Shuqing An

China's Natural Wetlands: Past Problems, Current Status, and Future Challenges

Abstract Natural wetlands, occupying 3.8% of Chinas land and providing 54.9% of ecosystem services, are unevenly distributed among eight wetland regions. Natural wetlands in China suffered great loss and degradation (e.g., 23.0% freshwater swamps, 51.2% costal wetlands) because of the wetland reclamation during Chinas long history of civilization, and the population pressure and the misguided policies over the last 50 years. Recently, with an improved understanding that healthy wetland ecosystems play a vital role in her sustainable economic development, China started major efforts in wetland conservation, as signified by the policy to return reclaimed croplands to wetlands, the funding of billions of dollars to restore degraded wetlands, and the national plan to place 90% of natural wetlands under protection by 2030. This paper describes the current status of the natural wetlands in China, reviews past problems, and discusses current efforts and future challenges in protecting Chinas natural wetlands.

Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds.

Classification trees (CT) have been used successfully in the past to classify aquatic vegetation from spectral indices (SI) obtained from remotely-sensed images. However, applying CT models developed for certain image dates to other time periods within the same year or among different years can reduce the classification accuracy. In this study, we developed CT models with modified thresholds using extreme SI values (CT(m)) to improve the stability of the models when applying them to different time periods. A total of 903 ground-truth samples were obtained in September of 2009 and 2010 and classified as emergent, floating-leaf, or submerged vegetation or other cover types. Classification trees were developed for 2009 (Model-09) and 2010 (Model-10) using field samples and a combination of two images from winter and summer. Overall accuracies of these models were 92.8% and 94.9%, respectively, which confirmed the ability of CT analysis to map aquatic vegetation in Taihu Lake. However, Model-10 had only 58.9-71.6% classification accuracy and 31.1-58.3% agreement (i.e., pixels classified the same in the two maps) for aquatic vegetation when it was applied to image pairs from both a different time period in 2010 and a similar time period in 2009. We developed a method to estimate the effects of extrinsic (EF) and intrinsic (IF) factors on model uncertainty using Modis images. Results indicated that 71.1% of the instability in classification between time periods was due to EF, which might include changes in atmospheric conditions, sun-view angle and water quality. The remainder was due to IF, such as phenological and growth status differences between time periods. The modified version of Model-10 (i.e. CT(m)) performed better than traditional CT with different image dates. When applied to 2009 images, the CT(m) version of Model-10 had very similar thresholds and performance as Model-09, with overall accuracies of 92.8% and 90.5% for Model-09 and the CT(m) version of Model-10, respectively. CT(m) decreased the variability related to EF and IF and thereby improved the applicability of the models to different time periods. In both practice and theory, our results suggested that CT(m) was more stable than traditional CT models and could be used to map aquatic vegetation in time periods other than the one for which the model was developed.

Seed plant endemism on Hainan Island: a framework for conservation actions.

Hainan, the second largest island of China, has the most extensive and best preserved tropical forests of this country. A network of 68 protected areas (54 of them are terrestrial) provides in situ conservation for the unique ecosystems of the island. We: (1) discuss an updated check-list of seed-plant species that are endemic to Hainan, (2) evaluate the extent to which the endemic flora has been the subject of molecular studies, and (3) investigate the conservation status of these species. We recognize 397 endemic species on the island, 271 of which are reported in the protected areas, and 144 of which have been Red-Listed (85 assigned to the Critically Endangered (40) or Endangered (45) IUCN categories). The families with the highest number of endemics are Rubiaceae (33 species), Lauraceae (27 species), and Poaceae (26 species). The island has only seven endemic genera, all of which are unispecific. Compared with other tropical islands, Hainan has a low number of endemics but our preliminary observations suggest that the island has a highly disharmonic flora when compared with that from the mainland. Nevertheless, most of the major clades of the seed-plant tree of life with representatives in China also have endemic species on the island. We argue that the low levels of endemism reflect the continental nature of Hainan and the fact that several areas of the island have not been fully inventoried. We were unable to find a single molecular systematic study focusing exclusively on the Hainan endemics; however, 24 of the endemic species have been included in phylogenetic studies targeting particular genera or families. Future research/conservation actions for the endemic flora of Hainan should focus in developing: (1) a red-list that assesses all 397 endemic species, (2) comprehensive floristic studies for the protected areas, (3) molecular phylogenetic and conservation genetic studies with a primary focus on the endemics, (4) studies to understand what ecological interactions are important in the biology of the endemic species, and (5) eco-geographical studies to identify Important Biodiversity Zones of endemism within Hainan and therefore potential new protected areas.

Spatio-Temporal Variability of Aquatic Vegetation in Taihu Lake over the Past 30 Years

It is often difficult to track the spatio-temporal variability of vegetation distribution in lakes because of the technological limitations associated with mapping using traditional field surveys as well as the lack of a unified field survey protocol. Using a series of Landsat remote sensing images (i.e. MSS, TM and ETM+), we mapped the composition and distribution area of emergent, floating-leaf and submerged macrophytes in Taihu Lake, China, at approximate five-year intervals over the past 30 years in order to quantify the spatio-temporal dynamics of the aquatic vegetation. Our results indicated that the total area of aquatic vegetation increased from 187.5 km2 in 1981 to 485.0 km2 in 2005 and then suddenly decreased to 341.3 km2 in 2010. Similarly, submerged vegetation increased from 127.0 km2 in 1981 to 366.5 km2 in 2005, and then decreased to 163.3 km2. Floating-leaf vegetation increased continuously through the study period in both area occupied (12.9 km2 in 1981 to 146.2 km2 in 2010) and percentage of the total vegetation (6.88% in 1981 to 42.8% in 2010). In terms of spatial distribution, the aquatic vegetation in Taihu Lake has spread gradually from the East Bay to the surrounding areas. The proportion of vegetation in the East Bay relative to that in the entire lake has decreased continuously from 62.3% in 1981, to 31.1% in 2005 and then to 21.8% in 2010. Our findings have suggested that drastic changes have taken place over the past 30 years in the spatial pattern of aquatic vegetation as well as both its relative composition and the amount of area it occupies.

Artificial regulation of water level and its effect on aquatic macrophyte distribution in Taihu Lake.

Management of water levels for flood control, water quality, and water safety purposes has become a priority for many lakes worldwide. However, the effects of water level management on the distribution and composition of aquatic vegetation has received little attention. Relevant studies have used either limited short-term or discrete long-term data and thus are either narrowly applicable or easily confounded by the effects of other environmental factors. We developed classification tree models using ground surveys combined with 52 remotely sensed images (15–30 m resolution) to map the distributions of two groups of aquatic vegetation in Taihu Lake, China from 1989–2010. Type 1 vegetation included emergent, floating, and floating-leaf plants, whereas Type 2 consisted of submerged vegetation. We sought to identify both inter- and intra-annual dynamics of water level and corresponding dynamics in the aquatic vegetation. Water levels in the ten-year period from 2000–2010 were 0.06–0.21 m lower from July to September (wet season) and 0.22–0.27 m higher from December to March (dry season) than in the 1989–1999 period. Average intra-annual variation (CVa) decreased from 10.21% in 1989–1999 to 5.41% in 2000–2010. The areas of both Type 1 and Type 2 vegetation increased substantially in 2000–2010 relative to 1989–1999. Neither annual average water level nor CVa influenced aquatic vegetation area, but water level from January to March had significant positive and negative correlations, respectively, with areas of Type 1 and Type 2 vegetation. Our findings revealed problems with the current management of water levels in Taihu Lake. To restore Taihu Lake to its original state of submerged vegetation dominance, water levels in the dry season should be lowered to better approximate natural conditions and reinstate the high variability (i.e., greater extremes) that was present historically.

Composition change and vegetation degradation of riparian forests in the altai plain, NW China

Riparian forests of the Altai Plain in China were studied usingDetrended Canonical Correspondence Analysis (DCCA) and Two-way Indictor SpeciesAnalysis (TWINSPAN). The species could be divided into hydrophytes,hygrophytes,hygro-mesophytes, xero-mesophytes, xerophytes, and high xerophytes. Riverrun-off, water table, and physical components of the soil decided thedistribution of the species. The forests could be classified into wood swamp,hygro-mesic forest, mesic forest and xeric forest. As a specific habitat in thedesert of northwest China, the river valleys harbored most of thePopulus and Salix species recorded inChina. However, the forest has been gradually invaded by adjacent desertspecies. Meanwhile, the native species diversity of the forest has beendeclining as the soil has become more saline and more xeric through intensiveirrigation practice and dam construction in the upper rivers.

A Hardy Plant Facilitates Nitrogen Removal via Microbial Communities in Subsurface Flow Constructed Wetlands in Winter.

The plants effect in subsurface flow constructed wetlands (SSF-CWs) is controversial, especially at low temperatures. Consequently, several SSF-CWs planted with Iris pseudacorus (CWI) or Typha orientalis Presl. (CWT) and several unplanted ones (CWC) were set up and fed with secondary effluent of sewage treatment plant during the winter in Eastern China. The 16S rDNA Illumina Miseq sequencing analysis indicated the positive effects of I. pseudacorus on the bacterial community richness and diversity in the substrate. Moreover, the community compositions of the bacteria involved with denitrification presented a significant difference in the three systems. Additionally, higher relative abundances of nitrifying bacteria (0.4140%, 0.2402% and 0.4318% for Nitrosomonas, Nitrosospira and Nitrospira, respectively) were recorded in CWI compared with CWT (0.2074%, 0.0648% and 0.0181%, respectively) and CWC (0.3013%, 0.1107% and 0.1185%, respectively). Meanwhile, the average removal rates of NH4+-N and TN in CWI showed a prominent advantage compared to CWC, but no distinct advantage was found in CWT. The hardy plant I. pseudacorus, which still had active root oxygen release in cold temperatures, positively affected the abundance of nitrifying bacteria in the substrate, and accordingly was supposed to contribute to a comparatively high nitrogen removal efficiency of the system during the winter.

Genetic structure and habitat selection of the tall form Spartina alterniflora Loisel. in China

Spartina alterniflora Loisel., a highly invasive species on the Chinese coast, is a focus of increasing management concerns due to its high expansion rate in tideland and the significant damages on native ecosystems, since its introduction into China in 1979. There are both tall and dwarf forms of the species in China. The tall form with strongly invasive ability has widely expanded. Genetic variation was examined within and among three tall form S. alterniflora populations in Jiangsu Province using amplified fragment length polymorphisms (AFLP) markers. Three populations were sampled along the coastal line, and each population was divided into three subpopulations relating with the three microenvironments: Foreland, mid-marsh and upland. Genetic diversity was low at both the population level (PPB = 22%, HE = 0. 0657 and Hpop = 0.099) and at the species level (PPB = 24.65%, HT = 0.0814 and Hsp = 0.1225). A low level of genetic differentiation among populations was detected based on analyses of coefficients of genetic differentiation (9.51%), Shannon’s diversity index (9.48%) and AMOVA (10.69%). The mean value of Gst among nine subpopulations was 22.02%. Habitat selections may occur and affect the genetic structure of S. alterniflora in the process of its spread because there are 7.2, 3.4 and 5.9% specific bands out of 158 polymorphic bands in foreland, mid-marsh, and upland, respectively. This genetic differentiation may result from seedling survival and colonization success based on the selection of specialized microhabitats. The results indicated that high capability of genetic differentiation within populations and strong adaptability of tall form S. alterniflora may be the reasons for the widespread expansion of the tall form S. alterniflora.

A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI) as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal) thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV) of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling) normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3%) and overall (92.0%–93.1%) accuracies. Our results suggest that Method of 0.1% index scaling provides a feasible way to apply CT models directly to images from sensors or time periods that differ from those of the images used to develop the original models.

Wetlands of Northeast Asia and High Asia: an overview

This review reports background information on wetlands in the Northeast Asia and High Asia areas, including wetland coverage and type, significance for local populations, and threats to their vitality and protection, with particular focus on the relationship of how global change influenced wetlands. Natural wetlands in these areas have been greatly depleted and degraded, largely due to global climate change, drainage and conversion to agriculture and silviculture, hydrologic alterations, exotics invasions, and misguided management policies. Global warming has caused wetland and ice-sheet loss in High Asia and permafrost thawing in tundra wetlands in Northeast Asia, and hence induced enormous reductions in water-storage sources in High Asia and carbon loss in Northeast Asia. This, in the long term, will exacerbate chronic water shortage and positively feed back global warming. Recently, better understanding of the vital role of healthy wetland ecosystems to Asia’s sustainable economic development has led to major efforts in wetland conservation and restoration. Nonetheless, collaborative efforts to restore and protect the wetlands must involve not only the countries of Northeast and High Asia but also international agencies. Research has been productive but the results should be more effectively integrated with policy-making and wetland restoration practices under future climatic scenarios.