed values). The error bar shows the standard deviation. QYZ site: temperate needleleaf evergreen tree; CBS site: temperate broadleaf deciduous tree; DHS site: broadleaf evergreen tree; HBG site: C3 grass.</p> Full article ">Figure 6
<p>The differences in SLA values between the observation data in China and the global average in the TRY database. The error bar shows the standard deviation. Abbreviations: NET, needleleaf evergreen tree; NDT, needleleaf deciduous tree; BET, broadleaf evergreen tree; BDT, broadleaf deciduous tree; BES, broadleaf evergreen shrub; BDS, broadleaf deciduous shrub.</p> Full article ">Figure 7
<p>The changes in SLA values during key development stages (DVS) for different crops (<b>a</b>) and observed SLA values in various growth periods (<b>b</b>). For (<b>a</b>), the dotted lines are the default SLA values at different DVS in the WOFOST model and the solid line is the observed SLA values of summer maize at the Gucheng site in China. DVS values range from −0.1 at sowing to 0.0 at emergence, 1.0 at flowering, and 2.0 at physiological maturity. For (<b>b</b>), the different lowercase letters indicate significant differences at the 0.05 level. More details about the species can be found in <a href="#app1-forests-14-00164" class="html-app">Table S2</a> [<a href="#B53-forests-14-00164" class="html-bibr">53</a>,<a href="#B54-forests-14-00164" class="html-bibr">54</a>,<a href="#B55-forests-14-00164" class="html-bibr">55</a>,<a href="#B56-forests-14-00164" class="html-bibr">56</a>,<a href="#B57-forests-14-00164" class="html-bibr">57</a>].</p> Full article ">Figure 8
<p>The SLA values of different PFTs under three soil moisture regimes (<b>a</b>) and different N addition levels (<b>b</b>). The different lowercase letters indicate significant differences at 0.05 level. More details about the species can be found in <a href="#app1-forests-14-00164" class="html-app">Table S2</a> [<a href="#B58-forests-14-00164" class="html-bibr">58</a>,<a href="#B59-forests-14-00164" class="html-bibr">59</a>,<a href="#B60-forests-14-00164" class="html-bibr">60</a>,<a href="#B61-forests-14-00164" class="html-bibr">61</a>,<a href="#B62-forests-14-00164" class="html-bibr">62</a>,<a href="#B63-forests-14-00164" class="html-bibr">63</a>,<a href="#B64-forests-14-00164" class="html-bibr">64</a>,<a href="#B65-forests-14-00164" class="html-bibr">65</a>].</p> Full article ">
18 pages, 6938 KiB  
Article
Effect of Multi-Walled Carbon Nanotubes on the Growth and Expression of Stress Resistance Genes in Birch
by Konstantin V. Zhuzhukin, Peter M. Evlakov, Tatiana A. Grodetskaya, Alexander A. Gusev, Olga V. Zakharova, Aleksey V. Shuklinov and Elena V. Tomina
Forests 2023, 14(1), 163; https://doi.org/10.3390/f14010163 - 16 Jan 2023
Cited by 7 | Viewed by 2957
Abstract
Recent studies have shown that nanomaterials, including carbon nanotubes, are associated with a wide range of effects on living organisms, from stimulation to toxic effects. Plants are an important object of such research, which is associated with the potential use of carbon nanomaterials [...] Read more.
Recent studies have shown that nanomaterials, including carbon nanotubes, are associated with a wide range of effects on living organisms, from stimulation to toxic effects. Plants are an important object of such research, which is associated with the potential use of carbon nanomaterials in agriculture and environmental protection. At the same time, the specific mechanisms of formation of plant resistance to the effects of carbon nanotubes remain not fully understood, especially in woody plants. Therefore, we studied the effect of aqueous colloids of multi-walled carbon nanotubes (MWCNTs) with an outer diameter of 10–30 nm and a length of about 2 μm at a concentration of 1, 10, 50, and 100 mg/L on morphometric parameters and the level of expression of stress resistance genes in Betula pubescens Ehrh. and B. pendula Roth. plants in greenhouse conditions. The results showed an increase in the length and diameter of the shoot in the studied plants. The dry biomass of the leaf increased by 30%, the stem by 42%, and the root by 49% when using MWCNTs at a concentration of 10 mg/L. The expression of the stress resistance genes DREB2 and PR-10 significantly increased under the influence of 1 mg/L MWCNTs on plants of both species. At the same time, the use of 100 mg/L nanoparticles led to a decrease in the studied parameters in Betula pendula, which may be associated with the negative effect of MWCNTs in high concentrations. The revealed positive effects of low concentrations of MWCNTs on morphometric parameters and stimulation of stress resistance genes by nanotubes open up prospects for their use in woody plant biotechnology. Full article
(This article belongs to the Special Issue Climate-Smart Forestry: Problems, Priorities and Prospects)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Raman spectrum of a MWCNT sample.</p> Full article ">Figure 2
<p>FTIR spectrum of MWCNTs.</p> Full article ">Figure 3
<p>Image in MWCNT in TEM (<b>a</b>–<b>d</b>) and SEM (<b>e</b>).</p> Full article ">Figure 4
<p>General view of colloidal solutions with different concentrations of MWCNTs (<b>a</b>) and the results of determining the zeta potential (<b>b</b>), certain concentrations of solutions.</p> Full article ">Figure 5
<p>Effect of MWCNTs on height (<b>a</b>), diameter (<b>b</b>), parameters of wet (<b>c</b>) and dry (<b>d</b>) biomass of downy birch (<span class="html-italic">B. pubescens</span>) plants. Bars represent the ±SE of three replicates. Letters on vertical bars represent significant differences between groups according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 6
<p>Effect of MWCNTs on height (<b>a</b>), diameter (<b>b</b>), parameters of wet (<b>c</b>) and dry (<b>d</b>) biomass of silver birch (<span class="html-italic">B. pendula</span>) plants. Bars represent the ±SE of three replicates. Letters on vertical bars represent significant differences between groups according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 7
<p>Leaf area (<b>a</b>) and total leaf surface (<b>b</b>).</p> Full article ">Figure 8
<p>Average number of leaves per plant.</p> Full article ">Figure 9
<p>Expression of stress resistance genes in downy birch (<b>a</b>) and silver birch (<b>b</b>) plants: white columns—nanotubes at a concentration of 1 mg/L, gray columns—10 mg/L, black columns—50 mg/L and shaded columns—100 mg/L. Asterisks in the vertical bars represent significant differences between groups according to Duncan’s multiple range test at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 10
<p>Possible process of the influence of MWCNTs on the synthesis of secondary metabolites.</p> Full article ">
17 pages, 16803 KiB  
Article
A Small-Target Forest Fire Smoke Detection Model Based on Deformable Transformer for End-to-End Object Detection
by Jingwen Huang, Jiashun Zhou, Huizhou Yang, Yunfei Liu and Han Liu
Forests 2023, 14(1), 162; https://doi.org/10.3390/f14010162 - 16 Jan 2023
Cited by 31 | Viewed by 5175
Abstract
Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components [...] Read more.
Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest scenes. Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). We use deformable DETR as a baseline containing the best sparse spatial sampling for smoke with deformable convolution and relation modeling capability of the transformer. We integrate a Multi-scale Context Contrasted Local Feature module (MCCL) and a Dense Pyramid Pooling module (DPPM) into the feature extraction module for perceiving features of small or inconspicuous smoke. To improve detection accuracy and reduce false and missed detections, we propose an iterative bounding box combination method to generate precise bounding boxes which can cover the entire smoke object. In addition, we evaluate the proposed approach using a quantitative and qualitative self-made forest fire smoke dataset, which includes forest fire smoke images of different scales. Extensive experiments show that our improved model’s forest fire smoke detection accuracy is significantly higher than that of the mainstream models. Compared with deformable DETR, our model shows better performance with improvement of mAP (mean average precision) by 4.2%, APS (AP for small objects) by 5.1%, and other metrics by 2% to 3%. Our model is adequate for early forest fire smoke detection with high detection accuracy of different-scale smoke objects. Full article
(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)
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Figure 1

Figure 1
<p>Samples of the FFS dataset (images show smoke objects at different scales, images of first row shows light smoke, the second row shows dense smoke).</p> Full article ">Figure 2
<p>The network structure of deformable DETR.</p> Full article ">Figure 3
<p>Illustration of Multi-scale Context Contrasted Local Feature module.</p> Full article ">Figure 4
<p>Illustration of the Dense Pyramid Pooling Module.</p> Full article ">Figure 5
<p>Location of the MCCL module in deformable DETR. MCCL module processes the lowest-resolution feature maps on C<sub>5</sub> stage.</p> Full article ">Figure 6
<p>Different detection samples before and after using iterative bounding box combination method. (<b>a</b>,<b>c</b>) Original detection results; (<b>a</b>) contains one missed detection; (<b>c</b>) contains one false detection. (<b>b</b>,<b>d</b>) The updated detection results where bounding boxes are generated by our method. The bounding boxes can cover the whole smoke accurately in both (<b>b</b>,<b>d</b>).</p> Full article ">Figure 7
<p>Detection results of our improved deformable DETR model. The first row shows small-target smoke images, the second row shows large-target smoke images.</p> Full article ">Figure 7 Cont.
<p>Detection results of our improved deformable DETR model. The first row shows small-target smoke images, the second row shows large-target smoke images.</p> Full article ">Figure 8
<p>Detection results using YOLOv5s.</p> Full article ">Figure 9
<p>Detection results using DETR.</p> Full article ">Figure 10
<p>Detection results using deformable DETR.</p> Full article ">Figure 11
<p>Detection results using our model.</p> Full article ">Figure 12
<p>Visualization results of the multi-scale deformable attention in encoder. We draw the sampling points and attention weights from feature maps in one image. Each circle represents a sampling point and its color represents the attention weight. Color from blue to red indicates the weight from small to large. (<b>a</b>) Raw image; (<b>b</b>) deformable DETR; (<b>c</b>) our improved model.</p> Full article ">
12 pages, 6596 KiB  
Article
Effects of Nitrogen Form on Root Activity and Nitrogen Uptake Kinetics in Camellia oleifera Seedlings
by Rui Wang, Zhilong He, Zhen Zhang, Ting Xv, Xiangnan Wang, Caixia Liu and Yongzhong Chen
Forests 2023, 14(1), 161; https://doi.org/10.3390/f14010161 - 16 Jan 2023
Cited by 4 | Viewed by 2037
Abstract
This study investigated the effects of nitrogen form on root activity and nitrogen uptake kinetics of Camellia oleifera Abel. seedlings, providing a scientific basis for improving nitrogen use efficiency and scientific fertilization in C. oleifera production. Taking one-year-old C. oleifera cultivar ‘Xianglin 27’ [...] Read more.
This study investigated the effects of nitrogen form on root activity and nitrogen uptake kinetics of Camellia oleifera Abel. seedlings, providing a scientific basis for improving nitrogen use efficiency and scientific fertilization in C. oleifera production. Taking one-year-old C. oleifera cultivar ‘Xianglin 27’ seedlings as subjects, 8 mmol·L−1 of nitrogen in varied forms (NO3−:NH4+ = 0:0, 10:0, 7:3, 5:5, 3:7, 0:10) was applied in this study as the treatment conditions to investigate the effects of different nitrogen forms on root activity and nitrogen uptake kinetics in C. oleifera seedlings. Comparing the performance of nutrient solutions with different NO3−:NH4+ ratios, the results showed that a mixed nitrogen source improved the root activity of C. oleifera seedlings based on total absorption area, active absorption area, active absorption area ratio, specific surface area, and active specific surface area. When NO3−:NH4+ = 5:5, the total absorption area and active absorption area of the seedling roots reached the maximum. The results of uptake kinetic parameters showed that Vmax NH4+ > Vmax NO3− and Km NO3− > Km NH4+, indicating that the uptake potential of ammonium–nitrogen by C. oleifera seedlings is greater than that of nitrate–nitrogen. The conclusion was that compared to either ammonium– or nitrate–nitrogen, the mixed nitrogen source was better for promoting the root activity of C. oleifera seedlings, and the best nitrate/ammonium ratio was 5:5. Full article
(This article belongs to the Special Issue Advances in Woody Oil Species: Past, Present and Future)
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Figure 1

Figure 1
<p>The effect of nitrogen form on the total absorption area of the roots of <span class="html-italic">C. oleifera</span> seedlings. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>The effect of nitrogen forms on the active absorption area of the roots of <span class="html-italic">C. oleifera</span> seedlings. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 3
<p>The effect of nitrogen forms on the active absorption area ratio of the roots of <span class="html-italic">C. oleifera</span> seedlings. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 4
<p>The effect of nitrogen forms on the specific surface area of roots of <span class="html-italic">C. oleifera</span> seedlings. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 5
<p>The effect of nitrogen forms on the active specific surface area of roots of <span class="html-italic">C. oleifera</span> seedlings. Different lowercase letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 6
<p>Absorption kinetics curve of nitrate in roots of <span class="html-italic">C. oleifera</span> seedlings.</p> Full article ">Figure 7
<p>Absorption kinetics curve of ammonium in roots of <span class="html-italic">C. oleifera</span> seedlings.</p> Full article ">
15 pages, 2349 KiB  
Article
Variability in Snowpack Isotopic Composition between Open and Forested Areas in the West Siberian Forest Steppe
by Dmitry Pershin, Natalia Malygina, Dmitry Chernykh, Roman Biryukov, Dmitry Zolotov and Lilia Lubenets
Forests 2023, 14(1), 160; https://doi.org/10.3390/f14010160 - 16 Jan 2023
Cited by 1 | Viewed by 1776
Abstract
The stable water isotopes in snow (primarily 18O and 2H) are widely used for tracing hydrological and ecological processes. However, isotopic signatures of snow can be significantly modified by topography and land cover. This study assesses spatial and temporal variability of [...] Read more.
The stable water isotopes in snow (primarily 18O and 2H) are widely used for tracing hydrological and ecological processes. However, isotopic signatures of snow can be significantly modified by topography and land cover. This study assesses spatial and temporal variability of the bulk snowpack isotopic composition (δ18O, δ2H, d-excess) between forested (pine and birch) and open areas in the West Siberian forest steppes. Isotopic samples were collected over the peak snow accumulation in 2017–2019. The snow isotopic composition within forested areas differed from open steppes, mainly in reducing d-excess (1.6‰ on average). We did not find a significant effect of canopy interception on snow enrichment in heavier isotopes. Snowpack in the pine forests was even lighter by 3.6‰ for δ2H compared to open areas, probably, due to low energy inputs and interception capacity. Additionally, snow depth significantly influenced the isotopic composition spatial variability. As snow depth increased, δ18O and δ2H values decreased due to conservation within the snowpack and less influence of sublimation and moisture exchange with the soil. However, this pattern was only evident in winters with below-average snow depth. Therefore, taking into account snow depth spatial and seasonal variability is advisable when applying the isotopic methods. Full article
(This article belongs to the Special Issue Forest Ecohydrology: From Theory to Practice)
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Figure 1
<p>The Kasmala basin in the south of Western Siberia with sampling locations and land cover composition. NP—northern part, CP—central part, SP—southern part.</p> Full article ">Figure 2
<p>Barnaul weather station air temperature, wind speed, snow depth for the studied winter seasons as well as the station period of record (1966–2020).</p> Full article ">Figure 3
<p>Temporal variability of the isotopic composition (δ<sup>2</sup>H and δ<sup>18</sup>O) of bulk snowpack over the basin parts (NP, CP, SP) with results of Kruskal–Wallis tests within sampling years. Violin and Tukey outlier box plots show the median value (horizontal line within the box), the 1st and 3rd quartile (ends of the box), minimum/maximum values (whiskers) and distribution.</p> Full article ">Figure 4
<p>δ<sup>2</sup>H vs. δ<sup>18</sup>O plot over the basin parts (NP, CP, SP) and sampling years, including their regression lines (blue), global meteoric water line (gray), and snow depth gradient.</p> Full article ">Figure 5
<p>D-excess values over the basin parts (NP, CP, SP) and sampling years. Individual samples are shown using dots. Tukey outlier box plots show the median value (horizontal line within the box), the 1st and 3rd quartile (ends of the box), and the minimum/maximum values (whiskers).</p> Full article ">Figure 6
<p>Partial residuals of each predictor for the sampling years selected by the stepwise multiple regression. The dashed lines represent linear relationships.</p> Full article ">Figure 7
<p>Relationship of the d-excess and snow depth, SWE over the sampling years and basin parts (NP, CP, SP).</p> Full article ">
35 pages, 467 KiB  
Editorial
Acknowledgment to the Reviewers of Forests in 2022
by Forests Editorial Office
Forests 2023, 14(1), 159; https://doi.org/10.3390/f14010159 - 16 Jan 2023
Viewed by 3054
Abstract
High-quality academic publishing is built on rigorous peer review [...] Full article
18 pages, 6831 KiB  
Article
Evaluation of Plant Growth and Potential of Carbon Storage in the Restored Mangrove of an Abandoned Pond in Lubuk Kertang, North Sumatra, Indonesia
by Rizka Amelia, Mohammad Basyuni, Alfinsyahri Alfinsyahri, Nurdin Sulistiyono, Bejo Slamet, Yuntha Bimantara, Salma Safrina Hashilah Harahap, Mikrajni Harahap, Insar Maulid Harahap, Shofiyah Sabilah Al Mustaniroh, Sigit D. Sasmito and Virni Budi Arifanti
Forests 2023, 14(1), 158; https://doi.org/10.3390/f14010158 - 15 Jan 2023
Cited by 10 | Viewed by 3755
Abstract
Mangrove forest in Lubuk Kertang Village, West Brandan sub-district has been converted around 20 ha annually (1996–2016) into various non-forest land use. Rehabilitation can be a solution to restore the condition of the ecosystem so that it can resume its ecological and economic [...] Read more.
Mangrove forest in Lubuk Kertang Village, West Brandan sub-district has been converted around 20 ha annually (1996–2016) into various non-forest land use. Rehabilitation can be a solution to restore the condition of the ecosystem so that it can resume its ecological and economic functions. This paper discusses the evaluation of mangrove rehabilitation carried out by planting 6000 propagules in December 2015 and 5000 seedlings in May 2016 with Rhizophora apiculata species in abandoned ponds. Monitoring was carried out every 6 months from 2016 to 2022. In the restored area, 11 true mangrove species and 3 associated mangrove species were found. The percentage of plants that survived after seven years was 69.42% for planting using propagules and 86.38% for planting with seedlings. The total biomass carbon stocks stored by 7-year-old plants using propagules was 51.18 Mg ha−1, while the carbon stored by planting using seedlings was 56.79 Mg ha−1. Soil carbon stocks at the planted site with propagules were 506.89 ± 250.74 MgC ha−1, and at the planted site with seedlings were 461.85 ± 102.23 MgC ha−1. The total ecosystem carbon stocks (including aboveground carbon) in the planted site using propagules were 558.07 MgC ha−1, while planting using seedlings were 518.64 MgC ha−1. The dataset and findings on the carbon storage evaluation of mangrove rehabilitation will be useful for blue carbon research community and policymakers in the context of the climate change mitigation strategy for Indonesia. Full article
(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)
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<p>Map of study location of mangrove restored area in Lubuk Kertang Village, West Brandan District, Langkat Regency, North Sumatra, Indonesia.</p> Full article ">Figure 2
<p>The picture of mangrove growth condition in the planting location obtained from Google Earth satellite images between 2014 and 2022.</p> Full article ">Figure 3
<p>The layout of the sampling plots of the planting (100 planting for each plot with a total of 800 planting); (<b>A</b>). Planting using seedlings, (<b>B</b>). Planting using propagules. The green circle represent health mangroves, the yellow are pest-affected mangroves, while the red were indicated mortality.</p> Full article ">Figure 4
<p>Above-ground (AGC) and below-ground biomass carbon (BGC) stocks stored and mean annual increment (MAI) in rehabilitated land planted with propagules and seedlings. The different letter notation shows significant differences in the Tukey at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 5
<p>Soil carbon stocks in two planted approaches in rehabilitated aquaculture ponds of Lubuk Kertang.</p> Full article ">Figure 6
<p>Relationships between height, diameter, above-ground biomass, underground biomass, number of leaves, and leaf thickness. (<b>A</b>) Planting with propagules; (<b>B</b>) planting with seedlings (the increasing intensity of the red color in the diagram indicates that the correlation was higher).</p> Full article ">
18 pages, 2701 KiB  
Article
Effects of Wild Forest Fires on Genetic Diversity and Population Structure of a Boreal Conifer, White Spruce (Picea glauca (Moench) Voss): Implications for Genetic Resource Management and Adaptive Potential under Climate Change
by Om P. Rajora, Manphool S. Fageria and Michael Fitzsimmons
Forests 2023, 14(1), 157; https://doi.org/10.3390/f14010157 - 14 Jan 2023
Cited by 5 | Viewed by 4631
Abstract
Climate change is predicted to increase forest fires in boreal forests, which can threaten the sustainability of forest genetic resources. Wildfires can potentially impact genetic diversity and population structure in forest trees by creating population bottlenecks, and influencing demography, effective population size ( [...] Read more.
Climate change is predicted to increase forest fires in boreal forests, which can threaten the sustainability of forest genetic resources. Wildfires can potentially impact genetic diversity and population structure in forest trees by creating population bottlenecks, and influencing demography, effective population size (Ne) and various evolutionary processes. We have investigated this critical issue in a widely-distributed, transcontinental, ecologically and economically important and fire-intolerant boreal conifer, white spruce (Picea glauca (Moench) Voss). We tested the hypothesis that in a predominantly outcrossing species with long distance gene flow, such as white spruce, located in primary undisturbed forests, normal forest fires do not adversely affect genetic diversity and population structure. We used 10 nuclear genic and genomic microsatellite loci to examine genetic diversity and population structure of post-fire pristine old-growth (PF-OG) and adjacent post-fire naturally regenerated young (PF-YR) stands. The genetic diversity, inbreeding and genetic differentiation levels, Bayesian population structure, Ne and latent genetic potential were statistically similar between the PF-OG and PF-YR populations. None of the microsatellites showed any signature of selection. Our study demonstrates that normal wild forest fires do not adversely affect genetic diversity, differentiation, and population genetic structure in white spruce. The results should have wide significance for sustainable forest management. Full article
(This article belongs to the Section Genetics and Molecular Biology)
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<p>The 1999 aerial photos showing post-fire old growth and post-fire young regeneration at the Sanctuary Lake site (<b>left</b>) and Tibiska Lake site (<b>right</b>).</p> Full article ">Figure 2
<p>Allele patterns across the white spruce populations. A, number of alleles per locus; A Frequency &gt;= 5%, number of alleles per locus with frequency of ≥0.05 (5%); Ae, effective number of alleles per locus; No. Private Alleles, total number of private alleles (A<sub>P</sub>); No. LComm Alleles (&lt;=50%) = number of locally common alleles (Freq. &lt;= 5%) found in 50% or fewer populations.</p> Full article ">Figure 3
<p>The UPGMA dendrogram, based on genetic distances. The number on the nodes represents the percent bootstrap support from 1000 bootstraps. Details of the populations are provided in <a href="#forests-14-00157-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 4
<p>The summary bar plot of the estimated membership coefficient (Q) of the studied white spruce individuals from four populations from the STRUCTURE analysis for K = 2–8. Each individual is represented by a single vertical line while each color represents one cluster/genetic group. TL-PF-OG, Tibiska Lake post-fire natural old-growth; SL-PG-OG, Sanctuary Lake post-fire natural old-growth; TL-PF-YR, Tibiska Lake post-fire natural young regeneration; SL-PF-YR, Sanctuary Lake post-fire natural young regeneration.</p> Full article ">Figure 5
<p>(<b>A</b>). A summary scatterplot of DeltaK values for white spruce populations testing K = 2–8 clusters, calculated from the STRUCTURE results using the Evanno et al. [<a href="#B54-forests-14-00157" class="html-bibr">54</a>] method. (<b>B</b>). Summary bar plot of estimated membership coefficient (Q) of the studied white spruce individuals from the four populations from the STRUCTURE analysis for K = 4. Each individual is represented by a single vertical line while each color represents one cluster/genetic group. TL-PF-OG, Tibiska Lake post-fire natural old-growth; SL-PG-OG, Sanctuary Lake post-fire natural old-growth; TL-PF-YR, Tibiska Lake post-fire natural young regeneration; SL-PF-YR, Sanctuary Lake post-fire natural young regeneration.</p> Full article ">
29 pages, 8802 KiB  
Article
Effect of Provenance and Environmental Factors on Tree Growth and Tree Water Status of Norway Spruce
by Adriana Leštianska, Peter Fleischer, Jr., Katarína Merganičová, Peter Fleischer, Sr., Paulína Nalevanková and Katarína Střelcová
Forests 2023, 14(1), 156; https://doi.org/10.3390/f14010156 - 14 Jan 2023
Cited by 5 | Viewed by 2703
Abstract
Changes in temperature regime, and a higher frequency of extreme weather conditions due to global warming are considered great risks for forest stands worldwide because of their negative impact on tree growth and vitality. We examined tree growth and water balance of two [...] Read more.
Changes in temperature regime, and a higher frequency of extreme weather conditions due to global warming are considered great risks for forest stands worldwide because of their negative impact on tree growth and vitality. We examined tree growth and water balance of two provenances of Norway spruce growing in Arboretum Borová hora (350 m a.s.l., Zvolen, central Slovakia) that originated from climatologically cooler conditions. The research was performed during three meteorologically different years from 2017 to 2019. We evaluated the impact of climatic and soil factors on intra-species variability in radial stem growth and tree water status that were characterised by seasonal radial increment, stem water deficit and maximum daily shrinkage derived from the records of stem circumference changes obtained from band dendrometers installed on five mature trees of each provenance. The impact of environmental factors on the characteristics was evaluated using the univariate factor analysis and four machine learning models (random forest, support vector machine, gradient boosting machine and neural network). The responses to climatic conditions differed between the provenances. Seasonal radial increments of the provenance from cooler conditions were greater than those of the provenance originating from cooler and wetter conditions due to the long-term shortage of water the latter provenance had to cope with in the current environment, while the provenance from the cooler region was more sensitive to short-term changes in environmental conditions. Full article
(This article belongs to the Special Issue Analyses of Stress Impact on Forest Ecosystems Connected with Global Climate Changes)
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<p>Anomalies of monthly average air temperature values (lines, °C) (<b>a</b>) and precipitation totals (bars, mm) (<b>b</b>) in the period April–October (A–O) of the years 2017–2019 in comparison to long-term average values from the period 1961–1990.</p> Full article ">Figure 2
<p>Climatic water balance (CWB, mm) in individual months during the growing seasons (April–October (A–O)) of the years 2017–2019.</p> Full article ">Figure 3
<p>Daily climatological characteristics: P (mm)—precipitation, ATavg (°C)—average air temperature, VPD (kPa)—vapour pressure deficit) and soil water potential SWP (MPa) and seasonal daily course of cumulative band dendrometer records (BDR) of stem circumference over bark of the provenance from cooler and wetter conditions (CW_PV) (grey line) and the provenance from cooler conditions (C_PV) of <span class="html-italic">P. abies</span> and their growth lines (red thin lines) in the study period (April–October) of the individual years 2017–2019 (<b>a</b>–<b>c</b>). Each line represents an average from five trees of the same provenance.</p> Full article ">Figure 4
<p>Maximum daily shrinkage (MDS) and stem water deficit (ΔW) of the investigated provenances (the provenance from cooler and wetter conditions (CW_PV) and the provenance from cooler conditions (C_PV)) of <span class="html-italic">P. abies</span> in the studied periods (April–October) of the years 2017–2019 (<b>a</b>–<b>c</b>).</p> Full article ">Figure 5
<p>Cumulative stem water deficit (ΔWcum) and cumulative maximum daily shrinkage (MDScum) of the investigated provenances (the provenance from cooler and wetter conditions (CW_PV) and the provenance from cooler conditions (C_PV)) of <span class="html-italic">P. abies</span> in the studied periods (April–October) of the years 2017–2019 (<b>a</b>–<b>c</b>).</p> Full article ">Figure 6
<p>Spearman rank-correlation coefficients between environmental characteristics and stem water deficit (ΔW) of provenance from cooler and wetter conditions (CW_PV) and provenance from cooler conditions (C_PV) of <span class="html-italic">P. abies</span> in individual years and the whole studied period of the years 2017–2019: global radiation (GR), average air temperature (ATavg), relative air humidity (RH), minimum air temperature (ATmin), maximum air temperature (ATmax), precipitation (P), vapour pressure deficit (VPD), potential evapotranspiration (PET), climatic water balance (CWB), cumulative water balance (CWBcum), soil water potential (SWP). Significance levels: * 95% significance; ** 99% significance; *** 99.9% significance.</p> Full article ">Figure 7
<p>Spearman rank-correlation coefficients between environmental characteristics and maximum daily shrinkage (MDS) of provenance from cooler and wetter conditions labelled as CW_PV and provenance from cooler conditions labelled as C_PV of <span class="html-italic">P. abies</span> in individual years and the whole studied period of the years 2017–2019: global radiation (GR), average air temperature (ATavg), relative air humidity (RH), minimum air temperature (ATmin), maximum air temperature (ATmax), precipitation (P), vapour pressure deficit (VPD), potential evapotranspiration (PET), climatic water balance (CWB), cumulative water balance (CWBcum), soil water potential (SWP). Significance levels: * 95% significance; ** 99% significance; *** 99.9% significance.</p> Full article ">Figure 8
<p>Partial dependence plots of derived machine learning models (RF = random forest, GBM = gradient boosting machine, SVM = support-vector machine, NN = neural network) for the values of stem water deficit (ΔW) of the examined provenances (from cooler and wetter conditions (CW_PV, left) versus the one from cooler conditions (C_PV, right)) of <span class="html-italic">P. abies</span> and the day of the year (DOY) and selected environmental factors of all study periods of the years 2017–2019 together: soil water potential (SWP), cumulative water balance (CWBcum).</p> Full article ">Figure 9
<p>Means (over 10 permutations) of permutation-based variable-importance measures for the explanatory variables included in the derived machine learning models (RF = random forest, GBM = gradient boosting machine, SVM = support vector machine, NN = neural network) for stem water deficit (ΔW) of the provenance from cooler and wetter conditions (CW_PV) (<b>left</b>) and the provenance from cooler conditions (C_PV) (<b>right</b>). The abbreviations of variables: day of the study period (DOY), global radiation (GR), average air temperature (ATavg), minimum air temperature (ATmin), maximum air temperature (ATmax), relative air humidity (RH), precipitation (P), vapour pressure deficit (VPD), potential evapotranspiration (PET), soil water potential (SWP), climatic water balance (CWB), cumulative water balance (CWBcum).</p> Full article ">Figure 10
<p>Partial dependence plots of derived machine learning models (RF = random forest, GBM = gradient boosting machine, SVM = support-vector machine, NN = neural network) for the values of maximum daily shrinkage (MDS) of provenance from cooler and wetter conditions (CW_PV) and provenance from cooler conditions (C_PV) of <span class="html-italic">P. abies</span> and day of the study period (DOY) and selected environmental factors of all study period of the years 2017–2019 all together: global radiation (GR), average air temperature (ATavg), minimum air temperature (ATmin), maximum air temperature (ATmax), relative air humidity (RH), precipitation (P), vapour pressure deficit (VPD), potential evapotranspiration (PET), soil water potential (SWP), climatic water balance (CWB), cumulative water balance (CWBcum).</p> Full article ">Figure 11
<p>Means (over 10 permutations) of permutation-based variable-importance measures for the explanatory variables included in the derived machine learning models (RF = random forest, GBM = gradient boosting machine, SVM = support-vector machine, NN = neural network) for maximum daily shrinkage (MDS) of provenance from cooler and wetter conditions (CW_PV) (<b>left</b>) and provenance from cooler conditions (C_PV) (<b>right</b>). The abbreviations of variable: day of the study period (DOY), global radiation (GR), average air temperature (ATavg), minimum air temperature (ATmin), maximum air temperature (ATmax), relative air humidity (RH), precipitation (P), vapour pressure deficit (VPD), potential evapotranspiration (PET), soil water potential (SWP), climatic water balance (CWB), cumulative water balance (CWBcum).</p> Full article ">Figure A1
<p>Monthly circumference increment and relative monthly circumference increment.</p> Full article ">Figure A2
<p>Partial dependence plots of derived machine learning models (RF = random forest, GBM = gradient boosting machine, SVM = support-vector machine, NN = neural network) for the values of stem water deficit (ΔW) of provenance from cooler and wetter conditions (CW_PV) and provenance from cooler conditions (C_PV) of <span class="html-italic">P. abies</span> and selected environmental factors of all study period of the years 2017–2019 all together: global radiation (GR), average air temperature (ATavg), minimum air temperature (ATmin), maximum air temperature (ATmax), relative air humidity (RH), precipitation (P), vapour pressure deficit (VPD), potential evapotranspiration (PET), climatic water balance (CWB).</p> Full article ">
18 pages, 5377 KiB  
Article
Identification, Classification and Characterization of bZIP Transcription Factor Family Members in Pinus massoniana Lamb.
by Mengyang Zhang, Peihuang Zhu, Romaric Hippolyte Agassin, Sheng Yao, Dengbao Wang, Zichen Huang, Chi Zhang, Qingqing Hao and Kongshu Ji
Forests 2023, 14(1), 155; https://doi.org/10.3390/f14010155 - 14 Jan 2023
Cited by 4 | Viewed by 2374
Abstract
Basic leucine zipper (bZIP) transcription factors (TFs) are ubiquitous in eukaryotes. Members of this family play significant roles in the regulation of plant growth, signal transduction, and various stresses. To date, bZIP TFs have been extensively studied in various plants, but there is [...] Read more.
Basic leucine zipper (bZIP) transcription factors (TFs) are ubiquitous in eukaryotes. Members of this family play significant roles in the regulation of plant growth, signal transduction, and various stresses. To date, bZIP TFs have been extensively studied in various plants, but there is little information about them in Pinus massoniana Lamb. In this study, 55 bZIP TFs were identified based on data from four different P. massoniana transcriptomes, and a systematic analysis was performed. According to the phylogenetic results, P. massoniana bZIP TFs were divided into 11 groups. Each bZIP protein contained a highly conserved bZIP domain, and the numbers and types of motifs were similar in the same group. The PmbZIPs were nuclear localization proteins. Based on the pine wood nematode inoculation transcriptome, the transcriptional profiles revealed that 25 PmbZIP genes could respond to pine wood nematodes at different levels. Genes PmbZIP3, PmbZIP4, PmbZIP8, PmbZIP20, and PmbZIP23 were selected to be upregulated in the process of inoculation with pine wood nematodes. These five genes had different expression levels in different tissues and were responsive to the related treatment conditions. Transcriptional activity analysis showed that PmbZIP3 and PmbZIP8 were transcriptional activators; PmbZIP4, PmbZIP20 and PmbZIP23 were transcriptional repressors. These findings provide preliminary information on PmbZIP TFs, which is helpful for further study of other physiological functions of bZIP TFs in P. massoniana. Full article
(This article belongs to the Section Genetics and Molecular Biology)
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<p>Sequence alignments of conserved domains of all bZIP family proteins in <span class="html-italic">P. massoniana</span> Lamb.. The dark blue, light red, and light blue backgrounds indicate protein identities of 100%, 75%, and 50%, respectively. The basic region and leucine zipper region are marked with black line boxes. The asterisks indicate the conserved amino acids of bZIP domain.</p> Full article ">Figure 2
<p>Phylogenetic tree of the bZIP TF family of <span class="html-italic">P. massoniana</span> and <span class="html-italic">A. thaliana</span>. The different color branches and surrounding letters represent different groups. The green stars represent AtbZIPs, and the purple stars represent PmbZIPs.</p> Full article ">Figure 3
<p>Motif analysis of the bZIP TFs in <span class="html-italic">P. massoniana</span>. The motif structures were obtained by MEME analysis. Eight conserved motifs of PmbZIP proteins are shown, and the different colors represent different kinds of motifs.</p> Full article ">Figure 4
<p>Subcellular localization analysis of PmbZIP4 and PmbZIP20 protein. Transient expression of GFP (control), PmbZIP4-GFP and PmbZIP20-GFP in <span class="html-italic">N. benthamiana</span> leaves. The scale in the images of GFP and PmbZIP20-GFP is 50 μM, and the scale in the images of PmbZIP4-GFP is 20 μM.</p> Full article ">Figure 5
<p>Transcriptional profiles of <span class="html-italic">PmbZIP</span> family members after inoculation with pine wood nematodes in <span class="html-italic">P. massoniana</span> corresponding to five stages: 0 (CK), 3, 10, 20, and 35 d. A heatmap was generated by the log<sub>2</sub>(FPKM + 1) value, and the color scale represents the relative expression level.</p> Full article ">Figure 6
<p>The expression levels of <span class="html-italic">PmbZIPs</span> in different tissues in <span class="html-italic">P. massoniana</span>. YN: young needles; MN: mature needles; S: stems; R: roots. The relative expression level is indicated as the mean ± standard deviation (SD). Different letters show significant differences at the 0.05 level with the Duncan method.</p> Full article ">Figure 7
<p>The expression levels of <span class="html-italic">PmbZIPs</span> in different treatments. (<b>A</b>) SA; (<b>B</b>) MeJA; (<b>C</b>) ETH; (<b>D</b>) H<sub>2</sub>O<sub>2</sub>. The relative expression level is indicated as the mean ± standard deviation (SD). Different letters show significant differences at the 0.05 level with the Duncan method.</p> Full article ">Figure 7 Cont.
<p>The expression levels of <span class="html-italic">PmbZIPs</span> in different treatments. (<b>A</b>) SA; (<b>B</b>) MeJA; (<b>C</b>) ETH; (<b>D</b>) H<sub>2</sub>O<sub>2</sub>. The relative expression level is indicated as the mean ± standard deviation (SD). Different letters show significant differences at the 0.05 level with the Duncan method.</p> Full article ">Figure 8
<p>Transcriptional activity analysis of PmbZIP proteins. The growth state of the pGBKT7 (control), pGBKT7-PmbZIP3, pGBKT7-PmbZIP4, pGBKT7-PmbZIP8, pGBKT7-PmbZIP20 and pGBKT7-PmbZIP23 on SD/-Trp, SD/-Ade/-His/-Trp and SD/-Ade/-His/-Trp + X-α-gal medium.</p> Full article ">
10 pages, 1954 KiB  
Article
Early Diagnosis of Pine Wilt Disease in Pinus thunbergii Based on Chlorophyll Fluorescence Parameters
by Fei Liu, Maojiao Zhang, Jiafeng Hu, Min Pan, Luyang Shen, Jianren Ye and Jiajin Tan
Forests 2023, 14(1), 154; https://doi.org/10.3390/f14010154 - 14 Jan 2023
Cited by 6 | Viewed by 2913
Abstract
As the most severe forestry quarantine disease in several countries, pine wilt disease (PWD) causes substantial economic losses and poses a significant threat to the forest ecosystem. It is necessary to find a rapid and sensitive method for the early diagnosis of the [...] Read more.
As the most severe forestry quarantine disease in several countries, pine wilt disease (PWD) causes substantial economic losses and poses a significant threat to the forest ecosystem. It is necessary to find a rapid and sensitive method for the early diagnosis of the disease to control the development of the disease effectively. This paper investigated the effect of Bursaphelenchus xylophilus (the pinewood nematode; PWN) on the chlorophyll fluorescence kinetic curve (OJIP curve) and the parameters of needles using four-year-old Pinus thunbergii as experimental materials and chlorophyll fluorescence analysis as a technical tool. It was shown by the results in the OJIP curve that the fluorescence intensity of the inoculated plants was significantly increased at points O and I. Additionally, the relative variable fluorescence intensity at points K and J was comparable to that of the control plants. Several chlorophyll fluorescence parameters of the treatment significantly increased or decreased with disease progression. At the same time, the control group had no significant changes in each parameter. Therefore, chlorophyll fluorescence parameters can be used as indicators for the early diagnosis of PWD, among which the DIo/RC parameter was the best. In summary, PWN invasion will produce fluorescence on the PSII of P. thunbergii, and its chlorophyll fluorescence parameters are expected to achieve early PWD diagnosis. Full article
(This article belongs to the Section Forest Health)
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<p>Effect of PWN on rapid chlorophyll fluorescence induction kinetic curves of <span class="html-italic">P. thunbergii</span> needles. (<b>A</b>) The trend of OJIP curves. (<b>B</b>) Analysis of the differences between O, J, I, and P points. CK1, CK4, CK7, CK10, and CK13 indicate day 1, day 4, day 7, day 10, and day 13 after inoculation in the control group (CK), whereas TR1, TR4, TR7, TR10, and TR13 indicate day 1, day 4, day 7, day 10, and day 13 after inoculation in the treatment group (TR). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and NS: not significant using the Duncan’s multiple range test.</p> Full article ">Figure 2
<p>Analysis of O-P and O-J curves to standardize <span class="html-italic">P. thunbergii</span> needles under PWN stress. (<b>A</b>) Trends of O-P normalized fluorescence curves; (<b>B</b>) Trends of O-J normalized fluorescence curves. (<b>C</b>) Trends of O-P standardized difference fluorescence curves; (<b>D</b>) Trends of O-J standardized difference fluorescence curves. The numbers 1, 4, 7, 10, and 13 indicate days 1, 4, 7, 10, and 13 after inoculation.</p> Full article ">Figure 3
<p>Analysis of chlorophyll fluorescence parameters of <span class="html-italic">P. thunbergii</span> needles under PWN stress. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 using Duncan’s multiple range test.</p> Full article ">
20 pages, 5445 KiB  
Article
Future Carbon Sequestration and Timber Yields from Chinese Commercial Forests under Shared Socioeconomic Pathways
by Fei Liu, Mingxing Hu, Wenbo Huang, Cindy X. Chen and Jinhui Li
Forests 2023, 14(1), 153; https://doi.org/10.3390/f14010153 - 13 Jan 2023
Cited by 3 | Viewed by 2297
Abstract
Socio-economic status, technologies, and policies are key factors affecting forest management planning and forest ecosystem functions. This study applied shared socioeconomic pathways (SSPs) to a forest-management model framework. The potential timber yields and carbon sinks of spatially allocate alternatives were examined by quantifying [...] Read more.
Socio-economic status, technologies, and policies are key factors affecting forest management planning and forest ecosystem functions. This study applied shared socioeconomic pathways (SSPs) to a forest-management model framework. The potential timber yields and carbon sinks of spatially allocate alternatives were examined by quantifying their consequent changes at the regional tree species level in Chinese commercial forests (CFs) under the harvest and afforestation restrictions. The results indicate that the annual carbon sequestration rate of China’s CFs over the next 50 years is estimated to be 152.0–162.5 Tg/a, which can offset approximately 5% of the anthropogenic CO2 emissions identified in 2019. Newly planted and regenerated forests can contribute more than 80% of this offset. The annual timber supply capacity is estimated to be 119.2–142.4 million m3/a with current policy interventions, which is not enough to meet the demand for China’s timber market. Although most existing forests are managed as the primary source for forest goods and carbon service, the total commercial forest area changes are not as large as expected, resulting in only 2.0–10.6% differences. Our results also demonstrate that socioeconomic factors (e.g., social preference, carbon price, and forest logging and silvicultural practices) have a strong impact on carbon sinks but a minor impact on timber yields timber, except for improving harvesting and processing technologies. Establishing local long-term effective forest management systems and making afforestation and regeneration as a priority at the national level are suggested to comprehensively enhance the carbon sequestration and timber-supplying abilities of regional CFs. Full article
(This article belongs to the Section Forest Economics, Policy, and Social Science)
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<p>Research framework.</p> Full article ">Figure 2
<p>Region distribution of the projected forest total timber yields and carbon sequestration in China, by SSPs.</p> Full article ">Figure 3
<p>Tree species of projected timber yields (<b>a</b>) and carbon sequestration (<b>b</b>) by SSPs in ten five-year periods. An explanation of each tree name abbreviation is shown in <a href="#forests-14-00153-t0A4" class="html-table">Table A4</a>.</p> Full article ">Figure 4
<p>Relationship between timber yields and carbon sequestration by regions (<b>a</b>) and by tree species (<b>b</b>) under all SSPs.</p> Full article ">Figure 5
<p>Key model estimates for weight parameter sensitivity in all SSPs. Forest area, total carbon sequestration, total timber yield, and total profits are selected in this study and listed above. The bars in pink, green, and blue colors represent the changes in percentage between weight sensitivity analysis and the SSP2. C75/T25, C25/T75, C0/T100 represent that the carbon weights are 75%, 25%, and 0%, while the timber weights are 25%, 75%, and 100%, respectively.</p> Full article ">Figure 6
<p>Key model estimates for other socioeconomic parameter sensitivity in all SSPs. The bars in green and orange colors represent the changes in percentage between sensitivity analysis result and the SSP2 result.</p> Full article ">Figure 7
<p>Commercial forest area changes across SSPs, from 2018 to 2068.</p> Full article ">Figure A1
<p>Regional projected timber production by SSPs in ten five-year rotation periods.</p> Full article ">Figure A2
<p>Regional projected carbon sequestration by SSPs in ten five-year rotation periods.</p> Full article ">
11 pages, 3012 KiB  
Article
COVID-19 and the Mystery of Lumber Price Movements
by Rebecca Zanello, Yin Shi, Atefeh Zeinolebadi and G. Cornelis van Kooten
Forests 2023, 14(1), 152; https://doi.org/10.3390/f14010152 - 13 Jan 2023
Cited by 4 | Viewed by 2719
Abstract
The COVID-19 pandemic led to unprecedented changes in the U.S. price of softwood lumber by more than 300% between 2020 and 2022. The increased volatility of lumber prices after the COVID-19 outbreak remains unexplained. In this paper, we examine how a calibrated random [...] Read more.
The COVID-19 pandemic led to unprecedented changes in the U.S. price of softwood lumber by more than 300% between 2020 and 2022. The increased volatility of lumber prices after the COVID-19 outbreak remains unexplained. In this paper, we examine how a calibrated random walk can induce similar price volatility through the development of a stochastic process. As a preferred approach, we employ an event model to estimate the impact of COVID-19 and other key events on the price of softwood lumber. The econometric model serves to provide evidence that the price volatility of softwood lumber is not completely random, and we can instead attribute part of the variation to recent regional and global events. We found that, while COVID-19 did result in a price jump, it was smaller than a rainfall event that restricted imports from Canada, while import duties and other trade actions had no discernible impact on U.S. lumber prices. Full article
(This article belongs to the Special Issue Forest Product Markets, Sustainability, and Societal Impacts)
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<p>Monthly prices of lumber sold in the U.S. and the composite framing price index in USD, and U.S. housing starts, January 1990 through July 2022. Source: Authors’ calculations using data from [<a href="#B4-forests-14-00152" class="html-bibr">4</a>,<a href="#B5-forests-14-00152" class="html-bibr">5</a>,<a href="#B6-forests-14-00152" class="html-bibr">6</a>].</p> Full article ">Figure 2
<p>Historical antidumping and countervailing duties on Canadian softwood lumber by the United States. Source: Authors’ calculations using data from [<a href="#B8-forests-14-00152" class="html-bibr">8</a>,<a href="#B9-forests-14-00152" class="html-bibr">9</a>].</p> Full article ">Figure 3
<p>Potential paths of composite and U.S. lumber prices based on Ornstein–Uhlenbeck Stochastic Mean-Reversion processes with means indicated by straight lines.</p> Full article ">Figure 4
<p>Composite framing price index in CAD January 1980 through July 2022 Source: Statista (2022) and Random Lengths (various issues).</p> Full article ">
16 pages, 5881 KiB  
Article
Effects of Extreme Drought and Heat Events on Leaf Metabolome of Black Alder (Alnus glutinosa L.) Growing at Neighboring Sites with Different Water Availability
by Lijun Zhu, Zhengqiao Liao, Lei Liu and Baoguo Du
Forests 2023, 14(1), 151; https://doi.org/10.3390/f14010151 - 13 Jan 2023
Cited by 2 | Viewed by 2721
Abstract
Riparian tree species are thought to be sensitive to the more frequent and intensive drought and heat events that are projected to occur in the future. However, compared to waterlogging, information about the responses of these tree species to water limitation and heat [...] Read more.
Riparian tree species are thought to be sensitive to the more frequent and intensive drought and heat events that are projected to occur in the future. However, compared to waterlogging, information about the responses of these tree species to water limitation and heat is still scare. Black alder (Alnus glutinosa L.) is a riparian tree species with significant ecological and economic importance in Europe. In the present study, we investigated the physiological responses of black alder (Alnus glutinosa L.) to different water availabilities growing at neighboring sites. Compared to trees with unlimited water source, trees with a limited water source had 20% lower leaf hydration, 39% less H2O2 contents, and 34% lower dehydroascorbate reductase activities. Concurrent with dramatically accumulated glutathione and phenolic compounds, leaf glutathione contents were two times higher in trees with limited water than in trees with sufficient water. Limited water availability also resulted in increased abundances of sugars, sugar acids, and polyols. Serine, alanine, as well as soluble protein related to nitrogen metabolism were also accumulated under limited water conditions. In contrast to sulfate, leaf phosphate contents were significantly increased under limited water. No significant effects of water conditions on malondialdehyde and ascorbate contents and fatty acid abundances were observed. The present study improves our understanding of the physiological responses of black alder to different water conditions. Our findings highlight this riparian species is at least to some extent resistant to future drought with a well-regulated system including antioxidative and metabolic processes and its potential as an admixture candidate for afforestation in either water-logged or dry areas, particularly in nitrogen limited habitats. Full article
(This article belongs to the Topic Plant Ecophysiology)
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<p>Map of sampling trees at the lake. Arrows and circles indicate the sample trees with sufficient and limited water resource, respectively.</p> Full article ">Figure 2
<p>Monthly air temperature (<b>a</b>) and precipitation (<b>b</b>) in 2018 (in line) and the average from 1998 to 2017 (in bar plot, mean ± standard deviation). Data from the Deutscher Wetterdienst.</p> Full article ">Figure 3
<p>Leaf hydration (<b>a</b>), hydrogen peroxide (<b>b</b>), and malondaldehy contents (<b>c</b>) of <span class="html-italic">Alnus glutinosa</span> grown under sufficient (+ water, blank bars) and limited water conditions (− water, grey bars). Data shown represent mean ± standard deviation (<span class="html-italic">n</span> = 5) on a dry weight basis. Asterisks indicate significant differences between trees with sufficient and limited water supply at <span class="html-italic">p</span> &lt; 0.05 (*) and 0.01 (**) respectively.</p> Full article ">Figure 4
<p>Leaf total sugar (<b>a</b>), soluble protein (<b>b</b>), sulfate (<b>c</b>) and phosphate contents (<b>d</b>) in leaves of <span class="html-italic">Alnus glutinosa</span> grown under sufficient (+ Water, blank bars) and limited water conditions (− Water, grey bars). Data shown represent mean ± standard deviation (<span class="html-italic">n</span> = 5) on a dry weight basis. Asterisks indicate significant differences between trees with sufficient and limited water supply at <span class="html-italic">p</span> &lt; 0.05 (*) and 0.01 (**) respectively.</p> Full article ">Figure 5
<p>Contents of total (<b>a</b>), reduced ascorbate (<b>b</b>), dehydroascorbate (<b>c</b>), and ratio between reduced and dehydroascorbate (<b>d</b>) in leaves of <span class="html-italic">Alnus glutinosa</span> grown under sufficient (+ water, blank bars) and limited water conditions (− water, grey bars). Data shown represent mean ± standard deviation (<span class="html-italic">n</span> = 5) on a dry weight basis. No significant differences (<span class="html-italic">p</span> &lt; 0.05) between trees with sufficient and limited water supply were found in all parameters.</p> Full article ">Figure 6
<p>Thiols of cysteine (<b>a</b>), γ-glutamylcysteine (<b>b</b>), total (<b>c</b>), and oxidized glutathione (<b>d</b>) contents in leaves of <span class="html-italic">Alnus glutinosa</span> grown under sufficient (+ water, blank bars) and limited water conditions (− water, grey bars). Data shown represent mean ± standard deviation (<span class="html-italic">n</span> = 5) on a dry weight basis. Asterisks indicate significant differences between trees with sufficient and limited water supply at <span class="html-italic">p</span> &lt; 0.05 (*) and 0.01 (**) respectively.</p> Full article ">Figure 7
<p>Activities of glutathione reductase (GR) (<b>a</b>) and dehydroascorbate reductase (DHAR) (<b>b</b>) in leaves of <span class="html-italic">Alnus glutinosa</span> grown under sufficient (+ water, blank bars) and limited water conditions (− water, grey bars). Data shown represent mean ± standard deviation (<span class="html-italic">n</span> = 5) on a dry weight basis. Asterisk (*) indicates significant differences between trees with sufficient and limited water supply at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 8
<p>Changes (log<sub>2</sub> limited water/sufficient water) of low molecular weight metabolites in leaves of <span class="html-italic">Alnus glutinosa</span> grown under sufficient (+ water) and limited (− water) water conditions. *, **, and *** indicate significant differences between trees with sufficient and limited water supply at <span class="html-italic">p</span> &lt; 0.05, 0.01, and 0.001, respectively. R002953 and D155405 are codes of N-methyl trans-4-hydroxy-L-proline (2S,4R)-4-hydroxy-1-methyl pyrrolidine-2-carboxylic acid and an unknown metabolite, respectively, in Golm library.</p> Full article ">Figure 9
<p>Clustering (<b>a</b>) of <span class="html-italic">Alnus glutinosa</span> grown under sufficient (+ water, triangle) and limited (− water, circle) water conditions. PLS-DA analysis was performed based on 46 with significant differences. Semi-transparent shadings indicate 95% confidence regions. (<b>b</b>) The most important 15 parameters according to VIP (Variable Importance for Projection) scores generated from PLS-DA analysis.</p> Full article ">
11 pages, 1528 KiB  
Article
Identification of Alnus incana (L.) Moenx. × Alnus glutinosa (L.) Gaertn. Hybrids Using Metabolic Compounds as Chemotaxonomic Markers
by Girmantė Jurkšienė, Vaida Sirgedaitė-Šėžienė, Aušra Juškauskaitė and Virgilijus Baliuckas
Forests 2023, 14(1), 150; https://doi.org/10.3390/f14010150 - 13 Jan 2023
Cited by 2 | Viewed by 2951
Abstract
Alnus glutinosa (L.) Gaertn. and Alnus incana (L.) Moenx. grow naturally in Lithuania, and their ranges overlap. They are considered ecologically and economically important species of forest trees. The objective of our study was to determine plant bioactive compounds, such as total phenolic [...] Read more.
Alnus glutinosa (L.) Gaertn. and Alnus incana (L.) Moenx. grow naturally in Lithuania, and their ranges overlap. They are considered ecologically and economically important species of forest trees. The objective of our study was to determine plant bioactive compounds, such as total phenolic (TPC) and flavonoid compounds (TFC), in the wood of alders and their hybrids in order to specify the opportunity to use secondary metabolites (SM) for the identification of alder hybrids. The samples from juvenile and mature alder woods (n = 270) were collected at three different sites in the natural forests of Lithuania. The TPC and TFC content was determined using spectrophotometric methods and was expressed in mg/g of fresh mass. Obtained results showed that the TPC of A. incana was statistically higher compared to A. glutinosa; however, in hybrid alder wood it was intermediate. The TFC was statistically higher in hybrid alder wood and lowest in A. glutinosa. The TFC was higher in mature trees; however, the TPC showed the opposite tendency. In our case, the TPC was higher in continental sites, while TFC was higher in mature alders at costal sites. Obtained data allow us to assume that TPC and TFC in alder wood can be used as taxonomic markers. This study expanded the knowledge of alder physiology and contributed to the identification of alder hybrids. The correct identification of tree species is very important for the conservation of natural resources and for the sustainable use of higher value-added products. Full article
(This article belongs to the Special Issue Studies in Tree Species Identification)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map showing the sites in Lithuania, where wood samples were collected. Location: 1. Biržai Regional Division (RD), 2. Kretinga RD, 3. Raseiniai RD.</p> Full article ">Figure 2
<p>Total amount of TPC in different wood samples in different Lithuania forests RD. Number of sites see <a href="#forests-14-00150-t001" class="html-table">Table 1</a>, abbreviations of species see <a href="#forests-14-00150-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 3
<p>Total amount of TFC in different wood samples in different Lithuanian forests RD. For number of sites see <a href="#forests-14-00150-t001" class="html-table">Table 1</a>; for abbreviations of species see <a href="#forests-14-00150-t002" class="html-table">Table 2</a>.</p> Full article ">
23 pages, 8792 KiB  
Article
Predicting Mangrove Distributions in the Beibu Gulf, Guangxi, China, Using the MaxEnt Model: Determining Tree Species Selection
by Lifeng Li, Wenai Liu, Jingwen Ai, Shuangjiao Cai and Jianwen Dong
Forests 2023, 14(1), 149; https://doi.org/10.3390/f14010149 - 13 Jan 2023
Cited by 10 | Viewed by 3269
Abstract
Mangrove restoration is challenging within protected coastal habitats. Predicting the dominant species distributions in mangrove communities is essential for appropriate species selection and spatial planning for restoration. We explored the spatial distributions of six mangrove species, including their related environmental factors, thereby identifying [...] Read more.
Mangrove restoration is challenging within protected coastal habitats. Predicting the dominant species distributions in mangrove communities is essential for appropriate species selection and spatial planning for restoration. We explored the spatial distributions of six mangrove species, including their related environmental factors, thereby identifying potentially suitable habitats for mangrove protection and restoration. Based on six dominant mangrove species present in the Beibu Gulf, Guangxi, China, we used a linear correlation analysis to screen environmental factors. In addition, we used the maximum entropy model to analyze the spatial distributions of potential mangrove afforestation areas. Based on the spatial superposition analysis, we identified mangrove conservation and restoration hot spots. The findings indicate that topographic and bioclimatic factors affect the distribution of suitable mangrove habitats in the Beibu Gulf, followed by land use type, salinity, and substrate type. We identified 13,816 hm2 of prime mangrove habitat in the Beibu Gulf that is primarily distributed in protected areas. The protection rate for existing mangroves was 42.62%. According to the predicted spatial distributions of the mangrove plants, the findings suggest that mangrove restoration should be based on suitable species and site selection. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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<p>The study area and sampling locations in Guangxi.</p> Full article ">Figure 2
<p>The analytical framework used to predict potential mangrove species distributions for restoration purposes.</p> Full article ">Figure 3
<p>Environment variable. (<b>a</b>) Bio2, (<b>b</b>) Bio3, (<b>c</b>) Bio5, (<b>d</b>) Bio6, (<b>e</b>) Bio10, (<b>f</b>) Bio15, (<b>g</b>) Bio18, (<b>h</b>) Bio19.</p> Full article ">Figure 4
<p>Environment variables. (<b>a</b>) Elevation, (<b>b</b>) WTI, (<b>c</b>) C_sss, (<b>d</b>) W_sss, (<b>e</b>) C_sst, (<b>f</b>) W_sst. (<b>g</b>) Land-use, (<b>h</b>) Substrate.</p> Full article ">Figure 5
<p>The receiver operating characteristic (ROC) curves of the mangrove species used to verify the MaxEnt model. (<b>a</b>) <span class="html-italic">A. marina</span> (<b>b</b>) <span class="html-italic">A. corniculatum</span> (<b>c</b>) <span class="html-italic">K. obovata</span> (<b>d</b>) <span class="html-italic">B. gymnorrhiza</span> (<b>e</b>) <span class="html-italic">R. stylosa</span> (<b>f</b>) <span class="html-italic">A. ilicifolius</span>.</p> Full article ">Figure 6
<p>The ROC curve of combined mangrove species used to verify the MaxEnt model.</p> Full article ">Figure 7
<p>The regularized training gains of the combined mangrove distribution. Dark blue entries represent independent test results of each variable, light green entries represent test results excluding the variable, and red entries represent test results including all variables [<a href="#B40-forests-14-00149" class="html-bibr">40</a>]. The length of each entry represents the size of its score (i.e., longer entries indicate more important variables).</p> Full article ">Figure 8
<p>The regularized training gains for the six mangrove species: (<b>a</b>) <span class="html-italic">A. marina</span>, (<b>b</b>) <span class="html-italic">A. corniculatum</span>, (<b>c</b>) <span class="html-italic">K. obovata</span>, (<b>d</b>) <span class="html-italic">B. gymnorrhiza</span>, (<b>e</b>) <span class="html-italic">R. stylosa</span>, and (<b>f</b>) <span class="html-italic">A. ilicifolius</span> (entry definitions are the same as in <a href="#forests-14-00149-f005" class="html-fig">Figure 5</a>).</p> Full article ">Figure 9
<p>The contributions of the six environmental variables to predicting the mangrove species distributions (<span class="html-italic">A. marina</span>: AM, <span class="html-italic">A. corniculatum</span>: AC, <span class="html-italic">K. obovata</span>: KO, <span class="html-italic">B. gymnorrhiza</span>: BG, <span class="html-italic">R. stylosa</span>: RS, and <span class="html-italic">A. ilicifolius</span>: AI).</p> Full article ">Figure 10
<p>The response curves of environmental variables for (<b>a</b>) <span class="html-italic">A. marina</span>, (<b>b</b>) <span class="html-italic">A. corniculatum</span>, (<b>c</b>) <span class="html-italic">K. obovata</span>, (<b>d</b>) <span class="html-italic">B. gymnorrhiza</span>, (<b>e</b>) <span class="html-italic">R. stylosa</span>, and (<b>f</b>) <span class="html-italic">A. ilicifolius</span> (entry definitions are the same as in <a href="#forests-14-00149-f005" class="html-fig">Figure 5</a>).</p> Full article ">Figure 11
<p>Mangrove elevation thresholds for <span class="html-italic">A. marina</span> (AM), <span class="html-italic">A. corniculatum</span> (AC), <span class="html-italic">K. obovata</span> (KO), <span class="html-italic">B. gymnorrhiza</span> (BG), <span class="html-italic">R. stylosa</span> (RS), and <span class="html-italic">A. ilicifolius</span> (AI).</p> Full article ">Figure 12
<p>Salinity preferences of the different mangrove species in the coldest and warmest seasons. Mean sea surface salinity in the (<b>a</b>) coldest and (<b>b</b>) warmest seasons.</p> Full article ">Figure 13
<p>Distributions of suitable mangrove habitats in the Beibu Gulf. (<b>a</b>) <span class="html-italic">A. marina</span>, (<b>b</b>) <span class="html-italic">A. corniculatum</span>, (<b>c</b>) <span class="html-italic">K. obovata</span>, (<b>d</b>) <span class="html-italic">B. gymnorrhiza</span>, (<b>e</b>) <span class="html-italic">R. stylosa</span>, and (<b>f</b>) <span class="html-italic">A. ilicifolius</span> (<b>g</b>) Overall mangrove community.</p> Full article ">Figure 14
<p>The map of the mangrove distribution and restoration hotspots in the Beibu Gulf.</p> Full article ">
12 pages, 1210 KiB  
Article
Economic and Environmental Analysis of Woody Biomass Power Generation Using Forest Residues and Demolition Debris in Japan without Assuming Carbon Neutrality
by Masaya Fujino and Masaya Hashimoto
Forests 2023, 14(1), 148; https://doi.org/10.3390/f14010148 - 12 Jan 2023
Cited by 2 | Viewed by 2408
Abstract
Despite the increasing importance of renewable energy worldwide, the argument that forest biomass power generation is not carbon neutral has been rising. This research used Gifu Biomass Power Co., Ltd. (GBP) in Japan as a case study to investigate this matter. An evaluation [...] Read more.
Despite the increasing importance of renewable energy worldwide, the argument that forest biomass power generation is not carbon neutral has been rising. This research used Gifu Biomass Power Co., Ltd. (GBP) in Japan as a case study to investigate this matter. An evaluation was conducted through an input–output analysis on the economic and environmental benefits (i.e., CO2 reduction) of forest biomass power generation without assuming carbon neutrality. GBP’s economic benefits were estimated to be 3452.18 million JPY during the construction period and 114.38 million JPY per year from operations. It was also estimated to generate 21.77 jobs per year in the forestry sector. CO2 emissions were estimated to increase by 423.02 tons during the construction period and 137,747 tons per year from operations. Although forests may offset CO2 by absorbing it, woody biomass power generation does not necessarily reduce CO2 emissions in Gifu Prefecture. The results indicate that woody biomass power generation is effective for the local economy but not necessarily for the global environment. The analysis should include more industrial sectors to clarify the environmental significance of wood biomass power generation without assuming carbon neutrality. Full article
(This article belongs to the Special Issue Economic and Societal Losses Due to Environmental Impacts on Forestry Productivity)
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<p>Map of Gifu Biomass Power Co., Ltd.; Gifu Prefecture, where Gifu Biomass Power Co., Ltd. Is located, is in the center of Japan.</p> Full article ">Figure 2
<p>The flow of materials. Gifu Biomass Power Co., Ltd. (GBP) purchases 86,000 tons of wood chips annually from wood chip companies, including Biomass Energy Tokai Co., Ltd. (BET).</p> Full article ">
13 pages, 3938 KiB  
Article
Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan
by Tadamichi Sato and Yasuhiro Shuin
Forests 2023, 14(1), 147; https://doi.org/10.3390/f14010147 - 12 Jan 2023
Cited by 3 | Viewed by 2567
Abstract
Extreme rainfall causes floods and landslides, and so damages humans and socioeconomics; for instance, floods and landslides have been triggered by repeated torrential precipitation and have caused severe damage in the Kyushu region, Japan. Therefore, evaluating extreme rainfall in Kyushu is necessary to [...] Read more.
Extreme rainfall causes floods and landslides, and so damages humans and socioeconomics; for instance, floods and landslides have been triggered by repeated torrential precipitation and have caused severe damage in the Kyushu region, Japan. Therefore, evaluating extreme rainfall in Kyushu is necessary to provide basic information for measures of rainfall-induced disasters. In this study, we estimated the probability of daily rainfall in Kyushu. The annual maximum values for daily rainfall at 23 long-record stations were normalized using return values at each station, corresponding to 2 and 10 years, and were combined by the station-year method. Additionally, the return period (RP) was calculated by fitting them to the generalized extreme value distribution. Based on the relationship between the normalized values of annual maximum daily rainfall and the RP, we obtained a regression equation to accurately estimate the RP up to 300 years by using data at given stations, considering outliers. In addition, we verified this equation using data from short-record stations where extreme rainfall events triggering floods and landslides were observed, and thereby elucidated that our method was consistent with previous techniques. Thus, this study develops strategies of measures for floods and landslides. Full article
(This article belongs to the Special Issue Economic and Societal Losses Due to Environmental Impacts on Forestry Productivity)
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<p>Location of rainfall stations. White circles and triangles indicate meteorological observatories and AMeDAS, respectively.</p> Full article ">Figure 2
<p>Kendall’s τ of annual maximum daily rainfall between stations plotted against distance.</p> Full article ">Figure 3
<p>Spatial distribution of the two-year value.</p> Full article ">Figure 4
<p>Spatial distribution of the 10-year value.</p> Full article ">Figure 5
<p>Boxplots for normalized values of annual maximum daily rainfall at 23 stations. Whiskers of the box show 25th (lower) and 75th (upper) percentile values. The gray line is the median value.</p> Full article ">Figure 6
<p>Quantile–quantile plot.</p> Full article ">Figure 7
<p>Relationship between normalized values of annual maximum daily rainfall and the RP.</p> Full article ">Figure 8
<p>Comparison of RP estimated by the proposed regression equation, RP estimated by the GEV, and RP by the empirical method (Cunnane [<a href="#B39-forests-14-00147" class="html-bibr">39</a>]) at AMeDAS Izumi (<b>a</b>), Morotsuka (<b>b</b>), Aso-Otohime (<b>c</b>), and Asakura (<b>d</b>).</p> Full article ">Figure 9
<p>Annual maximum daily rainfall at the AMeDAS Asakura between 1976 and 2020. Gray circles indicate annual maximum value. Bold line indicates Sen’s slope (Hipel and McLeod 1996 [<a href="#B47-forests-14-00147" class="html-bibr">47</a>]; Sen 1968 [<a href="#B48-forests-14-00147" class="html-bibr">48</a>]) (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
20 pages, 4222 KiB  
Article
Bamboo Scrimber’s Physical and Mechanical Properties in Comparison to Four Structural Timber Species
by Sarah Putri Sylvayanti, Naresworo Nugroho and Effendi Tri Bahtiar
Forests 2023, 14(1), 146; https://doi.org/10.3390/f14010146 - 12 Jan 2023
Cited by 13 | Viewed by 4465
Abstract
Bamboo scrimber is a sustainable engineered material that overcomes natural round bamboo’s various weaknesses. This study compared the bamboo scrimber’s mechanical (strength, stiffness, and ductility) to timber. The results showed that scrimber’s physical and mechanical properties are comparable, even superior, to wood, especially [...] Read more.
Bamboo scrimber is a sustainable engineered material that overcomes natural round bamboo’s various weaknesses. This study compared the bamboo scrimber’s mechanical (strength, stiffness, and ductility) to timber. The results showed that scrimber’s physical and mechanical properties are comparable, even superior, to wood, especially in compression. Scrimber has a higher density than timber. Its drier equilibrium moisture content indicates that scrimber is more hydrophobic than timbers. The maximum crushing strength (σc//), compressive stress perpendicular-to-fiber at the proportional limit (σcp⊥) and that at the 0.04” deformation (σc0.04⊥), shear strength (τ//), longitudinal compressive modulus of elasticity (Ec//), lateral compressive modulus of elasticity (Ec⊥), and modulus of rigidity (G) of scrimber are higher than those of timbers. Both scrimber’s and timber’s flexural properties (modulus of rupture (σb) and flexural modulus of elasticity (Eb)) are comparable. On the contrary, the tensile strength parallel-to-fiber (σt//) of scrimber is weaker than that of timber. Scrimber is high ductility (μ > 6) when subjected to compression perpendicular-to-fiber, medium ductility (4 < μ ≤ 6) when subjected to compression parallel-to-fiber, and low ductility (brittle) when subjected to bending, shear, or tensile parallel-to-fiber. The higher ductility of scrimber may give an alarm and more time before failure than timbers. Timbers have brittle to lower ductility when receiving each kind of loading scheme. The ratio of shear modulus to strength (G/τ) and compression modulus to strength parallel-to-fiber (EC∥/σC∥) strongly correlates with the ductility ratio. However, the ratio of the flexural modulus of elasticity to the modulus of rupture (Eb/σb) and the ratio of the modulus Young to compression stress perpendicular-to-fiber (Ec⊥/σcp⊥) do not strongly correlate to the ductility value. Full article
(This article belongs to the Section Wood Science and Forest Products)
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<p>Timber and scrimber specimens for tensile test parallel-to-fiber (<b>a</b>), timber (<b>b1</b>) and scrimber (<b>b2</b>) specimens for compressive test parallel-to-fiber, timber (<b>c1</b>) and scrimber (<b>c2</b>) specimens for compressive test perpendicular to fiber, timber (<b>d1</b>) and scrimber (<b>d2</b>) specimens for shear test, timber and scrimber specimens for bending test (<b>e</b>). (Note: the size unit is cm).</p> Full article ">Figure 2
<p>Methods used to obtain yield point: (<b>a</b>) Karacabeyli and Cecceotti; (<b>b</b>) CSIRO; (<b>c</b>) CEN; (<b>d</b>) Yasumura and Kawai; and (<b>e</b>) Equivalent Energy Elastic-Plastic Curve (EEEP).</p> Full article ">Figure 3
<p>Air-dried moisture content of all specimens.</p> Full article ">Figure 4
<p>Density (<span class="html-italic">ρ</span>) and specific gravity (<span class="html-italic">G<sub>b</sub></span>) of all specimens.</p> Full article ">Figure 5
<p>Tension parallel-to-fiber (<b>a</b>) results and (<b>b</b>) specimen failures in scrimber.</p> Full article ">Figure 6
<p>Compression parallel-to-fiber (<b>a</b>) results and (<b>b</b>) specimen failures in scrimber.</p> Full article ">Figure 7
<p>Compression perpendicular-to-fiber (<b>a</b>) results and (<b>b</b>) specimen failures in scrimber.</p> Full article ">Figure 8
<p>Shear parallel-to-fiber (<b>a</b>) results and (<b>b</b>) specimen failures in bamboo scrimber.</p> Full article ">Figure 9
<p>Flexural test (<b>a</b>) results and (<b>b</b>) specimen failures in bamboo scrimber.</p> Full article ">Figure 10
<p>Examples the failure of K<sub>10–40</sub> line that located offsite the load-displacement curve in (<b>a</b>) tension, (<b>b</b>) compression parallel-to-fiber, and (<b>c</b>) shear tests.</p> Full article ">Figure 11
<p>Position of yield points on the load-deformation curve of (<b>a</b>) tension parallel-to-fiber, (<b>b</b>) compression parallel-to-fiber, (<b>c</b>) compression perpendicular-to-fiber, (<b>d</b>) shear, and (<b>e</b>) flexural in bamboo scrimber.</p> Full article ">Figure 12
<p>Load-displacement curve of mechanical properties tested in (<b>a</b>) bamboo scrimber, (<b>b</b>) agathis, (<b>c</b>) mahogany, (<b>d</b>) red meranti, and (<b>e</b>) pine.</p> Full article ">Figure 13
<p>Linear regression between (<b>a</b>) <span class="html-italic">E<sub>b</sub>/σ<sub>b</sub></span>, (<b>b</b>) <span class="html-italic">G/τ<sub>//</sub></span>, (<b>c</b>) <span class="html-italic">E<sub>c</sub></span><sub>⊥</sub><span class="html-italic">/σ<sub>cp</sub></span><sub>⊥</sub>, (<b>d</b>) <span class="html-italic">E<sub>c</sub></span><sub>⊥</sub><span class="html-italic">/σ<sub>c</sub></span><sub>0.04⊥</sub>, (<b>e</b>) <span class="html-italic">E<sub>c</sub><sub>∥</sub> /σ<sub>C∥</sub></span> vs. ductility ratio.</p> Full article ">
12 pages, 3204 KiB  
Article
Spatial Prioritization of Ecosystem Services for Land Conservation: The Case Study of Central Italy
by Alessandro Sebastiani and Silvano Fares
Forests 2023, 14(1), 145; https://doi.org/10.3390/f14010145 - 12 Jan 2023
Cited by 12 | Viewed by 3662
Abstract
Ecosystem services delivered by natural ecosystems are increasingly important for climate change adaptation and mitigation and play a huge role in biodiversity conservation. For this reason, the EU has the ambitious goal of protecting at least 30% of land by 2030. Member states [...] Read more.
Ecosystem services delivered by natural ecosystems are increasingly important for climate change adaptation and mitigation and play a huge role in biodiversity conservation. For this reason, the EU has the ambitious goal of protecting at least 30% of land by 2030. Member states are called to improve and expand the network of protected areas within the next few years; to do so, scientific studies aimed at identifying areas with high ecological value, as well as at defining best management practices, are highly needed. In this study, we used the InVEST suite of models to spatially assess three regulating ecosystem services, that is, carbon storage, seasonal water yield, and urban flood risk mitigation in three administrative regions of central Italy. Using overlay analysis, we found areas with the highest delivery in each of the considered ESs; based on these findings, we eventually proposed four new protected areas, which combine for 888 km2, that is, 2.73% of the study area. Interestingly, each of the newly proposed protected areas has somehow been discussed and hypothesized by stakeholders, but only one is presumably going to be part of the national network of protected areas within the next years. Hopefully, by prioritizing areas according to the production of ecosystem services, this study can be intended as a step towards the systematic inclusion of ecosystem services studies for enhancing the network of areas under national protection schemes and achieving the goal of protecting at least 30% of land in Europe by 2030. Full article
(This article belongs to the Special Issue Nature-Based Solutions for Climate and Environmental Change)
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<p>Land use and land cover map of the study area (<b>a</b>); the administrative regions’ borders are marked in black, and EUAP areas are dotted. The location of the study area (<b>b</b>) and the digital elevation model (<b>c</b>) are also shown.</p> Full article ">Figure 2
<p>Ecosystem services’ delivery maps and high delivery areas (HDAs) for runoff retention (<b>a</b>), local recharge (<b>b</b>), baseflow (<b>c</b>), and carbon stock (<b>d</b>).</p> Full article ">Figure 3
<p>EUAP network (dotted), proposed protected areas (PPAs; the four identified patches are indicated by the letters A,B,C and D), and the ecosystem services’ priority areas (PA).</p> Full article ">
14 pages, 4801 KiB  
Article
Distinct Rainfall Interception Profiles among Four Common Pacific Northwest Tree Species
by Dylan G. Fischer, Spencer T. Vieira and Anand D. Jayakaran
Forests 2023, 14(1), 144; https://doi.org/10.3390/f14010144 - 12 Jan 2023
Cited by 2 | Viewed by 2667
Abstract
Forest tree canopies have a critical influence on water cycles through the interception of precipitation. Nevertheless, radial patterns of canopy interception may vary interspecifically. We analyzed canopy interception using catchments along radial transects underneath four common forest tree species (Acer macrophyllum, [...] Read more.
Forest tree canopies have a critical influence on water cycles through the interception of precipitation. Nevertheless, radial patterns of canopy interception may vary interspecifically. We analyzed canopy interception using catchments along radial transects underneath four common forest tree species (Acer macrophyllum, Alnus rubra, Pseudotsuga menziesii, and Thuja plicata) in the Pacific Northwest over two years. Near the center of the canopy in the leaf-off season, interception was 51.6%–67.2% in conifer species and only 20.1%–40.1% in broadleaf species, and interception declined to 19.9–29.9 for all species near the edge of the canopy. One deciduous species (A. rubra) showed spatially uniform interception during the leaf-off period (19.9%–20.96%), while another varied from 23.1%–40.1%. Patterns were more pronounced in the leaf-on period (under high vapor pressure deficit conditions), where conifers intercepted 36.5%–95.9% of precipitation, depending on the species and position under the canopy. Deciduous species similarly intercepted 42.1%–67.7% of rainfall, depending on species and canopy position. Total throughfall was curvilinearly related to the amount of rainfall near canopy centers for conifer trees but less so for deciduous trees. Soil moisture was predictably related to interception across and within species. These data highlight interspecific differences in radial interception patterns, with consequences for soil moisture, hydrologic processes, and ecosystem function. Full article
(This article belongs to the Section Forest Hydrology)
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<p>Study site used in the current study. Insets represent (right to left) broad geographic location, location relative to the Puget Sound (Washington state), and specific study location inside the Evergreen State College Forest Reserve. The main image represents the LiDAR-derived tree height (2017). White values represent maximum tree height (~63 m), and black represents ground. Trees used in the study ranged from 22–30 m tall within the study area.</p> Full article ">Figure 2
<p>Catchment interception and adjacent soil moisture in relation to distance from trees. Relationships represent cubic spline regression fits the data. Top panels: relationships between catchment sample point and estimated canopy interception of throughfall under four species of tree in leaf-off and leaf-on periods. Bottom panels: relationship between soil moisture and sample points. For all points, catchment and soil moisture values represented are averaged by species and collection period. Species codons in the legend refer to <span class="html-italic">A. macrophyllum</span> (ACMA), <span class="html-italic">A. rubra</span> (ALRU), <span class="html-italic">P. menziesii</span> (PSME), and <span class="html-italic">T. plicata</span> (THPL).</p> Full article ">Figure 3
<p>Simple linear regression relationships between precipitation (ΣRF) and throughfall (TF) averaged by species for each collection period and catchment sample point throughout the study. Sample point 1: represents locations adjacent to tree bases. Sample point 2: represents locations ~40% away from the tree base. Sample point 3: represents locations ~70% from the base. Sample point 4: at the canopy edge. In all panels, the colored lines reflect the different species, while the black line represents a 1:1 line. Species codons in the legend refer to <span class="html-italic">A. macrophyllum</span> (ACMA), <span class="html-italic">A. rubra</span> (ALRU), <span class="html-italic">P. menziesii</span> (PSME), and <span class="html-italic">T. plicata</span> (THPL). Deviations of slopes among species and curvilinear (polynomial) fits are more apparent for collections at the base of trees, while more linear relationships are common at distances greater than 40% of the canopy width.</p> Full article ">Figure 4
<p>Linear regression relationships between apparent canopy interception (%) and soil moisture (%) in the leaf-off period (left panel) and the leaf-on period (right panel). Species codons in the legend refer to <span class="html-italic">A. macrophyllum</span> (ACMA), <span class="html-italic">A. rubra</span> (ALRU), <span class="html-italic">P. menziesii</span> (PSME), and <span class="html-italic">T. plicata</span> (THPL). Significant relationships within leaf-off and leaf-on periods were only present for the two conifer species (leaf-off: <span class="html-italic">P. menziesii</span> R<sup>2</sup> = 0.22, <span class="html-italic">p</span> = 0.02, <span class="html-italic">T. plicata</span> R<sup>2</sup> = 0.58, <span class="html-italic">p</span> &lt; 0.001; leaf-on: <span class="html-italic">P. menziesii</span>: R<sup>2</sup> = 0.35, <span class="html-italic">p</span> = 0.001, <span class="html-italic">T. plicata</span> R<sup>2</sup> = 0.69, <span class="html-italic">p</span> &lt; 0.001). All species showed significant relationships when both periods were pooled (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
12 pages, 3924 KiB  
Article
Root-Growth-Related MaTCP Transcription Factors Responsive to Drought Stress in Mulberry
by Wuqi Wei, Jinzhi He, Yiwei Luo, Zhen Yang, Xiaoyu Xia, Yuanxiang Han and Ningjia He
Forests 2023, 14(1), 143; https://doi.org/10.3390/f14010143 - 12 Jan 2023
Cited by 2 | Viewed by 2080
Abstract
Root growth regulation plays a crucial role in the acclimatization of plants to their surroundings, but the molecular mechanisms underlying this process remain largely uncertain. Teosinte branched1/cycloidea/proliferating cell factor (TCP) transcription factors are crucial elements linking together plant growth and development, phytohormone signaling, [...] Read more.
Root growth regulation plays a crucial role in the acclimatization of plants to their surroundings, but the molecular mechanisms underlying this process remain largely uncertain. Teosinte branched1/cycloidea/proliferating cell factor (TCP) transcription factors are crucial elements linking together plant growth and development, phytohormone signaling, and stress response. In this study, 15 TCP transcription factors were identified in the mulberry (Morus alba) genome. Gene structure, conserved motif, and phylogenetic analyses revealed the conservation and divergence of these MaTCPs, thus providing insights into their functions. A promoter analysis uncovered distinct numbers and compositions of cis-elements in MaTCP gene promoter regions that may be connected to reproductive growth and phytohormone and stress responses. An expression pattern analysis of the 15 MaTCP genes in mulberry roots indicated that transcriptional levels of MaTCP2, MaTCP4-1, MaTCP8, MaTCP9-1, and MaTCP20-2 are correlated with root development. As revealed by changes in their expressions after drought treatment, these five MaTCP genes are involved in root growth and may increase mulberry tolerance to drought. Our findings lay the foundation for future functional studies of these genes. Full article
(This article belongs to the Special Issue Strategies for Tree Improvement under Stress Conditions)
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<p>The phylogenetic and protein structure analyses of MaTCP family members. (<b>A</b>) Neighbor-joining phylogenetic tree of TCP transcription factors in <span class="html-italic">M. alba</span>, <span class="html-italic">P. euphratica</span>, and <span class="html-italic">A. thaliana.</span> Three different colors were designated as three subclades of PCF, CYC/TB1, and CIN. (<b>B</b>) Conserved motifs of MaTCP proteins. Different motifs were shown by different colors numbered 1–7.</p> Full article ">Figure 1 Cont.
<p>The phylogenetic and protein structure analyses of MaTCP family members. (<b>A</b>) Neighbor-joining phylogenetic tree of TCP transcription factors in <span class="html-italic">M. alba</span>, <span class="html-italic">P. euphratica</span>, and <span class="html-italic">A. thaliana.</span> Three different colors were designated as three subclades of PCF, CYC/TB1, and CIN. (<b>B</b>) Conserved motifs of MaTCP proteins. Different motifs were shown by different colors numbered 1–7.</p> Full article ">Figure 2
<p>The promoter analyses of <span class="html-italic">MaTCP</span> genes. The number in parentheses indicated the number of <span class="html-italic">cis</span>-acting elements.</p> Full article ">Figure 3
<p>Expressions of <span class="html-italic">MaTCP</span> genes in GY12 seedlings during root development. (<b>A</b>) Images of 21-, 28-, 35-, 42- and 49-day-old GY12 seedlings. Scale bars, 10 cm. (<b>B</b>) Corresponding root lengths. Each <span class="html-italic">black triangle</span> represents a GY12 seedling. (<b>C</b>) RT-qPCR-based time series analysis of the expressions of 15 <span class="html-italic">MaTCP</span> genes in GY12 seedlings at five developmental time points. Roots were collected from 10 individuals at each time point (21, 28, 35, 42, and 49 days after germination), and their lengths were measured. (<b>D</b>) Clustering of <span class="html-italic">MaTCP</span> genes based on the results of the time series analysis.</p> Full article ">Figure 4
<p>Venn diagram of <span class="html-italic">MaTCP</span> genes significantly associated with root length in a correlation analysis (red circle) and those exhibiting a similar trend in regard to root development in a time series analysis (blue circle).</p> Full article ">Figure 5
<p>Subcellular localization of MaTCP2, MaTCP4-1, MaTCP8, MaTCP9-1, and MaTCP20-2 transcription factors. <span class="html-italic">35S:GFP</span> is an empty vector control, and DAPI was used for nucleic acid staining. Scale bars, 50 μm.</p> Full article ">Figure 6
<p>Expressions of <span class="html-italic">MaTCP</span> genes during drought stress. The plants were treated with 10% PEG6000 solution for 0, 1, 3, 5, and 7 days. The 0-day plants were the control. Error bars indicate SE. Different lowercase letters indicate statistically significant differences among treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
13 pages, 3085 KiB  
Article
Potential and Constraints on In Vitro Micropropagation of Juniperus drupacea Labill.
by Kostas Ioannidis, Ioanna Tomprou, Danae Panayiotopoulou, Stefanos Boutsios and Evangelia N. Daskalakou
Forests 2023, 14(1), 142; https://doi.org/10.3390/f14010142 - 12 Jan 2023
Cited by 7 | Viewed by 3053
Abstract
Juniperus drupacea Labill. (Cupressaceae) is a species with ecological and medicinal value. In Europe, it is native only in southern Greece, and is listed as endangered. Due to its uniqueness, this study attempted, for the first time, an in vitro propagation effort of [...] Read more.
Juniperus drupacea Labill. (Cupressaceae) is a species with ecological and medicinal value. In Europe, it is native only in southern Greece, and is listed as endangered. Due to its uniqueness, this study attempted, for the first time, an in vitro propagation effort of Syrian juniper. Explants of the lateral shoot tips were surface-sterilized and cultured on Murashige and Skoog (MS) medium. The cultures were subcultured on MS, woody plant medium (WPM), and Driver and Kuniyaki Walnut (DKW) supplemented with different concentrations of 6-benzylaminopurine (BA), thidiazuron (TDZ), or meta-topolin [6-(3-hy-droxybenzylamino)purine] for shoot induction. Explants derived from female trees exhibited 54.17% bud proliferation on DKW medium with 4 μM meta-topolin or 4 μM TDZ and on WPM with 4 μM meta-topolin or 4 μM BA. A total of 62.50% of the male tree derived explants produced multiple shoots on DKW with 4 μM BA. The maximum average number of shoots per explant were 1.17 per explant in both cases. The length of the shoot derived from explants of female origin was 2.94 mm compared to 2.69 mm of the in vitro shoots from the explants of male trees. Overall, the best medium and plant growth regulator combination for the explants derived from both female and male trees, for the traits under study, was proven to be DKW + 4 µM TDZ. Our experiments show that Juniperus drupacea, under in vitro conditions, shows recalcitrance in rooting, as the applications of IBA, NAA, and IAA concentrations were proven to be ineffective treatments. Although the results show low values, this avant-garde study provides a foundation for further research on the in vitro regeneration of Juniperus drupacea. Full article
(This article belongs to the Special Issue Application of Plant Biotechnology in Forestry)
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<p>Effect of the medium and plant growth regulator types and their concentrations on the average percentage of blastogenesis (%) of <span class="html-italic">Juniperus drupacea</span> explants in relation to their gender.</p> Full article ">Figure 2
<p>Effect of the medium and plant growth regulator types and their concentrations on the average shoot number per <span class="html-italic">Juniperus drupacea</span> explant in relation to their gender.</p> Full article ">Figure 3
<p>Effect of the medium and plant growth regulator types and their concentrations on the average shoot length per <span class="html-italic">Juniperus drupacea</span> explant in relation to their gender.</p> Full article ">Figure 4
<p>Culture establishment and shoot formation: (<b>a</b>) explants of <span class="html-italic">Juniperus drupacea</span> on MS medium containing 4 μΜ BA after 10 days of culture; (<b>b</b>) shoot formation on DKW medium containing 4 μΜ BA after 4 weeks of culture. Explant discoloration (browning) and necrotic zones of the explants are common among <span class="html-italic">Juniperus</span> species. Test tube diameter = 25 mm.</p> Full article ">
22 pages, 8984 KiB  
Article
Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level
by Xiaocheng Zhou, Hongyu Wang, Chongcheng Chen, Gábor Nagy, Tamas Jancso and Hongyu Huang
Forests 2023, 14(1), 141; https://doi.org/10.3390/f14010141 - 12 Jan 2023
Cited by 4 | Viewed by 2332
Abstract
With the rapid development of Unmanned Aerial Vehicle (UAV) technology, more and more UAVs have been used in forest survey. UAV (RGB) images are the most widely used UAV data source in forest resource management. However, there is some uncertainty as to the [...] Read more.
With the rapid development of Unmanned Aerial Vehicle (UAV) technology, more and more UAVs have been used in forest survey. UAV (RGB) images are the most widely used UAV data source in forest resource management. However, there is some uncertainty as to the reliability of these data when monitoring height and growth changes of low-growing saplings in an afforestation plot via UAV RGB images. This study focuses on an artificial Chinese fir (Cunninghamia lancelota, named as Chinese Fir) young forest plot in Fujian, China. Divide-and-conquer (DAC) and the local maximum (LM) method for extracting seedling height are described in the paper, and the possibility of monitoring young forest growth based on low-cost UAV remote sensing images was explored. Two key algorithms were adopted and compared to extract the tree height and how it affects the young forest at single-tree level from multi-temporal UAV RGB images from 2019 to 2021. Compared to field survey data, the R2 of single saplings’ height extracted from digital orthophoto map (DOM) images of tree pits and original DSM information using a divide-and-conquer method reached 0.8577 in 2020 and 0.9968 in 2021, respectively. The RMSE reached 0.2141 in 2020 and 0.1609 in 2021. The R2 of tree height extracted from the canopy height model (CHM) via the LM method was 0.9462. The RMSE was 0.3354 in 2021. The results demonstrated that the survival rates of the young forest in the second year and the third year were 99.9% and 85.6%, respectively. This study shows that UAV RGB images can obtain the height of low sapling trees through a computer algorithm based on using 3D point cloud data derived from high-precision UAV images and can monitor the growth of individual trees combined with multi-stage UAV RGB images after afforestation. This research provides a fully automated method for evaluating the afforestation results provided by UAV RGB images. In the future, the universality of the method should be evaluated in more afforestation plots featuring different tree species and terrain. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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<p>The study region is located in Jiangle County, Fujian Province, China. (<b>a</b>) Map of China; (<b>b</b>) Jiangle County, Fujian Province; (<b>c</b>) UAV image of tree pit on 22 January 2019; (<b>d</b>) UAV image of young forest on 2 October 2020; (<b>e</b>) UAV image of young forest on 2 October 2021; (<b>c-1</b>,<b>e-1</b>,<b>d-1</b>) are magnified images in red rectangle region in (<b>c</b>–<b>d</b>).</p> Full article ">Figure 2
<p>Field measurements of the scene. (<b>a</b>–<b>c</b>) shows the scenes of the test region in 2019, 2020, and 2021, (<b>d</b>) is the tree pit before afforestation in 2019, and (<b>e</b>,<b>f</b>) is the field scenes for tree height measurement in 2020 and 2021.</p> Full article ">Figure 3
<p>DEM derived from UAV images (<b>a</b>) and 3D visualization of UAV image in 2021 overlayed DEM (<b>b</b>) in the study area.</p> Full article ">Figure 4
<p>Fitting effect of DEM from different temporal UAV images.</p> Full article ">Figure 5
<p>Accuracy verification of ground point for DEM.</p> Full article ">Figure 6
<p>The location of tree pit and the determination of registration. (<b>a</b>) shows automatically obtained tree pits shapefile; (<b>b</b>) shows the result of manual correction; (<b>c</b>) shows optical UAV imaging in October, 2020; (<b>d</b>) shows optical UAV imaging in October 2021. The process of obtaining the location and size of tree pits, the red circle is the result of the 0.25 buffer and the black circle is the result of the 0.45 buffer.</p> Full article ">Figure 7
<p>Tree height extraction process via divide-and-conquer algorithm.</p> Full article ">Figure 8
<p>Divide and conquer diagram, where ‘a’ is the regional minimum value (2020): 479.49; ‘b’ is the regional maximum value (2020): 480.10; ‘c’ is the abnormal value (deleted).</p> Full article ">Figure 9
<p>Tree-height accuracy verification; the blue points are the verification results of DAC in 2020.</p> Full article ">Figure 10
<p>Tree height accuracy verification; the green points are the verification results of DAC in 2021; the orange points are the verification results of LM in 2021.</p> Full article ">Figure 11
<p>Local maximum value algorithm extract tree crown top; figure (<b>a</b>) shows red stars as true positives; figure (<b>b</b>) shows green stars as false positives; figure (<b>c</b>) shows blue stars as double tops.</p> Full article ">Figure 12
<p>Young forest height changing 3D map based on UAV image point cloud.</p> Full article ">Figure 13
<p>The different heights and growth of the young trees from 2019 to 2021.</p> Full article ">Figure 14
<p>The left boxplot shows the annual growth of the saplings from 2019 to 2021, and the right boxplot shows the average monthly growth of the saplings each year. The colors of the boxplot show the different stages of young tree growth. Green shows the survival of young trees from June 2019 to October 2020 (<span class="html-italic">n</span> = 1043); orange shows the survival of young trees from October 2020 to October 2021 (<span class="html-italic">n</span> = 894); gray shows the survival of young trees from June 2019 to October 2021 (<span class="html-italic">n</span> = 894). The median of the (a1) boxplot was 83.95 cm; the median of the (b1) boxplot was 124.25 cm; the median of the (c1) boxplot was 202.03 cm; the median of the (a2) boxplot was 5.25 cm; the median of the (b2) boxplot was 10.35 cm; the median of the (c2) boxplot was 7.22 cm.</p> Full article ">Figure 15
<p>Dynamic change of tree growth. (<b>a</b>–<b>c</b>) show changes in young forest height growth at single tree level at different stages from June 2019 to October 2020, October 2020 to October 2021, and June 2019 to October 2021. The color indicates an additional amount of growth. The pink growth range is 0.2–1.0 m; the blue growth range is 1.0–2.0 m; the light green growth range of 2.0–3.0 m; the green growth range is 3.0–4.0 m; the dark green grows in the 4.0–5.0 m range. According to (<b>c</b>), the growth of 0.2–1.0 m in three years is poor; 1.0–2.0 m growth medium; 2.0–4.0 m is healthy growth; above 4.0 m is considered very healthy.</p> Full article ">
15 pages, 3422 KiB  
Article
Benefits and Requirements of Mathematical Optimization in the Allocation of Wood to a Network of Forest Product Mills
by Maxime Auger, Luc LeBel and Edith Brotherton
Forests 2023, 14(1), 140; https://doi.org/10.3390/f14010140 - 12 Jan 2023
Cited by 2 | Viewed by 1755
Abstract
Supply planning is a challenge for the forest industry in the context of natural forests characterized by heterogeneity among raw materials. Several mathematical models have been proposed in the literature to support forest planning, though few have been used by companies. The complexity [...] Read more.
Supply planning is a challenge for the forest industry in the context of natural forests characterized by heterogeneity among raw materials. Several mathematical models have been proposed in the literature to support forest planning, though few have been used by companies. The complexity of the natural environment and the expertise required to use these models limit their application. Nevertheless, these tools can significantly improve profitability. Three main elements were analyzed to assess benefits fostered by computer-optimized planning: (i) assessing the potential of implementing mathematical optimization in companies, particularly by pinpointing the additional resources necessary; (ii) determining the benefits of mathematical optimization to support planning decisions in an industrial context; and (iii) analyzing the impact of variation in information precision. LogiLab, an optimization software was used to find the optimal allocation of raw materials to an industrial network of five mills. The plan produced using optimization was compared to the plan generated by company personnel. The optimized plan generated a nearly 20% greater net profit than the current planning method. This difference was in part due to the more efficient allocation of raw materials to mills. It also highlighted numerous benefits, including a 50% reduction in the time required to produce plans. Furthermore, if attributed volume can be distributed among sawmills, it would be possible to save CAD 3.21/m3 using optimized plans, greatly exceeding initial costs related to system implementation. Full article
(This article belongs to the Special Issue Recent Advances in Wood Harvesting, Timber Logistics and Road Planning)
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<p>Flowchart of the procurement optimization process used in this project.</p> Full article ">Figure 2
<p>An example of harvesting blocks before (<b>left</b>) and after aggregation by area (<b>right</b>) with the aggregation tool FPInterface<sup>TM</sup>.</p> Full article ">Figure 3
<p>Education received by employees in charge of planning.</p> Full article ">Figure 4
<p>Relative variation of net value with scenarios in comparison to the optimal scenario.</p> Full article ">Figure 5
<p>Proportion of volumes allocated to factories in the optimal scenario of reference in comparison to the manual scenario.</p> Full article ">Figure 6
<p>Comparing allocation of harvest blocks in mills between manual and optimal scenarios of reference.</p> Full article ">Figure 7
<p>Variation between real-time inventory and initial inventory.</p> Full article ">Figure 8
<p>Impact of changing inventory data on the optimal (<b>a</b>) and manual (<b>b</b>) plans.</p> Full article ">
15 pages, 2716 KiB  
Article
Refined Assessment of Economic Loss from Pine Wilt Disease at the Subcompartment Scale
by Feng Liu, Hongjun Su, Tiantian Ding, Jixia Huang, Tong Liu, Ning Ding and Guofei Fang
Forests 2023, 14(1), 139; https://doi.org/10.3390/f14010139 - 12 Jan 2023
Cited by 5 | Viewed by 2051
Abstract
Pine wilt disease is a major plant epidemic that has significantly impaired the ecological safety of pine wood, the national economy, and peoples’ livelihood. It is challenging to accurately assess the loss from pine wilt disease through academic research or field work. Based [...] Read more.
Pine wilt disease is a major plant epidemic that has significantly impaired the ecological safety of pine wood, the national economy, and peoples’ livelihood. It is challenging to accurately assess the loss from pine wilt disease through academic research or field work. Based on the 342,000 subcompartments of epidemic data of pine wilt disease in China in 2020, this study builds a refined assessment indicator system and measurement model for economic loss from disasters at the subcompartment scale and assesses direct economic loss and ecological service value loss. The results show that through direct economic loss and ecological service value loss, China lost USD 7.40 billion in 2020, including a direct economic loss of USD 1.11 billion and ecological service value loss of USD 6.29 billion. Of the direct economic loss, the forest material resource loss and protection expense reached USD 0.17 billion and USD 0.94 billion, respectively; of the ecological system service losses, regulation service, supporting service, and cultural service losses reached USD 4.58 billion, USD 1.35 billion, and USD 0.36 billion. Spatial distribution analysis showed that the loss declined from southeast to northwest, with Shandong, Zhejiang, and Jiangxi suffering the greatest losses. Based on the subcompartment scale, this study employs a more refined assessment indicator system and measurement model to provide accurate real-world assessment results. Full article
(This article belongs to the Section Forest Health)
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<p>Epidemic subcompartments distribution of pine wilt disease in China in 2020.</p> Full article ">Figure 2
<p>Losses caused by pine wilt disease in different regions in 2020. (<b>a</b>) Number of cleared trees, volume loss, and non-forested area. (<b>b</b>) Economic loss.</p> Full article ">Figure 3
<p>Spatial distribution of loss from pine wilt disease in China in 2020. (<b>a</b>) Total economic loss. (<b>b</b>) Ratio of ecological service loss to direct economic loss.</p> Full article ">Figure 4
<p>Level 2 economic loss indicators from pine wilt disease in different regions in 2020. (<b>a</b>) Direct economic loss indicators. (<b>b</b>) Ecological service value loss indicators.</p> Full article ">
21 pages, 6388 KiB  
Article
Harmonious Degree of Sound Sources Influencing Visiting Experience in Kulangsu Scenic Area, China
by Xuan Guo, Jiang Liu, Zhu Chen and Xin-Chen Hong
Forests 2023, 14(1), 138; https://doi.org/10.3390/f14010138 - 12 Jan 2023
Cited by 16 | Viewed by 2880
Abstract
Soundscapes are important resources and contribute to high-quality visiting experiences in scenic areas. Based on a public investigation of 195 interviewees in the Kulangsu scenic area, this study aimed to explore the relationships between the harmonious degree of sound sources (SHD) and visiting [...] Read more.
Soundscapes are important resources and contribute to high-quality visiting experiences in scenic areas. Based on a public investigation of 195 interviewees in the Kulangsu scenic area, this study aimed to explore the relationships between the harmonious degree of sound sources (SHD) and visiting experience indicators, in terms of soundscape perception, as well as the satisfaction degree of visual landscape and comprehensive impression. The results suggested that the dominating positions of human sounds did not totally suppress the perception of natural sounds such as birdsong and sea waves in the scenic area. Natural sound sources also showed a higher harmonious degree than other artificial sounds. Significant relationships existed between the SHD of most sound sources and the visiting experience indicators. Natural sounds were closely related to pleasant and comfortable soundscape perception, while mechanical sound sources were mainly related to eventful and varied soundscapes. The close relationships between certain sound sources and the satisfaction degree of the visual landscape and comprehensive impression evaluation indicated the effectiveness of audio-visual and even multi-sensory approaches to enhance visiting experience. The structural equation model further revealed that (1) natural sound was the most influential sound source of soundscape and visual landscape perception; (2) human sounds and mechanical sounds all showed significant positive effects on soundscape perception; and (3) indirect relationships could exist in the SHD of sound sources with comprehensive impression evaluation. The results can facilitate targeted soundscape and landscape management and landsense creation with the aim of improving visiting experience. Full article
(This article belongs to the Special Issue Landsenses in Green Spaces)
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<p>Aerial photo of Kulangsu scenic area (source: elaborated by the authors with Google earth).</p> Full article ">Figure 2
<p>Mean values of sound source perception indicators, perceived occurrences (POS), perceived loudness (PLS), preference (PFS), harmonious degree of sound source (SHD).</p> Full article ">Figure 3
<p>Mean values of harmonious degree of sound sources in different functional zones.</p> Full article ">Figure 4
<p>A conceptual model of the SHD influencing visiting experience in Kulangsu, SHD-NS: harmonious degree of natural sound, SHD-MS: harmonious degree of mechanical sound, SHD-HS: harmonious degree of human sound, SPE: soundscape perception, SVL: satisfaction degree of visual landscape, CIE: comprehensive impression evaluation.</p> Full article ">Figure 5
<p>The modified model of the SHD influencing visiting experience in Kulangsu, SHD-NS: harmonious degree of natural sound, SHD-MS: harmonious degree of mechanical sound, SHD-HS: harmonious degree of human sound, SPE: soundscape perception, SVL: satisfaction degree of visual landscape, CIE: comprehensive impression evaluation, significant paths are marked with * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01) or *** (<span class="html-italic">p</span> &lt; 0.001).</p> Full article ">
15 pages, 1860 KiB  
Article
Moderate Nitrogen Deposition Alleviates Drought Stress of Bretschneidera sinensis
by Xiao Wang, Gaoyin Wu, Deyan Li and Xiaohui Song
Forests 2023, 14(1), 137; https://doi.org/10.3390/f14010137 - 12 Jan 2023
Cited by 2 | Viewed by 1798
Abstract
Droughts are becoming more frequent and intense, and the nitrogen deposition rate is increasing worldwide due to human activities. Young seedlings of Bretschneidera sinensis Hemsl. are susceptible to mortality under drought conditions because their root tips have few root hairs. We studied the [...] Read more.
Droughts are becoming more frequent and intense, and the nitrogen deposition rate is increasing worldwide due to human activities. Young seedlings of Bretschneidera sinensis Hemsl. are susceptible to mortality under drought conditions because their root tips have few root hairs. We studied the effect of nitrogen deposition on the physiological characteristics of two-year-old B. sinensis seedlings under drought stress. Seedlings were grown under no nitrogen deposition (control; N0), low nitrogen deposition (N30, 30 kg·hm−2 year−1), medium nitrogen deposition (N60, 60 kg·hm−2 year−1), and high nitrogen deposition (N90, 90 kg·hm−2 year−1), and were subjected to either the normal watering regime (NW) or drought stress (DW). Under DW, the relative conductivity (RC) of seedlings receiving N60 was not significantly different from that of N0 seedlings, and the RC of seedlings receiving N90 was significantly higher than that of N0 seedlings. Under 10 d DW, N60 treatment increased antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities and content of soluble protein, chlorophyll a and a + b, with POD activity and soluble protein significantly increasing by 18.89% and 34.66%, respectively. Under DW, the proline (PRO) content of seedlings treated with N90 increased. Our data suggested that moderate nitrogen deposition could alleviate drought stress by decreasing cell membrane permeability, reducing cell membrane peroxidation, increasing the content of osmoregulatory substances, and reducing the tendency for chlorophyll to decline, whereas high nitrogen deposition increased the sensitivity of B. sinensis seedlings to drought stress and aggravated the degree of stress, thereby affecting growth. Full article
(This article belongs to the Special Issue Ecophysiology of Forest Trees and Responses to Environmental Changes)
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Figure 1
<p>(<b>a</b>) Effect of drought stress and nitrogen deposition on MDA content; (<b>b</b>) effect of drought stress and nitrogen deposition on RC. Water supply treatments: normal water supply (NW) or drought stress (DW) for 10 d or 30 d. Nitrogen deposition treatments: control (N0, 0 kg·hm<sup>−2</sup> year<sup>−1</sup>), low nitrogen deposition (N30, 30 kg·hm<sup>−2</sup> year<sup>−1</sup>), medium nitrogen deposition (N60, 60 kg·hm<sup>−2</sup> year<sup>−1</sup>), or high nitrogen deposition (N90, 90 kg·hm<sup>−2</sup> year<sup>−1</sup>). Abbreviations: MDA, malondialdehyde; RC, relative conductivity. Data points represent means ± the SD; <span class="html-italic">n</span> = 5. Data points with the same lowercase letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s test.</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Effect of drought stress and nitrogen deposition on MDA content; (<b>b</b>) effect of drought stress and nitrogen deposition on RC. Water supply treatments: normal water supply (NW) or drought stress (DW) for 10 d or 30 d. Nitrogen deposition treatments: control (N0, 0 kg·hm<sup>−2</sup> year<sup>−1</sup>), low nitrogen deposition (N30, 30 kg·hm<sup>−2</sup> year<sup>−1</sup>), medium nitrogen deposition (N60, 60 kg·hm<sup>−2</sup> year<sup>−1</sup>), or high nitrogen deposition (N90, 90 kg·hm<sup>−2</sup> year<sup>−1</sup>). Abbreviations: MDA, malondialdehyde; RC, relative conductivity. Data points represent means ± the SD; <span class="html-italic">n</span> = 5. Data points with the same lowercase letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s test.</p> Full article ">Figure 2
<p>(<b>a</b>) Effect of drought stress and nitrogen deposition on POD activity; (<b>b</b>) effect of drought stress and nitrogen deposition on SOD activity; (<b>c</b>) effect of drought stress and nitrogen deposition on CAT activity. Water supply treatments: normal water supply (NW) or drought stress (DW) for 10 d or 30 d. Nitrogen deposition treatments: control (N0, 0 kg·hm<sup>−2</sup> year<sup>−1</sup>), low nitrogen deposition (N30, 30 kg·hm<sup>−2</sup> year<sup>−1</sup>), medium nitrogen deposition (N60, 60 kg·hm<sup>−2</sup> year<sup>−1</sup>), or high nitrogen deposition (N90, 90 kg·hm<sup>−2</sup> year<sup>−1</sup>). Abbreviations: POD, peroxidase; SOD, superoxide dismutase; CAT, catalase. Data points represent means ± the SD; <span class="html-italic">n</span> = 5. Data points with the same lowercase letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s test.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>) Effect of drought stress and nitrogen deposition on POD activity; (<b>b</b>) effect of drought stress and nitrogen deposition on SOD activity; (<b>c</b>) effect of drought stress and nitrogen deposition on CAT activity. Water supply treatments: normal water supply (NW) or drought stress (DW) for 10 d or 30 d. Nitrogen deposition treatments: control (N0, 0 kg·hm<sup>−2</sup> year<sup>−1</sup>), low nitrogen deposition (N30, 30 kg·hm<sup>−2</sup> year<sup>−1</sup>), medium nitrogen deposition (N60, 60 kg·hm<sup>−2</sup> year<sup>−1</sup>), or high nitrogen deposition (N90, 90 kg·hm<sup>−2</sup> year<sup>−1</sup>). Abbreviations: POD, peroxidase; SOD, superoxide dismutase; CAT, catalase. Data points represent means ± the SD; <span class="html-italic">n</span> = 5. Data points with the same lowercase letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s test.</p> Full article ">Figure 3
<p>(<b>a</b>) Effect of drought stress and nitrogen deposition on PRO content; (<b>b</b>) effect of drought stress and nitrogen deposition on soluble sugar content; (<b>c</b>) effect of drought stress and nitrogen deposition on soluble protein content. Water supply treatments: normal water supply (NW) or drought stress (DW) for 10 d or 30 d. Nitrogen deposition treatments: control (N0, 0 kg·hm<sup>−2</sup> year<sup>−1</sup>), low nitrogen deposition (N30, 30 kg·hm<sup>−2</sup> year<sup>−1</sup>), medium nitrogen deposition (N60, 60 kg·hm<sup>−2</sup> year<sup>−1</sup>), or high nitrogen deposition (N90, 90 kg·hm<sup>−2</sup> year<sup>−1</sup>). Abbreviations: PRO, proline. Data points represent means ± the SD; <span class="html-italic">n</span> = 5. Data points with the same lowercase letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s test.</p> Full article ">Figure 4
<p>Effect of drought stress and nitrogen deposition on growth indicators. Water supply treatments: normal water supply (NW) or drought stress (DW) for 10 d or 30 d. Nitrogen deposition treatments: control (N0, 0 kg·hm<sup>−2</sup> year<sup>−1</sup>), low nitrogen deposition (N30, 30 kg·hm<sup>−2</sup> year<sup>−1</sup>), medium nitrogen deposition (N60, 60 kg·hm<sup>−2</sup> year<sup>−1</sup>), or high nitrogen deposition (N90, 90 kg·hm<sup>−2</sup> year<sup>−1</sup>). Data points represent means ± the SD; <span class="html-italic">n</span> = 5. Data points with the same lowercase letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s test.</p> Full article ">
24 pages, 2503 KiB  
Review
Unravelling the Role of Institutions in Market-Based Instruments: A Systematic Review on Forest Carbon Mechanisms
by Xinran Shen, Paola Gatto and Francesco Pagliacci
Forests 2023, 14(1), 136; https://doi.org/10.3390/f14010136 - 11 Jan 2023
Cited by 4 | Viewed by 3094
Abstract
Forest ecosystems provide various services that are crucial to human beings, in which carbon sequestration and storage is one of them with the most market potential and is usually governed by market-based instruments (MBIs). MBIs do not operate alone but in the hybrid [...] Read more.
Forest ecosystems provide various services that are crucial to human beings, in which carbon sequestration and storage is one of them with the most market potential and is usually governed by market-based instruments (MBIs). MBIs do not operate alone but in the hybrid governance arrangements. While the importance of public institutions has been identified, there is still a need to examine the specific role of public institutions in the market-oriented mechanism. Our work seeks answers to this question. This meta-study presents an up-to-date picture of MBIs targeted at forest carbon, in which 88 mechanisms are synthesized in a quantitative database. We analyze and discuss policy design features of these mechanisms and group them into nine types of MBIs. We find that many instruments coexist and/or interact with other instruments. In light of these results, we introduce the concept of policy mix and argue that the interplay among policy instruments can be complementary or interdependent. Using cluster analysis to identify underlying patterns, we reconfirm previous findings that there are distinct differences between public and private PES schemes, but also recognize a new cluster and label it as a ‘legally binding mechanism’. We discover that the role of public institutions is pronounced in the forest carbon mechanisms, and they can be the buyer, seller, regulator, coordinator, intermediary, and facilitator. Besides, public institutions tend to play an increasing role in the future climate policy arena. We believe that public institutions should stand out and create enabling conditions for private governance and finance. Full article
(This article belongs to the Section Forest Economics, Policy, and Social Science)
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<p>Diagram of systematic literature review (adapted from Page et al. [<a href="#B30-forests-14-00136" class="html-bibr">30</a>] and Maier et al. [<a href="#B28-forests-14-00136" class="html-bibr">28</a>]).</p> Full article ">Figure 2
<p>Geographical distribution of mechanisms reviewed (international mechanisms are excluded).</p> Full article ">Figure 3
<p>Left panel: temporal scale of mechanisms. Right panel: spatial scale of mechanisms.</p> Full article ">Figure 4
<p>Actor categories identified in the review from a multi-actor perspective (adapted from Avelino &amp; Wittmayer [<a href="#B57-forests-14-00136" class="html-bibr">57</a>] and IPBES [<a href="#B59-forests-14-00136" class="html-bibr">59</a>]).</p> Full article ">Figure 5
<p>Cluster analysis of mechanisms reviewed in the literature (points represent mechanisms, and inertia gain shows how much variance will be obtained by adding more clusters).</p> Full article ">Figure 6
<p>Conceptual model for governance of market-based instruments for forest carbon (adapted from McGinnis &amp; Ostrom [<a href="#B65-forests-14-00136" class="html-bibr">65</a>]).</p> Full article ">
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