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Article

Environmental Drivers of Landscape Fragmentation Influence Intraspecific Leaf Traits in Forest Ecosystem

by
Huifeng Guo
1,†,
Dantong Duan
1,†,
Hangyu Lei
1,
Yi Chen
1,
Jiangtao Li
1,
Gadah Albasher
2 and
Xiang Li
1,*
1
College of Landscape Architecture and Art, Northwest A&F University, Yangling, Xi’an 712100, China
2
Department of Zoology, College of Science, King Saud University, Riyadh 712100, Saudi Arabia
*
Author to whom correspondence should be addressed.
†
These two authors contributed equally to this work.
Forests 2023, 14(9), 1875; https://0-doi-org.brum.beds.ac.uk/10.3390/f14091875
Submission received: 14 August 2023 / Revised: 4 September 2023 / Accepted: 13 September 2023 / Published: 14 September 2023
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Habitat fragmentation threatens the sustainability of ecological restoration. Understanding the variation in intraspecific traits helped to reveal the functional resource-use strategies of plants in response to environmental changes. We sampled different landscape types of forest configurations, where the most widespread species was Robinia pseudoacacia. From each plot, from two to five R. pseudoacacia individuals were selected for further examinations. Plant development and leaf traits—leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), and leaf thickness (Lth)—were measured in 135 individuals in total. The effects of plant development and landscape fragmentation on R. pseudoacacia leaf traits were assessed using linear mixed-effects models. The environmental factors explained the changes in leaf traits of R. pseudoacacia individuals, and the effect of stand type was the most significant. Compared with continuous forests, R. pseudoacacia individuals in fragmented forests adopted a more conservative resource-use strategy, with smaller LA and SLA and larger Lth and LDMC values. With an increase in landscape heterogeneity, SLA increased and LDMC decreased. In conclusion, the occurrence of landscape fragmentation plays a substantial role in inducing changes in leaf characteristics. The restoration of fragmented forests to continuous forests requires the appropriate addition of land-use types and systematic adjustment of landscape configurations.

1. Introduction

Human land use has altered most natural ecosystems by fragmenting habitats into smaller and more isolated pieces. Habitat fragmentation has a strong degradative effect on ecological processes [1]. Landscape changes caused by fragmentation may lead to losses beyond biodiversity, including the loss of functional diversity (quantified using functional trait data) [2]. Based on the implementation background of the “Grain for Green” project on the Loess Plateau, the Chinese government has initiated an extensive reforestation campaign since the end of the last century. Forestland, grassland, and farmland are the main landscape types. There is growing evidence that intraspecific variability in functional traits is crucial for understanding the key physiological and ecological processes of plants in different environments [3,4,5]. Understanding intraspecific trait variability can improve the inference of functional trade-offs underlying biodiversity patterns, which allow species to alter their functional positions to seize more niche opportunities [6]. Thus, studying the dynamics of landscape fragmentation is beneficial for the rational planning and objective evaluation of land allocation and quality improvement [7].
Research on the variation of traits within a species has important implications for the dynamics of communities and the functioning of ecosystems [8,9]. It has been observed that the variation of traits within a species may be greater than the variation of traits between different species, as determined by the average trait values of each species [10]. Intraspecific trait variation plays a crucial role in determining individual and population performances, interactions among plants, interactions between plants and their environments, community dynamics, and ecosystem characteristics [3,11]. For example, intraspecific trait variations reduce the ability of trait-based models to predict the community structure [3]. Intraspecific trait variability reflects the functional resilience of plant communities due to their phenotypic plasticity or genotypic variation, which may be the basis of their resistance to environmental changes [12,13]. The direct kinetic impact of the environment on physiological and biochemical plant processes drives changes in the community dynamics and ecosystem functions [13].
Environmental factors and plant development are the two main drivers of intraspecific trait variation which may enable accurate tracking of the environmental variation [14]. Plant functional traits have been studied in terms of landscape composition and heterogeneity [15], and plant development and environment-mediated variation have been investigated from the perspective of intraspecific traits [16,17]. Based on previous studies, we aimed to combine plant development with landscape fragmentation to study intraspecific trait variations [18]. Regarding landscape fragmentation, landscape composition refers to the type and extent of habitats in a landscape, whereas landscape heterogeneity refers to the habitat diversity [15]. Landscape structures have been identified repeatedly as key drivers of ecosystem service delivery [19]. The richness and evenness of cover types can be incorporated through measures of landscape composition heterogeneity, such as the Shannon index [20,21]. The plant development stage is usually characterised by the plant size (diameter at breast height [DBH] and plant height) of woody angiosperms with longer lifespans [16,22]. For example, the plant height is related to the ability of a species to compete for light [23].
We focused on the functional traits of leaves to identify intraspecific trait variations. These plant functional traits respond to environmental drivers and ultimately influence their adaptability through their effects on growth, reproduction, and establishment. This demonstrates enormous potential for investigating the relationships between traits and the environment [24,25,26,27]. Ecological research targeting functional traits can help elucidate the response of natural ecosystems to ongoing global anthropogenic changes [28]. Leaf traits such as leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), and leaf thickness (Lth), which are the most commonly used and easily measured functional leaf traits, can reflect resource-uptake strategies and resource-use efficiency [29,30,31,32]. For example, LA predicts light interception, evapotranspiration, photosynthetic efficiency, plant growth, and stress tolerance [33]. SLA can reflect changes in the leaf photosynthetic capacity and N content [34]. The LDMC represents the construction cost per fresh weight mass unit [32,35] and indicates the ability of plants to resist environmental stress and acquire resources [36]. Leaf thickness responds to changes in light capture and leaf tissue moisture status, slowing the extent of water vapour and CO2 diffusion through the leaves [37].
Intraspecific variability in leaf traits that are resistant to landscape fragmentation is a key feature of the Loess Plateau ecosystem. We selected the widespread species Robinia pseudoacacia from a fragmented landscape on the Loess Plateau and aimed to address the following two questions that affect intraspecific trait variability. (1) From the perspective of environmental factors and plant development, is the relative contribution of environmental factors to intraspecific trait variation greater than that of plant development? The relative contributions of plant development and environmental factors may be related to the frequency of their changes [25]. This relative contribution may also be related to differences in the spatial extent and environmental heterogeneity included in the study [38]. (2) In terms of environmental factors, what are the differences in intraspecific functional traits under the influence of fragmented landscape structures and landscape heterogeneity? Owing to the transition from continuous to fragmented forests, landscape composition decreased in woodland areas, and landscape configuration and heterogeneity increased. We hypothesised that harsh environments led to a shift in plants towards more conservative resource-use strategies to resist environmental stress [16].

2. Materials and Methods

2.1. Establishment of Sample Plots and Buffer Zones

Landscape plots were selected according to previously published methods [15]. The dominant and widespread vegetation species was R. pseudoacacia (Leguminosae), which is a deciduous tree with a height of 6–21 m; leaves are composed of 2–12 pinnate elliptical leaflets that are 2–5 cm long and 1.5–2.2 cm wide. In early July 2022, we selected 28 sampling plots of R. pseudoacacia forest in Ansai District, Yan’an City, Shaanxi Province (N36°42′–N36°46′, E109°14′–E109°19′), located near the summit of the mountainside with a relatively consistent elevation (approximately 1300 m) and terrain (Figure S1). The sample plots had an uneven distribution seasonal distribution of precipitation, a dry environment, and severe soil erosion. An agricultural landscape consisting mainly of R. pseudoacacia plantations, grasslands, and horizontal terraces was formed around the plots. The topographies of the sample plots are essentially identical. Through field research, and later with visual remote sensing interpretation, the landscape was divided into fragmented and continuous forests. Sample plots were randomly selected from fragmented (15 plots) and continuous (13 plots) forests. The sizes of the 28 sample plots were borrowed from a study that examined deciduous-tree leaf traits [16], with a selection of 50 × 10 m (consisting of five 10 × 10 m plots to facilitate the selection of R. pseudoacacia individuals). Consequently, changes between locations may represent variations in plant traits in different landscape environments. The selected forestland was surrounded by landscape types, such as cultivated land, residential land, and orchards, which were divided into different patches under human influence. After approximately 20 years of succession, the population size remained relatively stable.
To measure landscape characteristics, each plot core was surrounded by a 1 km-diameter zone of buffering (Figure 1) [15]. Additionally, GIS 10.8 was used to visually evaluate remote sensing data to record the seven different land types that made up the buffer zone. These land types included woodland, grassland, water, arable land, garden land, other lands (such as bare ground and pathways), and residential land.

2.2. Measurement of Variables

The predictive variables were divided into plant development and environmental factors. Plant development factors included DBH and plant height in R. pseudoacacia individuals. Environmental factors included landscape configuration, composition, and heterogeneity. The landscape configuration was divided into fragmented and continuous forests. The proportion of forest land area (PLAND) in the buffer zone represents landscape composition, and Shannon’s diversity index (SHDI) represents landscape heterogeneity. We measured four leaf traits representing functional traits (SLA, LA, LDMC, and Lth) (Table 1). These plant variables were measured according to a new handbook for standardised measurements of plant functional traits worldwide [33]. We measured these in reproductively mature, healthy-looking R. pseudoacacia individuals. Plants located in sunny environments were randomly selected from the entire population. We sampled young (possibly more photosynthetically active), fully expanded, and hardened leaves from the outer canopy. The petioles of R. pseudoacacia compound leaves were excluded from the trait metrics. The SLA was higher in resource-poor environments. Leaves with high LDMC tended to be tough and more resistant to physical hazards (e.g., wind and hail) than those with low LDMC. The Lth values tended to be high in sunnier, drier, and less fertile fragmented forests.

2.2.1. Measurement of Environmental Factors

In the description of landscape fragmentation, the landscape structure includes both landscape configuration and composition. Using forest type as a categorical variable to characterise the differences in landscape configuration, the landscape characteristics of 28 plots were recorded during field research. Fragmentation was determined based on the tree density, DBH, height, and canopy density of R. pseudoacacia in the environment, which were divided into fragmented and continuous forests. To study the impact of the buffer zone on R. pseudoacacia in the core sample plot, Fragstats 4.2 was used to calculate PLAND and SHDI as descriptors of landscape composition and heterogeneity, respectively.

2.2.2. Measurement of Plant Development and Leaf Functional Traits

In studies on the effects of plant development and environmental factors on intraspecific trait variations, the sample size was approximately 100–200 individuals [16,17]. The sampled individuals were located at relatively consistent elevations near the mountainside summit. We randomly selected to 2–5 R. pseudoacacia individuals from each sample plot with different degrees of fragmentation (one R. pseudoacacia individual per 10 × 10 m plot) to obtain leaf samples and recorded their DBH and plant height (using a tree altimeter). A total of 135 R. pseudoacacia individuals were sampled in 28 plots. Considering the impact of microenvironments, individuals in each sample plot were randomly sampled under different microenvironments to comprehensively represent the overall level of each plot.
For the leaf sampling of 135 R. pseudoacacia individuals, three fully expanded compound leaves were randomly clipped from the outer canopy layer of each plant using high-pruning shears. The sampled leaves were placed in self-sealing bags, and the fresh weights of all sampled compound leaf leaflets, excluding the petiole and rachis, were measured (on the same day) using an electronic scale with a 0.001 g precision. An LI-3000C portable leaf area meter was used to measure the area of three compound leaves for each sampled individual. We randomly selected five leaflets from each compound leaf for stacking and used an electronic digital Vernier calliper to measure the average value three times between the top and bottom of the leaflets, avoiding the main vein. The average leaflet thickness was calculated to represent the thickness of the compound leaves. The average values of the leaf area and leaf thickness of the sampled compound leaves represented the LA and Lth of the sampled individuals. Each individual’s leaflets were then placed in 135 envelopes and dried in an oven at 85 ℃ for 48 h. The dry leaf mass was measured using an electronic scale. SLA was calculated for each individual by dividing the leaf areas of the sampled compound leaves by the sum of the dry leaf masses of the sampled compound leaves. The LDMC was calculated by dividing the sum of the dry leaf masses of the sampled leaves by the sum of the fresh weights of the sampled leaves.

2.3. Statistical Analysis

To investigate the changes in leaf traits of R. pseudoacacia mediated by plant development and environmental variables, we fitted linear mixed-effects models using the “lmer” function in the lmerTest package (Table S1) [39]. We fitted four models referring to research on the individual and environmental factors of another species in karst forests [16]. The four models were fitted with fixed (DBH, height, PLAND, forest type, and SHDI) and random (sampling plot) effects. The data for the variables of the models are shown in Table S1.
SLA (or LA/LDMC/Lth)~DBH + height + SHDI + PLAND + forest type + (1|plots)
We tested the variance inflation factor (VIF) for the predictor variables of the four models using the “vif” function in the companion to applied regression (car) package [40]. The VIF values for all four models were less than 2 and substantially less than 10 [41], indicating mild multicollinearity between the explanatory variables. Therefore, we calculated and compared the standardised coefficients of each variable (Figure 2).
Furthermore, we used the “glmm.hp” function in the glmm.hp package to “average out” the shared R2 in the linear mixed-effects model [42]. Unlike standardising variables, this function compares the fractions that are jointly influenced by different variables (Table S2) to determine the independent effects of the standardised coefficients. Moreover, standardisation of the data had no effect on the calculation of R2. The “r.squaredGLMM” function based on the MuMIn package calculated the marginal R2 explained by fixed effects and the conditional R2 explained by fixed and random effects together [43]. The “glmm.hp” function assigned the marginal R2 to each fixed-effects factor to calculate the individual R2 for each explanatory variable and then determine the relative importance of each factor. Thus, the R2 assigned to each fixed-effects factor was equal to the total marginal R2.
Finally, we used all potential variables as predictors and performed stepwise regression using the MuMIn package in R [44,45]. The fixed terms that led to a decrease in the Akaike information criterion for small samples (AICc) were sequentially removed, and the model with the lowest AICc was selected as the most supported model (Table S3). All analyses were performed using R version 4.2.2 [46].

3. Results

3.1. Differences in Leaf Traits of Plant Development and Environmental Factors

The variation in traits within a species was found to be more strongly influenced by environmental factors resulting from fragmentation, rather than by the developmental stage of the plants. Forest type significantly influenced the variation in all leaf traits (pLA < 0.05, pSLA < 0.001, pLDMC < 0.001, and pLth < 0.001), whereas SHDI influenced changes in SLA and LDMC (pSLA < 0.1; pLDMC < 0.05) (Figure 2).
Among the predictor variables, environmental factors were the most significant in explaining variations in leaf traits, with landscape structure accounting for the largest proportion of all environmental factors (all greater than 75%). Plant development had the least effect on SLA, LA, and LDMC, with the exception of a slightly larger contribution to Lth (5.17%). Landscape heterogeneity had no effect on Lth, indicating that landscape fragmentation had a weaker effect on the intraspecific traits of Lth at the landscape scale (Table 2 and Table S2).

3.2. Selection of the Linear Mixed-Effects Models

The four most-supported linear mixed-effects models explained the variance in the data considerably (Table 3 and Table S3). The variance explained by the fixed effects (marginal R2) of the four models ranged from 0.0697 to 0.4246. After adding random effects, the variance explained by conditional R2 ranged from 0.2005 to 0.5804. Both marginal R2 and conditional R2 were less in the model with LA than the response variable, indicating that the predictive variables had a weaker impact on LA than on other traits. DBH and PLAND were not retained in most of the supported models, indicating that plant development and landscape composition had no significant impact on the leaf traits of R. pseudoacacia individuals. The most important environmental factor was the forest type, which was significantly correlated with all traits. Furthermore, SHDI exhibited a positive association with SLA.

3.3. Response of Leaf Traits to Environmental Drivers under the Influence of Fragmentation

In terms of landscape configuration, forest type significantly affected all leaf traits (p < 0.05) (Figure 2; Table 3). In this study, we documented changes in four trait metrics in R. pseudoacacia individuals under the influence of forest type using principal component analysis (PCA) in R version 4.2.2. Dimension 1 of the PCA was primarily related to SLA, LDMC, and Lth. Larger values on dimension 1 of the PCA corresponded to larger LDMC and Lth but smaller SLA and LA. Specifically, as compared to continuous forests, fragmented forests had higher Lth and LDMC and lower LA and SLA, indicating that a conservative resource-use strategy was beneficial for the survival of individuals in fragmented forests (Figure 3 and Figure S2). Dimension 2 of the PCA was mainly related to LA, indicating that it was less influenced by the predictor variables than SLA, LDMC, or Lth (Table 3; Figure 3).
In the study on landscape heterogeneity, there was a significant correlation between SHDI and LDMC before model selection (p < 0.05), and the LDMC of R. pseudoacacia individuals decreased significantly with increasing SHDI (Figure 2 and Figure 4). After model selection, there was a significant correlation between the SHDI and SLA (p < 0.05). Moreover, the SLA of R. pseudoacacia individuals displayed a significant increase with higher SHDI values (Table 3; Figure 4).

4. Discussion

4.1. Relative Importance of Plant Development and Environmental Factors

Among the most-supported models, only LA and SLA retained the changes in height that characterised plant development, but none of these changes were statistically significant. Plant development contributed much less to the variation in leaf traits of R. pseudoacacia individuals than environmental factors (Table 2 and Table 3). The main contribution of environmental factors was reflected in the differences between the fragmented and continuous forests. The relative importance of the environment in driving intraspecific trait variation might increase with the spatial extent and environmental heterogeneity of the study system [38]. Our study covered a larger spatial extent and involved greater environmental heterogeneity, which would promote a more prominent role of environmental drivers on the variation of leaf traits within a species.
Our study is contrary to the Qinling Huangguan Forest Dynamics Plot. In a study by Qiu et al. [17], there were no significant differences in traits among six different habitats, such as high slopes and low ridges. The lack of trait variation between habitats might be common to the dominant species. They share tolerance mechanisms under various environments. In contrast, R. pseudoacacia was an exotic species introduced into planted forests in our study. This has led to the possibility that R. pseudoacacia individuals in our study may not have developed certain tolerance mechanisms to environmental changes under landscape fragmentation thereby exhibiting a limited capacity to withstand environmental stresses [47]. This may also be due to the rapid frequency of landscape fragmentation over a short period and delayed changes in plant development. R. pseudoacacia individuals do not ultimately adapt to the environment by influencing their growth, reproduction, and establishment [25]. In the later stages, after the thinning of monoculture plantations, native species can be intercropped to avoid the key problem of single cultivation, reduce ecological adaptability, and effectively improve ecological services and functions [48].

4.2. Changes in Leaf Traits Mediated by Landscape Structure in Landscape Fragmentation

4.2.1. Changes Mediated by Landscape Composition

In terms of landscape composition, changes in leaf traits mediated by PLAND were not significant and were not retained in any of the most-supported models (Figure 2, Figure 3 and Figure S3). However, this finding was not consistent with our hypothesis. This may be because woodland cultivation on the Loess Plateau has reached a sufficiently large scale, resulting in an increased woodland area in the buffer zone without causing significant changes in intraspecific traits. Moreover, successive planting of monocultures in the same location will lead to a decrease in forest production and soil nutrient availability but will not improve productivity and ecological services. Therefore, we should appropriately reduce the expansion of woodlands and urgently use a restoration model for mixed-species forests [49].

4.2.2. Changes Mediated by Landscape Configuration

Human activities have led to habitat fragmentation by dividing larger areas of habitat into smaller patches, which objectively increases landscape configuration heterogeneity. The results indicate that fragmented forests are more complex, with smaller LA and SLA and larger Lth and LDMC than continuous forests (Figure 3 and Figure S2). The resource-use strategies of plants are represented by their functional traits, and those with lower SLA and higher LDMC show slower returns on investment [50].
Owing to the severe conditions in fragmented forests, intraspecific leaf traits frequently reflect a more conservative resource-use strategy. Conservative strategies are typically characterised by slow growth, high LDMC, and low resource-acquisition capacities, such as low maximum photosynthetic rates, which are associated with low SLA and leaf nutrient content [51]. SLA may be reduced in environments with high light, low water content, and low soil nutrients [52], which are observed in fragmented forests. Under the extreme environmental stress of harsh summer droughts in the Loess Plateau, forest fragmentation reduces inter-individual competition for water. It is important to maintain the growth, function, and sustainability of forests under increasingly arid conditions [53]. Forest fragmentation mitigates the negative effects of drought on plant functioning [54]. Plants with a low SLA value typically have a high Lth value because more materials are used to create protective structures and enhance the density of mesophyll cells in the leaf [36]. In general, plants with a high Lth tend to have a high LA, which reduces the heat transfer between the leaves and the surrounding air and lowers the CO2 and water vapour diffusion rate from the leaves [55]. In contrast, our study showed high Lth but low LA values in fragmented woodlands. This is likely because fragmented forests require more energy to produce leaf defence mechanisms, and there is no additional energy to increase LA [36].

4.3. Changes in Leaf Traits Mediated by Landscape Heterogeneity in Landscape Fragmentation

The SHDI, which characterises landscape heterogeneity, significantly affected SLA and LDMC. As the SHDI increased, SLA increased significantly (Table 3) and LDMC decreased significantly (Figure 2 and Figure 4). A higher SLA and a lower LDMC indicate an effective resource-use strategy [53]. Habitat fragmentation leads to an increase in heterogeneity [56]. Thus, landscape heterogeneity can characterise the degree of fragmentation to some extent. The higher the SHDI, the greater the degree of fragmentation, and the corresponding resource-use strategy would be conservative. This indicates a positive response to fragmentation rather than a negative one, which does not support our hypotheses. Fahrig reviewed possible explanations for both the positive and negative effects of fragmentation [57]. The negative effects could be attributed to negative edge effects, minimum patch size requirements, and loss of connectivity [58]; however, a positive response to landscape heterogeneity was elicited by increasing the landscape complementarity [56]. In the studies of Pope et al. and Tscharntke et al., the importance of landscape complementarity was demonstrated [59,60]. Landscapes with higher mosaic quality may be less instable and stressful for plants than sample sites with relatively less landscape heterogeneity, leading to more acquisitive resource-use strategies [58,61].

4.4. Strategies of Landscape Management

Our results elucidate the adaptive functional strategies of R. pseudoacacia individuals at the intraspecific level and provide insights into future responses to environmental changes. The effect of the “Grain for Green” projects in China will be greatly reduced if we do not consider the response of the landscape environment to the intraspecific traits.

4.4.1. Plant Development and Environmental Factors

Based on the relative importance of plant development and the effects of environmental factors on leaf traits, we found that R. pseudoacacia did not develop a good tolerance mechanism [47] in complex and fragmented landscapes (Table 3; Figure 2). R. pseudoacacia is not an indigenous species but is monocultured. In subsequent restorations, native species with strong resistance can be planted to form a mixed forest and enhance tolerance mechanisms [62]. Leaf functional traits and growth rate have been used as the basis for selecting native tree species for planting in thinned, pure monoculture R. pseudoacacia plantations [48]. These measures are beneficial for the effective management of the stable relationship between the environment and plant interspecific traits, which improves ecological benefits and ecosystem services [63].

4.4.2. Landscape Structure and Heterogeneity

In the subsequent ecological restoration of the fragmented landscape on the Loess Plateau, it is inadequate to increase forest area merely based on landscape composition. We can reasonably increase the area of important land-use types (e.g., cropland and orchards) to achieve an optimal landscape configuration to improve forest resilience. The most effective actions can be implemented with the allocation of limited conservation resources [64]. Increasing cropland and orchard land increases economic benefits by promoting ecological benefits, allowing local farmers to realise the benefits of ecosystem services.
Regarding the study of landscape heterogeneity, strategies for appropriately increasing the SHDI must be implemented at the local scale because we found this at the scale of the 1 km buffer [61].

4.4.3. Sustainability in Forest Management

In the future, it will be important to rely on systematic restoration to coordinate ecosystems using intensive and integrated approaches. Intensive restoration of locations helps form barriers to control sand and conserve soil. There is a need to shift from the one-sided pursuit of ecosystem protection in specific areas to the pursuit of landscape sustainability in ecological restoration, thereby improving the natural ecosystem’s stability and derived social benefits [64]. In addition, public participation in forest planning and communication with the research community can be strengthened to promote a deeper understanding of the impact of future-oriented forest planning strategies on the functioning of forest ecosystems [65].

5. Conclusions

We aimed to investigate the changes in intraspecific leaf traits mediated by plant development and environmental factors in order to reveal the resource-use strategies of R. pseudoacacia individuals in fragmented environments with different landscape compositions and heterogeneity. We found that environmental factors explained the variations in leaf traits to a higher extent than plant development. The difference between the fragmented and continuous forests was significant. Fragmented forests had a more conservative resource-use strategy, with smaller LA and SLA and larger Lth and LDMC. A higher SHDI led to an increase in SLA and a decrease in LDMC among R. pseudoacacia individuals, indicative of an effective resource-use strategy. In summary, landscape fragmentation in the Loess Plateau influenced intraspecific traits of R. pseudoacacia. R. pseudoacacia individuals adopted conservative resource-use strategies to adapt to the complex and unstable landscape of the sample plot and its buffer zones. The restoration of fragmented landscapes promotes the sustainability of ecological restoration. A systematic restoration of mixed forests can change the landscape configuration from fragmented to continuous forests. A moderate increase in land-use type can enhance the heterogeneity of landscape composition and facilitate the ecological restoration of fragmented woodlands.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/f14091875/s1, Figure S1: Photographs of sampling sites taken in 2022; Figure S2: Cloud and rain chart of leaf traits responding to variations in forest type; Table S1: Summary of predictor and response variables for linear mixed-effects models; Table S2: The relative importance of explanatory variables in linear mixed-effects models; Table S3: Results of model selection for predicting leaf traits based on stepwise regression; Data S1: Sample leaf data.

Author Contributions

Conceptualization, X.L., H.G., G.A. and D.D.; Investigation, H.G., Y.C., H.L. and J.L.; Methodology, X.L., H.G., D.D., Y.C., H.L., G.A. and J.L.; software, H.G., D.D. and X.L.; Writing—original draft, H.G.; Writing—review & editing, H.G., D.D. and X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (no. 32201429). The authors would like to extend their sincere appreciation to the acknowledgment; research supporting project (RSP-2023/95, King Saud University, Riyadh, Saudi Arabia).

Data Availability Statement

Data are available upon reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 14 01875 g001
Figure 1. Distribution of sample plots and buffer zones.
Figure 1. Distribution of sample plots and buffer zones.
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Figure 2. Relative influences of predictor variables on leaf traits of R. pseudoacacia individuals. The effect size was a standardised coefficient estimated separately for each predictor variable in the linear mixed-effects model. Statistical significance at different p-values is represented as: ‘***’ p < 0.001; ‘*’ p < 0.05; ‘.’ p < 0.1. SLA: specific leaf area; LA: leaf area; LDMC: leaf dry matter content; Lth: leaf thickness.
Figure 2. Relative influences of predictor variables on leaf traits of R. pseudoacacia individuals. The effect size was a standardised coefficient estimated separately for each predictor variable in the linear mixed-effects model. Statistical significance at different p-values is represented as: ‘***’ p < 0.001; ‘*’ p < 0.05; ‘.’ p < 0.1. SLA: specific leaf area; LA: leaf area; LDMC: leaf dry matter content; Lth: leaf thickness.
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Figure 3. Principal component analysis (PCA) of intraspecific traits of R. pseudoacacia individuals. The yellow dots represent individuals in fragmented forest and the blue dots represent individuals in continuous forest. SLA: specific leaf area; LA: leaf area; LDMC: leaf dry matter content; Lth: leaf thickness.
Figure 3. Principal component analysis (PCA) of intraspecific traits of R. pseudoacacia individuals. The yellow dots represent individuals in fragmented forest and the blue dots represent individuals in continuous forest. SLA: specific leaf area; LA: leaf area; LDMC: leaf dry matter content; Lth: leaf thickness.
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Figure 4. Response of leaf traits to SHDI after linear mixed-effects model selection. Each bubble represents a R. pseudoacacia individual. In the residual analysis graphic, the colour gradient and bubble area size correspond to the absolute value of the residual, which was used to represent actual data points. The fitting data points were represented by small hollow circles and placed on a grey fitting curve. SLA: specific leaf area; LDMC: leaf dry matter content.
Figure 4. Response of leaf traits to SHDI after linear mixed-effects model selection. Each bubble represents a R. pseudoacacia individual. In the residual analysis graphic, the colour gradient and bubble area size correspond to the absolute value of the residual, which was used to represent actual data points. The fitting data points were represented by small hollow circles and placed on a grey fitting curve. SLA: specific leaf area; LDMC: leaf dry matter content.
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Table 1. Description of predictor variables and functional traits in analysis.
Table 1. Description of predictor variables and functional traits in analysis.
AbbreviationDescription
Predictor variables
Plant
development		DBHMeasured diameter at breast height of R. pseudoacacia individuals
HeightThe plant height of R. pseudoacacia individuals
Environmental factorsPLANDPercentage of woodland area in the landscape area of the entire buffer zone
Forest typeDivided into fragmented forest and continuous forest based on different landscape configurations
SHDICharacterising landscape heterogeneity using Shannon’s diversity index
Functional traits
SLASpecific leaf area
LALeaf area
LDMCLeaf dry matter content
LthLeaf thickness
Table 2. The contributions of plant development and environmental factors to intraspecies trait variation in linear mixed-effects models.
Table 2. The contributions of plant development and environmental factors to intraspecies trait variation in linear mixed-effects models.
Individual R2SLALALDMCLth
Plant development0.00350.00800.01690.0175
Landscape structure0.3876 ***0.0540 *0.2862 ***0.3205 ***
Landscape heterogeneity0.03220.00890.04780.0004
The relative importance of the contributions was calculated based on the individual R2 assigned to each explanatory variable by the glmm.hp package in R. Statistical significance at different p-values is represented as: ‘***’ p < 0.001; ‘*’ p < 0.05. SLA: specific leaf area; LA: leaf area; LDMC: leaf dry matter content; Lth: leaf thickness.
Table 3. Coefficients of the effects of plant size and environmental factors on variations in intraspecific leaf traits by the most-supported models.
Table 3. Coefficients of the effects of plant size and environmental factors on variations in intraspecific leaf traits by the most-supported models.
Fixed EffectsSLALALDMCLth
Plant sizeDBH
Height−0.04−0.09
Landscape heterogeneitySHDI0.20 *0.13
Landscape structurePLAND
Forest type−1.39 ***−0.58 *1.10 ***1.20 ***
Most supported models do not display unreserved factors. Among the most-supported models, the marginal R2 values of SLA, LA, LDMC, and Lth were 0.4246, 0.0697, 0.2978, and 0.3374, respectively, and their conditional R2 values were 0.5567, 0.2005, 0.4665, and 0.5804, respectively. Statistical significance at different p-values is represented as: ‘***’ p < 0.001; ‘*’ p < 0.05. SLA: specific leaf area; LA: leaf area; LDMC: leaf dry matter content; Lth: leaf thickness.
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Guo, H.; Duan, D.; Lei, H.; Chen, Y.; Li, J.; Albasher, G.; Li, X. Environmental Drivers of Landscape Fragmentation Influence Intraspecific Leaf Traits in Forest Ecosystem. Forests 2023, 14, 1875. https://0-doi-org.brum.beds.ac.uk/10.3390/f14091875

AMA Style

Guo H, Duan D, Lei H, Chen Y, Li J, Albasher G, Li X. Environmental Drivers of Landscape Fragmentation Influence Intraspecific Leaf Traits in Forest Ecosystem. Forests. 2023; 14(9):1875. https://0-doi-org.brum.beds.ac.uk/10.3390/f14091875

Chicago/Turabian Style

Guo, Huifeng, Dantong Duan, Hangyu Lei, Yi Chen, Jiangtao Li, Gadah Albasher, and Xiang Li. 2023. "Environmental Drivers of Landscape Fragmentation Influence Intraspecific Leaf Traits in Forest Ecosystem" Forests 14, no. 9: 1875. https://0-doi-org.brum.beds.ac.uk/10.3390/f14091875

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