d characteristics of original large patches (area over 200 ha) among three years, which were almost consecutively distributed as the vegetation belt in the eastern part of study area. We extended the 17 transect lines by edit tools under ArcGIS 9.2, and recorded the geographical coordinates of intersection points between each transect and both edges of large patches for each year. The expansion distance was calculated to detect the spread difference in east/sea and west/inland directions among three years.

3. Results

3.1. Fuzzy Accuracy Assessment

MAX function values exceeded 73% in all 3 years, which are the conservational estimates of accuracy (Table 2, Appendix 1 and 2). These accuracies from MAX are identical to that derived from the conventional binary assessment, i.e. the numbers in the “Matches” column.

Table 2. Fuzzy accuracy tables for 2005 map classification result of the study area.

T = Total number of sites in each group; M = number of matched sites; N = number of mismatched sites.

Using the less restrictive assessment metric, RIGHT, improved the correct classifications by 17.7% in 2003, 18.1% in 2005, and 14.4% in 2008. However, improvements in individual land cover classifications varied from 0% to 29.8% (in the case of sparse S. glauca in 2005). The sparse and dense S. glauca improved very much in comparison with the other land cover classes. MAX values for the alien S. alterniflora indicated 100% correct classification in 2003 and 2008, thus no improvement in classification was possible for the RIGHT function in this community in those years. In 2005, however, there was a 16.7% improvement in classification with the RIGHT function.

The results of DIFFERENCE functions were very similar for the mean of matches (4 scores), which were 28.1% in 2003, 25.8% in 2005, and 29.3% in 2008, in the field samples, respectively. The maximum mismatches (−4 scores) occurred in 2003 (3.6% of the field sites), primarily of a result in classification error for the unvegetated marsh. These field samples were confused with sparse S. glauca community, and their spectra were mixed with vegetation as soil background, thus leading to misclassification. The nature of the mismatches indicated that there was little ambiguity or uncertainty in determining the land cover conditions at each plot.

Results for the MEMBERSHIP functions suggested that a small number (14.37% in 2003, 25.8% in 2005, and 17.36 in 2008) of field samples are members of Sparse S. glauca and unvegetated marsh, they are distributed close to evenly between matches and non-matches. These results indicated that land cover classifications were clearly defined and showed little confusion to field crews when they were assigning a linguistic score.

3.2. Changes in Area of Eight Land Cover Classes

Eight land cover classes were mapped in the study area (Figure 2).

The area of S. alterniflora and P. australis increased by 28.8% and 47.3% in 2003-2008, respectively (Table 3). The annual increase rate of invasive plant in 2003-2005 (6.7%) is higher than that in 2005-2008 (4.5%), while the annual increase rate of P. australis was less in 2003-2005 (5.5%) than in 2005-2008 (10.9%). The sparse S. glauca, unvegetated marshes, tidal channels and rivers, and intertidal mudflats decreased in areas in 2003-2008 (Table 3). The dense S. glauca increased by 69.0% in area in 2003-2005, and decreased in 2005-2008 (−1.9%). The overall area in dense S. glauca increased by 65.7% (728 ha) in 2003-2008 (Table 3). The entire area of intertidal mudflats in 2003 was completely occupied by the S. alterniflora by 2008 (Figure 2 and Table 3).

3.3. Expansion Characteristics of S. alterniflora Patches

The numbers of S. alterniflora patches increased by 2.5 times from 742 individuals in 2003 to 2608 in 2008, with annual average increase of 373 patches. The number of patches within each 1000-m interval kept increasing

Figure 2. Maps of eight land cover classes of study area generated from SPOT-5 images in 2003 (A), 2005 (B), and 2008 (C).

from 2003 to 2008, except those in 0 - 1000 m interval between 2003 and 2005 (Figure 3(A)). The maximum increase occurred in the interval from 2001 to 3000 m, which are 422 patches in 2003-2005 and 548 patches in 2005-2008. However, the area of patches increased to the most extent in 0 - 1000 m interval, which are 126 ha in 2003-2005, and 142 ha in 2005-2008 (Figure 3(B)). The annual average number and area of patches at over 3001-m interval in 2005-2008 increased faster than those in 2003-2005. Contrarily, those at less than 3000-m interval in 2005-2008 increased slower than in 2003-2005. The distance of the most western patch in 2008 (5931 m) was approximately 700 m far from the most one in 2003 (5247 m).

Specifically, the number of S. alterniflora patches in 2003 indicated to be decreasing at each 1000-m interval in the following years, except those at 0 - 1000 m interval (Figure 4(A)). This was attributed to the large area of patches in this interval and most of them have been connected with each other with the expansion in 2005 and 2008. The number of these patches in 2003 decreased to the most occurred at 2001 - 3000 m interval, which the area expanded to the most extent at the 1001 - 2000 m interval, and the second expansion occurred at 2001 - 3000 m interval (Figure 4(B)).

Contrarily, the number of newly formed patches showed increasing at each 1000-m interval between 2005 and 2008 (Figure 5(A)). The number and area increased to the most occurred at 2001 - 3000 m interval in 2005, which were 680 patches and 1077 ha, respectively. The most increasing of number and area in 2005-2008 appeared at 3001 - 4000 m interval, which increased by 479 patches in number and by 1025 ha in area (Figure 5(A) and Figure 5(B)). At 0 - 1000 m interval, the number and area of new patches formed very slightly, which mainly distributed in the tidal channels.

Table 3. Area and percentage of eight land cover classes of study area in 2003, 2005 and 2008 (the bold numbers presented the overall increase in area of four land covers).

Figure 3. Changes in number of S. alterniflora patches (A) and area of this community (B) at each 1000-m interval against the eastern baseline in 2003, 2005 and 2008. The number of patches was consistent with the number of patch centroids in the corresponding interval belt.

Figure 4. Changes in number (A) and mean expansion area (B) of S. alterniflora patches at each 1000-m interval in 2005 and 2008, which were based on the number and mean area of S. alterniflora patches in 2003.

Figure 5. The number (A) and area (B) of S. alterniflora patches newly formed at each 1000-m interval in 2005 and 2008.

3.4. Changes in Diminutive and Large S. alterniflora Patches

The accumulative number of S. alterniflora patches with area less than 200 m2 accounted for 81.0%, 79.7%, and 79.4% of the total number of S. alterniflora patches in 2003, 2005 and 2008, respectively (Table 4). However, the accumulative area only occupied 0.18%, 0.31% and 0.59% of the total area in each year. The number of patches with the area less than 100 m2 showed distinctively increasing between 2003, 2005 and 2008. The mean increases in 2003-2005 and 2005-2008 were 23.9 ± 10.1, and 50.4 ± 4.1 patches, respectively (n = 16, p < 0.05). Accordingly, the number of patches with the area between 106.25 and 200 m2, also showed distinctively increasing between three years (5.6 ± 0.4 in 2003-2005, and 12.1 ± 1.6 in 2005-2008). While, the increase range is lower than those in smaller patches.

The numbers of patches with area more than 1 ha were 13 individuals in 2003 (1.75%), 7 in 2005 (0.52%), and 16 in 2008 (0.61%), respectively (Table 4). However, the area of these patches accounted for 99.11%, 98.44% and 97.30% of the total area of S. alterniflora community in each year.

3.5. Spread Characteristics of Original Dominant S. alterniflora Patches

The numbers of original dominant patches with area over 200 ha were 4 individuals for each year, which accounted for 91.2%, 98.0%, and 92.8% of the total area of S. alterniflora community in 2003, 2005 and 2008, respectively. The mean spread width increased by 405.2 ± 80.7 m along 17 transects between 2003 and 2008, including 254.6 ± 53.2 m towards east direction and 148.9 ± 44.7 m towards west direction. The east spread was significantly larger than that in west direction (118.04 ± 54.13, t = 2.181, df = 16, p = 0.044).

Mean east spread distance between 2003-2005 was 124.3 ± 36.5 m, which did not show significant increase in comparison with that (66.5 ± 31.0 m) in west direction (57.8 ± 47.0 m, t = 1.23, df = 16, p = 0.236). Mean east spread distance between 2005-2008 was (133.3 ± 44.2 m), which also did not indicate significant increase compared with that (82.9 ± 22.6 m) in west direction (47.4 ± 40.8 m, t = 1.161, df = 16, p = 0.263) (Figure 6).

Table 4. Number of patches with area less than 200 m2 and more than 1 ha at each 1000-m interval in 2003, 2005 and 2005.

Figure 6. Spread distances of original dominant patches of S. alterniflora along 17 transects between 2003-2005 and 2005-2008, which distributed in the outer periphery of the mudflat. Its mean widths were 1591.8 ± 103.0 m, 1779.9 ± 117.2 m, and 1997.0 ± 122.5 m in 2003, 2005 and 2008, respectively.

4. Discussion

This study further proved the fuzzy accuracy assessment approach combined with abundant field samples can significantly improve the map accuracy compared with the conventional binary assessment [34] [44] . The overall accuracies for the land cover maps in 2003, 2005 and 2008 were satisfactory high on a basis of a conventional error matrix (MAX function in fuzzy set assessment), ranging from the lowest 73.5% in 2005 to the highest 81.4% in 2008. When the degrees of acceptability of mis-classification are introduced (in the fuzzy assessment approach), the distribution maps were much better, with acceptable classifications in 91.5% in 2003 and in up to 93.4% in 2008.

These results are encouraging, given the fact that a simple method (maximum-likelihood classification) was chosen for land cover classification in this study. A number of more sophisticated algorithms are currently available, including several that evaluate and use the spectral local texture of images to improve pixel classification [13] [47] . Based on works done in other contexts, it is likely that this approach can still result in higher classification accuracies in wetlands and marshes. The results can satisfy to recognize invasive process and expansion pattern of exotic S. alterniflora, and to be applied to coastal wetland management.

The classification maps derived from higher resolution satellite data can elaborate the expansion characteristics of S. alterniflora, especially in the early infestation stage when the invasive species has not yet gained dominance. For early detection and rapid response to invasions, higher resolution imagery are therefore more appropriate [44] [48] . In this study, the MAX values for S. alterniflora varied from 83.3% to 100% in three years, and a 16.7% improvement in classification occurred in RIGHT function in 2005. These were largely associated with high spatial resolution of images and small size of field samples (one in 2003, and 18 in 2005 and 2008). A combination of high spatial resolution imagery and fuzzy set assessment would effectively improve the overall classification accuracy and early detection abilities of this invasive plant.

The results showed the area of S. alterniflora community increased by 479 ha (28.8%) from 2003 to 2008, while the number of patches increased by 2.5 times from 742 individuals in 2003 to 2,608 in 2008, with average annual increase of 373 patches. Specifically, the area of patches increased much larger in 2003-2005 and the number of patches increased much more in 2005-2008. The number of patches with area less than 100 m2 increased by 605 (32.4% of newly formed patches) and 405 (21.7%) patches in 2001 - 3000 m and 3001 - 4000 m intervals, respectively. The newly formed patches with area less than 200 m2 accounted for 67.7% (1,264 patches) of all increased patches. These indicated salt marshes located in 2001 - 3000 m interval are the most vulnerable to the invader, and in 3001 - 4000 m interval is the second.

The S. alterniflora can accrete and hold sediment in intertidal areas that they invade [49] . The rigid, densely packed stems decrease the rate of tidal flow, causing suspended sediment to precipitate, while dense root mats cause sediment accumulation [14] [50] . In New Zealand, rates of sediment accumulation in both S. alterniflora and S. townsendii have been reported at around 4 cm/year, while adjacent open mud showed no change [51] .

Tidal water can carry the seeds, root stocks, or other parts of the plants deep into the native salt marshes [52] . S. alterniflora can rapidly settled along the tidal channels and in lower lands of salt marshes. Growth of Spartina spp. along river banks and tidal channels can restrict water flow and cause widening of the floodplain [14] . Introduced S. alterniflora that modifies their physical environment in distinct ways, like through altering substrate characteristics, often has great ecological effects on native communities [52] . The original dominant S. alterniflora patches with area over 200 ha covered over 90% of total invasive area for each year in the eastern vegetation belt. The mean eastern spread width (254.6 ± 53.2 m) was distinctively larger than that in western direction (148.9 ± 44.7 m). This indicated that this alien plant invaded mudflats more effective than salt marshes. Its invasion of open mud in the intertidal salt marshes will affect availability of open-mud shorebird feeding areas there [53] [54] .

In this study, sparse S. glauca kept shrinking in the study period, and dense S. glauca showed increasing in the first stage and shrinking afterwards. S. alterniflora outcompeted S. glauca due to changes in growth conditions [50] [55] . The expansion of S. alterniflora reduced the area of native saline S. glauca, which is the important habitats for globally vulnerable Larus saundersi [36] [38] and endangered G. japonensis [39] [40] . The degradation and shrinking of native salt marshes may cause a decline in numbers of birds and eventually decrease local bird diversity. Our previous study indicated that the degradation of breeding habitats would continue, and suitable breeding habitats for L. saundersi would disappear by 2018 [38] .

5. Management Implications

The ecological impacts of S. alterniflora invasion have been documented in previously numerous studies, and recognized worldwide [14] [21] [22] [52] [54] -[57] . In this study area, introduced S. alterniflora has clear negative effects on native S. glauca and P. australis communities, and potentially negative effect on invertebrate communities, and eventually affects the local biodiversity and coastal ecosystem integrity [27] [36] [38] [40] [49] [58] . A major issue is how much human resources should be expended in an effort to eliminate present invasions and prevent future invasion.

A number of different strategies have been used to control the invasion of S. alterniflora, including physical, chemical, biological or integrated methods [59] . There have been large variations in its effectiveness ranging from no effect to complete elimination.

Based on our findings related to the specific expansion characteristics of S. alterniflora, combining with its biology, ecology and management in published literatures, we recommend the following management proposals targeting to conserve globally threatened waterbirds and coastal ecosystem.

1) Considering the S. alterniflora spread along the tidal channels in the study area, it is recommended to dredge the channels in some appropriate way to ensure that the tide currents can flood the saltmarshes regularly. The tide currents can also swash the dead stems of S. glauca plant to keep low plant coverage and density. These would provide suitable habitats for nesting and feeding by waterbirds [38] .

2) For the small patches with area less than 200 m2 in 2001 - 4000 m vertical interval, the mowing combined with the herbicides would be suitable to maintain the growth of native saline plant. [58] concluded that the most effective method Spartina control technique used in Washington combines a single mowing followed by Rodeo application once its growth reaches 30 - 45 cm in height or in the early infestation. [59] illustrated mowing at early florescence is more efficient for controlling S. alterniflora. From a management point of view, each control method has its optimal treatment timing in relation to the phenology of the target plant. However, the effectiveness of different herbicides appears to be highly variable. The use of herbicides could also have deleterious impacts on native flora, and lead to other biological changes within a system. The opportunity also poses great challenge for decision-makers on how to manage this risk.

3) For the original large and dominated patches in the eastern vegetation belt, it is recommended to apply the waterlogging together with mowing. Water irrigation has been used as a means to control S. alterniflora in the marshes and estuaries [17] [60] , and even S. alterniflora can survive by respiring aerobically and anaerobically [61] . [62] concluded that waterlogging with water depth less than 50 cm could promote the growth of S. alterniflora, while waterlogging with water depth more than 50 cm could effectively inhibit the vegetative growth and sexual reproduction, being an effective water depth for controlling the expansion of S. alterniflora.

Managing invaded ecosystems is a great challenge for managers and conservationists, especially in the face of scientific uncertainty and ecological stochasticity [63] . The existing methods for control of S. alterniflora are also uncertain to some extent, and require long-term planning, monitoring and financing. Therefore, it is important to conduct the demonstrations at the first stage to avoid the huge loss of time, money and effort.


This study was supported by the National Natural Science Foundation of China (No. 41201431 and No. 31372226). We thank Yancheng National Nature Reserve for giving us permission to conduct this study. We would like to thank Mr. Wang Hui, Sun Guorong, and Liu Xiaoyun for their involvement in the field works for several years. We also thank the anonymous reviewers for their comments on an earlier version of this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.


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