Next Article in Journal
Ecosystem Services Response to the Grain-for-Green Program and Urban Development in a Typical Karstland of Southwest China over a 20-Year Period
Next Article in Special Issue
Effects of Bacillus cereus NJSZ-13 on Fatty Acid Metabolism of Bursaphelenchus xylophilus
Previous Article in Journal
Properties of Plywood Made of Thermally Treated Veneers Bonded with Castor Oil-Based Polyurethane Adhesive
Previous Article in Special Issue
Early Diagnosis of Pine Wood Nematode Disease Based on Chlorophyll Fluorescence Parameters and Organic Acids
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 8
10.3390/f14081636
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Responses of Plant Species Diversity and Biomass to Forest Management Practices after Pine Wilt Disease

by
Jinliang Liu
1,*,
Weiyong Liu
1,
Jianbin Wu
2,
Boliang Wei
3,4,
Jing Guo
3,
Lei Zhong
3,4 and
Mingjian Yu
3
1
College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
2
Taishun County Bureau of Natural Resources and Planning, Wenzhou 325500, China
3
College of Life Sciences, Zhejiang University, Hangzhou 310058, China
4
Zhejiang Wuyanling National Natural Reserve Management Bureau, Wenzhou 325500, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(8), 1636; https://doi.org/10.3390/f14081636
Submission received: 22 July 2023 / Revised: 7 August 2023 / Accepted: 11 August 2023 / Published: 14 August 2023
(This article belongs to the Special Issue Advance in Pine Wilt Disease)
Browse Figures
Versions Notes

Abstract

:
Pine wilt disease (PWD), which is caused by the Bursaphelenchus xylophilus, is one of the most serious biological invasions in East Asia. Removal of infected pine trees is generally used to prevent the spread of PWD. However, how this strategy affects plant species diversity and ecosystem functions needs to be clarified. We compared alpha diversity, species composition, and biomass for all woody plant species, adults, saplings, and seedlings in infected Masson pine forests where removal of infected trees occurred (CTD) and where infected trees were retained (UTD), uninfected Masson pine forests (PMF), and evergreen broad-leaved forests (EBF). UTD had the highest alpha diversity of all species and saplings, and UTD and PMF had the lowest alpha diversity of seedlings. CTD and EBF had a similar composition of seedlings, and CTD and UTD had similar species composition of all plant species and saplings. UTD had the lowest biomass for all species and adults but had the highest saplings biomass. Soil properties were significantly related to plant biomass. The retention of infected trees likely maintained soil fertility which increased species alpha diversity and the biomass of saplings, and unchanged species composition compared to the removal of infected trees, indicating that the retention strategy could help to maintain ecosystem functions.

1. Introduction

Pine wilt disease (PWD) caused by Bursaphelenchus xylophilus Nickle (pine wood nematode, PWN) is a devastating epidemic of pine trees and one of the most serious biological natural hazards in East Asia and Europe [1,2]. PWN is native to North America and has rapidly spread to East Asian and European countries since it was first recorded in Japan in 1905 [3,4,5]. Short-distance dispersal of PWN is mainly carried by the pine sawyer beetle (Monochamus alternatus Hope), or the longhorn beetle (Monochamus galloprovincialis Olivier), and long-distance dispersal is mainly through timber transportation and trading [6,7]. PWD in China was first noted in Nanjing in Jiangsu Province in 1982 and has since spread rapidly in the country, causing a significant impact on China’s forestry production and ecological environment [8]. PWN can infect most pine species, such as Masson pine (Pinus massoniana Lamb.), P. thunbergia Parlatore, and P. pinaster Aiton [5,9,10]. By 2022, PWD had reached 737 counties and cities in 19 provinces in China (Data from: www.forestry.gov.cn, accessed on 4 May 2023), and PWD will continue to expand [11]. Among them, most provinces contain Masson pine forests and are in subtropical regions. Given its extensive spread, it is important to understand the impact of PWD on species diversity and community structure within Masson pine forests to prevent the dispersal of PWD in subtropical regions.
To prevent the spread of PWD, one forest management practice is to remove the infected individuals from the forest [3,12]. However, Robinet et al. [13] found that clear-cutting a 500 m radius around infected trees was not an effective management strategy in non-fragmented pine forests, and instead, the radius of clear-cutting should be between 14 and 38 km to significantly reduce the number of transmissions. Thus, preventing the spread of PWD by the removal of infected trees requires a huge economic cost and is likely not technically nor ethically feasible [2]. In addition, due to poor economic conditions and complex topography, not all infected trees can be removed from forests in some regions. Although there are other practices to control the spread of PWD, such as biological and chemical controls, there is not yet a consensus on the effects of these approaches [4,6]. As Masson pine forests continue to rapidly become infected over large areas, it will become increasingly difficult to effectively control the spread of PWD by removing infected trees [2,14]. Therefore, it is necessary to understand the impact of the retention of infected trees on pine forest ecosystems. This different forest management practice may differently affect species diversity and ecosystem functions compared to tree removal; however, these effects have so far been understudied [15,16].
Some studies have analyzed the community dynamics of pine forests after PWD. For example, Yu et al. [3] found that individual death caused by PWD can accelerate the transformation of Masson pine forests into evergreen broad-leaved forests. Similar results have also been found in other areas in Japan and China (e.g., [16,17]), indicating that PWD may promote dramatic changes in species diversity in communities. However, previous studies did not consider the effects of different management strategies after PWD, such as the removal or retention of infected pine trees. The removal of infected trees will promote the formation of forest gaps and thus could affect the species composition of seedlings under the canopy [18]. For example, forest gaps can promote the regeneration of deciduous broad-leaved species or shade-intolerant species in subtropical forests [19] and might increase the diversity of seedlings. The removal of infected pine trees may also promote the growth and survival of saplings under the forest canopy and could accelerate the transformation of Masson pine forests into evergreen broad-leaved forests [3,20]. However, it is still unclear what effect infected tree removal has on species diversify for different growth forms, such as adults, saplings, and seedlings. It is important to distinguish effects by different growth forms to better reflect the dynamics and direction of community succession.
The death of pine trees could change the physical and chemical properties of the soil and the composition of soil microorganisms [16,21]. Carbon (C) storage and soil CO2 efflux rates have been shown to be impacted by PWD in red pine forests, with a significant C reduction in severely damaged forests [21]. Kim et al. [22] found that soil fertility was generally higher in undamaged pine forests than in damaged pine forests after PWD. Gao et al. [23] also found that the greatest rates of infection by PWD occurred in lower-diameter individuals, and overstory biomass decreased with the extent of PWD. Compared to the retention of infected trees, removing infected pine trees may affect the decomposition of litter and woody debris and reduce the soil fertility, in turn creating varying impacts on the growth of adults, saplings, and seedlings. Therefore, the retention and removal of infected trees may have different impacts on biomass, which is a key ecosystem function associated with C dynamics and productivity [24].
In this study, we aimed to better understand how the removal or retention of infected pine trees affects species diversity and biomass of plants with different growth forms (i.e., adults, saplings, and seedlings) in Masson pine forests. We calculated and compared the alpha diversity, species composition, and biomass for different growth forms in various forest conditions in Taishun County, eastern China: (1) infected Masson pine forests where removal of infected pine trees occurred, (2) infected Masson pine forests where infected pine trees were retained five years ago, (3) uninfected Masson pine forests, and (4) evergreen broad-leaved forests.

2. Materials and Methods

2.1. Study Sites

The study sites are located in Taishun County (27°17′–27°50′, 119°37′–120°15′) in Zhejiang Province, China (Figure 1). This region has a humid, subtropical monsoon climate. The mean annual temperature is 16 °C, and the mean annual precipitation is 2000 mm. Taishun County has had high rates of PWD infestation in the last five years. The first record of PWD occurrence in Taishun County was in Yayang Town in 2016. The total land area of Taishun County is 1761.5 km2, of which the forest cover is 76.9%. According to unpublished data from the local forestry department, the PWD census in the autumn of 2020 showed an epidemic area of about 19 km2, with 218,708 dead pine trees in the county.
When trees are infected in Taishun County, there are two main practices for the management of infected pine trees: (1) removal of the affected trees and (2) retention of the affected pine trees with no other pest treatment. The second practice is especially common where there are large areas of infection or limited human and financial resources to manage diseased trees.

2.2. Plant Data Collection

In 2021, we investigated 6 forest plots (30 m × 30 m) in Masson pine forests infected by pine nematode in 2016 according to the record in the local forestry department and the appearance of the pine forest, in which 3 plots removed the infected Masson pine trees in 2016 (CTD) and the other 3 plots retained the infected trees (UTD). We also sampled 3 plots in uninfected Masson pine forests (PMF) and sampled 3 plots in evergreen broad-leaved forests (EBF) as comparisons. These pine forests developed from the secondary succession of cleared evergreen broad-leaved forests following logging in the 1950s. In each plot, all trees with a diameter at breast height (DBH) ≥ 1 cm were tagged, measured, identified, and mapped. All surveyed tree species were firstly classified as tree, shrub, and under-shrub according to the description of the Flora of China (http://www.iplant.cn/foc, accessed on 1 March 2023) and the Flora of Zhejiang (New Edition) [25]. Then, we classified saplings as individuals with DBH smaller than 10 cm for trees, 5 cm for shrubs, and 2 cm for under-shrubs, and adults as individuals with DBH greater than the set values for each growth form group [26].
PWD caused the death of pine trees and may affect the diversity of seedlings under the forest at a small scale. Therefore, each 30 m × 30 m plot was divided into 9 10 m × 10 m plots for seedlings investigation and soil sampling. We set up 2 m × 2 m subplots at the center of each 10 m × 10 m plot and investigated all seedling individuals with DBH < 1 cm in the subplots.

2.3. Environmental Data Measurement

Soil attributes were investigated at the center of each 10 m × 10 m plot. Soil samples were collected in June 2021. One 100-cm3 core was randomly collected at the center of each subplot for the measurement of soil bulk density (g/cm3) and maximum water holding capacity (MWHC, g/kg) using the cutting ring method (LY/T 1215-1999). Three 0–10 cm deep soil cores were randomly collected around the center of each selected subplot after removing the litter layer, mixing, and passing through a sieve (2 mm mesh size) to form one soil sample. The sampled soil was separated into two for each subplot: one part was stored at 4 °C for the measurement of ammonium nitrogen (NH4+-N, mg/kg) and nitrate nitrogen (NO3−-N, mg/kg) within 72 h, and the other was air-dried for the measurement of other soil chemical properties, e.g., total carbon (TC, %), total nitrate (TN, %), total phosphorus (TP, mg/kg), available phosphorus (AP, mg/kg), and soil pH. The slope, aspect (i.e., the orientation of the slope), canopy openness (LAI), and soil depth were measured at the center of the plot. LAI was quantified for each plot with hemispherical photographs taken 1.3 m above the soil surface. Hemispherical photographs were taken with a Canon 6D MARK II digital camera (Tokyo, Japan) connected to a Sigma 4.5-mm fisheye lens (Kawasaki, Japan)mounted on a tripod and processed using Hemiview v. 2.1 software.

2.4. Biomass Estimation

We calculated the biomass of each plot, 10 m × 10 m or 30 m × 30 m, for all woody plant species (DBH ≥ 1 cm), adults, and saplings. The biomass of each plot was estimated by calculating the total biomass for each tree (stem + branches + foliage + roots) using allometric equations for the primary subtropical species and functional groups in southern China [27].

2.5. Data Analysis

To reflect the difference in alpha diversity between different forest types (i.e., UTD, CTD, PMF, and EBF), we calculated the species richness for all species (DBH ≥ 1 cm), adults, saplings, and seedlings in each 10 m × 10 m plot (small scale) and in each 30 m × 30 m plot (large scale) separately. We also calculated the Shannon–Wiener index [28] and bias-corrected Chao index [29] when considering the species abundance using the function “estimateR” in the vegan package on small and large scale separately. A higher value in Shannon–Wiener index means more evenness in species abundance distribution, and a higher value in Chao’s index means more rare species in the community.
To visualize the difference in species composition between different forest types on small and large scales, we conducted Principal Coordinate Analysis (PCoA) based on the Bray–Curtis dissimilarity index using the vegan package in R. We also used permutational analysis of variance (PERMANOVA test, with Bray–Curtis distances) using 999 permutations in the adonis function of the vegan package to test whether there is a significant difference in species composition between groups [30,31,32].
We used a one-way analysis of variance followed by Tukey’s HDC (honestly significant difference) post hoc test to compare the differences in alpha diversity and biomass across forest types on a large scale. On a small scale, to test how forest types affect the alpha diversity and biomass of different growth forms, we compared the alpha diversity and biomass between forest types using the linear mixed effects models (LME) with the “lmer” function in the lme4 package [33]. In LME models, the forest types were taken as a fixed effect, and the 30 m × 30 m plot was used as the random effect to account for the lack of independence among 9 10 m × 10 m plots in one 30 m × 30 m plot.
To further test whether the environmental factors affect alpha diversity (species richness, Shannon–Wiener index, and Chao’s index) and biomass on small and large scales, a principal component analysis (PCA) was performed on 13 standardized environmental variables (i.e., soil bulk density, MWHC, soil depth, aspect, slope, LAI, TP, TC, TN, AP, NH4+-N, NO3−-N, and soil pH), and the first two principal components (PC1 and PC2) were selected to measure environmental factors. The simple linear relationships between PC1 or PC2 and the alpha diversity and biomass were plotted to test the effect of environmental factors. On a large scale, the environmental factors in each 30 m × 30 m plot were calculated by the average of the environmental factors in 9 10 m× 10 m plots.

3. Results

3.1. Differences in Plant Alpha Diversity across Forest Types

The woody plant species in EBF were dominated by the Castanopsis eyrei (Champ. ex Benth.) Tutch. and Schima superba Gardn. et Champ.; the species in PMF were dominated by the P. massoniana, and Loropetalum chinense (R. Br.) Oliver; the species in CTD were dominated by P. massoniana, L. chinense, Vaccinium carlesii Dunn, Adinandra milletii (Hook. and Arn.) Benth. and Hook.f. ex Hance, and the species in UTD were dominated by the A. milletii, V. carlesii, Eurya muricata Dunn, and Lindera aggregata (Sims) Kosterm. The EBF had the lowest species richness, while the UTD had the highest species richness for all species (Figure 2a) and saplings (Figure 2c), and the CTD and PMF had similar species richness on a small scale (Figure 2a). UTD and PMF had the highest species richness for all species (Figure A1a) and adults (Figure A1b) on a large scale. There was no significant difference in species richness between CTD, UTD, and PMF for adults on a small scale (Figure 2b). UTD and PMF had the lowest species richness, CTD and EBF had the highest richness for seedlings on a small scale (Figure 2d), and UTD and CTD had similar species richness for seedlings on a large scale (Figure A1d). When considering the species abundance distributions in forests, the differences in the Shannon–Wiener index and Chao’s index across forest types showed similar patterns with species richness for all species, adults, and saplings (Figure 2a and Figure A1).
To further test whether environmental factors affect alpha diversity on a small scale, PCA analysis showed that the first two axes (PCs) of the PCA explained 38.37% of the variation in 13 environmental factors (Figure 3). When using the averaged environmental factors of each 30 m × 30 m plot on a large scale, PCA analysis showed that the first two axes (PCs) of the PCA explained 53.73% of environmental factors (Figure A2). On a small scale, PC1, which we refer to as the “soil physical property” axis, explained 23.79% of the variance and was significantly correlated with MWHC, soil bulk density, and soil depth. PC2, referred to as the “soil chemical property” axis, explained 14.58% of the variance and was significantly correlated with NH4+-N, NO3−-N, AP, soil pH, and LAI. On a large scale, the PCs have similar ecological reference to the PCs on a small scale. There were significant positive correlations between PC2 and species richness, the Shannon–Wiener index, and Chao’s index for all species and adults on a small scale (Figure 4), but there were no significant correlations between PCs and species diversity on a large scale (Figure A3).

3.2. Differences in Plant Species Composition across Forest Types

Differences in species composition across forest types showed similar patterns on small and large scales (Figure 5 and Figure A4). PCoA analysis revealed clear distinctions in species composition between PMF, EBF and the infected Masson pine forests (CTD and UTD) for all species, adults, saplings, and seedlings (Table 1; Figure 5). CTD and UTD had similar species composition for all plant species (Table 1; Figure 5a) and saplings (Figure 4c), while the species composition of saplings in PMF showed higher dissimilarity with CTD and UTD. CTD and PMF had similar species compositions with the lowest Bray-Curtis index values for adults (Table 1; Figure 5b). CTD and EBF had similar species composition values with the lowest Bray-Curtis index for seedlings, while PMF, CTD, and UTD had the highest Bray-Curtis index for seedlings (Table 1; Figure 5d).

3.3. Differences in Plant Biomass across Forest Types

Differences in biomass across forest types showed similar patterns on a small and large scale (Figure 6 and Figure A5). The EBF had the highest biomass compared to other forest types (PMF, CTD, and UTD), and the UTD had the lowest biomass for all species (Figure 6a) and adults (Figure 6b) but the highest for saplings (Figure 6c). There were no significant differences in biomass for all species (Figure 6a), adults (Figure 6b) and saplings between CTD and PMF. There were significant negative correlations between PC2 and the biomass for all species (Figure 7b) and adults (Figure 7d), while there were positive correlations between PC2 and the biomass of saplings (Figure 7f) on a small scale. There were significant negative correlations between PC1 and the biomass for all species (Figure A6b) and adults (Figure A6d) on a large scale.

4. Discussion

Although many studies have been conducted on how to prevent the spread of PWD [2,6,34] and its influence on ecosystem functions [3,20], the impact of management practices following PWD on the species diversity and ecosystem functions of Masson pine forests has been comparatively understudied (but see [15,16]). Here, we tested the effect of retaining infected pine trees compared to removing them on the alpha diversity, species composition, and biomass in Masson pine forests on a small and large scale separately. Our results indicated that retaining infected pine trees as a management strategy may have a positive effect on maintaining ecosystem functions after PWD in Masson pine forests.

4.1. Impact of the Removal of Diseased Pine Trees on Alpha Diversity

Biological invasion of diseases generally negatively affects native species diversity, and invasive tree pests tend to have a significant negative impact on forest ecosystems [35,36,37]. While our results showed that there was no significant difference in alpha diversity between the CTD and PMF, and the retention of infected pine trees could increase the alpha diversity (Figure 2 and Figure A1). Retention of infected pine trees was shown to improve the alpha diversity of saplings but reduced the alpha diversity of seedlings when compared with CTD (Figure 2). These results suggested that this management practice can help to maintain the alpha diversity of the whole community, which is important to note, given that previous research has found that the removal of diseased pine trees is not effective in preventing the spread of PWD [13]. Compared with the removal approach, the retention management practice has the added benefit of avoiding human disturbance in infected Masson pine forests. Removal of infected pine trees may further affect the growth and survival of other species around the infected tree, such as the spatial dependence or by destroying the biological interaction of individuals [38]. The removal of infected individuals could also have an impact on habitat conditions, such as an increase in light conditions under the forest canopy due to the formation of forest gaps, perhaps due to an increase in the alpha diversity of seedlings [19,39]. Our results also found that the removal of infected pine trees can increase the alpha diversity of seedlings, likely because the increase in lighting conditions promoted the regeneration of understory seedlings. While retention of infected pine trees can increase the alpha diversity of saplings, it was shown to also reduce the alpha diversity of seedlings. Retention of infected pine trees could increase the forest litter substrate quality and help to maintain soil nutrients [40]. In addition, we found that the alpha diversity of saplings was significantly positively correlated with PC2, and the UTD had higher PC2 values on a small scale (Figure 4), supporting that retained pine trees could help maintain higher soil fertility, thereby increasing the diversity of saplings.

4.2. Impact of the Removal of Diseased Pine Trees on Species Composition

Previous studies have found that the death of Masson pine trees caused by PWD could promote the succession to subtropical evergreen broad-leaved forests, which would be evidenced by the similarity in species composition between EBF and infected Masson pine forests over time [3,16,17]. The results of this study are consistent with this as CTD and EBF had similar species compositions for seedlings, and the species composition of seedlings in pine forests infected by PWN was dissimilar with PMF (Table 1; Figure 5 and Figure A4). The results indicated that the removal of infected pine trees might accelerate the succession of the Masson pine forests to subtropical evergreen broad-leaved forests faster than the retention of infected trees. We also found that CTD and UTD had similar species composition for all plant species and saplings (Table 1; Figure 5 and Figure A4), indicating that the removal of infected trees did not significantly cause differentiation in species composition during succession. While the species composition in saplings was not similar between PMF and infected pine forests (CTD and UTD), CTD and PMF had similar species composition for adults (Table 1; Figure 5b). These results suggest that PWD in Masson pine forests will first cause changes in the species composition of sapling trees because the death of dominant pine species will provide space and light for the growth of saplings, thus accelerating the turnover of the species composition of saplings.

4.3. Impact of the Removal of Diseased Pine Trees on Biomass

The invasion of PWD in Masson pine forests kills many pine trees, which generally leads to C reduction and biomass decreases [21]. In this study, we found that UTD had the lowest biomass when compared with EBF and PMF for all species and adults, while the biomass in CTD and PMF showed no significant difference (Figure 6 and Figure A5). In addition, we further found that almost all pine adults had died five years later (the mean number of adult pine trees = 0.703 ± 0.993 in one 10 m × 10 m plot) in the retention of infected pine plots (UTD), but there were still pine adults alive in the CTD (mean number of adult pine trees = 5.296 ± 4.177 in one 10 m × 10 m plot), which may explain why there was lower biomass in UTD. There was no significant difference between CTD and PMF for adults. However, we cannot rule out that the biomass of adults in CTD will not be further reduced because of tree death and removal over a longer time scale. However, the death of dominant tree or canopy species in the forest is likely to provide sufficient lighting and nutrient conditions for understory plants or sapling trees [40]. Our results also found that UTD had the highest biomass for saplings (Figure 6c). In addition, there exists a significantly positive relationship between PC2 and biomass for saplings on a small scale (Figure 7). Because the PC2 related to “soil chemical properties”, the UTD had the highest mean value, suggesting that the retention of infected pine trees can maintain or even increase the nutrient elements in the soil, thus promoting the growth of saplings. Moreover, there were significant negative correlations between PC1 and the biomass for all species (Figure A6b) and adults (Figure A6d) on a large scale, indicating that the soil bulk density and maximum water holding capacity could also affect the forest biomass.

5. Conclusions

We evaluated the effect of different management practices on plant species diversity and ecosystem functions after PWD in Masson pine forests. Our research found that: (1) retention of infected pine trees can better maintain soil fertility, thus maintaining higher alpha diversity of all plant species and saplings, but the removal of infected pine trees is conducive to increasing species diversity of seedlings; (2) although removal of infected pine trees did not significantly cause differentiation in species composition of saplings between the retained infected pine trees during the succession, the removal of infected pine trees could accelerate the succession of Masson pine forest to subtropical evergreen broad-leaved forests; (3) the PWD reduced the biomass of adults while the retaining of infected pine trees increased the biomass of saplings. Therefore, we suggest retaining infected pine trees in infected forests can be a preferred strategy for forest management that also balances economic concerns when removal of infected individuals from the pine forest is not effective in preventing the spread of PWD and requires considerable human and financial resources. To control the spread of PWD, additional attention should be paid to other management practices beyond the removal of infected trees, including beetle mass trapping, reconstruction of biotic interactions (e.g., increase predation on vector beetles), and chemical defenses [6,34,41].

Author Contributions

Conceptualization, J.L. and M.Y.; software: W.L.; methodology, W.L. and J.L.; formal analysis, W.L.; investigation, W.L., J.W., B.W., J.G., L.Z. and J.L.; resources, J.L., J.W. and M.Y.; data curation, M.Y. and J.L.; writing—original draft preparation, W.L. and J.L.; writing—review and editing, W.L., B.W., J.G., L.Z. and J.L.; visualization, W.L.; supervision, J.L.; project administration, J.L. and M.Y.; funding acquisition, M.Y. and J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Scientific Research Project of Wenzhou (grant number: N2020005), International Collaborative Project of National Key R&D Plan (grant number: 2018YFE0112800), National Natural Science Foundation of China (grant number: 32271606), “Pioneer” and “Leading Goose” R&D Program of Zhejiang (2023C03137), and the Graduate Scientific Research Foundation of Wenzhou University (grant number: 3162023004074).

Data Availability Statement

Some data are acquired from other collaborators, which are not allowed for the public repository. Data can be obtained by contacting the corresponding author.

Acknowledgments

We thank Mengyuan Chen, Mengsi Zhou, Dan Long at Wenzhou University, Zhibin Mao, and Xixi Yang at Zhejiang University for participating in fieldwork.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Forests 14 01636 g0a1
Figure A1. The difference in species richness (a–d), Shannon–Wiener index (e–h), and Chao’s index (i–l) between forest types for all species (a,e,i), adults (b,f,j), saplings (c,g,k), and seedlings (d,h,l) on a large scale. Different letters denote the significant differences between forest types. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Figure A1. The difference in species richness (a–d), Shannon–Wiener index (e–h), and Chao’s index (i–l) between forest types for all species (a,e,i), adults (b,f,j), saplings (c,g,k), and seedlings (d,h,l) on a large scale. Different letters denote the significant differences between forest types. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Forests 14 01636 g0a1
Forests 14 01636 g0a2
Figure A2. Principal component analysis (PCA) for 13 environmental factors on a large scale. The environmental factors include soil bulk density (g/cm3), maximum water holding capacity (MWHC, g/kg), ammonium nitrogen (NH4+-N, mg/kg), nitrate nitrogen (NO3−-N, mg/kg), total carbon (TC, %), total nitrate (TN, %), total phosphorus (TP, mg/kg), available phosphorus (AP, mg/kg), soil pH, slope, aspect, canopy openness (LAI, %), and soil depth (cm).
Figure A2. Principal component analysis (PCA) for 13 environmental factors on a large scale. The environmental factors include soil bulk density (g/cm3), maximum water holding capacity (MWHC, g/kg), ammonium nitrogen (NH4+-N, mg/kg), nitrate nitrogen (NO3−-N, mg/kg), total carbon (TC, %), total nitrate (TN, %), total phosphorus (TP, mg/kg), available phosphorus (AP, mg/kg), soil pH, slope, aspect, canopy openness (LAI, %), and soil depth (cm).
Forests 14 01636 g0a2
Forests 14 01636 g0a3
Figure A3. Simple linear relationships between PC1 and PC2 and species richness, Shannon–Wiener index, and Chao’s index for all species (a–f), adults (g–l), saplings (m–r), and seedlings (s–x) on a large scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Figure A3. Simple linear relationships between PC1 and PC2 and species richness, Shannon–Wiener index, and Chao’s index for all species (a–f), adults (g–l), saplings (m–r), and seedlings (s–x) on a large scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Forests 14 01636 g0a3
Forests 14 01636 g0a4
Figure A4. The ordination of (a) all species, (b) adults, (c) saplings, and (d) seedlings communities by principal coordinate analysis (PCoA) on a large scale. Ellipses show 75% confidence limits within each forest type (EBF: evergreen broad-leaved forests; CTD: infected Masson pine forests in 2016 and selective cutting of infected trees; UTD: infected Masson pine forests in 2016 and retention of infected trees; PMF: Masson pine forests). Two dimensions and Bray–Curtis distance were applied in the analysis. Points close together in principal coordinate analysis (PCoA) ordination space indicate plots with similar species composition.
Figure A4. The ordination of (a) all species, (b) adults, (c) saplings, and (d) seedlings communities by principal coordinate analysis (PCoA) on a large scale. Ellipses show 75% confidence limits within each forest type (EBF: evergreen broad-leaved forests; CTD: infected Masson pine forests in 2016 and selective cutting of infected trees; UTD: infected Masson pine forests in 2016 and retention of infected trees; PMF: Masson pine forests). Two dimensions and Bray–Curtis distance were applied in the analysis. Points close together in principal coordinate analysis (PCoA) ordination space indicate plots with similar species composition.
Forests 14 01636 g0a4
Forests 14 01636 g0a5
Figure A5. The differences in mean biomass among forest types for all tree species (a), adults (b), and saplings (c) on a large scale. Different letters denote the significant differences between forest types. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Figure A5. The differences in mean biomass among forest types for all tree species (a), adults (b), and saplings (c) on a large scale. Different letters denote the significant differences between forest types. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Forests 14 01636 g0a5
Forests 14 01636 g0a6
Figure A6. Relationships between biomass and PC1 and PC 2 for all species (a,b), adults (c,d), and saplings species (e,f) on a large scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Figure A6. Relationships between biomass and PC1 and PC 2 for all species (a,b), adults (c,d), and saplings species (e,f) on a large scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Forests 14 01636 g0a6

References

  1. Webster, J.; Mota, M. Pine Wilt Disease: Global Issues, Trade and Economic Impact; Springer: Dordrecht, The Netherlands, 2008. [Google Scholar]
  2. de la Fuente, B.; Saura, S.; Beck, P.S.A.; Fortin, M.-J. Predicting the spread of an invasive tree pest: The pine wood nematode in Southern Europe. J. Appl. Ecol. 2018, 55, 2374–2385. [Google Scholar] [CrossRef]
  3. Yu, M.J.; Xu, X.H.; Ding, P. Economic loss versus ecological gain: The outbreaks of invaded pinewood nematode in China. Biol. Invasions 2011, 13, 1283–1290. [Google Scholar] [CrossRef]
  4. Kim, B.-N.; Kim, J.H.; Ahn, J.-Y.; Kim, S.; Cho, B.-K.; Kim, Y.-H.; Min, J. A short review of the pinewood nematode, Bursaphelenchus xylophilus. Toxicol. Env. Health 2020, 12, 297–304. [Google Scholar] [CrossRef]
  5. Hirata, A.; Nakamura, K.; Nakao, K.; Kominami, Y.; Tanaka, N.; Ohashi, H.; Takano, K.T.; Takeuchi, W.; Matsui, T. Potential distribution of pine wilt disease under future climate change scenarios. PLoS ONE 2017, 12, e0182837. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. de la Fuente, B.; Beck, P.S.A. Management measures to control pine wood nematode spread in Europe. J. Appl. Ecol. 2019, 56, 2577–2580. [Google Scholar] [CrossRef]
  7. Filipiak, A. The pine wilt disease. Sylwan 2008, 152, 9–19. [Google Scholar]
  8. Yang, B.J. The History, Dispersal and Potential Threat of Pine Wood Nematode in China; Brill Academica Publishers: Leiden, The Netherlands, 2003. [Google Scholar]
  9. Fonseca, L.; Cardoso, J.; Lopes, A.; Pestana, M.; Abreu, F.; Nunes, N.; Mota, M.; Abrantes, I. The pinewood nematode, Bursaphelenchus xylophilus, in Madeira Island. Helminthologia 2012, 49, 96–103. [Google Scholar] [CrossRef] [Green Version]
  10. Vicente, C.; Espada, M.; Vieira, P.; Mota, M. Pine Wilt Disease: A threat to European forestry. Eur. J. Plant Pathol. 2011, 133, 89–99. [Google Scholar] [CrossRef]
  11. Tang, X.; Yuan, Y.; Li, X.; Zhang, J. Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China. Front. Plant Sci. 2021, 12, 652500. [Google Scholar] [CrossRef]
  12. Kwon, T.S.; Shin, J.H.; Lim, J.H.; Kim, Y.K.; Lee, E.J. Management of pine wilt disease in Korea through preventative silvicultural control. Forest. Ecol. Manag. 2011, 261, 562–569. [Google Scholar] [CrossRef]
  13. Robinet, C.; Castagnone-Sereno, P.; Mota, M.; Roux, G.; Sarniguet, C.; Tassus, X.; Jactel, H.; Marini, L. Effectiveness of clear-cuttings in non-fragmented pine forests in relation to EU regulations for the eradication of the pine wood nematode. J. Appl. Ecol. 2020, 57, 460–466. [Google Scholar] [CrossRef] [Green Version]
  14. Gordillo, L.F.; Kim, Y. A simulation of the effects of early eradication of nematode infected trees on spread of pine wilt disease. Eur. J. Plant Pathol. 2011, 132, 101–109. [Google Scholar] [CrossRef]
  15. Hao, X.; Liu, X.; Chen, J.; Wang, B.; Li, Y.; Ye, Y.; Ma, W.; Ma, L. Effects on community composition and function Pinus massoniana infected by Bursaphelenchus xylophilus. BMC Microbiol. 2022, 22, 157. [Google Scholar] [CrossRef] [PubMed]
  16. Gao, R.; Shi, J.; Huang, R.; Wang, Z.; Luo, Y. Effects of pine wilt disease invasion on soil properties and Masson pine forest communities in the Three Gorges reservoir region, China. Ecol. Evol. 2015, 5, 1702–1716. [Google Scholar] [CrossRef] [PubMed]
  17. Fujihara, M. Development of secondary pine forests after pine wilt disease in western Japan. J. Veg. Sci. 1996, 75, 729–738. [Google Scholar] [CrossRef]
  18. Dalling, J.W.; Hubbell, S.P. Seed size, growth rate and gap microsite conditions as determinants of recruitment success for pioneer species. J. Ecol. 2002, 90, 557–568. [Google Scholar] [CrossRef]
  19. Jin, Y.; Russo, S.E.; Yu, M.J. Effects of light and topography on regeneration and coexistence of evergreen and deciduous tree species in a Chinese subtropical forest. J. Ecol. 2018, 106, 1634–1645. [Google Scholar] [CrossRef]
  20. Hu, G.; Xu, X.; Wang, Y.; Lu, G.; Feeley, K.J.; Yu, M. Regeneration of different plant functional types in a Masson pine forest following pine wilt disease. PLoS ONE 2012, 7, e36432. [Google Scholar] [CrossRef] [PubMed]
  21. Jeong, J.; Kim, C.; Lee, K.S.; Bolan, N.S.; Naidu, R. Carbon storage and soil CO2 efflux rates at varying degrees of damage from pine wilt disease in red pine stands. Sci. Total. Environ. 2013, 465, 273–278. [Google Scholar] [CrossRef]
  22. Kim, C.; Jang, K.-S.; Kim, J.-B.; Byun, J.-K.; Lee, C.-H.; Jeon, K.-S. Relationship between soil properties and incidence of pine wilt disease at stand level. Landsc. Ecol. Eng. 2010, 6, 119–124. [Google Scholar] [CrossRef]
  23. Gao, R.; Luo, Y.; Wang, Z.; Yu, H.; Shi, J. Patterns of biomass, carbon, and nitrogen storage distribution dynamics after the invasion of pine forests by Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) in the three Gorges Reservoir Region. J. For. Res. 2017, 29, 459–470. [Google Scholar] [CrossRef]
  24. Reu, J.C.; Catano, C.P.; Spasojevic, M.J.; Myers, J.A. Beta diversity as a driver of forest biomass across spatial scales. Ecology 2022, 103, e3774. [Google Scholar] [CrossRef]
  25. Editorial Committee of Flora of Zhejiang (New Edition). Flora of Zhejiang (New Edition); Zhejiang Science and Technology Press: Hangzhou, China, 2021. [Google Scholar]
  26. Liu, J.; Zhong, Y.; Zhong, L.; Wei, B.; Zheng, S.; Xie, Y.; Jin, Y.; Yu, M. The asymmetric relationships of the distribution of conspecific saplings and adults in forest fragments. J. Plant Ecol. 2020, 13, 398–404. [Google Scholar] [CrossRef]
  27. Ouyang, S.; Xiang, W.; Wang, X.; Zeng, Y.; Lei, P.; Deng, X.; Peng, C. Significant effects of biodiversity on forest biomass during the succession of subtropical forest in south China. For. Ecol. Manag. 2016, 372, 291–302. [Google Scholar] [CrossRef]
  28. Jost, L. Partitioning diversity and independent alpha and beta components. Ecology 2007, 88, 2427–2439. [Google Scholar] [CrossRef] [Green Version]
  29. Chiu, C.H.; Wang, Y.T.; Walther, B.A.; Chao, A. An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics 2014, 70, 671–682. [Google Scholar] [CrossRef]
  30. Anderson, M.J. Permutational Multivariate Analysis of Variance (PERMANOVA). In Wiley StatsRef: Statistics Reference Online; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 1–15. [Google Scholar]
  31. Anderson, M.J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001, 26, 32–46. [Google Scholar] [CrossRef]
  32. Anderson, M.J.; Walsh, D.C. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecol. Monogr. 2013, 83, 557–574. [Google Scholar] [CrossRef]
  33. Kuznetsova, A.; Brockhoff, P.B.; Christensen, R.H.B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 2017, 82, 1–25. [Google Scholar] [CrossRef] [Green Version]
  34. Menéndez-Gutiérrez, M.; Alonso, M.; Jiménez, E.; Toval, G.; Mansilla, P.; Abelleira, A.; Abelleira-Sanmartín, A.; Díaz, R. Interspecific variation of constitutive chemical compounds in Pinus spp. xylem and susceptibility to pinewood nematode (Bursaphelenchus xylophilus). Eur. J. Plant Pathol. 2017, 150, 939–953. [Google Scholar] [CrossRef]
  35. Bonello, P.; Campbell, F.T.; Cipollini, D.; Conrad, A.O.; Farinas, C.; Gandhi, K.J.K.; Hain, F.P.; Parry, D.; Showalter, D.N.; Villari, C.; et al. Invasive tree pests devastate ecosystems—A proposed new response framework. Front. For. Glob. Change 2020, 3, 2. [Google Scholar] [CrossRef]
  36. Frost, C.M.; Allen, W.J.; Courchamp, F.; Jeschke, J.M.; Saul, W.C.; Wardle, D.A. Using network theory to understand and predict biological invasions. Trends Ecol. Evol. 2019, 34, 831–843. [Google Scholar] [CrossRef]
  37. Crystal-Ornelas, R.; Lockwood, J.L. Cumulative meta-analysis identifies declining but negative impacts of invasive species on richness after 20 yr. Ecology 2020, 101, e03082. [Google Scholar] [CrossRef] [PubMed]
  38. Akesson, A.; Curtsdotter, A.; Eklof, A.; Ebenman, B.; Norberg, J.; Barabas, G. The importance of species interactions in eco-evolutionary community dynamics under climate change. Nat. Commun. 2021, 12, 4759. [Google Scholar] [CrossRef] [PubMed]
  39. Liu, F.; Tan, C.; Yang, Z.; Li, J.; Xiao, H.; Tong, Y. Regeneration and growth of tree seedlings and saplings in created gaps of different sizes in a subtropical secondary forest in southern China. For. Ecol. Manag. 2022, 511, 120143. [Google Scholar] [CrossRef]
  40. Ge, X.; Zeng, L.; Xiao, W.; Huang, Z.; Geng, X.; Tan, B. Effect of litter substrate quality and soil nutrients on forest litter decomposition: A review. Acta Ecol. Sin. 2013, 33, 102–108. [Google Scholar] [CrossRef]
  41. Prospero, S.; Botella, L.; Santini, A.; Robin, C. Biological control of emerging forest diseases: How can we move from dreams to reality? For. Ecol. Manag. 2021, 496, 119377. [Google Scholar] [CrossRef]
Forests 14 01636 g001
Figure 1. Map and photos of the study sites in Taishun County, Zhejiang Province, East China.
Figure 1. Map and photos of the study sites in Taishun County, Zhejiang Province, East China.
Forests 14 01636 g001
Forests 14 01636 g002
Figure 2. The difference in species richness (a–d), Shannon–Wiener index (e–h), and Chao’s index (i–l) between forest types for all species (a,e,i), adults (b,f,j), saplings (c,g,k), and seedlings (d,h,l) on a small scale. Different letters denote the significant differences between forest types according to the linear mixed effect models. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Figure 2. The difference in species richness (a–d), Shannon–Wiener index (e–h), and Chao’s index (i–l) between forest types for all species (a,e,i), adults (b,f,j), saplings (c,g,k), and seedlings (d,h,l) on a small scale. Different letters denote the significant differences between forest types according to the linear mixed effect models. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Forests 14 01636 g002
Forests 14 01636 g003
Figure 3. Principal component analysis (PCA) for 13 environmental factors on a small scale. The environmental factors include soil bulk density (g/cm3), maximum water holding capacity (MWHC, g/kg), ammonium nitrogen (NH4+-N, mg/kg), nitrate nitrogen (NO3−-N, mg/kg), total carbon (TC, %), total nitrate (TN, %), total phosphorus (TP, mg/kg), available phosphorus (AP, mg/kg), soil pH, slope, aspect, canopy openness (LAI, %), and soil depth (cm).
Figure 3. Principal component analysis (PCA) for 13 environmental factors on a small scale. The environmental factors include soil bulk density (g/cm3), maximum water holding capacity (MWHC, g/kg), ammonium nitrogen (NH4+-N, mg/kg), nitrate nitrogen (NO3−-N, mg/kg), total carbon (TC, %), total nitrate (TN, %), total phosphorus (TP, mg/kg), available phosphorus (AP, mg/kg), soil pH, slope, aspect, canopy openness (LAI, %), and soil depth (cm).
Forests 14 01636 g003
Forests 14 01636 g004
Figure 4. Simple linear relationships between PC1 and PC2 and species richness, Shannon–Wiener index, and Chao’s index for all species (a–f), adults (g–l), saplings (m–r), and seedlings (s–x) on a small scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Figure 4. Simple linear relationships between PC1 and PC2 and species richness, Shannon–Wiener index, and Chao’s index for all species (a–f), adults (g–l), saplings (m–r), and seedlings (s–x) on a small scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Forests 14 01636 g004
Forests 14 01636 g005
Figure 5. The ordination of (a) all species, (b) adults, (c) saplings, and (d) seedlings communities by principal coordinate analysis (PCoA) on a small scale. Ellipses show 75% confidence limits within each forest type (EBF: evergreen broad-leaved forests; CTD: infected Masson pine forests in 2016 and selective cutting of infected trees; UTD: infected Masson pine forests in 2016 and retention of infected trees; PMF: Masson pine forests). Two dimensions and Bray–Curtis distance were applied in the analysis. Points close together in principal coordinate analysis (PCoA) ordination space indicate plots with similar species composition.
Figure 5. The ordination of (a) all species, (b) adults, (c) saplings, and (d) seedlings communities by principal coordinate analysis (PCoA) on a small scale. Ellipses show 75% confidence limits within each forest type (EBF: evergreen broad-leaved forests; CTD: infected Masson pine forests in 2016 and selective cutting of infected trees; UTD: infected Masson pine forests in 2016 and retention of infected trees; PMF: Masson pine forests). Two dimensions and Bray–Curtis distance were applied in the analysis. Points close together in principal coordinate analysis (PCoA) ordination space indicate plots with similar species composition.
Forests 14 01636 g005
Forests 14 01636 g006
Figure 6. The differences in mean biomass among forest types for all tree species (a), adults (b), and saplings (c) on a small scale. Different letters denote the significant differences between forest types according to the linear mixed effect models. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Figure 6. The differences in mean biomass among forest types for all tree species (a), adults (b), and saplings (c) on a small scale. Different letters denote the significant differences between forest types according to the linear mixed effect models. Pink boxes represent evergreen broad-leaved forests (EBF); green boxes represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue boxes represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple boxes represent the Masson pine forests (PMF).
Forests 14 01636 g006
Forests 14 01636 g007
Figure 7. Relationships between biomass and PC1 and PC 2 for all species (a,b), adults (c,d), and saplings species (e,f) on a small scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Figure 7. Relationships between biomass and PC1 and PC 2 for all species (a,b), adults (c,d), and saplings species (e,f) on a small scale. Pink dots represent evergreen broad-leaved forests (EBF); green dots represent the infected Masson pine forests in 2016 and selective cutting of infected trees (CTD); blue dots represent the infected Masson pine forests in 2016 and retention of infected trees (UTD); purple dots represent the Masson pine forests (PMF).
Forests 14 01636 g007
Table 1. The Bray–Curtis values and the tests for significant differences in species composition (PERMANOVA) of all species, adults, saplings, and seedlings communities between forest types.
Table 1. The Bray–Curtis values and the tests for significant differences in species composition (PERMANOVA) of all species, adults, saplings, and seedlings communities between forest types.
Bray–CurtisSums of SquaresR2Fp Value
All species
EBF vs. CTD0.6574.3750.30222.496≤0.001
EBF vs. UTD0.8055.9640.39133.317≤0.001
EBF vs. PMF0.7905.4600.34627.459≤0.001
CTD vs. UTD0.4721.6980.16310.095≤0.001
CTD vs. PMF0.5672.7820.22114.0794≤0.001
UTD vs. PMF0.5902.9680.24917.194≤0.001
Adults
EBF vs. CTD0.6224.2760.29121.349≤0.001
EBF vs. UTD0.8707.2300.39834.430≤0.001
EBF vs. PMF0.8105.6790.35828.955≤0.001
CTD vs. UTD0.7523.7600.22615.150≤0.001
CTD vs. PMF0.4321.2600.0945.378≤0.001
UTD vs. PMF0.7353.8510.23315.782≤0.001
Saplings
EBF vs. CTD0.7243.2440.20213.173≤0.001
EBF vs. UTD0.7994.4060.27219.433≤0.001
EBF vs. PMF0.7955.4600.34627.459≤0.001
CTD vs. UTD0.4341.6990.16310.095≤0.001
CTD vs. PMF0.6113.2440.24016.446≤0.001
UTD vs. PMF0.5612.6760.22515.061≤0.001
Seedlings
EBF vs. CTD0.4430.9720.0663.6960.002
EBF vs. UTD0.5662.3690.1348.068≤0.001
EBF vs. PMF0.6903.2030.16210.087≤0.001
CTD vs. UTD0.5041.5040.0844.799≤0.001
CTD vs. PMF0.7062.5340.1267.512≤0.001
UTD vs. PMF0.6781.8370.0884.994≤0.001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, J.; Liu, W.; Wu, J.; Wei, B.; Guo, J.; Zhong, L.; Yu, M. Responses of Plant Species Diversity and Biomass to Forest Management Practices after Pine Wilt Disease. Forests 2023, 14, 1636. https://doi.org/10.3390/f14081636

AMA Style

Liu J, Liu W, Wu J, Wei B, Guo J, Zhong L, Yu M. Responses of Plant Species Diversity and Biomass to Forest Management Practices after Pine Wilt Disease. Forests. 2023; 14(8):1636. https://doi.org/10.3390/f14081636

Chicago/Turabian Style

Liu, Jinliang, Weiyong Liu, Jianbin Wu, Boliang Wei, Jing Guo, Lei Zhong, and Mingjian Yu. 2023. "Responses of Plant Species Diversity and Biomass to Forest Management Practices after Pine Wilt Disease" Forests 14, no. 8: 1636. https://doi.org/10.3390/f14081636

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop