Restoration of Understory Plant Species and Functional Diversity in Temperate Plantations Along Successional Stages
1
College of Landscape Architecture and Art, Northwest A&F University, Yangling 712100, China
2
Key Laboratory of Ecological Prewarning, Protection and Restoration of Bohai Sea, Ministry of Natural Resources, School of Life Sciences, Shandong University, Qingdao 266237, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work and should be considered co-first authors.
Forests 2025, 16(6), 956; https://doi.org/10.3390/f16060956
Submission received: 21 April 2025
/
Revised: 24 May 2025
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Accepted: 28 May 2025
/
Published: 5 June 2025
(This article belongs to the Section Forest Ecology and Management)
Abstract
Context: Planting forests is an important strategy to combat biodiversity loss and ecosystem service degradation, but its effects on biodiversity and ecosystem services remain uncertain. Objectives: This study aimed to investigate the restoration of plants along successional and environmental gradients in planted forests by examining how understory plant diversity (species richness, composition, functional diversity), functional diversity—the range of species’ traits influencing ecosystem functions and services and their environmental drivers—evolve in temperate plantations over time. Methods: We examined a total of 36 plots with different stand ages in Chongli District, China, and compared the differences in species richness, biodiversity, composition, and functional diversity across different successional stages and over time. We also analyzed the response mechanisms of species richness and functional diversity to environmental factors at both the local and landscape scales. Results and Discussion: Our results showed species diversity, species richness, and functional diversity tended to increase with time in most plots and stabilized after 45 years. Although species richness was lower in mature plots (>100 years), functional diversity was higher, and species composition was significantly differentiated. This trade-off reflects environmental filtering selecting for competitively dominant species with distinct functional traits, while continuous species turnover prevents compositional convergence. The increase in functional diversity was not directly related to the rise in species richness, but it depended on the relative dominance of several species with different functional characteristics in the ecosystem. Simulation analysis confirmed this pattern aligns with a Simpson’s index-driven trait complementarity mechanism. At the local scale, stand age was the most significant positive factor influencing species richness and functional diversity. Soil total nitrogen and organic matter only negatively affected species richness in interactions. At the landscape scale, landscape heterogeneity plays an important role in restoring functional diversity. Historical afforestation since the 1950s restricted comparisons to secondary forests, lacking primary forest baselines. Conclusions: The results suggest that the effects of the successional stage and multiscale environmental factors should be comprehensively considered in the restoration strategy of restored forests.
1. Introduction
Planting forests has the potential to reverse various forest loss and degradation trends [1] and provide forest products for local people [2]. Afforestation of plantations may replace natural forests and enhance hydrological regulation and nutrient cycling. In regions experiencing natural forest loss, plantation stands preserve partial ecological functionality and serve as refugia for specialized species [3]. Additionally, it has become an important method for mitigating climate change [4]. However, there is still a lack of empirical studies on the recovery of forests in terms of biodiversity after planting [5].
The ecological impact of reforestation projects is still controversial [6]. The planting of forests is primarily used for commercial purposes such as timber and crop production, with restoration of biodiversity being a secondary consideration. The planted forests can change the land-use properties of surface habitats, such as grasslands and farmland, which will likely have a profound impact on biodiversity. Nevertheless, research in this field remains scarce [7]. Most converted forests are monoculture plantations, which often exhibit simplified vegetation structures and lack the diverse understory plant communities found in natural forests [3], often due to low structural complexity and functional redundancy [8]. Previous studies have shown that converting single-use forest land to other uses can lead to declines in the diversity of various organisms [9]. There is still a lack of studies on ecosystem functioning, especially biodiversity restoration, after planted forests are planted.
The ultimate aim of reforestation is to restore ecosystem functions and increase ecosystem services [10]. Therefore, there is a need to assess the quality of restoration by evaluating ecosystem services. Some commonly used methods to determine ecosystem services and functions are based on functional plant traits [11]. However, these methods have limitations. Many studies on plant functional traits have focused primarily on leaves, which are weakly correlated with ecosystem services, while ignoring other important traits [12]. This may not be conducive to accurately assessing changes in ecological functions and services. To address these limitations, our study adopts a service-oriented trait selection framework [13] to assess how temperate plantations regain multi-dimensional ecosystem services through understory recovery. Furthermore, the relationship between plant functional diversity and species diversity remains uncertain. Although enhanced biodiversity is widely considered a driver of restoration efficacy, this paradigm exhibits limited applicability in forest ecosystems. Elevated floristic richness observed in degraded forests exposed to anthropogenic logging or natural disturbances frequently signifies ecosystem retrogression rather than functional rehabilitation. Paradoxically, spatiotemporal heterogeneity in resource distribution or disturbance patterns may facilitate resilience-building via community reassembly processes [14]. Furthermore, increased temporal and spatial variability in resource availability or disturbance regimes may enhance ecosystem resilience through adaptive reorganization [14], grazing or disturbance exclusion over time can lead to structurally stable and diverse vegetation, especially under moderate intensity [15]. Nonetheless, in some cases, services such as ecosystem cultural functions, medicinal resources, esthetic values, or mediating functions are determined by a dominant species or key species with specific ecological functions [16]. Consequently, assessing the biodiversity may not be sufficient to fully comprehend the alterations in ecosystem functions and services.
The adoption of natural regeneration strategies post-establishment in plantations is advocated under the premise of optimizing restoration outcomes. However, this paradigm faces cross-scale limitations: (1) localized reforestation success through in situ protection proves susceptible to landscape-level perturbations [17] and (2) dissynchronicity between taxonomic and functional responses to environmental filters generates systemic mismatches in ecological complexity restoration. Evidence suggests fragmented landscapes increase species richness due to edge effects, but landscape fragmentation can lead to the inability of some species with specific traits to survive and recolonize [18] so that trait-based functional diversity may decline [19]. It is necessary to explore the response mechanism of functional diversity to environmental variables. Their changing patterns over time may also be different [20]. Several studies have demonstrated that the natural restoration method can effectively restore biodiversity [21], but whether it can quickly restore ecosystem functions and services is still controversial [22]. It is crucial to comprehend the variables influencing biodiversity and functional diversity in plantations at different stages of succession because this knowledge is essential for developing sustainable management practices and restoring degraded sites [23].
This study investigated the understory plants in planted forests at different time periods (Table A1). We aimed to record and reveal changes in understory plant diversity and functional diversity along successional and environmental gradients. We used data collected from 36 plots in a converted forest with different stand ages to address the following questions: (i) Are species diversity, species composition, and functional diversity significantly different in the different successional stages? (ii) Are trends in species diversity and functional diversity consistent? (iii) Do species diversity and functional diversity respond consistently to environmental factors at different scales?
2. Materials and Methods
2.1. Study Region and Plot Data Collection
2.1.1. Site Description
The study region is located in Chongli District (40°47′–41°17′ N, 114°17′–115°34′ E), Zhangjiakou City, Hebei Province, China, which is a transition zone between the Inner Mongolia Plateau and North China Plain (Figure 1). This region experiences an East Asian continental monsoon climate, with a mean annual temperature of ~4.5 °C and a mean annual precipitation exceeding 488 mm. Approximately 80% of Chongli District’s territory is mountainous, with a forest coverage rate of 52.38%. Since the 1950s, the area has been affected by deforestation and overgrazing (see interviews with local farmers). Beginning in 1999, Chongli District has been carrying out large-scale afforestation activities.
2.1.2. Sampling Design
To avoid excessive environmental differences in the sampling sites, all the sampling sites located in the north of Chongli District were excluded to avoid the influence of altitude (Figure 1). All the sample plots were located in an artificially planted Pinus sylvestris (Pinus sylvestris var. mongolica) forest. Preliminary investigations during the pilot phase revealed suspected large-scale, recurrent mining operations in the western sector of the study area, though systematic field measurements were not yet obtained at this exploratory stage. Coal mining reduces plant species richness, favoring pollution-tolerant species over sensitive ones, and alters community composition [24]. To eliminate the confounding effects of mining activities and geographical heterogeneity on the experimental results and minimize external interference in causal inference, we set all sample plots in the central and eastern parts of the study area. Additionally, to prevent spatial autocorrelation, we removed all sample plots less than 1 km apart. Ultimately, we established 36 sample plots and categorized all plots into six age groups: 10, 23, 34, 45, 58, and >100 years, as defined by the time since recovery. Specifically, the “time since recovery” refers to the duration of natural restoration following the establishment of plantations of Pinus sylvestris var. mongolica. The restoration time ranges for each group were defined as follows: 10-year group (6–12 years), 23-year group (21–26 years), 34-year group (33–36 years), 45-year group (43–46 years), 58-year group (53–63 years), and >100-year group (Table 1). All plots were exclusively composed of monocultures of Mongolian Scots pine, with understory herbaceous communities spontaneously colonizing through natural succession. All plots > 100 years were coniferous forests and should be at least 100–200 years old.
Given that all sampled plantations were pure stands of Pinus sylvestris var. mongolica with a similar stand structure and management history, we did not measure canopy cover (a proxy for light availability) as an environmental variable. Field observations confirmed that canopy closure was relatively consistent across the sampled successional stages, minimizing its potential confounding effect on understory plant diversity. This homogenized light environment allowed us to focus on other drivers without the need for explicit light measurements.
2.1.3. Vegetation Data
We investigated vascular plants in June and September 2016 and 2017. We used the quadrat method to sample plants and set a 20 × 20 m2 in the middle of each sample plot. We divided each plot into four 10 × 10 m2 subplots and recorded all trees and shrubs in the subplots. We further recorded all herbaceous species in four randomly placed 1 × 1 m2 plots, one within each subplot. We carried out plant species identification and recording on the spot. We brought plants that could not be identified on the spot back to the laboratory for identification by taxonomy experts or by referring to the “Electronic Edition of Flora of China” (https://www.plantplus.cn/frps2019/) (accessed on 19 February 2025). We recorded all plant species coverage as species abundance.
2.2. Environmental Factors
2.2.1. Environmental Variables at the Landscape Scale
We took remote sensing images (Landsat-8 satellite 2016) as the base map and placed all the sample points within a radius of 1 km from the center to conduct the landscape surveys. We selected the Landscape Fragmentation Index to represent the degree of fragmentation of the landscape, reflect the complexity of the landscape’s spatial structure, and demonstrate the degree of human interference in the landscape. We selected the patch size of sample forests as an indicator to reflect the size of the habitat patches. We based the land-use maps we used in our analysis on field surveys and calculated the landscape metrics using FRAGSTATS 4.2 [25]. The landscape metric parameters classification thresholds were defined based on ecological rationale and rigorously validated through remote sensing data analysis. Multi-scale sensitivity testing (500–2000 m) achieved an accuracy of 86% (κ = 0.79).
2.2.2. Environmental Variables at Local Scale
Soil sampling and plant investigation were carried out at the same time. We selected soil indicators. The total soil nitrogen (SN) in the soil can reflect the intensity of land use before the conversion of farmland. The second soil index is soil organic matter (SOM), which has an important impact on species richness and species composition [26]. We took five randomly selected soil samples at a depth of 0–20 cm using a 50 mm diameter sand auger at each plot, and we sieved the samples < 2 mm to remove roots and other large organic debris. We homogenized and air-dried them before chemical analysis, pooled dried samples within each plot, and ground each one in a ball mill until the material had a talcum powder consistency. We then analyzed SN using the Kjeldahl method [27] and studied SOM using the dichromate titration method.
2.2.3. Selection of Plant Traits
We selected a series of representative species characteristics and chose seven plant traits for each recorded plant species (Table 2). We chose every trait explicitly for its relevance to ecosystem service provision: (1) Life form: Complex vegetation structure is conducive to maintaining microclimate conditions and higher biodiversity. (2) Growing cycle: Perennial species require a longer succession than annual or biennial species, but once established, they are more competitive and resistant [28]. In addition, the continuous soil cover ensured by perennials reduces erosion and nutrients, and it improves nutrient availability [29]. (3) Dominance in situ: This indicator is related to soil and water conservation and microclimate regulation, and it also characterizes the influence and adaptability of the species. (4) Edible or healing effect: This indicator reflects the provision of ecosystem services. (5) Nectariferous plants: This function characterizes the supply function of pollination services. (6) Flower color: It is known to influence the perception of scenic beauty [30] and also has a very important cultural value. (7) Red List species: The “Red List of Chinese Species” (https://www.mee.gov.cn/), (accessed on 3 April 2025), which is related to the function of biodiversity conservation.
2.3. Data Analysis
We tested sampling independence among the sampling plots by a Mantel statistic based on Pearson’s product-moment correlation between two matrices: spatial (Euclidean bidimensional) and compositional distance (Sorensen). We first analyzed whether the forest land with different ages varied significantly regarding plant species richness (without the influence of abundance), Simpson’s Diversity Index (we chose the inversed Simpson index), and functional diversity. We used the inverse of Simpson to make it more intuitive, in which higher values indicate lower dominance (therefore, high diversity) in the assemblages. Functional diversity (RaoQ) was calculated using Gower distances to accommodate mixed trait types. We performed the computations using the function dbFD() in the R package FD [31]. We used a one-way ANOVA with post hoc contrast tests to compare the differences in the three indicators between different ages. To explore the differences between species composition in different plots, we used nonmetric multidimensional scaling (NMDS) for analysis and chose the Bray–Curtis distance. We conducted this analysis using CANOCO 5.0 [32] and used linear regression to analyze the relationship between functional diversity, Simpson’s Diversity Index, and species richness. Finally, we treated the plant species richness, Simpson’s Diversity Index, and functional diversity as dependent variables. Their change is linked to potential environmental predictor variables on the local (age, total soil nitrogen, soil organic matter) and landscape (patch size, landscape richness) scales in a set of generalized linear models (GLMs) with fixed variance (vegan package; [33]) in R 3.1.5 (R Development Core Team 2018). We also considered all interaction effects but only showed effects with a significant response.
3. Results
We identified and recorded a total of 199 species and 49 families belonging to understory vascular plant species (Online Resource 1). Due to the lack of some characteristics, a total of nine plots with species were not identified. Among them, we identified one species from Cruciferae at the family level and four species from Artemisia, Agrostis, Bromus, and Apium at the genus level. The abundance of these unidentified species accounts for less than 1% of the total species abundance and does not affect the calculation of all the indicators.
The comparison of species richness in forests of different ages (Figure 2) showed the species richness in plots that have been converted for more than 100 years (the oldest forest land) and the plots that have been converted for 13 years (the youngest forest land) is relatively low, significantly lower than the 58-, 45-, and 33-year plots. The highest value of species richness appeared in the plots with a conversion time of 58 years, which was significantly higher than the 13-, 26-, and 100-year plots. In comparing plant diversity, we found no significant difference between the oldest forest land and other forest land, but the 58 and 45-year plots are significantly higher than the 33-, 26-, and 13-year plots. Functional diversity tends to increase with the age of forests, with the oldest forest land exhibiting significantly higher diversity than younger forest land. Nonmetric multiscale analysis (NMDS) showed the difference in species composition between the samples (Figure 3). The results showed six sample plots in the oldest forest land were far away from other plots, meaning their species composition was significantly different from other samples. Of all the plots in the oldest forest land, the distance between each other is also relatively long, indicating the species composition of different places is also quite different. The species composition of the 13- and 25-year-old plots differed from the other age plots, in addition to the oldest plots.
The linear regression analysis results indicate there is no linear correlation between species richness and functional diversity. However, functional diversity increases with increasing plant Simpson’s diversity (y = 3.9503x − 1.7035, R2 = 0.310, p < 0.001).
This study thus revealed that woodland age has a significant impact on species richness. Additionally, the area of the patch where the plot was located showed a positive correlation with species richness (Table 3). Furthermore, an increase in landscape fragmentation was found to significantly affect species richness (Figure 4). The soil variables we tested, namely soil total nitrogen and soil organic matter, did not significantly impact species richness. However, we found their interaction to be significantly related to species richness (Table 3). The age of forest land is the most important factor influencing functional diversity, whereas total nitrogen and organic matter do not show a significant correlation. Landscape richness and fragmentation positively correlate with functional diversity, but patch area is insignificant (Table 4). No other interactions in this model showed a significant correlation with any dependent variables.
4. Discussion
4.1. Accumulation of Species Richness, Diversity, and Functional Diversity
Our results showed species richness, diversity, and functional diversity all tended to increase with time (except for plots > 100 years). However, the increase is not significant after 45 years (Figure 2). A similar study demonstrated the initial 10–30 years of the restoration process in newly established forest plots exhibited the most rapid increase in species [1]. In our study, the turning point occurred at 45 years. This may be due to the specificity of the converted forest land, which, after the loss of human disturbance, has become a new habitat with superior conditions, providing many opportunities for plant species to invade. Another possibility is that the local temperature is relatively low, and the ecological processes are slow, prolonging the length of this process.
For the >100 years plots, species richness was lower than all other plots, and species diversity was not significantly different from the other plots. But its functional diversity was significantly higher than other plots. On the one hand, this is because, after environmental filtering, species compete with each other and become the main resistance to survival [34]. Species with strong competitiveness occupy an ecological niche, and after forming an advantage, other species are slowly eliminated and cannot invade anymore. This also explains the low species richness but high functional diversity of the oldest plots. On the other hand, local climatic conditions have changed, preventing some species from adapting [35].
By establishing multi-species mixed plantations, we can accelerate the transition towards natural forest stands [36]. Thinning treatments may potentially expedite the development of late-successional species composition in young stands by creating structural features analogous to old-growth forest ecosystems [37]. Additionally, incorporating predictive successional modeling into silvicultural planning could help address current knowledge gaps in characterizing the developmental trajectories of newly restored forests [38].
4.2. Divergence of Species Composition
Our study showed significant differences in species composition between the oldest plots and woodlands of other ages. There was no convergence between the oldest growth (Figure 3). A possible explanation for this is that the species composition continued to change after 50 years [39], the time span between the oldest plot and the other plots was greater, and there was also a time gradient between the plots, so the species composition differences were greatest and did not converge. Our study showed that the species composition of woodlands increases over time and is constantly changing. In the early stages of restoration (13 and 26 years), species composition changed more rapidly. Other researchers have previously reported the occurrence of divergence in species composition along the successional gradient [40]. Plant species change rapidly in the early stages of succession, but it may take hundreds of years to reach a relatively stable final succession (or similar to natural forest) [41].
Maintaining mixed canopies alongside pure-stand mosaics can amplify microhabitat contrasts while preserving natural disturbances and landscape-level structural variability. Regulating canopy light regimes, managing species-specific litter properties, and selecting site-adapted trees to alter soil chemistry and water-nutrient dynamics further reinforce compositional distinctions. Integration of pure and mixed forests across successional stages may sustain habitat transitions, balancing competitive and facilitative interactions to perpetuate divergence [42].
4.3. Relationship Between Functional Diversity and Species Diversity
The results showed that increasing species richness does not increase functional diversity or improve ecosystem services. Functional diversity was positively correlated with the Simpson’s Diversity Index, related to the calculation method. A high Simpson’s index is likely to indicate the dominance of a wide range of species with different functional characteristics [43]. Many ecological restoration evaluation projects tend to choose species richness as an indicator of biodiversity or ecological function restoration. This study, as well as some other related studies, has shown that such assessments may not accurately reflect the functional status of ecosystems [44].
In this study, we chose traits that are associated with the ecological services that plants can provide, so the level of functional diversity in this study reflects the level of ecosystem services to some extent. This approach is a good way to link traits—functional diversity—to ecosystem services. It can simultaneously achieve the restoration of ecosystem functions and improvements of ecosystem services [45]. However, this method is currently not widely used [45]. The lower abundance of nectariferous plants in >100-year stands highlights potential human pressures on pollination services. Monoculture plantations and pesticide use in adjacent agricultural areas may reduce floral diversity, limiting pollinator habitats. While direct pollinator data were not measured, this decline in nectar-rich species could disrupt plant–pollinator networks—a critical trade-off to address in sustainable forest management.
4.4. Response of Functional Diversity to the Environment
At the local scale, functional diversity and species richness respond similarly to the environment. Forest age has a positive effect on species richness, which is consistent with the results of other studies on plant restoration after returning farmland to forest. This relationship aligns with observations from managed Pinus tabuliformis plantations in northern China, where moderate thinning (30%–50%) in younger stands (e.g., half-mature forests ≤ 30 years) promotes understory plant richness by creating heterogeneous light conditions and alleviating resource competition, ultimately facilitating species establishment [37]. Furthermore, the results show that forest age has a positive effect on restoring functional diversity, demonstrating that planted forests can restore some ecosystem functions after prolonged periods of low disturbance. Soil total nitrogen and organic matter had no significant effects on functional diversity and species richness, respectively, but their interaction was significantly negatively correlated with species richness. One possible reason for this is that the synergistic effect of the two soil factors has a potential positive effect on certain highly competitive plants, which may inhibit less competitive species. Management strategies should seek a balance between species composition and nutrient resource interactions to avoid resource competition and exclusion.
At the landscape scale, functional diversity and species richness do not respond to the environment in the same way. Landscape richness and landscape fragmentation constitute two important dimensions of landscape heterogeneity. Our results showed that both landscape richness and landscape fragmentation were significantly positively correlated with functional diversity because heterogeneous environments provide more ecological niches, allowing multiple species with different functional characteristics to coexist. The results emphasize the important role of landscape heterogeneity in restoring functional diversity. To achieve this, management strategies must consider the complex effects of landscape fragmentation on functional diversity and species richness and seek to maintain and restore ecosystem function while conserving species diversity.
4.5. Soil Biogeochemical Drivers of Biodiversity Dynamics
The antagonistic interaction between soil TN and SOM in reducing plant species richness could arise through multiple interrelated mechanisms: (1) accelerated microbial mineralization of nitrogen-enriched SOM releasing NH4+ pulses that enhance light competition among plants, alongside [46] (2) SOM-TN coupling inducing microbial C/N imbalances that reduce recalcitrant organic matter persistence, possibly exacerbating rhizotoxicity through pH shifts in weakly buffered soils. However, critical unknowns remain regarding threshold responses of organic acid fluxes, microbial depolymerization kinetics, and SOM-pH buffering capacities across TN gradients, requiring targeted quantification of extracellular enzyme cascades and microbe-mineral interactions to resolve these drivers [47].
4.6. Limitations in Experimental Design
The study area (Chongli District) has undergone uninterrupted land-use transitions since the 1950s, including extensive deforestation followed by large-scale afforestation, resulting in near-total eradication of primary forests. Consequently, our comparisons are restricted to secondary forests at different successional stages, without reference to pristine forest conditions as a baseline. Additionally, while our trait-based analysis inferred pollination service potential through nectariferous plants, direct measurements of pollinator abundance and interactions were not conducted.
5. Conclusions
In this study, we aimed to investigate the differences in species richness, biodiversity, species composition, and functional diversity in artificial forests at different successional stages, noting trends over time. Meanwhile, we also focused on the response mechanisms of species richness and functional diversity to local and landscape-scale environmental variables. The results of the study showed species richness, species diversity, and functional diversity increased over time in all age plots except for the >100-year plots and leveled off after 45 years. The species richness of the >100-year plots was lower than all plots, but the functional diversity was higher, and the differentiation of species composition was most significant. The increase in functional diversity was not significantly correlated with the increase in species richness but relied on the relative dominance of species with different functional characteristics in the ecosystem. Our results showed environmental variables at different scales had different effects on species richness and functional diversity. At the local scale, stand age had a positive effect on both species richness and functional diversity, whereas soil total nitrogen and soil organic matter showed a negative effect on species richness only in interactions. At the landscape scale, landscape heterogeneity (landscape richness and landscape fragmentation) played an important role in restoring functional diversity. Our study thus demonstrated restoration projects for returned forests should comprehensively consider the effects of succession stage and multiscale environmental factors. For young returned forests, functional diversity can be increased by altering landscape heterogeneity; for mature returned forests, species richness can be restored under natural succession while focusing on maintaining functional diversity and enhancing the protection of species with important functional characteristics.
Author Contributions
Conceptualization, Y.C. and X.L.; methodology, X.L.; software, W.Z.; formal analysis, M.F.S.; investigation, X.L.; data curation, Y.C.; writing—original draft preparation, X.L. and W.Z.; writing—review and editing, M.F.S. and Y.C.; visualization, X.L.; supervision, W.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (32201429) and (41671181), the Natural Science Foundation of Shaanxi Province of China (2021JQ-153).
Data Availability Statement
The datasets generated during this study are available from the corresponding author on responsible request.
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix A
Table A1.
List of plant species found in the study areas.
| Species Name | Generic Name | Family Name |
|---|---|---|
| Artemisia argyi | Artemisia | Asteraceae |
| Rumex patientia | Rumex | Polygonaceae |
| Cyperus nipponicus | Cyperus | Cyperaceae |
| Imperata cylindrica | Imperata | Poaceae |
| Thymus mongolicus | Thymus | Labiatae |
| Patrinia scabiosaefolia | Patrinia | Valerianaceae |
| Echinochloa crusgalli | Echinochloa | Poaceae |
| Viola variegata | Viola | Violaceae |
| Ixeris sonchifolia | Ixeris | Asteraceae |
| Bupleurum chinense | Bupleurum | Umbellifera |
| Agropyron cristatum | Agropyron | Poaceae |
| Vicia faba | Vicia | Leguminosae |
| Xanthium sibiricum | Xanthium | Asteraceae |
| Melilotus suaveolens | Melilotus | Leguminosae |
| Stellaria dichotoma | Stellaria | Caryophyllaceae |
| Potentilla supina | Potentilla | Rosaceae |
| Plantago asiatica | Plantago | Plantaginaceae |
| Arabis pendula | Arabis | Brassicaceae |
| Salix babylonica | Salix | Salicaceae |
| Cirsium selosum | Cirsium | Asteraceae |
| Allium fistulosum | Allium | Liliaceae |
| Cleistogenes caespitosa | Cleistogenes | Poaceae |
| Geranium dahuricum | Geranium | Geraniaceae |
| Saussurea davurica | Saussurea | Asteraceae |
| Astragalus dahuricus | Astragalus | Leguminosae |
| Calystegia hederacea | Calystegia | Convolvulaceae |
| Cirsium japonicum | Cirsium | Asteraceae |
| Artemisia sieversiana | Artemisia | Asteraceae |
| Euphorbia humifusa | Euphorbia | Euphorbiaceae |
| Cynanchum thesioides | Cynanchum | Asclepiadaceae |
| Sanguisorba officinalis | Sanguisorba | Rosaceae |
| Androsace umbellata | Androsace | Primulaceae |
| Malva crispa | Malva | Malvaceae |
| Lepidium apetalum | Lepidium | Brassicaceae |
| Heracleum hemsleyanum | Heracleum | Umbellifera |
| Clematis brevicaudata | Clematis | Ranunculaceae |
| Potentilla multicaulis | Potentilla | Rosaceae |
| Schizonepeta multifida | Schizonepeta | Labiatae |
| Roegneria kamoji | Roegneria | Poaceae |
| Cynanchum chinense | Cynanchum | Asclepiadaceae |
| Potentilla anserina | Potentilla | Rosaceae |
| Potentilla bifurca | Potentilla | Rosaceae |
| Potentilla discolor | Potentilla | Rosaceae |
| Amaranthus retroflexus | Amaranthus | Amaranthaceae |
| Saposhnikovia divaricata | Pastinaca | Umbellifera |
| Clinopodium chinense | Clinopodium | Labiatae |
| Saussurea runcinata | Saussurea | Asteraceae |
| Chrysanthemum lavandulaefolium | Chrysanthemum | Asteraceae |
| Brassica oleracea | Brassica | Brassicaceae |
| Polygonum alpinum | Polygonum | Polygonaceae |
| Setaria viridis | Setaria | Poaceae |
| Cynodon dactylon | Cynodon | Poaceae |
| Lycium chinense | Lycium | Solanaceae |
| Vicia cracca | Vicia | Leguminosae |
| Bidens pilosa | Bidens | Asteraceae |
| Rorippa indica | Rorippa | Brassicaceae |
| Campylotropis macrocarpa | Campylotropis | Leguminosae |
| Salix chaenomeloides | Salix | Salicaceae |
| Pinus thunbergii | Pinus | Pinaceae |
| Caragana rosea | Caragana | Leguminosae |
| Ostryopsis davidiana | Ostryopsis | Betulaceae |
| Halenia corniculata | Genthianaceae | Halenia |
| Medicago ruthenica | Medicago | Leguminosae |
| Scabiosa tschiliensis | Scabiosa | Dipsacaceae |
| Larix principis-rupprechtii | Larix | Pinaceae |
| Cucumis sativus | Cucumis | Cucurbitaceae |
| Artemisia annua | Artemisia | Asteraceae |
| Polygonatum sibiricum | Polygonatum | Liliaceae |
| Chenopodium glaucum | Chenopodium | Chenopodiaceae |
| Leontopodium leontopodioides | Leontopodium | Asteraceae |
| Agastache rugosa | Labiatae | Agastache |
| Kummerowia striata | Kummerowia | Leguminosae |
| Panicum miliaceum | Panicum | Poaceae |
| Populus × canadensis | Populus | Salicaceae |
| Chenopodium acuminatum | Chenopodium | Chenopodiaceae |
| Equisetum ramosissimum | Equisetum | Equisetaceae |
| Arthraxon hispidus | Arthraxon | Poaceae |
| Vicia sativa | Vicia | Leguminosae |
| Sonchus arvensis | Sonchus | Asteraceae |
| Diospyros lotus | Diospyros | Ebenaceae |
| Mulgedium tataricum | Mulgedium | Asteraceae |
| Sonchus oleraceus | Sonchus | Asteraceae |
| Ixeris polycephala | Ixeris | Asteraceae |
| latifolius Tausch | Echinops | Asteraceae |
| Oxytropis caerulea | Oxytropis | Leguminosae |
| Stellera chamaejasme | Stellera | Euphorbiaceaec |
| Geranium wilfordii | Geranium | Geraniaceae |
| Lomatogonium carinthiacum | Lomatogonium | Gentianaceae |
| Salix chaenomeloides | Salix | Salicaceae |
| Asparagus schoberioides | Aspargus | Aspargus |
| Agrimonia pilosa | Agrimonia | Rosaceae |
| Phragmites australis | Phragmites | Poaceae |
| Rhamnus bungeana | Rhamnus | Rhamnaceae |
| Vigna radiata | Vigna | Leguminosae |
| Ampelopsis humulifolia | Ampelopsis | Vitaceae |
| Portulaca oleracea | Portulaca | Portulacaceae |
| Iris lectea | Iris | Iridaceae |
| Solanum tuberosum | Solanum | Solanaceae |
| Digitaria sanguinalis | Digitaria | Poaceae |
| Datura stramonium | Datura | Solanaceae |
| Erodium stephanianum | Erodium | Geraniaceae |
| Rosa bella | Rosa | Rosaceae |
| Gueldenstaedtia verna | Gueldenstaedtia | Leguminosae |
| Clematis hexapetala | Clematis | Ranunculaceae |
| Unknown | Astragalus | Leguminosae |
| Unknown | Polygonum | Polygonaceae |
| Medicago sativa | Medicago | Leguminosae |
| Hemistepta lyrata | Hemistepta | Asteraceae |
| Caragana korshinskii | Caragana | Leguminosae |
| Caragana korshinskii | Caragana | Leguminosae |
| Aconitum barbatum | Aconitum | Ranunculaceae |
| Myosoton aquaticum | Malachium | Caryophyllaceae |
| Eleusine indica | Eleusine | Poaceae |
| Galium verum | Galium | Rubiaceae |
| Elymus dahuricus | Elymus | Poaceae |
| Thermopsis lanceolata | Thermopsis | Betulaceae |
| Plantago depressa | Plantago | Plantaginaceae |
| Potentilla flagellaris | Potentilla | Rosaceae |
| Taraxacum mongolium | Taraxacum | Asteraceae |
| Stemmacantha uniflora | Stemmacantha | Asteraceae |
| Capsella bursa-pastoris | Capsella | Brassicaceae |
| Rubia cordifolia | Rubia | Rubiaceae |
| Clematis aethusifolia | Clematis | Ranunculaceae |
| Gentiana macrophylla | Gentiana | Gentianaceae |
| Corydalis repens | Corydalis | Papaveraceae |
| Euphorbia esula | Euphorbia | Euphorbiaceae |
| Hippophae rhamnoides | Hippophae | Elaeagnaceae |
| Oxytropis psamocharis | Oxytropis | Leguminosae |
| Allium senescens | Allium | Liliaceae |
| Kalimeris lautureana | Kalimeris | Asteraceae |
| Armeniaca sibirica | Armeniaca | Rosaceae |
| Potentilla kleiniana | Potentilla | Rosaceae |
| Duchesnea indica | Duchesnea | Rosaceae |
| Adenophora polyantha | Adenophora | Campanulaceae |
| Geranium sibiricum | Geranium | Geraniaceae |
| Amethystea caerulea | Amethystea | Labiatae |
| Ammannia baccifera | Ammannia | Lythraceae |
| Polygonum lapathifolium | Polygonum | Polygonaceae |
| Medicago lupluina | Medicago | Leguminosae |
| Asparagus cochinchinensis | Asparagus | Liliaceae |
| Hyoscyamus niger | Hyoscyamus | Solanaceae |
| Convolvulus arvensis | Convolvulus | Convolvulaceae |
| Beta vulgaris | Beta | Chenopodiaceae |
| Acalypha australis | Acalypha | Euphorbiaceaec |
| Mazus japonicus | Mazus | Ranunculaceae |
| Spiraea pubescens | Spiraea | Rosaceae |
| Orostachys fimbriatus | Orostachys | Crassulaceae |
| Vicia unijuga | Vicia | Leguminosae |
| Potentilla chinensis | Potentilla | Rosaceae |
| Equisetum arvense | Equisetum | Equisetaceae |
| Saussurea ussuriensis | Saussurea | Asteraceae |
| Myosotis silvatica | Myosotis | Boraginaceae |
| Carex rigescens | Carex | Cyperaceae |
| Berberis poiretii | Berberis | Berberidaceae |
| Leonurus sibiricus | Leonurus | Labiatae |
| Polygala tenuifolia | Polygala | Polygalaceae |
| Unknown | Sedum | Crassulaceae |
| Dracocephalum moldavica | Dracocephalum | Labiatae |
| Elsholtzia ciliata | Elsholtzia | Labiatae |
| Chrysanthemum chanetii | Chrysanthemum | Asteraceae |
| Bidens parviflora | Bidens | Asteraceae |
| Oxytropis glabra | Oxytropis | Leguminosae |
| Rhamnus parvifolia | Rhamnus | Rhamnaceae |
| Populus simonii | Populus | Salicaceae |
| Inula japonica | Inula | Asteraceae |
| Scorzonera austriaca | Scorzonera | Asteraceae |
| Linum usitatissimum | Linum | Linaceae |
| Corydalis yanhusuo | Corydalis | Papaveraceae |
| Leymus chinense | Leymus | Poaceae |
| Glycine soja | Glycine | Leguminosae |
| Arundinella hirta | Arundinella | Poaceae |
| Deyeuxia arundinacea | Deyeuxia | Poaceae |
| Avena fatua | Avena | Poaceae |
| Papaver nudicaule | Papaver | Papaveraceae |
| Iris dichotoma | Iris | Iridaceae |
| Leonurus artemisia | Leonurus | Labiatae |
| Artemisia capillaris | Artemisia | Asteraceae |
| Poa sphondylodes | Poa | Poaceae |
| Pinus tabulaeformis | Pinus | Pinaceae |
| Ulmus pumila | Ulmus | Ulmaceae |
| Ulmus pumila | Ulmus | Ulmaceae |
| Achnatherum sibiricum | Achnatherum | Poaceae |
| Zea mays | Zea | Poaceae |
| polygonatum odratum | Polygonatum | Liliaceae |
| Pharbitis purpurea | Pharbitis | Convolvulaceae |
| Polygala tenuifolia | Polygala | Polygalaceae |
| Viola prioantha | Viola | Violaceae |
| Poa annua | Poa | Poaceae |
| Thalictrum squarrosum | Thalictrum | Ranunculaceae |
| Potentilla bifurca | Potentilla | Rosaceae |
| Halerpestes ruthenica | Halerpestes | Ranunculaceae |
| Stipa capillata | Stipa | Poaceae |
| Astragalus adsurgens | Astragalus | Leguminosae |
| Axyris amaranthoides | Axyris | Chenopodiaceae |
| Salsola collina | Salsola | Chenopodiaceae |
| Artemisia scoparia | Artemisia | Asteraceae |
| Oplismenus compositus | Oplismenus | Poaceae |
| Aster tataricus | Aster | Asteraceae |
| Oxalis corniculata | Oxalis | Oxalidaceae |
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Figure 1.
Location of the study area and the plots. The different colored dots represent different forest ages, and the numbers represent median forest ages.
Figure 1.
Location of the study area and the plots. The different colored dots represent different forest ages, and the numbers represent median forest ages.
Figure 2.
Plant species richness, plant diversity, and functional diversity in different successional age classes (the same letter indicate no significant difference, different letters indicate significant differences).
Figure 2.
Plant species richness, plant diversity, and functional diversity in different successional age classes (the same letter indicate no significant difference, different letters indicate significant differences).
Figure 3.
Nonmetric multidimensional scaling (NMDS) analysis based on Jaccard dissimilarity. Ellipses showing 60% confidence interval for each age class (stress = 0.58, Bray–Curtis distance).
Figure 3.
Nonmetric multidimensional scaling (NMDS) analysis based on Jaccard dissimilarity. Ellipses showing 60% confidence interval for each age class (stress = 0.58, Bray–Curtis distance).
Figure 4.
Relationship between species richness and functional diversity (a) (R2 = 0.071, p > 0.05), and the relationship between Simpson diversity and functional diversity (b) (R2 = 0.310, p < 0.001).
Figure 4.
Relationship between species richness and functional diversity (a) (R2 = 0.071, p > 0.05), and the relationship between Simpson diversity and functional diversity (b) (R2 = 0.310, p < 0.001).
Table 1.
Basic situation of the plots.
| Age | Number of Plots | Age of Midpoint |
|---|---|---|
| 6~12 | 6 | 10 |
| 21~26 | 6 | 23 |
| 33~36 | 6 | 34 |
| 43~46 | 6 | 45 |
| 53~63 | 6 | 58 |
| >100 | 6 | >100 |
Table 2.
Species traits and related ecosystem services.
| Plant Trait | Trait Characteristics | Data Sources | Related Ecosystem Services |
|---|---|---|---|
| Life form | arbor, shrub, herbaceous | field observations | soil conservation, climate change |
| Growth cycle | annual, perennial | database query 1 | community stability, soil retention |
| Dominance in situ | scattered, small groups, larger groups, larger stands, dominating large areas | field observations | regulation, support |
| Flower color | none (too small to see), green, white, yellow, violet, purple, orange, red, blue, pink, mix | field observations | biological control, culture |
| Red List species | yes, no | database query 2 | biodiversity |
| Edible or healing effect | yes, no | database query 3 | supply |
| Nectariferous plant | yes, no | database query 4 | pollination |
database query 1: https://www.iplant.cn/ (accessed on 15 March 2024). database query 2: https://www.mee.gov.cn/ (accessed on 28 May 2024). database query 3: https://www.chsla.org.cn/ (accessed on 28 May 2024). database query 4: https://www.data.ac.cn/table/tbf78 (accessed on 12 January 2025).
Table 3.
Relationship between the plant richness and environmental factors in a generalized linear model.
Table 3.
Relationship between the plant richness and environmental factors in a generalized linear model.
| Independent Variables | Coefficient | Standard Error | Z-Value | p-Value |
|---|---|---|---|---|
| Age | 1.179 | ±0.185 | 2.061 | 0.039 * |
| Soil total nitrogen | 0.369 | ±0.215 | 1.717 | 0.085 |
| Soil organic matter | 0.003 | ±0.007 | 0.463 | 0.643 |
| Landscape richness | 0.416 | ±0.318 | 1.907 | 0.060 |
| Patch size | 2.319 | ±1.172 | 1.979 | 0.047 * |
| Landscape fragmentation | −0.648 | ±0.315 | −2.055 | 0.039 * |
| Soil total N × Soil organic matter | −0.005 | ±0.003 | −1.964 | 0.049 * |
Significance levels: * p < 0.05.
Table 4.
Coefficients, standard errors, Z-values, and p-values in the generalized linear model for the relationship between plant richness and environmental factors.
Table 4.
Coefficients, standard errors, Z-values, and p-values in the generalized linear model for the relationship between plant richness and environmental factors.
| Independent Variables | Coefficient | Standard Error | Z-Value | p-Value |
|---|---|---|---|---|
| Age | 3.367 | ±0.001 | 2.850 | 0.003 ** |
| Soil total nitrogen | 0.069 | ±0.034 | 1.085 | 0.085 |
| Soil organic matter | 0.003 | ±0.151 | 0.463 | 0.643 |
| Landscape richness | 0.806 | ±0.068 | 1.907 | 0.050 * |
| Patch size | 0.319 | ±0.026 | 1.979 | 0.077 |
| Landscape fragmentation | 2.368 | ±0.049 | 2.964 | 0.022 * |
Significance levels: * p < 0.05; ** p < 0.01.
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MDPI and ACS Style
Zhao, W.; Chen, Y.; Sardar, M.F.; Li, X. Restoration of Understory Plant Species and Functional Diversity in Temperate Plantations Along Successional Stages. Forests 2025, 16, 956. https://doi.org/10.3390/f16060956
AMA Style
Zhao W, Chen Y, Sardar MF, Li X. Restoration of Understory Plant Species and Functional Diversity in Temperate Plantations Along Successional Stages. Forests. 2025; 16(6):956. https://doi.org/10.3390/f16060956
Chicago/Turabian StyleZhao, Weiwei, Yanting Chen, Muhammad Fahad Sardar, and Xiang Li. 2025. "Restoration of Understory Plant Species and Functional Diversity in Temperate Plantations Along Successional Stages" Forests 16, no. 6: 956. https://doi.org/10.3390/f16060956
APA StyleZhao, W., Chen, Y., Sardar, M. F., & Li, X. (2025). Restoration of Understory Plant Species and Functional Diversity in Temperate Plantations Along Successional Stages. Forests, 16(6), 956. https://doi.org/10.3390/f16060956
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