Habitat Suitability Modeling of Asian-Moon Scallop (Amusium pleuronectes) in Brebes District Waters, Central Java, Indonesia  

Achmad Sahri , Sutrisno Anggoro , Jusup Suprijanto
Master Program of Coastal Resources Management, Diponegoro University, Semarang, Indonesia
Author    Correspondence author
International Journal of Marine Science, 2014, Vol. 4, No. 61   doi: 10.5376/ijms.2014.04.0061
Received: 03 Mar., 2014    Accepted: 11 Sep., 2014    Published: 28 Oct., 2014
© 2014 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Sahri et al., 2014, Habitat Suitability Modeling of Asian-Moon Scallop (Amusium pleuronectes) in Brebes District Waters, Central Java, Indonesia, International Journal of Marine Science, Vol.4, No.61 1-13 (doi: 10.5376/ijms.2014.04.0061)


The purpose of this study was to analyze the characteristics of the environment that determines the bottom habitat of the scallop from Brebes District and to map the potential habitat of scallop using spatial modeling. Research areas include the waters covering 806.03 km2 of Brebes District extending 12 miles outward the sea. Habitat suitability mapping was conducted using Ecological Niche Factor Analysis (ENFA). Kolmogorov-Smirnov test was used to test differences in each environment occupied by scallop with the overall study area.

ENFA results showed that the scallop required habitats with very different conditions from the mean habitat conditions existing in all study areas (p <0.05). The suitable habitat for scallop Amusium pleuronectes in Brebes District waters were the bottom water column with high plankton density (11,001-14,500 ind/L), between 18-31 m of water depth, low total suspended solids (<3.61 mg/L), soft sediments substrate (mud, φ ≥ 6) and a relative distant from estuary (> 5 km). In addition, the scallop also required bottomwatershabitat with current velocity and high salinity (> 0.06 m/sec; 31.85-32.65 ‰) and low temperature of the bottom waters (29.92-30.06 °C), while towards the pH level of the bottom waters, scallop were relatively more tolerant (7.3-7.4). Based on spatial modeling of habitat suitability in the study area, there were three categories obtained: 1) “suitable habitat” area of ​​4,629.01 ha (5.8%), 2) “marginal habitat” area of 9,291.09 ha (11.5%), and 3) “unsuitable habitat” for scallops covering an area of ​​66,682.68 ha (82.7%).

Amusium pleuronectes; ENFA; Habitat modeling; GIS; Scallop

Scallop is one of the fisheries resources that have the potential to be exploited optimally as it has a high economic value in the international trade. Some destination countries for export purpose of scallop from Indonesia include Singapore, Taiwan and Hong Kong (Suprijanto, 2003; Prasetya, 2009). On the north coast of Central Java, Brebes District is one of the areas known as a significant scallop producer, because in certain seasons, scallop from this area has managed to meet the supply needs of exporters both in Jakarta and in domestic market.
One of the distinguished constraints of fishing effort, and exporting the scallop is the continuity of production that has not sustained yet. It is due to the fishing season factor (Prasetya, 2009; Widowati et al., 2008) as well as the lack of information on what kind of waters preference for their habitat. Knowledge of scallop habitat characteristics, such as sediment type, depth, current speed, total suspended solid (TSS), salinity, and temperature (Franklin et al., 1980; Williams, 2002), can be a valuable guide in determining their habitat. By knowing the characteristics of scallop habitat, the mapping of areas as their potential habitat can be easily performed.
Some researches on mapping the potential of scallop have been carried out in several countries such as Queensland (Williams, 2002) and the U.S. (Hart, 2006). Meanwhile in Indonesia, the mapping potential of scallop, especially regarding the suitability of the habitat has become less concerned.
Habitat mapping of scallop can provide several advantages, including obtaining an estimation of how great the potential of the water areas that is suitable for habitat scallop is. Prasetya (2009) reported that assessing the potential of scallop in Brebes District waters is one of the most important priorities in resource management. Wilson et al. (2007) implied that the exploitation activity of marine resources both commercially and sustainably required information on habitat mapping. By knowing the habitat of scallop, the fishing ground of them can be located, so that the efficiency of fishing effort can be accomplished. In addition, habitat suitability maps can also be applied as a database for conservation and resource management (Danker et al., 2001; Compton, 2004; Leverette, 2004; Mandleberg, 2004; Bryan and Metaxas, 2007; Wilson et al., 2007; MacLeod et al., 2008; Praca and Gannier, 2008).
As benthic marine organism, a specific method in mapping the areas of scallop habitat is required in order to describe the precise and appropriate benthic environment. Habitat suitability maps can be generated through spatial habitat modeling, which is a method examining the relationship between the presence and/or the absence of species with relevant environmental parameters termed as eco-geographical variables (EGV) (Hirzel, 2001; Hirzel et al., 2002) forming the basis of environmental variables waters. The program is also capable of performing descriptive statistical analysis and GIS-based. Therefore, it will be able to deliver a habitat suitability map.
This study aimed to analyze the characteristics of the benthic environment that determines the scallop habitat and maps the potential habitat using spatial modeling based on the environmental parameters, so the potential habitats for scallop is acquainted.
1 Material and Method
1.1 Types and Sources of Data
Primary data were obtained directly from the field such as the environmental parameters of the seabed and the surface including: plankton, sediment, temperature, depth, and distance from the estuary along with suspended solids, salinity and pH. Scallop samples and catching coordinate data were also collected as validation of the established habitat suitability models.
Secondary data were collected by reviewing some related literature sources. In addition, satellite data and supporting maps were obtained from several local agencies and institutes. Those satellites data included: the 2008 ASTER satellite images, the 2009 Landsat 7 ETM+ satellite images, the 2002 Java Sea Bathymetry Map from Hydro-Oceanographic Office of Indonesian Navy (Dishidros), the 1991 Distribution of Sediment Surface Seabed Map from the Marine Geology Research and Development Centre (P3GL), the 2001 Topography Map from Geospatial Information Agency (Bakosurtanal), the 2010 tidal data from Dishidros, the 2010 wind data of Meteorology, Climatology and Geophysics Agency (BMKG) of Tegal. Then, other supporting data on the study area originated from Department of Marine and Fisheries Affairs (DKP) of Brebes District and previous researches were also collected.
1.2 Data Analysis Methods
Some data such as tentative maps from the secondary data were analyzed before the survey. Those data included the shoreline updating with ASTER satellite image and the seabed sediments mapping by using the digit on screen method (Prahasta, 2002; Nuarsa, 2005), the depth mapping by applying the interpolation model of Inverse Distance Weighted (IDW) in geostatistics-ArcGIS (ESRI, 2000; Prahasta, 2002; Radiarta et al., 2003). The TSS data extraction from Landsat 7 ETM+ satellite image and the tides modeling with the SMS program (Luetich and Westerink, 2004; Nugroho, 2005) were also analyzed.
Samples obtained from the field survey were analyzed in the laboratory, including: seabed sediment samples by multilevel sieve (sieve shaker) method-pipetting (Tahrir et al., 1986) and sediment classification triangle diagram (Folk, 1980). Then, plankton samples by APHA method (1976) based on the type of plankton identification book of Yamaji (1984), as well as the TSS samples of the sea surface by spectrophotometer at 810 nm wavelength and distilled water as a blank (APHA, 1980).
Mapping of estuary ranged by using the buffering technique in geostatistics-ArcGIS (ESRI, 2000; Prahasta, 2001), the spatial mapping of seabed environmental parameters with IDW method in geostatistics-ArcGIS (ESRI, 2000), the mapping of scallop availability with Generate Random Point models contained in the program Animal Movement SA v2.04 beta in ArcView 3.3 software, as well as habitat modeling were made to determine the suitability of habitat scallop with ENFA.
The species availability map was created by converting the presence of 3.366 data of scallop into a grid size of 100 × 100 m2 with ArcGIS software. The grid conversion has resulted 721 species presence grids.
1.3 Scallop Habitat Modeling with ENFA
Habitat suitability map generated by Ecological Niche Factor Analysis (ENFA). ENFA could produce habitat suitability maps with the data linking to the species availability with environmental variables (Table 1) in order to determine the ecological niche of a species (Hirzel et al., 2002). This technique integrated in the BioMapper software (Hirzel, 2001). The program also combined descriptive statistics with Geographic Information Systems (GIS).

Table 1 Eco-geographical variable applied in the ENFA

In BioMapper, each variables value will be transformed to Box-Cox format, thereby it normally distributed and could be overlaid. For each thematic map, the value of each location where scallop species calculated produced a score appearing as several classes in the frequency histogram. By assuming the data distributed normally, the maximum score was around the median and decreased on both sides (Hirzel et al., 2002). Hereafter, class of each grid within the study area would be determined and the value of "partial suitability" of each thematic map was generated based on scores from classes in the histogram. The farther the grid from the median, the lower the habitat suitability was.
Furthermore, global suitability maps would be generated by calculating the weighted averages of several partial suitability value of each thematic map, producing a rescaled habitat suitability index value in the isopleths methods ranging from 0-100, where a value of 0 indicated that there were no suitable habitat and vice versa (Hirzel, 2001). ENFA summarized all eco-geographical variables (thematic maps) into a number of unrelated components with each other, such as Principal Component Analysis (PCA) (Manly, 1986; Reutter et al., 2003). These components represented the combined factors explaining variability. One thing that distinguished ENFA from PCA was the formed components had a direct ecological significance.
The first component referred to as species ecological niche marginality (marginality) which described the distribution of species in relation to the mean of global distribution (study area). The higher the marginality coefficient, the different the habitats of species from the average condition of enviro
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