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Interaction mechanism between floristic quality and environmental factors during ecological restoration in a mine area based on structural equation modeling
Ecological Engineering 124 (2018) 23–30
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Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Interaction mechanism between floristic quality and environmental factors during ecological restoration in a mine area based on structural equation modeling
T
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Xiaoyun Hou, Shiliang Liu , Shuang Zhao, Yueqiu Zhang, Xue Wu, Fangyan Cheng, Shikui Dong State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Floristic Quality Index Environmental factors Structural equation modeling Toxicity Mining restoration
Open-pit mining activities cause great damage to the local ecosystems. It is therefore necessary to assess and recover the vegetation status to maintain ecological stability. In this study, the Floristic Quality Index (FQI) was used to assess the vegetation habitat and the structural equation model (SEM) was applied to quantify the influences of different environmental factors on FQI in the Kunyang open-pit phosphate rock mine in Yunnan Province, China. Non-metric multidimensional scaling analyses revealed that great differences of vegetation community composition existed in the sampled plots, even those at similar distances to mining areas, which indicates that disturbing distance was not the only factor to determine the vegetation community. SEM results showed that Cu promoted the FQI most obviously (0.84), followed by Co (0.75), while the inhibition of Cd content in soil to the FQI was the most significant (−0.88), followed by TK (−0.82), and C (−0.79). Soil fertility quality and soil pollution indexes were also established to analyze the effects of comprehensive soil parameters on FQI. The results showed that the soil fertility quality index had a strong negative effect on FQI, which revealed that higher levels of TP, TN, TK, and other nutrients in the soil would produce ‘toxicity’ to the growth of vegetation. Findings from our study could provide a scientific method for assessing the ecological restoration results in the mining area.
1. Introduction Open-pit mining makes an important contribution to the local economic development, but it also generates environmental pollution, vegetation destruction, and ecological degradation (Wick et al., 2014; Hu et al., 2015). These issues have gained more and more attention, especially in China’s move towards ecological civilization (Harantová et al., 2017; Lee et al., 2017). In order to restore the local ecosystem function, vegetation in open-pit mining areas should be restored to accelerate the processes of soil natural restoration and to enhance biodiversity (Hu et al., 2015; Hendrychová and Kabrna, 2016). Monitoring and assessing the status during vegetation restoration is essential when evaluating the restoration success (Hobbs and Harris, 2001), and it needs meaningful and interpretable metrics. There are many metrics to describe the plant community, such as species abundance, species richness, or evenness (Bauman et al., 2015; Li et al., 2017; Shackelford et al., 2017). Each metric provides certain information and has application limitations (Taft et al., 2006). Alatalo (1981) considered that evenness measures of calculations which include
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richness were limited by sampling biases. Abundance and diversity as the commonly used metrics were used to describe the essential characteristics of vegetation communities, but they were not weighted by vegetation composition (Taft et al., 1997). Ecosystems consist of a complex set of temporally and spatially variable components; thus, it is difficult to characterize their integrity. Vegetation community composition could be a diversity indicator of ecosystem function and reflect site conditions (Cadotte et al., 2011). Government agencies and land managers require reproducible and quantifiable metrics to monitor a natural region’s vegetation community. Therefore, a scientific and comparable approach for assessing the vegetation conditions during the restoration process is urgently needed. Wilhelm (1977) proposed the Coefficient of Conservatism (CC) to quantify the tolerance of each individual native species to human disturbance. Swink and Wilhelm (1994) combined the richness of the native species with the vulnerability measure CC value to create the Floristic Quality Index (FQI). The CC value is a number from 0 to 10 which is assigned to each taxa within a region. Species with high CC values have high allegiance to a specific habitat where disturbance is
Corresponding author. E-mail address: [email protected] (S. Liu).
https://doi.org/10.1016/j.ecoleng.2018.09.021 Received 7 April 2018; Received in revised form 10 September 2018; Accepted 19 September 2018 0925-8574/ © 2018 Elsevier B.V. All rights reserved.
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2.2. Experimental design and field sampling
minimal. Species with low CC values are somewhat tolerant of human disturbance and can be found in common plant communities (Taft et al., 1997). The FQI value is calculated by multiplying the square root of native species richness for a site by the mean CC value for the same sampling unit, thus combining the plant diversity with weighted value of vegetation composition (Taft et al., 1997). At present, some scholars have studied the application of Floristic Quality Index to assess vegetation condition. It has been a proven indicator of human disturbance when evaluating various habitat conditions (Chamberlain and Brooks, 2016; Bell et al., 2017). A study by Maginel et al. (2016) suggested that FQI could be a good indicator for monitoring the restoration of vegetation communities in forest ecosystems after fire. FQI has been used by conservation organizations and government agencies to monitor and assess a variety of areas (Freyman et al., 2016). However, studies on FQI change associated with multiple and comprehensive environmental factors are still lacking. Struckhoff et al. (2013) found that Zn and Pb concentrations in the soil had negative relationships to FQI in southeast Missouri, USA while their relative contributions of these factors is not well understood. Soil, as a major environmental factor affecting vegetation reconstruction, is often accompanied by a shortage or excess of nutrient or pollutant contents in mining areas, and it would accelerate or inhibit the growth of vegetation (Lei et al., 2016; Ahirwal et al., 2017). Therefore, it is necessary to measure the effects of soil and other environmental factors on vegetation restoration (van Swaay et al., 2011). However, understanding the mechanisms by which environmental factors influence FQI is important when implementing ecological restoration in the mining areas. To reveal the mechanism between environmental factors and FQI for the ecological restoration, many quantifiable methods have been used. Among them, the structural equation modeling (SEM) is an effective approach to explore the mechanisms between selected multi-factors. In the past decade, SEM has been used in some scientific disciplines (Sarstedt et al., 2014; Mammides et al., 2015a; Zhang et al., 2017). In this study, we used the SEM approach to study the mechanisms of the interaction between FQI and environmental factors in restoration areas of the Kunyang phosphate mine. The objectives were to: 1) determine vegetation community change during restoration and apply FQI index to reflect the habitat quality in restoration areas; and 2) reveal the influential mechanism of environmental factors on FQI using the structural equation model.
A typical sample method was used to conduct a vegetation survey in phosphate rock mine areas (Fang et al., 2009). In September 2016, we investigated 18 plots of restoration area along different distances from the mining area. Three quadrats were randomly selected in each plot (Fig. 2). Typical sample method was adopted for the arbor, shrub, and herb layers. The arbor layer was investigated in a 10 × 10 m-quadrat, the internal shrub layer was chosen in a 5 × 5 m-quadrat, and the herb layer was surveyed in a 1 × 1 m-quadrat. In the quadrats, each tree was identified and measured for total height and layer coverage. Each shrub species was identified and measured for total height and layer coverage, whereas each herb was identified for layer coverage and height. In each quadrat, we also investigated habitat characteristics, including altitude, slope, slope position, aspect, and the distance to nearest road (Dr) and the mining area (Dm). For soil sampling, we collected the 0–10 cm topsoil and took a quarter of the total after mixing four random soil samples from each quadrat. Soil samples were ground and sieved after removing the large roots and stones, and the soil properties were determined in our laboratory. We used a Euro EA3000 elemental analyzer to analyze total carbon (TC) and total nitrogen (TN), and used ICP-AES to define total phosphorus (TP), total potassium (TK), Zn, Pb, As, Mn, Co, Ni, Cd, Cu, Cr, and Ca (Bai et al., 2011). 2.3. Data analysis Based on the obtained vegetation and environmental data, we first analyzed the influence of Dm on the composition of the vegetation community using non-metric multidimensional scaling (NMDS) analyses to determine that Dm was not the only determinant. Then we calculated the FQI, soil pollution index, and soil fertility quality index, respectively. Finally, SEM was used to analyze the effects of environmental factors on FQI. The schematic diagram of the study framework was shown in Fig. 3. 2.3.1. Floristic Quality Assessment FQI and mean CC value were calculated as the primary dependent variables. Based on each native species’ affinity for natural vegetation communities and its tolerance of human interference, each native species in the geographic area was assigned a CC value from 1 to 10. The least conservative species could adapt to extremely naturally- or artificially-degraded habitats (CC values < 3). Matric species could tolerate human disturbance and occur in common vegetation communities (CC values from 4 to 6), while the most conservative species depend mainly on undisturbed sites (CC values > 7). For each vegetation sampling quadrat (n = 54), we calculated total species richness, mean CC value, and the FQI as follows:
2. Methods 2.1. Study area This study was conducted in the Kunyang phosphate rock mine (24°43′ N, 102°34′ E) located in Jinning county of Yunnan Province (Fig. 1). The mine is the largest open-pit phosphate rock mine in China and has been exploited for 52 years. It is the first phosphate recovery area for vegetation restoration and the first batch of national green mine pilot units in China. The annual average temperature is 14.7 °C, the altitude of the landscape is between 2118 m and 2828 m, the mean annual rainfall is 918 mm, and the rainfall events are mainly concentrated from May to August. The mining area is located in the subtropical evergreen broadleaf forest zone. The soil type is mainly red soil and the soil texture is mainly loam. The local natural forest has experienced destruction due to long-term human activities in the mining area, and the existing vegetation types are mainly secondary vegetation formed under the vegetation restoration process. The area of vegetation restoration has exceeded 1000 acres. For the vegetation restoration of this mining area, the surface soils that have been stripped prior to mining activities were stored separately for later vegetation restoration. The reclamation area is mostly covered by forests, as well as some shrubs and herbs.
S = number of species (native + exotic) Mean CC =
∑ CC/S
FQI = Mean CC ×
(1) (2)
N
(3)
where CC is the Coefficient of Conservatism for each species occurring in a quadrat, mean CC is the average Coefficient of Conservatism for the quadrat, S is number of all species, N is native species richness, and FQI is the Floristic Quality Index. Additionally, we assigned all exotic species a CC value of 0 in mean CC calculations (Francis et al., 2000). 2.3.2. Analysis of vegetation community composition and distribution Non-metric multidimensional scaling analyses (NMDS) based on the lower-triangular dissimilarity matrix was used to display the differences between vegetation community compositions in the Vegan package in R 3.4.2 (R Development Core and Team, 2017). The result of the NMDS analysis was measured by stress. The two-dimensional point map of 24
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Fig. 1. Distribution of plots in the Kunyang phosphate rock mine in Yunnan Province, China.
software to determine the weight of various factors; and 3) Establishment of comprehensive metrics. The index value of each nutrient was synthesized by multiplication according to the additive and multiplication rule, and then the comprehensive index value of soil fertility quality was calculated (Appendix A). 2.3.4. Soil pollution evaluation Soil pollution index (PI) reflected the relative soil pollution degree. High PI value meant higher soil pollution degree. We determined the PI using a modified Nemerow index evaluation method (Han et al., 2017), which was generally divided into three steps: 1) We calculated the pollution index of single soil heavy metal factor; 2) The uncorrected Nemerow index was calculated by synthesizing the each pollution index; and 3) We modified the Nemerow index according to the toxicology and the impact degree of pollution factors on environment (Appendix A). 2.3.5. Initial SEM construction We used the structural equation modeling to study the relationships between explanatory variables and Floristic Quality Index. Structural equation modeling combined a set of linear equations into a model that represents the hypothetical causal relationships between selected variables, and it could simultaneously analyze the causal relationships among multiple variables in a system and determine the strength of each relationship clearly (Fornell, 1982). To build the structural equation modeling, we first used multiple collinearity diagnosis and reliability analysis to screen the explanatory variables and response variables in SPSS 20.0. Then, we built an initial model based on corresponding ecological knowledge which represented the hypothesized mechanism of linking the selected variables (Fig. 4). Observed variables could be measured directly in the process of observation or experiment. By contrast, latent variables could not be measured directly from observation or experiment, but they could be estimated by other measurable metrics. Latent variable was usually used to represent a relatively abstract concept, such as the reasons or factors could not be measured. The composite variable was another variable type in SEM that represents abstract concept. It reflected the overall effect of multiple observed variables or latent variables and was a collection of multiple ‘causes’. In this study, observed variables could be divided into three kinds:
Fig. 2. Schematic framework of the vegetation investigation in Yunnan Province, China.
NMDS had certain analytical significance when the stress was between 0.1 and 0.2 (Legendre and Legendre, 2012).
2.3.3. Soil fertility quality evaluation Soil fertility quality index (QI) reflected the relative soil quality degree. Its value ranged from 0 to 1, and higher value meant higher soil quality degree. QI was calculated using the integrated soil fertility quality evaluation method (Fu et al., 2003). The evaluation of integrated QI could generally be divided into three steps: 1) Selection of factors. We selected the vegetation nutrients elements, TN, TP, TK, Ca, and C, as factors to participate in the evaluation; 2) Determination of weight. We used principal component analysis (PCA) in SPSS 20.0 25
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Fig. 3. Schematic diagram of the study framework.
was 10, the minimum was 3, the average value was 5.74, and the Standard Deviation was 2.27. Species which appeared in multiple areas had higher CC values. Artemisia princeps and Chloris virgata were the most common herb vegetation in the study area, so they had the lowest CC values based on Floristic Quality Assessment method. Alnus nepalensis and Pinus massoniana were the rare arbor species with the highest CC values that were only distributed in three quadrats. Bidens pilosa, Ageratina adenophora, Erigeron canadensis, Tagetes patula and Conyza sumatrensis were exotic species with a CC value of 0 (Appendix B). NMDS can compare the differences in vegetation community composition at different quadrats. Based on the lower-triangular dissimilarity matrix calculation, the spatial distance revealed whether there existed significant differences between the quadrats. The data analysis using NMDS produced a two-dimensional solution with a final stress of 0.11 which means the results had certain analytical significance (Fig. 5). We compared the composition differences between the vegetation communities in the restoration area with Dm. The results showed that similar and different vegetation community compositions exist in some quadrats even with similar Dm values, while the community compositions of some quadrats with different Dm values were similar. This result showed that the Dm factor was not the only factor determining the vegetation community composition. The results also indicated that the maximum FQI value of different quadrats in study area was 21.82, the minimum was 2, the average value was 9.74, and the Standard Deviation was 4.82 (Appendix C). Furthermore, the result of Spearman’s correlation analysis showed that there were no significant correlations between FQI and Dm as well as Dr (p > 0.05). This result indicated that Dm and Dr had no significantly direct impact on FQI, but they could indirectly affect FQI in the quadrats by affecting other soil parameters.
Fig. 4. The initial frame for the SEM model. Square represents the observed variable, circle represents the latent variable, and broken line represents the transformation through the mathematical formula.
vegetation nutrients (TN, TK, TP, C, Ca), soil heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn), and habitat characteristics (Dm, Dr, Slope, Altitude). Latent variables included QI and PI, and the composite variable was FQI. Vegetation nutrients and soil heavy metals were hypothesized to have a direct impact on the FQI, and also on QI and PI. The disturbance of habitat characteristics, such as the migration of dust and other minerals (including high concentrations of soil heavy metals and vegetation nutrients) was mainly affected by Dm, which indirectly affect the FQI by affecting QI and PI. Comparative fit index and root mean square error of approximation were used to test the model. Correlation links were allowed to alter during model selection.
3.2. Environmental characteristics and soil quality assessment
3. Results
We selected fourteen soil factors and four habitat characteristics to analyze the effects of different environmental factors on FQI (Fig. 6). In the fourteen selected soil factors, the average values of As, Co, and Cu contents were all lower than their background values in the Yunnan Province. The content of TN was basically equal to the background value, while the average contents of the other ten soil factors (TP, TK,
3.1. Vegetation community composition and habitat condition In total, 48 plant species were identified in all 18 plots. The maximum richness of vegetation community in different sampling quadrats 26
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plant in a region. The plant with a large distribution range is assigned a low CC value, indicating that this plant has strong adaptability to different habitats. A high CC value suggests that the plant has poor adaptability to habitat and is vulnerable to die and disappear; therefore, it needs to be especially protected. The existence of such plants can improve the biodiversity of the region, so the species with higher CC values appear more important and should be protected with priority. In this study, there were 10 native species and no exotic species in the quadrat with the highest FQI value. Five of the ten native species were found only in this quadrat, and four of them were trees (Cedrus deodara and Acer oliverianum) or shrubs (Hedera nepalensis and Phyllodium pulchellum). Trees or shrubs have a stronger effect than herbs on the development and maintenance of biodiversity, but their recovery ability is far less than herbs once they are exposed to strong external disturbances. Therefore, trees and shrubs have a higher protection value and should be more thoroughly protected to avoid external interference. In summary, the areas in the restoration segment with higher FQI values have more native species and priority protected species than the areas with lower FQI. These areas play a significant role in improving local biodiversity, which should be maintained with priority for ecosystem stability. Maginel et al. (2016) found that the increases in species richness might be attributed to the increase of annual species with a low CC value following fire. The difference between FQI and a traditional diversity index was that FQI could determine the priority protected species by distinguishing between ruderal species and conservative species. FQI is a flexible index, its evaluation is based on the value of local plant species to ecological restoration, which ensures that the priority species can be accurately selected in a large region and makes the external environment more conducive to species growth of priority protection by changing the environment artificially. Therefore, FQI is a very suitable index for evaluating the results of vegetation restoration in mining areas, and it provides new theoretical support for local ecological restoration work. This is especially true in China, where 1.4 million km2 of land was affected by mining (Wu et al., 2013).
Fig. 5. NMDS plot of the phosphate rock mine restoration area based on vegetation composition.
TC, Zn, Pb, Mn, Ni, Cd, Cr, and Ca) were higher than the background values found in the Yunnan Province. QI and PI reflect the relative soil quality degree and soil pollution degree under different mining disturbance, respectively. The results showed that the maximum, minimum, and average values of QI were 0.64, 0.09 and 0.28, respectively. The maximum, minimum, and average values of PI were 10.92, 0.74, and 3.59, respectively (Appendix C). Furthermore, Spearman’s correlation analysis showed that there was a significant correlation between QI and PI (p < 0.01). 3.3. The structural equation modeling result The impact of environmental factors on the FQI was analyzed using structural equation modeling. Firstly, we performed a multiple collinearity diagnosis and a reliability analysis for environmental factors to screen the explanatory variables suitable for the model. The altitude and slope factors were deleted in the next analysis because they did not pass the collinearity tests (Condition Index > 10). The other factors passed the reliability analysis (Cronbach's alpha > 0.7), of which Cronbach's alpha of heavy metal was 0.82, nutrient was 0.73, and habitat was 0.79. This structural equation modeling mainly considered the direct impact of soil parameters on FQI (Fig. 7). The variables included in our model could explain 78% of FQI variation. The results showed that among all selected factors, the content of Cu in soil promoted the FQI most obviously (0.84), followed by Co (0.75), while the inhibition of Cd content in soil to the FQI was the most significant (−0.88), followed by TK (−0.82) and C (−0.79). QI and PI had a certain effect on the FQI, and QI (0.95) was more effective than PI (0.52). Dm and Dr did not have direct impacts on the FQI, but had indirectly negative impacts on the FQI by affecting QI and PI. The results of SEM between QI and PI confirmed and quantified the significantly positive correlation between them.
4.2. Reasons for the difference of community composition in vegetation restoration In the process of vegetation restoration, the composition of vegetation community was affected by mining action, but it was not entirely determined by the mining. In this study, the vegetation community compositions in some plots were similar. However, the vegetation community composition in other plots had great differences due to the following reasons: 1) Human activities, such as roads in restoration area, could also have a certain effect on the vegetation restoration, which would eventually change the vegetation community composition in some regions (Liu et al., 2008). Müllerová et al. (2017) showed that roads had a great influence on vegetation structure, especially in undernourished ecosystems; 2) Due to the fact that the restoration area did not have existing vegetation communities at the beginning of restoration, exotic species could easily invade at this stage. If invasive species were formed, they would have a strong impact on native species, thus altering the eventual result of the restoration. Rijal et al. (2017) presented evidence that the invasive species Heracleum persicum rapidly multiplied due to the lack of natural enemies, which inhibited the growth of native species. In our research, a total of 54 quadrats were surveyed, among which 37 quadrats had exotic species, and the mean value of vegetation richness in quadrats with exotic species was less than that in quadrats with non-exotic species. In the future, the exotic species should be introduced cautiously when vegetation restoration is carried out, in case they inhibit the growth of native species and reduce the biodiversity of the region.
4. Discussion 4.1. Habitat quality evaluation FQI is a comprehensive index which is determined by CC values and richness of native species in the area, and there is a positive correlation between them. The CC value depends on the distribution range of a 27
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Fig. 6. Environmental factors of the Kunyang phosphate rock mine restoration area in Yunnan Province, China. Red represents the habitat characteristics, blue represents the nutrient elements, and yellow represents heavy metal elements in soil. Green lines are the soil factors background value in this study area. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
PI level. In order to verify the correctness of this result, we reviewed the calculation process of PI. The original data showed that the content of Cd in the plots exceeded the background value by more than 5 times. The contents of most heavy metals exceeded the background values by 1–2 times, As, Co, and Cu fell below the background value. In addition, Cd had the greatest toxicity in these heavy metals and had the greatest impact on the development of vegetation and the health of human body. Combined with the evaluation formula we chose, Cd content should have the greatest effect on PI in all soil factors. Similarly, the result that Ca content in soil had the greatest impact on QI could also be judged by combining the calculation formula with the original data. In summary, the results of SEM were considered to be correct. The results of SEM showed that excessive nutrients (C, TP, and TK) in the soil could inhibit the growth and development of vegetation (especially for rare species), which was unfavorable to the improvement of habitat quality. An important finding of our study is that excessive nutrient contents in the soil of the phosphate mine (i.e., when C, TP,
4.3. The joint contribution of vegetation nutrient and heavy metal element Using the structural equation modeling, we were able to link and to quantify the different factors influencing the FQI in the Kunyang phosphate rock mine, revealing the underlying mechanism. In this study, we evaluated the QI and PI using the integrated soil fertility quality evaluation and the modified Nemerow soil pollution evaluation method, then added them to the SEM for further analysis. The effects of soil elements on vegetation restoration in the mining area were revealed through a comprehensive index, which further revealed the effect mechanism of the soil chemical properties on vegetation. The SEM had been applied in the fields of natural science, such as ecology and environmental science (Mammides et al., 2015a, b; Zhang et al., 2017), and the reliability of the model had been confirmed. In this study, we integrated different metrics through the environmental formula to test whether the model could judge the contribution of the index. SEM indicated that Cd content in the soil had the greatest impact on 28
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Fig. 7. The regulation process of environmental factors on FQI in the study of Kunyang phosphate rock mining restoration in Yunnan Province, China. The red line indicates positive effects, and the blue line indicates negative effects. The dashed lines indicate that removing these relationships would contribute to improving the model quality. The numbers on the lines indicate influence coefficients. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) were used to test the model: CFI = 0.91 and RMSEA = 0.04. The model was considered to pass the test. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Acknowledgements
and TK contents in the soil far exceed the background values), would have a toxic effect on the vegetation growth, thereby inhibiting the development of vegetation communities. Cross and Lambers (2017) indicated that some species adapted to severely P-poor soils were highly sensitive to raised P utilization, showing severe P toxicity symptoms. However, the increase of heavy metal content in soil did not always inhibit the development of vegetation communities and the improvement of FQI. For example, the contents of Cu and Co in soil were significantly positively correlated with FQI in the study area. This result was consistent with the law of limiting factors in ecology, which restrains the growth of vegetation when the factor was either shortage or excess. Therefore, TN, TP, TK, and other nutrients could not be blindly added in future vegetation restoration in the Kunyang phosphate rock mine. Local managers should combine the selected restoration species with the physical and various additives according to local conditions. By speeding up the efficiency of vegetation restoration, the stability of regional ecosystems could be ensured and ecosystem services in the region could be enhanced.
The research was supported by National Key Research and Development Project, China (No. 2016YFC0502103) and National Natural Sciences Foundation of China (No. 41571173). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoleng.2018.09.021. References Ahirwal, J., Maiti, S.K., Reddy, M.S., 2017. Development of carbon, nitrogen and phosphate stocks of reclaimed coal mine soil within 8 years after forestation with Prosopis juliflora (Sw.) Dc. Catena 156, 42–50. https://doi.org/10.1016/j.catena.2017.03.019. Alatalo, R.V., 1981. Problems in the measurement of evenness in ecology. Oikos 37, 199–204. https://doi.org/10.2307/3544465. Bai, J.H., Xiao, R., Cui, B.S., Zhang, K.J., Wang, Q.G., Liu, X.H., Gao, H.F., Huang, L.B., 2011. Assessment of heavy metal pollution in wetland soils from the young and old reclaimed regions in the Pearl River Estuary, South China. Environ. Pollut. 159, 817–824. https://doi.org/10.1016/j.envpol.2010.11.004. Bauman, J.M., Cochran, C., Chapman, J., Gilland, K., 2015. Plant community development following restoration treatments on a legacy reclaimed mine site. Ecol. Eng. 83, 521–528. https://doi.org/10.1016/j.ecoleng.2015.06.023. Bell, J.L., Boyer, J.N., Crystall, S.J., Nichols, W.F., 2017. Floristic quality as an indicator of human disturbance in forested wetlands of northern New England. Ecol. Indic. 83, 227–231. https://doi.org/10.1016/j.ecolind.2017. 08.010. Cadotte, M.W., Carscadden, K., Mirotchnick, N., 2011. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087. https://doi.org/10.1111/j.1365-2664.2011.02048.x. Chamberlain, S.J., Brooks, R.P., 2016. Testing a rapid Floristic Quality Index on headwater wetlands in central Pennsylvania, USA. Ecol. Indic. 60, 1142–1149. https:// doi.org/10.1016/j.ecolind.2015.09.004. Cross, A.T., Lambers, H., 2017. Young calcareous soil chronosequences as a model for ecological restoration on alkaline mine tailings. Sci. Total Environ. 607–608, 168–175. https://doi.org/10.1016/j.scitotenv.2017.07.005. Development Core Team, R., 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Fang, J.Y., Wang, X.P., Shen, Z.H., Tang, Z.Y., He, J.S., Yu, D., Jiang, Y., Wang, Z.H., Zheng, C.Y., Zhu, J.L., Guo, Z.D., 2009. Methods and protocols for plant community inventory. Biodivers. Sci. 17, 533–548. https://doi.org/10.3724/SP.J.1003.2009. 09253. Fornell, C., 1982. A Second Generation of Multivariate Analyses: Volumes I and II. Praeger Publishers, New York, USA. Francis, C.M., Austen, M.J.W., Bowles, J.M., Draper, W.B., 2000. Assessing floristic quality in southern Ontario woodlands. Nat. Areas J. 20, 66–77.
5. Conclusion In this study, we first analyzed the community composition of different vegetation restoration plots, and then assessed the influences of environmental factors on FQI in a Kunyang phosphate rock mine. Our results indicated that vegetation community composition varied depending on a variety of environmental factors. Among all the environmental metrics, Cu content in the soil had the greatest promotional effect on FQI, while Cd and TK contents had a significantly inhibitory effect. With our findings, we believe that the general increase of soil nutrients does not necessarily promote the growth of vegetation in the process of vegetation restoration in the study area, and it is possible to produce ‘toxicity’ to the vegetation. The increase of some heavy metal elements, however, can promote the vegetation restoration. Our research could provide some theoretical guidance for vegetation restoration in the mining area. In future studies, more factors should be included in the model, and their contributions to vegetation restoration in the mining area could be examined, such as soil microbial community and water availability. 29
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Interaction mechanism between floristic quality and environmental factors during ecological restoration in a mine area based on structural equation modeling
T
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Xiaoyun Hou, Shiliang Liu , Shuang Zhao, Yueqiu Zhang, Xue Wu, Fangyan Cheng, Shikui Dong State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Floristic Quality Index Environmental factors Structural equation modeling Toxicity Mining restoration
Open-pit mining activities cause great damage to the local ecosystems. It is therefore necessary to assess and recover the vegetation status to maintain ecological stability. In this study, the Floristic Quality Index (FQI) was used to assess the vegetation habitat and the structural equation model (SEM) was applied to quantify the influences of different environmental factors on FQI in the Kunyang open-pit phosphate rock mine in Yunnan Province, China. Non-metric multidimensional scaling analyses revealed that great differences of vegetation community composition existed in the sampled plots, even those at similar distances to mining areas, which indicates that disturbing distance was not the only factor to determine the vegetation community. SEM results showed that Cu promoted the FQI most obviously (0.84), followed by Co (0.75), while the inhibition of Cd content in soil to the FQI was the most significant (−0.88), followed by TK (−0.82), and C (−0.79). Soil fertility quality and soil pollution indexes were also established to analyze the effects of comprehensive soil parameters on FQI. The results showed that the soil fertility quality index had a strong negative effect on FQI, which revealed that higher levels of TP, TN, TK, and other nutrients in the soil would produce ‘toxicity’ to the growth of vegetation. Findings from our study could provide a scientific method for assessing the ecological restoration results in the mining area.
1. Introduction Open-pit mining makes an important contribution to the local economic development, but it also generates environmental pollution, vegetation destruction, and ecological degradation (Wick et al., 2014; Hu et al., 2015). These issues have gained more and more attention, especially in China’s move towards ecological civilization (Harantová et al., 2017; Lee et al., 2017). In order to restore the local ecosystem function, vegetation in open-pit mining areas should be restored to accelerate the processes of soil natural restoration and to enhance biodiversity (Hu et al., 2015; Hendrychová and Kabrna, 2016). Monitoring and assessing the status during vegetation restoration is essential when evaluating the restoration success (Hobbs and Harris, 2001), and it needs meaningful and interpretable metrics. There are many metrics to describe the plant community, such as species abundance, species richness, or evenness (Bauman et al., 2015; Li et al., 2017; Shackelford et al., 2017). Each metric provides certain information and has application limitations (Taft et al., 2006). Alatalo (1981) considered that evenness measures of calculations which include
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richness were limited by sampling biases. Abundance and diversity as the commonly used metrics were used to describe the essential characteristics of vegetation communities, but they were not weighted by vegetation composition (Taft et al., 1997). Ecosystems consist of a complex set of temporally and spatially variable components; thus, it is difficult to characterize their integrity. Vegetation community composition could be a diversity indicator of ecosystem function and reflect site conditions (Cadotte et al., 2011). Government agencies and land managers require reproducible and quantifiable metrics to monitor a natural region’s vegetation community. Therefore, a scientific and comparable approach for assessing the vegetation conditions during the restoration process is urgently needed. Wilhelm (1977) proposed the Coefficient of Conservatism (CC) to quantify the tolerance of each individual native species to human disturbance. Swink and Wilhelm (1994) combined the richness of the native species with the vulnerability measure CC value to create the Floristic Quality Index (FQI). The CC value is a number from 0 to 10 which is assigned to each taxa within a region. Species with high CC values have high allegiance to a specific habitat where disturbance is
Corresponding author. E-mail address: [email protected] (S. Liu).
https://doi.org/10.1016/j.ecoleng.2018.09.021 Received 7 April 2018; Received in revised form 10 September 2018; Accepted 19 September 2018 0925-8574/ © 2018 Elsevier B.V. All rights reserved.
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2.2. Experimental design and field sampling
minimal. Species with low CC values are somewhat tolerant of human disturbance and can be found in common plant communities (Taft et al., 1997). The FQI value is calculated by multiplying the square root of native species richness for a site by the mean CC value for the same sampling unit, thus combining the plant diversity with weighted value of vegetation composition (Taft et al., 1997). At present, some scholars have studied the application of Floristic Quality Index to assess vegetation condition. It has been a proven indicator of human disturbance when evaluating various habitat conditions (Chamberlain and Brooks, 2016; Bell et al., 2017). A study by Maginel et al. (2016) suggested that FQI could be a good indicator for monitoring the restoration of vegetation communities in forest ecosystems after fire. FQI has been used by conservation organizations and government agencies to monitor and assess a variety of areas (Freyman et al., 2016). However, studies on FQI change associated with multiple and comprehensive environmental factors are still lacking. Struckhoff et al. (2013) found that Zn and Pb concentrations in the soil had negative relationships to FQI in southeast Missouri, USA while their relative contributions of these factors is not well understood. Soil, as a major environmental factor affecting vegetation reconstruction, is often accompanied by a shortage or excess of nutrient or pollutant contents in mining areas, and it would accelerate or inhibit the growth of vegetation (Lei et al., 2016; Ahirwal et al., 2017). Therefore, it is necessary to measure the effects of soil and other environmental factors on vegetation restoration (van Swaay et al., 2011). However, understanding the mechanisms by which environmental factors influence FQI is important when implementing ecological restoration in the mining areas. To reveal the mechanism between environmental factors and FQI for the ecological restoration, many quantifiable methods have been used. Among them, the structural equation modeling (SEM) is an effective approach to explore the mechanisms between selected multi-factors. In the past decade, SEM has been used in some scientific disciplines (Sarstedt et al., 2014; Mammides et al., 2015a; Zhang et al., 2017). In this study, we used the SEM approach to study the mechanisms of the interaction between FQI and environmental factors in restoration areas of the Kunyang phosphate mine. The objectives were to: 1) determine vegetation community change during restoration and apply FQI index to reflect the habitat quality in restoration areas; and 2) reveal the influential mechanism of environmental factors on FQI using the structural equation model.
A typical sample method was used to conduct a vegetation survey in phosphate rock mine areas (Fang et al., 2009). In September 2016, we investigated 18 plots of restoration area along different distances from the mining area. Three quadrats were randomly selected in each plot (Fig. 2). Typical sample method was adopted for the arbor, shrub, and herb layers. The arbor layer was investigated in a 10 × 10 m-quadrat, the internal shrub layer was chosen in a 5 × 5 m-quadrat, and the herb layer was surveyed in a 1 × 1 m-quadrat. In the quadrats, each tree was identified and measured for total height and layer coverage. Each shrub species was identified and measured for total height and layer coverage, whereas each herb was identified for layer coverage and height. In each quadrat, we also investigated habitat characteristics, including altitude, slope, slope position, aspect, and the distance to nearest road (Dr) and the mining area (Dm). For soil sampling, we collected the 0–10 cm topsoil and took a quarter of the total after mixing four random soil samples from each quadrat. Soil samples were ground and sieved after removing the large roots and stones, and the soil properties were determined in our laboratory. We used a Euro EA3000 elemental analyzer to analyze total carbon (TC) and total nitrogen (TN), and used ICP-AES to define total phosphorus (TP), total potassium (TK), Zn, Pb, As, Mn, Co, Ni, Cd, Cu, Cr, and Ca (Bai et al., 2011). 2.3. Data analysis Based on the obtained vegetation and environmental data, we first analyzed the influence of Dm on the composition of the vegetation community using non-metric multidimensional scaling (NMDS) analyses to determine that Dm was not the only determinant. Then we calculated the FQI, soil pollution index, and soil fertility quality index, respectively. Finally, SEM was used to analyze the effects of environmental factors on FQI. The schematic diagram of the study framework was shown in Fig. 3. 2.3.1. Floristic Quality Assessment FQI and mean CC value were calculated as the primary dependent variables. Based on each native species’ affinity for natural vegetation communities and its tolerance of human interference, each native species in the geographic area was assigned a CC value from 1 to 10. The least conservative species could adapt to extremely naturally- or artificially-degraded habitats (CC values < 3). Matric species could tolerate human disturbance and occur in common vegetation communities (CC values from 4 to 6), while the most conservative species depend mainly on undisturbed sites (CC values > 7). For each vegetation sampling quadrat (n = 54), we calculated total species richness, mean CC value, and the FQI as follows:
2. Methods 2.1. Study area This study was conducted in the Kunyang phosphate rock mine (24°43′ N, 102°34′ E) located in Jinning county of Yunnan Province (Fig. 1). The mine is the largest open-pit phosphate rock mine in China and has been exploited for 52 years. It is the first phosphate recovery area for vegetation restoration and the first batch of national green mine pilot units in China. The annual average temperature is 14.7 °C, the altitude of the landscape is between 2118 m and 2828 m, the mean annual rainfall is 918 mm, and the rainfall events are mainly concentrated from May to August. The mining area is located in the subtropical evergreen broadleaf forest zone. The soil type is mainly red soil and the soil texture is mainly loam. The local natural forest has experienced destruction due to long-term human activities in the mining area, and the existing vegetation types are mainly secondary vegetation formed under the vegetation restoration process. The area of vegetation restoration has exceeded 1000 acres. For the vegetation restoration of this mining area, the surface soils that have been stripped prior to mining activities were stored separately for later vegetation restoration. The reclamation area is mostly covered by forests, as well as some shrubs and herbs.
S = number of species (native + exotic) Mean CC =
∑ CC/S
FQI = Mean CC ×
(1) (2)
N
(3)
where CC is the Coefficient of Conservatism for each species occurring in a quadrat, mean CC is the average Coefficient of Conservatism for the quadrat, S is number of all species, N is native species richness, and FQI is the Floristic Quality Index. Additionally, we assigned all exotic species a CC value of 0 in mean CC calculations (Francis et al., 2000). 2.3.2. Analysis of vegetation community composition and distribution Non-metric multidimensional scaling analyses (NMDS) based on the lower-triangular dissimilarity matrix was used to display the differences between vegetation community compositions in the Vegan package in R 3.4.2 (R Development Core and Team, 2017). The result of the NMDS analysis was measured by stress. The two-dimensional point map of 24
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Fig. 1. Distribution of plots in the Kunyang phosphate rock mine in Yunnan Province, China.
software to determine the weight of various factors; and 3) Establishment of comprehensive metrics. The index value of each nutrient was synthesized by multiplication according to the additive and multiplication rule, and then the comprehensive index value of soil fertility quality was calculated (Appendix A). 2.3.4. Soil pollution evaluation Soil pollution index (PI) reflected the relative soil pollution degree. High PI value meant higher soil pollution degree. We determined the PI using a modified Nemerow index evaluation method (Han et al., 2017), which was generally divided into three steps: 1) We calculated the pollution index of single soil heavy metal factor; 2) The uncorrected Nemerow index was calculated by synthesizing the each pollution index; and 3) We modified the Nemerow index according to the toxicology and the impact degree of pollution factors on environment (Appendix A). 2.3.5. Initial SEM construction We used the structural equation modeling to study the relationships between explanatory variables and Floristic Quality Index. Structural equation modeling combined a set of linear equations into a model that represents the hypothetical causal relationships between selected variables, and it could simultaneously analyze the causal relationships among multiple variables in a system and determine the strength of each relationship clearly (Fornell, 1982). To build the structural equation modeling, we first used multiple collinearity diagnosis and reliability analysis to screen the explanatory variables and response variables in SPSS 20.0. Then, we built an initial model based on corresponding ecological knowledge which represented the hypothesized mechanism of linking the selected variables (Fig. 4). Observed variables could be measured directly in the process of observation or experiment. By contrast, latent variables could not be measured directly from observation or experiment, but they could be estimated by other measurable metrics. Latent variable was usually used to represent a relatively abstract concept, such as the reasons or factors could not be measured. The composite variable was another variable type in SEM that represents abstract concept. It reflected the overall effect of multiple observed variables or latent variables and was a collection of multiple ‘causes’. In this study, observed variables could be divided into three kinds:
Fig. 2. Schematic framework of the vegetation investigation in Yunnan Province, China.
NMDS had certain analytical significance when the stress was between 0.1 and 0.2 (Legendre and Legendre, 2012).
2.3.3. Soil fertility quality evaluation Soil fertility quality index (QI) reflected the relative soil quality degree. Its value ranged from 0 to 1, and higher value meant higher soil quality degree. QI was calculated using the integrated soil fertility quality evaluation method (Fu et al., 2003). The evaluation of integrated QI could generally be divided into three steps: 1) Selection of factors. We selected the vegetation nutrients elements, TN, TP, TK, Ca, and C, as factors to participate in the evaluation; 2) Determination of weight. We used principal component analysis (PCA) in SPSS 20.0 25
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Fig. 3. Schematic diagram of the study framework.
was 10, the minimum was 3, the average value was 5.74, and the Standard Deviation was 2.27. Species which appeared in multiple areas had higher CC values. Artemisia princeps and Chloris virgata were the most common herb vegetation in the study area, so they had the lowest CC values based on Floristic Quality Assessment method. Alnus nepalensis and Pinus massoniana were the rare arbor species with the highest CC values that were only distributed in three quadrats. Bidens pilosa, Ageratina adenophora, Erigeron canadensis, Tagetes patula and Conyza sumatrensis were exotic species with a CC value of 0 (Appendix B). NMDS can compare the differences in vegetation community composition at different quadrats. Based on the lower-triangular dissimilarity matrix calculation, the spatial distance revealed whether there existed significant differences between the quadrats. The data analysis using NMDS produced a two-dimensional solution with a final stress of 0.11 which means the results had certain analytical significance (Fig. 5). We compared the composition differences between the vegetation communities in the restoration area with Dm. The results showed that similar and different vegetation community compositions exist in some quadrats even with similar Dm values, while the community compositions of some quadrats with different Dm values were similar. This result showed that the Dm factor was not the only factor determining the vegetation community composition. The results also indicated that the maximum FQI value of different quadrats in study area was 21.82, the minimum was 2, the average value was 9.74, and the Standard Deviation was 4.82 (Appendix C). Furthermore, the result of Spearman’s correlation analysis showed that there were no significant correlations between FQI and Dm as well as Dr (p > 0.05). This result indicated that Dm and Dr had no significantly direct impact on FQI, but they could indirectly affect FQI in the quadrats by affecting other soil parameters.
Fig. 4. The initial frame for the SEM model. Square represents the observed variable, circle represents the latent variable, and broken line represents the transformation through the mathematical formula.
vegetation nutrients (TN, TK, TP, C, Ca), soil heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn), and habitat characteristics (Dm, Dr, Slope, Altitude). Latent variables included QI and PI, and the composite variable was FQI. Vegetation nutrients and soil heavy metals were hypothesized to have a direct impact on the FQI, and also on QI and PI. The disturbance of habitat characteristics, such as the migration of dust and other minerals (including high concentrations of soil heavy metals and vegetation nutrients) was mainly affected by Dm, which indirectly affect the FQI by affecting QI and PI. Comparative fit index and root mean square error of approximation were used to test the model. Correlation links were allowed to alter during model selection.
3.2. Environmental characteristics and soil quality assessment
3. Results
We selected fourteen soil factors and four habitat characteristics to analyze the effects of different environmental factors on FQI (Fig. 6). In the fourteen selected soil factors, the average values of As, Co, and Cu contents were all lower than their background values in the Yunnan Province. The content of TN was basically equal to the background value, while the average contents of the other ten soil factors (TP, TK,
3.1. Vegetation community composition and habitat condition In total, 48 plant species were identified in all 18 plots. The maximum richness of vegetation community in different sampling quadrats 26
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plant in a region. The plant with a large distribution range is assigned a low CC value, indicating that this plant has strong adaptability to different habitats. A high CC value suggests that the plant has poor adaptability to habitat and is vulnerable to die and disappear; therefore, it needs to be especially protected. The existence of such plants can improve the biodiversity of the region, so the species with higher CC values appear more important and should be protected with priority. In this study, there were 10 native species and no exotic species in the quadrat with the highest FQI value. Five of the ten native species were found only in this quadrat, and four of them were trees (Cedrus deodara and Acer oliverianum) or shrubs (Hedera nepalensis and Phyllodium pulchellum). Trees or shrubs have a stronger effect than herbs on the development and maintenance of biodiversity, but their recovery ability is far less than herbs once they are exposed to strong external disturbances. Therefore, trees and shrubs have a higher protection value and should be more thoroughly protected to avoid external interference. In summary, the areas in the restoration segment with higher FQI values have more native species and priority protected species than the areas with lower FQI. These areas play a significant role in improving local biodiversity, which should be maintained with priority for ecosystem stability. Maginel et al. (2016) found that the increases in species richness might be attributed to the increase of annual species with a low CC value following fire. The difference between FQI and a traditional diversity index was that FQI could determine the priority protected species by distinguishing between ruderal species and conservative species. FQI is a flexible index, its evaluation is based on the value of local plant species to ecological restoration, which ensures that the priority species can be accurately selected in a large region and makes the external environment more conducive to species growth of priority protection by changing the environment artificially. Therefore, FQI is a very suitable index for evaluating the results of vegetation restoration in mining areas, and it provides new theoretical support for local ecological restoration work. This is especially true in China, where 1.4 million km2 of land was affected by mining (Wu et al., 2013).
Fig. 5. NMDS plot of the phosphate rock mine restoration area based on vegetation composition.
TC, Zn, Pb, Mn, Ni, Cd, Cr, and Ca) were higher than the background values found in the Yunnan Province. QI and PI reflect the relative soil quality degree and soil pollution degree under different mining disturbance, respectively. The results showed that the maximum, minimum, and average values of QI were 0.64, 0.09 and 0.28, respectively. The maximum, minimum, and average values of PI were 10.92, 0.74, and 3.59, respectively (Appendix C). Furthermore, Spearman’s correlation analysis showed that there was a significant correlation between QI and PI (p < 0.01). 3.3. The structural equation modeling result The impact of environmental factors on the FQI was analyzed using structural equation modeling. Firstly, we performed a multiple collinearity diagnosis and a reliability analysis for environmental factors to screen the explanatory variables suitable for the model. The altitude and slope factors were deleted in the next analysis because they did not pass the collinearity tests (Condition Index > 10). The other factors passed the reliability analysis (Cronbach's alpha > 0.7), of which Cronbach's alpha of heavy metal was 0.82, nutrient was 0.73, and habitat was 0.79. This structural equation modeling mainly considered the direct impact of soil parameters on FQI (Fig. 7). The variables included in our model could explain 78% of FQI variation. The results showed that among all selected factors, the content of Cu in soil promoted the FQI most obviously (0.84), followed by Co (0.75), while the inhibition of Cd content in soil to the FQI was the most significant (−0.88), followed by TK (−0.82) and C (−0.79). QI and PI had a certain effect on the FQI, and QI (0.95) was more effective than PI (0.52). Dm and Dr did not have direct impacts on the FQI, but had indirectly negative impacts on the FQI by affecting QI and PI. The results of SEM between QI and PI confirmed and quantified the significantly positive correlation between them.
4.2. Reasons for the difference of community composition in vegetation restoration In the process of vegetation restoration, the composition of vegetation community was affected by mining action, but it was not entirely determined by the mining. In this study, the vegetation community compositions in some plots were similar. However, the vegetation community composition in other plots had great differences due to the following reasons: 1) Human activities, such as roads in restoration area, could also have a certain effect on the vegetation restoration, which would eventually change the vegetation community composition in some regions (Liu et al., 2008). Müllerová et al. (2017) showed that roads had a great influence on vegetation structure, especially in undernourished ecosystems; 2) Due to the fact that the restoration area did not have existing vegetation communities at the beginning of restoration, exotic species could easily invade at this stage. If invasive species were formed, they would have a strong impact on native species, thus altering the eventual result of the restoration. Rijal et al. (2017) presented evidence that the invasive species Heracleum persicum rapidly multiplied due to the lack of natural enemies, which inhibited the growth of native species. In our research, a total of 54 quadrats were surveyed, among which 37 quadrats had exotic species, and the mean value of vegetation richness in quadrats with exotic species was less than that in quadrats with non-exotic species. In the future, the exotic species should be introduced cautiously when vegetation restoration is carried out, in case they inhibit the growth of native species and reduce the biodiversity of the region.
4. Discussion 4.1. Habitat quality evaluation FQI is a comprehensive index which is determined by CC values and richness of native species in the area, and there is a positive correlation between them. The CC value depends on the distribution range of a 27
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Fig. 6. Environmental factors of the Kunyang phosphate rock mine restoration area in Yunnan Province, China. Red represents the habitat characteristics, blue represents the nutrient elements, and yellow represents heavy metal elements in soil. Green lines are the soil factors background value in this study area. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
PI level. In order to verify the correctness of this result, we reviewed the calculation process of PI. The original data showed that the content of Cd in the plots exceeded the background value by more than 5 times. The contents of most heavy metals exceeded the background values by 1–2 times, As, Co, and Cu fell below the background value. In addition, Cd had the greatest toxicity in these heavy metals and had the greatest impact on the development of vegetation and the health of human body. Combined with the evaluation formula we chose, Cd content should have the greatest effect on PI in all soil factors. Similarly, the result that Ca content in soil had the greatest impact on QI could also be judged by combining the calculation formula with the original data. In summary, the results of SEM were considered to be correct. The results of SEM showed that excessive nutrients (C, TP, and TK) in the soil could inhibit the growth and development of vegetation (especially for rare species), which was unfavorable to the improvement of habitat quality. An important finding of our study is that excessive nutrient contents in the soil of the phosphate mine (i.e., when C, TP,
4.3. The joint contribution of vegetation nutrient and heavy metal element Using the structural equation modeling, we were able to link and to quantify the different factors influencing the FQI in the Kunyang phosphate rock mine, revealing the underlying mechanism. In this study, we evaluated the QI and PI using the integrated soil fertility quality evaluation and the modified Nemerow soil pollution evaluation method, then added them to the SEM for further analysis. The effects of soil elements on vegetation restoration in the mining area were revealed through a comprehensive index, which further revealed the effect mechanism of the soil chemical properties on vegetation. The SEM had been applied in the fields of natural science, such as ecology and environmental science (Mammides et al., 2015a, b; Zhang et al., 2017), and the reliability of the model had been confirmed. In this study, we integrated different metrics through the environmental formula to test whether the model could judge the contribution of the index. SEM indicated that Cd content in the soil had the greatest impact on 28
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Fig. 7. The regulation process of environmental factors on FQI in the study of Kunyang phosphate rock mining restoration in Yunnan Province, China. The red line indicates positive effects, and the blue line indicates negative effects. The dashed lines indicate that removing these relationships would contribute to improving the model quality. The numbers on the lines indicate influence coefficients. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) were used to test the model: CFI = 0.91 and RMSEA = 0.04. The model was considered to pass the test. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Acknowledgements
and TK contents in the soil far exceed the background values), would have a toxic effect on the vegetation growth, thereby inhibiting the development of vegetation communities. Cross and Lambers (2017) indicated that some species adapted to severely P-poor soils were highly sensitive to raised P utilization, showing severe P toxicity symptoms. However, the increase of heavy metal content in soil did not always inhibit the development of vegetation communities and the improvement of FQI. For example, the contents of Cu and Co in soil were significantly positively correlated with FQI in the study area. This result was consistent with the law of limiting factors in ecology, which restrains the growth of vegetation when the factor was either shortage or excess. Therefore, TN, TP, TK, and other nutrients could not be blindly added in future vegetation restoration in the Kunyang phosphate rock mine. Local managers should combine the selected restoration species with the physical and various additives according to local conditions. By speeding up the efficiency of vegetation restoration, the stability of regional ecosystems could be ensured and ecosystem services in the region could be enhanced.
The research was supported by National Key Research and Development Project, China (No. 2016YFC0502103) and National Natural Sciences Foundation of China (No. 41571173). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoleng.2018.09.021. References Ahirwal, J., Maiti, S.K., Reddy, M.S., 2017. Development of carbon, nitrogen and phosphate stocks of reclaimed coal mine soil within 8 years after forestation with Prosopis juliflora (Sw.) Dc. Catena 156, 42–50. https://doi.org/10.1016/j.catena.2017.03.019. Alatalo, R.V., 1981. Problems in the measurement of evenness in ecology. Oikos 37, 199–204. https://doi.org/10.2307/3544465. Bai, J.H., Xiao, R., Cui, B.S., Zhang, K.J., Wang, Q.G., Liu, X.H., Gao, H.F., Huang, L.B., 2011. Assessment of heavy metal pollution in wetland soils from the young and old reclaimed regions in the Pearl River Estuary, South China. Environ. Pollut. 159, 817–824. https://doi.org/10.1016/j.envpol.2010.11.004. Bauman, J.M., Cochran, C., Chapman, J., Gilland, K., 2015. Plant community development following restoration treatments on a legacy reclaimed mine site. Ecol. Eng. 83, 521–528. https://doi.org/10.1016/j.ecoleng.2015.06.023. Bell, J.L., Boyer, J.N., Crystall, S.J., Nichols, W.F., 2017. Floristic quality as an indicator of human disturbance in forested wetlands of northern New England. Ecol. Indic. 83, 227–231. https://doi.org/10.1016/j.ecolind.2017. 08.010. Cadotte, M.W., Carscadden, K., Mirotchnick, N., 2011. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087. https://doi.org/10.1111/j.1365-2664.2011.02048.x. Chamberlain, S.J., Brooks, R.P., 2016. Testing a rapid Floristic Quality Index on headwater wetlands in central Pennsylvania, USA. Ecol. Indic. 60, 1142–1149. https:// doi.org/10.1016/j.ecolind.2015.09.004. Cross, A.T., Lambers, H., 2017. Young calcareous soil chronosequences as a model for ecological restoration on alkaline mine tailings. Sci. Total Environ. 607–608, 168–175. https://doi.org/10.1016/j.scitotenv.2017.07.005. Development Core Team, R., 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Fang, J.Y., Wang, X.P., Shen, Z.H., Tang, Z.Y., He, J.S., Yu, D., Jiang, Y., Wang, Z.H., Zheng, C.Y., Zhu, J.L., Guo, Z.D., 2009. Methods and protocols for plant community inventory. Biodivers. Sci. 17, 533–548. https://doi.org/10.3724/SP.J.1003.2009. 09253. Fornell, C., 1982. A Second Generation of Multivariate Analyses: Volumes I and II. Praeger Publishers, New York, USA. Francis, C.M., Austen, M.J.W., Bowles, J.M., Draper, W.B., 2000. Assessing floristic quality in southern Ontario woodlands. Nat. Areas J. 20, 66–77.
5. Conclusion In this study, we first analyzed the community composition of different vegetation restoration plots, and then assessed the influences of environmental factors on FQI in a Kunyang phosphate rock mine. Our results indicated that vegetation community composition varied depending on a variety of environmental factors. Among all the environmental metrics, Cu content in the soil had the greatest promotional effect on FQI, while Cd and TK contents had a significantly inhibitory effect. With our findings, we believe that the general increase of soil nutrients does not necessarily promote the growth of vegetation in the process of vegetation restoration in the study area, and it is possible to produce ‘toxicity’ to the vegetation. The increase of some heavy metal elements, however, can promote the vegetation restoration. Our research could provide some theoretical guidance for vegetation restoration in the mining area. In future studies, more factors should be included in the model, and their contributions to vegetation restoration in the mining area could be examined, such as soil microbial community and water availability. 29
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