Distribution Patterns and Environmental Determinants of Invasive Alien Plants on Subtropical Islands (Fujian, China)
by
,
Yanqiu Xie
1,
Xinran Xie
1,
Feifan Weng
1,
Liebo Nong
1,
Manni Lin
1,
Jingyao Ou
1,
Yingxue Wang
1,
Yue Mao
1,
Ying Chen
1,
Zhijun Qian
1,
Xiaoxue Lu
1,
Zujian Chen
1,
Yushan Zheng
1,
Chuanyuan Deng
1,* and
Hui Huang
2,* 1
College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350100, China
2
College of Architecture and Civil Engineering, Fujian College of Water Conservancy and Electric Power, Sanming 365000, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(7), 1273; https://doi.org/10.3390/f15071273
Submission received: 5 June 2024
/
Revised: 19 July 2024
/
Accepted: 20 July 2024
/
Published: 22 July 2024
(This article belongs to the Section Forest Ecology and Management)
Abstract
:Plant invasions threaten the biodiversity of islands, causing serious impacts on their ecosystems. To investigate the distribution patterns of invasive alien plants on subtropical islands, the environmental determinants of species richness, and the growth forms of invasive alien plants, this study analyzed the composition and origin of invasive alien plants on 77 islands in Fujian. The similarity in the distribution of invasive alien plants between islands was assessed using the UPGMA. Moreover, feature selection, best-subset regression, and variance decomposition were performed using 19 environmental variables characterizing climate, anthropogenic disturbance, and landscape/geography, as well as the species richness and growth forms of invasive alien plants. Through the analysis, the main environmental factors affecting the species richness and growth forms of invasive alien plants on the Fujian islands were identified. The results showed 142 species of invasive alien plants in 38 families and 102 genera on 77 islands in Fujian. Annual herbs constituted the most representative growth form of invasive alien plants and tropical America was the main origin of invasive alien plants. The distribution of invasive alien plants across the 77 islands in Fujian showed a high degree of similarity, suggesting a nested pattern in their distribution. The proportions of building and farmland area (BFA), island area (A), and maximum elevation (ME) were the main driving factors of species richness and growth forms for invasive alien plants. In particular, BFA played a key role in driving plant invasion. The results of this study can help establish an early warning mechanism for invasive alien plants and better implement island ecological management, which are important for the protection of subtropical island ecosystems.
1. Introduction
Biological invasions are a major threat to global ecosystems. Invasive plants not only compete with native plants for resources, reducing their abundance, but also alter the genetic diversity of native plants through hybridization and gene flow, leading to floristic homogenization [1,2]. Furthermore, invasive plants can decrease the abundance of native animals through trophic interactions, resulting in the loss of ecosystem services [3] and posing a severe threat to biodiversity [4]. As the third-largest country in the world, China has identified more than 268 species of invasive alien plants [5], facing serious ecological threats and economic losses [6]. Therefore, exploring the driving factors that determine the success of plant invasions is imperative for controlling invasive alien plants [7].
Island ecosystems are particularly susceptible to the impacts of invasive species. Firstly, islands have lower species richness and reduced competitive ability among species [8], making them more vulnerable to invasions compared to mainland ecosystems [9]. Furthermore, islands are ideal for studying plant invasion mechanisms because of their small size and isolation characteristics [10]. The species richness (SR) of island-invasive alien plants in relation to environmental factors is of particular concern [11]. For example, island biogeography suggests that the species richness of islands is mainly influenced by size and isolation effects [12,13,14]. Despite numerous investigations, the environmental determinants that facilitate the species richness of invasive alien plants remain to be explored. In addition to the influence of area and the degree of isolation, these determinants depend on other environmental factors such as climate (temperature and precipitation), elevation, and latitude [15,16,17,18,19]. At the same time, human activities have led to unprecedented interactions among species on islands [20], changing the distribution patterns of species. However, the predictive power of these environmental factors in terms of species richness varies widely [21,22]. Therefore, it is necessary to determine the contributions of climate, anthropogenic disturbance, and landscape/geography to plant invasion on islands.
The determinants of invasive alien plants may vary among taxa [23]. It has been confirmed that there are differences between the influencing factors of plants with different growth forms [24,25]. Because different growth forms have different habitat preferences, species spreading abilities, and environmental demands [26], studies on growth forms can show the effects of specific environmental factors, space usage, and possible competitive relationships among different growth forms [25]. However, the current environmental driving factors of the species richness and growth forms of invasive alien plants on islands still need to be investigated. Therefore, in addition to assessing the effects of environmental factors such as climate, landscape/geography, and anthropogenic disturbance, adding plant growth patterns to the analysis can provide a more comprehensive insight into the distribution patterns of invasive alien plants and their driving factors.
The islands of the subtropical Fujian archipelago harbor many endangered and endemic plants. This area is one of the important areas for the conservation of island biodiversity and species resource utilization [27,28]. However, the suitable climate and the frequent exchanges caused by economic development have provided avenues for the successful invasion of invasive alien plants, threatening the biodiversity and ecosystems of these islands. Therefore, exploring the distribution patterns of invasive alien plants on subtropical islands under the influence of environmental factors can provide the necessary information for relevant departments to predict and manage invasive alien plants on subtropical islands. The primary objectives of this study were as follows: (1) assessing the similarities in invasive plant distribution between islands and analyzing the distribution patterns of invasive alien plants on islands and (2) determining the major environmental variables affecting the species richness and growth forms of island-invasive alien plants, assessing the relative importance values of major environmental variables affecting the species richness of island-invasive alien plants. The identification of environmental factors can facilitate the prediction of invasive plant spread and the implementation of island ecological management, which are important for protecting subtropical island ecosystems and mitigating the impact of invasive alien plants on islands.
2. Materials and Methods
2.1. Study Area
The 77 islands in Fujian (117°31′61″–120°68′66″ E, 23°60′90″–26°93′08″ N, Figure 1) were selected for this study, including 56 uninhabited islands and 21 inhabited islands (Appendix A). Geologically, the islands in Fujian are all continental islands [29], with close connections to the neighboring continent. The Fujian islands are located at low latitudes and have a predominantly subtropical maritime monsoon climate [30]. In this area, south of the Min River has a southern subtropical maritime monsoon climate and north of the Min River has a central subtropical maritime monsoon climate [31]. The average annual temperature is between 17.43 °C and 21.27 °C and the average annual precipitation is from about 995 mm to nearly 1600 mm. The islands are the windiest areas in Fujian, with annual average wind speeds ranging from 2 to 12 m/s (Appendix Table A2). The vegetation types on the islands are mainly as follows: (1) evergreen coniferous forests dominated by Pinus thunbergii, Pinus elliottii, and Cunninghamia lanceolata; (2) broadleaf evergreen forests dominated by Acacia confusa, Celtis sinensis, and Ficus microcarpa; and (3) scrub communities dominated by Elaeagnus oldhamii, Eurya emarginata, Glochidion puberum, Breynia fruticosa, Phyllanthus leptoclados, Scaevola taccada, Ipomoea pes-caprae, and Imperata cylindrica.
2.2. Plant Species Data
This study conducted field surveys on islands in Fujian from 2018 to 2021. To comprehensively record the plant species on each island, surveys were carried out during the key growing seasons in Fujian (May–July and September–November), when plant species and their numbers peak, maximizing the accuracy and completeness of species records. All plant species on the islands were meticulously documented. The classification and identification of plants were based on the Flora of China (FOC, http://www.efloras.org, accessed on 13 December 2021) database. To ensure accuracy, photographs were taken and field specimens were collected. These specimens were then reviewed and identified by botanical experts to confirm species identification accuracy. All voucher specimens collected are stored in the Herbarium of the College of Landscape Architecture and Art, Fujian Agriculture and Forestry University. Additionally, species richness data for the 77 islands were obtained by integrating publicly available data from Datuyu [32], 12 islands in the Xiamen region [33], and 13 islands in Fujian [32].
Using the China Invasive Alien Species Database (https://www.iplant.cn/ias/, accessed on 5 March 2022), we screened the species richness data from 77 islands to identify the list of invasive alien plants on each island and determine their origins. Additionally, the invasive alien plants were classified into four growth forms: trees, shrubs, perennial herbs, and annual herbs (hereafter referred to as growth forms) (Table A1).
To identify what invasive alien plants pose the greatest threat to the ecosystems of Fujian islands, it is necessary to determine the invasive alien plants with the highest distribution frequency on these islands. We used the following formula for this calculation: Distribution frequency of invasive alien plants = (Number of islands where a species is present/Total number of surveyed islands) × 100%.
2.3. Environmental Data
To analyze the influence of environmental factors on the distribution patterns of invasive alien plants on islands in Fujian, this study classified environmental factors into three domains: climate, anthropogenic disturbance, and landscape/geography, with a total of 19 potential explanatory variables (Table 1). Based on the latitude and longitude of the 77 islands, the climate data for each island were extracted using “kriging interpolation”. Moreover, the “Extract by Mask” tool was used to extract the proportions of building and farmland area (BFA), island area (A), and maximum elevation (ME). The BFA is the proportion of building and farmland area to IA, and vegetation coverage is the ratio of vegetation coverage to A. The extraction and analysis of these data were performed using Arc GIS 10.8.
2.4. Data Analysis
To reveal the overall distribution patterns of invasive alien plants and determine whether there are regularities in their distribution among different islands, particularly the presence of nested patterns [35], we conducted a similarity analysis of the composition of invasive alien plants across the islands. Based on the distribution data of plant growth forms, we performed a Hellinger transformation to obtain the Hellinger distance matrix. Gower distance and same-phenotype correlation were used to compare the results of four common hierarchical clusters and the best clustering method was obtained by the unweighted-pair group method with arithmetic means (UPGMA). In addition, the optimal number of clusters and clustering results of different growth forms were evaluated using the fusion level value and silhouette width of the clustering tree.
Boruta feature selection (BFS) analysis of random forests was used to identify feature annotations that were significantly separated between treatments [36]. In order to extract important variables affecting total species richness and species richness of different growth forms from 19 environmental factors in 3 domains, BFS was performed (1000 trees, p < 0.01). The original dataset was replicated and the values in each column were disrupted (shadow features). Afterward, the random forest classifier was trained on these datasets to compare whether the importance of the variables calculated from the original data was significantly higher than the importance of the shadow features. The training was completed until all predictor variables were classified as “Confirmed” or “Rejected” at the 0.01 alpha level [37].
Best-subset regression was performed on the environmental factors confirmed by BFS with the total species richness and the species richness of different growth forms. Then, a comprehensive assessment was performed using four goodness-of-fit evaluation metrics: Adjusted R2 (Adj-R2), Mallows’ Cp, Akaike Information Criterion (AIC), and Schwarz’s Bayesian Criterion (SBC). Through the evaluation, the environmental factors affecting the total species richness and species richness of growth forms and their number were determined, obtaining the optimal fitting model. Moreover, the variance decomposition of the best-fit model was performed to assess the contribution of the major environmental variables to the total variance of species richness, thus achieving the quantitative analysis of the relative importance of the major environmental variables.
The above data analysis was mainly completed using the adespatial package (https://cran.r-project.org/web/packages/adespatial/, accessed on 14 February 2023), vegan package (https://cran.r-project.org/web/packages/vegan/, accessed on 15 February 2023 ), Boruta package (https://cran.r-project.org/web/packages/Boruta/, accessed on 20 February 2023), olsrr package (https://cran.r-project.org/web/packages/olsrr/, accessed on 25 February 2023), and relaimpo package (https://cran.r-project.org/web/packages/relaimpo/, accessed on 5 March 2023) of R (Version 4.1.1).
3. Results
3.1. Analysis of Species Composition and Distribution Patterns
There are 142 species of invasive alien plants (including infraspecific taxonomic units) belonging to 38 families and 104 genera on 77 islands in Fujian. These invasive alien plants include 13 species of trees, 20 species of shrubs, 29 species of perennial herbs, and 80 species of annual herbs. Casuarina equisetifolia, Agave americana, Tetragonia tetragonioides, Lantana camara, and Malvastrum coromandelianum are the invasive species with high distribution frequency, appearing on 56, 45, 44, 41, and 35 islands, respectively. On the other hand, Axonopus compressus, Physalis angulata, and Mimosa bimucronata have a low distribution frequency, appearing on only one island.
As shown in Figure 2, plants originating from South America are the main invasive alien plants on the islands in Fujian. Among all the plants on 77 islands, the ones that have originated from South America are mainly Erigeron sumatrensis, Praxelis clematidea, Alternanthera pungens, Amaranthus viridis, Erigeron bonariensis, and Bromus catharticus. Furthermore, invasive alien plants originating from North America also account for a certain proportion of the islands in Fujian, mainly including Lepidium virginicum, Symphyotrichum subulatum, Erigeron annuus, Erigeron canadensis, and Spartina alterniflora. In addition to South and North America, plants with African and Asian origins also account for a large proportion of invasive alien plants on the islands in Fujian.
Based on the UPGMA, the 77 islands in Fujian were clustered into four categories (Figure 3). It can be seen that 63 islands, including Haitandao, Daduidao, Tayu, Duimiandao, and other islands, were clustered into one category due to the distribution of trees, shrubs, perennial herbs, and annual herbs. Nine islands, including Qingyu, Dayuzai, Guluoyu, Geshaqingyu, Xiaweidao, and others, were clustered into one category because only annual herbs are found on these islands. In addition, due to the presence of only trees on these islands, Dashengdao, Shixhengdadao, and Chixietedao were clustered into one category. Dongzhuodao and Toujinyu were clustered into one category since no invasive alien plants were found on these islands.
3.2. Analysis of Environmental Factors of Invasive Alien Plants on the Islands
Through Boruta feature selection, the importance ranking of the 19 environmental factors affecting the total species richness was obtained. A total of 12 significant environmental factors were identified, in the order of ND, BFA, A, PAR, P, LAT, AMT, ME, AMW, NLB, PWM, and DNI. Meanwhile, seven environmental factors with importance below the threshold were rejected, including DNC, PDM, TCM, AP, SI, TWM, and VC (Figure 4a). In the feature selection for the species richness of trees, shrubs, perennial herbs, and annual herbs, fourteen (Figure 4b), twelve (Figure 4c), seven (Figure 4d), and ten (Figure 4e) variables were identified, respectively. Overall, the factors with high importance were mainly BFA and ND in the domain of anthropogenic disturbance and IA, P, PAR, and ME in the domain of landscape/geography. Although BFA and ND were also selected as environmental factors for annual herbs, the importance of both was lower.
In this study, goodness-of-fit evaluation was performed on the subset of environmental factors selected by Boruta feature selection and the total species richness and species richness of different growth forms. The results showed the five optimal environmental factors suitable for total species richness (Figure 5), including BFA in the domain of anthropogenic disturbance, AMT in the domain of climate, and ME, A, and LAT in the domain of landscape/geography. Furthermore, the optimal environmental factors suitable for trees were BFA and ND in the domain of anthropogenic disturbance and LAT, ME, A, and PAR in the domain of landscape/geography. There were five optimal environmental factors for shrubs, including BFA and ND in the domain of anthropogenic disturbance, AMT in the domain of climate, and ME and LAT in the domain of landscape/geography. For perennial herbs, the optimal environmental factors were BFA and DNI in the domain of anthropogenic disturbance and ME and A in the domain of landscape/geography. There were also three major environmental variables for annual herbs, including BFA in the domain of anthropogenic disturbance and DNC and A in the domain of landscape/geography.
The results of the variance decomposition (Figure 6) revealed that the total species richness and growth forms of invasive alien plants on the islands were differently influenced by environmental factors. It can be seen that among 19 environmental factors in three domains, BFA, A, and ME were significantly and positively correlated predictors for total species richness and the species richness of different growth forms. Among them, BFA in the domain of anthropogenic disturbance was the main determinant of species richness. The importance of BFA on total species richness was 22.07%. For trees, shrubs, perennial herbs, and annual herbs, the importance values of BFA were 16.41%, 16.40%, 19.68%, and 14.26%, respectively. The second important determinant was A in the geographic/landscape domain, with importance values of 26.04% for total species richness and 13.81%, 18.91%, and 38.61% for trees, perennial herbs, and annual herbs, respectively. In addition, ME in the geographic/landscape domain positively influenced species richness and different growth forms. The importance of ME to total species richness was 17.74%, and to trees, shrubs, and perennial herbs, it was 12.33%, 16.41%, and 16.18%, respectively.
4. Discussion
4.1. Distribution Pattern of Invasive Alien Plants
The islands of Fujian are vulnerable to invasion by herbaceous plants, especially annual herbs. Due to their high adaptability and reproductive capacity, annual herbs can easily occupy empty ecological niches and crowd out native plants to become the main taxon of invasive alien plants [6]. Tropical America is the main origin of invasive alien plants on the islands in Fujian, which may be determined by both climate and frequent maritime trade. Invasive alien plants native to similar climatic regions are more adaptable and have a higher success rate of settlement and spreading after introduction [38]. Although Fujian is geographically distant from tropical America, human activities such as frequent exchanges, convenient transportation, and long-term trade have gradually broken down the barrier of geographical isolation. These activities have facilitated the successful invasion and spread of plants native to tropical America. Similar climatic environments also provide the necessary climatic background for invasive alien plants from the Americas, facilitating their successful invasion and colonization. Moreover, the hypothesis of continental drift further demonstrates that plants between the two sites have similar genetic backgrounds [39,40].
The relatively high similarity between the islands in Fujian is not surprising as species that exhibit invasive behavior in one area have a high likelihood of invading other areas [41]. The high similarity in the distribution of different growth forms suggests the nestedness in the distribution of invasive alien plants on the islands in Fujian. Nestedness refers to island ecosystems in which most species distributed on small islands also appear on larger islands with relatively high species richness [42]. On islands, invasive alien plants often exhibit more significant nested patterns than native species [43]. As found in this study, the invasive alien plants distributed on the small islands of Shanbaidao, Toujinyu, Guluoyu, and Nanchidao are also distributed on the larger islands of Haitandao, Dongshandao, and Dayushandao. When differences in species richness on islands are expressed as the small islands being subsets of larger islands, the plant distribution forms a nested pattern [44]. For invasive alien plants, both plant adaptation to the environment and anthropogenic disturbance may lead to nested patterns.
4.2. Environmental Determinants of Invasive Alien Plants on Islands
4.2.1. The Proportion of Building and Farmland Area
This study showed that BFA, A, and ME are the main driving factors of total species richness and the species richness of different growth forms. In particular, BFA plays a key role in driving species richness. The invasion of species in natural ecosystems is heavily dependent on disturbance [45]. Our findings are consistent with previous studies conducted on the small Mediterranean islands of Sardinia [11] and the Lesser Antilles islands [46]. They confirm that human activity is a key factor in promoting the distribution of invasive alien plants on islands. In this study, BFA in the domain of anthropogenic disturbance was the dominant factor affecting species richness. Disturbances create new ecological niches, affecting the availability of various resources (nutrients, water, light) [47]. As a result, space and resources are freed up, promoting the distribution of invasive alien plants. In addition, a large BFA on an island means a large population. Humans have changed the types of land through agriculture and construction, generating trade, transportation, and economic activities. These activities bring more opportunities for the intentional and unintentional introduction of invasive alien plants [48], increasing the number of invasive alien plants on islands. However, eliminating anthropogenic disturbances is not sufficient to prevent invasive alien plants. Other environmental factors in the climate and landscape/geography domains such as A, ME, and LAT also influence the invasive plant compositions of islands in Fujian.
4.2.2. Island Area
Island biogeography theory suggests that species richness in island ecosystems follows a species–area relationship [13]. In this study, the species richness values of invasive alien plants, trees, perennial herbs, and annual herbs on islands in Fujian were significantly and positively correlated with island area (Figure 6), consistent with the species–area relationship hypothesis. This result may have been due to the following reasons: (1) larger islands provide larger areas and are more likely to intercept randomly spread propagules, increasing the chances of colonization and spread of invasive alien plants [49]; (2) larger islands have more habitats and higher habitat diversity than smaller islands, providing more ecological niches [50] to support more plants and larger populations; and (3) the marine environment (salt fog, storm surge, strong winds) can act as an environmental filter for invasive alien plants [51]. As the area of an island increases and the distance between the ocean and the interior of the island increases, the plants on the island may be less affected by the marine environment. Therefore, larger islands are less affected by the marine environment. For smaller islands, the influence of the marine environment may penetrate deep into the center of the island and disturb the distribution of plants [52].
4.2.3. Maximum Elevation
The highest elevation has been proven to affect species richness on islands [53,54]. Previous studies have shown that the richness of exotic plant species on islands generally decreases with increasing elevations [55]. For instance, research on the Canary Islands [56] found that the richness of exotic species peaks at around 500 m in elevation and then decreases with higher elevations. Similar results were found in a study in New Caledonia [57].
However, our research on islands in Fujian found a significant positive correlation between the richness of invasive plant species and elevation, which contrasted with many studies in other global regions. Our study covered 77 islands in Fujian, all of which are low-elevation areas, with Dayushan Island being the highest at 541.40 m (Table A2). These unique geographic conditions may have been the main reason for this difference. On high-elevation islands, harsh environmental conditions such as high solar radiation and large diurnal temperature variations [58] may limit human activity and act as filters for potential invaders. In contrast, low-elevation islands, due to their lower isolation and frequent human disturbances, are more susceptible to the introduction of exotic plants. Additionally, low-elevation islands often provide more suitable moisture and temperature conditions for the spread of exotic plants [59], facilitating their introduction and establishment.
Although our findings differed from those of previous studies, this does not indicate a contradiction but rather reveals that the factors influencing exotic species richness may vary under different geographical and environmental contexts. Our study suggested that in low-elevation areas, a higher richness of invasive plant species may be due to more favorable environmental conditions and frequent human disturbances in these regions.
4.2.4. Latitude
Previous studies considered latitude as one of the factors influencing the species richness of invasive alien plants [51], which was confirmed by our study. The optimal temperature for most plants to adapt is between 10 °C and 20 °C [60]. A higher latitude means lower effective cumulative temperature, which is less conducive to plant adaptation [61]. Islands in high latitudes have minimum temperatures of 5 °C to 7 °C in the coldest months (Table A2); the primary productivity levels of their plants are limited by climatic factors. Although the total species richness and species richness of trees and shrubs on subtropical islands decrease with increasing latitudes, the species richness values of perennial herbs and annual herbs are not affected by the latitude. The distribution of herbaceous plants is less influenced by latitude because of their greater environmental adaptability, shorter life cycles, diverse reproduction, and fruiting rates, which are higher than those of woody plants [6,47,62].
4.2.5. Early Warning and Monitoring of Invasive Alien Plants
Establishing effective early warning systems can significantly reduce the damage caused by invasive species to ecosystems [63]. The results of this study provide important references for the early warning and monitoring of invasive species. By identifying and quantifying key environmental driving factors, we gain a deeper understanding of the distribution patterns of invasive plants. For example, the ratio of building and farmland area (BFA) is a key driving factor, indicating that areas with intensive human activity are more prone to plant invasion. This finding can help managers take preventive measures in these high-risk areas, such as strengthening monitoring and implementing early warning mechanisms, thereby reducing the potential threat of invasive plants. Larger islands may need more resources for effective monitoring and management while higher-altitude islands may require different management approaches. This is important for developing regional invasive-plant management strategies. Additionally, we collected species richness data on invasive plants in the study area, which can serve as a baseline for identifying potential invasive plants. When abnormal changes or new plant invasions are detected, appropriate measures can be taken quickly. Overall, this study provided a scientific basis for predicting and managing future plant invasions by identifying the main environmental factors affecting the distribution of invasive plants. This information is crucial for establishing effective early warning mechanisms and formulating targeted ecological management strategies, significantly enhancing the protection of subtropical island ecosystems.
5. Conclusions
This study demonstrated that no single factor or factor associated with a single domain can fully explain the species richness of invasive alien plants. The differences in the effects of environmental factors (three domains of climate, anthropogenic disturbance, and landscape/geography) on the species richness of invasive alien plants are related to growth forms. The study found that the proportion of building and farmland area (BFA) is a significant driver of the species richness of invasive plants on Fujian islands. These islands are currently facing anthropogenic disturbances such as residential expansion, overfarming, and tourism. These disturbances are the main causes of the widespread distribution of invasive plants on the islands. Therefore, in the development and management of islands, priority should be given to these factors to control the excessive proliferation of invasive plants. This study further elucidated the driving roles of climate, human disturbance, and landscape factors in the distribution patterns of invasive plants on the Fujian islands. However, there were some limitations in the context of climate change. The climate data used in this study were primarily based on current data analysis, which may not have reflected the long-term effects of climate change on the distribution and growth forms of invasive plants. Future research will consider incorporating climate change scenario models to explore the long-term impacts of climate change on invasive plants. Additionally, future studies could include factors such as animal behavior and soil physicochemical properties, which may contribute to a more comprehensive understanding of the species richness distribution patterns of invasive plants on subtropical islands and further refine the research framework.
Author Contributions
Conceptualization: Y.X., C.D. and Y.Z.; methodology: Y.X., H.H., F.W. and X.X.; software: Y.X. and H.H.; investigation: Y.X., H.H., X.X., F.W., L.N., M.L. and J.O.; validation: Y.X., Y.W., and Y.M.; formal analysis: Y.X., H.H., and C.D.; writing—original draft preparation: Y.X.; writing—review and editing: Y.X.; supervision: Y.X., Y.C., Z.Q., X.L. and Z.C.; project administration: C.D. and H.H.; funding acquisition: C.D. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Introduction and application of suitable species in abandoned sandy land, Science and Technology Development Center of Changle District, Fuzhou (115/KH230123A); the Fujian Province Regional Development Science and Technology Plan (2018Y3006); the Discipline and Professional Construction of the School of Landscape Architecture, School of Art, Fujian A&F University (YSYLbdpy3); the Fujian Water Resources and Electric Power Vocational and Technical College 2023 Annual School-level Research Projects: A Study on the Diversity and Biogeography of Lycophytes and Ferns on the Coastal Islands of Fujian(YJKJ2304B); and the Fujian A&F University Science and Technology Innovation Special Fund (CXZX2019086).
Data Availability Statement
Data are contained within the article.
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix A
Table A1.
Basic information and invasive alien plant species richness of 77 subtropical islands.
| NO. | Islands | Longitude (°) | Latitude (°) | Total Species Richness | Tree | Shrub | Perennial Herb | Annual Herb |
|---|---|---|---|---|---|---|---|---|
| 1 | Dadengdao | 118.32 | 24.56 | 43 | 6 | 11 | 12 | 14 |
| 2 | Huoshaoyu | 118.06 | 24.49 | 18 | 3 | 5 | 7 | 3 |
| 3 | Datuyu | 118.06 | 24.49 | 11 | 3 | 2 | 1 | 5 |
| 4 | Xiamenjiyu | 118.01 | 24.43 | 12 | 4 | 3 | 0 | 5 |
| 5 | Gulangyu | 118.06 | 24.45 | 8 | 2 | 4 | 2 | 0 |
| 6 | Haicangdayu | 118.05 | 24.46 | 7 | 3 | 3 | 1 | 0 |
| 7 | Dalipuyu | 118.15 | 24.56 | 13 | 3 | 4 | 2 | 4 |
| 8 | Eeyuyu | 118.17 | 24.59 | 9 | 2 | 2 | 3 | 2 |
| 9 | Baozhuyu | 118.07 | 24.54 | 9 | 2 | 3 | 1 | 3 |
| 10 | XIamentuyu | 118.19 | 24.45 | 10 | 1 | 4 | 2 | 3 |
| 11 | Huoyu | 118.06 | 24.47 | 8 | 0 | 4 | 3 | 1 |
| 12 | Baituyu | 118.05 | 24.48 | 7 | 1 | 1 | 0 | 5 |
| 13 | Wuyayu | 118.06 | 24.48 | 7 | 1 | 3 | 2 | 1 |
| 14 | WUyu | 118.07 | 24.51 | 5 | 2 | 1 | 1 | 1 |
| 15 | Dashengdao | 119.02 | 25.08 | 1 | 1 | 0 | 0 | 0 |
| 16 | Huanggandao | 119.02 | 25.04 | 16 | 3 | 4 | 0 | 10 |
| 17 | Huiyu | 119.00 | 25.18 | 30 | 5 | 6 | 5 | 14 |
| 18 | Nanridao | 119.49 | 25.21 | 20 | 3 | 4 | 7 | 6 |
| 19 | Meizhuodao | 119.12 | 25.07 | 21 | 3 | 4 | 6 | 8 |
| 20 | Huoqingyu | 119.36 | 25.33 | 4 | 1 | 1 | 1 | 1 |
| 21 | Shichengdayu | 119.40 | 25.26 | 2 | 2 | 0 | 0 | 0 |
| 22 | Toujinyu | 119.39 | 25.25 | 0 | 0 | 0 | 0 | 0 |
| 23 | Qingyu | 119.39 | 25.25 | 2 | 0 | 0 | 0 | 2 |
| 24 | Dayuzai | 119.40 | 25.25 | 1 | 0 | 0 | 0 | 1 |
| 25 | Dongyuzai | 119.40 | 25.25 | 1 | 0 | 0 | 0 | 1 |
| 26 | Caoyudao | 119.71 | 25.37 | 18 | 5 | 5 | 4 | 4 |
| 27 | Daliandao | 119.69 | 25.65 | 16 | 4 | 4 | 2 | 6 |
| 28 | Yutoudao | 119.59 | 25.65 | 7 | 3 | 1 | 2 | 1 |
| 29 | Dongxiangdao | 119.89 | 25.59 | 5 | 2 | 0 | 1 | 2 |
| 30 | Dayudao | 119.66 | 25.45 | 7 | 1 | 0 | 2 | 4 |
| 31 | Dasongdao | 119.80 | 25.65 | 9 | 4 | 2 | 1 | 2 |
| 32 | Xiaosongdao | 119.79 | 25.65 | 5 | 1 | 1 | 1 | 2 |
| 33 | Pingzhuodao | 119.82 | 25.67 | 1 | 0 | 0 | 0 | 1 |
| 34 | Shanzhuodao | 119.82 | 25.67 | 4 | 1 | 0 | 1 | 2 |
| 35 | Chixietedao | 119.79 | 25.65 | 2 | 1 | 0 | 0 | 0 |
| 36 | Guluoyu | 119.81 | 25.67 | 1 | 0 | 0 | 0 | 1 |
| 37 | DOngzhuodao | 119.83 | 25.67 | 0 | 0 | 0 | 0 | 0 |
| 38 | Guangyouyu | 119.83 | 25.58 | 5 | 1 | 2 | 1 | 1 |
| 39 | Hongshanyu | 119.84 | 25.58 | 4 | 1 | 1 | 1 | 1 |
| 40 | Beixiangluyu | 119.69 | 25.62 | 5 | 1 | 2 | 1 | 1 |
| 41 | Huangmendao | 119.68 | 25.46 | 8 | 1 | 2 | 1 | 4 |
| 42 | Jiangshandao | 119.80 | 25.44 | 7 | 1 | 0 | 3 | 3 |
| 43 | Beiguanyu | 119.69 | 25.35 | 4 | 1 | 1 | 0 | 2 |
| 44 | Longmuyu | 119.69 | 25.35 | 5 | 2 | 2 | 0 | 1 |
| 45 | Dahuaiyu | 119.71 | 25.55 | 3 | 1 | 0 | 1 | 1 |
| 46 | Hengyu | 119.70 | 25.53 | 4 | 0 | 0 | 1 | 3 |
| 47 | Baijiangdao | 119.82 | 25.43 | 2 | 1 | 0 | 0 | 1 |
| 48 | Shanbaidao | 119.85 | 25.71 | 1 | 0 | 0 | 0 | 1 |
| 49 | Culudao | 119.63 | 26.16 | 19 | 3 | 3 | 7 | 6 |
| 50 | Langqidao | 119.60 | 26.09 | 11 | 5 | 1 | 3 | 2 |
| 51 | Muyudao | 119.72 | 26.24 | 5 | 1 | 1 | 0 | 3 |
| 52 | LianjiangWuyu | 119.72 | 26.27 | 5 | 1 | 0 | 1 | 3 |
| 53 | Huchudao | 119.66 | 26.15 | 6 | 1 | 1 | 0 | 4 |
| 54 | Huangwanyu | 119.82 | 26.29 | 7 | 1 | 1 | 1 | 4 |
| 55 | Jinpaidao | 119.72 | 26.36 | 9 | 1 | 3 | 0 | 5 |
| 56 | Geshaqingyu | 119.74 | 26.28 | 2 | 0 | 0 | 0 | 2 |
| 57 | Sandudao | 119.69 | 26.66 | 27 | 3 | 8 | 9 | 7 |
| 58 | Dayushandao | 120.35 | 26.95 | 25 | 3 | 6 | 7 | 9 |
| 59 | Xiyangdao | 120.05 | 26.51 | 20 | 2 | 5 | 7 | 6 |
| 60 | Zhujiangdao | 119.95 | 26.78 | 14 | 3 | 3 | 4 | 4 |
| 61 | Xiaweidao | 120.12 | 26.66 | 5 | 0 | 0 | 1 | 4 |
| 62 | Xiluodao | 119.65 | 25.76 | 2 | 0 | 1 | 0 | 1 |
| 63 | Yuanyangyu | 120.38 | 26.96 | 6 | 2 | 1 | 0 | 3 |
| 64 | Tayu | 117.55 | 23.73 | 22 | 3 | 8 | 7 | 4 |
| 65 | Duimiandao | 117.52 | 23.75 | 17 | 3 | 4 | 7 | 3 |
| 66 | Xiaochiyu | 117.52 | 23.75 | 7 | 2 | 1 | 0 | 4 |
| 67 | Nanyu | 117.52 | 23.72 | 15 | 2 | 2 | 2 | 9 |
| 68 | Nanchidao | 117.49 | 23.71 | 16 | 3 | 4 | 1 | 8 |
| 69 | Dazuidao | 118.77 | 24.83 | 20 | 3 | 4 | 11 | 2 |
| 70 | Zhangpuxiaosongdao | 117.94 | 24.18 | 16 | 2 | 3 | 1 | 10 |
| 71 | Daisongdao | 117.96 | 24.17 | 29 | 4 | 7 | 5 | 13 |
| 72 | Niguyu | 120.15 | 26.56 | 9 | 2 | 1 | 1 | 5 |
| 73 | Xiamendao | 118.13 | 24.50 | 45 | 4 | 12 | 12 | 17 |
| 74 | Zinidao | 117.85 | 24.44 | 23 | 3 | 6 | 5 | 9 |
| 75 | Jiangyindao | 119.31 | 25.50 | 16 | 4 | 4 | 5 | 3 |
| 76 | Dongshandao | 117.42 | 23.70 | 37 | 5 | 9 | 11 | 12 |
| 77 | Haitandao | 119.77 | 25.54 | 63 | 9 | 10 | 13 | 31 |
Table A2.
19 Environmental factor data of 77 subtropical islands.
| NO. | Islands | Landscape/Geography Domain | Climate Domain | Anthropogenic Disturbance Domain | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LAT | A /km2 | P /km | ME /m | VC % | PAR | SI | AMW /m/s | AMT /°C | TWM /°C | TCM /°C | AP /mm | PWM /mm | PDM /mm | DNC /km | DNI /km | BFA% | ND | NLB | ||
| 1 | Dadengdao | 24.56 | 12.99 | 18.80 | 41.80 | 29.62% | 1.45 | 1.47 | 3.66 | 20.80 | 32.05 | 8.89 | 1072 | 166 | 23 | 0.62 | 5.74 | 70.38% | 8 | 2 |
| 2 | Huoshaoyu | 24.49 | 0.27 | 2.75 | 34.80 | 62.59% | 10.34 | 1.50 | 4.77 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 0.27 | 0.57 | 10.85% | 2 | 0 |
| 3 | Datuyu | 24.49 | 0.09 | 1.62 | 41.80 | 78.60% | 18.00 | 1.52 | 4.41 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 0.41 | 1.33 | 12.40% | 1 | 0 |
| 4 | Xiamenjiyu | 24.43 | 0.40 | 3.64 | 64.40 | 90.15% | 9.02 | 1.62 | 4.83 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 1.10 | 3.12 | 0.91% | 1 | 0 |
| 5 | Gulangyu | 24.45 | 1.84 | 7.40 | 92.60 | 20.65% | 4.02 | 1.54 | 2.94 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 1.22 | 0.52 | 66.20% | 6 | 0 |
| 6 | Haicangdayu | 24.46 | 0.18 | 2.49 | 57.20 | 97.65% | 13.84 | 1.66 | 5.12 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 0.27 | 1.16 | 0.21% | 1 | 0 |
| 7 | Dalipuyu | 24.56 | 0.02 | 0.70 | 16.80 | 70.16% | 38.42 | 1.46 | 4.88 | 21.16 | 32.57 | 9.03 | 1113 | 175 | 25 | 3.83 | 0.68 | 29.19% | 1 | 0 |
| 8 | Eeyuyu | 24.59 | 0.08 | 1.56 | 17.60 | 89.46% | 19.82 | 1.57 | 4.88 | 20.80 | 32.05 | 8.89 | 1072 | 166 | 23 | 4.50 | 1.42 | 0.99% | 1 | 0 |
| 9 | Baozhuyu | 24.54 | 0.01 | 0.29 | 19.50 | 74.33% | 46.63 | 1.04 | 4.67 | 21.16 | 32.57 | 9.03 | 1113 | 175 | 25 | 1.70 | 2.53 | 0.00% | 0 | 0 |
| 10 | XIamentuyu | 24.45 | 0.00 | 0.17 | 11.30 | 74.76% | 86.45 | 1.09 | 6.26 | 20.77 | 31.47 | 9.45 | 995 | 146 | 23 | 10.27 | 1.41 | 0.00% | 0 | 0 |
| 11 | Huoyu | 24.47 | 0.01 | 0.52 | 20.00 | 80.67% | 39.51 | 1.28 | 4.76 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 1.82 | 0.91 | 9.82% | 1 | 0 |
| 12 | Baituyu | 24.48 | 0.01 | 0.36 | 19.20 | 64.44% | 56.14 | 1.27 | 4.77 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 0.67 | 2.20 | 0.00% | 0 | 0 |
| 13 | Wuyayu | 24.48 | 0.01 | 0.40 | 19.30 | 86.03% | 50.92 | 1.27 | 3.96 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 0.60 | 0.58 | 0.00% | 0 | 0 |
| 14 | WUyu | 24.51 | 0.01 | 0.42 | 15.80 | 76.25% | 47.61 | 1.26 | 4.28 | 21.16 | 32.57 | 9.03 | 1113 | 175 | 25 | 0.24 | 1.05 | 8.86% | 0 | 0 |
| 15 | Dashengdao | 25.08 | 0.07 | 1.30 | 44.20 | 75.18% | 17.78 | 1.36 | 7.90 | 20.32 | 31.19 | 9.28 | 1131 | 207 | 23 | 2.54 | 1.28 | 0.00% | 0 | 0 |
| 16 | Huanggandao | 25.04 | 0.55 | 4.52 | 72.40 | 71.30% | 8.27 | 1.73 | 6.34 | 20.32 | 31.19 | 9.28 | 1131 | 207 | 23 | 0.72 | 3.99 | 0.20% | 0 | 0 |
| 17 | Huiyu | 25.18 | 0.41 | 5.40 | 59.90 | 20.74% | 13.14 | 2.38 | 6.58 | 20.04 | 31.35 | 8.37 | 1196 | 223 | 24 | 1.11 | 1.28 | 55.23% | 2 | 0 |
| 18 | Nanridao | 25.21 | 29.60 | 66.40 | 116.30 | 13.45% | 2.24 | 3.44 | 6.19 | 20.30 | 31.34 | 9.10 | 1190 | 226 | 27 | 7.34 | 3.37 | 85.41% | 10 | 0 |
| 19 | Meizhuodao | 25.07 | 14.35 | 33.32 | 95.20 | 27.76% | 2.32 | 2.48 | 4.64 | 20.32 | 31.19 | 9.28 | 1131 | 207 | 23 | 2.19 | 31.55 | 67.32% | 13 | 0 |
| 20 | Huoqingyu | 25.33 | 0.50 | 4.64 | 54.00 | 23.19% | 9.32 | 1.86 | 8.67 | 20.30 | 31.34 | 9.10 | 1190 | 226 | 27 | 5.79 | 1.82 | 0.00% | 0 | 0 |
| 21 | Shichengdayu | 25.26 | 0.04 | 0.78 | 31.00 | 22.30% | 19.09 | 1.09 | 9.01 | 20.30 | 31.34 | 9.10 | 1190 | 226 | 27 | 3.12 | 4.31 | 0.00% | 0 | 0 |
| 22 | Toujinyu | 25.25 | 0.01 | 0.44 | 21.10 | 49.38% | 52.37 | 1.35 | 8.22 | 20.30 | 31.34 | 9.10 | 1190 | 226 | 27 | 2.40 | 4.85 | 0.00% | 0 | 0 |
| 23 | Qingyu | 25.25 | 0.01 | 0.39 | 20.00 | 6.26% | 48.63 | 1.22 | 8.20 | 20.30 | 31.34 | 9.10 | 1190 | 226 | 27 | 1.95 | 5.32 | 0.00% | 0 | 0 |
| 24 | Dayuzai | 25.25 | 0.01 | 0.15 | 13.98 | 56.23% | 17.67 | 0.45 | 8.30 | 20.30 | 31.34 | 9.10 | 1190 | 226 | 27 | 3.33 | 4.38 | 0.00% | 0 | 0 |
| 25 | Dongyuzai | 25.25 | 0.02 | 0.45 | 16.00 | 74.76% | 29.80 | 1.03 | 8.28 | 20.30 | 31.34 | 9.10 | 1190 | 226 | 27 | 3.31 | 4.11 | 0.00% | 0 | 0 |
| 26 | Caoyudao | 25.37 | 7.05 | 16.11 | 212.30 | 63.47% | 2.29 | 1.71 | 6.30 | 19.94 | 30.90 | 9.06 | 1258 | 244 | 30 | 4.29 | 3.63 | 36.53% | 10 | 0 |
| 27 | Daliandao | 25.65 | 9.96 | 20.25 | 238.50 | 69.45% | 2.03 | 1.81 | 5.96 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 7.88 | 1.18 | 17.01% | 16 | 1 |
| 28 | Yutoudao | 25.65 | 8.56 | 22.04 | 77.90 | 10.19% | 2.57 | 2.12 | 5.17 | 20.02 | 31.47 | 8.76 | 1206 | 237 | 28 | 2.40 | 10.64 | 85.76% | 17 | 0 |
| 29 | Dongxiangdao | 25.59 | 4.80 | 19.68 | 134.60 | 35.06% | 4.10 | 2.53 | 5.56 | 19.75 | 30.60 | 8.96 | 1280 | 230 | 36 | 26.50 | 2.30 | 45.54% | 8 | 0 |
| 30 | Dayudao | 25.45 | 0.22 | 2.72 | 45.10 | 59.35% | 12.38 | 1.64 | 7.76 | 19.94 | 31.10 | 8.88 | 1211 | 239 | 26 | 2.47 | 1.05 | 12.92% | 1 | 0 |
| 31 | Dasongdao | 25.65 | 0.28 | 2.50 | 51.80 | 73.41% | 8.86 | 1.33 | 9.06 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 20.14 | 1.16 | 0.00% | 0 | 0 |
| 32 | Xiaosongdao | 25.65 | 0.06 | 1.34 | 36.80 | 32.93% | 24.17 | 1.61 | 7.87 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 19.43 | 0.28 | 0.00% | 0 | 0 |
| 33 | Pingzhuodao | 25.67 | 0.05 | 0.91 | 40.00 | 43.79% | 18.12 | 1.14 | 7.80 | 20.03 | 30.97 | 9.00 | 1259 | 229 | 36 | 21.09 | 3.38 | 0.00% | 0 | 0 |
| 34 | Shanzhuodao | 25.67 | 0.04 | 0.88 | 52.20 | 26.84% | 21.98 | 1.24 | 7.74 | 20.03 | 30.97 | 9.00 | 1259 | 229 | 36 | 20.74 | 3.30 | 0.00% | 0 | 0 |
| 35 | Chixietedao | 25.65 | 0.02 | 0.74 | 28.90 | 34.95% | 31.89 | 1.37 | 7.88 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 19.77 | 1.14 | 0.00% | 0 | 0 |
| 36 | Guluoyu | 25.67 | 0.01 | 0.43 | 33.20 | 48.43% | 41.84 | 1.19 | 7.88 | 20.03 | 30.97 | 9.00 | 1259 | 229 | 36 | 20.33 | 2.64 | 0.00% | 0 | 0 |
| 37 | DOngzhuodao | 25.67 | 0.03 | 0.94 | 36.00 | 39.32% | 27.73 | 1.44 | 7.95 | 20.03 | 30.97 | 9.00 | 1259 | 229 | 36 | 21.27 | 4.11 | 0.00% | 0 | 0 |
| 38 | Guangyouyu | 25.58 | 0.06 | 1.08 | 24.20 | 59.01% | 17.30 | 1.22 | 8.14 | 19.75 | 30.60 | 8.96 | 1280 | 230 | 36 | 22.57 | 0.32 | 2.87% | 3 | 0 |
| 39 | Hongshanyu | 25.58 | 0.03 | 1.02 | 20.80 | 49.84% | 33.09 | 1.63 | 7.98 | 19.75 | 30.60 | 8.96 | 1280 | 230 | 36 | 23.05 | 0.67 | 0.00% | 0 | 0 |
| 40 | Beixiangluyu | 25.62 | 0.04 | 0.85 | 15.60 | 75.81% | 20.83 | 1.18 | 8.44 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 9.79 | 0.60 | 0.00% | 0 | 0 |
| 41 | Huangmendao | 25.46 | 0.07 | 1.42 | 34.70 | 6959.41% | 19.73 | 1.49 | 6.85 | 19.94 | 30.90 | 9.06 | 1258 | 244 | 30 | 3.89 | 0.15 | 0.00% | 0 | 0 |
| 42 | Jiangshandao | 25.44 | 0.47 | 4.74 | 68.10 | 22.58% | 10.01 | 1.94 | 10.71 | 19.94 | 30.90 | 9.06 | 1258 | 244 | 30 | 17.33 | 1.12 | 0.00% | 0 | 0 |
| 43 | Beiguanyu | 25.35 | 0.13 | 1.71 | 40.90 | 84.71% | 12.94 | 1.33 | 8.22 | 19.94 | 30.90 | 9.06 | 1258 | 244 | 30 | 3.29 | 8.42 | 0.00% | 0 | 0 |
| 44 | Longmuyu | 25.35 | 0.03 | 0.98 | 20.20 | 38.67% | 37.97 | 1.72 | 7.59 | 19.94 | 30.90 | 9.06 | 1258 | 244 | 30 | 3.95 | 0.57 | 0.00% | 0 | 0 |
| 45 | Dahuaiyu | 25.55 | 0.03 | 1.03 | 26.80 | 72.96% | 31.78 | 1.61 | 6.79 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 9.17 | 0.63 | 0.00% | 0 | 0 |
| 46 | Hengyu | 25.53 | 0.03 | 0.94 | 22.40 | 24.63% | 31.23 | 1.53 | 7.06 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 9.04 | 0.47 | 0.00% | 0 | 0 |
| 47 | Baijiangdao | 25.43 | 0.05 | 1.49 | 33.60 | 49.50% | 27.50 | 1.81 | 8.86 | 19.94 | 30.90 | 9.06 | 1258 | 244 | 30 | 17.00 | 3.92 | 0.00% | 0 | 0 |
| 48 | Shanbaidao | 25.71 | 0.02 | 0.58 | 34.60 | 64.44% | 32.20 | 1.22 | 7.86 | 20.03 | 30.97 | 9.00 | 1259 | 229 | 36 | 22.75 | 8.21 | 0.00% | 0 | 0 |
| 49 | Culudao | 26.16 | 14.40 | 20.40 | 232.60 | 46.75% | 1.42 | 1.52 | 4.85 | 19.35 | 31.69 | 7.42 | 1352 | 244 | 37 | 0.26 | 2.13 | 48.73% | 9 | 1 |
| 50 | Langqidao | 26.09 | 55.50 | 32.48 | 275.00 | 34.38% | 0.59 | 1.23 | 3.09 | 19.35 | 31.69 | 7.42 | 1352 | 244 | 37 | 0.56 | 2.30 | 59.72% | 21 | 5 |
| 51 | Muyudao | 26.24 | 0.19 | 2.57 | 17.81 | 68.55% | 13.53 | 1.66 | 5.85 | 18.81 | 31.18 | 6.85 | 1436 | 249 | 39 | 3.64 | 2.52 | 0.59% | 0 | 0 |
| 52 | LianjiangWuyu | 26.27 | 0.11 | 1.74 | 41.68 | 69.70% | 16.45 | 1.51 | 5.36 | 18.81 | 31.18 | 6.85 | 1436 | 249 | 39 | 0.62 | 2.59 | 0.33% | 0 | 0 |
| 53 | Huchudao | 26.15 | 0.07 | 1.69 | 33.72 | 46.72% | 22.56 | 1.74 | 5.50 | 19.35 | 31.69 | 7.42 | 1352 | 244 | 37 | 5.20 | 0.38 | 0.20% | 0 | 0 |
| 54 | Huangwanyu | 26.29 | 0.07 | 1.96 | 26.90 | 41.26% | 28.88 | 2.12 | 6.46 | 18.81 | 31.18 | 6.85 | 1436 | 249 | 39 | 1.20 | 1.15 | 0.71% | 0 | 0 |
| 55 | Jinpaidao | 26.36 | 0.05 | 0.92 | 18.82 | 15.00% | 17.27 | 1.12 | 4.53 | 18.54 | 30.98 | 6.62 | 1493 | 257 | 41 | 1.36 | 0.44 | 0.00% | 0 | 1 |
| 56 | Geshaqingyu | 26.28 | 0.02 | 0.77 | 21.05 | 86.03% | 42.14 | 1.60 | 4.53 | 18.81 | 31.18 | 6.85 | 1436 | 249 | 39 | 0.66 | 1.49 | 0.00% | 0 | 0 |
| 57 | Sandudao | 26.66 | 29.60 | 31.17 | 460.60 | 76.92% | 1.05 | 1.62 | 2.16 | 18.00 | 30.60 | 5.84 | 1591 | 276 | 42 | 2.08 | 1.93 | 21.97% | 43 | 0 |
| 58 | Dayushandao | 26.95 | 21.22 | 30.02 | 541.40 | 72.02% | 1.41 | 1.84 | 4.70 | 17.43 | 29.68 | 5.13 | 1406 | 213 | 40 | 6.62 | 5.27 | 21.81% | 6 | 0 |
| 59 | Xiyangdao | 26.51 | 29.80 | 21.59 | 221.00 | 69.66% | 0.72 | 1.12 | 5.00 | 18.14 | 30.20 | 6.23 | 1495 | 240 | 46 | 10.23 | 6.99 | 25.05% | 5 | 0 |
| 60 | Zhujiangdao | 26.78 | 0.15 | 1.97 | 47.30 | 22.57% | 12.82 | 1.42 | 3.47 | 18.32 | 31.09 | 5.68 | 1589 | 266 | 45 | 0.81 | 8.83 | 66.37% | 2 | 0 |
| 61 | Xiaweidao | 26.66 | 0.08 | 1.76 | 35.51 | 58.31% | 22.42 | 1.77 | 6.49 | 18.14 | 30.20 | 6.23 | 1495 | 240 | 46 | 0.27 | 2.63 | 0.00% | 0 | 0 |
| 62 | Xiluodao | 25.76 | 0.10 | 1.60 | 39.85 | 65.00% | 15.96 | 1.42 | 7.46 | 20.03 | 30.97 | 9.00 | 1259 | 229 | 36 | 1.74 | 14.24 | 0.00% | 0 | 1 |
| 63 | Yuanyangyu | 26.96 | 0.64 | 5.72 | 149.81 | 96.49% | 9.01 | 2.02 | 5.03 | 17.43 | 29.68 | 5.13 | 1406 | 213 | 40 | 12.85 | 0.25 | 0.00% | 0 | 0 |
| 64 | Tayu | 23.73 | 1.01 | 6.88 | 91.30 | 76.69% | 6.82 | 1.93 | 5.53 | 21.22 | 30.81 | 10.74 | 1239 | 204 | 27 | 2.44 | 1.16 | 25.77% | 3 | 0 |
| 65 | Duimiandao | 23.75 | 0.10 | 1.99 | 25.80 | 83.19% | 20.71 | 1.81 | 6.30 | 21.22 | 30.81 | 10.74 | 1239 | 204 | 27 | 5.78 | 0.49 | 1.21% | 2 | 0 |
| 66 | Xiaochiyu | 23.75 | 0.01 | 0.22 | 19.20 | 55.00% | 16.85 | 0.54 | 5.83 | 21.22 | 30.81 | 10.74 | 1239 | 204 | 27 | 0.43 | 0.36 | 0.00% | 2 | 0 |
| 67 | Nanyu | 23.72 | 0.04 | 0.85 | 22.70 | 47.82% | 20.09 | 1.17 | 6.39 | 21.22 | 30.81 | 10.74 | 1239 | 204 | 27 | 0.12 | 0.12 | 1.48% | 0 | 1 |
| 68 | Nanchidao | 23.71 | 0.02 | 0.69 | 49.52 | 68.86% | 28.19 | 1.25 | 5.73 | 21.28 | 31.08 | 10.35 | 1296 | 217 | 26 | 0.05 | 0.03 | 1.37% | 0 | 1 |
| 69 | Dazuidao | 24.83 | 0.61 | 5.12 | 103.10 | 81.30% | 8.39 | 1.85 | 7.19 | 20.57 | 31.12 | 9.48 | 1085 | 183 | 24 | 2.33 | 40.97 | 3.78% | 2 | 0 |
| 70 | Zhangpuxiaosongdao | 24.18 | 0.02 | 0.52 | 26.00 | 84.95% | 30.37 | 1.12 | 5.41 | 20.96 | 32.11 | 9.09 | 1142 | 179 | 25 | 0.35 | 1.39 | 0.00% | 0 | 1 |
| 71 | Daisongdao | 24.17 | 0.36 | 2.76 | 27.30 | 16.57% | 7.71 | 1.30 | 4.81 | 20.96 | 32.11 | 9.09 | 1142 | 179 | 25 | 0.40 | 1.40 | 79.45% | 6 | 1 |
| 72 | Niguyu | 26.56 | 10.48 | 5.95 | 231.83 | 88.50% | 0.57 | 0.52 | 11.67 | 18.14 | 30.20 | 6.23 | 1495 | 240 | 46 | 10.99 | 0.59 | 0.00% | 0 | 0 |
| 73 | Xiamendao | 24.50 | 134.84 | 66.30 | 339.60 | 19.03% | 0.49 | 1.61 | 2.65 | 20.96 | 32.12 | 9.24 | 1093 | 169 | 25 | 0.71 | 5.42 | 80.07% | 90 | 7 |
| 74 | Zinidao | 24.44 | 28.56 | 36.10 | 5.00 | 1.98% | 1.26 | 1.91 | 2.31 | 21.19 | 32.56 | 9.23 | 1126 | 176 | 26 | 0.28 | 16.70 | 90.90% | 65 | 9 |
| 75 | Jiangyindao | 25.50 | 69.75 | 50.32 | 429.10 | 49.49% | 0.72 | 1.70 | 4.33 | 20.27 | 31.84 | 8.67 | 1193 | 231 | 25 | 0.21 | 20.85 | 41.00% | 14 | 7 |
| 76 | Dongshandao | 23.70 | 220.18 | 111.53 | 274.30 | 74.33% | 0.51 | 2.12 | 3.07 | 21.28 | 31.08 | 10.35 | 1296 | 217 | 26 | 0.31 | 23.13 | 24.32% | 35 | 5 |
| 77 | Haitandao | 25.54 | 267.13 | 129.09 | 438.20 | 35.76% | 0.48 | 2.23 | 4.67 | 19.84 | 31.01 | 8.77 | 1257 | 238 | 32 | 3.59 | 1.19 | 61.54% | 51 | 2 |
Table A3.
List of invasive alien plants on Fujian islands.
| Family | Genus | Species | Family | Genus | Species |
|---|---|---|---|---|---|
| Asteraceae | Ageratum | Ageratum conyzoides | Asteraceae | Sonchus | Sonchus asper |
| Asteraceae | Symphyotrichum | Symphyotrichum subulatum | Asteraceae | Sonchus | Sonchus oleraceus |
| Asteraceae | Bidens | Bidens pilosa | Asteraceae | Tithonia | Tithonia diversifolia |
| Asteraceae | Erigeron | Erigeron annuus | Fabaceae | Leucaena | Leucaena leucocephala |
| Asteraceae | Praxelis | Praxelis clematidea | Fabaceae | Acacia | Acacia confusa |
| Asteraceae | Erigeron | Erigeron canadensis | Fabaceae | Albizia | Albizia lebbeck |
| Asteraceae | Glebionis | Glebionis coronaria | Fabaceae | Senna | Senna surattensis |
| Asteraceae | Sphagneticola | Sphagneticola trilobata | Fabaceae | Acacia | Acacia mearnsii |
| Asteraceae | Tridax | Tridax procumbens | Fabaceae | Mimosa | Mimosa pudica |
| Asteraceae | Parthenium | Parthenium hysterophorus | Fabaceae | Senna | Senna occidentalis |
| Asteraceae | Coreopsis | Coreopsis basalis | Fabaceae | Acacia | Acacia auriculiformis |
| Asteraceae | Erigeron | Erigeron sumatrensis | Fabaceae | Erythrina | Erythrina corallodendron |
| Asteraceae | Tagetes | Tagetes erecta | Fabaceae | Cajanus | Cajanus cajan |
| Asteraceae | Ambrosia | Ambrosia artemisiifolia | Fabaceae | Crotalaria | Crotalaria pallida |
| Asteraceae | Bidens | Bidens bipinnata | Fabaceae | Melilotus | Melilotus indicus |
| Asteraceae | Chromolaena | Chromolaena odorata | Fabaceae | Senegalia | Senegalia catechu |
| Asteraceae | Coreopsis | Coreopsis lanceolata | Fabaceae | Vachellia | Vachellia farnesiana |
| Asteraceae | Cosmos | Cosmos bipinnatus | Fabaceae | Arachis | Arachis duranensis |
| Asteraceae | Erigeron | Erigeron bonariensis | Fabaceae | Medicago | Medicago sativa |
| Asteraceae | Pluchea | Pluchea sagittalis | Fabaceae | Mimosa | Mimosa bimucronata |
| Asteraceae | Rudbeckia | Rudbeckia laciniata | Fabaceae | Robinia | Robinia pseudoacacia |
| Poaceae | Melinis | Melinis repens | Euphorbiaceae | Euphorbia | Euphorbia maculata |
| Poaceae | Chloris | Chloris virgata | Euphorbiaceae | Euphorbia | Euphorbia prostrata |
| Poaceae | Spartina | Spartina alterniflora | Euphorbiaceae | Jatropha | Jatropha curcas |
| Poaceae | Avena | Avena fatua | Amaranthaceae | Amaranthus | Amaranthus tricolor |
| Poaceae | Bromus | Bromus catharticus | Amaranthaceae | Alternanthera | Alternanthera philoxeroides |
| Poaceae | Cymbopogon | Cymbopogon citratus | Amaranthaceae | Celosia | Celosia cristata |
| Poaceae | Lolium | Lolium perenne | Amaranthaceae | Amaranthus | Amaranthus viridis |
| Poaceae | Paspalum | Paspalum conjugatum | Amaranthaceae | Alternanthera | Alternanthera bettzickiana |
| Poaceae | Pennisetum | Pennisetum purpureum | Amaranthaceae | Alternanthera | Alternanthera pungens |
| Poaceae | Arrhenatherum | Arrhenatherum elatius | Amaranthaceae | Amaranthus | Amaranthus spinosus |
| Poaceae | Axonopus | Axonopus compressus | Amaranthaceae | Dysphania | Dysphania ambrosioides |
| Poaceae | Cymbopogon | Cymbopogon nardus | Amaranthaceae | Amaranthus | Amaranthus hybridus |
| Poaceae | Paspalum | Paspalum urvillei | Solanaceae | Cestrum | Cestrum nocturnum |
| Euphorbiaceae | Euphorbia | Euphorbia hirta | Solanaceae | Datura | Datura stramonium |
| Euphorbiaceae | Euphorbia | Euphorbia pulcherrima | Solanaceae | Atropa | Atropa belladonna |
| Euphorbiaceae | Euphorbia | Euphorbia peplus | Solanaceae | Physalis | Physalis angulata |
| Euphorbiaceae | Euphorbia | Euphorbia cyathophora | Solanaceae | Physalis | Physalis peruviana |
| Euphorbiaceae | Euphorbia | Euphorbia hypericifolia | Solanaceae | Solanum | Solanum capsicoides |
| Euphorbiaceae | Pedilanthus | Pedilanthus tithymaloides | Solanaceae | Solanum | Solanum erianthum |
| Euphorbiaceae | Ricinus | Ricinus communis | Malvaceae | Malvastrum | Malvastrum coromandelianum |
| Euphorbiaceae | Euphorbia | Euphorbia antiquorum | Malvaceae | Sida | Sida acuta |
| Malvaceae | Abelmoschus | Abelmoschus esculentus | Plantaginaceae | Scoparia | Scoparia dulcis |
| Malvaceae | Abutilon | Abutilon theophrasti | Plantaginaceae | Plantago | Plantago lanceolata |
| Convolvulaceae | Ipomoea | Ipomoea nil | Apocynaceae | Catharanthus | Catharanthus roseus |
| Convolvulaceae | Ipomoea | Ipomoea purpurea | Apocynaceae | Asclepias | Asclepias curassavica |
| Convolvulaceae | Ipomoea | Ipomoea alba | Polygonaceae | Antigonon | Antigonon leptopus |
| Convolvulaceae | Ipomoea | Ipomoea cairica | Polygonaceae | Muehlenbeckia | Muehlenbeckia platyclada |
| Lamiaceae | Salvia | Salvia splendens | Verbenaceae | Lantana | Lantana camara |
| Lamiaceae | Mentha | Mentha spicata | Verbenaceae | Duranta | Duranta erecta |
| Lamiaceae | Ocimum | Ocimum basilicum | Cyperaceae | Cyperus | Cyperus rotundus |
| Acanthaceae | Andrographis | Andrographis paniculata | Cyperaceae | Cyperus | Cyperus involucratus |
| Acanthaceae | Justicia | Justicia adhatoda | Amaryllidaceae | Hippeastrum | Hippeastrum vittatum |
| Acanthaceae | Justicia | Justicia gendarussa | Amaryllidaceae | Zephyranthes | Zephyranthes candida |
| Onagraceae | Oenothera | Oenothera drummondii | Caryophyllaceae | Cerastium | Cerastium glomeratum |
| Onagraceae | Oenothera | Oenothera parviflora | Caryophyllaceae | Stellaria | Stellaria aquatica |
| Onagraceae | Oenothera | Oenothera biennis | Myrtaceae | Psidium | Psidium guajava |
| Passifloraceae | Passiflora | Passiflora caerulea | Myrtaceae | Eucalyptus | Eucalyptus robusta |
| Passifloraceae | Passiflora | Passiflora edulis | Asparagaceae | Agave | Agave americana |
| Passifloraceae | Passiflora | Passiflora suberosa | Asparagaceae | Agave | Agave sisalana |
| Nyctaginaceae | Bougainvillea | Bougainvillea glabra | Cactaceae | Opuntia | Opuntia dillenii |
| Nyctaginaceae | Bougainvillea | Bougainvillea spectabilis | Cactaceae | Selenicereus | Selenicereus undatus |
| Nyctaginaceae | Mirabilis | Mirabilis jalapa | Commelinaceae | Tradescantia | Tradescantia pallida |
| Commelinaceae | Tradescantia | Tradescantia zebrina | |||
| Papaveraceae | Argemone | Argemone mexicana | |||
| Papaveraceae | Papaver | Papaver rhoeas | |||
| Casuarinaceae | Casuarina | Casuarina equisetifolia | |||
| Aizoaceae | Tetragonia | Tetragonia tetragonioides | |||
| Brassicaceae | Lepidium | Lepidium virginicum | |||
| Pontederiaceae | Eichhornia | Eichhornia crassipes | |||
| Crassulaceae | Bryophyllum | Bryophyllum pinnatum | |||
| Oxalidaceae | Oxalis | Oxalis corymbosa | |||
| Portulacaceae | Portulaca | Portulaca grandiflora | |||
| Basellaceae | Basella | Basella alba | |||
| Bignoniaceae | Macfadyena | Macfadyena unguis-cati | |||
| Apiaceae | Cyclospermum | Cyclospermum leptophyllum | |||
| Urticaceae | Pilea | Pilea microphylla | |||
| Orobanchaceae | Orobanche | Orobanche brassicae | |||
| Rubiaceae | Spermacoce | Spermacoce alata |
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Figure 1.
Distribution profile of the 77 islands in Fujian. Some dense areas of the islands overlap due to the way the pictures are presented, and islands 1–77 are listed in Table A1. The 4 colors represent 4 clusters of the growth forms. The specific clustering is described below.
Figure 1.
Distribution profile of the 77 islands in Fujian. Some dense areas of the islands overlap due to the way the pictures are presented, and islands 1–77 are listed in Table A1. The 4 colors represent 4 clusters of the growth forms. The specific clustering is described below.
Figure 2.
Distribution map of the origin of total species richness and different growth forms of invasive alien plants.
Figure 2.
Distribution map of the origin of total species richness and different growth forms of invasive alien plants.
Figure 3.
Cluster diagram of Fujian islands based on the distribution patterns of different growth forms of invasive alien plants. The names of the clustered islands are shown in Table A1. The 77 islands in Fujian are clustered into 4 categories: 63 islands in the red section, 9 islands in the yellow section; 2 islands in the blue section, and 3 islands in the green section.
Figure 3.
Cluster diagram of Fujian islands based on the distribution patterns of different growth forms of invasive alien plants. The names of the clustered islands are shown in Table A1. The 77 islands in Fujian are clustered into 4 categories: 63 islands in the red section, 9 islands in the yellow section; 2 islands in the blue section, and 3 islands in the green section.
Figure 4.
Boruta feature selection of 19 environmental factors and total species richness and different growth forms of invasive alien plants. The blue boxes represent shadow features, used for comparing the importance of feature variables. The green boxes represent confirmed environmental factors and the red boxes represent the rejected environmental factors. Being more to the right indicates higher importance. Usually, feature variables with importance higher than shadowMax can be confirmed, but the Max Temperature of Warmest Month (TWM) variable in (b) was rejected by the algorithm even though it was higher than shadowMax.
Figure 4.
Boruta feature selection of 19 environmental factors and total species richness and different growth forms of invasive alien plants. The blue boxes represent shadow features, used for comparing the importance of feature variables. The green boxes represent confirmed environmental factors and the red boxes represent the rejected environmental factors. Being more to the right indicates higher importance. Usually, feature variables with importance higher than shadowMax can be confirmed, but the Max Temperature of Warmest Month (TWM) variable in (b) was rejected by the algorithm even though it was higher than shadowMax.
Figure 5.
Four goodness-of-fit results for the subset of environmental factors selected by Boruta feature selection with the total species richness and species richness of different growth forms. The colored nodes are the numbers of optimal environmental factors suitable for total species richness and species richness of different growth forms. The red cross points represent total species richness. The solid blue square points indicate tree species richness. The hollow green square points show shrub species richness. The solid purple triangle points denote perennial herb species richness. The solid yellow circle points represent annual herb species richness.
Figure 5.
Four goodness-of-fit results for the subset of environmental factors selected by Boruta feature selection with the total species richness and species richness of different growth forms. The colored nodes are the numbers of optimal environmental factors suitable for total species richness and species richness of different growth forms. The red cross points represent total species richness. The solid blue square points indicate tree species richness. The hollow green square points show shrub species richness. The solid purple triangle points denote perennial herb species richness. The solid yellow circle points represent annual herb species richness.
Figure 6.
Variance decomposition of the subset of environmental factors with the total species richness of invasive alien plants and the species richness of different growth forms. Colors indicate Spearman correlations between individual environmental variables and species richness. The bar chart in (a) PAR represents the total explained variance of the screened environmental factors in the total species richness and growth form species richness. In (b), the environmental factors are indicated by circles, with the sizes of the circles representing the importance of individual environmental factors. The colors indicate the Spearman correlation of each environmental variable with species richness: red denotes a positive correlation and blue denotes a negative correlation, and the deeper the color is, the stronger the correlation is. *** p < 0.001. PAR: Perimeter Area Ratio; ME: Maximum Elevation; A: Island Area; LAT: Latitude; ND: Number of Docks; BFA: The proportion of Building and Farmland Area; DNI: Distance from the nearest big island; NDC: Distance from the nearest continent; AMT: Annual Mean Temperature.
Figure 6.
Variance decomposition of the subset of environmental factors with the total species richness of invasive alien plants and the species richness of different growth forms. Colors indicate Spearman correlations between individual environmental variables and species richness. The bar chart in (a) PAR represents the total explained variance of the screened environmental factors in the total species richness and growth form species richness. In (b), the environmental factors are indicated by circles, with the sizes of the circles representing the importance of individual environmental factors. The colors indicate the Spearman correlation of each environmental variable with species richness: red denotes a positive correlation and blue denotes a negative correlation, and the deeper the color is, the stronger the correlation is. *** p < 0.001. PAR: Perimeter Area Ratio; ME: Maximum Elevation; A: Island Area; LAT: Latitude; ND: Number of Docks; BFA: The proportion of Building and Farmland Area; DNI: Distance from the nearest big island; NDC: Distance from the nearest continent; AMT: Annual Mean Temperature.
Table 1.
Abbreviations and data sources of 19 environmental variables in 3 domains.
| Domain | Description | Abbreviation | Source |
|---|---|---|---|
| C | Annual Mean Wind | AMW | NOAA 10 m × 10 m wind speed vector map (http://www.worldclim.org, accessed on 20 October 2022) |
| C | Annual Mean Temperature | AMT | Worldclim global climate dataset (http://www.worldclim.org, accessed on 20 October 2022) |
| C | Annual Precipitation | AP | Worldclim global climate dataset (http://www.worldclim.org, accessed on 20 October 2022) |
| C | Precipitation of Wettest Month | PWM | Worldclim global climate dataset (http://www.worldclim.org, accessed on 20 October 2022) |
| C | Precipitation of Driest Month | PDM | Worldclim global climate dataset (http://www.worldclim.org, accessed on 20 October 2022) |
| C | Max Temperature of Warmest Month | TWM | Worldclim global climate dataset (http://www.worldclim.org, accessed on 20 October 2022) |
| C | Min Temperature of Coldest Month | TCM | Worldclim global climate dataset (http://www.worldclim.org, accessed on 20 October 2022) |
| A | The proportion of Building and Farmland Area | BFA | The 2020 global 30 m precision land cover vector map released by the Institute of Space and Space Information Innovation, Chinese Academy of Sciences (https://data.casearth.cn/, accessed on 7 November 2022) |
| A | Distance from the nearest continent | DNC | Google Earth (http://www.google.com/earth, accessed on 3 December 2022) |
| A | Distance from the nearest big island | DNI | Google Earth (http://www.google.com/earth, accessed on 3 December 2022) |
| A | Number of Docks | ND | Google Earth (http://www.google.com/earth, accessed on 3 December 2022) |
| A | Number of land bridges | NLB | Google Earth (http://www.google.com/earth, accessed on 3 December 2022) |
| L/G | Island Area | A | The 2020 global 30 m precision land cover vector map released by the Institute of Space and Space Information Innovation, Chinese Academy of Sciences (https://data.casearth.cn/, accessed on 12 December 2022) |
| L/G | Maximum Elevation | ME | A 30 m precision DEM elevation map of the geospatial data cloud (http://www.gscloud.cn, accessed on 15 December 2022) |
| L/G | Latitude | LAT | Google Earth (http://www.google.com/earth, accessed on 3 December 2022) |
| L/G | Perimeter | P | A 30 m precision DEM elevation map of the geospatial data cloud (http://www.gscloud.cn, accessed on 21 December 2022) |
| L/G | Vegetation Coverage | VC | A 30 m precision DEM elevation map of the geospatial data cloud (http://www.gscloud.cn, accessed on 22 December 2022) |
| L/G | Perimeter Area Ratio | PAR | P/A |
| L/G | Shape Index | SI | SI = p/[2 × (π × a)0.5], where p is the island perimeter and a is the island area [34]. |
C is the abbreviation for the climate domain, A is for the anthropogenic disturbance domain, and L/G is the landscape/geography domain.
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Xie, Y.; Xie, X.; Weng, F.; Nong, L.; Lin, M.; Ou, J.; Wang, Y.; Mao, Y.; Chen, Y.; Qian, Z.; et al. Distribution Patterns and Environmental Determinants of Invasive Alien Plants on Subtropical Islands (Fujian, China). Forests 2024, 15, 1273. https://doi.org/10.3390/f15071273
AMA Style
Xie Y, Xie X, Weng F, Nong L, Lin M, Ou J, Wang Y, Mao Y, Chen Y, Qian Z, et al. Distribution Patterns and Environmental Determinants of Invasive Alien Plants on Subtropical Islands (Fujian, China). Forests. 2024; 15(7):1273. https://doi.org/10.3390/f15071273
Chicago/Turabian StyleXie, Yanqiu, Xinran Xie, Feifan Weng, Liebo Nong, Manni Lin, Jingyao Ou, Yingxue Wang, Yue Mao, Ying Chen, Zhijun Qian, and et al. 2024. "Distribution Patterns and Environmental Determinants of Invasive Alien Plants on Subtropical Islands (Fujian, China)" Forests 15, no. 7: 1273. https://doi.org/10.3390/f15071273
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