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Forests
Volume 10
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Forests, Volume 10, Issue 2 (February 2019) – 124 articles

Cover Story (view full-size image): Natural forests that have not been disturbed for many decades are called old-growth forest (OGF.) These forests are important as stores of carbon and biodiversity, but are rare and threatened, particularly in Europe where very little remains. We demonstrate that publically available satellite images combined with Random Forest image classification can be used to identify OGF in the Ukrainian Carpathians. The identification and protection of OGF is a priority for environmental organisations, and we hope our research can encourage the use of remote sensing in this important work. View this paper.
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12 pages, 3851 KiB  
Article
Thermal Insulating and Mechanical Properties of Cellulose Nanofibrils Modified Polyurethane Foam Composite as Structural Insulated Material
by Weiqi Leng and Biao Pan
Forests 2019, 10(2), 200; https://doi.org/10.3390/f10020200 - 25 Feb 2019
Cited by 42 | Viewed by 5067
Abstract
Cellulose nanofibrils (CNF) modified polyurethane foam (PUF) has great potential as a structural insulated material in wood construction industry. In this study, PUF modified with spray-dried CNF was fabricated and the physical and mechanical performance were studied. Results showed that CNF had an [...] Read more.
Cellulose nanofibrils (CNF) modified polyurethane foam (PUF) has great potential as a structural insulated material in wood construction industry. In this study, PUF modified with spray-dried CNF was fabricated and the physical and mechanical performance were studied. Results showed that CNF had an impact on the foam microstructure by increasing the precursor viscosity and imposing resistant strength upon foaming. In addition, the intrinsic high mechanical strength of CNF imparted an extra resistant force against cells expansion during the foaming process and formed smaller cells which reduced the chance of creating defective cells. The mechanical performance of the foam composite was significantly improved by introducing CNF into the PUF matrix. Compared with the PUF control, the specific bending strength, specific tensile strength, and specific compression strength increased up to three-fold for the CNF modified PUF. The thermal conductivity of PUF composite was mainly influenced by the closed cell size. The introduction of CNF improved thermal insulating performance, with a decreased thermal conductivity from 0.0439 W/mK to 0.02724 W/mK. Full article
(This article belongs to the Special Issue Wood Productions and Renewable Materials)
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<p>Scanning electron microscopy (SEM) images of PUF modified with different amount of CNF (0–30%).</p> Full article ">Figure 2
<p>FTIR spectra of CNF and PUF.</p> Full article ">Figure 3
<p>Compressive stress–strain relation for PUF.</p> Full article ">Figure 4
<p>Tensile stress–strain relation for PUF.</p> Full article ">
12 pages, 3640 KiB  
Article
The Impact of Anatomical Characteristics on the Structural Integrity of Wood
by Lukas Emmerich, Georg Wülfing and Christian Brischke
Forests 2019, 10(2), 199; https://doi.org/10.3390/f10020199 - 24 Feb 2019
Cited by 9 | Viewed by 4258
Abstract
The structural integrity of wood is closely related to its brittleness and thus to its suitability for numerous applications where dynamic loads, wear and abrasion occur. The structural integrity of wood is only vaguely correlated with its density, but affected by different chemical, [...] Read more.
The structural integrity of wood is closely related to its brittleness and thus to its suitability for numerous applications where dynamic loads, wear and abrasion occur. The structural integrity of wood is only vaguely correlated with its density, but affected by different chemical, physico-structural and anatomical characteristics, which are difficult to encompass as a whole. This study aimed to analyze the results from High-Energy Multiple Impact (HEMI) tests of a wide range of softwood and hardwood species with an average oven-dry wood density in a range between 0.25 and 0.99 g/cm³ and multifaceted anatomical features. Therefore, small clear specimens from a total of 40 different soft- and hardwood species were crushed in a heavy vibratory ball mill. The obtained particles were fractionated and used to calculate the ‘Resistance to Impact Milling (RIM)’ as a measure of the wood structural integrity. The differences in structural integrity and thus in brittleness were predominantly affected by anatomical characteristics. The size, density and distribution of vessels as well as the ray density of wood were found to have a significant impact on the structural integrity of hardwoods. The structural integrity of softwood was rather affected by the number of growth ring borders and the occurrence of resin canals. The density affected the Resistance to Impact Milling (RIM) of neither the softwoods nor the hardwoods. Full article
(This article belongs to the Special Issue Wood Properties and Processing)
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<p>The relationship between the average oven-dry density and Resistance to Impact Milling (RIM): (<b>a</b>) all wood species (y = 3.1629x + 82.887); (<b>b</b>) softwoods (y = 1.1035x + 83.791); (<b>c</b>) ring- and semi-ring-porous hardwoods (y = 19.634x + 72.545); and (<b>d</b>) diffuse-porous hardwoods (y = 1.8475x + 84.086).</p> Full article ">Figure 2
<p>The fracture pattern in the softwoods: (<b>a</b>) Cross section of the Scots pine heartwood, fracture along a growth ring border; (<b>b</b>) The radial fracture section of the Douglas fir heartwood, fracture along the rays.</p> Full article ">Figure 3
<p>The fracture pattern in the ring-porous hardwoods: (<b>a</b>) Cross section of the Ash, fracture within a ring of the earlywood vessels; (<b>b</b>) Cross section of the English oak heartwood, fracture along the field of the latewood pores and the adjacent parenchyma cells.</p> Full article ">Figure 4
<p>The relationship between the average ray density and the Resistance to Impact Milling (RIM): (<b>a</b>) all wood species (y = 0.2354x + 83.247); (<b>b</b>) softwoods (y = −0.0252x + 84.454); (<b>c</b>) ring- and semi-ring-porous hardwoods (y = −0.5083x + 87.875); (<b>d</b>) and diffuse-porous hardwoods (y = 0.4365x + 81.997).</p> Full article ">Figure 5
<p>The fracture pattern in semi-ring-porous and diffuse-porous hardwoods: (<b>a</b>) the cross section of the Wild cherry, the fracture along a growth ring border; (<b>b</b>) the radial fracture section of the Alder, the fracture along the rays.</p> Full article ">Figure 6
<p>The relationship between the average earlywood vessel diameters and the resistance to impact milling (RIM): (<b>a</b>) all wood species (y = −0.0213x + 86.982); (<b>b</b>) softwoods (y = −0.0814x + 86.425); (<b>c</b>) ring- and semi-ring-porous hardwoods (y = −0.0213x + 87.857); and (<b>d</b>) diffuse-porous hardwoods (y = −0.0445x + 90.309).</p> Full article ">Figure 7
<p>The fracture pattern in the diffuse-porous hardwoods: (<b>a</b>) the cross section of the Bongossi, the tangential fractures; (<b>b</b>) the cross section of the Amaranth—the radial fractures along the rays.</p> Full article ">
17 pages, 5182 KiB  
Article
Modelling the Incursion and Spread of a Forestry Pest: Case Study of Monochamus alternatus Hope (Coleoptera: Cerambycidae) in Victoria
by John Weiss, Kathryn Sheffield, Anna Weeks and David Smith
Forests 2019, 10(2), 198; https://doi.org/10.3390/f10020198 - 22 Feb 2019
Cited by 4 | Viewed by 3326
Abstract
Effective and efficient systems for surveillance, eradication, containment and management of biosecurity threats require methods to predict the establishment, population growth and spread of organisms that pose a potential biosecurity risk. To support Victorian forest biosecurity operations, Agriculture Victoria has developed a landscape-scale, [...] Read more.
Effective and efficient systems for surveillance, eradication, containment and management of biosecurity threats require methods to predict the establishment, population growth and spread of organisms that pose a potential biosecurity risk. To support Victorian forest biosecurity operations, Agriculture Victoria has developed a landscape-scale, spatially explicit, spatio-temporal population growth and dispersal model of a generic pest pine beetle. The model can be used to simulate the incursion of a forestry pest from a nominated location(s), such as an importation business site (approved arrangement, AA), into the surrounding environment. The model provides both illustrative and quantitative data on population dynamics and spread of a forestry pest species. Flexibility built into the model design enables a range of spatial extents to be modelled, from user-defined study areas to the Victoria-wide area. The spatial resolution of the model (size of grid cells) can be altered from 100 m to greater than 1 km. The model allows core parameters to be altered by the user, enabling the spread of a variety of windborne insect species and pathogens to be investigated. We verified the model and its parameters by simulating and comparing the outputs with the 1999/2000 Melbourne incursion, but no establishment of a forestry pest beetle was believed to be Monochamus alternatus Hope (Coleoptera: Cerambycidae). The model accurately predicts the distance and direction of the historic incursion, and the subsequent failure to establish is due to low overall population density of the pest species. Full article
(This article belongs to the Special Issue Exotic Forest Pest and Pathogen Risks)
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<p>Life cycle of the <span class="html-italic">Monochamus</span> beetle, the nematode (<span class="html-italic">Bursaphelenchus xylophilus</span>) and impact on pine trees (from Akbulut and Stamps [<a href="#B13-forests-10-00198" class="html-bibr">13</a>]).</p> Full article ">Figure 2
<p>Flow diagram illustrating decisions within the model for the adult component of the beetle’s life cycle.</p> Full article ">Figure 3
<p>Relationships between minimum and maximum temperatures and degree days.</p> Full article ">Figure 4
<p>Phenological stages and degree day thresholds for the <span class="html-italic">Monochamus</span> genus model based on United States Department of Agriculture, Animal and Plant Health Inspection Service (USDA, APHIS) model [<a href="#B19-forests-10-00198" class="html-bibr">19</a>]. Representative date ranges are the median value for the Melbourne region, averaged over the period 2010–2018, assuming egg laying in mid-winter.</p> Full article ">Figure 5
<p>Relationship between beetle density and subsequent number of infected trees, showing the probabilities (<b>a</b>) and the number of infected trees (<b>b</b>).</p> Full article ">Figure 6
<p>Illustrative components of a dispersal sub-model showing (<b>a</b>) calculation of beetle flight distance probability density function (PDF) as a function of windspeed, maximum beetle flight time and number of healthy trees in initial cell, (<b>b</b>) corresponding dispersion PDF calculated in 8 cardinal directions, N, NE, E, SE, S, SW, W, NW, (<b>c</b>) flight-direction weighting based on likelihood of wind direction and (<b>d</b>) healthy tree weighting.</p> Full article ">Figure 7
<p>Historic records of trees sampled for nematodes (positive—solid yellow dots and negative —crosses) from a suspected pine beetle incursion in Melbourne during 1999/2000 [<a href="#B21-forests-10-00198" class="html-bibr">21</a>]. Concentric circles correspond to distances of 1, 5, 10, 20, 40 and 60 km from the original infected tree (red cross).</p> Full article ">Figure 8
<p>Observed positive nematode locations (yellow dots) and modelled beetle dispersion (black dots) of 100 adults from the Melbourne Ports area, based on median October wind speed (2010–2018) from the Melbourne (Olympic Park) weather station. The colour bar illustrates the PDF that underpins beetle dispersal, based on the wind speed and direction, tree density and maximum beetle fight time.</p> Full article ">Figure 9
<p>Comparison of observed positive nematode locations (yellow dots) and 10,000 modelled dispersed beetles (small black dots).</p> Full article ">Figure 10
<p>Percentages of observations for both observed nematode records (<span class="html-italic">n</span> = 29) and modelled beetle records (<span class="html-italic">n</span> = 10,000) according to the cardinal direction from the initial release point in Port Melbourne.</p> Full article ">Figure 11
<p>Average distances of records from the initial release point in Port Melbourne by cardinal direction for both observed nematode records (<span class="html-italic">n</span> = 29) and modelled beetle records (<span class="html-italic">n</span> = 10,000).</p> Full article ">Figure 12
<p>Boxplot comparisons of distance (<b>a</b>) and angle (<b>b</b>) of modelled and observed beetles/nematodes from the assumed release point.</p> Full article ">Figure 13
<p>Relationship between the number of adult beetles in the initial release and the number of cells with a least one mating couple in a 100 m × 100 m cell.</p> Full article ">
18 pages, 2124 KiB  
Article
Biomass Accumulation and Carbon Sequestration in an Age-Sequence of Mongolian Pine Plantations in Horqin Sandy Land, China
by Xiao Zhang, Xueli Zhang, Hui Han, Zhongjie Shi and Xiaohui Yang
Forests 2019, 10(2), 197; https://doi.org/10.3390/f10020197 - 22 Feb 2019
Cited by 37 | Viewed by 5016
Abstract
The Mongolian pine (Pinus sylvestris L. var. mongolica Litv.) was first introduced to the southeastern Horqin sandy land in the mid-1950s. Since then, it has been widely planted and has become the most important conifer species in Northern China, providing significant ecological, [...] Read more.
The Mongolian pine (Pinus sylvestris L. var. mongolica Litv.) was first introduced to the southeastern Horqin sandy land in the mid-1950s. Since then, it has been widely planted and has become the most important conifer species in Northern China, providing significant ecological, economic and social benefits. However, its function in sequestering carbon at different developmental stages has been little studied. In this study, twenty plots inventory and destructive sampling of eight trees were conducted in 12-, 19-, 34-, 48- and 58-year-old Mongolian pine stands of China. Allometric biomass equations (ABEs) for tree components were established and used to determine the magnitude and distribution of tree biomass and carbon density. The carbon density of the understory, forest floor and soil was also determined. The ABEs with age as the second variable could simply and accurately determine the biomass of plantation tree branches, foliage and fruit, which were considerably influenced by age. With increasing stand age, the proportion of stem biomass to total tree biomass increased from 22.2% in the 12-year-old stand to 54.2% in the 58-year-old stand, and the proportion of understory biomass to total ecosystem biomass decreased, with values of 7.5%, 4.6%, 4.4%, 4.1% and 3.0% in the five stands. The biomass of the forest floor was 0.00, 1.12, 2.04, 6.69 and 3.65 Mg ha−1 in the five stands. The ecosystem carbon density was 40.2, 73.4, 92.9, 89.9 and 87.3 Mg ha−1 in the 12-, 19-, 34-, 48-, and 58-year-old stands, in which soil carbon density accounted for the largest proportion, with values of 67.4%, 76.8%, 73.2%, 63.4%, and 57.7% respectively. The Mongolian pine had the potential for carbon sequestration during its development, especially in the early stages, however, in the later growth stage, the ecosystem carbon density decreased slightly. Full article
(This article belongs to the Special Issue Forest Stand Management and Biomass Growth)
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<p>Location of the five Mongolian pine stands (<b>a</b>) and pictures of the 12-year-old (<b>b</b>) and 58-year-old (<b>c</b>) Mongolian pine stands.</p> Full article ">Figure 2
<p>Biomass percentage distribution of tree individual components (<b>a</b>) and ecosystem components (<b>b</b>) in the 12-, 19-, 34-, 48-, and 58-year-old Mongolian pine (<span class="html-italic">Pinus sylvestris</span> L. var. <span class="html-italic">mongolica</span> Litv.) plantations.</p> Full article ">Figure 3
<p>Distribution patterns of the carbon concentration (<b>a</b>) and carbon density (<b>b</b>) at different soil depths in the 12-, 19-, 34-, 48-, and 58-year-old Mongolian pine (<span class="html-italic">Pinus sylvestris</span> L. var. <span class="html-italic">mongolica</span> Litv.) stands.</p> Full article ">Figure 4
<p>Biomass density of tree components versus stand age of Mongolian pine (<span class="html-italic">Pinus sylvestris</span> L. var. <span class="html-italic">mongolica</span> Litv.). The solid line and dotted line represent linear regression and polynomial regression, respectively.</p> Full article ">
16 pages, 2429 KiB  
Article
The Cumulative Effects of Forest Disturbance and Climate Variability on Streamflow in the Deadman River Watershed
by Krysta Giles-Hansen, Qiang Li and Xiaohua Wei
Forests 2019, 10(2), 196; https://doi.org/10.3390/f10020196 - 22 Feb 2019
Cited by 21 | Viewed by 4542
Abstract
Climatic variability and cumulative forest cover change are the two dominant factors affecting hydrological variability in forested watersheds. Separating the relative effects of each factor on streamflow is gaining increasing attention. This study adds to the body of literature by quantifying the relative [...] Read more.
Climatic variability and cumulative forest cover change are the two dominant factors affecting hydrological variability in forested watersheds. Separating the relative effects of each factor on streamflow is gaining increasing attention. This study adds to the body of literature by quantifying the relative contributions of those two drivers to the changes in annual mean flow, low flow, and high flow in a large forested snow dominated watershed, the Deadman River watershed (878 km2) in the Southern Interior of British Columbia, Canada. Over the study period of 1962 to 2012, the cumulative effects of forest disturbance significantly affected the annual mean streamflow. The effects became statistically significant in 1989 at the cumulative forest disturbance level of 12.4% of the watershed area. The modified double mass curve and sensitivity-based methods consistently revealed that forest disturbance and climate variability both increased annual mean streamflow during the disturbance period (1989–2012), with an average increment of 14 mm and 6 mm, respectively. The paired-year approach was used to further investigate the relative contributions to low and high flows. Our analysis showed that low and high flow increased significantly by 19% and 58%, respectively over the disturbance period (p < 0.05). We conclude that forest disturbance and climate variability have significantly increased annual mean flow, low flow and high flow over the last 50 years in a cumulative and additive manner in the Deadman River watershed. Full article
(This article belongs to the Special Issue Forest Hydrology and Watershed)
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<p>The location of watershed boundary, hydrometric station, stream network, forest logging, and mountain pine beetle (MPB) infestation of the Deadman River watershed, located in British Columbia, Canada.</p> Full article ">Figure 2
<p>Equivalent clear-cut area (ECA) (%) since year of disturbance by mountain pine beetle (MPB).</p> Full article ">Figure 3
<p>Annual area disturbed (km<sup>2</sup>) and Cumulative Equivalent clear-cut area (CECA) in percent (% of watershed area) by disturbance type and total in the Deadman River watershed from 1960 to 2012.</p> Full article ">Figure 4
<p>Modified Double Mass Curve (MDMC) of cumulative effective precipitation (P<sub>ae</sub>) versus cumulative annual mean flow (Q<sub>a</sub>) in the Deadman River watershed from 1960 to 2012. The ‘Predicted’ line is from the linear equation shown on the graph. The breakpoint is in 1989.</p> Full article ">
21 pages, 3148 KiB  
Article
Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China
by Jian Li, Jingwen He, Ying Liu, Daojie Wang, Loretta Rafay, Can Chen, Tao Hong, Hailan Fan and Yongming Lin
Forests 2019, 10(2), 195; https://doi.org/10.3390/f10020195 - 21 Feb 2019
Cited by 14 | Viewed by 3846
Abstract
Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged [...] Read more.
Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas. Full article
(This article belongs to the Special Issue Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches)
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<p>The distribution of damaged vegetation in nine worst-hit cities and counties and the typical CEBRS-02B image screenshots of damaged vegetation in the Wenchuan earthquake affected area.</p> Full article ">Figure 2
<p>The digital elevation model (DEM) of the 25 m × 25 m cell size (<b>a</b>), aspect (<b>b</b>), and slope (<b>c</b>) in nine worst-hit cities and counties.</p> Full article ">Figure 3
<p>The relationships between the correlograms of the Moran’s <span class="html-italic">I</span> of ten vegetation types and those of all the destructed vegetation at four different aggregation levels including (<b>a</b>) the 1 × 1 aggregation level, (<b>b</b>) the 2 × 2 aggregation level, (<b>c</b>) the 4 × 4 aggregation level, and (<b>d</b>) the 8 × 8 aggregation level. The points in black are significant (<span class="html-italic">P</span> &lt; 0.001). The vegetation type abbreviations are defined in <a href="#forests-10-00195-t004" class="html-table">Table 4</a>.</p> Full article ">Figure 4
<p>The correlograms of the Moran’s <span class="html-italic">I</span> in 19 potential driving factors at the aggregation level 1 × 1. (<b>a</b>): Correlograms with Moran’s <span class="html-italic">I</span> in the distances of factors and elevation; (<b>b</b>): Correlograms with Moran’s <span class="html-italic">I</span> in seismic intensity zones; (<b>c</b>): Correlograms with Moran’s <span class="html-italic">I</span> in slope classes; (<b>d</b>): Correlograms with Moran’s <span class="html-italic">I</span> in slope aspects. The points in black are significant (<span class="html-italic">P</span> &gt; 0.001). The driving factor abbreviations are defined in <a href="#forests-10-00195-t004" class="html-table">Table 4</a>.</p> Full article ">Figure 5
<p>Cluster (<b>a</b>) and significance (<b>b</b>) maps of local indicators of spatial association (LISA) for all damaged vegetation at the 1 × 1 aggregation level.</p> Full article ">Figure 6
<p>The correlograms of the Moran’s <span class="html-italic">I</span> of all damaged vegetation in the residuals of three models at the aggregation level 1 × 1. The points in black are significant (<span class="html-italic">P</span> &lt; 0.001). The points in red are insignificant (<span class="html-italic">P</span> &gt; 0.001). OLS: the standard linear model based on the ordinary least squares; SLM: spatial lag model; and SEM: spatial error model.</p> Full article ">
13 pages, 8645 KiB  
Article
Characterisation of Physical and Mechanical Properties of Unthinned and Unpruned Plantation-Grown Eucalyptus nitens H.Deane & Maiden Lumber
by Mohammad Derikvand, Nathan Kotlarewski, Michael Lee, Hui Jiao and Gregory Nolan
Forests 2019, 10(2), 194; https://doi.org/10.3390/f10020194 - 21 Feb 2019
Cited by 29 | Viewed by 5709
Abstract
The use of fast-growing plantation eucalypt (i.e., pulpwood eucalypt) in the construction of high-value structural products has received special attention from the timber industry in Australia and worldwide. There is still, however, a significant lack of knowledge regarding the physical and mechanical properties [...] Read more.
The use of fast-growing plantation eucalypt (i.e., pulpwood eucalypt) in the construction of high-value structural products has received special attention from the timber industry in Australia and worldwide. There is still, however, a significant lack of knowledge regarding the physical and mechanical properties of the lumber from such plantation resources as they are mainly being managed to produce woodchips. In this study, the physical and mechanical properties of lumber from a 16-year-old pulpwood Eucalyptus nitens H.Deane & Maiden resource from the northeast of Tasmania, Australia was evaluated. The tests were conducted on 318 small wood samples obtained from different logs harvested from the study site. The tested mechanical properties included bending modulus of elasticity (10,377.7 MPa) and modulus of rupture (53 MPa), shear strength parallel (5.5 MPa) and perpendicular to the grain (8.5 MPa), compressive strength parallel (42.8 MPa) and perpendicular to the grain (4.1 MPa), tensile strength perpendicular to the grain (3.4 MPa), impact bending (23.6 J/cm2), cleavage (1.6 kN) and Janka hardness (23.2 MPa). Simple linear regression models were developed using density and moisture content to predict the mechanical properties. The variations in the moisture content after conventional kiln drying within randomly selected samples in each test treatment were not high enough to significantly influence the mechanical properties. A relatively high variation in the density values was observed that showed significant correlations with the changes in the mechanical properties. The presence of knots increased the shear strength both parallel and perpendicular to the grain and significantly decreased the tensile strength of the lumber. The results of this study created a profile of material properties for the pulpwood E. nitens lumber that can be used for numerical modelling of any potential structural product from such a plantation resource. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p>The arrangement of the three general annual growth ring orientations in the cross-section of the samples.</p> Full article ">Figure 2
<p>The location of knot in samples prepared for shear strength parallel (<span class="html-italic">SPA</span>) test (<b>a</b>), perpendicular (<span class="html-italic">SPE</span>) test (<b>b</b>) and tensile strength perpendicular (<span class="html-italic">TPE</span>) test (<b>c</b>,<b>d</b>).</p> Full article ">Figure 3
<p>The correlation between <span class="html-italic">MC</span> and basic density.</p> Full article ">Figure 4
<p>The most frequently observed failure modes of the bending test samples. Combined compression and bending tension failure (<b>a</b>) and grain tension failure (<b>b</b>).</p> Full article ">Figure 5
<p>Fibre deformations in hardness samples with different annual growth ring orientations.</p> Full article ">Figure 6
<p>Failure modes of cleavage samples.</p> Full article ">Figure 7
<p>Failure modes of <span class="html-italic">SPE</span> (left) and <span class="html-italic">SPA</span> (right) with and without knot.</p> Full article ">Figure 8
<p>Failure modes of clear (left) and knotted (right) <span class="html-italic">TPE</span> samples.</p> Full article ">Figure 9
<p>Failure modes of the <span class="html-italic">CPA</span> samples. (<b>a</b>,<b>e</b>) compression and shearing parallel to grain, (<b>b</b>,<b>f</b>) end-rolling and shearing, (<b>c</b>,<b>d</b>) crushing.</p> Full article ">
16 pages, 3081 KiB  
Article
Expression Patterns of MYB (V-myb Myeloblastosis Viral Oncogene Homolog) Gene Family in Resistant and Susceptible Tung Trees Responding to Fusarium Wilt Disease
by Xue Wang, Qiyan Zhang, Ming Gao, Liwen Wu, Yangdong Wang and Yicun Chen
Forests 2019, 10(2), 193; https://doi.org/10.3390/f10020193 - 21 Feb 2019
Cited by 3 | Viewed by 3048
Abstract
Vernicia fordii (tung oil tree) is famous in the world for its production of tung oil. Unfortunately, it was infected by the soil-borne fungus Fusarium oxysporum f. sp. fordii 1 (Fof-1) and suffered serious wilt disease. Conversely, its sister species V. [...] Read more.
Vernicia fordii (tung oil tree) is famous in the world for its production of tung oil. Unfortunately, it was infected by the soil-borne fungus Fusarium oxysporum f. sp. fordii 1 (Fof-1) and suffered serious wilt disease. Conversely, its sister species V. montana is highly resistant to Fof-1. The MYB (v-myb myeloblastosis viral oncogene homolog) transcription factors were activated during the pathogen Fof-1 infection according to our previous comparative transcriptomic results. Depending on whether the sequence has a complete MYB-DNA-binding domain, a total of 75 VfMYB and 77 VmMYB genes were identified in susceptible V. fordii and resistant V. montana, respectively. In addition, we detected 49 pairs of one-to-one orthologous Vf/VmMYB genes with the reciprocal-best BLAST-hits (RBH)method. In order to investigate the expression modes and the internal network of MYB transcription factors in the two species responding to Fusarium wilt disease, the expressions of Vf/VmMYBs were then investigated and we found that most orthologous Vf/VmMYB genes exhibited similar expression patterns during the Fof-1 infection. However, four pairs of Vf/VmMYB genes, annotated as unknown proteins and mediator of root architecture, demonstrated absolute opposite expression patterns in the two Vernicia species responding to Fof-1. The interaction network of VmMYB genes were further constructed using weighted gene co-expression network analysis (WGCNA) method and four hub genes showing extremely high interaction with the other 1157 genes were identified. RT-qPCR result verified the opposite expression pattern of the hub gene VmMYB011 and VmMYB041 in two Vernicia species. In summary, co-expression network of the Vf/VmMYBs and significantly opposite related pairs of genes in resistant and susceptible Vernicia species provided knowledge for understanding the molecular basis of Vernicia responding to Fusarium wilt disease. Full article
(This article belongs to the Special Issue Functional and Phylogenetic Signals of Forest Tree Communities)
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<p>The sequence logos of R2 (<b>a</b>) and R3 (<b>b</b>) repeats are distributed in all R2R3-MYB proteins of the <span class="html-italic">Vernicia</span> species. The analysis of conserved domains of the R2 and R3 MYB sequences are based on all the full-length alignments of <span class="html-italic">VfR2R3-MYB</span> and <span class="html-italic">VmR2R3-MYB</span> proteins in MEME Suite version 4.10.0.</p> Full article ">Figure 2
<p>Phylogenetic relationships of MYB family proteins in <span class="html-italic">Arabidopsis</span>, <span class="html-italic">V. fordii</span> and <span class="html-italic">V. montana</span>. 278 MYB proteins from <span class="html-italic">Arabidopsis</span>, <span class="html-italic">V. fordii</span> and <span class="html-italic">V. montana</span> were used to do the multiple sequence alignment and construct the unrooted phylogenetic tree. BioEdit 5.0.6 with the default settings and MEGA 7.0.7 with the UPGMA method and 1000 bootstrap replications were used. Different colored dots represent different subgroups.</p> Full article ">Figure 3
<p>The conserved motifs of MYBs in <span class="html-italic">V. fordii</span> and <span class="html-italic">V. montana</span>. The unrooted phylogenetic tree of 64 <span class="html-italic">VfMYBs</span> and 69 <span class="html-italic">VmMYBs</span> was constructed by MEGA 7.0.7 software with the neighbour-joining (NJ) method and 1000 bootstrap replications. All subgroups named C1 to C35 are marked in different colors. The analysis of conserved motifs was carried out using the MEME online tool, which was set to search for at most twenty motifs with lengths between 6 and 200 amino acids. The colored boxes represent corresponding motifs.</p> Full article ">Figure 4
<p>Expression profiles of Vf/VmMYB homologous genes under infection by <span class="html-italic">Fof-1</span> in <span class="html-italic">V. fordii</span> and <span class="html-italic">V. montana</span>. F0-F3 represented the expression of VfMYB homologous genes in <span class="html-italic">V. fordii</span> during the infection stage (0, 1, 2, 3) by the pathogen <span class="html-italic">Fof</span>1; M0-M3 represented the expression of VmMYB homologous genes in <span class="html-italic">V. montana</span> during the infection stage (0, 1, 2, 3) by the pathogen <span class="html-italic">Fof</span>1. The expression levels were illustrated by the gradient color. Red and green represent high and low expression level respectively.</p> Full article ">Figure 5
<p>Network analysis of <span class="html-italic">VmMYB013, VmMYB034, VmMYB011</span> and <span class="html-italic">VmMYB041</span> hub genes in response to <span class="html-italic">Fof-1</span>. The four hub genes interacted with 634 genes and exhibited a total of 1157 interactions. The red nodes represent the four hub genes. Bottle blue nodes represent the interaction genes.</p> Full article ">Figure 6
<p>Tissue-specific expression analyses of three hub genes in <span class="html-italic">Vernicia</span>. The y-axis indicates the relative levels of expression. The left vertical axis indicates the expression levels of <span class="html-italic">VmMYB</span>; the right vertical axis indicates the expression levels of <span class="html-italic">VfMYB</span>. The expression levels were analysed using RT-qPCR in triplicate. The x-axis indicates root (R), stem (S), leaf (L), kernel (K), stamen (St), petal (P) and bud (B) tissues used for the expression analysis (<b>a</b>). Root tissues of <span class="html-italic">V. fordii</span> and <span class="html-italic">V. montana</span> were used for the expression analysis responding to <span class="html-italic">Fof-1</span> (<b>b</b>). The numbers in the x-axis represent the four stages of infection, as follows: 0, uninfected stage; 1, early stage of infection; 2, middle stage of infection; 3, late stage of infection.</p> Full article ">
11 pages, 1699 KiB  
Article
Forest Soil Profile Inversion and Mixing Change the Vertical Stratification of Soil CO2 Concentration without Altering Soil Surface CO2 Flux
by Xiaoling Wang, Shenglei Fu, Jianxiong Li, Xiaoming Zou, Weixin Zhang, Hanping Xia, Yongbiao Lin, Qian Tian and Lixia Zhou
Forests 2019, 10(2), 192; https://doi.org/10.3390/f10020192 - 21 Feb 2019
Cited by 12 | Viewed by 3714
Abstract
In order to gain more detailed knowledge of the CO2 concentration gradient in forest soil profiles and to better understand the factors that control CO2 concentration along forest soil profiles, we examined the soil surface CO2 flux, soil properties and [...] Read more.
In order to gain more detailed knowledge of the CO2 concentration gradient in forest soil profiles and to better understand the factors that control CO2 concentration along forest soil profiles, we examined the soil surface CO2 flux, soil properties and soil profile CO2 concentration in upright (CK), inverted and mixed soil columns with a depth of 60 cm in two subtropical forests in China from May 2008 to December 2009. The results showed that: (1) The SOC (soil organic carbon), TN (total N) and microbial biomass were higher in the deeper layers in the inverted soil column, which was consistent with an increase in CO2 concentration in the deeper soil layer. Furthermore, the biogeochemical properties were homogenous among soil layers in the mixed soil column. (2) CO2 concentration in the soil profile increased with depth in CK while soil column inversion significantly intensified this vertical stratification as the most active layer (surface soil) was now at the bottom. The stratification of CO2 concentration along the soil profile in the mixed soil column was similar to that in CK but it was not intensified after soil was mixed. (3) The soil surface CO2 flux did not significantly change after the soil column was inverted. The surface CO2 flux rate of the mixed soil column was higher compared to that of the inverted soil column but was not significantly different from CK. Our results indicated that the profile soil CO2 production was jointly controlled by soil properties related to CO2 production (e.g., SOC content and soil microbial biomass) and those related to gas diffusion (e.g., soil bulk density and gas molecular weight), but the soil surface CO2 flux was mainly determined by soil surface temperature and may be affected by the intensity of soil disturbance. Full article
(This article belongs to the Special Issue Disturbance Effects on Soil Carbon and Greenhouse Gas Emissions in Forest Ecosystems)
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<p>Seasonal variations of CO<sub>2</sub> concentration along soil profiles in a coniferous forest (CF) and a broad-leaved forest (BF). Different soil column treatments were: (<b>a</b>) CF-CK (upright soil column); (<b>b</b>) BF-CK; (<b>c</b>) CF-Inverted; (<b>d</b>) BF-Inverted; (<b>e</b>) CF-Mixed; and (<b>f</b>) BF-Mixed. Data are shown as means ± SE, <span class="html-italic">n</span> = 6.</p> Full article ">Figure 2
<p>Fluxes of CO<sub>2</sub> in a coniferous forest (CF) (<b>a</b>) and a broad-leaved forest (BF) (<b>b</b>) in different soil column treatments: CK, inverted and mixed. Error bars represent standard errors of the mean (<span class="html-italic">n</span> = 6).</p> Full article ">Figure 3
<p>Relationship between soil profile CO<sub>2</sub> concentration and soil temperature. Different soil column treatments were: (<b>a</b>) CF-CK; (<b>b</b>) BF-CK; (<b>c</b>) CF-Inverted; (<b>d</b>) BF-Inverted; (<b>e</b>) CF-Mixed; and (<b>f</b>) BF-Mixed.</p> Full article ">Figure A1
<p>Diagram depicting soil column manipulation for field measurements of CO<sub>2</sub>.</p> Full article ">Figure A2
<p>Precipitation and air temperature in Heshan station during the study period.</p> Full article ">
14 pages, 1701 KiB  
Article
Profile, Level of Vulnerability and Spatial Pattern of Deforestation in Sulawesi Period of 1990 to 2018
by Syamsu Rijal, Roland A. Barkey, Nasri Nasri and Munajat Nursaputra
Forests 2019, 10(2), 191; https://doi.org/10.3390/f10020191 - 20 Feb 2019
Cited by 16 | Viewed by 4746
Abstract
Deforestation is an event of loss of forest cover to another cover. Sulawesi forests have the potential to be deforested as with Sumatra and Kalimantan. This study aims to provide information on deforestation events in Sulawesi from 1990 to 2018. The data used [...] Read more.
Deforestation is an event of loss of forest cover to another cover. Sulawesi forests have the potential to be deforested as with Sumatra and Kalimantan. This study aims to provide information on deforestation events in Sulawesi from 1990 to 2018. The data used in this study are (1) land cover in 1990, 2000, 2010; (2) Landsat 8 imagery in 2018; (3) administrative map of BIG in 2018. The methods used are (1) image classification with on-screen digitation techniques following the PPIK land cover classification guidelines, Forestry Planning Agency (2008) using ArcGIS Desktop 10.6 from ESRI; (2) overlapping maps; (3) analysis of deforestation; (4) analysis of deforestation profiles, (5) vulnerability analysis; and (6) analysis of distribution patterns of deforestation. The results showed that the profile of deforestation occurring on Sulawesi Island in the 1990–2018 observation period was dominated by profile 3-1-1 (the proportion of large forest area, the highest incidence of deforestation early stage at the beginning, at a low rate) in 13 districts. The level of vulnerability to deforestation is a non-vulnerable category (37 districts) which is directed to become a priority in handling deforestation in Sulawesi. Spatial patterns of the deforestation that occurred randomly and were scattered are dominated by shrubs, dryland agricultural activities, and small-scale plantations. Full article
(This article belongs to the Special Issue Impact of Land Use Change on Forest Biodiversity)
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<p>Forest Cover Loss in 1990–2018.</p> Full article ">Figure 2
<p>Tabulation Graph of Profile of Deforestation in Sulawesi Period 1990 to 2018.</p> Full article ">Figure 3
<p>Level of vulnerability to deforestation in Sulawesi from 1990 to 2018.</p> Full article ">Figure 4
<p>The spatial pattern of deforestation in Sulawesi in (<b>a</b>–<b>c</b>).</p> Full article ">Figure 4 Cont.
<p>The spatial pattern of deforestation in Sulawesi in (<b>a</b>–<b>c</b>).</p> Full article ">
14 pages, 3624 KiB  
Article
Predicting the Potential Distribution of Paeonia veitchii (Paeoniaceae) in China by Incorporating Climate Change into a Maxent Model
by Keliang Zhang, Yin Zhang and Jun Tao
Forests 2019, 10(2), 190; https://doi.org/10.3390/f10020190 - 20 Feb 2019
Cited by 65 | Viewed by 6569
Abstract
A detailed understanding of species distribution is usually a prerequisite for the rehabilitation and utilization of species in an ecosystem. Paeonia veitchii (Paeoniaceae), which is an endemic species of China, is an ornamental and medicinal plant that features high economic and ecological values. [...] Read more.
A detailed understanding of species distribution is usually a prerequisite for the rehabilitation and utilization of species in an ecosystem. Paeonia veitchii (Paeoniaceae), which is an endemic species of China, is an ornamental and medicinal plant that features high economic and ecological values. With the decrease of its population in recent decades, it has become a locally endangered species. In present study, we modeled the potential distribution of P. veitchii under current and future conditions, and evaluated the importance of the factors that shape its distribution. The results revealed a highly and moderately suitable habitat for P. veitchii that encompassed ca. 605,114 km2. The central area lies in northwest Sichuan Province. Elevation, temperature seasonality, annual mean precipitation, and precipitation seasonality were identified as the most important factors shaping the distribution of P. veitchii. Under the scenario with a low concentration of greenhouse gas emissions (RCP 2.6), we predicted an overall expansion of the potential distribution by 2050, followed by a slight contraction in 2070. However, with the scenario featuring intense greenhouse gas emissions (RCP 8.5), the range of suitable habitat should increase with the increasing intensity of global warming. The information that was obtained in the present study can provide background information related to the long-term conservation of this species. Full article
(This article belongs to the Special Issue Geographic Information Systems and Their Applications in Forests)
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<p>Processing methodology showing a summary of the full workflow, which served as the basis of the analyses.</p> Full article ">Figure 2
<p>Distribution records of <span class="html-italic">Paeonia veitchii</span> in China. Outlines of provinces and other administrative areas are shown.</p> Full article ">Figure 3
<p>Predicted current distribution model (<b>A</b>) and the core distribution shifts (<b>B</b>) under four climate scenario/year for <span class="html-italic">Paeonia veitchii</span>. Arrow indicates the magnitude and direction of predicted change through time. ① Tibet; ② Qinghai; ③ Gansu; ④ Ningxia; ⑤ Shaanxi; ⑥ Shanxi; ⑦ Sichuan; and, ⑧ Yunnan.</p> Full article ">Figure 4
<p>Jackknife test for evaluating the relative importance of environmental variables for <span class="html-italic">Paeonia veitchii</span> in China. <a href="#forests-10-00190-t001" class="html-table">Table 1</a> provides the full definitions of each variable.</p> Full article ">Figure 5
<p>Response curves for important environmental predictors in the species distribution model for <span class="html-italic">Paeonia veitchii</span>.</p> Full article ">Figure 6
<p>Future species distribution models (SDMs) of <span class="html-italic">Paeonia veitchii</span> under climate change scenarios representative concentration pathway 2.6 (RCP 2.6) and RCP 8.5. (<b>A</b>) SDM for <span class="html-italic">P. veitchii</span> under RCP 2.6 in 2050; (<b>B</b>) comparison between the current SDM and the SDM under RCP 2.6 in 2050; (<b>C</b>) SDM for <span class="html-italic">P. veitchii</span> under RCP 2.6 in 2070; (<b>D</b>) comparison between the current SDM and the SDM under RCP 2.6 in 2070; (<b>E</b>) SDM for <span class="html-italic">P. veitchii</span> under RCP 8.5 in 2050; (<b>F</b>) comparison between the current SDM and the SDM under RCP 8.5 in 2050; (<b>G</b>) SDM for <span class="html-italic">P. veitchii</span> under RCP 8.5 in 2050; and, (<b>H</b>) comparison between the current SDM and the SDM under RCP 8.5 in 2070. ① Tibet; ② Qinghai; ③ Gansu; ④ Ningxia; ⑤ Shaanxi; ⑥ Shanxi; ⑦ Sichuan; ⑧ Yunnan.</p> Full article ">
22 pages, 12311 KiB  
Article
Influence of Site Conditions and Quality of Birch Wood on Its Properties and Utilization after Heat Treatment. Part I—Elastic and Strength Properties, Relationship to Water and Dimensional Stability
by Vlastimil Borůvka, Roman Dudík, Aleš Zeidler and Tomáš Holeček
Forests 2019, 10(2), 189; https://doi.org/10.3390/f10020189 - 20 Feb 2019
Cited by 24 | Viewed by 4249
Abstract
This work deals with the quality of birch (Betula pendula) wood from different sites and the impact of heat treatment on it. Two degrees of heat treatment were used, 170 °C and 190 °C. The resulting property values were compared with [...] Read more.
This work deals with the quality of birch (Betula pendula) wood from different sites and the impact of heat treatment on it. Two degrees of heat treatment were used, 170 °C and 190 °C. The resulting property values were compared with reference to untreated wood samples. These values were wood density, compressive strength, modulus of elasticity (MOE), bending strength (MOR), impact bending strength (toughness), hardness, swelling, limit of hygroscopicity, moisture content and color change. It was supposed that an increase in heat-treatment temperature could reduce strength properties and, adversely, lead to better shape and dimensional stability, which was confirmed by experiments. It was also shown that the properties of the wood before treatment affected their condition after heat treatment, and that the characteristic values and variability of birch properties from 4 sites, 8 stems totally, were reflected in the properties of the heat-treated wood. Values of static MOR were the exception, where the quality of the input wood was less significant at a higher temperature, and this was even more significant in impact bending strength, where it manifested at a lower temperature degree. Impact bending strength also proved to be significantly negatively affected by heat treatment, about 48% at 170 °C, and up to 67% at 190 °C. On the contrary, the most positive results were the MOE and hardness increases at 170 °C by about 30% and about 21%, respectively, with a decrease in swelling at 190 °C by about 31%. On the basis of color change and other ascertained properties, there is a possibility that, after suitable heat treatment, birch could replace other woods (e.g., beech) for certain specific purposes, particularly in the furniture industry. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p>Location of study areas in the Czech Republic and description of particular forest stands.</p> Full article ">Figure 2
<p>Cutting diagram for test specimen preparation. (<b>a</b>) Basal section of the trunk, (<b>b</b>) cutting of selected trunk section, (<b>c</b>) cutting of the disc and method for the experimental measurement of hardness and (<b>d</b>) cutting diagram for preparation of testing samples. Blue coloring = reference, with no treatment; yellow coloring = heat treatment at 170 °C; red coloring = heat treatment at 190 °C; upper set of samples = for the determination of impact bending strength; bottom set of samples = for the determination of dynamic elasticity modulus, static elasticity modulus and bending strength; small samples marked A and B = for determination of density, compressive strength, swelling and moisture content.</p> Full article ">Figure 2 Cont.
<p>Cutting diagram for test specimen preparation. (<b>a</b>) Basal section of the trunk, (<b>b</b>) cutting of selected trunk section, (<b>c</b>) cutting of the disc and method for the experimental measurement of hardness and (<b>d</b>) cutting diagram for preparation of testing samples. Blue coloring = reference, with no treatment; yellow coloring = heat treatment at 170 °C; red coloring = heat treatment at 190 °C; upper set of samples = for the determination of impact bending strength; bottom set of samples = for the determination of dynamic elasticity modulus, static elasticity modulus and bending strength; small samples marked A and B = for determination of density, compressive strength, swelling and moisture content.</p> Full article ">Figure 3
<p>(<b>a</b>) Photograph of the thermal chamber used for the treatment, (<b>b</b>) including the arrangement of the samples inside.</p> Full article ">Figure 4
<p>Diagram of the heat-treatment procedure at (<b>a</b>) 170 °C and (<b>b</b>) 190 °C.</p> Full article ">Figure 5
<p>Photo of Tira 50 kN testing machine, detailed picture of loading and schema of bending moment with two load forces.</p> Full article ">Figure 6
<p>(<b>a</b>) Untreated birch, (<b>b</b>) birch treated at 160 °C, (<b>c</b>) birch treated at 170 °C, (<b>d</b>) birch treated at 180 °C, (<b>e</b>) birch treated at 190 °C, (<b>f</b>) birch treated at 200 °C, (<b>g</b>) untreated beech, (<b>h</b>) steamed beech.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Untreated birch, (<b>b</b>) birch treated at 160 °C, (<b>c</b>) birch treated at 170 °C, (<b>d</b>) birch treated at 180 °C, (<b>e</b>) birch treated at 190 °C, (<b>f</b>) birch treated at 200 °C, (<b>g</b>) untreated beech, (<b>h</b>) steamed beech.</p> Full article ">Figure 7
<p>Graphic visualization of the effect of heat-treatment temperature on (<b>a</b>) wood density, (<b>c</b>) compressive strength and (<b>d</b>) specific compressive strength. Graphic visualization of the effect of heat-treatment temperature and site on (<b>e</b>) density and (<b>g</b>) compressive strength. Graphic visualization of the effect of heat-treatment temperature and tree on (<b>f</b>) density and(<b>h</b>) compressive strength. The relationship between density and compressive strength, regardless of the heat-treatment degree, is shown in (<b>b</b>). Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 7 Cont.
<p>Graphic visualization of the effect of heat-treatment temperature on (<b>a</b>) wood density, (<b>c</b>) compressive strength and (<b>d</b>) specific compressive strength. Graphic visualization of the effect of heat-treatment temperature and site on (<b>e</b>) density and (<b>g</b>) compressive strength. Graphic visualization of the effect of heat-treatment temperature and tree on (<b>f</b>) density and(<b>h</b>) compressive strength. The relationship between density and compressive strength, regardless of the heat-treatment degree, is shown in (<b>b</b>). Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 8
<p>Graphic visualization of the effect of heat-treatment temperature on (<b>a</b>) oven-dry density, (<b>b</b>) basic density, (<b>c</b>) limit of hygroscopicity (fiber saturation point—FSP), (<b>d</b>) radial and tangential swelling, and (<b>e</b>) volumetric swelling. (<b>f</b>) The relationship between density and volumetric swelling, regardless of the heat-treatment degree. (<b>g</b>) Graphic visualization of the effect of heat-treatment temperature and site on volumetric swelling. (<b>h</b>) Graphic visualization of the effect of heat-treatment temperature and tree on volumetric swelling. Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 8 Cont.
<p>Graphic visualization of the effect of heat-treatment temperature on (<b>a</b>) oven-dry density, (<b>b</b>) basic density, (<b>c</b>) limit of hygroscopicity (fiber saturation point—FSP), (<b>d</b>) radial and tangential swelling, and (<b>e</b>) volumetric swelling. (<b>f</b>) The relationship between density and volumetric swelling, regardless of the heat-treatment degree. (<b>g</b>) Graphic visualization of the effect of heat-treatment temperature and site on volumetric swelling. (<b>h</b>) Graphic visualization of the effect of heat-treatment temperature and tree on volumetric swelling. Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 9
<p>Graphic visualization of the effect of heat-treatment temperature on (<b>a</b>) dynamic elasticity modulus, (<b>b</b>) static elasticity modulus, (<b>c</b>) bending strength, (<b>d</b>) impact bending strength and (<b>i</b>) hardness in the radial plane. Graphic visualization of the effect of heat-treatment temperature on (<b>j</b>) hardness profile in the horizontal direction along the radius of the tree. Graphic visualization of the effect of heat-treatment temperature and site on (<b>e</b>) bending strength, (<b>g</b>) impact bending strength and (<b>k</b>) hardness of the radial plane. Graphic visualization of the effect of heat-treatment temperature and tree on (<b>f</b>) bending strength, (<b>h</b>) impact bending strength, and (<b>l</b>) hardness in the radial plane. Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 9 Cont.
<p>Graphic visualization of the effect of heat-treatment temperature on (<b>a</b>) dynamic elasticity modulus, (<b>b</b>) static elasticity modulus, (<b>c</b>) bending strength, (<b>d</b>) impact bending strength and (<b>i</b>) hardness in the radial plane. Graphic visualization of the effect of heat-treatment temperature on (<b>j</b>) hardness profile in the horizontal direction along the radius of the tree. Graphic visualization of the effect of heat-treatment temperature and site on (<b>e</b>) bending strength, (<b>g</b>) impact bending strength and (<b>k</b>) hardness of the radial plane. Graphic visualization of the effect of heat-treatment temperature and tree on (<b>f</b>) bending strength, (<b>h</b>) impact bending strength, and (<b>l</b>) hardness in the radial plane. Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 10
<p>The equilibrium moisture content of birch wood after heat treatment and subsequent air conditioning at temperature 20 ± 2 °C and a relative humidity of 65 ± 5%. Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 11
<p>The change of volumetric swelling after repeating one and two cycles of storage under the water surface and re-drying. Significance level is 95%. REF = reference, with no treatment; 170 = heat treatment at 170 °C; 190 = heat treatment at 190 °C.</p> Full article ">Figure 12
<p>(<b>a</b>) Sample of untreated birch veneer. (<b>b</b>) Sample of treated veneer under a temperature of 200 °C for 2 h.</p> Full article ">
12 pages, 1498 KiB  
Article
Variability of Aboveground Litter Inputs Alters Soil Carbon and Nitrogen in a Coniferous–Broadleaf Mixed Forest of Central China
by Renhui Miao, Jun Ma, Yinzhan Liu, Yanchun Liu, Zhongling Yang and Meixia Guo
Forests 2019, 10(2), 188; https://doi.org/10.3390/f10020188 - 20 Feb 2019
Cited by 95 | Viewed by 5935
Abstract
Global changes and human disturbances can strongly affect the quantity of aboveground litter entering soils, which could result in substantial cascading effects on soil biogeochemical processes in forests. Despite extensive reports, it is unclear how the variations in litter depth affect soil carbon [...] Read more.
Global changes and human disturbances can strongly affect the quantity of aboveground litter entering soils, which could result in substantial cascading effects on soil biogeochemical processes in forests. Despite extensive reports, it is unclear how the variations in litter depth affect soil carbon and nitrogen cycling. The responses of soil carbon and nitrogen to the variability of litter inputs were examined in a coniferous–broadleaf mixed forest of Central China. The litter input manipulation included five treatments: no litter input, natural litter, double litter, triple litter, and quadruple litter. Multifold litter additions decreased soil temperature but did not affect soil moisture after 2.5 years. Reductions in soil pH under litter additions were larger than increases under no litter input. Litter quantity did not affect soil total organic carbon, whereas litter addition stimulated soil dissolved organic carbon more strongly than no litter input suppressed it. The triggering priming effect of litter manipulation on soil respiration requires a substantial litter quantity, and the impacts of a slight litter change on soil respiration are negligible. Litter quantity did not impact soil total nitrogen, and only strong litter fluctuations changed the content of soil available nitrogen (nitrate nitrogen and ammonium nitrogen). Litter addition enhanced soil microbial biomass carbon and nitrogen more strongly than no litter input. Our results imply that the impacts of multifold litter inputs on soil carbon and nitrogen are different with a single litter treatment. These findings suggest that variability in aboveground litter inputs resulting from environmental change and human disturbances have great potential to change soil carbon and nitrogen in forest ecosystems. The variability of aboveground litter inputs needs to be taken into account to predict the responses of terrestrial soil carbon and nitrogen cycling to environmental changes and forest management. Full article
(This article belongs to the Special Issue Forest Carbon Dynamics under Changing Climate and Disturbance Regimes)
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<p>Effects of litter quantity on soil temperature (<b>A</b>), moisture (<b>B</b>), and pH (<b>C</b>). Different letters indicate significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05. Values are presented as the means ± standard error. NL: no litter input; L: natural litter; DL: double litter; TL: triple litter; QL: quadruple litter.</p> Full article ">Figure 2
<p>Effects of litter quantity on soil total organic carbon (<b>A</b>), dissolved organic carbon (DOC) (<b>B</b>), total nitrogen (<b>C</b>), the C:N ratio (<b>D</b>), nitrate nitrogen (<b>E</b>), and ammonium nitrogen (<b>F</b>). Different letters indicate significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05. Values are presented as the means ± standard error. See <a href="#forests-10-00188-f001" class="html-fig">Figure 1</a> for abbreviations.</p> Full article ">Figure 3
<p>Effects of litter quantity on soil microbial biomass carbon (<b>A</b>) and nitrogen (<b>B</b>). Different letters indicate significant differences between treatments at <span class="html-italic">p</span> &lt; 0.05. Values are presented as the means ± standard error. See <a href="#forests-10-00188-f001" class="html-fig">Figure 1</a> for abbreviations.</p> Full article ">Figure 4
<p>Effects of litter quantity on soil respiration. Values are presented as the means ± standard error. See <a href="#forests-10-00188-f001" class="html-fig">Figure 1</a> for abbreviations.</p> Full article ">
20 pages, 1733 KiB  
Article
Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms
by Qiangxin Ou, Xiangdong Lei and Chenchen Shen
Forests 2019, 10(2), 187; https://doi.org/10.3390/f10020187 - 20 Feb 2019
Cited by 52 | Viewed by 4834
Abstract
Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), [...] Read more.
Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth. Full article
(This article belongs to the Section Forest Ecology and Management)
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<p>Tuning random forest (RF)—the effect of mtry on RF model performance. The tenfold cross-validation was used for model validating. The error bar is standard deviation. The parameter, mtry, is the number of predictive variables randomly sampled as candidates at each split and controls the correlations between trees.</p> Full article ">Figure 2
<p>Tuning boosted regression tree (BRT)—the effect of n.trees, shrinkage and interaction.depth on BRT model performance. The tenfold cross-validation was used for validating. The error bar is standard deviation. The parameters, n.trees, shrinkage and interaction.depth are the total number of trees to grow, the step-size reduction or learning rate and the maximum depth of variable interactions for each tree, respectively.</p> Full article ">Figure 3
<p>Tuning Cubist—the effect of neighbors and committees on Cubist model performance. The tenfold cross-validation was used for validating. The error bar is standard deviation. The parameters, committees and neighbors, are the number of iterative model trees to grow in sequence and the number of neighboring training points which can be used to adjust the optimal predictions, respectively.</p> Full article ">Figure 4
<p>Tuning multivariate adaptive regression splines (MARS)—the effect of degree and nprune on MARS model performance. The tenfold cross-validation was used for validating. The error bar is standard deviation. The parameters, degree and nprune, are the maximum degree of interaction and the maximum number of terms (including intercept), respectively.</p> Full article ">Figure 5
<p>Relative importance of each variable as determined from (<b>A</b>) random forest (RF), and (<b>B</b>) boosted regression tree (BRT), which were normalized to 100%. The explanations of stand factors: BAL is the sum of basal area larger than the subject tree, Dg is average DBH, BA is the basal area per hectare, RD is the ratio of DBH of subject tree to average DBH, DL is the sum of square DBH in trees which are larger than subject tree, D0 is the individual tree DBH at the beginning of 5-year interval, DDM is the ratio of DBH of subject tree to the maximal DBH, TreeSpe_Code is the tree species, and N is the number of trees per hectare. The explanations of site factors: Aspect is aspect, Slope is slope and Altitude is altitude. The explanations of climate factors were presented in <a href="#forests-10-00187-t002" class="html-table">Table 2</a>.</p> Full article ">
16 pages, 2547 KiB  
Article
Effects of Arbuscular Mycorrhizal Fungi on Growth, Photosynthesis, and Nutrient Uptake of Zelkova serrata (Thunb.) Makino Seedlings under Salt Stress
by Jinping Wang, Zhiyuan Fu, Qiong Ren, Lingjun Zhu, Jie Lin, Jinchi Zhang, Xuefei Cheng, Jieyi Ma and Jianmin Yue
Forests 2019, 10(2), 186; https://doi.org/10.3390/f10020186 - 20 Feb 2019
Cited by 43 | Viewed by 4730
Abstract
Salinity is the primary restriction factor for vegetation conservation and the rehabilitation of coastal areas in Eastern China. Arbuscular mycorrhizal fungi (AMF) have been proved to have the ability to alleviate salt stress in plants. However, the role of AMF in relieving salt [...] Read more.
Salinity is the primary restriction factor for vegetation conservation and the rehabilitation of coastal areas in Eastern China. Arbuscular mycorrhizal fungi (AMF) have been proved to have the ability to alleviate salt stress in plants. However, the role of AMF in relieving salt stress among indigenous trees species is less well known, limiting the application of AMF in the afforestation of local area. In this study, a salt-stress pot experiment was conducted to evaluate the effects of AMF on Zelkova serrata (Thunb.) Makino, a tree species with significant potential for afforestation of coastal area. The Z. serrata seedlings inoculated with three AMF strains (Funneliformis mosseae 1, Funneliformis mosseae 2, and Diversispora tortuosa) were subjected to two salt treatments (0 and 100 mM NaCl) under greenhouse conditions. The results showed that the three AMF strains had positive effects, to a certain extent, on plant growth and photosynthesis under normal condition. However, only F. mosseae 1 and F. mosseae 2 alleviated the inhibition of growth, photosynthesis, and nutrient uptake of Z. serrata seedlings under salt stress. The two AMF strains mitigated salt-induced adverse effects on seedlings mainly by increasing the leaf photosynthetic ability and biomass accumulation by reducing Na+ content, increasing P, K+, and Mg2+ content, as well as by enhancing photosynthetic pigments content and the stomatal conductance of leaves. These results indicated that AMF inoculation is a promising strategy for the afforestation of coastal areas in Eastern China. Full article
(This article belongs to the Special Issue Ecto- and Endomycorrhizal Relationships in Forest Trees)
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<p>Development of the three arbuscular mycorrhizal fungi in <span class="html-italic">Z. serrata</span> seedling roots. (<b>A</b>) represents the figure of colonization status. (<b>B</b>) represents the figure of mycorrhizal colonization. CK represents the control without salt and AMF. NM represents the control treatments without AMF, FM<sub>1</sub> represents treatments inoculated with the AMF <span class="html-italic">Funneliformis mosseae</span> 1, FM<sub>2</sub> represents treatments inoculated with the AMF <span class="html-italic">Funneliformis mosseae</span> 2, DT represents treatments inoculated with the AMF <span class="html-italic">Diversispora tortuosum</span>. Different lowercase letters indicate significant differences between the three AMF under control and salt stress at the 0.05 significance level; NS not significant, * <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.</p> Full article ">Figure 2
<p>Effects of salt treatments and AMF inoculation on the plant net photosynthetic rate (<b>a</b>), stomatal conductance (<b>b</b>), transpiration rate (<b>c</b>), and water use efficiency (<b>d</b>) of <span class="html-italic">Z. serrata</span> seedlings leaves. Vertical bars indicate the standard error of the mean (<span class="html-italic">n</span> = 9). Different lowercase letters indicate significant differences between the three AMF under control and salt stress at the 0.05 significance level; NS not significant, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 3
<p>Effects of salt treatments and AMF inoculation on the photosynthetic pigments (<b>A</b>–<b>C</b>) and chlorophyll fluorescence (<b>D</b>–<b>F</b>) of <span class="html-italic">Z. serrata</span> seedlings leaves. Vertical bars indicate the standard error of the mean (<span class="html-italic">n</span> = 4). Different lowercase letters indicate significant differences between the three AMF under control and salt stress at the 0.05 significance level; NS not significant, * <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.</p> Full article ">Figure 4
<p>Effects of salt treatments and AMF inoculation on the relative water content and cell membrane stability index of <span class="html-italic">Z. serrata</span> seedlings leaves. Vertical bars indicate the standard error of the mean (<span class="html-italic">n</span> = 4). Different lowercase letters indicate significant differences between the three AMF under control and salt stress at the 0.05 significance level; NS not significant, *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">
18 pages, 2382 KiB  
Article
Caribou Conservation: Restoring Trees on Seismic Lines in Alberta, Canada
by Angelo T. Filicetti, Michael Cody and Scott E. Nielsen
Forests 2019, 10(2), 185; https://doi.org/10.3390/f10020185 - 20 Feb 2019
Cited by 40 | Viewed by 6124
Abstract
Seismic lines are narrow linear (~3–8 m wide) forest clearings that are used for petroleum exploration in Alberta’s boreal forest. Many seismic lines have experienced poor tree regeneration since initial disturbance, with most failures occurring in treed peatlands that are used by the [...] Read more.
Seismic lines are narrow linear (~3–8 m wide) forest clearings that are used for petroleum exploration in Alberta’s boreal forest. Many seismic lines have experienced poor tree regeneration since initial disturbance, with most failures occurring in treed peatlands that are used by the threatened woodland caribou (Rangifer tarandus caribou). Extensive networks of seismic lines, which often reach densities of 40 km/km2, are thought to have contributed to declines in caribou. The reforestation of seismic lines is therefore a focus of conservation. Methods to reforest seismic lines are expensive (averaging $12,500 per km) with uncertainty of which seismic lines need which treatments, if any, resulting in inefficiencies in restoration actions. Here, we monitored the effectiveness of treatments on seismic lines as compared to untreated seismic lines and adjacent undisturbed reference stands for treed peatlands in northeast Alberta, Canada. Mechanical site preparation (mounding and ripping) increased tree density when compared to untreated lines, despite averaging 3.8-years since treatment (vs. 22 years since disturbance for untreated). Specifically, treated lines had, on average, 12,290 regenerating tree stems/ha, which is 1.6-times more than untreated lines (7680 stems/ha) and 1.5-times more than the adjacent undisturbed forest (8240 stems/ha). Using only mechanical site preparation, treated seismic lines consistently have more regenerating trees across all four ecosites, although the higher amounts of stems that were observed on treated poor fens are not significant when compared to untreated or adjacent undisturbed reference stands. Full article
(This article belongs to the Section Forest Ecology and Management)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Examples of seismic lines in northern Alberta, Canada: (<b>a</b>) treated (mounding and planting) poor fen; (<b>b</b>) treated (mounding and planting) bog; (<b>c</b>) untreated poor mesic forest; and, (<b>d</b>) untreated rich fen. All photographs by Angelo T. Filicetti.</p> Full article ">Figure 2
<p>Location of the study areas: (<b>a</b>) notable population centers and the location of the three restoration projects (Kirby, LiDea 1, and LiDea 2) within this study; and, (<b>b</b>) outline of the province of Alberta, Canada within North America, and location of study.</p> Full article ">Figure 3
<p>Mean and standard error (error bars) of tree regeneration (diameter at breast height (DBH) &lt; 1 cm), across four ecosites and three treatments. Significance of treatments within each ecosite was tested with a pairwise comparison (Bonferroni adjustment) with different letters indicating significant (<span class="html-italic">p</span> &lt; 0.017) differences within an ecosite. Note, dashed line represents the amount of planted stems per hectare in treated lines (1300 stems/ha).</p> Full article ">Figure A1
<p>Mean and standard error (error bars) of tree regeneration (DBH &lt; 1 cm), across four tree species and three treatments. Where each ecosite is represented by: (<b>i</b>) bog; (<b>ii</b>) poor fen; (<b>iii</b>) rich fen; and (<b>iv</b>) poor mesic. Significance of treatments within each ecosite was tested with a pairwise comparison (Bonferroni adjustment) with different letters indicating significant (<span class="html-italic">p</span> &lt; 0.017) differences within a species. Note, dashed line represents the amount of planted stems per hectare in treated lines (1300 stems/ha). Scales vary.</p> Full article ">
18 pages, 5315 KiB  
Article
Attribution Analysis for Runoff Change on Multiple Scales in a Humid Subtropical Basin Dominated by Forest, East China
by Qinli Yang, Shasha Luo, Hongcai Wu, Guoqing Wang, Dawei Han, Haishen Lü and Junming Shao
Forests 2019, 10(2), 184; https://doi.org/10.3390/f10020184 - 20 Feb 2019
Cited by 14 | Viewed by 3750
Abstract
Attributing runoff change to different drivers is vital in order to better understand how and why runoff varies, and to further support decision makers on water resources planning and management. Most previous works attributed runoff change in the arid or semi-arid areas to [...] Read more.
Attributing runoff change to different drivers is vital in order to better understand how and why runoff varies, and to further support decision makers on water resources planning and management. Most previous works attributed runoff change in the arid or semi-arid areas to climate variability and human activity on an annual scale. However, attribution results may differ greatly according to different climatic zones, decades, temporal scales, and different contributors. This study aims to quantitatively attribute runoff change in a humid subtropical basin (the Qingliu River basin, East China) to climate variability, land-use change, and human activity on multiple scales over different periods by using the Soil and Water Assessment Tool (SWAT) model. The results show that runoff increased during 1960–2012 with an abrupt change occurring in 1984. Annual runoff in the post-change period (1985–2012) increased by 16.05% (38.05 mm) relative to the pre-change period (1960–1984), most of which occurred in the winter and early spring (March). On the annual scale, climate variability, human activity, and land-use change (mainly for forest cover decrease) contributed 95.36%, 4.64%, and 12.23% to runoff increase during 1985–2012, respectively. On the seasonal scale, human activity dominated runoff change (accounting for 72.11%) in the dry season during 1985–2012, while climate variability contributed the most to runoff change in the wet season. On the monthly scale, human activity was the dominant contributor to runoff variation in all of the months except for January, May, July, and August during 1985–2012. Impacts of climate variability and human activity on runoff during 2001–2012 both became stronger than those during 1985–2000, but counteracted each other. The findings should help understandings of runoff behavior in the Qingliu River and provide scientific support for local water resources management. Full article
(This article belongs to the Special Issue Forest Hydrology and Watershed)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of the Qingliu River basin, East China.</p> Full article ">Figure 2
<p>The inter-annual change of hydro-meteorological variables ((<b>a</b>) precipitation, maximum daily precipitation, and runoff depth; (<b>b</b>) temperature and pan evaporation; and (<b>c</b>) wind speed and solar radiation) between 1960 and 2012 for the Qingliu River basin.</p> Full article ">Figure 3
<p>Change detection of runoff during 1960–2012 for the Qingliu River basin, China. <span class="html-italic">UF</span> and <span class="html-italic">UB</span> present the forward trend and backward trend line of the runoff series. <span class="html-italic">UF</span> &gt;0 and <span class="html-italic">UF</span> &lt;0 indicate the increasing trend and decreasing trend, respectively.</p> Full article ">Figure 4
<p>Double-mass curve of the accumulated annual runoff depth and the accumulated annual precipitation.</p> Full article ">Figure 5
<p>Intra-annual variations of meteorological variables during the disturbed period (1985–2012) relative to the baseline period (1960–1984) for the Qingliu River basin, East China.</p> Full article ">Figure 6
<p>Land-use classification results in 1981 (<b>a</b>) and 2000 (<b>b</b>) for the Qinliu River basin, East China.</p> Full article ">Figure 7
<p>Comparison of the observed and simulated runoff in the Qingliu River for the calibration (1960–1971) and validation (1972–1984) periods (<b>a</b>), and the scatter plot of the observed versus simulated runoff (<b>b</b>). Jan means January.</p> Full article ">Figure 8
<p>Contributions of climate variability, human activity, and land-use change to runoff variation on the seasonal scale over three different periods (1985–2012, 1985–2000, and 2001–2012) for the Qingliu River basin, East China.</p> Full article ">Figure 9
<p>Runoff change (mm) induced by climate variability and human activity on the monthly scale over three different periods (1985–2012, 1985–2000, and 2001–2012) for the Qingliu River basin, East China. J–D are the abbreviations of January to December.</p> Full article ">
13 pages, 2429 KiB  
Article
Experimental Data on Maximum Rainfall Retention on Crowns of Deciduous Tree Species of the Middle Ural (Russia)
by Dmitry Klimenko, Anna Ostakhova and Alina Tuneva
Forests 2019, 10(2), 183; https://doi.org/10.3390/f10020183 - 20 Feb 2019
Cited by 7 | Viewed by 2925
Abstract
Metering of actual volume of rainfall flowing under deciduous stock canopy is essential for correct calculation of the water balance of forest watersheds of small rivers. This article includes the results of a physical (experimental) simulation of maximum rainfall retention on the laminae [...] Read more.
Metering of actual volume of rainfall flowing under deciduous stock canopy is essential for correct calculation of the water balance of forest watersheds of small rivers. This article includes the results of a physical (experimental) simulation of maximum rainfall retention on the laminae of deciduous tree species. The authors developed the experimental methodology, assembled the testing machine, assessed results, and suggested ways of interpreting the obtained results in calculations of flood runoff. According to experimental data, rainfall is retained on laminae both in film and drip form. Specific retention value per unit area of leaf surface is mostly determined by the level of physical roughness of a leaf, which, in turn, depends on the type of venation, typical for different types of analyzed trees. The value of complete raindrops retention by crowns of deciduous species is determined by the leaf surface area and rainfall intensity. Dependencies of the maximum mass of the retained moisture on the leaf surface area, which are characterized by the correlation coefficient of 0.98, were obtained on the basis of branch sprinkling experiments. The maximum mass of water retention on crowns of single deciduous trees can reach up to 77 kg, or 3.0–4.0 mm per projection area of a crown. This is significantly less than the maximum mass of water retention on crowns of coniferous species (for comparison, larch retains up to 150 kg of rain moisture or 5.9 mm of layer). Evaporation from crowns, as well as wind oscillations of laminae, result in larger volumes of interception as compared to the results obtained from experiments. Metering of irrecoverable losses values has great practical value in the assessment of the water balance of forest lands, moisture balance in soil layer under the forest canopy, as well as the flood runoff of small watersheds of forest zones. Full article
(This article belongs to the Section Forest Ecology and Management)
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<p>Photograph showing the experimental set up for the artificial sprinkling and weighing of tree branches.</p> Full article ">Figure 2
<p>Dependence of the maximum weight of retained water (g) on the leaf surface area of the analyzed species (<span class="html-italic">LA</span>, m<sup>2</sup>).</p> Full article ">Figure 3
<p>Dependency of specific water retention on the leaf surface of deciduous species on the density of veins of a “normal” leaf in the analyzed sample.</p> Full article ">
11 pages, 3555 KiB  
Article
The First Record of a North American Poplar Leaf Rust Fungus, Melampsora medusae, in China
by Wei Zheng, George Newcombe, Die Hu, Zhimin Cao, Zhongdong Yu and Zijia Peng
Forests 2019, 10(2), 182; https://doi.org/10.3390/f10020182 - 20 Feb 2019
Cited by 8 | Viewed by 3737
Abstract
A wide range of species and hybrids of black and balsam poplars or cottonwoods (Populus L., sections Aigeiros and Tacamahaca) grow naturally, or have been introduced to grow in plantations in China. Many species of Melampsora can cause poplar leaf rust [...] Read more.
A wide range of species and hybrids of black and balsam poplars or cottonwoods (Populus L., sections Aigeiros and Tacamahaca) grow naturally, or have been introduced to grow in plantations in China. Many species of Melampsora can cause poplar leaf rust in China, and their distributions and host specificities are not entirely known. This study was prompted by the new susceptibility of a previously resistant cultivar, cv. ‘Zhonghua hongye’ of Populus deltoides (section Aigeiros), as well as by the need to know more about the broader context of poplar leaf rust in China. Rust surveys from 2015 through 2018 in Shaanxi, Sichuan, Gansu, Henan, Shanxi, Qinghai, Beijing, and Inner Mongolia revealed some samples with urediniospores with the echinulation pattern of M. medusae. The morphological characteristics of urediniospores and teliospores from poplar species of the region were further examined with light and scanning electron microscopy. Phylogenetic analysis based on sequences of the rDNA ITS region (ITS1, 5.8S rRNA gene, and ITS2) and the nuclear large subunit rDNA (D1/D2) was used to further confirm morphology-based identification. Based on combined analyses, five of the fifteen fully characterized samples were identified as Melampsora medusae: one from Shaanxi and four from Sichuan. Two of the five were from Populus deltoides cv. ‘Zhonghua hongye’. Three others were identified on Populus szechuanica, P. simonii, and P. yunnanensis. Additional samples of M. medusae were collected in Shaanxi in 2017 and 2018, and from Henan in 2015 through 2018. Altogether these findings show that this introduced pathogen is widespread and persistent from year to year in China. This is the first report of this North American poplar leaf rust species, Melampsora medusae, in China. It has previously been reported outside North America in Argentina, Europe, Australia, New Zealand, Japan, and Russia. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p>Sites (red dots) for rust sampling of <span class="html-italic">P. deltoides</span> cv. ‘Zhonghua hongye’. Dark dots represent the potential distribution of this formerly rust-free cultivar.</p> Full article ">Figure 2
<p>Echinulation patterns of urediniospores and associated paraphyses of <span class="html-italic">Melampsora medusae</span> and <span class="html-italic">Melampsora larici-populina</span> (the two species of <span class="html-italic">Melampsora</span> found on sections <span class="html-italic">Tacamahaca</span> and <span class="html-italic">Aigeiros</span> in our surveys in China) as observed in the SEM. (<b>a</b>–<b>d</b>) <span class="html-italic">Melampsora medusae</span>; (<b>e</b>–<b>h</b>) <span class="html-italic">Melampsora larici-populina</span>; (<b>a, e</b>) abundant paraphyses under SEM; (<b>b, f</b>) Uredinium under SEM; (<b>c, j</b>) urediniospores with echinulate spines under SEM; (<b>d, h</b>) spines on the surface of urediniospores under SEM.</p> Full article ">Figure 3
<p>Germination of urediniospores of <span class="html-italic">M. medusae</span> and <span class="html-italic">M. larici-populina</span> on a medium of 2% agar. (<b>a</b>–<b>c</b>) <span class="html-italic">M. medusae</span>; (<b>d</b>–<b>f</b>) <span class="html-italic">M. larici-populina</span>; (<b>a</b>) the flowing nuclei (arrows) after 6 h; (<b>b</b>) the swollen top of the germ tube (arrows) after 10 h; (<b>c</b>) germ nucleates (arrows) with 4 nucleates after 16 h; (<b>d</b>) the flowing nuclei of MLP (arrows) after 8 h; (<b>e</b>) the germ tube with 2 nuclei (arrows) after 10 h; (<b>f</b>) the germ tube with 2 nuclei (arrows) after 16 h.</p> Full article ">Figure 4
<p>Maximum Likelihood (ML) phylogenetic tree of nrDNA-ITS sequences of species of <span class="html-italic">Melampsora</span> from HMAS collections (red triangles) made during poplar leaf rust surveys in China, 2015–2018. Bootstrap values &gt;50% are shown.</p> Full article ">Figure 5
<p>Maximum likelihood (ML) phylogenic tree of 28S rDNA(D<sub>1</sub>/D<sub>2</sub>) regions. Note: Bootstrap values &gt;50% are shown and red marks are specimens we collected.</p> Full article ">Figure 6
<p>Amplification with primer clc3a2f/clc3a2r and primer clc3a3f/clc3a3r. Lanes 1 and 10: Marker DM2000; lanes 2–6: Amplification from uredinia of the five specimens of <span class="html-italic">M. medusae</span> (HMAS 247969-71, HMAS 247972-73) using primers clc3a2f/clc3a2r; Lanes 11–15: Amplification from uredinia of the five specimens of <span class="html-italic">M. medusae</span> (HMAS 247969-71, HMAS 247972-73) using primers clc3a3f/clc3a3r; Lanes 7–8 and 16–17(HMAS247968, HMAS247978): The positive control with DNA of <span class="html-italic">M. larici-populina</span>; Lanes 9 and 18: The negative control with ddH<sub>2</sub>O.</p> Full article ">
10 pages, 2289 KiB  
Article
Within-Site Variation in Seedling Survival in Norway Spruce Plantations
by Emma Holmström, Helena Gålnander and Magnus Petersson
Forests 2019, 10(2), 181; https://doi.org/10.3390/f10020181 - 19 Feb 2019
Cited by 19 | Viewed by 3215
Abstract
Seedling survival was evaluated from inventories of a large set of Norway spruce plantations in privately owned forests in southern Sweden. The inventories were conducted at the time of planting and a subset was re-inventoried three years later. This enabled comparison of regeneration [...] Read more.
Seedling survival was evaluated from inventories of a large set of Norway spruce plantations in privately owned forests in southern Sweden. The inventories were conducted at the time of planting and a subset was re-inventoried three years later. This enabled comparison of regeneration success after soil scarification and planting. The acquired data enabled evaluation of annual and climatic variation of seedling mortality since inventories were made on newly established clearcuts distributed spatially throughout three regions in southern Sweden and repeated in five consecutive years. Within-site variation was also captured via the use of a large number of sample plots on each clearcut. To do so, thirty sample plots were established within weeks of planting on 150 clearcuts. Small- and large-scale site and management variables were recorded as well as the numbers of suitable planting spots and planted seedlings. Three years later, 60 of the initially surveyed clearcuts were revisited and the numbers of both planted and naturally regenerated seedlings counted. On average, 2000 seedlings ha−1 were planted and 1500 seedlings ha−1 had survived after three years. However, there was high variation, and in 42% of the revisited sample plots no mortality was recorded. Important variables for seedling survival identified by linear regression analysis included the number of suitable planting spots, soil moisture conditions and annual variation in available soil water. Full article
(This article belongs to the Special Issue The Forest Renewal Issue: New Problems and Findings in the Establishment of Tree Regeneration)
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<p>The distribution of forest owners associated with the Södra company in southern Sweden, showing management regions 1–3.</p> Full article ">Figure 2
<p>Box and whiskers plot of the digital depth to water value in the field-measured soil moisture classes: dry, mesic, moist and wet. The number of sample plots in each category (<span class="html-italic">n</span>) is shown in red below each box. Black dots indicate median, the border of the box indicates the first and third quartile, length of whiskers represents approximately two standard deviations of the data, and the unfilled dots indicate outliers of the whiskers.</p> Full article ">Figure 3
<p>Accumulated available water (mm) in the years 2006–2010 and geographic locations of the clearcuts included in the re-inventory marked in planting years.</p> Full article ">Figure 4
<p>Proportions of the sample plots (total number of plots summarize to 1) with indicated numbers of seedlings, ranging from 0 to max registered value 14 (corresponding to 0–5600 ha<sup>−1</sup>): TPS, Total number of planted seedlings; PPS, Properly planted seedlings; Survival, Number of planted seedlings after three years; NR + PL, Number of naturally regenerated and planted seedlings after three years.</p> Full article ">Figure 5
<p>Densities of (<b>a</b>) all planted seedlings and (<b>b</b>) properly planted seedlings in the planting year, and (<b>c</b>) number of surviving planted seedlings three years after planting, in sample plots of indicated soil moisture classes derived from DTW raster values. The <span class="html-italic">x</span>-axes represent the number of suitable planting spots ha<sup>−1</sup> in corresponding sample plots.</p> Full article ">Figure 6
<p>The variation coefficient of seedling density as a function of the clearcut mean, at the time of planting (dark blue) and three years later (light blue) and smoothed lines of the overall inventory tendencies.</p> Full article ">
46 pages, 2359 KiB  
Article
A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States
by Adam Erickson and Nikolay Strigul
Forests 2019, 10(2), 180; https://doi.org/10.3390/f10020180 - 19 Feb 2019
Cited by 4 | Viewed by 4667
Abstract
State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed [...] Read more.
State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS R 2 ¯ = 0.73 , + 0.07 , R M S E ¯ = 4.68 , − 9.96 ; JERC-RD R 2 ¯ = 0.73 , + 0.01 , R M S E ¯ = 2.18 , − 1.64 ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>LANdscape DIsturbance and Succession II Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN) model structure.</p> Full article ">Figure 2
<p>Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) model structure; Raich et al. [<a href="#B116-forests-10-00180" class="html-bibr">116</a>]; Domke et al. [<a href="#B117-forests-10-00180" class="html-bibr">117</a>].</p> Full article ">Figure 3
<p>Harvard Forest (HF) EMS flux tower and landcover classes.</p> Full article ">Figure 4
<p>Jones Ecological Research Center (JERC) RD flux tower and landcover classes.</p> Full article ">Figure 5
<p>Overall model performance (<math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math>) for both models and sites; left = HF-EMS; right = JERC-RD; periwinkle = PPA-SiBGC; pink = LANDIS-II NECN; violet = intersection.</p> Full article ">Figure 6
<p>Simulated and observed NEE, <math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi>AG</mi> </msub> </semantics></math>, ANPP, and <math display="inline"><semantics> <msub> <mi mathvariant="normal">r</mi> <mi>Soil</mi> </msub> </semantics></math> for the HF-EMS site; black = observations; red = PPA-SiBGC; blue = LANDIS-II NECN; (<b>a</b>) = NEE; (<b>b</b>) = <math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi>AG</mi> </msub> </semantics></math>; (<b>c</b>) = ANPP; (<b>d</b>) = <math display="inline"><semantics> <msub> <mi mathvariant="normal">r</mi> <mi>Soil</mi> </msub> </semantics></math>.</p> Full article ">Figure 7
<p>HF-EMS: Simulated and observed species aboveground biomass and relative abundance; (<b>a</b>) = biomass; (<b>b</b>) = abundance; left = observations, middle = PPA-SiBGC, right = LANDIS-II NECN; note that different scales are used for biomass.</p> Full article ">Figure 8
<p>Simulated and observed NEE, <math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi>AG</mi> </msub> </semantics></math>, ANPP, and <math display="inline"><semantics> <msub> <mi mathvariant="normal">r</mi> <mi>Soil</mi> </msub> </semantics></math> for the JERC-RD site; black = observations; red = PPA-SiBGC; blue = LANDIS-II NECN; (<b>a</b>) = NEE; (<b>b</b>) = <math display="inline"><semantics> <msub> <mi mathvariant="normal">C</mi> <mi>AG</mi> </msub> </semantics></math>; (<b>c</b>) = ANPP; (<b>d</b>) = <math display="inline"><semantics> <msub> <mi mathvariant="normal">r</mi> <mi>Soil</mi> </msub> </semantics></math>.</p> Full article ">Figure 9
<p>JERC-RD: Simulated and observed species aboveground biomass and relative abundance; (<b>a</b>) = biomass; (<b>b</b>) = abundance; left = observations, middle = PPA-SiBGC, right = LANDIS-II NECN; note that different scales are used for biomass.</p> Full article ">Figure A1
<p>HF-EMS tower daily averages.</p> Full article ">Figure A2
<p>HF-EMS tower daily diurnal averages.</p> Full article ">Figure A3
<p>JERC-RD tower daily averages.</p> Full article ">Figure A4
<p>JERC-RD tower daily diurnal averages.</p> Full article ">
14 pages, 1528 KiB  
Article
Evaluation of Anti-Tyrosinase and Antioxidant Properties of Four Fern Species for Potential Cosmetic Applications
by Adrià Farràs, Guillermo Cásedas, Francisco Les, Eva María Terrado, Montserrat Mitjans and Víctor López
Forests 2019, 10(2), 179; https://doi.org/10.3390/f10020179 - 19 Feb 2019
Cited by 20 | Viewed by 5955
Abstract
Ferns are poorly explored species from a pharmaceutical perspective compared to other terrestrial plants. In this work, the antioxidant and tyrosinase inhibitory activities of hydrophilic and lipophilic extracts, together with total polyphenol content, were evaluated in order to explore the potential cosmetic applications [...] Read more.
Ferns are poorly explored species from a pharmaceutical perspective compared to other terrestrial plants. In this work, the antioxidant and tyrosinase inhibitory activities of hydrophilic and lipophilic extracts, together with total polyphenol content, were evaluated in order to explore the potential cosmetic applications of four Spanish ferns collected in the Prades Mountains (Polypodium vulgare L., Asplenium adiantum-nigrum L., Asplenium trichomanes L., and Ceterach officinarum Willd). The antioxidant activity was evaluated using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, oxygen radical absorbance capacity (ORAC) and xanthine/xanthine oxidase (X/XO) assays. The potential to avoid skin hyperpigmentation was tested by inhibiting the tyrosinase enzyme, as this causes melanin synthesis in the epidermis. All ferns were confirmed as antioxidant and anti-tyrosinase agents, but interestingly hydrophilic extracts (obtained with methanol) were more potent and effective compared to lipophilic extracts (obtained with hexane). Polypodium vulgare, Asplenium adiantum-nigrum, and Ceterach officinarum methanolic extracts performed the best as antioxidants. Polypodium vulgare methanolic extract also showed the highest activity as a tyrosinase inhibitor. Full article
(This article belongs to the Special Issue Forest, Foods and Nutrition)
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Graphical abstract
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<p>Antioxidant activity against DPPH radicals of methanol extracts (<b>A</b>) and hexane extracts (<b>B</b>) using trolox as a reference.</p> Full article ">Figure 2
<p>Correlation studies between polyphenol content and antioxidant activity (<b>A</b>) and between polyphenol content and tyrosinase inhibition (<b>B</b>). Pearson r values confirm that there is a correlation between polyphenol content and antioxidant activity measured by the ORAC method, whereas no correlation exists between polyphenol content and the inhibition of the tyrosinase enzyme.</p> Full article ">Figure 3
<p>Antioxidant activity of fern methanolic (<b>A</b>) and hexane (<b>B</b>) extracts against superoxide radicals generated by xanthine/xanthine oxidase using trolox as a reference. *** <span class="html-italic">p</span> &lt; 0.001 versus the hexane extract in the same species.</p> Full article ">Figure 4
<p>Inhibition of tyrosinase by methanolic (<b>A</b>) and hexane (<b>B</b>) extracts. Kojic acid was used as reference.</p> Full article ">
13 pages, 3021 KiB  
Article
Proportional Relationship between Leaf Area and the Product of Leaf Length and Width of Four Types of Special Leaf Shapes
by Peijian Shi, Mengdi Liu, Xiaojing Yu, Johan Gielis and David A. Ratkowsky
Forests 2019, 10(2), 178; https://doi.org/10.3390/f10020178 - 19 Feb 2019
Cited by 43 | Viewed by 7325
Abstract
The leaf area, as an important leaf functional trait, is thought to be related to leaf length and width. Our recent study showed that the Montgomery equation, which assumes that leaf area is proportional to the product of leaf length and width, applied [...] Read more.
The leaf area, as an important leaf functional trait, is thought to be related to leaf length and width. Our recent study showed that the Montgomery equation, which assumes that leaf area is proportional to the product of leaf length and width, applied to different leaf shapes, and the coefficient of proportionality (namely the Montgomery parameter) range from 1/2 to π/4. However, no relevant geometrical evidence has previously been provided to support the above findings. Here, four types of representative leaf shapes (the elliptical, sectorial, linear, and triangular shapes) were studied. We derived the range of the estimate of the Montgomery parameter for every type. For the elliptical and triangular leaf shapes, the estimates are π/4 and 1/2, respectively; for the linear leaf shape, especially for the plants of Poaceae that can be described by the simplified Gielis equation, the estimate ranges from 0.6795 to π/4; for the sectorial leaf shape, the estimate ranges from 1/2 to π/4. The estimates based on the observations of actual leaves support the above theoretical results. The results obtained here show that the coefficient of proportionality of leaf area versus the product of leaf length and width only varies in a small range, maintaining the allometric relationship for leaf area and thereby suggesting that the proportional relationship between leaf area and the product of leaf length and width broadly remains stable during leaf evolution. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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Figure 1
<p>Examples of the four types of leaf shapes.</p> Full article ">Figure 2
<p>Linear regression of the Montgomery equation for four species of plants. (<b>a</b>) <span class="html-italic">H. vulgaris</span>; (<b>b</b>) <span class="html-italic">G. biloba</span>; (<b>c</b>) <span class="html-italic">O. sulcatum</span>; (<b>d</b>) <span class="html-italic">P. perfoliatum</span>. In each panel, <span class="html-italic">A</span> denotes leaf area; <span class="html-italic">L</span> represents leaf length; <span class="html-italic">W</span> represents leaf width; RMSE represents the root-mean-square error; <span class="html-italic">r</span> represents the correlation coefficient; <span class="html-italic">n</span> represents the number of leaves sampled; exp(<math display="inline"><semantics> <mover accent="true"> <mi>a</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>) represents the estimate of the Montgomery parameter; 95% CI denotes the 95% confidence interval of the estimate of the Montgomery parameter. Open circles represent the observations, and the red straight line represents the fitted regression line.</p> Full article ">Figure 3
<p>The estimates of the Montgomery parameters (MP) and the correlation coefficients for 20 bamboo species. Blue open circles represent the estimates of the MP; and red open triangles represent the correlation coefficients. The number on the <span class="html-italic">x</span>-axis represents the bamboo species code (see <a href="#app1-forests-10-00178" class="html-app">Table S1</a> in the online <a href="#app1-forests-10-00178" class="html-app">Supplementary Materials</a> for details).</p> Full article ">Figure 4
<p>Linear regression of the Montgomery equation for the pooled data of 20 bamboo species. <span class="html-italic">A</span> denotes leaf area; <span class="html-italic">L</span> represents leaf length; <span class="html-italic">W</span> represents leaf width; RMSE represents the root-mean-square error; <span class="html-italic">r</span> represents the correlation coefficient; <span class="html-italic">n</span> represents the number of leaves sampled; exp(<math display="inline"><semantics> <mover accent="true"> <mi>a</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>) represents the estimate of the Montgomery parameter; 95% CI denotes the 95% confidence interval of the estimate of the Montgomery parameter. Open circles (among which different colors represent different bamboo species) represent the observations, and the solid straight line represents the fitted regression line.</p> Full article ">Figure 5
<p>Comparison of the ratios of leaf width to length (and the ratios of leaf perimeter to area) among the four species of plants. (<b>a</b>) The ratio of leaf width to length; (<b>b</b>) the ratio of leaf perimeter to area. In panel (<b>a</b>), the red percentage number above the box is the coefficient of variation of the ratios of leaf width to length; the green number above the box is the skewness of the ratios of leaf width to length. The letters A, B, C, and D in each panel were used to indicate a significant difference of species means using Tukey’s HSD (<span class="html-italic">α</span> = 0.05). Data set 1 represents <span class="html-italic">H. vulgaris</span>; data set 2 represents <span class="html-italic">G. biloba</span>; data set 3 represents <span class="html-italic">O. sulcatum</span>; data set 4 represents <span class="html-italic">P. perfoliatum</span>.</p> Full article ">Figure 6
<p>Linear regression of the leaf area–length allometry for the four species of plants. (<b>a</b>) <span class="html-italic">H. vulgaris</span>; (<b>b</b>) <span class="html-italic">G. biloba</span>; (<b>c</b>) <span class="html-italic">O. sulcatum</span>; (<b>d</b>) <span class="html-italic">P. perfoliatum</span>. In each panel, <span class="html-italic">A</span> denotes leaf area; <span class="html-italic">L</span> represents leaf length; RMSE represents the root-mean-square error; <span class="html-italic">r</span> represents the correlation coefficient; <span class="html-italic">n</span> represents the number of leaves sampled; 95% CI denotes the 95% confidence interval of the estimate of the slope.</p> Full article ">Figure 7
<p>Gielis fit to the leaf edge data of three species of plants. (<b>a</b>) <span class="html-italic">H. vulgaris</span>; (<b>b</b>) <span class="html-italic">O. sulcatum</span>; (<b>c</b>) <span class="html-italic">P. perfoliatum</span>. The gray curve represents the scanned (observed) leaf edge; the red curve represents the predicted leaf edge by the Gielis equation; the intersection of two blue dashed lines represents the polar point of the Gielis equation; the gray dashed line represents the rotated straight line that was the previous <span class="html-italic">x</span>-axis for a standard Gielis shape.</p> Full article ">Figure 8
<p>Estimation of the leaf angle of <span class="html-italic">G. biloba</span>. (<b>a</b>) Numerical solution of the leaf angle of the population of <span class="html-italic">G. biloba</span>; (<b>b</b>) histogram of the numerical solutions for different individual leaves sampled. In panel (<b>a</b>), the red horizontal straight line represents the estimate of the Montgomery parameter; the blue curve represents <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>/</mo> <mrow> <mo>[</mo> <mrow> <mrow> <mn>4</mn> <mtext> </mtext> <mi>sin</mi> </mrow> <mrow> <mo>(</mo> <mrow> <mi>δ</mi> <mo>/</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> (see Equation (3)), where <span class="html-italic">δ</span> represents the candidate of leaf angle that was initially set from <span class="html-italic">π</span>/4000 to <span class="html-italic">π</span> by an increment of <span class="html-italic">π</span>/4000; the point at which the red straight line and the blue curve intersect is used to quantify the leaf angle of the population (namely all the leaves sampled). In panel (<b>b</b>), red bars represent the densities of different groups of leaf angles.</p> Full article ">
17 pages, 3090 KiB  
Article
Abundance and Impacts of Competing Species on Conifer Regeneration Following Careful Logging in the Eastern Canadian Boreal Forest
by Louis-Philippe Ménard, Jean-Claude Ruel and Nelson Thiffault
Forests 2019, 10(2), 177; https://doi.org/10.3390/f10020177 - 19 Feb 2019
Cited by 10 | Viewed by 3713
Abstract
Managing competing vegetation is crucial in stand establishment strategies; forecasting the abundance, composition, and impact of competing vegetation after harvesting is needed to optimize silviculture scenarios and maintain long-term site productivity. Our main objective was to identify factors influencing the short-term abundance and [...] Read more.
Managing competing vegetation is crucial in stand establishment strategies; forecasting the abundance, composition, and impact of competing vegetation after harvesting is needed to optimize silviculture scenarios and maintain long-term site productivity. Our main objective was to identify factors influencing the short-term abundance and composition of competing vegetation over a large area of the Canadian boreal forest. Our second objective was to better understand the mid-term evolution of the regeneration/competing vegetation complex in cases of marginal regeneration conditions. We used operational regeneration surveys of 4471 transects sampled ≈5 years after harvesting that contained data on regeneration, competing vegetation, elevation, ecological classification, soil attributes, and pre-harvest forest stands. We performed a redundancy analysis to identify the relationships between competing vegetation, harvesting and biophysical variables. We then estimated the probability of observing a given competing species cover based on these variables. In 2015, we re-sampled a portion of the sites, where conifer regeneration was marginal early after harvesting, to assess the temporal impact of different competing levels and species groups on the free-to-grow stocking, vigour and basal area of softwood regeneration. Results from the first inventory showed that, after careful logging around advance growth, ericaceous shrubs and hardwoods were not associated with the same sets of site attributes. Ericaceous shrubs were mainly found on low fertility sites associated with black spruce (Picea mariana (Mill.) BSP) or jack pine (Pinus banksiana Lamb.). The distinction between suitable environments for commercial shade-intolerant hardwoods and non-commercial hardwoods was less clear, as they responded similarly to many variables. Analysis of data from the second inventory showed a significant improvement in conifer free-to-grow stocking when commercial shade-intolerant hardwood competing levels were low (stocking 0%–40%) and when ericaceous shrubs competing levels were moderate (percent cover 26%–75%). In these conditions of marginal regeneration, the different types and intensities of competition did not affect the vigour or basal area of softwood regeneration, 9–14 years after harvesting. Full article
(This article belongs to the Special Issue Enhancing Forest Productivity, Value, and Health through Silviculture in a Changing World)
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Figure 1
<p>Location of the study area and sampling sites in Quebec (Canada). Gray dots represent transects from the 2007–2011 inventory and red dots represent transects that were re-sampled in 2015. Ecological regions are those defined by [<a href="#B16-forests-10-00177" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) Configuration of the micro-plots in a transect used in the 2007–2011 monitoring of post-harvest regenerating stands. (<b>b</b>) Configuration of the plot and the microplots in a transect for the 2015 stratified inventory.</p> Full article ">Figure 3
<p>Redundancy analysis (RDA) ordination biplot showing the correlation between competing species groups (black arrows) and explanatory variables selected using a forward selection approach (grey arrows). Only variables with the highest contribution to axes RDA1 or RDA2 are labeled (i.e., coordinate on one axis was &gt;90th quantile or &lt;10th quantile of the distribution of variables coordinates on the same axis). Refer to <a href="#forests-10-00177-t002" class="html-table">Table 2</a> for variables description.</p> Full article ">Figure 4
<p>Free-to-grow conifer stocking per strata of competing cover and over two time periods. LIH: low competing level of commercial shade-intolerant hardwoods, MIH: moderate competing level of commercial shade-intolerant hardwoods, HIH: high competing level of commercial shade-intolerant hardwoods, LES: low competing level of ericaceous shrubs, MES: moderate competing level of ericaceous shrubs, HES: high competing level of ericaceous shrubs. Bold indicate significance at α = 0.05.</p> Full article ">
18 pages, 3204 KiB  
Article
Assessing Hydrological Ecosystem Services in a Rubber-Dominated Watershed under Scenarios of Land Use and Climate Change
by Kevin Thellmann, Reza Golbon, Marc Cotter, Georg Cadisch and Folkard Asch
Forests 2019, 10(2), 176; https://doi.org/10.3390/f10020176 - 19 Feb 2019
Cited by 10 | Viewed by 4432
Abstract
Land use and climate change exert pressure on ecosystems and threaten the sustainable supply of ecosystem services (ESS). In Southeast-Asia, the shift from swidden farming to permanent cash crop systems has led to a wide range of impacts on ESS. Our study area, [...] Read more.
Land use and climate change exert pressure on ecosystems and threaten the sustainable supply of ecosystem services (ESS). In Southeast-Asia, the shift from swidden farming to permanent cash crop systems has led to a wide range of impacts on ESS. Our study area, the Nabanhe Reserve in Yunnan province (PR China), saw the loss of extensive forest areas and the expansion of rubber (Hevea brasiliensis Müll. Arg.) plantations. In this study, we model water yield and sediment export for a rubber-dominated watershed under multiple scenarios of land use and climate change in order to assess how both drivers influence the supply of these ESS. For this we use three stakeholder-validated land use scenarios, varying in their degree of rubber expansion and land management rules. As projected climate change varies remarkably between different climate models, we combined the land use scenarios with datasets of temperature and precipitation changes, derived from nine General Circulation Models (GCMs) of the Fifth Assessment Report of the IPCC (Intergovernmental Panel on Climate Change) in order to model water yield and sediment export with InVEST (Integrated Valuation of Ecosystem Services and Trade-offs). Simulation results show that the effect of land use and land management decisions on water yield in Nabanhe Reserve are relatively minor (4% difference in water yield between land use scenarios), when compared to the effects that future climate change will exert on water yield (up to 15% increase or 13% decrease in water yield compared to the baseline climate). Changes in sediment export were more sensitive to land use change (15% increase or 64% decrease) in comparison to the effects of climate change (up to 10% increase). We conclude that in the future, particularly dry years may have a more pronounced effect on the water balance as the higher potential evapotranspiration increases the probability for periods of water scarcity, especially in the dry season. The method we applied can easily be transferred to regions facing comparable land use situations, as InVEST and the IPCC data are freely available. Full article
(This article belongs to the Special Issue Adaptive Forest Management and Decision Making Models under Climate Change (Risk and Uncertainty))
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Figure 1
<p>Annual mean temperature (<b>a</b>) and annual precipitation (<b>b</b>) in Nabanhe Reserve for two Relative Concentration Pathways (RCP 4.5 and RCP 8.5 of IPCC5) derived from 9 General Circulation Models (GCMs) for the time slices of 2030 (20-year average from 2020–2040), 2050 (20-year average from 2040–2060) and 2070 (20-year average from 2060–2080). Boxes and whiskers show the 25/75 and 10/90 percentiles respectively. Lines in boxes show the ensemble median, whereas crosses show the ensemble mean. The dotted lines show long term annual average precipitation and long term annual mean temperature used as a baseline (WorldClim v1.4. [<a href="#B39-forests-10-00176" class="html-bibr">39</a>,<a href="#B40-forests-10-00176" class="html-bibr">40</a>]).</p> Full article ">Figure 2
<p>Land use maps of Nabanhe Reserve; the initial condition in 2015 (<b>a</b>), the Business-As-Usual Scenario in 2040 (<b>b</b>), based on linear extrapolation of past rubber expansion rates, the 5-Years-Plan scenario in 2040 (<b>c</b>), based on province-level policy land use guidelines, and the Balanced-Trade-Offs scenario in 2040 (<b>d</b>), based on the 5-Years-Plan and additional measures such as water protection zones and riparian buffer zones. Maps are taken from Thellmann et al. [<a href="#B61-forests-10-00176" class="html-bibr">61</a>].</p> Full article ">Figure 3
<p>Annual average evapotranspiration in Nabanhe Reserve calculated with InVEST and ensemble data (9 GCMs) of two Relative Concentration Pathways (RCP 4.5 (<b>a</b>) and RCP 8.5 (<b>b</b>) of the Fifth Assessment Report of the IPCC) for the time slices of 2030 (20-year average from 2020–2040), 2050 (20-year average from 2040–2060) and 2070 (20-year average from 2060–2080). Boxes and whiskers show the 25/75 and 10/90 percentiles respectively. Lines in boxes show the ensemble median, whereas crosses show the ensemble mean. Colors represent land use conditions: Initial case of 2015 (INIT, yellow), Business-As-Usual scenario (BAU, red), 5-years-plan scenario (5YP, green), Balanced-Trade-Offs scenario (BTO, blue). The dotted lines show annual average evapotranspiration calculated with long-term annual average climate data as a baseline (WorldClim v1.4. [<a href="#B39-forests-10-00176" class="html-bibr">39</a>,<a href="#B40-forests-10-00176" class="html-bibr">40</a>]).</p> Full article ">Figure 4
<p>Annual average water yield in Nabanhe Reserve calculated with InVEST using ensemble data (9 GCMs) of two Relative Concentration Pathways (RCP 4.5 (<b>a</b>) and RCP 8.5 (<b>b</b>) of the Fifth Assessment Report of the IPCC) for the time slices of 2030 (20-year average from 2020–2040), 2050 (20-year average from 2040–2060) and 2070 (20-year average from 2060–2080). Boxes and whiskers show the 25/75 and 10/90 percentiles respectively. Lines in boxes show the ensemble median, whereas crosses show the ensemble mean. Colors represent land use conditions: Initial case of 2015 (INIT, yellow), Business-As-Usual scenario (BAU, red), 5-years-plan scenario (5YP, green), Balanced-Trade-Offs scenario (BTO, blue). The dotted lines show annual average water yield calculated with long term annual average climate data as a baseline (WorldClim v1.4. [<a href="#B39-forests-10-00176" class="html-bibr">39</a>,<a href="#B40-forests-10-00176" class="html-bibr">40</a>]).</p> Full article ">Figure 5
<p>Annual average sediment export in Nabanhe Reserve calculated with InVEST using ensemble precipitation data (9 GCMs) of two Relative Concentration Pathways (RCP 4.5 (<b>a</b>) and RCP 8.5 (<b>b</b>) of IPCC5) for the time slices of 2030 (20-year average from 2020–2040), 2050 (20-year average from 2040–2060) and 2070 (20-year average from 2060–2080). Boxes and whiskers show the 25/75 and 10/90 percentiles respectively. Lines in boxes show the ensemble median, whereas crosses show the ensemble mean. Colors represent land use conditions: Initial case of 2015 (INIT, yellow), Business-As-Usual scenario (BAU, red), 5-years-plan scenario (5YP, green), Balanced-Trade-Offs scenario (BTO, blue). The dotted lines shows annual average sediment export calculated with long-term precipitation data as a baseline (WorldClim v1.4. [<a href="#B39-forests-10-00176" class="html-bibr">39</a>,<a href="#B40-forests-10-00176" class="html-bibr">40</a>]).</p> Full article ">Scheme 1
<p>Comprehensive scheme of the modeling methodology used in this study. Model inputs are depicted as black arrows. Model outputs are depicted as purple arrows. * Data &amp; parameterization is based on Thellmann et al. [<a href="#B61-forests-10-00176" class="html-bibr">61</a>].</p> Full article ">
14 pages, 3643 KiB  
Article
Effects of Prescribed Fire, Site Factors, and Seed Sources on the Spread of Invasive Triadica sebifera in a Fire-Managed Coastal Landscape in Southeastern Mississippi, USA
by Shaoyang Yang, Zhaofei Fan, Xia Liu, Andrew W. Ezell, Martin A. Spetich, Scott K. Saucier, Sami Gray and Scott G. Hereford
Forests 2019, 10(2), 175; https://doi.org/10.3390/f10020175 - 19 Feb 2019
Cited by 9 | Viewed by 3688
Abstract
In the Gulf of Mexico coastal region, prescribed fire has been increasingly used as a management tool to restore declining native ecosystems, but it also increases the threat posed by biological invasion, since the treated sites are more susceptible to invasive species such [...] Read more.
In the Gulf of Mexico coastal region, prescribed fire has been increasingly used as a management tool to restore declining native ecosystems, but it also increases the threat posed by biological invasion, since the treated sites are more susceptible to invasive species such as Chinese tallow (Triadica sebifera). We chose Mississippi Sandhill Crane National Wildlife Refuge (MSCNWR), a fire-managed landscape, to examine the potential effect of prescribed fire and landscape/community features on tallow invasion and spread. We took a complete survey of roadways and fire lines for tallow and measured a systematic sample of 144 10 × 3 m2 rectangular plots along two selected roadways and a simple random sample of 56 0.04-ha circular plots across burn units. We used pair correlation function for marked point pattern data, zero-inflated negative binomial models for count data, as well as multivariate Hotelling’s T2 test, to analyze the effect of prescribed fire and landscape/community characteristics on tallow invasion and spread along habitat edges and into interiors. Our results show that tallow spread along habitat edges and into interiors in a spatially clustered pattern. Tallow invasion risk decreases with the distance to seed trees and shrub coverage, and with the time since last fire if seed trees are outside the effective seed dispersal range (~300 m), but increases with the time since last fire if seed trees are within the effective seed dispersal range. Tallow seedling (≤2 years old) densities increase with the time since last fire and with increasing overstory tree basal area, but decrease with the distance to seed trees. Tallow-invaded interior plots have significantly shorter mean fire return intervals (2.7 years), lower shrub coverage (8.6%), and are closer to edges (20.3 m) than non-invaded plots (4.3 years, 18.4%, 167.6 m, respectively). Full article
(This article belongs to the Special Issue Impacts of Nonnative Species on the Health of Natural and Planted Forests)
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<p>Map of Mississippi Sandhill Crane National Wildlife Refuge (MSCNWR) west block, showing the burn bunts, selected edges, and the locations of sample plots.</p> Full article ">Figure 2
<p>The design of rectangular plots and site/stand condition variables collected to study tallow colonization and spread along habitat edges (roadways and file lines).</p> Full article ">Figure 3
<p>(<b>A</b>) Cumulative increases of tallow clusters between 2003–2011 and 2012–2015, (<b>B</b>) spatiotemporal spread patterns of tallow clusters along habitat edges (roadways and fire lines) and (<b>C</b>) the spatial relationship of tallow clusters between 2003–2011 and 2012–2015.</p> Full article ">Figure 4
<p>The cumulative increase in density and age structure (inserted) of tallow trees along selected roadways: (<b>A</b>) With seed trees; (<b>B</b>) without seed trees.</p> Full article ">Figure 5
<p>Change of tallow seedling (≤2 years) densities by risk factors: (<b>A</b>) Distance to seed sources; (<b>B</b>) year since last fire; (<b>C</b>) overstory tree basal area; and (<b>D</b>) understory shrub coverage.</p> Full article ">Figure 6
<p>The spatial relationship and site conditions between tallow-invaded plots and non-invaded plots: (<b>A</b>) The PCF curve shows the invaded plots and non-invaded plots are spatially independent; (<b>B</b>) tallow-invaded plots are significantly closer to roads than non-invaded plots; (<b>C</b>) tallow-invaded plots have significantly lower shrub coverage than non-invaded plots; (<b>D</b>) tallow-invaded plots have significantly shorter mean fire return intervals than non-invaded plots.</p> Full article ">
11 pages, 1936 KiB  
Article
Torreya jackii (Taxaceae): A Special Species that is Genetically Admixed, Morphologically Distinct, and Geographically Sympatric with Parent Species
by Yu-Jin Wang, Kun Xiao and Yi-Xuan Kou
Forests 2019, 10(2), 174; https://doi.org/10.3390/f10020174 - 19 Feb 2019
Cited by 7 | Viewed by 3290
Abstract
Torreya jackii Chun is an endangered species (Taxaceae) confined to a few localities in China. However, the species status of T. jackii within Torreya Arn. has not been clearly elucidated under a phylogenetic context. In this study, phylogenetic analyses based on the nuclear [...] Read more.
Torreya jackii Chun is an endangered species (Taxaceae) confined to a few localities in China. However, the species status of T. jackii within Torreya Arn. has not been clearly elucidated under a phylogenetic context. In this study, phylogenetic analyses based on the nuclear internal transcribed spacer (ITS) and amplified fragment length polymorphism (AFLP) indicated that T. jackii is closely related with a sympatric species T. grandis Fort. ex Lindl. that is present due to cultivation. However, analysis based on the concatenated sequences of seven chloroplast loci resolved T. jackii as the first branch within the genus. Given their overlapping distribution and synchronous blooming, we suggest that the plastid-nuclear incongruence was derived from the dilution of the nuclear genome of T. jackii by T. grandis via pollen-mediated introgression hybridization when the two species met due to cultivation. Introgressive hybridization is fairly common in plants but few cases have been recognized as independent species. Our study highlights the complexity of protecting endangered species and the need for caution to prevent the unreasonable expansion of economic crops into the distribution ranges of their wild relatives. Full article
(This article belongs to the Section Forest Ecology and Management)
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<p>Geographical distribution of the extant <span class="html-italic">Torreya</span> species. (<b>A</b>), worldwide distribution (orange regions); (<b>B</b>), distribution in Eastern Asia; (<b>C</b>), distribution in North America.</p> Full article ">Figure 2
<p>Strict consensus of the most parsimonious tree obtained for <span class="html-italic">Torreya</span> based on the seven combined chloroplast regions. Numbers above the branches denote the bootstrap values based on 1000 replicates from the maximum parsimony (MP) and maximum likelihood (ML) analyses, and the posterior probabilities from Bayesian analyses. EA, Eastern Asia; NA, North America.</p> Full article ">Figure 3
<p>Strict consensus of the most parsimonious tree obtained for <span class="html-italic">Torreya</span> based on the nuclear internal transcribed spacer (ITS). The other details are the same as those in <a href="#forests-10-00174-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>Neighbor-joining tree based on the amplified fragment length polymorphism (AFLP) data. Numbers above the branches denote the bootstrap values based on 1000 replicates from MP.</p> Full article ">
16 pages, 1752 KiB  
Article
The Effects of DNA Methylation Inhibition on Flower Development in the Dioecious Plant Salix Viminalis
by Yun-He Cheng, Xiang-Yong Peng, Yong-Chang Yu, Zhen-Yuan Sun and Lei Han
Forests 2019, 10(2), 173; https://doi.org/10.3390/f10020173 - 18 Feb 2019
Cited by 21 | Viewed by 3781
Abstract
DNA methylation, an important epigenetic modification, regulates the expression of genes and is therefore involved in the transitions between floral developmental stages in flowering plants. To explore whether DNA methylation plays different roles in the floral development of individual male and female dioecious [...] Read more.
DNA methylation, an important epigenetic modification, regulates the expression of genes and is therefore involved in the transitions between floral developmental stages in flowering plants. To explore whether DNA methylation plays different roles in the floral development of individual male and female dioecious plants, we injected 5-azacytidine (5-azaC), a DNA methylation inhibitor, into the trunks of female and male basket willow (Salix viminalis L.) trees before flower bud initiation. As expected, 5-azaC decreased the level of DNA methylation in the leaves of both male and female trees during floral development; however, it increased DNA methylation in the leaves of male trees at the flower transition stage. Furthermore, 5-azaC increased the number, length and diameter of flower buds in the female trees but decreased these parameters in the male trees. The 5-azaC treatment also decreased the contents of soluble sugars, starch and reducing sugars in the leaves of the female plants, while increasing them in the male plants at the flower transition stage; however, this situation was largely reversed at the flower development stage. In addition, 5-azaC treatment decreased the contents of auxin indoleacetic acid (IAA) in both male and female trees at the flower transition stage. These results indicate that hypomethylation in leaves at the flower transition stage promotes the initiation of flowering and subsequent floral growth in Salix viminalis, suggesting that DNA methylation plays a similar role in vegetative–reproductive transition and early floral development. Furthermore, methylation changes during the vegetative–reproductive transition and floral development were closely associated with the biosynthesis, metabolism and transportation of carbohydrates and IAA. These results provide insight into the epigenetic regulation of carbohydrate accumulation. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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<p><span class="html-italic">Salix viminalis</span> in its growing and dormant seasons. (<b>a</b>) <span class="html-italic">Salix viminalis</span> in the growing season. (<b>b</b>) <span class="html-italic">Salix viminalis</span> in the dormant season.</p> Full article ">Figure 2
<p>Development of flower buds and annual shoots in male and female plants treated with 5-azaC. (<b>a</b>) Total (vegetative and floral) bud numbers in the annual shoots. (<b>b</b>) Flower bud numbers in annual shoots. (<b>c</b>) The ratio of flower buds to total buds in annual shoots. (<b>d</b>) Length of flower buds. (<b>e</b>) Diameter of flower buds. (<b>f</b>) Length of annual shoots. (<b>g</b>) Diameter of annual shoots. Least significant difference (LSD) tests were used to determine any significant differences between the treatment and control groups. Data are mean ± SE, <span class="html-italic">n</span> = 3. *, Significant difference between treatment and control (<span class="html-italic">p</span> &lt; 0.05); **, significant difference between treatment and control (<span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 3
<p>Effects of 5-azaC on the carbohydrate contents of male and female <span class="html-italic">Salix viminalis</span> trees. (<b>a–f</b>) Contents of soluble sugars (<b>a</b>,<b>d</b>), starch (<b>b</b>,<b>e</b>) and reducing sugars (<b>c</b>,<b>f</b>) in the female (<b>a–c</b>) and male (<b>d–f</b>) trees. FTS, flower transition stage; FDS, flower development stage. Least significant difference (LSD) tests were used to determine any significant differences between the treatment and control groups. Data are mean ± SE, <span class="html-italic">n</span> = 3. *, Significant difference between treatment and control (<span class="html-italic">p</span> &lt; 0.05); **, significant difference between treatment and control (<span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 4
<p>Effects of 5-azaC on the phytohormone contents of male and female <span class="html-italic">Salix viminalis</span> trees. (<b>a–j</b>) Contents of indoleacetic acid (IAA; <b>a</b>,<b>b</b>), abscisic acid (ABA; <b>c</b>,<b>d</b>), trans-zeatin-riboside (ZT; <b>e</b>,<b>f</b>), gibberellin 3 (GA3; <b>g</b>,<b>h</b>) and gibberellin 4 (GA4; <b>i</b>,<b>j</b>) in female (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>) and male (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>) trees. Least significant difference (LSD) tests were used to determine any significant differences between the treatment and control groups. Data are mean ± SE, <span class="html-italic">n</span> = 3. *, Significant difference between treatment and control (<span class="html-italic">p</span> &lt; 0.05); **, significant difference between treatment and control (<span class="html-italic">p</span> &lt; 0.01).</p> Full article ">
16 pages, 3601 KiB  
Article
Seasonal Variations and Thinning Effects on Soil Phosphorus Fractions in Larix principis-rupprechtii Mayr. Plantations
by Huixia Tian, Xiaoqin Cheng, Hairong Han, Hongyuan Jing, Xujun Liu and Zuzheng Li
Forests 2019, 10(2), 172; https://doi.org/10.3390/f10020172 - 18 Feb 2019
Cited by 14 | Viewed by 3125
Abstract
Thinning is a common management practice in forest ecosystems. However, understanding whether thinning treatment will change the availability of phosphorus (P) in soils, and the effect of thinning on the seasonal dynamics of soil P fractions, are still limited. The objective of the [...] Read more.
Thinning is a common management practice in forest ecosystems. However, understanding whether thinning treatment will change the availability of phosphorus (P) in soils, and the effect of thinning on the seasonal dynamics of soil P fractions, are still limited. The objective of the present study was to assess seasonal variations in soil P fractions under different forest thinning management strategies in a Larch (Larix spp.) plantation in northern China. To accomplish this, we examined soil P fractions, soil physical–chemical properties, and litter biomass under control (CK), light (LT), moderate (MT) and high thinning (HT) treatments. Data were collected during the growing season of 2017. We found that most P fractions varied seasonally at different soil depths, with the highest values occurring in the summer and autumn. When compared to CK, MT enhanced the inorganic P (Pi) concentration extracted by resin strip (R-Pi). Labile organic P (Labile Po), moderately labile P and total P (TP) also increased in both MT and HT treatments irrespective of season. In contrast, less-labile Pi and Po fractions were lower in LT than in CK, especially when examining deeper soil layers. Our results suggest that LT leads to a strong ability to utilize Po and less-labile Pi. Moreover, the effect of thinning did not tend to increase with thinning intensity, P availability was maximized at the MT. Ultimately, we show that MT can improve soil P bioavailability and is recommended in Larix principis-rupprechtii Mayr. plantations of North China. Our results emphasize that the effect of thinning management on soil microenvironment is an important basis for evaluating soil nutrients such as soil P bioavailability. Full article
(This article belongs to the Special Issue Organic Matter Production and Decomposition in Forest Soils)
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<p>Flow chart of sequential P fractions.</p> Full article ">Figure 2
<p>Seasonal changes in total (<b>a</b>), labile (<b>b</b>), moderately labile (<b>c</b>) and non-labile P (<b>d</b>) in <span class="html-italic">L. principis-rupprechtii</span> plantations. Each bar represents an average value across thinning treatments and soil depths (<span class="html-italic">n</span> = 36), i.e., twelve plots × three soil depths. Error bars indicate standard error. Values within each sampling time followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Response of total inorganic and organic P to thinning treatments at the three depths (0−10, 10−20 and 20−30 cm) at various sampling times in L. principis-rupprechtii plantations. CK, control site; LT, light thinning; MT, moderate thinning; HT, high thinning. Error bars indicate standard error (<span class="html-italic">n</span> = 3). Values within each sampling time followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). When the difference was not statistically significant, no letter notation was noted.</p> Full article ">Figure 4
<p>Response of labile P fractions to thinning treatments at the three depths (0−10, 10−20 and 20−30 cm) at various sampling times in <span class="html-italic">L. principis-rupprechtii</span> plantations. CK, control site; LT, light thinning; MT, moderate thinning; HT, high thinning. Error bars indicate standard error (<span class="html-italic">n</span> = 3). Values within each sampling time followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). When the difference was not statistically significant, no letter notation was noted.</p> Full article ">Figure 5
<p>Response of moderately labile P fractions to thinning treatments at the three depths (0−10, 10−20 and 20−30 cm) at various sampling times in <span class="html-italic">L. principis-rupprechtii</span> plantations. CK, control site; LT, light thinning; MT, moderate thinning; HT, high thinning. Error bars indicate standard error (<span class="html-italic">n</span> = 3). Values within each sampling time followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). When the difference was not statistically significant, no letter notation was noted.</p> Full article ">Figure 6
<p>Response of non-labile P fractions to thinning treatments at the three depths (0−10, 10−20 and 20−30 cm) at various sampling times in <span class="html-italic">L. principis-rupprechtii</span> plantations. CK, control site; LT, light thinning; MT, moderate thinning; HT, high thinning. Error bars indicate standard error (<span class="html-italic">n</span> = 3). Values within each sampling time followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). When the difference was not statistically significant, no letter notation was noted.</p> Full article ">Figure 7
<p>Effects of thinning treatments on soil temperature (<b>a</b>) and moisture (<b>b</b>) at various sampling times in <span class="html-italic">L. principis-rupprechtii</span> plantations. CK, control site; LT, light thinning; MT, moderate thinning; HT, high thinning. Values within each sampling time followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). When the difference was not statistically significant, no letter notation was noted.</p> Full article ">Figure 8
<p>Effects of sampling times on soil pH in four thinning treatments in <span class="html-italic">L. principis-rupprechtii</span> plantations. CK, control site; LT, light thinning; MT, moderate thinning; HT, high thinning. Error bars indicate standard error (<span class="html-italic">n</span> = 9), i.e., three plots repeats × three soil depths. Values within each treatment followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). When the difference was not statistically significant, no letter notation was noted.</p> Full article ">Figure 9
<p>Effects of thinning treatments on P in litter of L layer (<b>a</b>) and F/H layer (<b>b</b>) at various sampling times in <span class="html-italic">L. principis-rupprechtii</span> plantations. CK, control site; LT, light thinning; MT, moderate thinning; HT, high thinning; L layer, undecomposed litter; F/H layer, mixture of partly decomposed litter and amorphous humus. Error bars indicate standard error (<span class="html-italic">n</span> = 3). Values within each sampling time followed by different letters differ significantly according to Tukey’s test (<span class="html-italic">p</span> &lt; 0.05). When the difference was not statistically significant, no letter notation was noted.</p> Full article ">
13 pages, 1838 KiB  
Article
Effects of Invasive Spartina alterniflora Loisel. and Subsequent Ecological Replacement by Sonneratia apetala Buch.-Ham. on Soil Organic Carbon Fractions and Stock
by Jianxiang Feng, Shugong Wang, Shujuan Wang, Rui Ying, Fangmin Yin, Li Jiang and Zufu Li
Forests 2019, 10(2), 171; https://doi.org/10.3390/f10020171 - 17 Feb 2019
Cited by 27 | Viewed by 4317
Abstract
Background and Objectives: The rapid spread of invasive Spartina alterniflora Loisel. in the mangrove ecosystems of China was reduced using Sonneratia apetala Buch.-Ham. as an ecological replacement. Here, we studied the effects of invasion and ecological replacement using S. apetala on soil organic [...] Read more.
Background and Objectives: The rapid spread of invasive Spartina alterniflora Loisel. in the mangrove ecosystems of China was reduced using Sonneratia apetala Buch.-Ham. as an ecological replacement. Here, we studied the effects of invasion and ecological replacement using S. apetala on soil organic carbon fractions and stock on Qi’ao Island. Materials and Methods: Seven sites, including unvegetated mudflat and S. alterniflora, rehabilitated mangroves with different ages (one, six, and 10 years) and mature native Kandelia obovata Sheue, Liu, and Yong areas were selected in this study. Samples in the top 50 cm of soil were collected and then different fractions of organic carbon, including the total organic carbon (TOC), particulate organic carbon (POC), soil water dissolved carbon (DOC) and microbial biomass carbon (MBC), and the total carbon stock were measured and calculated. Results: The growth of S. alterniflora and mangroves significantly increased the soil TOC, POC, and MBC levels when compared to the mudflat. S. alterniflora had the highest soil DOC contents at 0–10 cm and 20–30 cm and the one-year restored mangroves had the highest MBC content. S. alterniflora and mangroves both had higher soil total carbon pools than the mudflat. Conclusions: The invasive S. alterniflora and young S. apetala forests had significantly lower soil TOC and POC contents and total organic carbon than the mature K. obovata on Qi’ao Island. These results indicate that ecological replacement methods can enhance long term carbon storage in Spartina-invaded ecosystems and native mangrove species are recommended. Full article
(This article belongs to the Special Issue Impacts of Nonnative Species on the Health of Natural and Planted Forests)
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<p>Map of the study sites in Qi’ao Island in Guangdong, China.</p> Full article ">Figure 2
<p>Vertical variation of different carbon in the sediment at different sites.</p> Full article ">Figure 3
<p>Total soil organic carbon stock down to 50 cm at different sites (error bar represents standard deviation of total carbon stock, letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Redundancy Analysis (RDA) ordination showing the relationship between carbon contents of top 10 cm and environmental parameters of surface 0–10 cm soil (Sal means <span class="html-italic">S. alterniflora</span> age, Mangrove means mangrove age).</p> Full article ">
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