Jurnal Ilmu Kelautan
SPERMONDE Vol. 4 No. 1, Maret 2018
Diterbitkan oleh Program Studi Ilmu Kelautan Fakultas Ilmu Kelautan dan Perikanan, Universitas Hasanuddin Bekerjasama Dengan Ikatan Sarjana Oseanologi Indonesia (ISOI)
ISSN: 2460-0156 EISSN: 2614-5049
Jurnal Ilmu Kelautan
SPERMONDE Jurnal Ilmu Kelautan SPERMONDE merupakan majalah ilmiah yang mempublikasikan artikel-artikel ilmiah hasil penelitian dalam bidang Ilmu Kelautan. Jurnal Ilmu Kelautan SPERMONDE diterbitkan secara berkala dua kali setahun, pada bulan Maret dan September. Jurnal Ilmu Kelautan SPERMONDE diterbitkan oleh Program Studi Ilmu Kelautan, Jurusan Ilmu Kelautan, Fakultas Ilmu Kelautan dan Perikanan, Universitas Hasanuddin. Naskah yang diterima merupakan hasil penelitian yang belum pernah diterbitkan sebelumnya dan tidak sedang dalam proses untuk dipublikasikan di jurnal penerbitan lain. PENANGGUNG JAWAB Dekan Fakultas Ilmu Kelautan dan Perikanan Universitas Hasanuddin DEWAN REDAKSI: - Dr.Ir. Amir Hamzah Muhiddin, M.Si. (Ketua) - Dr. Muhammad Anshar Amran, M.Si - Dr. Inayah Yasir, M.Sc. - Dr.Ir. Muh. Farid Samawi, M.Si. - Dr.Ir. Rahmadi Tambaru, M.Si. - Dr. Khaerul Amri, ST, M.Sc.Stud - Dr. Yayu A. La Nafie, ST, M.Sc - Dr. Muhammad Banda Selamat, S.Pi, M.Si - Dr. Wasir Samad Daming, M.Sc. - Dr. Rantih Isyrini, ST, M.Sc
ALAMAT REDAKSI: Jurnal Ilmu Kelautan SPERMONDE, Program Studi Ilmu Kelautan, Departemen Ilmu Kelautan, Fakultas Ilmu Kelautan dan Perikanan, Universitas Hasanuddin. Jl. Perintis Kemerdekaan Km.10 Tamalanrea, Makassar 90245 Telp/Fax : 081355008985/(0411) 586025 E-mail :
[email protected] Website : http://journal.unhas.ac.id/index.php/jiks Indexed : Directory of Open Acces Journals (DOAJ) https://doaj.org/toc/2460-0156
FORMAT MANUSKRIP
Jurnal Ilmu Kelautan SPERMONDE I. Petunjuk umum -
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Manuskrip diformat pada kertas A4 (21 x 30cm), menggunakan Bahasa Indonesia baku sesuai dengan kaidah Bahasa Indonesia yang baik dan benar Semua halaman dilengkapi nomor halaman dan tersusun teratur Menggunakan Times New Roman font 12, tanpa menggunakan huruf italic, bold atau tipe penulisan lain yang tidak standar. Jangan meratakan margin kanan. Setiap paragraf baru diberi indentasi Gunakan spasi 2 untuk keseluruhan manuskrip termasuk halaman judul, abstrak, daftar pustaka, keterangan tabel dan gambar. Margin kiri, kanan, atas dan bawah adalah 2,5 cm (1 inchi) Rasio seperti contoh pada unit diharapkan menggunakan garis miring (contoh mg/L) Nama ilmiah seharusnya dilengkapi dengan nama umum pada judul dan saat untuk pertama kali dituliskan pada abstrak dan pada teks. Author dari nama ilmiah tidak menyertai genus atau nama spesies kecuali bila dibutuhkan penjelasan lebih lanjut. Tuliskan dengan lengkap angka satu hingga sepuluh, kecuali bila diikuti oleh unit pengukuran (empat ikan, 4 ekor ikan, 4kg, 14 ikan). Hindari memulai kalimat dengan angka. Gunakan ‘1.000’ untuk ‘1000’; 0,13 dan % (bukan ‘persen’). Untuk menuliskan waktu (jam), gunakan sistem 24 jam (13.30 bukan 01.30; 02.30 bukan ‘2.30’). Data kalender dituliskan dalam susunan hari, bulan, tahun (14 Januari 2014). Semua referensi yang dikutip pada teks, harus terdata pada Daftar Pustaka manuskrip, begitu pula sebaliknya. Sitasi literatur pada teks mengikuti sistem “nama dan tahun: 1. Untuk seorang penulis: Hamzah (2013) atau (Hamzah, 2013) 2. Untuk dua orang penulis: Hamzah dan Amran (2013) atau (Hamzah dan Amran, 2013) 3. Tiga atau lebih penulis: Hamzah et al. (2013) atau (Hamzah et al., 2013) 4. Menggunakan manuskrip yang telah diterima untuk dipublikasi: Hamzah (in press) atau (Hamzah, in press). 5. Sangat diharapkan untuk menghindari penggunaan data atau komunikasi dengan orang untuk dimasukkan ke dalam manuskrip. Bila itu dirasa sangat perlu, maka dituliskan: A. Hamzah (Universitas Hasanuddin, unpublished data) atau A. Hamzah (Universitas Hasanuddin, komunikasi pribadi). 6. Bila menggunakan multisitasi, gambar dan tabel, gunakan semicolon: (Hamzah, 2013; Amran,2010) (Tabel 1; Gambar 2). - Manuskrip tersusun atas: halaman judul, halaman abstrak, teks (pendahuluan-simpulan/saran), daftar kepustakaan, daftar tabel dan gambar yang ada, tabel
II. Petunjuk Lainnya dan Template Dapat dilihat dan diunduh di Website Jurnal Ilmu Kelautan SPERMONDE: http://journal.unhas.ac.id/index.php/jiks atau dapat menghubungi Dewan Redaksi di Email:
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Jurnal Ilmu Kelautan
SPERMONDE SPERMONDE
Vol. 4
No. 1
Hal. 1-47
Makassar, Maret 2018
ISSN: 2460-0156 EISSN: 2614-5049
Alimuddin Hamzah Assegaf, Wasir Samad, Sakka Some Characteristics of Atmospheric Boundary Layer Over Makassar Alinda. N. Hasanah, Nita Rukminasari, Budiman Yunus, Dewi Yanuarita, Jamaluddin Jompa, Suharto
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7-11
The Effect of Temperature on Zooxanthellae of Isopora Palifera and Acropora Hyacinthus From Karanrang Island, Indonesia Chair Rani, M. Natsir Nessa, Jamaluddin Jompa, Ahmad Faisal, Shinta Werorilangi, Akbar Tahir
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Temporal Dynamics of Eutrophication Level and Sedimentation Rate in Coral Reef Area of Spermonde and Sembilan Islands, South Sulawesi Esa Fajar Hidayat, Sri Pujiyati, Ali Suman, Totok Hestirianoto
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Distribution of Pelagic Fish In South China Sea Using Geostatistical Approach Magdalena Litaay, Slamet Santosa, Eva Johannes, Rosana Agus, Willem Moka, Jennyta Dhewi Darmansyah Tanjung
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Biodiversity of Marine Tunicates at Samalona Waters, Sangkarang Archipelago, Indonesia Muhammad Banda Selamat, Mahatma Lanuru, Amir Hamzah Muhiddin
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Spatial Composition of Benthic Substrate around Bontosua Island Muh. Farid Samawi, Shinta Werorilangi, Rahmadi Tambaru, Rastina
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Accumulation of Heavy Metals in Hard Coral Porites lutea at Spermonde Archipelago, South Sulawesi Wasir Samad, Muhammad Anshar Amran, Amir Hamzah Muhiddin, Rahmadi Tambaru Spatial-Temporal Distribution of Chlorophyll-a in The Southern Part of The Makassar Strait
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SPERMONDE (2018) 4(1): 1-6
ISSN: 2460-0156 EISSN: 2614-5049
SOME CHARACTERISTICS OF ATMOSPHERIC BOUNDARY LAYER OVER MAKASSAR Alimuddin Hamzah Assegaf1*, Wasir Samad2, Sakka1 Submitted: 14 January 2018 Accepted: 19 February 2018
ABSTRACT. Some upper air atmospheric parameters measured during period of 2011-2016 by means of radiosonde located at Hasanuddin International Airport were examined for characterization of boundary layer over Makassar, Indonesia. These data, combined with surface atmospheric parameters were used to calculate some boundary layer parameters using AERMET model which based on Monin-Obukhov similarity theory. The obtained Monin-Obukhov length which reflecting atmospheric stability then converted into traditional Pasquill-Gifford stability classification. Examination of wind characteristics of wind showing clearly their dependence of the day, season and height. Winds dominantly flows from the southeast during the daytime with the relatively larger velocity and from the northwest with smaller velocity during the nighttime. Interpretation of monin-obukhov length using Pasquill-Gifford stability classification showing that the atmosphere was dominantly unstable during the daytime and dominantly stable during the nighttime. These atmospheric stabilities were also varied during seasons. The height of convective boundary layer (CBL) was start to rise in the morning and reaching its maximum in the afternoon (18:00) at the mean value of 2 km. Meanwhile, the height of mechanical boundary layer (MBL) during the day time forming parabolic curve with its maximum value of 1.2 km at noon. These indicated that any released pollution from the stack will be less dispersed during the nighttime due to the fact of lower mixing height, lower wind speed, atmosphere become more stable, and it dispersed in different direction compare to the daytime. Keywords: atmospheric boundary layer, atmospheric stability, AERMET, boundary layer height
INTRODUCTION Study on atmospheric boundary layer plays important role in air quality modelling. Information about mixing height, atmospheric stability, wind profile, and turbulence characteristics will be valuable on predicting the behaviour of air pollutant dispersion. The wind will determine the direction and the speed of pollutant away from its source. The atmospheric turbulence will determine the turbulence dispersion. The temperature affects the rise of a buoyant plume (Stull, 1988); (Stull, 2017); (AERMOD, 1988a). Preparation of meteorological data for air quality modelling is one of important step, that challenging for development country, such as Indonesia. The data can be obtained from satellite, global model, or surface station. Satellite can cover the whole globe, but its resolution is too coarse to be used in short range model. Other choice is to use the global meteorological model, such as MM5 or WRF (Assegaf et al., 2015), (Grell et al., 1994), (Jayadipraja et al., 2016), (Jesse et al., 2011). We start from the coarse grid, then downscaling at the area of interest. Preparation of the model for generating long term prediction (in the order of 5 years or more) required huge computer resource and some detail of parameters setting need advance expertise. The best data is from the measurement at local surface station. Unfortunately, the availability *
Alimuddin Hamzah Assegaf Department of Physiscs, Hasanuddin University 2 Departmen of Marine Science, Hasanuddin University Jl. Perintis Kemerdekaan Km.10, Makassar 90245, Indonesia Email:
[email protected] 1
of surface meteorological station, that provide upper air meteorological profile data is very few in the development country, such as Indonesia. Until recently, some major airports in Indonesia are equipped with radiosonde to measure the meteorological profile around the airport. This paper describes the preparation of meteorological data based on the local station. Both surface and upper meteorological data were processed to obtain boundary layer parameters such as sensible heat flux, friction velocity, convective velocity scale, boundary layer height, as well as Monin-Obukhov length (L) (Monin and Obukhov, 1954). MATERIALS AND METHODS Location and Data Preparation The data was measured at Hasanuddin International Airport, Makassar, Indonesia (WMO station code: 971800) located at (5.080553S, 119.551243E) can be seen in the Figure 1. The data period of 20112016 covering 336,525 records of surface and upper air data was used. The upper air data was taken four times a day at 00:00, 06:00, 12:00 and 18:00 local standard time (LST). It consists of height of measurement (m), temperature (0C), dew point temperature (0C), pressure (mbar), wind velocity (m/s) and direction (degree) at correspondence height. In this study, we only use profile data at significant level (code 5 at FSL file). The hourly surface data consists of station pressure (mbar), surface temperature (0C), dew point temperature (0C), surface wind velocity (m/s) and direction (degree), and sky cover. The measurement was
SPERMONDE (2018) 4(1): 1-6 taken at 30 m above mean sea level. The portion of
missing data is less than 2%.
Figure 1. Location map of Sultan Hasanuddin International Airport (inset on map)
AERMET Model The AERMET model is a meteorological processor for the AERMOD model (Cimorelli et al., 2005), (AERMOD, 1988A), together with AERSURFACE model (as a terrain/morphology processor) (Perry et al., 2005), (AERMOD, 1998b). AERMOD system is regulated model in USA, Canada, and some European countries. It utilizes the principle of surface heat equilibrium to calculate friction velocity, convective velocity scale, as well as Monin-Obukhov length (L) (Monin and Obukhov, 1954) and determine whether the atmosphere is stable or unstable. Planetary boundary layer (PBL) is divided into two types: convective boundary layer (CBL) and stable boundary layer (SBL). CBL is developed during the day. It is driving by surface heating and can cause moderate to strong vertical mixing (Stull, 1988). Meanwhile, SBL develops at night, driven by surface cooling and causing little to no vertical mixing. In calculating the transfer of surface heat to the atmosphere and vice versa, AERMET refers to the formula proposed (Holtslag and Ulden, 1983), (Holtslag and Bruin, 1988), (Ulden and Holtslag, 1985), which calculated the solar radiation flux as a function of temperature, cloud cover and angle of the sun. Further more sensible heat flux is calculated as a function of solar radiation flux and Bowen ratio. Monin-Obukhov length will be calculated iteratively based on convective velocity scale information, temperature and wind speed. Next steps are calculating the
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stability of the atmosphere and the thickness of the boundary layer (convective and mechanical boundary layers). The AERMET convention is used as follows: day time period is 07:00~18:00 (CBL period) and night time period is 18:00~07:00 LST (SBL period). 07:00 is the transition between SBL and CBL; vice versa 19:00 is the transition between CBL and SBL. The result of AERMET is processed further to have quarterly quantity (25%, 50% and 75% percentile) of the processed results. RESULTS AND DISCUSSION Wind Roses and Upper Air Profile To identify possible daily variations that cause a preference for one direction over another, the wind roses for the night - day and the whole year (from 2011 to 2016) were derived (Figure 2a and 2b). In the day (Figure.2a) the wind direction is mostly oriented from South-South East (SSE). Otherwise, in the night (Figure.2b) the wind direction is dominantly from North. It also indicated that the wind speed was larger during the day time. The analysis of the upper air profile referred to the whole year during 2011 – 2016 shows the wind speed logarithmically increase with height in the boundary layer and then constant in the rest of stratosphere layer. The same also for the pressure which tends to decrease logarithmically with height. The temperature decreases in the troposphere and then increases in stratosphere layer as can be seen in Figure 3-c.
Alimuddin Hamzah Assegaf
ISSN: 2460-0156 EISSN: 2614-5049
(a)
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Figure 2. Wind roses of the daily variation (a) day time (b) night time, and (c) day-night during period 2011-2016
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Figure 3. Upper air profile: (a) Wind velocity and dicrection, (b) pressure, (c) temperature
Sensible Heat Lux The sensible heat flux is the energy flux from the atmosphere to the ground driven by temperature differences between the ground and the atmosphere. It is the energy flux transferred from or to the ground. During the daytime, energy radiates from the ground into the atmospheric boundary layer, while during the night the boundary layer supplies Alimuddin Hamzah Assegaf
energy to the ground. The main driver of the daytime heat balance is the incoming solar radiation. It determines the level of turbulence both during the day and the night, and governs the evolution of the CBL. The daytime solar heating will generate buoyancy which will cause rapid vertical spread of plume and growth of the mixed layer. In Figure-4a, the daily variation of sensible heat flux form
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SPERMONDE (2018) 4(1): 1-6 June (Figure-4b) and overall median is 186.mm Watt/m2.
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parabolic curve with the maximum at noon (291.8 Watt/m2). There is a little variation over the month, where it has maximum on October and minimum on
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Convective and Friction Velocities Convective velocity scale, w* is an estimate of the turbulent velocity created by buoyancy or free convection. It is dominated during the day time. It turns out that the standard deviation of the horizontal turbulent velocity fluctuations is about 0.6 of w* through the depth of the boundary layer (Jesse et al., 2011). Figure 5 shows the diurnal of convective and friction velocity. The convective velocity has maximum 2.64 m/s at noon (14:00 LST) with range of 2.09 m/s (25 percentile to 75 percentile data). Over all median are 2.36 m/s. The curve path is following the sensible heat flux as the energy supply for the turbulence dynamic. Air flowing over a surface exerts a shear stress that
depends on the level of turbulence in the boundary layer. Turbulence in the stable boundary layer is generated by wind shear, and inhibited by the stable potential temperature gradient. Observations indicate that the height of the boundary layer, which is the height to which the turbulence extends, is related to the surface friction velocity. The maximum friction velocity is 0.73 m/s with range of 0.69 m/s, and overall median is 0.15 m/s. Figure 5 (right panel) shows that the maximum convective velocity is 2.29 m/s which occur on September. Small variation (0.1 m/s) of friction velocity which is has median of 0.88 m/s. The surface friction velocity, u*, is a measure of mechanical turbulence and is directly related to the surface roughness. 2
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Monin-Obukhov Length Monin-Obukhov (MO) length reflects the stability of the atmosphere. When MO length (Figure 6) is negative, it indicates unstable conditions (positive surface temperature flux), infinite at neutral, and positive under stable conditions. Instability arises in the morning and tends to increase as more heat accumulated in the day time. It reached its
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maximum in the afternoon, just before transition time. When the MO length is converted into Pasquill-Gilford (PG) stability criteria (see Table 1), information on atmospheric stability distribution can be more explore. During the night time, the atmospheric stability is dominated by class G (moderately stable) and following by class H (very stable). On the contrary, the atmospheric condition is very unstable (class A) during the day time. Small Alimuddin Hamzah Assegaf
ISSN: 2460-0156 EISSN: 2614-5049 variations are over the months, but mostly very stable (during the night time) and very unstable
during the day time (Figure 7)
Table 1. Conversion of MO length into PG stability criteria (Pelliconi et al., 2012)
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Figure 7. Atmospheric stability distribution over day and month
Height of Boundary Layers The rapid boundary layer growth in the morning starting at 7 to 15 LST and tends to slow at 16~18 LST. It reflects contribution of turbulence to push up the boundary layer ceiling and ended at dissipation due to decrease of solar heat in the afternoon. The maximum height of CBL is 1.9 km at 17 LST. The MBL is also growth in the period of 7~13 LST and has maximum 1.2 km at 14 LST and
finally decreases. MBL is strongly influenced by wind friction. As shown in Figure 8 (left panel), the height of CBL and MBL is about 1.3~1.5 km and 0.4~0.7 km, respectively. High boundary layer episodes typically occur in the period of August ~ October. It is coincidence with dry air due to strong sensible heat which can be seen in Figure-4 and Figure-8 (right panel).
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Figure 8. Variation of ABL height of daily (left panel) and monthly (right panel)
Alimuddin Hamzah Assegaf
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SPERMONDE (2018) 4(1): 1-6 CONCLUSION The following conclusion can be drawn from the discussion: The wind dominantly blows from SouthSoutheast (SSE) in the day time and from North in the night time with lower speed. The convective and friction velocity is about 1.9 m/s and 0.2 m/s, respectively. The height of CBL and MBL is about 1.5 km and 0.5 km, respectively REFERENCES Assegaf, AH. and Jayadipraja EA, Modeling of CO Dispersion from Tonasa Cement Factory Stack Using AERMOD Model, in Physics National Seminar: Makassar 2015, Makassar, Indonesia, Faculty of Mathematics and Natural Sciences, Hasanuddin University, (2015) (in Bahasa Indonesia). Cimorelli, A. J, Steven G.Perry, Akula Venkatram, Jeffrey C.Weil, Robert J.Paine, Robert B.Wilson, Russell F.Lee, Warren D.Peters, Roger W.Brode, 2005: AERMOD: A Dispersion Model for Industrial Source Application. Part I: General Model Formulation and Boundary Layer Characterization, Journal of Applied Meteorology 44, 682-693. Grell, G. A., J. Dudhia and D. R. Stauffer, 1994: A Description of the fifth generation Penn State/ NCAR mesoscale model (MM5), NCAR Tech Note, NCAR/TN-398+STR, 117 Holtslag A.A.M. and van Ulden A.P. (1983). A simple scheme for daytime estimates of the surface fluxes from routine weather data. J. Clim. Appl. Meteorol. 22, 517–529. Holtslag A.A.M. and de Bruin H.A.R. (1988). Applied Modeling of the Nighttime Surface Energy Balance over Land. J. Appl. Meteorol. 27, 689–704. Jayadipraja EA, Daud A, Assegaf AH, Maming. Applying Spatial Analysis Tools in Public Health: The Use of AERMOD in Modeling the Emission Dispersion of SO2 and NO2 to Identify Exposed Area to Health Risks. Public Health of Indonesia 2016;2(1): 20-27 Jesse L. Thé, Russell Lee, Roger W. Brode (2011): Worldwide Data Quality Effects on PBL
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Short-Range Regulatory Air Dispersion Models, Weblakes Environment Consultants Inc. Monin, A.S. and A.M. Obukhov (1954): Basic laws of turbulent mixing in the surface layer of the atmosphere (English translation by John Miller for Geophysics Research Directorate, AF Cambridge Research Centre, Cambridge, Massachusetts, by the American Meteorological Society), Originally published in Tr. Akad. Nauk SSSR Geophiz. Inst. 24(151):163-187 Perry, S. G. et. al., 2005: AERMOD: A Dispersion Model for Industrial Source Application. Part II: Model Performance against 17 Field Study Databases, Journal of Applied Meteorology 44, 696-708. Pelliccioni, A., P. Monti, C. Gariazzo, and G. Leuzzi. “Some Characteristics of Urban Boundary Layer Above Rome, Italy, and Applicability of Monin-Obukhov Similarity.” Environ Fluid Mechanics 12 (2012): 405-428 Stull, Roland B., 1988; An Introduction to boundary layer meteorology. Published by Kluwer Academic Publishers. The Netherlands. Stull, Roland B., 2017; Practical Meteorology: An Algebra-based Survey of Atmospheric Science. Department of Earth, Ocean and Atmospheric Sciences. University of British Colombia. Vancouver Canada. US Environmental Protection Agency, 1998a: AERMOD: Revised Draft – User‟s Guide for the AMS/EPA Regulatory Model – AERMOD. Office of Air Quality Planning and Standards, Research Triangle Park, NC US Environmental Protection Agency, 1998b: Revised Draft – User‟s Guide to the AERMOD Terrain Preprocessor (AERMAP). Office of Air Quality Planning and Standards, Research Triangle Park, NC. Van Ulden A.P. and Holtslag A.A.M. (1985). Estimation of atmospheric boundary layer parameters for diffusion applications. J. Climate Appl. Meteorol. 24, 1196–1207.
Alimuddin Hamzah Assegaf
SPERMONDE (2018) 4(1): 7-11
ISSN: 2460-0156 EISSN: 2614-5049
THE EFFECT OF TEMPERATURE ON ZOOXANTHELLAE OF ISOPORA PALIFERA AND ACROPORA HYACINTHUS FROM KARANRANG ISLAND, INDONESIA Alinda. N. Hasanah1*, Nita Rukminasari1, Budiman Yunus1, Dewi Yanuarita1,Jamaluddin Jompa1,Suharto1, Dwi Fajriati Inaku1 Submitted: 15 January 2018 Accepted: 22 February 2018
ABSTRACT Climate change and global warming cause massive damage to the environment. One of the major events that are threatening the marine ecosystem is coral bleaching. Coral bleaching occurs when corals are exposed to above or below normal temperatures. The aims of this study are to compare the resistance of Isopora palifera and Acropora hyacinthus from Karanrang Island to temperature stress. Four treatment temperatures (28ºC, 30ºC, 32ºC, and 34ºC) were tested to assess the role of temperature stress and bleaching to Isopora palifera and Acropora hyacinthus for 48-hours. The abundance of zooxanthellae counted as the temperature stress variable. The results showed that there was a difference of coral response tothe treatment based on the time of experiment, after 48-hours experimentexposed at temperature treatment of 34°C the abundance of zooxanthellae from Isopora paliferawas 0,06 x105 cm-2 and the abundance of zooxanthellae from Acropora hyacinthus is 0,18 x105cm-2. In comparison between species, Isoporapalifera taken from Karanrang Island was more resistant to temperature stress thanAcroporahyacinthus. Keywords:Climate change, coral bleaching, temperature rise, abundance of zooxanthellae.
INTRODUCTION Coral reefs constitute some of the largest and most diverse ecological communities on earth and result from interactions between symbiotic organisms composed of photosynthetic dinoflagellate algae and cnidarian corals (Dustan, 1999, Stone 1999). The Coral Triangle as the heart of the world coral reefs is located across the coastal waters of Indonesia, Malaysia, Papua New Guinea, Philippines, Solomon Islands and Timor-Leste. Nearly 30% of the total coral reef area and 75% of all known coral species are found in this area, and it is home to over 3,000 species of fish twice the number found elsewhere in the world (Burke, et al, 2012). Indonesia with a total of 590 hard coral species represents more than 95% of the species diversity in the world. The greatest threats to today's coral reef ecosystems come from the anthropogenic pressures and global climate change that trigger a rise in seawater temperatures. In 2010, sea water temperature led to mass coral bleaching throughout Southeast Asia impacting many coral reefs in Indonesia. The worst affected areas are around Sumatra and Sulawesi, with 8090% of coral reefs bleaching around Aceh (in the northern tip of Sumatra) (IPCC, 2007). The experiences of the last two decades suggest that bleaching happens when zooxanthellae are expelled from the coral tissue. There are numerous factors which are responsible for those events, with high temperatures and intense light being major contributors (Hoegh- Guldberg, 1999; Fitt et al., 2001). 1
Faculty of Marine Science and Fisherires, Hasanuddin University Jl. Perintis Kemerdekaan Km.10 Makassar 90245, Indonesia * Alinda. N. Hasanah Email:
[email protected]
At high seawater temperatures, the damagetophotosynthetic and mitochondrial membranes in corals generates oxidative stress (Weis, 2008; Higuchi et al 2010), and this stress induces the loss of symbionts from coral tissues which gradually leads to coral bleaching (Jones, 1997). Zooxanthellae are symbiotic dinoflagellates that form a mutualistic relationship with coral polyps and a wide range of marine invertebrates (Taylor 1974, Trench 1987). They have chlorophyll a and accessory pigments (e.g. other chlorophylls, carotenoids and phycobilins) for photosynthesis and provide products of photosynthesis to their host corals. It is estimated that there are about one million symbiotic zooxanthellae cells per square centimeter of coral tissues (S. Li, 2007), and up to 90% of the coral metabolic demand comes from the by-products of photosynthesis by the symbiotic zooxanthellae (R. Trench, 1979). Therefore, the growth of reef-building corals and the status of coral reef ecosystems are closely related to the photosynthesis of zooxanthellae. A previous study observed preferential elimination of clade C zooxanthellae, which are associated with low irradiance, from multi-clade communities of Symbiodinium spp. in Montastrea annularis and Montastre afaveolata during bleaching (Rowan et al. 1997). Another study observed the selective release of symbionts with decreased photosynthesis at elevated temperatures from the same host species (Perez et al.2001). This study clarified that the algal partner is more susceptible to thermal stress than their coral hosts, suggesting that algal symbionts play a significant role in determining bleaching susceptibility of corals.
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Field studies on mass bleaching have reported differences in bleaching susceptibility among coral species. It was reported that corals with faster growth rates (e.g. acroporids and pocilloporids) have been more severely affected by bleaching than slower growing species (e.g. poritids and faviids) in the Indo-Pacific region (Brown and Suharsono, 1990). The aim of this study is to examine and compare temperature stress responses in corals from the family Acroporidae (Isopora palifera,Lamarck 1918 and Acropora hyacinthus, Dana 1984) from Karanrang Island.
This acclimation to reduce the possible effect of thermal or irradiance history on the stress susceptibility of the corals (Brown et al., 2000, 2002) Experimental Design. Four treatment groups were tested to assess the roles of temperature stress and bleaching on Isopora palifera and Acropora hyacinthus: 28o, 30oC (control), 32oC and 34oC, each with three replicates. Visual data of coral bleaching was collected for every 4hours by photographing the samples,density of zooxanthellae and counting per 12hours for period of 48hours.
MATERIAL AND METHOD The experiment about temperature stress on coralsIsopora palifera and Acropora hyacinthuswas conducted at Hasanuddin University Marine Station from September to Oktober 2016. Mitotic index and abundance of zooxanthellae counted as temperature stress parameter. Sampling Collection. Samples were collected from Karanrang Island E.119.37690⁰S.04.85259⁰ (Figure 1).Samples of Isopora palifera and Acropora hyacinthus were collected in September 2016 from water depths between 1-3 m. One colony per species was collected and placed in separate plastic bag, which filled with sea water. Insitu water temperature was 29.8oC, measured with a HANNA multiparameter water quality checker series HI 98194. The coral colonies were placed in the same outdoor tank filled with ambient seawater (29-30oC) for two weeks to recover from damage during fragmentation and transportation from sampling time.
Coral Surface Measurement. The coral surface area was measured based on the method of Marsh (1970). Aluminum foil (1, 2, and 3 cm2) were cut from a roll of standard kitchen foil and weighed to determine the weight per unit area of the foil. The procedure was repeated three times to provide an average of 2.90 ± 0.03 mg/cm2 (mean ±SD) (Veal et al, 2010). Each coral skeleton was then carefully wrapped in the foil to minimize the overlapping of the foil. The weight of the foil required to cover each coral was then used to estimate the surface area of the coral skeleton. Zooxanthellae Density Counting. Coral tissues were airbrushed into a plastic bag filled with 50 mL of filtered seawater until all tissue was removed (the time for this process varied depending on the size of the coral branch; from five to ten minutes). Each of the samples was shaken vigorously; then, using a clean pipette, the sample was placed onto a Neubauer Improved (0.100 mm) haemocytometer, and viewed under 40x magnification with a light microscope. To mitigate „edge effects‟ (i.e. counting cells lying on quadrat margins more than once) only the cells which touched the top and left-hand side of each square were counted. There were three replicate counts from each branch (McCowan et al. 2011). Zooxanthellae densities were calculated by following formula:
Where D is the abundance of zooxanthellae, P is dilution, Q is number of zooxanthellae cells counted, L is number of coral fragment surface area and 10000 is convert 0.1 mm³ to 1 cm³. Statistical Analyses. Figure 1. Map of the Spermonde Archipelago in southern Sulawesi. The Karanrang Island is circled. Map modified from Cornils et al. (2010).
8
The results of the experiment were analyzed using a two-way analysis of variance (ANOVA; factors: treatment and time) using GraphPad Prism 5 for Windows.
Alinda N Hasanah
ISSN: 2460-0156 EISSN: 2614-5049 RESULT AND DISCUSSION Tests of between-subject effect of the experiment show that the time of experiment significantly affected the density of zooxanthellae (two-way ANOVA, p< 0.01; Table 1). There is no significant difference in all temperature treatment. This result indicates that the coral samples affected by temperature due to prolonged exposure to high temperature. The Abundance of Zooxhantellae. Samples from 28oC temperature treatment, showed a dramatical decreased of zooxanthellae abundance after 12 hours of temperature experiment (Figure.2a) since anin-situ water temperature from sampling site was 29.8oC so the treatment of 28oC was 1oC lower
than the ambient seawater.These results were consistent with the previous study, who have reported that bleaching of Acropora spp. was occurred when the temperature was increasing or decreasing slightly (Williams and Bunkley-Williams 1990; Marshall and Baird 2000). Hoegh-Guldberg (1994) studied a variety of bleached scleractinian corals from French Polynesia and found that low temperatures caused bleaching in numerous species, and Acropora spp. showed the greatest susceptibilityto low temperature. Our finding contradicted with a study on Montastrea annularis, M. cavernosa, Agaricia lamarcki, A. agaricites, and Siderastrea radiansthat reported that those corals were not stressed at temperatures
Figure 6. Biplot analysis fish density on oceanography factors
CONCLUSION The highest density of pelagic fish was found near in around Tambelan Island, and Anambas Waters. The oceanographic condition in those spots area were have temperature at 30.5oC – 31oC, salinity at 32 psu – 33 psu, chlorophyll-a at 0.2 - 0.3 mg/L, and sea current velocity at 130 cm/s. The environment temperature was prove has close correlation to fish density. Based on spatial analysis, areas in high fish density are potential to state as fishing ground. Statistically, there were two parameters that most REFERENCES Akhir, M.F.M. 2012. Surface Circulation and Temperature Distribution of Southern South China Sea from Global Ocean Model (OCCAM). Sains Malaysiana. 41(6) : 701714. Bertrand, A., E. Josse, P. Bach, P. Gros, dan L. Dagorn. 2002. Hydrological And Trophic Characteristics Of Tuna Habitat: Consequences On Tuna Distribution And Log Line Catchability. Canadian Journal of Fisheries and Aquatic Science 59 (6): 1002 – 1013. doi.org/10.1139/f02-073. Johannesson, K.A. dan R.B. Mitson. 1983. Fisheries Acoustic a Practical Manual for Aquatic Biomass Estimation. FAO Fisheries Technical Paper. Roma. 24
influential on pelagic fish density in this research; temperature and sea current velocity. ACKNOWLEDGMENT Deep thank to the Director and staff of Marine Fisheries Research Board of Research and Development of Marine and Fisheries Republic of Indonesia for permission and acoustic facilities during the research. Also thank to Suprapto as a head of team survey and all crew for the assistance in field. Kang, M. 2014. Overview of the Applications of Hydroacoustic Methods in South Korea and Fish Abunandce Estimation Methods. Fisheries and Aquatic Sciences. 17(3):369376. doi.org/10.5657/FAS.2014.0369. Laevastu, T. 1993. Marine Climate, Weather, and Fisheries. London(GB): Fishing News Books. Maclennan, D.N., E.J. Simmonds. 1992. Fisheries Acoustic. London(EN): Chapman and Hall. Matsunuma, M., H. Motomura, K. Matsuura, N.A.M. Shazili, M.A. Ambak. 2011. Fishes of Trenggani East Coast of Malay Peninsula. Trengganu (MY). National Museum of Natural and Science.
Esa Fajar Hidayat
ISSN: 2460-0156 EISSN: 2614-5049 Melvin, G.D., R. Kloser dan T. Honkalehto. 2015. The Adaptation of Acoustic Data From Commercial Fishing Vessels In Resource Assessment And Ecosystem Monitoring. Fisheries Research. 178: 13-25. doi.org/10.1016/j.fisheries.2015.09.010. Nurhakim, S., V.P.H. Nikijuluw, D. Nugroho, B.I. Prisantoso. 2007. Status Perikanan Menurut Wilayah Pengelolaan. Pusat Riset Perikanan Tangkap. Jakarta Pond, S. dan G.L. Pickard. 1978. Introductory Dynamic Oceanography. London (GB) : Pergamon Press. Priatna, A. dan Wijopriona. 2011. Estimasi Stok Sumberdaya Ikan dengan Metode Hidroakustik di Perairan Kabupaten Bengkalis. Jurnal Litbang Perikanan Indonesia. 1(3) : 1-10. doi.org/10.15578 /jppi.17.1.2011.1-10. Rasyid, A.J., N. Nurjannah, A.B. Iqbal dan M. Hatta. 2014. Kajian Daerah Penangkapan Ikan Pelagis Kecil Terkait dengan Kondisi Oseanografi di Perairan Kota Makassar pada Musim Barat. Simposium Nasional I Kelautan and Perikanan Makassar, 3 Mei 2014. Simanjuntak, M. 2009. Hubungan Faktor Lingkungan Kimia, Fisika Terhadap Distribusi Plankton di Perairan Belitung Timur, Bangka Belitung. Jurnal Perikanan. 11(1): 31-45.
Esa Fajar Hidayat
Solanki, H.U., R.M. Dwivedi, S.R. Nayak, S.K. Naik, M.E. John dan V.S. Somvanshi. 2005. Cover: Application Of Remotely Sensed Closely Coupled Biological And Physical Process For Marine Fishery Resources Exploration. International Journal of Remote Sensing 26 (10): 2029 –2034.doi.org/ 10.1080/014311603100 01595028. Suman, A., H.E Irianto, F. Satria dan K. Amri. 2016. Potency and Exploitation Level of Fish Resources 2015 in Fisheries Management Area of Indonesian Republic (FMAs) and Its Management Option. Jurnal Kebijakan Perikanan Indonesia. 8(2): 97-110. doi.org/10.15578/jkpi.8.2.2016.97-100. Syaifullah, M.D. 2015. Suhu permukaan Laut Perairan Indonesia and Hubungannya dengan Pemanasan Global. Jurnal Sagara. 11(2): 103113. Tubalawony, S., E. Kusmanto, Muhadjirin. 2012. Suhu and Salinitas Permukaan merupakan Indikator Upwelling Sebagai Respon Terhadap Angin Muson Tenggara di Perairan Bagian Utara Laut Sawu. Ilmu Kelautan.17(4) : 226-239. doi.org/10.14710 /ik.ijms.17.4.226-239 Zainuddin, M., A. Nelwan, S.A. Farhum, Najamuddin, M.A.I Hajar, M. Kurnia dan Sudirman. 2013. Characterizing Potential Fishing Zone of Skipjack Tuna during the Southest Monsoon in the Bone Bay-Flores Sea Using Remotely Sensed Oceanography Data. International Journal of Geosciences. 4: 259-266. doi.org/10.4236/ijg.2013.41 A023
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ISSN: 2460-0156 EISSN: 2614-5049
BIODIVERSITY OF MARINE TUNICATES IN SAMALONA WATERS, SANGKARANG ARCHIPELAGO, INDONESIA Magdalena Litaay1*, Slamet Santosa1, Eva Johannes1, Rosana Agus1, Willem Moka1, Jennyta Dhewi Darmansyah Tanjung1 Submitted: 19 February 2018 Accepted: 26 February 2018
ABSTRACT The study aims to know the biodiversity and community structure of marine tunicate in Samalona waters. The present study is part of biodiversity assessment for marine resources of Sangkarang Archipelago SW Makassar Indonesia. Field campaign was conducted from October to November 2016. Sample collection was done at 3 and 7 m depth by using Line Intersection Transect (LIT) method combined with a quadrat (plot). Two 50 m transects were placed parallel to a shore line at three stations (sta.) at Samalona waters. A quadrat (plot) (2.5 m x 2.5 m) was placed side by side of the line transect and all tunicates in the transect was recorded, identified, counted and photographed. Samples were collected by using SCUBA and under water camera. Environmental parameters including water temperatue, salinity, dissolved oxygen, clarity, current and wind speed, were measured in situ. Data were analysed using ecological indices including species composition and density, Shanon Wienner species diversity, Evenness, and Morisita Indices. The result indicates that there are 18 species of tunicates present at 3 m as well as 7 m depth of Samalona waters.. Result of the ecological analysis shows that species diversity can be categorized as moderate and there were no dominant species. Environmental parameters indicates that water quality at Samalona waters was in good condition to support tunicates. Key words: ascidian, chordata, coral reefs, Spermonde
INTRODUCTION Marine invertebrates as major group living incoral reefs of the Indo-Pacific region are rich for secondary metabolite and are targeted for studying lead compound as marine drug discovery (Sabdono and Radjasa 2008). Radjasa et al (2011) stated that coral reef ecosystem is a source for bioactive compound origin from its associated biotas such as sponge, ascidians, mollusks, bryozoans and cnidarian. Biodiversity of marine biotas has pushed the discovery of marine natural products that can be developed as a therapeutics candidate. Marine tunicates were potential for inoculum source for endo-symbiotic that can produce anti-bacterial and anti-fungi (Karthikeyan et al. 2009; Litaay et al. 2015; Christine et al. 2015; Nurfadillah et al. 2015; Sardiani et al. 2015; Tahir et al. 2016). Tunicates are also potential antiviral (Murti and Agrawal, 2010), anticancer (Shaala and Youssef, 2015), inhibitor and induces apoptosis of breast (MCF-7; MDA-MB) cancer cells and also used for phase II cancer treatment (Zelek et al. 2006; Michaelson et al. 2012; Atmaca et al. 2013), as inhibitor of breast cancer cells by JNK dependent apoptosis (GonzalezSantiago et al. 2006), breast and prostate cancer (Kalimuthu et al. 2014). One of the bioactive compounds produced by tunicate is used to cure refractory soft-tissue sarcomas (Sinko et al. 2012). Sangkarang Archipelago which was previously known as Spermonde is located South West off Makassar,
consisting more than a hundred islands. Various marine biodiversity in Sangkarang area have been studied (Moll 1983; Verheij 1993; Massin 1999; Renema and Simon 2001; de Voogd et al. 2006, Pogoreutz et al. 2012; Priosambodo et al.2014). However, diversity of marine tunicates in the Sangkarang region is less studied (Fikruddin, 2013; Mawaleda, 2014). In order to explore bioprospecting of marine tunicate, a basic research on species diversity is needed. Information on biodiversity and distribution of tunicate in this region will provide useful baseline data to support sustainable use of marine resources. MATERIALS AND METHODS Sample Collection Sampling of tunicates was done at three different sites (sta.) of Samalona waters, Sangkarang Archipelago South Sulawesi Indonesia (Figure 1). SCUBA was used in collecting sample and LIT
1
Department of Biology, Hasanuddin University Jl Perintis Kemerdekaan Km 10. Makassar 90245, Indonesia. * Magdalena Litaay Email:
[email protected]
Figure 1. Location at Samalona waters, red spot indicating o the o sampling sites. o sta 1 (5 7.390‟S; o 119 20.576‟E), sta 2 (5 7.513‟S; 119 20.330‟E) o o and sta 3 (5 7.699‟S; 119 20.506‟E).
ISSN: 2460-0156 EISSN: 2614-5049 at 3 m and 7 m depth at three stations (Figure 1). In each station, a 50 m line transect was placed parallel to shore line and a quadrat (2.5 m x 2.5 m) was applied side by side of transects. All tunicates in the quadrat were recorded, identified, counted and photographed. Sub-sample was taken and brought to laboratory for identification purposes. Environmental parameter was measured in situ. Underwater camera Canon GIS was used to obtain images of species and habitat (English et al. 1997; Brower at al. 1998). Water parameter including type of substrate, pH, temperature, current, salinity, clarity and dissolved oxygen were measured during sampling. Identification of tunicates was based on main morphological characters according to Kott (2005), Page and Kelly (2013), WoRMS (2017). Data Analysis Data analysis were based on ecological indices including density, Shanon Wiener diversity index, Dominance Index, Dispersion Index of Morisita (Odum, 1993) as follows Di = ni / A Where; Di = Density species i ni = Total no of individuals of species i in all quadrats A = Total area of quadrats (plot)
Species composition = ( No of individual sp-i /Total no of individual) x 100 % Dominance Index (Odum, 1993) C = ∑ (ni / N)2 Where; ni = No of individual species i N = Total no of individuals of all spesies C = Dominance Index Criteria : 0 < C ≤ 0.5 = Low 0.5 < C ≤ 0.75 = Moderate 0.75 < C ≤ 1.00 = High
H = -∑ (ni / N) ln (ni / N)
N
NN 1 Where; n = total number of plot/transect =F(x) N = Total no of individual inside = [F(X)] (X)
Magdalena Litaay
Species Composition The result shows that there are 18 species of tunicate present at reef flat (3 m) and reef slope (7 m) of Samalona waters (Table 1). The highest percentage of species diversity was found at 3 m depth at sta. 3 (83.3%) and lower in Sta 1 and 2 (77.2 %). At 7 m depth, we found similar number of tunicate species at sta. 2 and 3 (72,2%), yet higher compared to sta. 1. We recorded 7 families and number of species of tunicates (in brackets) as follows Clavelinidae (3), Didemnidae (3), Diazonidae (1), Styelidae (4), Ascidiidae (1), Perophoridae (3), Pyuridae (3) (Table 1). Species Richness The result of species richness is provided in Table 2. Table 2 shows that D. molle were more abundant at 3 m depth reef flat in all stations. This species is small in size, living solitary or forming a colony. Hence, D. molle can be found in a big colony at different habitats and depths. Previous finding indicated that Didemnum can grow at different habitats (Bullard and Whitlatch 2008; Carman et al., 2010).
Table 4 shows the value of dominance index ranges from 0 to 0.68. High values were recorded for Didemnum molle at 3 m depth at sta. 2 as well as sta. 1. This indicates a moderate dominance of particular species at those sites. As also described in Table 4, value of dominance index was less than 0.5 or close to zero.
Morisita Index 2
RESULTS AND DISCUSSION
Table 3 shows value of species diversity and Morisita indices at different sites and depths in Samalona waters. Based on criteria used in this study, diversity of tunicate is classified as low at 3 m depth at sta. 1 and sta. 2, while moderate at other depths and sites. Values of Morisita index indicates that tunicates are randomly distributed at all depths and sites (Table 3).
Where; ni = Number of spesies - i N = Total number of species H‟ = Diversity Index Criteria : 0 < H‟ ≤ 2.0 = Low 2 < H‟ ≤ 3.0 = Modertae 3.0 < H‟ ≤ 4.0 = High
X
Correspondance Analysis (CA) was used to observe the relationship between tunicates with water parameters (SPSS ver 23).
Diversity, Morisita and Dominance Indices
Shanon-Wiener Diversity Index
Id n
X2 = Sum of the squares of the number of individual inside plot = [F(X)] (X2) Criteria : Id < 1.0 = Randomly distributed Id = 1.0 = Uniformly distributed Id > 1.0 = Clumped distributed
plot/transect
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Table1. The occurrence of tunicates at different sites and depth of Samalona water Tunicates Family & species Sta 1 Sta 2 3m 7m 3m 7m Clavelinidae + + + + Clavelina robusta Kott 1990 (white spot) + + + + Clavelina lepadiformis Muller, 1776 + + + Oxycornia fascicularis Drasche, 1882 Didemnidae + + + + Didemnum molle Hermann, 1986 + Lissoclinum patella Gottschaldt 1898 + + + + Trididemnum della Ritter & Forsyth, 1917 Diazonidae + + + + Rhopalea crassa Hermann, 1880 Styelidae + + + + Polycarpa papillata Sluiter, 1886 + + + + Polycarpa aurata Quoys & Gaimard, 1834 + + + Polycarpa nigricans Heller, 1878 + Polycarpa spongiabilis Traustedt 1883 Ascidiidae + + + Ascidia sydneinsis Monniot F. 1898 Perophoridae + Pherophora sp (soft blue) + + Pherophora sp (orange) + + Perophora annectens Ritter, 1893 Pyuridae + Pyura molina Blainville, 1824 + + + Halocynthia verrill Dumosa Simpson, 1885 + + Microcosmus juinii Drasche 1884 77.7 66.6 77.7 72.2 Total percentage (%)
Sta 3 3m 7m + + +
+ + +
+ + +
+ +
+
+
+ + + +
+ + + -
+
+
+
+ -
+ + + + 83.3 72.2
Table 2. Species richness at different sites and depths of Samalona waters (ind/500m2) Sta 1 Sta 2 No. Tunicates species 3m 7m 3m 7m 0.126 0.054 0.004 0.058 1 Clavelina robusta Kott 1990 (white spot) 0.04 0.014 0.008 0.004 2 Clavelina lepadiformis Muller, 1776 0 0.03 0.006 0.054 3 Oxycornia fascicularis Drasche, 1882 2.286 0.79 2.16 0.564 4 Didemnum molle Hermann, 1986 0 0.028 0.012 0.036 5 Lissoclinum patella Gottschaldt 1898 0.01 0.002 0.048 0.012 6 Trididemnum della Ritter & Forsyth, 1917 0.012 0.01 0.002 0.032 7 Rhopalea crassa Hermann, 1880 0.352 0.052 0.068 0.09 8 Polycarpa papillata Sluiter, 1886 0.178 0.25 0.092 0.212 9 Polycarpa aurata Quoys & Gaimard, 1834 0.134 0.128 0.186 0.078 10 Polycarpa nigricans Heller, 1878 0.008 0 0 0 11 Polycarpa spongiabilis Traustedt 1883 0 0.008 0.002 0 12 Ascidia sydneinsis Monniot F. 1898 0.006 0 0 0 13 Pherophora sp (soft blue) 0.004 0 0.002 0 14 Pherophora sp (orange) 0.004 0 0 0.002 15 Perophora annectens Ritter, 1893 0 0.132 0 0 16 Pyura molina Blainville, 1824 0.018 0 0.016 0.074 17 Halocynthia verrill Dumosa Simpson, 1885 0.002 0 0.002 0 18 Microcosmus juinii Drasche 1884 Tabel 3. Value of Diversity and Morisita Indices Sites Depth Diversity (m) Index 3 0.99 Sta 1 Sta 2 Sta 3
28
7 3 7 3 7
1.55 0.73 1.68 1.67 1.35
Sta 3 3m 7m 0.042 0.372 0.052 0.872 0.026 0.012 0.01 0.12 0.344 0.206 0.01 0.004 0 0.002 0 0.094 0.002 0
0.016 0.04 0.03 0.416 0.004 0.008 0.014 0.056 0.096 0 0 0.002 0 0 0.002 0.024 0.002 0
Morisita Index 0.35 0.53 0.25 0.58 0.57 0.46
Magdalena Litaay
ISSN: 2460-0156 EISSN: 2614-5049 Table 4. The Result of Dominance Analysis No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Tunicate Species Clavelina robusta Kott 1990 (white spot) Clavelina lepadiformis Muller, 1776 Oxycornia fascicularis Drasche, 1882 Didemnum molle Hermann, 1986 Lissoclinum patella Gottschaldt 1898 Trididemnum della Ritter & Forsyth, 1917 Rhopalea crassa Hermann, 1880 Polycarpa papillata Sluiter, 1886 Polycarpa aurata Quoys & Gaimard, 1834 Polycarpa nigricans Heller, 1878 Polycarpa spongiabilis Traustedt 1883 Ascidia sydneinsis Monniot F. 1898 Pherophora sp (soft blue) Pherophora spp (orange) Perophora annectens Ritter, 1893 Pyura molina Blainville, 1824 Halocynthia verrill Dumosa Simpson, 1885 Microcosmus juinii Drasche 1884
Environmental Parameters The range of environmental parameters in study sites (Table 5) are as follows: temperature 30-31oC, salinity 31-32.5o/oo, dissolved oxygen (DO) 6.1-6.2 ppm, wind velocity 5.3-10.5 km/h), current 0.1-1.1 km/h, clarity 8.2-11.2 m, and pH 8.4. In general, water parameters are preferable for marine invertebrates. Table 5. Environment parameters at Samalona waters Water Parameters sta 1 sta 2 sta 3 Temperature oC Salinity o/oo DO (ppm) Wind Velocity (km/h) Current (km/h) Clarity (m) pH
31 31 6.2 10.5 0.1 8.2 8.4
30 31.3 6.2 9.8 1.1 10 8.4
30 32.5 6.1 5.3 0.7 11.2 8.4
Relationship Between Tunicates With Environment Parameters The result of Correspondence Analysis (CA) between species composition and environmental parameters is given in Figure 2.
Figure 2. The relationship between species composition with environmental parameters (Note: 1-18 tunicates species as indicated in Table 1; 19 = temperature, 20 = salinity, 21 = DO, 22 = wind velocity, 23 = current, 24 = clarity, 25 = pH) Magdalena Litaay
Sta1 3m 7m
Sta 2 3m
7m
Sta 3 3m
7m
0.002 0 0 0.517 0 0 0 0.012 0.003 0.002 0 0 0 0 0 0 0 0
0 0 0 0.686 0 0 0 0.001 0.001 0.005 0 0 0 0 0 0 0 0
0.002 0 0.002 0.215 0.001 0 0.001 0.005 0.030 0.004 0 0 0 0 0 0 0.004 0
0 0.029 0.001 0.162 0 0 0 0.003 0.025 0.009 0 0 0 0 0 0.002 0 0
0.001 0.003 0.002 0.343 0 0 0 0.006 0.018 0 0 0 0 0 0 0.001 0 0
0.001 0 0 0.278 0 0 0 0.001 0.028 0.007 0 0 0 0 0 0.008 0 0
As shown in figure 2, distribution of most of the tunicate are related to environmental parameters as they are close to center of the quadrant. While Clavelina lepadiformis (2), Ascidia sydneinsis (12), and Halocynthia verrill (17) are different compare to other ascidians as indicated in Figure 2. These species were found solitary living amongst brain coral (A. sydneinsis), H. verrill in small size and abundant at 3 m. This may indicate that other factors may influence the distribution of these particular tunicates. Tunicates from Sangkarang archipelago is less documented. This study was the first record for marine tunicate from Samalona waters of this archipelago. This study indicates that a number of tunicate species for this area is less than recorded from Baranglompo island of Sangkarang. Mawaleda (2014) found 33 species of tunicates at coral reef areas of Baranglompo waters. However, this study shows more tunicates species are found in Samalona waters compared to those recorded for other islands of Sangkarang: 7 species (Lae-Lae), 9 species (Bone Batang) and 10 species (Badi) waters (Fikruddin, 2013). Tunicate class Ascidiacea is the most diverse group, as 700 species have been recorded in the Australian waters, while Thaliacea is having less than 100 species worldwide and Appendicularia is about 60 species known worldwide (Kott, 2005). Kiuru et al. 2014 estimated that marine and coastal environments host about 90% of all organisms living on earth. Here, we found 18 species tunicate at Samalona waters, this number is still low compare to a worldwide known tunicates. Marine resources particularly in coastal areas and small islands received main pressure from natural catastrophic and anthropogenic activities. Human activities in main land as use of unfriendly fishing method, overfishing, pollution , coastal development and global warning has have big impact on marine 29
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resources. These also can contribute to loss of biodiversity of marine resources. Sangkarang archipelago has more than a hundred islands, biodiversity of tunicates in this area are still questionable. Therefore, more studies on biodiversity of tunicates in this area is needed. ACKNOWLEDGEMENTS Funding for this project was provided to Magdalena Litaay from Hasanuddin University through the research scheme of Indonesia Maritime Specific Topic II (BMIS II) 2016. Thanks to Nenis Sardiani, Reignildis Regina, Ilham, Muh Nurdin, Wahyulfatwatul, Ayub Wirabuana, Bachtiar Anas, Marjuni from Biological Celebes Diving Club (BCDC Unhas) for their assistance with field work. REFERENCES Atmaca H, Bozkurt E, Uzunoglu S, Uslu R, Karaca B. 2013. A diverse induction of apoptosis by trabectedin in MCF-7 (HER2- /ER+) and MDA-MB-453 (HER2+/ER-) breast cancer cells. Toxicology Letters 221:128-36. Brower JE, ZR JH, Von Ende CN. 1998. Field and Laboratory Methods for General Ecology, Mc Graw Hill Company.
Gonzalez-Santiago L, Suarez Y, Zarich N, MunozAlonso M, Cuadrado A, Martinez T, Goya, Iradi A, Saez-Tormo G, Maier J. 2006. Aplidin® induces JNK-dependent apoptosis in human breast cancer cells via alteration of glutathione homeostasis, Rac1 GTPase activation, and MKP-1 phosphatase downregulation. Cell Death & Differentiation 13:1968-81 Karthikeyan MM, Ananthan G, Balasubramanian T. 2009. Antimicrobial Activity of Crude Extracts of Some Ascidians (Urochordata: Ascidiacea), from Palk Strait, (Southeast Coast of India). World Journal of Fish and Marine Sciences 1 (4): 262-267. Kalimuthu S, Venkatesan J and Kim Se-Kwon. 2014. Marine Derived Bioactive Compounds for Breast and Prostate Cancer Treatment: A Review. Current Bioactive Compounds 10: 62-74. Kiuru P, Valeria DʼAuria M, Muller CD, Tammela P, Vuorela H, Yli-Kauhaluoma J. 2014. Exploring Marine Resources for Bioactive Compounds. Planta Med 2014; 80: 1234– 1246. DOI http://dx.doi.org/ 10.1055/s-00341383001 Published online September 9, 2014
Bullard SG and Whitlach RB. 2009. In situ growth of the colonial ascidian Didemnumvexillum under different condition. Aquatic Invasion 4, 275-278.
Kott P. 2005. Catalogue of Tunicata in Australian Waters. Queensland Museum, Brisbane, Australia. ISBN 0 642 56842 1.301. 301 p.
Carman MR, Morris JA, Karney RC, Grunden DW.2010. An initial assessment of native and invasive tunicates in shellfish aquaculture of the northeast coast. Journal Applied Ichthyology 26:8-11.
Litaay M, Christine G, Gobel RB, Dwyana Z. 2015. Bioactivity of tunicate Polycarpa aurata simbion as antimicrobe. In: Ohee (eds.) Proceeding the 23rd National Seminar of Indonesia Biology Society. Jayapura, 18 September 2015.
Christine G, Litaay M, Gobel RB, Dwyana Z. 2015. Potency of tunicate Polycarapa aurata as inoculum source for endosymbiotic bacteria; characterization of isolates. In: Tahir (eds). Proceeding National Seminar on Marine and Fisheries. FIKP Unhas. Makassar, May 2015. [Indonesian] de Voogd NJ, Cleary DFR, Hoeksema BW, Noor A, van Soest RWM. 2006. Sponge beta diversity in the Sangkarang Archipelago, SW Sulawesi, Indonesia. Marine Ecological Progress Series 309:131–142. English S, Wilkinson C, Baker V. 1997. Survey Manual for Tropical Marine Resources.Australian Institute of Marine Science. Townsville Fikruddin MBAbdH. 2013. Distribution and diversity of Tunicate (Ascidiacea) at different habitat at waters of Badi, Bonebatang and Laelae Island. BSc Hon Thesis FIKP Hasanuddin University. 30
Massin 1999. Reef Dwelling Holothuroidea (Echinodermata) of The Sangkarang Archipelago (South-West Sulawesi, Indonesia). Zoologische Verhandelingen 329. National Natuurhistorisch Museum. Leiden Mawaleda R. 2014. Distribution and habitat preference Urochordata class Ascidiacea at coral reefs area of Barrang Lompo island of Makassar. BSc Hon Thesis FIKP Hasanuddin University. Michaelson M, Bellmunt J, Hudes G, Goel S, Lee R, Kantoff P, Stein C, Lardelli P, Pardos I, Kahatt C. 2012. Multicenter phase II study of trabectedin in patients with metastatic castrationresistant prostate cancer. Annals of Oncology 23: 1234-40 Misset
J. 2006. A phase II study of Yondelis®(trabectedin, ET 743) as a 24-h continuous intravenous infusion in pretreated advanced breast cancer. Britainia Journal Cancer 94. Magdalena Litaay
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H. 1983. Zonation and diversity of scleractinian on reefs of South Sulawesi Indonesia. [Thesis]. Netherland: Leiden University.
Murti Y and Agrawal T. 2010. Marine derived pharmaceuticals development of natural health products from marine biodiversity. International Journal of ChemTech Research 2 (4): 2198-2217 Nurfadillah A, Litaay M, Gobel RB, Haedar N. 2015. Potency of tunicate Polycarpa aurata as inoculum source of sebagai sumber endosimbyotic fungsi that produce antimicrobe. Jurnal Alam and Lingkungan 6 (12): 10-16. Page M and Kelly M. 2013. Awesome Ascidians, a Guide to the Sea Squirts of New Zealand. TC Media Ltd.53 pp Priosambodo D, Kneer D, Asmus H, Zamani NP, von Juterzenka K, Litaay M, Soekendarsi E. 2014. Community analysis of burrower shrimp in Bone Batang seagrass bed South Sulawesi. Proceeding of The First International Conference on Science (ICOS1) 2014 ISBN 978-602-72198-0-9: 209 -219. Pogoreutz, C, H. Asmus, H. Anelt. D. Kneer, M. Litaay. 2012. The influence of canopy structure and tidal level on fish assemblages in tropical Southeast Asian seagrass meadows. Eustarine Coastal and Shelf Science. 107 (2): 58-68. Odum EP. 1993. General Ecology. Indonesia version: Dasar-Dasar Ekologi (Tjahjono Samingan; penyunting B. Srigandono). Yogyakarta, Gadjah Mada University Press. 667 pp. Radjasa OK, Vaske YM, Navarro G, Vervoort HC, Tenney K, Linington RG, Crews P. 2011. Highlights of marine invertebrate-derived biosynthetic products: Their biomedical potential and possible production by
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microbial associants Bioorganic & Medicinal Chemistry 19: 6658–6674 Renema WT and Simon R. 2001. Larger foraminifera distribution on a mesotrophic carbonate shelf in SW Sulawesi (Indonesia). Palaeogeography, Palaeo climatology, Palaeoecology 175:125-146 Sabdono A and Radjasa OK. 2008. Microbial symbionts in marine sponges: Marine natural product factory. Journal Coastal Development 11: 57–61. Sardiani N, Litaay M, Gobel RB, Dwyana Z. 2015. Potency of tunicate Rhopalaea sp as source of bacterial inoculum that produce antibacterial; isolates characterization. J. Alam dan Lingkungan 6: 11. Shaala
LA and Diaa Youssef TA. 2015. Identification and Bioactivity of Compounds from the Fungus Penicillium sp. CYE-87 Isolated from a Marine Tunicate. Marine Drugs 2015, 13, 1698-1709; doi:10.3390/md13041698.
Sinko J, Rajchard J, Balounova Z, Fikotova L. 2012. Biologically active substances from water invertebrates: a review. Veterinarni Medicina, 57(4): 177–184 Tahir E, Litaay M, Gobel RB, Haedar N, Prisambodo D, Syahribulan. 2016. Potency of tunicate Rhopalaea crassa as inoculum source of endosymbiont fungi that produce antimirobe. Marine Science Journal Spermonde 2(2): 33-37 [Indonesian] WoRMS. 2017. World Register of Marine Species. http://www.marinespecies.org/ aphia.php?p=taxdetails&id=146420. Verheij E. 1993. Marine plants on the reefs of the Sangkarang Archipelago, SW Sulawesi, Indonesia: aspect of taxonomy, floristics and ecology. [Dissertation] Leiden: Rijksherbarium / Hortus Botanicus
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ISSN: 2460-0156 EISSN: 2614-5049
SPATIAL COMPOSITION OF BENTHIC SUBSTRATE AROUND BONTOSUA ISLAND Muhammad Banda Selamat1*, Mahatma Lanuru1, Amir Hamzah Muhiddin1 Submitted:17 Januari 2018 Accepted: 12 Februari 2018
ABSTRACT Coral reefs and seagrass are natural fortress for small islands from waves and ocean currents. The spatial distribution of these benthic substrate should be known and monitored regularly. This study aims were to map existing benthic substrates on the reef flat of Bontosua Island, determine the spatial composition and develop index ratio. Benthic substrates were surveyed using geotagging technique. Their distribution were estimate using Quickbird image that was rectified and classified using ISOcluster method and validate by 240 selected photos. The seagrass were surveyed at 8 stasions to record percent cover and species composition. Depth profiles were track along 10 reef flat line segment. Bontosua Island has an elongated shape from South to Northwest. This study had produced a benthic substrate distribution map with thematic accuracy 76%. Total area able to map were 54.2 hectares. About 43% benthic substrates at Bontosua were mixture of coral rubble, seagrass and algae, 20% was mixture of rubble and algae, 16% dominated by seagrass, 13% mixture of sand and seagrass and 8% substrate were dominated by live coral. There were eight seagrass species found with average percent cover 37.2 ± 12.5 percent. The spatial ratio of live coral, seagrass and mixed substrate for West side reef flat was 2:20:49 and 1:9:9 for East side. This indicate that the distribution of benthic substrates on the West side is much wider than on the East side. This approach potentially applied to study the relationship between benthic substrate composition and the deformation of small islands. Keywords: benthic substrate, spatial composition, spatial ratio index, small islands, seagrass
INTRODUCTION Coastal ecosystems such as coral reefs, seagrass and mangroves play an important role in maintaining coastal stability (Spalding et al. 2014). The ecosystem services are function of various variables such as ecosystem size, season, type of disturbance, and species interaction (Barbier et al. 2008). For example, sea wave reduction by seagrass beds is only optimal when the size and density of the sea grass is maximum.. Coral reefs in Spermonde, South Sulawesi are threatened by sedimentation, destructive fishing (Sawall et al. 2013) and coral bleaching. Based on Landsat image analysis, the rate of coral damage in Spermonde is about 300 hectares per year (Rauf and Yusuf, 2004). Recent mapping found that there has been a decline in live coral cover over period of 20 years starting from 1994 with a rate of 174 ha/year (Yasir Haya and Fujii, 2017). Indicates that detailed information of benthic substrate distribution in this region is still highly needed. Moreover, the coral reefs may act as absorbant of wave energy that propagate to the shore (Ferrario et al., 2014), so the knowledge of its distribution also important for disaster mitigation. Satellite imagery proved to be effective for mapping coral reefs habitat when supported by sufficient field data (Roelfsema et al, 2013; Selamat et al, 2012). The spatial pattern of coral reefs has a positive relationship with topographic variation form (Fuad, 2010). 1
Department of Marine Science, Hasanuddin University Jl. Perintis Kemerdekaan Km.10, Makassar 90245, Indonesia * Muhammad Banda Selamat Email:
[email protected]
The complexity of coral reefs occupies a wide spatial range that can be approximated using different satellite imagery depend on study objective (Ferrari et al., 2016). The Small islands in Spermonde can be classified as coral islands by the origin of its formation. The islands like these are commonly surrounded by seagrass and coral reef ecosystems with various variations of geomorphic zones. Coral reefs and seagrass beds have a very important role for these islands, especially in blocking waves and ocean currents so that coastal erosion remains minimum. This study aims were to map existing benthic substrates on the reef flat of Bontosua Island, determine their spatial composition and develop a simple index value to represent the composition of live coral, seagrass and mixed substrate. This information can be optimized to see how variation of benthic substrate composition may affect the magnitude of environmental services, for example in maintaining the stability of the shoreline. MATERIALS AND METHODS This study was conducted from August to October 2016 at Bontosua Island, Liukang Tupabbiring District, Pangkejene Islands (Figure 1). The equipment and materials used are presented in Table 1. Research was generally divided into two major sections. The first section were a hydrographic and ecological survey that includes measurements of bathymetry on reef flat area, continuous substrate photo shooting (coral, macroalgae, seagrass) and seagrass cover survey.
ISSN: 2460-0156 EISSN: 2614-5049 The second section focuses on the processing and analysis of photographs and satellite imagery to map the distribution of benthic substrates in the study area. Spatial analysis was performed to see the
distribution of substrate types along the reef flat profile at wind direction and to calculate the substrate (distance) composition from the shoreline.
Table 1. Equipments and Materials in The Study Material and Equipments
Functions
Quickbird satellite image
To produce benthic map
Quadrats with rectangular grid (50 cm X 50 cm)
Seagrass observation
Global Positioning System (GPS) e10
Positioning the sampling stasions
Roll Meter (30 m)
Line transect on seagrass sampling
Navigation compass
Direction guidance on seagrass sampling
Seagrass cover photos (Mc Kenzie, 2003)
Guidance on seagrass percent cover estimation
Underwater cameras
To potray benthic substrate
Mapsonder 420
Depth profiling
Katingting
Survey mobilization
IDRISI Terrset 18
Image processing and GIS data analysis
Picasa 3
Photo processing
Mapsource 6
GPS data processing
Ms. Excell
Statistical spreadsheets
using two satellite navigation constellations which are GPS and Glonass in order to limit Horizontal Dilution Of Precession (HDOP) better than 3 meters. Benthic Substrate Survey Benthic substrate survey was done by using lapse time photo shooting technique. The cameras were placed at the side of the katingting which parallel to the mapsonder about 30 cm below the waterline. The position of camera lens was faced downward and perpendicular to the seabed. Camera time clock was unified to GPS time clock so that made possible to apply geotagging technique. Each photos then have same coordinate system as on satellite image. Recording of GPS positioning was done by using tracking technique (Selamat et al, 2012). Of the 1.954 photographs produced there were 240 selected photos to analyzed. Seagrass Survey Figure 1. Study Location and Sampling Stations at Bontosua Island
Determination of Survey Track The tracks plan was plotted on to the image of Bontosua Island using Google Earth software. These tracks were then input to GPS using mapsource software. The profile and distribution of benthic depth on reef flats of western side Bontosua Island are represented by transects A to F and on the East side by G to L (Figure 1). Field positioning was
Muhammad Banda Selamat
The seagrass survey was conducted by modifying Seagrass Watch method (Mc Kenzie, 2003). Each sampling stations were consist of three parallel lines and separated 15 meters far. The length of each transect lines were about 30 meters. The position of start and end point of transects were defined and recorded by using GPS. After line transect installation was complete, then observation of seagrass cover on each plot was done by using quadratic frame which size 50 cm x 50 cm. The
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distance between plots were 5 meter starting from marked line 0 meter to meter 30. Thus the number of observation plots in each station were 21 and there were 8 seagrass stations surveyed (Figure 1). The seagrass species on each plot was identified and the percentage of seagrass cover was estimated based on the number of grids. Seagrass data processing was refer to sea grass monitoring guide book (Rahmawati et al. 2014). Seagrass cover in one squared was calculated according to the formula Seagrass cover (%) = total seagrass cover value / 4 The average seagrass cover per station was calculated according to the formula: Average seagrass cover (%) = Number of seagrass cover of all transects / 21 Quickbird Image Processing
rear zone, reef flat and buttress zone (Goreau, 1959). The reef flat zone commonly has average 0.5 to 3 metres depth. Corals are rarely occur in reef flat except in deeper area. This is because the area is normally dry at low tides. The reef flat zone around Bontosua Island can be divided into two parts: windward areas along West side and leeward areas along East side. Bontosua Island has an elongated shape from the south to the Northwest. If measured from the midpoint of the island then the reef flat of the west side has a wider size than the Eastern side. The northwest reef flat is the most wide zone compare to other side. The depth at reef flat is less than 1 meter and exposed at the lowest tide. Generally the depth change on East side is fast and form a steep cliff edge or can be named as reef slope. This is contrast to west side area where the depth change is small along reef flat zone to reef slope (Figure 2)
The satellite imagery used was the Quickbird satellite image of the November 1, 2014 recording date obtained freely from Google Earth (https://www.google.com/earth/). About 52 tile images were mosaicing and geometrically rectified to RMSE = 0,5. The original color composite image was then classified using ISOcluster unsupervissed technique with minimum number of cell in a valid class is 10 and the sample interval is 5 pixel. The classification map contains five benthic classes: 1) class wich dominated by seagrass, abbreviate as: dom.sg 2) class that mixed between sand and seagrass, abbreviate as: mix.sa.sg 3) class that mixed between rubble, seagrass and macroalgae, abbreviate as: mix.rb.sg.al 4) class that mixed between rubble and macroalga, abbreviate as:mix.rb.al 5) class wich dominated by live coral, , abbreviate as: dom.livc We use 230 benthic photos to validate and produced image thematic accuracy matrix using method that similar to that developed by Congalton and Green (2009) and Stehman (2009). With all procedures Depth Profile of Reef Flat Zone at Bontosua and data limitations it was reasonable to set passing Figure 2. Island. A to F are represent West side of the value for accuracy minimal 75%. Zone and G to L for East side (see Figure 1 for detail location)
RESULTS AND DISCUSSION Geomorphic Profile of Bontosua Reef Flat Terminology for coral reef structures is often contextually defined, sometimes referring only to photographs or images (Stoddart, 1978). Studies about reef are commonly focus on the organism distribution along the area and rarely connected to variation of depth (stoddart, 1969). The geomorphic of a reef system may consist of back reef region, reef crest, seaward slope or fore reef. Each region divided into several zone like shore zone, lagoon, 34
The substrate distribution along depth profile line are obtained from field photographs. The live coral substrate (often mix with dead corals) was commonly found at west side reef slope at minimum depth was 1 meter. Live corals at east side of Bontosua island are limited and usually damage due boat anchoring. Seagrass substrates are common on both sides but more widely distribute on west side. Macro algae are easier to find at northwest and southwest of the island. The substrate of algae and rubble was more dominant at western reef flats compared to the eastern part (Figure 3).
Muhammad Banda Selamat
ISSN: 2460-0156 EISSN: 2614-5049
Figure 3. Benthic Substrate around Bontosua Island. A to F are represent West side of The Zone and G to L for East side (see Figure 1 for detail location)
Spatial Characterization of Substrate Quickbird image classification using ISOcluster unsupervised techniques had produced a map of benthic substrate distribution for Bontosua Island (Figure 4). The thematic error for this map was 24% hence means that its level for thematic accuracy was 76% (Table 2). This level of accuracy was pass the critical value and the map was available for advance analysis. The total area spatially mapped was about 54.2 hectares. Thematic accuracy is influenced by many things, including the number of thematic classes displayed. Selamat et al. (2012) had compared two algorithms for mapping the benthic substrates with satellite imagery and concluding that for the same satellite imagery source, higher thematic accuracy can be generated by reducing the
thematic class presented. Roelfsema et al. (2013 study had resulted in thematic accuracy of benthic community maps for Kubulau, Kadavu and Roviana areas in Fiji Islands respectively 66%, 68% and 65%. Satellite images used were Quickbird and IKONOS. According to the map produced, benthic substrate composition around Bontosua island are 43% consist of mixture of coral rubble, seagrass and algae; mixture of rubble and algae about 20%; dominated by seagrass about 16%; mixture of sand and seagrass about 13% and substrate that dominated by live coral about 8%. Live corals communities were more easy to found at reef slope area of Nortwest side of the island. Seagrass beds are commonly found at West coast of the island and distribute sparsely around the reef flat.
Figure 4. The Map of Benthic Substrate Distribution at Bontosua Island
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Table 2. The Matrix Accuracy for Bontosua Substrat Benthic Image Classification dom.sg mix.sa.sg mix.rb.sg.al dom.livc mix.rb.al 24
0
3
0
3
Total 30
mix.sa.sg
9
11
2
0
0
22
0.50
mix.rb.sg.al
7
1
100
8
5
121
0.17
dom.livc
0
0
2
34
0
36
0.06
mix.rb.al Total
7 47
2 14
4 111
2 44
6 14
21
0.71
230
Error0
0.49
0.21
0.10
0.23
0.57
dom.sg
Seagrass Cover and Composition There were 8 (eight) seagrass species found on the reefs of Bontosua Island in August 2016, those are Enhalus acoroides, Thalassia hemprichii, Halophila ovalis, Cymodocea rotundata, C. Serrulata, Halodule uninervis, Syringodium isoetifolium and S. isoetifolium. The percentage of seagrass cover at those observation stations were varied from 20.5 ± 13.3 to 67.5 ± 11.9 percent with average about 37.2 ± 12.5 percent (Figure 5a). The highest seagrass cover was found at station 6 which was the closest location to the residential area and the lowest seagrass cover was found at station 8 located on reef flat of southside the island. The seagrasses are generally spread over the northern side of the island and have the highest cover at the western bank of the island shore.
ErrorC 0.20
0.24
The species Cymodocea rotundata and Thalassia hemprichii were found in almost all stations, indicating that they have a wide distribution compared to other species (Figure 5b). The composition of seagrass in an area is more influenced by the adaptability of the seagrass to the environmental factor. The species Cymodocea rotundata and Thalassia hemprichii generally grow predominantly at carbonate and rubble sand substrate, forming a mixed community (Waycott et al., 2004). Based on field observations, several factors that cause the low percentage of seagrass cover at South side of Bontosua Island were: ship anchorage activity around the seagrass area, coral reef damage that causing rubble to cover the seagrass and the presence of biota that digging hole around seagrass communities
Figure 5. The Seagrass Cover (a) and Composition (b) at Bontosua Sampling Stations
Spatial Composition of Benthic Substrate Spatial composition of benthic substrate is a comparison of distribution length of each benthic class on reef flat area. This study had profile 10 (ten) line cross section reef flat and five classes of benthic substrate presented in the form of spreading map (Table 3). The class benthic of dom.livc and dom.sg were containing unique and specific substrate, while benthic class of mix.sa.sg,
36
mix.rb.sg.al and mix.rb.al were actually contain several substrates or not dominated by certain substrates. Therefore, the length distribution values for these three classes can be unified to form a new class called the mixed class. Furthermore the ratio between benthic classes can be calculated based on the longest segment distance (i.e segment A). The calculation results of spatial benthic substrate ratios for the Western and Eastern sides of Bontosua island are presented in Table 4
Muhammad Banda Selamat
ISSN: 2460-0156 EISSN: 2614-5049 Table 3. The Length Segment of Benthic Substrate Distribution along Reef Flat (unit in meters) Segment of Cross line Benthic Classes A B C D E F G H I J dom.livc 31.5 6.2 3.6 5.7 7.2 7.8 0.0 0.0 5.4 6.8 dom.sg 266.9 80.3 126.2 31.1 82.9 74.8 81.9 14.5 22.4 72.8 mix.sa.sg 75.4 34.5 0.0 116.3 64.6 162.3 0.0 8.3 15.0 0.0 mix.rb.sg.al 150.2 277.2 74.7 170.9 50.4 133.0 16.0 11.9 7.3 9.7 mix.rb.al 34.5 36.2 8.3 17.6 221.8 0.0 44.1 7.0 13.4 0.0 Sum 558.4 434.4 212.8 341.6 427.0 377.9 141.9 41.7 63.6 89.3 Table 4. The Spatial Benthic Substrate Ratios
Benthic Classes dom.livc dom.sg mixed
Spatial Ratio West East 2 1 20 9 49 9
K 3.7 54.9 0.0 31.7 0.0 90.2
L 8.2 43.3 88.7 44.0 18.7 202.8
in order to see its relationship with small island coastline change. ACKNOWLEDGMENT
The authors would like to thank PT. MARS which has provided the software IDRISI Terrset and financing during surveys. Thanks and high appreciation are also given to field assistants: The spatial ratios of these benthic substrates are able Taufikurrahman, Mustono and Asgar Saputra. to show us that the reef flats on the western side of Bontosua island are generally dominated by a mixed REFERENCES substrate. The seagrass may found more on the West side than on the East side. Furthermore, this value Barbier, E. B., Koch, E. W., Silliman, B. R., Hacker, S. D., Wolanski, E., Primavera, J., …Reed, D. can also indicate us that the distribution of benthic J. 2008. Coastal ecosystem-based substrates on the West side is much wider than on the management with nonlinear ecological East side based on the magnitude of values for each functions and values supporting material. benthic class. Science (New York, N.Y.), 319(5861), 321–3. This spatial based valuation approach is potentially http://doi.org/10.1126/science.1150349. applied to neighbour islands in order to compare their benthic substrate composition variation in Congalton, R. G., & Green, K. 2009. Assessing the Accuracy of Remotely Sensed Data: context long term and large scale area monitoring. Principles and Practices. The This index also potential to deploy in the study of Photogrammetric Record (Vol. 2). environmental services that benthic substrate http://doi.org/10.1111/j.1477provided. As well as how much this spatial ratio 9730.2010.00574_2.x may indicate the shifting shapes of small islands. CONCLUSION In order to monitor the benthic substrate composition variation it is important first to develop a simple index value. This study had produced benthic substrate distribution map for 54.2 hectares of Bontosua reef flat. There were five benthic substrate classes with thematic accuracy 76%. Most of reef flat area (43%) were cover by mixture of coral rubble, seagrass and algae. Only 8% substrate were cover by live coral. Of the 16% substrate dominated by seagrass there were eight seagrass species found with average percent cover about 37.2 ± 12.5 percent. The benthic spatial composition or may stated as a spatial benthic substrate ratio index is actually a comparison of live coral, seagrass and mixed substrate line segment for the reef flat at area of study. In this study the spatial benthic substrate ratio shows that the distribution of mixed substrate was much higher at West side than East side of Bontosua reef flat. It is also shows that West side reef flat much more wider than East side.Further study that employ this approach to monitor long term of benthic substrate composition variation is needed
Muhammad Banda Selamat
Ferrari, R., McKinnon, D., He, H., Smith, R., Corke, P., González-Rivero, M., Upcroft, B. 2016. Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling. Remote Sensing, 8(2), 113. Ferrario, F., Michael, W.B., Curt, D. S., Fiorenza, M., Christine, C. S. dan Laura, A. 2014. The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nature Communication, hal 1-9 Fuad, M. A. Z. 2010. Coral Reef Rugosity and Coral Biodiversity. Bunaken National Park-North Sulawesi, Indonesia. Tourism, 60. Goreau, T. F. 1959. The Ecology of Jamaican Coral Reefs I. Species Composition and Zonation. Ecology, 40(1), 67–90. http:// doi.org/ 10.2307/1929924 McKenzie, L.J. 2003. Guidelines for the rapid assessment and mapping of tropical seagrass habitats. QFS, NFS, Cairns. 46 hal
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Rahmawati, S., Irawan, A., Supriyadi, I.H., Azkab, Spalding, M. D., Ruffo, S., Lacambra, C., Meliane, M.H. 2014. Panduan Monitoring Padang I., Hale, L. Z., Shepard, C. C., & Beck, M. W. Lamun, ed. M. Hutomo, A. Nontji. 2014. The role of ecosystems in coastal COREMAP CTI LIPI, Jakarta, pp 45 protection: Adapting to climate change and coastal hazards. Ocean and Coastal Rauf, A., & Yusuf, M. 2004. Studi Distribusi dan Management, 90, 50–57. http:// Kondisi Terumbu Karang dengan doi.org/10.1016/j.ocecoaman.2013.09.007 Menggunakan Teknologi Penginderaan Jauh di Kepulauan Spermonde , Sulawesi Selatan. Stehman, S. V. 2009. Sampling designs for accuracy Ilmu Kelautan, 9(2), 74–81. assessment of land cover. International Journal of Remote Sensing, 30(20), 5243– Roelfsema, C., Phinn, S., Jupiter, S., Comley, J., & 5272. Albert, S. 2013. Mapping coral reefs at reef to http://doi.org/10.1080/01431160903131000 reef-system scales, 10s-1000s km2, using object-based image analysis. International Stoddart, D. R. 1969. Ecology and Morphology Of Journal of Remote Sensing, 34(18), 6367– Recent Coral Reefs. Biological Reviews, 6388. 44(4), 433–498. http://doi.org/10.1111/j.1469http://doi.org/10.1080/01431161.2013.800660 185X.1969.tb00609.x Sawall, Y., Jompa, J., Litaay, M., Maddusila, A., & Stoddart, D.R. 1978. Descriptive reef terminology in Richter, C. 2013. Coral recruitment and Monographs on oceanographic methodology, potential recovery of eutrophied and blast 5. Coral reefs: research methods, ed. D. R. fishing impacted reefs in Spermonde Stoddart & R. E. Johannes.UNESCO, Archipelago, Indonesia. Marine Pollution Norwich, 17-22 Bulletin, 74(1), 374–382 Yasir Haya, L. O. M., & Fujii, M. 2017. Mapping the Selamat, M. B., Jaya, I., Siregar, V. P., & change of coral reefs using remote sensing Hestirianoto, T. 2012. Akurasi Tematik Peta and in situ measurements: a case study in Substrat Dasar dari Citra Quickbird (Studi Pangkajene and Kepulauan Regency, Kasus Gusung Karang Lebar, Kepulauan Spermonde Archipelago, Indonesia. Journal Seribu, Jakarta). Ilmu Kelautan, 17(3), 132– of Oceanography, 73(5), 623–645. 140. http://doi.org/10.1007/s10872-017-0422-4
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Muhammad Banda Selamat
SPERMONDE (2018) 4(1): 39-42
ISSN: 2460-0156 EISSN: 2614-5049
ACCUMULATION OF HEAVY METALS WITHIN HARD CORAL Porites lutea IN SPERMONDE ARCHIPELAGO, SOUTH SULAWESI Muhammad Farid Samawi1*, Shinta Werorilangi1, Rahmadi Tambaru1, Rastina Rastina1 Submitted: 3 January 2018 Accepted: 15 February 2018
ABSTRACT Hard coral Porites lutea is an animal that lives on the ocean floor. This species may live for years and accumulate heavy metals from its surrounding environments. The aims of this study was to know accumulation of heavy metals (Pb, Cd, and Cu) pollution by Porites lutea at different islands in Spermonde Archipelago waters. This study used field surveys around Laelae, Bonebatang and Badi Islands of South Sulawesi. Field parameters measured were oceanographic parameters, metals in water and sediment. Hard coral was extracted using nitric acid, then measured its heavy metal levels using Atomic Absorption Spectrophotometer. Several field parameters such as temperature, salinity, turbidity, pH and dissolved oxygen indicated no differences at each location, whereas the difference was observed in the values of Total Suspended Solid and dissolved oxygen. The results showed the accumulation of heavy metals in the skeleton of Porites lutea was Pb>Cu>Cd and Laelae>Bonebatang>Badi Island. Keywords: accumulation, heavy metals, pollution, skeleton Porites lutea
INTRODUCTION Development of both residential and industrial activities in coastal areas has increased along with the population growth. These activities produce byproducts such as solid and liquid waste that will pollute the marine environment. The composition of this waste consists of organic and inorganic materials (heavy metals). Industrial activities are done by humans are source of metal pollution. Heavy metals are common marine pollutants that emanate from such sources as industrial and sewage treatment discharges and antifouling paints (Mitchelmore et al. 2003). These industrial wastes containing heavy metals such as mercury (Hg), cadmium (Cd), lead (Pb), copper (Cu), and zinc (Zn). These elements have strong toxicity (high toxicity), so that reduce the quality of water and to poison the organisms that live in it. Some organisms that live in the sea have the ability to accumulate heavy metals in their bodies. Some plant species such as seaweed, seagrass and animal species such as shellfish are able to accumulate heavy metals. Sponge is one type of coral that capable to accumulate heavy metals. Van Hansen et al. (2000) reported that the marine sponge Halichondria panicea cosmopolitan species capable of accumulating metals Cu, Zn and Cd. The same thing was reported by Cebrian et al. (2003) who observed the sponge Crambe crambe accumulate copper, tin and vanadium in bulk. The increase in the heavy metals in coral skeleton may reflect the environmental factors besides the anthropogenic impacts (Esslemont et al. 2000). There is a rapid increase of threats to corals owing to the increase of coastal activities along the coastline 1
Department of Marine Science, Hasanuddin University Jl. Perintis Kemerdekaan Km. 10, Makassar 90245, Indonesia * Muhammad Farid Samawi Email:
[email protected]
expansion, damage by maritime activities, inorganic and organic pollution, oil pollution, shipping processes, tourism and lack of public awareness (Dar and Abdel-Wahab 2005; Madkour and Dar 2007). However, studies conducted in Spermonde showed that the sediment and sea waters near the mainland have experienced contamination (Mallongi, 2014; Werorilangi et al., 2016). While, there is no study about monitoring of heavy metals using hard coral Porites lutea in Spermonde Archipelago waters. In addition, contribution of the mainland to accumulation of heavy metal may different from the Makassar due to differences in magnitude length. The aim of this study was to monitor heavy metals contamination using hard coral Porites lutea accumulation in different oceanographic conditions. We tested the hypothesis that accumulation level of heavy metals Pb, Cd and Cu in Porites lutea would be greater on island closer to the mainland. MATERIALS AND METHODS Study Sites and Sampling Spermonde Archipelago is located in the west of South Sulawesi. Samples of hard coral Porites lutea were collected from three different localities representing different distances from the mainland (Laelae Island, 0.68 mile from mainland; Bonebatang Island 8.16 mile and Badi Island 11.4 mile (Figure 1). Preparation of hard coral Porites lutea samples was conducted in the Laboratory of Chemical Oceanography, Faculty of Marine Science and Fisheries, Hasanuddin University. Levels of heavy metals measurements performed in the Laboratory of Environmental Health Makassar. The research was conducted with explorative method designed to describe the accumulation of heavy metals from waters by hard coral Porites lutea
SPERMONDE (2018) 4(1): 39-42
Sample Preparation Hard coral preparation was done through the following steps: sample was put in the oven to be dried at a temperature of 1050C for 2 days. Samples were then weighed and the weight by 5g was put into porcelain dish and was added HNO3 and H2SO4, respectively 5ml-1. After the sample was cooled and then dissolved the sample in distilled water and then filtered using filter paper as much as 50ml. Samples to red in glass bottles are subsequently measured their metal concentrations using Atomic Absorption Spectrophotometer (flame, 6200 Shimadzu) (Denton and Burdon-Jones, 1986).
Figure. 1 . Site location
(levels of heavy metals Pb, Cd,and Cu). All glassware and sampling bottles and bags were then soaked in 1M nitric acid for 24 hours. Before being used, all the glassware, sampling bags and bottles were washed with deionised water and dried. Collection of hard coral colonies from various study sites in order to quantify the degree of pollution in the field. Small coral colonies were collected from the reef flats of Laelae, Bonebatang and Badi Islands from three different sites as specified in Figure 1, using a hammer and a chisel. Thecoral colonies were placed into plastic bags and transported to the laboratory, Sampling was done three times on each reef field parameters measured during sampling trip were temperature, flow rate, turbidity, total suspended solids (TSS), salinity, pH and dissolved oxygen.
Bioaccumulation is a process in which a chemical substance is absorbed within an organism by all routes of exposure as occurs in the natural environment, i.e., ambient environment sources. Calculation Bioaccumulation Factor (BAF) of hard coral Porites lutea used the following equation (Arnot and Gobas, 2006): [
]
BAFw =[
BAFs =[
and
] [
] ]
The statistical analyses of hard coral in the studied localities were made using the one-wayANOVA and LSD method using SPSS (Ver. 16). RESULTS AND DISCUSSION Conditions of field parameters in Laelae, Bonebatang, and Badi Islands waters were shown in Table 1.
Table1. Conditions of field parameters in Laelae, Bonebatang, and Badi Islands waters
Parameters
Unit
Laelae
Bonebatang
Badi
Temperature
oC
29±0.0
28.6±0.3
28.17±0.21
Salinity
ppt
34.33±0.58
33.66±0.58
34.33±1.53
Turbidity
NTU
1.51±0,15
0.22±0.14
0.38±0.24
Total Suspended Solid
ppm
108.12±54.7
65.0±17.2
66.18±8.19
pH
-
7.15±0.05
7.15±0.01
7.20±0.02
Dissolved Oxygen
ppm
4.49±0.53
5.01±0.08
6.18±0.16
Table 1shows that the salinity, temperature, pH, turbidity, dissolved oxygen to support life of the coral, no differences at each location, but the Total
40
Suspended Solid and dissolved oxygen different between location. The high value of Total
Muhammad Farid Samawi
ISSN: 2460-0156 EISSN: 2614-5049 Suspended Solid because the waters around the mouth of the river there are Tallo, Maros,and Pangkep rivers. Table 2. Heavy metals content in Laelae, Bonebatang and Badi Islands waters Metals Laelae Bonebatang Badi (mgL-1) Pb 0.315±0.058 0.245±0.036 0.229±0.051 Cd
0.078±0.001
0.075±0.002
0.079±0.002
Cu
0.032±0.001
0.020±0.001
0.016±0.002
Table 2 shows that the concentration of heavy metals Pb, Cd and Cu in seawater is different at three location. The high concentrations of Pb, Cd and Cu in waters of Laelae Island because it is located near the mainland, so that under the influence of heavy metal input from land.
72-102 ppm, greater than Cd and Cu in skeletal and Pb heavy metal accumulation by hard coral Porites lutea highest found on the island Laelae. Anu et al. (2007), found hard coral accumulated heavy metal Pb 4.50–24.18 ppm; Shah (2008) found Pb concentrations range from 2.07 to 23.1 ppm and Muhammed and Dar (2010) found 2.91–12.68 ppm.These data demonstrate that the accumulation of heavy metals Pb by hard coral Porites lutea in the Laelae Island water greater than in other countries. Bioaccumulation Factor (BAF) is used to determine the ability of an organism to accumulate in the body element of the surrounding environment. Hard coral Porites lutea is a species that lives macrozoobenthos settled at the bottom of the ocean waters. Porites lutea able to adapt to the environment so that it can absorb pollutants. Figure 2 shows the calculation of BAF several types of heavy metals by hard coral Porites lutea.
Tabel 3. Concentration of heavy metals Pb, Cd and Cu in sediment of Laelae, Bonebatang and Badi Islands Metals (µg.gLaelae Bonebatang Badi 1 d.wt) Pb
17.33±1,34
16.58±0,49
18.32±2,06
Cd
0.22±0,08
0.25±0,04
0.27±0,02
Cu
0.43±0,16
0.24±0,03
0.31±0,22
Table 3 shows that the concentration of heavy metals Pb, Cd and Cu in sediment quality is below the standard (IADC/CEDA. 1997). Lead concentrations in the coral skeletons were similar to that of the sediments obtained by Morrison et al (2001). Other possible sources of Pb in the marine environment could be from industrial discharge, agricultural runoff or from urban/storm water runoff and transportation.Results of the measurement of the concentration of heavy metals Pb, Cd and Cu in the skeleton of hard coral Porites lutea at each observation station are shown in Table 4. Table 4. Concentration of heavy metals in hard coral Porites lutea in Laelae, Bonebatang and Badi Islands Metals (µg.g-1 Laelae Bonebatang Badi d.wt) Pb
102.37±21.09
72.85±24.22
79.42±17.20
Cd
1.33±0.63
1.23±0.30
1.31±0.14
Cu
2.04±0.57
2.43±1.46
2.75±0.33
Table 4 shows that in skeleton of hard coral Porites lutea contained heavy metals. ANOVA test results to the accumulation of heavy metals in the order Pb hard coral Porites lutea showed significant differences between site for heavy metal Pb (P .05). Thus the hard coral Porites lutea able to accumulate heavy metals Pb
Muhammad Farid Samawi
Figure 2. BCFs value heavy metals from waters by hard coral Porites lutea
Figure 2 shows the accumulation of heavy metals by hard coral Porites lutea from the waters. Pb accumulated ranged from 297.2 to 347 times the concentration in the waters. Further Cu ranged from 63.1 to 175.7 times, ranging from 15.8 to 18.8 times the metal Cd. Thus proving that the hard coral Porites lutea as bioaccumulator considerable heavy metal from water. BAFs calculation results of heavy metals from sediments (Figure 3) shows that Porites lutea accumulate 4-10 times in sediment. This is very small amount heavy metals accumulated from sediment
Figure 3. BCFs value heavy metals from sediment by hard coral Porites lutea
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SPERMONDE (2018) 4(1): 39-42
CONCLUSION Consentration of Pb in waters and sediment was higher than Cd and Cu, while metals consentration was not different among island waters. The levels of heavy metals in the skeleton of hard coral
Porites lutea in Laelae Island was higher than Bonebatang and Badi island waters. Based on the value of BAF, metal accumulation derived from water. Suspended material inhibits the accumulation of metals from waters
REFERENCES Anu
G, Kumar NC, Jayalakshmi KJ, Nair SM.(2007). Monitoring of heavy metal partitioning in reef corals of Lakshadweep Archipelago, Indian Ocean. Environ Monit Assess 128:195–208
Arnot,J.A and F. Gobas. (2006). A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environ. Rev. 14: 257–297 (2006) Cebrian, E. R. Martí, J. M. Uriz and X. Turon.(2003). Sublethal effects of contamination on the Mediterranean sponge Crambe crambe: metal accumulation and biological responses. Marine Pollution Bulletin. 46(10):1273-1284. Dar
Dar
MA, Abdel-Wahab M.(2005). Particulate sediments and trace metal uptakes in some recent corals, Red Sea coast, Egypt. Bull Fac Sci Zagazig Uni Chem Geol 27:115–135 MA, Mohammed TAA. (2006). Biomineralization processes and heavy metal incorporations in the scleractinian coral skeletons, Red Sea, Egypt. Egypt J Aquat Res 32(special issue): 87–104
Denton, G.R.W., and Burdon-Jones, C. (1986). Trace Metals in Corals from The Greath Barrier Reef, Marine Pollution Bulletin, 17, 209-213 IADC/CEDA. (1997). Convention, Codes, and Conditions: Marine Disposal. Environmental Aspects of Dredging 2a. 71 hal. Madkour HA, Dar M.(2007). The anthropogenic effects of the human activities on the Red Sea coast at Hurghada harbour (case study). Egypt J Aquat Res 33(1):43–58
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Mallongi, A. (2014). Environmental Risks of Mercury Contamination in Losari Coastal Area of Makassar City, Indonesia. International Journal of Scientific and Research Publications. 4:1-6 Mitchelmore CL, Alan-Verde E, Ringwood AH, Weis VM. (2003). Differential accumulation of heavy metals in the sea anemone Anthopleuraelegantissima as a function of symbiotic state. Aquat Toxicol 64:317–329 Morrison, R.J., Narayan, S.P., and Gangaiya, P. (2001). Trace element studies in Laucala Bay, Suva, Fiji, Marine Pollution Bulletin, 42, 397-404 Muhammed, T.A.A. and M.A. Dar. (2010). Ability of corals to accumulate heavy metals, Northern Red Sea, Egypt. Environ Earth Sci 59:1525–1534 Shah,
S.B. (2008). Study of Heavy Metal Accumulation in Scleractinian Corals of Viti Levu, Fiji Islands. Thesis. University of the South Pacific Suva, Fiji Islands
Werorilangi,S., M. F. Samawi, Rastina , A. Tahir, A. Faizal and A. Massinai.(2016). Bioavailability of Pb and Cu in Sediments of Vegetated Seagrass, Enhalus acoroides, from Spermonde Islands, Makassar, South Sulawesi, Indonesia. Research Journal of Environmental Toxicology, 10: 126-134. Van Hansen, I. (1995). Accumulation of copper, zinc, cadmium and chromium by the marine spongeHalichondria panicea Pallas and the implications for biomonitoring . Marine Pollution Bulletin. 31(1-3):133-138.
Muhammad Farid Samawi
SPERMONDE (2018) 4(1): 43-47
ISSN: 2460-0156 EISSN: 2614-5049
SPATIAL-TEMPORAL DISTRIBUTION OF CHLOROPHYLL-A IN SOUTHERN PART OF THE MAKASSAR STRAIT Wasir Samad Daming1*, Muhammad Anshar Amran1, Amir Hamzah Muhiddin1, Rahmadi Tambaru1 Submitted: 14 January 2018 Accepted: 19 February 2018
ABSTRACT Surface chlorophyll-a (Chl-a) distribution have been analyzed with seasonal variation during southeast monsoon in southern part of Makassar Strait and Flores Sea. Satellite data of Landsat-8 is applied to this study to formulate the distribution of chlorophyll concentration during monsoonal wind period. The distribution of chlorophyll concentration was normally peaked condition in August during southeast monsoon. Satellite data showed that a slowdown in the rise of the distribution of chlorophyll in September with a lower concentration than normal is likely due to a weakening the strength of southeast trade winds during June – July – August 2016. Further analysis shows that the southern part of the Makassar strait is likely occurrence of upwelling characterized by increase in surface chlorophyll concentrations were identified as the potential area of fishing ground. Keywords: chlorophyll, Landsat-8, southeast monsoon, Makassar Strait
INTRODUCTION The southern part of the Makassar Strait is one of the relatively more fertile waters because it is suspected that there is an increase in the mass of water in to the local and temporary surface layers in a narrow area. The fertile waters of the Makassar Strait occur throughout the year not only in the west season but also in the east seasons. In the western season, fertility occurs due to the run off of the mainland of Borneo and Sulawesi in large numbers due to high rainfall, while in the east season the fertility occurs due to the increase in water mass (upwelling) in the southern Makassar Strait. Nababan et al., (2009) revealed that high concentrations of chlorophyll-a surface are strongly suspected of upwelling in the eastern seasons until the start of the transition season (July-September) in the southern waters of the Makassar Strait indicated by cold temperatures and relatively high chlorophyll-a concentration. Kurniawati, et al., 2015 revealed that the distribution of chlorophyll-a concentration in the ocean varies according to geographical location as well as depth of water. This variation is caused by the differences in the intensity of sunlight and the concentration of nutrients contained in the waters. Distribution of achlorophyll concentration is higher in coastal and coastal waters, as well as low chlorophyll-a concentration in offshore waters.
productivity is identified by the high concentration of chlorophyll-a in these waters (Rashid, 2010). Chlorophyll-a concentration is one of the parameters that determine primary productivity at sea.. Spreading and high chlorophyll concentration is strongly associated with oceanic conditions Distribution of chlorophyll-a concentrations is generally high in coastal waters as a result of high nutrient supplies derived from land via river runoff, and low in offshore waters. However, high chlorophyll-a concentrations can also be found in offshore waters, due to the mass water circulation process (Sukoraharjo, 2012; Syahdan et al., 2014). In addition, the exchange of water masses with the Pacific Ocean through the Sulawesi Sea, Flores Sea and Java Sea affects the primary productivity level in the southern waters of the Makassar Strait (Inaku, 2015, Kurniawati et al., 2015) to become one of the causes of the southern waters of the Makassar Strait to be highly dynamic which is closely related to the potential area of fisheries caused by the enrichment of nutrients in the area. This paper discusses the spatial dynamics of temporal distribution of chlorophyll-a southern waters of the Makassar Strait in relation to the potential location of fishing. MATERIAL AND METHOD
The rise of the inner layer water to the surface layers in the southern waters of the Makassar Strait was not only due to the circulatory impact of Indonesia's cross currents but the strong role of southeastern airflow (Samad, et al, 2015) and the influence of dipole modes from the Indian Ocean (Susanto et al., 2006 Curie et al., 2013). High water
The materials used in this research are Landsat-8 imagery covering the Southern waters of Makassar Strait on path / row 114/064 (downloaded from www.glovis.usgs.gov), Map of Indonesia, scale 1: 50.000, published by BAKOSURTANAL, as a reference in geometric correction, computer equipped with ENVI 5.1 image processing software.
1
This research was conducted at the beginning of the signal of east season period until the transition season (from east to west season) ie period from May to October 2016, covering data of chlorophylla and sea surface temperature (SST). The
Department of Marine Science, Hasanuddin University Jl. Perintis Kemerdekaan Km. 10, Makassar 90245, Indonesia * Wasir Samad Daming Email:
[email protected]
SPERMONDE (2018) 4(1): 43-47
chlorophyll and SST data are obtained from Landsat-8 imagery acquired on (a) May 21, 2016, (b) June 6, 2016, (c) June 22, 2016, (d) July 24, 2016, (e) August 9, 2016 (f) September 10, 2016, (g) September 26, 2016, (h) October 12, 2016. Chlorophyll-a Landsat-8 image processing for the distribution of chlorophyll-a using Ocean Color OC2 algorithm, i.e: Log (Chlorophyyll-a) = a0 + ∑ ai Log (R2/R3)i mg/m3; Where : i = 1 – 4. R2 = reflectance of band-2 (blue) R3 = reflectance of band -3 (green) a0,ai = coeficient (a0 = 0,1977; a1 = - 1,8117; a2 = 1,9743; a3 = -2,5635; a4 = -0,7218).
Sea Surface Temperature The thermal band data can be converted from spectral radiance to brightness temperature by using the thermal constants provided in image‟s metadata files (USGS, 2013): T
K2 273 K1 ln 1 L
Where: T = brightness temperature (oC) L = TOA spectral radiance (Watts/( m2 * srad * μm)) K1= conversion constant for (K1_CONSTANT_BAND) K2 = conversion constant for (K2_CONSTANT_BAND) K1 and K2 listed in the image metadata file.
wind that blows in the period of the east season is the result of the sun in the northern hemisphere, causing a difference of pressure between the continent of Asia and the continent of Australia, as the impact of the Australian continent faster cooling and high pressure compared to the warmer and lower-pressure Asia continent. In the period of east seasons, the southern waters of the Makassar Strait are influenced by east winds. This east wind is the southeast wind (east season) has a role to the formation of surface water circulation in the southern waters of the Makassar Strait. The pattern of east-east wind circulation over the waters of the Makassar Strait can affect the distribution of surface temperatures as they are with the circulation of wind-induced surface currents. The circulation of the inner water mass to the surface layer is the role of the southeast wind blowing force, the stronger the wind blow the greater the effect on the temperature distribution and the surface chlorophyll. The pattern of east wind circulation can be seen in Figure 1. Based on wind data at the study site, in May the dominant wind direction comes from south to southeast to north to northwest with a speed of 1.04.0 m / s. In July-August wind direction is still dominant from southeast to east to west and northwest with speeds between 2-8 m / s. Then in Sep-Oct the wind direction is more dominant from east to west with speeds between 2-7 m / s. Although it has entered the transition season (transition) precisely wind direction is uncertain.
The above equation produces a T10 image. Converting pixel value to radiance value (TOA radiance) is done by using formula (USGS, 2013): L λ M LQcal A L
Where: Lλ = TOA spectral radiance (Watts/( m2 * srad*μm)) pada band- λ ML = multiplier factor of each band (RADIANCE_MULT_BAND) AL = additional factor of each band (RADIANCE_ADD_BAND) Qcal = pixel digital values
ML and AL listed in the image metadata file. Sea surface temperature is obtained from the transformation: SST = (5,971 + 1,859 T10 – 0,035 T102 ) oC
RESULT AND DISCUSSION East Wind Pattern The wind blowing in the east-season period is identified when entering April - October especially in the southern waters of the Makassar Strait. The
44
Figure 1. Profile of wind intensification during east season period (June-July-August)
Distribution of Chlorophyll-a To investigate the evolution of 16-daily chlorophylla by using spatial and temporal analysis during the east-season period of Landsat-8 imagery shows the presence of surface chlorophyll concentration fluctuations. Figure 2 shows the distribution of 16daily chlorophyll-a concentrations in the southern waters of the Straits of Makassar that on May 21, June 22, August 9 and September 26 the surface chlorophyll concentrations showed higher values compared to chlorophyll-a concentrations on June 6, July 24, September 10 and October 12 with an almost uniform distribution of concentration from the central waters to the southern part of the waters
Wasir Samad Daming
ISSN: 2460-0156 EISSN: 2614-5049 of South Sulawesi covering the southern waters of the Makassar Strait including the Java Sea and Flores Sea. Syahdan et al., (2014) revealed that the surface chlorophyll-a in the Makassar strait varies tends to increase toward the coast and vice versa decreases towards offshore. The high concentration of chlorophyll is one factor due to the high intensity of solar radiation that can cause blooming chlorophyll can form around the surface. This phenomenon is also reinforced by Rahardjo (2012) and Rashid (2010) which explains that oceanic oceanic conditions greatly affect the high-low concentration of chlorophyll on the surface. Distribution of this chlorophyll is generally concentrated more near sea level along the offshore of South Sulawesi from the west to the south of the coastal plains of South Sulawesi. It should be noted, however, that surface chlorophyll concentrations are inseparable from the dynamics of vertical water mass motions that tend to carry nutrients that can be identified as highly potential locations for pelagic fisheries. The fish response to fluctuations in chlorophyll-a which tends to be stable due to the nutrient availability required by phytoplankton is always available so that it impacts the chlorophyll-a concentration in the waters as shown in Figure 2. This condition will cause the fish to respond to other environmental factors suspected availability of stable chlorophyll-a, so it remains within tolerable limits, consequently small pelagic fish will tend to respond to fluctuations of other oceanographic factors, such as temperature, density and current. Different concentrations of chlorophyll-a each month in the southern waters of the Makassar Strait provide an indication that the presence of fish also fluctuates. Distribution of Sea Surface Temperature (SST) The variation of SST around the southern waters of the Makassar strait shows the 16-daily evolution of Landsat-8 products. The SST distribution is processed based on the emergence of the eastern seasons signal up to the transition period (from the east to the west season) each periodic turn of the season. The monthly SST variation taken every 16 days during the period of the east season shows the fluctuation of sea surface temperature of the imaging results on May 21 and June 6 with values between 29.5oC - 30.5oC. However, the results of imaging at the time of intensive east winds blow show SST spreading in southern region of Makassar Strait is not evenly distributed, especially on July 24th. The result of the imaging shows the SST value reached in the temperature range between 28oC 30,5oC as shown in Figure 3. The state of the sea surface temperature during May 2016 showed a uniform pattern of temperature spread. The temperature distribution in May 2016 from the daily 16 cycles shows the temperature
Wasir Samad Daming
(a)
(b)
(c)
(d)
(e) (e)
(f)
(g) (g)
(h) (mg/m3) 0,9-1,0 0,8-0,9 0,7-0,8 0,6-0,7 0,5-0,6 0,4-0,5 0,3-0,4 0,2-0,3 0,1-0,2 0,0-0,1
Figure 2. Map of Landsat-8 satellite imagery of surface chlorophyll-a concentration (mg / m3) in east season 2016; (a) May 21, (b) June 6, (c) June 22, (d) July 24, (e) August 9, (f) Sept 10, (g) Sept 26, (h) October 12
range at 29.5oC - 30.0oC, which then increases in the first week of June (June 6, 2016) in the range 30.0 oC - 30.5oC. Entering the third week (June) again decreased in the range 29.0 oC - 29.5 oC. However, at the end of July the sea surface temperature significantly changed, in the southern part of mainland of South Sulawesi tends to decrease in the range 28.0 oC - 28,5oC and this phenomenon has been strongly suspected the influence of southeast wind that has been very intensive blowing from the southeast. While in the north just in the range 30.0 oC - 30.5 oC. The presence of temperature fluctuations is possible by the influence of rivér inputs, and declining rainfall conditions This phenomenon becomes interesting, because the results of imaging on July 24 showed a decrease of SST in southern of South Sulawesi did not increase
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chlorophyll concentration so that there was time lag between SST and chlorophyll-a. In contrast, Rasyid, (2010) revealed that increased chlorophyll concentrations are characterized along with decreasing sea surface temperatures. This phenomenon gives the assumption that the decrease of SST value is not necessarily followed by the increase of chlorophyll concentration. This study should be more comprehensive because it may be that the cause of the rise of the inner surface water mass to the surface is not the main cause of increased concentration of chlorophyll on the surface but there is a possibility of the influence of river runoff from the mainland, the internal sea due to the throughflow of Indonesia as well as the effect of eddy currents, so it needs a more in-depth study. Sea surface temperatures when associated with estimation of fishing areas tend to fluctuate, in Rasyid, (2010) revealed that the tendency of small pelagic fish has the ability to adapt to the temperature range of measurement results ie 28 oC 30 oC. However, the optimum trend of catching is in the temperature range of 29oC - 30oC. Furthermore, it is said that fish have the ability to recognize and select a certain temperature range that provides the opportunity to perform activities to the maximum and ultimately affect the abundance and distribution. Upwelling and Catching Areas
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
oC
28,0 – 28,5 28,5 – 29,0
The strength of the southeastern wind causes the mass transfer of the inner layer water to the surface. This displacement is followed by the motion of water particles that bring the life source to the ecosystem to grow and multiply.
29,0 – 29,5 29,5 – 30,0 30,0 – 30,5
Areas indicated by upwelling are closely related to the catchment area. Upwelling area is not only fertile but is a fish area for foraging. Rasyid, et al., (2010) explains that the presence of fish in tropical waters is related to monsoon variations of the marine environment. The influence of daytime long monsoon variations and the temperature of the tropical waters is not very influential when compared with equatorial regions. In the tropics wind and monsoon variations are more influential on marine ecosystems; where monsoon variations will affect the availability of quantities and types of food that directly affect the presence of fish in tropical marine ecosystems. In the southern part of Makassar Strait area identified upwelling occurs because during the period of east season in these waters have below normal surface temperatures ranging from 26 oC - 28oC found in July – September.
Figure 3. Landsat-8 images of sea surface temperature (oC) during the eastern seasons of 2016; (a) May 21, (b) June 6, (c) June 22, (d) July 24, (e) August 9, (f) Sept 10, (g) Sept 26, (h) Oct 12.
Figure 4 shows the monthly average of sea surface temperature and chlorophyll-a in the southern waters of the Makassar Strait, SST tends to decrease from June until October, while in May-October the chlorophyll concentration fluctuates. In July the concentration of chlorophyll increased but not
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30,5 – 31,0 31,0 – 31,5
Figure 4. Graph between SST and Chlorophyll-a in the southern waters of Makassar Strait.
significant compared to the previous month, but in October it increased sharply to exceed 1 mg / m3. It is suspected that there is an impact of the massive increase of Makassar Strait water mass by the flow of Indonesian traffic. Strengthened by Sukoraharjo (2012) and Susanto, et.al. (2006) revealing that the role of Indonesia's cross-currents could trigger upwelling in the east season, so this area is highly potential for fish catching areas.
Wasir Samad Daming
ISSN: 2460-0156 EISSN: 2614-5049 Kecil di Perairan Laut Jawa pada Musim Barat dan Musim Timur dengan Menggunakan Citra Aqua Modis,” Geo Image 4 (2) 2015.
CONCLUSION Distribution of surface chlorophyll concentrations began to be detected by Landsat-8 imagery at the time of the east season signal around the southern waters of the Makassar Strait. The high concentration of surface chlorophyll is not accompanied by decreasing sea surface temperature but there is a time lag. During the eastern seasons of 2016 there is upwelling in the southern waters of the Makassar Strait but is very weak and the period is short enough that marked the weakening of the southeast pasig wind, so it is not strong enough to push the water mass layer to the surface. The variation of chlorophyll-a concentration content in the southern waters of the Makassar Strait is quite high in coastal areas and decreases in coastal waters offshore suspected to occur due to the influence of the flow of rivers from the mainland of South Sulawesi which empties into the waters of Makassar Strait. REFERENCES
J. C. Currie, M. Lengaigne, J. Vialard, D. M. Kaplan, O. Aumont, S. W. A. Naqvi, and O.Maury, “Indian Ocean Dipole and El Niño/Southern Oscillation impacts on regional chlorophyll anomalies in the Indian Ocean”, Ballast Water, vol. 6, no. 4, pp. 465–485, Aug. 2007. M. Syahdan, A.S. Atmadipoera, S.B. Susilo, J.L Gaol, “Variability of surface Chlorophyll-a in the Makassar Strait-Java Sea, Indonesia”, Intr.Journal of Sciences: Basic and Apllied Research (IJSBAR). Vol 14 no 2, pp. 103116. 2014. S.
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B. Nababan, D. Zulkarnain, J.L Gaol., “variabilitas konsentrasi klorofil-a di perairan utara sumbawa berdasarkan data satelit SeaWiFS”, E-jurnal Ilmu dan Kelautan Kelautan Tropis”, vol. 1 (2), pp. 72-83, 2009.
USGS, 2013, Using USGS Landsat-8 Product, diakses melalui http://landsat.usgs.gov.
D. F. Inaku, “Analisis Pola Penyebaran dan Perkembangan Area Upwelling di Bagian Selatan Selat Makassar”, Jurnal Torani vol. 25 (2) Agustus, pp.67-74, 2015. F. Kurniawati, T.B Sanjoto, dan Juhadi, “Pendugaan Zona Potensi Penangkapan Ikan Pelagis
Wasir Samad Daming
USGS/EROS, 2012, Landsat Data Continuity Mission (LDCM) Lavel 1 (L1), Data Format Control Book (DFCB), diakses melalui http://landsat.usgs.gov W.Samad, J-H Oh, D.A. Suriamihardja, D. Widyanuriyawan” Variasi Suhu Permukaan Laut dan Distribusi Klorofil di Perairan Indonesia”, Prosiding Seminar Nasional Perikanan dan Kelautan V Universitas Brawijaya. pp. 334-338, 2015.
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