Diverse Patterns of Understory Plant Species across Different Types of Plantations in a Mountainous Ecosystem

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Diverse Patterns of Understory Plant Species across Different Types of Plantations in a Mountainous Ecosystem


1. Introduction

Many countries have adopted the practice of artificial afforestation as a strategy to protect and preserve mountainous ecosystems. Mountain ecosystems have received increasing attention from ecologists in recent decades. Due to its physical isolation from other ecosystems, the mountain ecosystem can often form unique habitats that support the survival of specific species [1]. However, compared to other ecosystems, mountain ecosystems are more fragile and more susceptible to human disturbance. Afforestation is widely used as a strategy to protect mountain ecosystems by restoring forest cover, thereby mitigating climate change, conserving biodiversity and achieving sustainable development [2].

China has been carrying out large-scale afforestation activities since the 1980s. Chongli District in Zhangjiakou City, Hebei Province, is a major corridor for wind and sand intrusion into Beijing from the Inner Mongolian Plateau. In order to prevent soil erosion and green the ecological environment of the Beijing–Tianjin area, the government has implemented key national greening projects such as the “Three North Protective Forests” and the construction of the “Saibei Forestry Farm”. After several years of afforestation, the species of artificial forests include spruce, camphor pine, white birch, tufted white birch, five-horned maple, tufted five-horned maple, Mongolian oak, tufted Mongolian oak, Hebei poplar, silver poplar, Beijing poplar, etc., with the heights of the seedlings ranging from 70 cm to 3 m, and the design of afforestation heights ranging from 4 m to 10 m; the shrubs include saffron grass and sandy cypress.

As an important part of terrestrial ecosystems, planted forests play an important role in restoring and rebuilding forest ecosystems, providing forest products, increasing forest carbon sinks, and improving the ecological environment [3]. However, afforestation activities have caused extensive habitat changes, which will inevitably lead to variations in biodiversity. Some scientists have carried out research on related issues [4,5]; however, most related studies focus on large-scale assessments, and there is still a lack of specific empirical studies on the impact of plantations on biodiversity [6].
In artificial forests, plant diversity is mainly reflected through understory vegetation due to the homogeneity of stand types [7]. The presence of understory vegetation plays a significant role in plantation ecosystems, influencing the flow of energy and the cycling of nutrients within forest ecosystems [8]. Understory vegetation responds quickly to natural and anthropogenic disturbances, and if the environment changes, the diversity of understory plants will change significantly, which in turn will affect other species [9]. Studies have verified that understory plant diversity is highly correlated with soil microorganisms [10], Coleoptera [11], bees [12], and other organisms’ diversity; therefore, understory plants can be used as indicator species for biodiversity in plantation ecosystems. At present, research on understory biodiversity in plantations has mostly targeted oak, pine, etc., and has focused on natural forests, secondary forests, and other stand types, while research on understory biodiversity in plantation forests is still lacking [13].
There is controversy over differences in understory plant diversity between monoculture and mixed forests. At present, the artificial forests in most areas are monoculture plantations. Studies have found that although monocultures are good at supporting carbon emissions and soil and water conservation ecological services, their impact on biodiversity is controversial [14]. Many studies have found that monocultures may have problems such as low nutrient turnover efficiency, poor stand stability, low productivity, and weak disease resistance due to their simple structure [15]. However, ecological processes in mixed forests are more complex and stable, and interspecific competition among tree species has been observed over an extended period of time in these forests [16]. Also in young stands, mixed plantation forests store more carbon than monoculture forests [17]. Therefore, artificial afforestation in several areas has begun to adopt multi-species mixed planting methods. However, evidence suggests that biodiversity in mixed multi-species woodlands is not always better than in monoculture forests [18], that differences in understory biodiversity may only be reflected in species composition rather than species number, and that significant differences in species often take at least 10 years to develop [19]. There is still a lack of research on specific species, the completion of which will help to further reveal the causes and patterns of biodiversity differences in monocultures and mixed forests.
Many environmental factors affect the diversity of understory vegetation. At the local scale, the distribution of plant species is influenced by various factors such as the species of the upper trees, the soil pH, organic matter, nitrogen, phosphorus, and potassium [20]. Studies have found that the spatial heterogeneity of soil organic matter in mountainous areas is high, which may make soil organic matter a decisive factor affecting plant diversity [21]. In addition, human interference is an important but sometimes overlooked influencing factor; cars, pedestrians, and other activities may disperse plant seeds, dust, grazing, trampling, and other disturbances may change the plant community [22]. In recent years, research has gradually paid attention to environmental variables at the landscape scale. Some studies have found that patch area has a significant positive effect on plant species diversity [23], and habitat fragmentation can lead to biodiversity loss [24]; nevertheless, drivers of biodiversity in fragmented habitats are complex and controversial [25,26]. Fragmented landscapes may increase landscape heterogeneity, which may have a positive effect on plant diversity within the habitat [27]. At present, there are few studies that explore the influence of understory plant diversity by multi-scale environmental factors; the responses of different types of forests to environmental factors are still unclear [6]. This is not conducive to predicting and assessing the ecological impacts of afforestation, as well as to the development of management measures for planted forests.

In this research, the mountainous regions situated in North China were chosen as a case study to examine two distinct types of plantation forests: monoculture (MON) and mixed (MIX). We analyzed the differences in plant diversity in different types of plantations in mountainous areas and explored the influencing mechanism of environmental factors on plant diversity under the forest. We evaluated (1) whether there are differences in understory plant diversity between two different types of plantations. (2) What are the differences in the composition of understory vegetation communities between two different types of plantations? (3) Does understory plant diversity respond consistently to environmental variables in pure and mixed forests?

2. Materials and Methods

The study area is located in Chongli District, Zhangjiakou City, Hebei Province (40°47′~41°17′ N, 114°17′~115°34′ E). This is the transition zone between the Inner Mongolian Plateau and the North China Plain, which belongs to the East Asian continental monsoon climate and is in the middle temperate sub-arid zone. The overall temperature in this area is low, the annual average temperature is −2 °C~12 °C, the winter is cold and long, the summer is cool and short, and the temperature difference between day and night is large. Eight percent of the total area of Chongli District is mountainous, with steep mountains, and the altitude ranges from 813 to 2174 m. Due to the protection of forests and artificial afforestation, the overall forest cover of the area exceeds 52%.

2.1. Plantation Situation

Before the 1960s, the forest coverage rate of Chongli District was only 15%, and the forest and grass coverage was less than 50%. The types of forest soil are mainly brown soil, chestnut soil, gray forest soil, brown soil, and meadow soil. The primary tree species are Pinus tabuliformis, Larix gmelinii, Cinnamomum camphora, Ulmus pumila, and other species. In order to improve the ecology and benefit the people, the implementation of large-scale afforestation activities commenced. Since 2009, the local construction of larch, Cinnamomum camphora, and other pure forest and mixed forest plantings has been undertaken (Figure A1).

2.2. Vegetation Data

In June and mid-September 2016, sample plots of monoculture and mixed forest plantings were introduced in Chongli District, with coniferous forest comprising the monoculture and a mixture of additional species composing the mixed coniferous forest component. A total of 18 sample plots were selected, 9 each from the monoculture forest and the mixed forest (Figure 1). The age of the forests in which all samples are located is at least 10 years old, and the largest difference in the age of the forests between the samples is within 10 years (Table A1). All of the plantations in the study area are less than 20 years old, and the organic matter in the forest is low. In the study area, monoculture and mixed forest patches with similar forest ages and similar altitudes were selected, and the area of all forest patches was greater than 30,000 m2, and the distance between the edges of each patch was greater than 1 km. The method of stratified random sampling was used in the plot studies, with 50 m × 50 m designated plots located at the center of each woodland patch. Three 20 m × 20 m subplots were randomly established within each plot to record tree and shrub species and cover and four 1 m × 1 m subplots were randomly selected to allow for the recording of herbaceous species and cover. The data in all of the subplots were combined as averages. Plant species were identified on-site or specimen collections were made and brought back for expert identification (Table A2). Plant species that can adapt to the local environment of Chongli for a long time and grow well are regarded as native species [27]. For determining native species, refer to the Flora of China (http://www.iplant.cn/frps accessed on 1 December 2022).

2.3. Environmental Factors at the Local Scale

Among the environmental factors at the local scale, because we controlled the other variables such as sunlight and terrain in the selection of plots, only soil physical and human disturbance were selected as local variables. Available phosphorus, soil organic matter, and the distance from the road were selected as key environmental indicators at the local scale. In each plot, the soil was randomly drilled three times with a soil drill, and the topsoil (0–20 cm) was collected, mixed into one soil sample, air-dried, and passed through a 2 mm sieve. The content of total soil organic matter and available phosphorus was determined using an element analyzer.

2.4. Environmental Factors at the Landscape Scale

We used Google remote sensing images with a resolution of 0.2 m in our study area to develop site and plot maps. Using the remote sensing image as the base map with a radius of 1 km around the center of all sample points, digital mapping was carried out according to the site research purpose and the land use composition of the study area on the basis of field verification. Landscapes with a minimum side length greater than 2 m were recorded as patches, and landscapes with a maximum side length of less than 2 m were recorded as linear landscapes or spots. Through manual digitization, the information obtained from the survey was converted into a unified GIS database, and after digitization was completed, layer stitching, correction, and registration were carried out, and the necessary repairs were implemented to obtain a complete spatial vector data layer that met the topological requirements. Three indicators were calculated at each sample site, including the patch density, landscape fragmentation, and Shannon diversity index value.

2.4.1. Landscape Fragmentation

Fragmentation analysis refers to the degree of distribution of landscape elements in the study area, which consists of the number of patches (NP), the patch density (PD), and the average patch area (AREA_MN). Patch density refers to the number of patches per unit area in a landscape that includes all heterogeneous landscape element patches and is able to characterize the degree of fragmentation of the landscape pattern. The calculation formula is as follows:
where n is the total number of landscape types, Mi is the number of landscape patches of category i, and LA is the total area of the landscape within the study area.

2.4.2. Patch Shannon Diversity Index (SHDI)

The Shannon diversity index is an important indicator reflecting the heterogeneity of the landscape, which can more accurately identify the spatial non-equilibrium distribution of each patch type in the landscape. The calculation formula is as follows:

S H D I = i = 1 n ( p i I n p i )

where pi is the ratio occupied by landscape patch type i.

2.5. Biodiversity Index

The species richness index (R) and Simpson diversity index (D) were used to assess biodiversity. The calculation formula is as follows:
where S is the number of species in the simple plot and Pi is the proportion of individuals of this species to the total number of individuals.

2.6. Statistical Analyses

In the process of data analysis, the species richness and Simpson diversity indices of plants in the monoculture and mixed forest groups were calculated. One-way ANOVA and the t-test were used to analyze the significance of the difference between the indices of understory plant diversity. NMDS analysis based on the Bray–Curtis distance algorithm was used to analyze the species composition. Stepwise linear regression was utilized to analyze the influence of organic matter and other factors on understory plant diversity. Linear regression analysis was performed on different types of forest understory plant diversity and different environmental variables. PAST 3.0 and SPSS 26.0 software were used for statistical analysis. ArcGIS 10.0 software was employed for the landscape survey and digitization. Fragstats4.0 software was utilized for landscape index calculation. Origin 2021 software was used for mapping.


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