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Article

The Use of Molluscan Fauna as Model Taxon for the Ecological Classification of River Estuaries

by
Rei Itsukushima
1,*,
Kai Morita
2 and
Yukihiro Shimatani
2
1
Department of Decision Science for a Sustainable Society, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
2
Department of Urban and Environmental Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
*
Author to whom correspondence should be addressed.
Water 2017, 9(5), 356; https://doi.org/10.3390/w9050356
Submission received: 21 March 2017 / Revised: 5 May 2017 / Accepted: 9 May 2017 / Published: 18 May 2017
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Abstract

:
River estuaries are important aquatic environments characterized by large environmental gradients in their water quality, riverbed material, and microtopography in the longitudinal and transverse directions. The geography or habitats in river estuaries differ depending on the energy from the tide, waves, and river; therefore, the biota inhabiting river estuaries vary depending on the river estuary type. In view of this, for effective conservation in river estuaries, there is a need for information about potential habitats and biota based on objective data about the river estuary type. The objective of this study thus was to classify river estuaries by their molluscan fauna and physical indicators to reveal the relationship between molluscan fauna and the physical environment. The classification results using physical indicators indicated three types of river estuaries (wave energy-dominated group, tide energy-dominated group, and low tide and wave energy group). This classification result was similar to the classification of molluscan fauna. Therefore, it was suggested that molluscan fauna is extremely useful as a variable representing the river estuary environment. From the comparison between molluscan fauna and the physical environment, some rivers were not classified into the same group in the classification of molluscan fauna, despite them having similar physical environments. Some of these rivers with a molluscan fauna that diverged from expectations had undergone channel modification, which is expected to have caused a shift in this fauna group. These results suggest that this approach could be used to identify rivers that have been degraded by human activities.

1. Introduction

Estuaries are located at the boundary between land and sea, and are a particular environment influenced by periodic tides and waves [1,2]. Estuaries provide multiple ecological services, such as nutrient cycling, climate change adaptation, and function as habitats and spaces for recreation [3]. Costanza et al. (1997) estimated that the total value of annual ecosystem services of estuaries is $22,832 ha−1·yr−1, which is among the highest of 21 biomes [4]. In contrast, estuaries were reported to be highly degraded by human activities because of their rich biodiversity, high levels of nutrients in the land, and abundant natural resources [5,6]. Approximately four billion people now inhabit land within 60 km of the world’s coastlines, and they have placed considerable pressure on estuaries [7]. Anthropogenic impacts induce changes of estuary environments, such as habitat loss, deterioration of water quality, and degradation of resources [7,8,9,10,11,12].
Knowledge about the relationship between estuarine biota and habitat characteristics is important to establish appropriate conservation plans. A multidisciplinary approach was adopted to evaluate estuarine ecological integrity in the United States using the indicators of water quality, bed material, and habitats [13]. Ecological conditions were evaluated using the Benthic Quality Index, based on the recognition of regional benthic fauna [14]. In Australia, a conservation plan for each estuary has been designed and conservation actions have been progressing. In the Australian system, estuaries are classified by the strength of impact of the energy of their tide, waves, and rivers [15,16]. In this Australian system, estuaries are categorized into six main types (tide-dominated, tide-dominated estuary, tidal flat, wave-dominated delta, wave-dominated estuary, and strandplain), and potential habitat types associated with each estuary type are suggested [17]. Research on the relationships between fauna and estuary type and reference conditions for each estuary type was also conducted [18]. In European countries, a Water Framework Directive (WFD) is used to define a framework for the protection of European waters in order to reach “good status” objectives for water bodies throughout the EU [19]; many studies have also evaluated the environmental condition of estuaries to achieve their integrated management [20]. Phytoplankton or benthic fauna have been used as biological indicators [21,22] and physicochemical indicators have also been used to evaluate the condition of estuaries [23,24,25]. In addition, Galván (2010) classified Northeast Atlantic estuaries using hydrological and geological data and revealed that transitional waters with a complex morphology showed the highest values of species diversity, while those with a smaller tidal influence showed lower species diversity [26].
A conservation plan for estuaries should be based on knowledge of an estuary’s classification results based on biological or physical/environmental factors. Many studies have thus established systems for classifying estuaries; their geography, physical environment, and biota are representative of the indicators that have been used. Pritchard (1952) focused on estuarine geography and established three classification types (coastal plain estuaries, fjords, and bar-built estuaries) [27]. In contrast, Davies (1964) classified estuaries into microtidal, mesotidal, macrotidal, and hypertidal, in accordance with the difference of tidal levels [28]. In addition, Fairbridge (1980) presented a comprehensive classification method using geomorphological history, river discharge of water and sediment, tidal current and waves, and coastal processes [29]. Darlymple et al. (1992) further developed the classification concept of Fairbridge and presented the estuarine habitat type based on river energy, wave energy, and tide energy [15]. On the other hand, fish fauna was used for classifying South African estuaries and the classification results were associated with water temperature or geological formation [30,31,32,33,34]. Colloty et al. (2002) categorized 92 estuaries of Eastern Cape Province using 54 plant species and suggested a relationship between the classification results and topographical factors (permanently open and temporarily open/closed) [35].
The examples of conservation efforts or research tools developed in the US, Europe, Australia, and South Africa mentioned above are mainly intended for estuaries of large rivers or coastal zones. However, few studies or conservation plans have been established for river estuaries of small and medium-sized rivers. In Japan and Southeast Asia, where the land is composed of many peninsulas or islands, there is an extended and complex coastline, geographically variable tides, and rivers of various sizes. Therefore, there are many types of estuaries according to the complex geography or physical condition of these areas, and the relationship between the ecological system and the physical environment has not been revealed. Furthermore, the anthropogenic impact on estuaries is extremely high in Japan because of the high population density in lowland areas which means that the reference condition is difficult to determine. Therefore, to establish conservation strategies for the great diversity of existing estuaries, it is important to comprehend the environmental condition by drawing comparisons with other estuaries. Furthermore, predicting the habitat or biota occurring in the river estuaries from physical factors enables the establishment of specific targets for environmental restoration.
Against this background, the aims of this study were to reveal the relationship between physical factors and the composition of the biotic community. To achieve this objective, we investigate the relationship between physical indicators and molluscan fauna and compare the classification results of physical indicators and molluscan fauna and consider the cause of non-correspondence between physical indicators and molluscan fauna.
We selected molluscan fauna to evaluate the integrity of the river estuaries. Molluscan fauna respond sensitively to water quality or bottom sediment and include species that inhabit only one particular environment or have a low capacity to thrive in different habitats. Molluscan species at individual locations directly reflect the environmental conditions at these sites [36]. Therefore, molluscan species are ideal for evaluating the environmental conditions or determining the impact of human activities [37,38,39].

2. Materials and Methods

2.1. Study Area

The present study focused on 19 river estuaries in Kyushu, Japan (Figure 1). River estuaries are characterized by large environmental gradients of water quality, riverbed material, and microtopography in the longitudinal and transverse directions [40]. We selected river estuaries for study here by including those with a variety of physical environments.

2.2. Molluscan Fauna Data

Molluscan fauna collections were performed at low tide at the middle and spring tides from 28 April 2015 to 22 January 2016. The molluscan species were sampled at a dried out habitat one reach (about ten times of the river width) from the mouth. We defined the land of low flow channel as a habitat and set one to three sampling points at each habitat according to the habitat area. Eight kinds of habitats were set from the viewpoint of particle size of the sediment, vegetation, and artificial structure (silt, sand, gravel, boulder, bedrock, riprap, concrete construct, and vegetation). We collected molluscan (Bivalvia and Gastropoda) fauna in a region of a 50 cm square and 10 cm deep at each onshore habitat in a stream reach.

2.3. Physical Environment Data

We collected riverbed material at each molluscan sampling site. The grain size accumulation curve was obtained by a sieve analysis test, as specified in JIS Z 8815 [41], for the grain size range of 3.0–75 mm. For grain sizes greater than 75 mm, we measured the short diameter (ds) and long diameter (dl) of 50 pebbles and calculated the grain diameter as (ds × dl)0.5. When the sampled material did not contain particles larger than 3.0 mm, the grain size accumulation curve was calculated by laser diffraction particle size analysis (SALD, 2000). In addition, the morphology of each habitat and the cross section and longitudinal shape of the river were surveyed by Real Time Kinematic-Global Positioning System (RTK-GPS). The habitat planform and cross-sectional profiles of each river were created by the measurements.

2.4. Calculation of Physical Indicators

In this study, we focused on physical indicators at the watershed scale and the habitat scale. The indicators of river energy, tide energy, and wave energy were adopted for the watershed scale, and the indicators of river bed material and habitat type were adopted for the habitat scale. Specific indicators were as follows: indicator of tide energy: (1) tidal range; indicators of wave energy; (2) wave exposure; (3) direct fetch; indicators of river energy; (4) topographic gradient; (5) form ratio; (6) specific discharge of occurrence probability 1/5, and indicators of habitat scale; (7) silt; (8) sand; (9) gravel; (10) boulder; (11) bedrock; (12) riprap; (13) concrete construct; and (14) vegetation.
(1) Tidal range was calculated by the average of the difference of monthly maximum and minimum tide levels for the period from January–December 2014. The tidal data were obtained from the Japanese Coastguard. To represent the tide at the investigated site, the recorded value from the nearest Japanese Coastguard observation point was adopted; (2) Wave exposure was quantified using the Baardseth Index [42]. To calculate this index, the center of a transparent circular disc with a radius of 15 cm (representing 7.5 km) was placed at the investigation site on a 1:50,000 scale chart. The disc was divided into 40 sectors, with the angle of each sector being 9°. Sectors containing peninsulas, islands, or parts of the mainland shore were ignored [43]; (3) We calculated direct fetch by integrating the distance to the opposite shore from the investigation point, extending the radius in each direction with a width of ±22.5° from the center point [44]. A maximum distance of 200 km was set as the distance to the opposite shore, in accordance with the concept of wave height saturation [45]; (4) The topographic gradient was obtained by dividing the altitude of the headstream by the length of the main river; (5) The form ratio was calculated by dividing the basin area by the square of the length of the main river; (6) The specific discharge of occurrence probability 1/5 was calculated by a rational run-off formula using a rainfall intensity formula released by each river’s administrator; For (7) silt; (8) sand; (9) gravel; and (10) boulder; based on the grain size accumulation curve of riverbed materials collected at each habitat, we defined silt as an average grain diameter of 0.005–0.075 mm, sand as an average grain diameter of 0.075–2.0 mm, gravel as an average grain diameter of 2.0–75 mm, and boulder as an average grain diameter greater than 75 mm; For (12) riprap and (13) concrete construct, when the function of a habitat was changed between a concrete construct or stone material, we defined large stones acting as coastal defenses as riprap and bed beaching as a concrete construct. We used presence-absence data of the indicators (7–14).

2.5. Statistical Analysis

To identify the possible groupings of similar molluscan fauna between river estuaries, we conducted non-metric multidimensional scaling analysis (nMDS) to summarize the composition of molluscan fauna using the Bray–Curtis similarity. nMDS was conducted for 14 physical indicators and species that appeared in over three rivers, to exclude the influence of species appearing at a low frequency. To approach a normal distribution, the number of individual molluscs was used after logarithmic conversion [log(e + 1)]. As a result of the permutation test, physical indicators (p < 0.05) were shown as vectors. Additionally, indicator species of each group were obtained using the indicator value method (IndVal) [46]. Secondly, molluscan fauna and physical indicators were classified by hierarchical cluster analysis (Ward method). Euclidean distance was used for calculating the distance between objectives. To investigate the difference of a physical indicator among groups, the average values of physical indicators of each group were analyzed with the Kruskal–Wallis test and Steel Dwass test. These analyses were conducted using the statistical analysis software R.

3. Results

3.1. Molluscan Fauna Survey

In total, 27 families, 55 species, and 6003 individuals were collected in 19 river estuaries (Table 1). The species for which the highest number of individuals were collected was Batillaria multiformis, for which a total of 1105 individuals were confirmed in 12 rivers. At the other end of the spectrum, species belonging to Bivalvia, such as Nuttallia commode, Moerella iridescens, and Laternula boschasina, were confirmed in a few rivers. Regarding the numbers of species and individuals in each river, the Hai River was associated with the highest numbers of species and individuals (17 species and 1249 individuals). In contrast, no molluscan species were found in the Obaru River.

3.2. Relationship between Molluscan Fauna and Physical Indicators

Figure 2 indicates the plotted molluscan species and physical indicators on nMDS dimensions. As a result of the permutation test, tidal range, direct fetch, silt, sand, boulder, concrete construct, and vegetation were selected. Littoraria intermedia, Cerithidea rhizophorarum, Assiminea sp., or Cerithidea cingulate were plotted in the first quadrant, characterized by a large tidal range and abundant silt and vegetation habitat. The second quadrant represented habitats with large direct fetch and an abundant concrete construct. Mytilus galloprovincialis and Littorina brevicula were plotted in this quadrant. Ruditapes philippinarum, Monodonta labio form confusa, or Nipponacmea radula belonged to the third quadrant, characterized by habitats with large direct fetch and sand habitat. Pattelloida pygmaea form heroldi, Nipponacmea nigrans, and Turbo cornatus coreensis were plotted in the fourth quadrant, characterized by abundant sand and boulder habitat.
In the nMDS dimensions, the Cerithidea genus, which inhabits muddy bottoms or reed fields in tidal wetlands, was plotted in the first quadrant. The first quadrant in this analysis represented a high tide and vegetation-rich environment. Additionally, Mytilus galloprovincialis, which inhabits wave-dominated rock reefs or concrete constructs via byssus attachment, was plotted into the second quadrant. The second quadrant in this analysis represented high direct fetch and a gravel-abundant environment. Therefore, the nMDS results appropriately reflect the habitat environment of these species.

3.3. River Estuary Classification Using Molluscan Fauna

From the results of the nMDS analysis, the molluscan fauna of the 19 rivers were divided into four groups (Figure 3). Group A comprised fauna from three rivers flowing into the Genkai Sea and the Kurosaki River flowing into the Sumou Sea. Group B comprised five rivers flowing into the Genkai Sea and Imari Bay. Group C comprised four rivers flowing into the Imari Sea and the Kusami River. Group D comprised six rivers flowing into the Imari Sea and the Buzen Sea. The Obaru River was not classified into any groups because no molluscan species were found.
Table 2 shows the IndVal index of molluscan fauna in the four groups.
In group A, the IndVal score of all species was lower than 20. The IndVal indices of Littorina brevicula and Mytilus galloprovincialis were relatively higher than those of the other species. The IndVal indices of Batillaria multiformis and Batillaria attramentaria exceeded 40 in group B. In group C, the IndVal indices of eight species exceeded 40, and especially, the value of Cerithidea ornata was the highest. The IndVal indices of Littoraria intermedia and Assiminea sp. exceeded 50 in group D.

3.4. River Estuary Classification Using Physical Indicators

Figure 4 shows the results of the cluster analysis using physical indicators. In this case, the 19 rivers were divided into three groups (groups α–γ). Group α was composed of the rivers flowing into the Ariake Sea and the Genkai Sea. All of the rivers flowing into the Imari Sea belonged to group β. Group γ was mainly composed of the rivers flowing into the Buzen Sea and the Ariake Sea.
The differences of six quantitative physical indicators (tidal range, wave exposure, direct fetch, topographic gradient, form ratio, specific discharge) among the three groups are indicated in a boxplot (Figure 5). Group γ exhibited the highest tidal range (388.4 cm), followed by group α (238.5 cm) and group β (220.0 cm). The results of the Kruskal-Wallis test indicated a significant difference in this regard among the three groups (p < 0.01). The Steel-Dwass multiple comparison test also showed a significant difference between group γ and the other two groups (p < 0.05). Direct fetch varied in the order of group α (583.0), group γ (110.7), and group β (11.7). The Kruskal-Wallis test showed a significant difference among the three groups (p < 0.001), as did the Steel-Dwass multiple comparison test. Wave exposure was the highest (9.4) in group α. The results of the Kruskal-Wallis test also indicated a significant difference among the three groups for this variable (p < 0.001), and the Steel-Dwass multiple comparison test indicated a significant difference between group α and the other two groups (p < 0.05). In contrast, topographic gradient, form ratio, and specific discharge were not confirmed to differ significantly among the three groups in one-way analysis of variance. The average values of eight qualitative physical indicators in each group are shown in Table 3. The value of sand was higher than those of the other bed materials in group α. The rivers belonging to group α were dominated by sand-based habitats. In addition, vegetation was not observed in group α. In group β, the values of silt, sand, gravel, boulder, and riprap were high. The rivers belonging to group β were characterized by various types of riverbed material. The rivers classified into group γ were characterized by various riverbeds and vegetation habitats, except for the Yamashiro River.

4. Discussion

4.1. Physical Indicators Affecting Molluscan Fauna

As a result of the nMDS, seven physical indicators were selected as factors having a strong relationship with molluscan fauna. In Australia, estuarine morphology has been reported to be strongly influenced by the strength of impacts of energy from the tide, waves, and rivers [15,16]. Also in this study, the tidal range reflecting tide energy and direct fetch reflecting wave energy were selected as important variables to the inhabiting molluscan species. The reason why the indicator representing the energy of the river was not selected is that the river estuaries included in this study belong to the same climate zone and the difference in specific flow rate is not significant (Figure 5). The habitat scale indicators of silt, sand, boulder, concrete construct, and vegetation were also selected, in addition to watershed scale indicators. These findings indicate that the planning of estuary conservation schemes, including for molluscan fauna, requires information on the habitat at the micro-habitat scale, in addition to the watershed scale.
Regions of vegetation in estuaries, such as reed fields, have important functions as habitats for estuarine organisms [47,48,49]. However, reed fields have been modified to paddy fields and fishery ponds in recent years [50,51], which has led to discussions about the need for conservation in this context. On the other hand, the invasion of common reeds (Phragmites australis) has reduced ecosystem quality due to a change in the hydroperiod of salt marshes and its planform [52,53,54]. However, the results of this study suggest that the vegetation habitat is an important environmental element of river estuaries for rarer species, such as Cerithidea rhizophorarum and Assiminea sp.
The findings obtained here indicate that many molluscan species are closely related to physical indicators at the watershed scale and the habitat scale; therefore, molluscan species at individual locations directly reflect the environmental conditions of a particular site.

4.2. Comparison of the Classification Results of Molluscan Fauna and Physical Indicators

In this section, we discuss a comparison of the classification results of molluscan fauna and physical indicators. As a result of the classification using physical indicators, 19 rivers were classified into three groups.
Group α comprised all rivers belonging to group A (the Yahagi River, the Saigou River, and the Kurosaki River) in the classification of molluscan fauna and the Obaru River and the Ekawauchi River. Common species that inhabit all rivers were not confirmed in group α. In contrast, species closely related to direct fetch in nMDS, such as Nipponacmea radula and Monodonta labio form confuse, were confirmed in multiple rivers belonging to group α. The characteristic of the physical environment of rivers in group α was high wave energy (Figure 5); therefore, the results of a classification using physical indicators may reflect the characteristics of molluscan fauna.
The physical characteristic of group β was low wave energy (Figure 5). Compared with the classification results of molluscan fauna, this group comprises five rivers, each belonging to groups B (the Imahuku River, the Sato River, the Sakurai River, and the Jyurou River) and C (the Hai River and the Saozoro River). The species belonging to the Cerithidea genus were frequently confirmed in these rivers. These species were marked at the low direct fetch and high tide range areas in nMDS.
The tidal range of group γ was significantly larger than those of the other two groups (Figure 5), and rivers dominated by tide energy belonged to this group. Compared with the classification results of molluscan fauna, six rivers (the Naka River, the Iida river, the Yamashiro River, the Kakuta River, the Kuroki River, and the Otsubo River) belonging to this group were classified into group D. Littoraria intermedia and Assiminea sp. were frequently confirmed in these rivers. These species were marked at the high tide area in nMDS. Therefore, the classification results of physical indicators fit with the molluscan fauna.
Martins et al. (2014) investigated benthic molluscan communities on the Portuguese continental shelf; they evaluated the utility of using molluscan fauna as an environmental indicator because of the strong relationship between molluscan fauna and sediment particle size [55]. Moreover, they pointed out the application of other fauna such as polychaete assemblages in areas where the molluscan fauna is poor [56,57]. In this study, many rivers belonging to group C showed poor molluscan fauna; therefore, it is necessary to consider the introduction of other taxa to evaluate environmental integrity.

4.3. Physical Environment Degradation by Human Activities and Molluscan Fauna

As mentioned above, the classification results of molluscan fauna and those of physical indicators were generally in agreement. However, the results of the classification of molluscan fauna and physical indicators did differ in multiple rivers. This was thought to have been due to the molluscan fauna being modified by human activities, such as river channel improvement. In this section, we discuss the relationship between the impact of channel improvement and molluscan fauna by comparing the Sato River and the Hai River. These two rivers flow into Imari Bay (Figure 1) and have very similar physical indicators (Figure 4). Therefore, their molluscan fauna would also be expected to be similar. However, the classification results using molluscan fauna indicated a difference between the two rivers (Figure 3). In total, 17 molluscan species and 1249 individuals were confirmed in the Hai River, whereas 7 species and 34 individuals were confirmed in the Sato River. We consider that this difference can be explained by habitat characteristics.
Habitat distributions of these two rivers are indicated in Figure 6. Six types of habitat (silt, sand, gravel, bedrock, vegetation, and riprap) were confirmed in the Hai River. However, only two types (silt, gravel) were confirmed in the Sato River. Moreover, the proportion of total habitat area to river channel area was 59% in the Sato River, whereas it was 10% in the Hai River. Regarding the difference of molluscan fauna, Batillaria attramentaria, Cerithidea djadjariensis, and Cerithidea cingulata, which were abundant in the Hai River, were not confirmed in the Sato River. Since these species mainly inhabit muddy bottoms or reed fields in tidal flats, the difference of habitat diversity and habitat size may influence the molluscan fauna in the two rivers.
The cross-sectional profiles of the Hai River and the Sato River are shown in Figure 7. The riverbed of the Sato River has been flattened by channel improvement, and the area of dried habitat that appears at low tide is small. In contrast, the Hai River exhibits substantial variation in the elevation of its channel. The existence of various elevations along a river channel is thought to have a major influence on the habitat of shellfish fauna because it causes variations in the concentration of salt in the river and in the type of material on the riverbed.
As mentioned above, the molluscan fauna was expected to be similar between these rivers because of their closely analogous physical environments. However, river channel improvement had affected the molluscan fauna of the Sato River. These results of cluster analysis of molluscan fauna and physical indicators suggested that this approach could be used to identify rivers that have been degraded by human activities.

5. Conclusions

The objective of this study was to classify river estuaries by their molluscan fauna and physical indicators, in order to reveal the relationship between molluscan fauna and physical environments. The major conclusions and recommendations of this study include the following:
  • As a result of nMDS, seven physical indicators (tidal range, direct fetch, specific discharge, silt, gravel, concrete construct, and vegetation) were selected with a strong relationship with molluscan fauna. At the watershed scale, the energy levels of the tide and waves were found to influence the molluscan fauna of a river estuary, while at the habitat scale, the factors of silt, gravel, concrete construct, and vegetation exerted this same influence.
  • The classification results using physical indicators indicated three types of river estuaries (wave energy-dominated group, tide energy-dominated group, and low tide and wave energy group). This classification result was similar to the classification of molluscan fauna. Therefore, it was suggested that molluscan fauna is extremely useful as a feature representing the river estuary environment.
  • From the comparison between molluscan fauna and the physical environment, some rivers were not classified into the same group as in the classification of molluscan fauna, despite them having similar physical environments. Some of these rivers with molluscan fauna that diverged from expectations had undergone channel modification, which is expected to have caused a shift in the fauna group. Comparing the classification results of the biota and the physical indicators suggested that it was possible to extract rivers with degraded biota by artificial influence.

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number JP15K18144 and the River Fund managed by The River Foundation.

Author Contributions

Rei Itsukushima integrated the research program and wrote this paper. Kai Morita conducted the field survey and data reduction. Yukihiro Shimatani provided editorial comments towards the writing of the paper.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Dyer, K.R. Estuaries, a Physical Introduction, 2nd ed.; John Wiley & Sons: Chichester, UK, 1997. [Google Scholar]
  2. Schröder-Adams, C.J.; Boyd, R.L.; Tran, T. Estuarine foraminiferal biofacies pattern compared to the brackish ichnofacies model: Port Stephens, southeast Australia. Estuar. Coast. Shelf Sci. 2014, 139, 78–87. [Google Scholar] [CrossRef]
  3. Carpenter, S.R.; Mooney, H.A.; Agard, J.; Capistrano, D.; Defries, R.S.; Diaz, S.; Dietz, T.; Duraiappah, A.K.; Oteng-Yeboah, A.; Pereira, H.M.; et al. Science for managing ecosystem services: Beyond the Millennium Ecosystem Assessment. Proc. Natl. Acad. Sci. USA 2009, 106, 1305–1312. [Google Scholar] [CrossRef] [PubMed]
  4. Costanza, R.; D’Arge, R.; De Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; O’Neill, R.V.; Paruelo, J.; et al. The value of the world’s ecosystem services and natural capital. Nature 1997, 387, 253–260. [Google Scholar] [CrossRef]
  5. Edgar, G.J.; Barrett, N.S.; Graddon, D.J.; Last, P.R. The conservation significance of estuaries: A classification of Tasmanian estuaries using ecological, physical and demographic attributes as a case study. Biol. Conserv. 2000, 92, 383–397. [Google Scholar] [CrossRef]
  6. Cohen, A.N.; Carlton, J.T. Accelerating invasion rate in a highly invaded estuary. Science 1998, 279, 555–558. [Google Scholar] [CrossRef] [PubMed]
  7. Kennish, M.J. Environmental threats and environmental future of estuaries. Environ. Conserv. 2002, 29, 78–107. [Google Scholar] [CrossRef]
  8. Clark, R.B. Marine Pollution, 3rd ed.; Clarendon Press: Oxford, NY, USA, 1992. [Google Scholar]
  9. McIntyre, A.D. Human impact on the oceans: The 1990s and beyond. Mar. Pollut. Bull. 1995, 31, 147–151. [Google Scholar] [CrossRef]
  10. Kennish, M.J. Coastal salt marsh systems in the U.S.: A review of anthropogenic impacts. J. Coast. Res. 2001, 17, 731–748. [Google Scholar]
  11. Howarth, R.W. Coastal nitrogen pollution: A review of sources and trends globally and regionally. Harmful Algae 2008, 8, 14–20. [Google Scholar] [CrossRef]
  12. Brown, A.C.; McLachlan, A. Sandy shore ecosystems and the threats facing them: Some predictions for the year 2025. Environ. Conserv. 2002, 29, 62–77. [Google Scholar] [CrossRef]
  13. Borja, A.; Bricker, S.B.; Dauer, D.M.; Demetriades, N.T.; Ferreira, J.G.; Forbes, A.T.; Hutchings, P.; Jia, X.; Kenchington, R.; Marques, J.C.; et al. Overview of integrative tools and methods in assessing ecological integrity in estuarine and coastal systems worldwide. Mar. Pollut. Bull. 2008, 56, 1519–1537. [Google Scholar] [CrossRef] [PubMed]
  14. Diaz, R.J.; Solan, M.; Valente, R.M. A review of approaches for classifying benthic habitats and evaluating habitat quality. J. Environ. Manag. 2004, 73, 165–181. [Google Scholar] [CrossRef] [PubMed]
  15. Dalrymple, R.W.; Zaitlin, B.A.; Boyd, R. Estuarine facies models: Conceptual basis and stratigraphic implications. J. Sediment. Petrol. 1992, 62, 1130–1146. [Google Scholar] [CrossRef]
  16. Boyd, R.; Dalrymple, R.; Zaitlin, B.A. Classification of clastic coastal depositional environments. Sediment. Geol. 1992, 80, 19–150. [Google Scholar] [CrossRef]
  17. Heap, A.; Bryce, S.; Ryan, D.; Redke, L.; Smith, R. Australian Estuaries and Coastal Waterways: A Geoscience Perspective for Improved and Integrated Resource Management; Record 2001/07; Australian Geological Survey Organization: Canberra, Australia, 2001. [Google Scholar]
  18. Fairweather, P.G. Determining the ‘health’ of estuaries: Priorities for ecological research. Aust. Ecol. 1999, 24, 441–451. [Google Scholar] [CrossRef]
  19. Voulvoulis, N.; Arpon, K.D.; Giakoumis, T. The EU Water Framework Directive: From great expectations to problems with implementation. Sci. Total Environ. 2017, 575, 358–366. [Google Scholar] [CrossRef] [PubMed]
  20. Borja, A.; Elliott, M.; Andersen, J.H.; Cardoso, A.C.; Carstensen, J.; Ferreira, J.G.; Heiskanen, A.-S.; Marques, J.C.; Neto, J.M.; Teixeira, H.; et al. Good environmental status of marine ecosystems: What is it and how do we know when we have attained it? Mar. Pollut. Bull. 2013, 76, 16–27. [Google Scholar] [CrossRef] [PubMed]
  21. Borja, A.; Franco, J.; Pérez, V. The application of a Marine Biotic Index to different impact sources affecting soft-bottom benthic communities along European coasts. Mar. Pollut. Bull. 2000, 40, 1110–1114. [Google Scholar] [CrossRef]
  22. Muxika, I.; Borja, A.; Bald, J. Using historical data, expert judgement and multivariate analysis in assessing reference conditions and benthic ecological status, according to the European Water Framework Directive. Mar. Pollut. Bull. 2007, 55, 16–29. [Google Scholar] [CrossRef] [PubMed]
  23. Bald, J.; Borja, A.; Muxika, I.; Franco, J.; Valencia, V. Document Assessing reference conditions and physico-chemical status according to the European Water Framework Directive: A case-study from the Basque Country (Northern Spain). Mar. Pollut. Bull. 2005, 50, 1508–1522. [Google Scholar] [CrossRef] [PubMed]
  24. Borja, A.; Heinrich, H. Implementing the European Water Framework Directive: The debate continues. Mar. Pollut. Bull. 2005, 50, 486–488. [Google Scholar] [CrossRef] [PubMed]
  25. Rodríguez, J.G.; Tueros, I.; Borja, A.; Belzunce, M.J.; Franco, J.; Solaun, O.; Valencia, V.; Zuazo, A. Maximum likelihood mixture estimation to determine metal background values in estuarine and coastal sediments within the European Water Framework Directive. Sci. Total Environ. 2006, 370, 278–293. [Google Scholar] [CrossRef] [PubMed]
  26. Galván, C.; Juanes, J.A.; Puente, A. Ecological classification of European transitional waters in the North-East Atlantic eco-region. Estuar. Coast. Shelf Sci. 2010, 87, 442–450. [Google Scholar] [CrossRef]
  27. Pritchard, D.W. Salinity distribution and circulation in the Chesapeake Bay estuarine system. J. Mar. Res. 1952, 11, 106–123. [Google Scholar]
  28. Davies, J.H. A morphogenetic approach to world shorelines. Z. Geomorphol. 1964, 8, 127–142. [Google Scholar]
  29. Fairbridge, R.W. The estuary: Its definition and geodynamic cycle. In Chemistry and Biogeochemistry of Estuaries; Wiley: New York, NY, USA, 1980; pp. 1–35. [Google Scholar]
  30. Harrison, T.D.; Whitfield, A.K. Estuarine typology and the structuring of fish communities in South Africa. Environ. Biol. Fishes 2006, 75, 269–293. [Google Scholar] [CrossRef]
  31. Harrison, T.D.; Whitfield, A.K. Geographical and typological changes in fish guilds of South African estuaries. J. Fish Biol. 2008, 73, 2542–2570. [Google Scholar] [CrossRef]
  32. Harrison, T.D.; Whitfield, A.K. Fish trophic structure in estuaries, with particular emphasis on estuarine typology and zoogeography. J. Fish Biol. 2012, 81, 2005–2029. [Google Scholar] [CrossRef] [PubMed]
  33. Cooper, J.A.G.; Ramm, A.E.L.; Harrison, T.D. The estuarine health index: A new approach to scientific information transfer. Ocean Coast. Manag. 1994, 25, 103–141. [Google Scholar] [CrossRef]
  34. Whitfield, A.K. An estuary-association classification for the fishes of southern Africa. S. Afr. J. Sci. 1994, 90, 441–447. [Google Scholar]
  35. Colloty, B.M.; Adams, J.B.; Bate, G.C. Classification of estuaries in the Ciskei and Transkei regions based on physical and botanical characteristics. S. Afr. J. Bot. 2002, 68, 312–321. [Google Scholar] [CrossRef]
  36. Sato, S. Report on Four Academic Societies Joint Symposium of Biodiversity Conservation of Ariake Bay. Jpn. J. Benthol. 2011, 66, 102–116. [Google Scholar] [CrossRef]
  37. Blanchet, H.; Gouillieux, B.; Alizier, S.; Amouroux, J.-M.; Bachelet, G.; Barillé, A.-L.; Dauvin, J.-C.; de Montaudouin, X.; Derolez, V.; Desroy, N.; et al. Multiscale patterns in the diversity and organization of benthic intertidal fauna among French Atlantic estuaries. J. Sea Res. 2014, 90, 95–110. [Google Scholar] [CrossRef]
  38. Zenetos, A. Classification and interpretation of the established Mediterranean biocoenoses based solely on bivalve molluscs. J. Mar. Biol. Assoc. UK 1996, 76, 403–416. [Google Scholar] [CrossRef]
  39. Koutsoubas, D.; Dounas, C.; Arvanitidis, C.; Kornilios, S.; Petihakis, G.; Triantafyllou, G.; Eleftheriou, A. Macrobenthic community structure and disturbance assessment in Gialova Lagoon, Ionian Sea. ICES J. Mar. Sci. 2000, 57, 1472–1480. [Google Scholar] [CrossRef]
  40. Kusuda, T.; Yamamoto, K. River Brackish Area; Gihodo Shuppan: Tokyo, Japan, 2008. [Google Scholar]
  41. Japanese Geotechnical Society. Japanese Geotechnical Society Standards Laboratory Testing Standards of Geomaterials; Japanese Geotechnical Society: Tokyo, Japan, 2014; Volume 1. [Google Scholar]
  42. Baardseth, E. A Square Scanning, Two Stage Sampling Method of Estimating Sea Weed Quantities; Norskinstitutt for Tang-Ogtareforskning; Tapir: Oslo, Norway, 1970; Volume 33, pp. 1–41. [Google Scholar]
  43. Ruuskanen, A.; Bäck, S.; Reitalu, T. A Comparison of Two Cartographic Exposure Methods Using Fucusvesicu-losus as an Indicator. Mar. Biol. 1999, 134, 139–145. [Google Scholar] [CrossRef]
  44. Keddy, P.A. Quantifying a within-lake gradient of wave energy in Gillfillan Lake, Nova Scotia. Can. J. Bot. 1982, 62, 301–309. [Google Scholar] [CrossRef]
  45. Burrows, M.; Harvey, R.; Robb, L. Wave Exposure Indices from Digital Coastlines and the Prediction of Rocky Shore Community Structure. Mar. Ecol.-Prog. Ser. 2008, 353, 1–12. [Google Scholar] [CrossRef]
  46. Dufrêne, M.; Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical ap-proach. Ecol. Monogr. 1997, 67, 345–366. [Google Scholar] [CrossRef]
  47. Vermeiren, P.; Sheaves, M. Predicting habitat associations of five intertidal crab species among estuaries. Estuar. Coast. Shelf Sci. 2014, 149, 133–142. [Google Scholar] [CrossRef]
  48. Bancroft, G.T.; Gawlik, D.E.; Rutchey, K. Distribution of wading birds relative to vegetation and water depths in the northern Everglades of Florida, USA. Waterbirds 2002, 25, 265–277. [Google Scholar] [CrossRef]
  49. Kobayashi, S. Distribution pattern and ecology of brachyuran crabs in the riverine environment: Their significance in the ecosystem and present condition. Ecol. Civil Eng. 2000, 3, 113–130. [Google Scholar] [CrossRef]
  50. Ke, C.-Q.; Zhang, D.; Wang, F.-Q.; Chen, S.-X.; Schmullius, C.; Boerner, W.-M.; Wang, H. Analyzing coastal wetland change in the Yancheng National Nature Reserve, China. Reg. Environ. Chang. 2011, 11, 161–173. [Google Scholar] [CrossRef]
  51. Zuo, P.; Wan, S.W.; Qin, P.; Du, J.J.; Wang, H. A comparison of the sustainability of original and constructed wetlands in Yancheng Biosphere Reserve, China: Implications from emergy evaluation. Environ. Sci. Policy 2004, 7, 329–343. [Google Scholar] [CrossRef]
  52. Weinstein, M.P.; Balletto, J.H. Does the common reed, Phragmites australis reduce essential habitat for fishes? Estuaries 1999, 22, 793–802. [Google Scholar] [CrossRef]
  53. Hagan, S.M.; Brown, S.A.; Able, K.W. Production of mummichog (Fundulus heteroclitus): Response in marshes treated for common reed (Phragmites australis) removal. Wetlands 2007, 27, 54–67. [Google Scholar] [CrossRef]
  54. Weinstein, M.P.; Litvin, S.Y.; Guida, V.G. Essential fish habitat and wetland restoration success: A tier III approach to the biochemical condition of common mummichog Fundulus heteroclitus in common reed Phragmites australis- and smooth cordgrass Spartina alterniflora-dominated salt marshes. Estuaries Coasts 2009, 32, 1011–1022. [Google Scholar] [CrossRef]
  55. Martins, R.; Sampaio, L.; Quintino, V.; Rodrigues, A.M. Diversity, distribution and ecology of benthic molluscann communities on the Portuguese continental shelf. J. Sea Res. 2014, 93, 75–89. [Google Scholar] [CrossRef]
  56. Martins, R.; Sampaio, L.; Rodrigues, A.M.; Quintino, V. Soft-bottom portuguese continental shelf polychaetes: Diversity and distribution. J. Mar. Syst. 2013, 123–124, 41–54. [Google Scholar] [CrossRef]
  57. Martins, R.; Quintino, V.; Rodrigues, A.M. Diversity and spatial distribution patterns of the soft-bottom macrofauna communities on the Portuguese continental shelf. J. Sea Res. 2013, 83, 56–64. [Google Scholar] [CrossRef]
Water 09 00356 g001 550
Figure 1. Location of the study sites.
Figure 1. Location of the study sites.
Water 09 00356 g001
Water 09 00356 g002 550
Figure 2. Plotted molluscan species and physical indicators on non-metric multidimensional scaling analysis (nMDS) dimensions.
Figure 2. Plotted molluscan species and physical indicators on non-metric multidimensional scaling analysis (nMDS) dimensions.
Water 09 00356 g002
Water 09 00356 g003 550
Figure 3. nMDS for similarity between the rivers based on molluscan fauna.
Figure 3. nMDS for similarity between the rivers based on molluscan fauna.
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Water 09 00356 g004 550
Figure 4. Cluster analysis dendrogram for the environmental variables.
Figure 4. Cluster analysis dendrogram for the environmental variables.
Water 09 00356 g004
Water 09 00356 g005 550
Figure 5. Comparison of tidal range (a); direct fetch (b); wave exposure (c); form ratio (d); terrain gradient (e) and specific discharge (f) among the three groups.
Figure 5. Comparison of tidal range (a); direct fetch (b); wave exposure (c); form ratio (d); terrain gradient (e) and specific discharge (f) among the three groups.
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Water 09 00356 g006 550
Figure 6. Habitat distribution of the Sato River (a) and the Hai River (b).
Figure 6. Habitat distribution of the Sato River (a) and the Hai River (b).
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Water 09 00356 g007 550
Figure 7. Comparison of the cross-sectional profile of the Sato River and the Hai River.
Figure 7. Comparison of the cross-sectional profile of the Sato River and the Hai River.
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Table
Table 1. Molluscan fauna of 19 river estuaries.
Table 1. Molluscan fauna of 19 river estuaries.
SpeciesRiver No
12345678910111213141516171819
Chitonidae
Ischnochiton comptus0005000000000000000
Nacellidae
Cellana nigrolineata2200000000000000600
Lottiidae
Nipponacmea gloriosa0001000000000000000
Nipponacmea radula55012000000000100000
Nipponacmea nigrans008914310000000000000
Patelloida pygmaea0000000100010000000
Patelloida pygmaea form conulus0021080000000000000
Pattelloida pygmaea form heroldi0240084000000000000
Trochidae
Chlorostoma xanthostigma0100000000000000000
Monodonta labio form confusa419111030001000000000
Omphalius rusticus0000000001000000000
Turbinidae
Turbo coronoatus coronatus0202000000000000000
Turbo cornatus coreensis051302130019000000000
Neritidae
Nerita japonica427919002340000000000000
Clithon retropicta001496044400500080000
Neripteron cornucopia00000000000010001200
Phenacolepadidae
Phenacolepas unguiformis0000000200000000000
Cinnalepeta pulchella0060000000000000000
Cerithiidae
Ceritium coralium0000001000000000000
Batillariidae
Batillaria multiformis0281381112216761010160002051208003
Batillaria attramentaria031089073092052000100000
Potamididae
Cerithidea djadjariensis00000396136000000000000
Cerithidea largillierti00000030000080003300
Cerithidea ornata0010015110000002101300
Cerithidea rhizophorarum0018011653000005446309900
Cerithidea cingulata000003198500000040067500
Littorinidae
Cerithidea rhizophorarum11000042130070029232114313
Littoraria intermedia00000000000011487104106
Littoraria articulata000000000000000200730
Assimineidae
Assiminea sp.003800000000018274232800442
Muricidae
Thais clavigera2004000009000000000
Reishia bronni0100000000000000000
Nassariidae
Reticunassa festiva0010000000000000000
Nassarius multigranosa0000060000000000000
Buccinidae
Japeuthria ferrea0400000000000000000
Ellobiidae
Laemodonta exaratoides0000000000000001000
Arcidae
Barbatia virescens0202001000000000000
Mytilidae
Modiolus nipponicus00000000003300000000
Hormomya mutabilis21500002000000000000
Mytilus galloprovincialis40605907200011011503210
Mactridae
Raetellops pulchellus0000000200000000000
Mesodesmatidae
Coecella chinensis0003000000000000000
Tellinidae
Nitidotellina hokkaidoensis0000000002000000000
Moerella iridescens0000000001000000000
Psammobiidae
Nuttallia commoda0000000100000000000
Psammotaea virescens00000000008100000000
Psammotaea minor0000000300000000000
Trapezidae
Trapezium oblongum00001211000000000090
Corbiculidae
Corbicula japonica0000000000000200000
Glauconomidae
Glauconome chinensis00000000000075000501
Veneridae
Ruditapes philippinarum00020001083100000000
Cyclina sinensis00100250001000000000
Meretrix lusoria0000000200000000000
Laternulidae
Laternula boschasina0000000000000100000
Pomacea canaliculate00001800000000000000
number of species8141615717171201053713871046
number of individuals7216826826434124945926401855422896301263621125157475
Table
Table 2. IndVal index of molluscan species in each group.
Table 2. IndVal index of molluscan species in each group.
SpeciesIndval
ABCD
Nipponacmea radula111660
Nipponacmea nigrans014490
Pattelloida pygmaea form heroldi04590
Monodonta labio form confusa825170
Turbo cornatus coreensis027420
Nerita japonica93440
Clithon retropicta723162
Batillaria multiformis0404010
Batillaria attramentaria041360
Cerithidea ornata00758
Cerithidea rhizophorarum025915
Cerithidea cingulata00613
Littorina brevicula1822328
Littoraria intermedia00664
Assiminea sp.001952
Mytilus galloprovincialis1663716
Trapezium oblongum05293
Glauconome chinensis00833
Ruditapes philippinarum123900
Table
Table 3. Average value of qualitative physical indicators.
Table 3. Average value of qualitative physical indicators.
GroupRiverSiltSandGravelBoulderBed RockRiprapConcrete ConstructVegetationNumber of Habitats
αKurosak.R001010103
Ekawachi.R011100003
Ohara.R010000001
Saigou.R010100103
Yahagi.R010000102
Average00.80.40.40.200.602.4
βImahuku.R011101004
Sato.R110000002
Hai.R111111016
Sozoro.R111101016
Sakurai.R111101005
Jyurou.R010100002
Average0.710.70.80.20.700.34.2
γKashii.R011100014
Otsubo.R111100015
Kusami.R010101014
Kakuta.R111000014
Naka.R101101004
Yamashiro.R100000012
Kuroki.R101101004
Iida.R101000103
Average0.750.50.750.6300.380.130.633.75

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Itsukushima, R.; Morita, K.; Shimatani, Y. The Use of Molluscan Fauna as Model Taxon for the Ecological Classification of River Estuaries. Water 2017, 9, 356. https://doi.org/10.3390/w9050356

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Itsukushima R, Morita K, Shimatani Y. The Use of Molluscan Fauna as Model Taxon for the Ecological Classification of River Estuaries. Water. 2017; 9(5):356. https://doi.org/10.3390/w9050356

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Itsukushima, Rei, Kai Morita, and Yukihiro Shimatani. 2017. "The Use of Molluscan Fauna as Model Taxon for the Ecological Classification of River Estuaries" Water 9, no. 5: 356. https://doi.org/10.3390/w9050356

APA Style

Itsukushima, R., Morita, K., & Shimatani, Y. (2017). The Use of Molluscan Fauna as Model Taxon for the Ecological Classification of River Estuaries. Water, 9(5), 356. https://doi.org/10.3390/w9050356

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