In the Merbok Estuary of Kedah Farahiyah

SOME ECOLOGICAL ASPECTS OF Plicofollis argyropleuron

(SILURIFORMES: ARIIDAE) IN THE MERBOK ESTUARY OF

KEDAH

FARAHIYAH KHADIJAH BINTI AMBRI

UNIVERSITI SAINS MALAYSIA

2013

SOME ECOLOGICAL ASPECTS OF Plicofollis argyropleuron

(SILURIFOMES: ARIIDAE) IN THE MERBOK ESTUARY OF

KEDAH

FARAHIYAH KHADIJAH BINTI AMBRI

Thesis submitted in fulfillment of the requirements

for the degree of Master of Science

NOVEMBER 2013 ACKNOWLEDMENTS

Alhamdulillah for the completeness of this research. First and foremost, my heartfelt appreciations go to my supervisor, Dr Mansor Mat Isa for his invaluable guidances, friendliness, advices and support throughout the course of this study. His helpful expertise and patience have benefited and encouraged me immensely in this effort.

Special thanks and appreciation goes to my co-supervisor, Dr Khairun Yahya for her guidance and expert advice. I am also deeply grateful to my labmates and my lab assistants for their great helps during the sampling period and labworks.

My deepest gratitude goes to my mom, sister, fiance and other family members for everything they have done, who always be there for me for the betterment of my life. I couldn’t have done this without all of you. Thanks for the support, unconditional love and encouragement. I couldn’t ask for more.

A special note of thanks also goes out to Shafiq, Aiman, Erna, Balkhis, my housemates and all friends who have contributed a lot either directly or indirectly for helping, companion and support throughout the research. I sincerely thank all of you for your contribution and friendship. It has been a pleasure knowing wonderful people like all of you.

Last but not least, I wish to express my thanks and warm grateful to all members of the Centre for Marine And Coastal Studies (CEMACS) and School of Biological Sciences for their assistance and technical support.

Thank you so much.

TABLES OF CONTENTS

ACKNOWLEDMENTS ...... ii

TABLES OF CONTENTS ...... iii

LIST OF FIGURES ...... vii

LIST OF TABLES ...... x

LIST OF PLATES ...... xi

LIST OF ABBREVIATIONS ...... xii

LIST OF PUBLICATIONS ...... xiii

ABSTRAK ...... xiv

ABSTRACT ...... xv

CHAPTER 1: GENERAL INTRODUCTION ...... 1

1.1 INTRODUCTION ...... 1

CHAPTER 2: LITERATURE REVIEW ...... 5

2.1 ESTUARY ECOSYSTEM ...... 5

2.2 MERBOK ESTUARY ...... 6

2.3 GENERAL INFORMATION OF FISH ...... 7

2.4 STUDIED SPECIES ...... 8

2.5 FISH AS BIOINDICATOR ...... 11

2.6 FISH ASSEMBLAGES ...... 13

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2.7 FACTORS INFLUENCING THE DISTRIBUTION OF FISH ...... 14

2.7.1 WATER TEMPERATURE ...... 14

2.7.2 SALINITY ...... 15

2.7.3 CONDUCTIVITY ...... 16

2.7.4 WATER DEPTH ...... 16

2.7.5 TURBIDITY ...... 17

2.7.6 RAINFALL ...... 17

2.7.7 PH ...... 18

2.8 SEX RATIO ...... 18

2.9 FISH BREEDING STRATEGY ...... 19

2.10 REPRODUCTIVE BIOLOGY ...... 20

2.11 GONADOSOMATIC INDEX (GSI) ...... 21

2.12 SPAWNING SEASON ...... 22

2.13 FECUNDITY ...... 22

2.14 LENGTH AT FIRST MATURITY ...... 23

2.15 GROWTH IN FISH ...... 23

2.16 LENGTH-WEIGHT RELATIONSHIP ...... 24

2.17 MORTALITY ...... 27

2.18 RECRUITMENT ...... 28

CHAPTER 3: DISTRIBUTIONS AND ABUNDANCES OF Plicofollis argyropleuron

AND THEIR RELATIONSHIP WITH PHYSICAL PARAMETERS IN MERBOK

ESTUARY, KEDAH ...... 29

3.1 INTRODUCTION ...... 29

3.2 OBJECTIVES ...... 30

3.3 MATERIALS AND METHODS ...... 31

3.3.1 STUDY AREA ...... 31

3.3.2 SAMPLING TECHNIQUE ...... 32

3.3.3 DATA ANALYSIS ...... 34

3.4 RESULTS ...... 36

3.5 DISCUSSION ...... 42

CHAPTER 4: REPRODUCTIVE BIOLOGY OF Plicofollis argyropleuron IN

MERBOK ESTUARY, KEDAH ...... 56

4.1 INTRODUCTION ...... 56

4.2 OBJECTIVES ...... 57

4.3 MATERIALS AND METHODS ...... 58

4.3.1 SAMPLING TECHNIQUE ...... 58

4.3.2 LABORATORY WORKS ...... 58

4.3.3 HISTOLOGICAL ANALYSIS ...... 61

4.4 RESULTS ...... 62

4.5 DISCUSSION ...... 73

CHAPTER 5: GROWTH, MORTALITY AND RECRUITMENT PATTERN OF

Plicofollis argyropleuron IN MERBOK ESTUARY, KEDAH ...... 83

5.1 INTRODUCTION ...... 83

5.2 OBJECTIVES ...... 84

5.3 MATERIALS AND METHODS ...... 85

5.3.1 DATA ANALYSIS ...... 85

5.3.1.1 CONDITION FACTOR (K) ...... 85

5.3.1.2 BHATTACHARYA'S PLOT ...... 85

5.3.1.3 LENGTH-WEIGHT RELATIONSHIP (LWR) ...... 86

5.3.1.4 LENGTH FREQUENCY DATA ...... 87

5.4 RESULTS ...... 89

5.5 DISCUSSION ...... 95

CHAPTER 6: CONCLUSION AND RECOMMENDATION ...... 104

6.1 CONCLUSION ...... 104

6.2 RECOMMENDATION ...... 112

REFERENCES ...... 115

LIST OF FIGURES

FIGURE 2.1 Upper tooth patches of Plicofollis argyropleuron……………..10

FIGURE 2.2 Dorsal view of head (left side) and skull (right side) of Plicofollis argyropleuron………………………………………………….11

FIGURE 3.1 (A) Location of sampling site, Merbok estuary in the northern of Peninsular Malaysia (in square). (B) A few tributaries (in blue circle) that connected with Merbok estuary (in red square)…...33

FIGURE 3.2 Monthly total number of individuals collected in Merbok estuary, Kedah…………………………………………………36

FIGURE 3.3 The percentage of fish abundance (%CPUE) by month collected in Merbok estuary, Kedah…………………………..37

FIGURE 3.4 The percentage of frequency of occurrence (%FO) by month collected in Merbok estuary, Kedah…………………...37

FIGURE 4.1 Measuring steps and devices………………………………….59

FIGURE 4.2 A fish morphological feature and measurement of total length and standard length…………………………………………...59

FIGURE 4.3 The location of fish gonad (red circle) in Plicofollis argyropleuron…………………………………………………60

FIGURE 4.4 Monthly gonadosomatic index (GSI mean ± standard deviation) of male and female of Plicofollis argyropleuron in Merbok estuary, Kedah…………………………………… 63

vii

FIGURE 4.5 Monthly condition factor in Plicofollis argyropleuron in Merbok estuary, Kedah………………………………………………...64

FIGURE 4.6 Relationship between GSI mean and condition factor of Plicofollis argyropleuron in Merbok estuary, Kedah…………65

FIGURE 4.7 Monthly gonad maturity stages in percentage (%) of Plicofollis argyropleuron in Merbok estuary, Kedah………...66

FIGURE 4.8 Relationship between fecundity with (a) total length, (b) body weight and (c) gonad weight of Plicofollis argyropleuron from Merbok estuary, Kedah……………………………………….71

FIGURE 4.9 Length at first maturity of (a) male and (b) female Plicofollis argyropleuron from Merbok estuary, Kedah…………………72

FIGURE 5.1 Length-weight relationship of Plicofollis argyropleuron in a linear form (left side) and power form (right side)………………….90

FIGURE 5.2 Probability of capture of each length class of Plicofollis

argyropleuron (L25 = 19.44 cm, L50 = 20.49 cm, L75 = 20.87 cm N = 488)…………………………………………………………...92

FIGURE 5.3 Length-converted catch curve for Plicofollis argyropleuron. Regression statistic: y-intercept, a = 4.73; slope, b = -1.18; r = 0.998, N = 488……………………………………………………….92

viii

FIGURE 5.4 Growth curve of Plicofollis argyropleuron by ELEFAN I superimposed on the restructured length-frequency diagram (L∞ = 34.10cm, K = 0.88 year-1)……………………………………..94

FIGURE 5.5 Recruitment pattern of Plicofollis argyropleuron (L∞ = 34.10 cm, K

= 0.88 per year, to = -0.18)…………………………………….94

LIST OF TABLES

TABLE 3.1 Details of Plicofollis argyropleuron sampled from Merbok estuary, Kedah…………………………………………………………..38

TABLE 3.2 Details of Plicofollis argyropleuron sampled from Merbok estuary, Kedah…………………………………………………………..39

TABLE 3.3 Details of Plicofollis argyropleuron sampled from Merbok estuary, Kedah…………………………………………………………..41

TABLE 4.1 Details of Plicofollis argyropleuron sampled from Merbok estuary, Kedah…………………………………………………………..62

TABLE 4.2 Gonad descriptions for macroscopic and microscopic observations …………………………………………………...... 67 and 68

TABLE 5.1 Condition factor (K) of Plicofollis argyropleuron on monthly basis…………………………………………………………...89

TABLE 5.2 Estimated growth parameters of Plicofollis argyropleuron in FiSAT II 1.2.2…………………………………………………………98

TABLE 5.3 Comparative growth parameters (L∞ and K) and indices of growth performance (φ’) of Plicofollis argyropleuron in Merbok estuary, Kedah with other locations…………………………………….98

TABLE 5.4 Estimated population parameters of Plicofollis argyropleuron in Merbok estuary, Kedah………………………………………100

LIST OF PLATES

PLATE 4.1 Testis of Plicofollis argyropleuron showing the immature, maturing matured, ripe and spent……………………………………….69

PLATE 4.1 Ovaries (b) of Plicofollis argyropleuron showing the immature, maturing, matured and ripe…………………………………...70

LIST OF ABBREVIATIONS

FISAT = FAO-ICLARM Stock Assessment Tools

GPS = Global Positioning System

SCT = Salinity,Conductivity, Temperature vBGF = von Bertalanffy Growth Function

DATABASE ABBREVIATIONS AND SYMBOLS

CAO = Cortical Aveoli Ooocyte Stage I = Immature

CPUE = Catch Per Unit Effort Stage II = Maturing

FO = Frequency of Occurrence Stage III = Matured

GSI = Gonadosomatic Index Stage IV = Ripe

LST = Late Spermatid Stage V = Spent

LWR = Length-Weight Relationship YG = Yolk Globule

Og = Oogonia YV = Yolk Vesicle ol = Ovarian Lamellae ZR = Zona Radiata

PS = Primary Spermatocyte sc = sertoli cell

SG = Spermatogonia

SS = Secondary Spermatocyte st = Seminiferous Tubules

ST = Spermatid

SZ = Spermatozoa

xii

LIST OF PUBLICATIONS

Farahiyah-Khadijah, A., Mansor, M. I. and Khairun, Y. (2011). Ecology of Plicofollis argyropleuron in the Merbok Estuary of Kedah. Proceedings for CEMACS First Postgraduate Colloquium.

Farahiyah-Khadijah, A., Mansor, M. I. and Khairun, Y. (2012). Length-weight relationship and condition factor of Plicofollis argyropleuron in the Merbok Estuary of Kedah. Proceedings for CEMACS Second Postgraduate Colloquium.

Mansor, M. I., Nur-Ili-Alia, D. and Farahiyah-Khadijah, A. (2012). Reproductive biology of the sleeper goby, Butis gymnopomus (Bleeker, 1853) from the Merbok Estuary, Kedah, Malaysia. Indian J Fish 59(4): 147-155.

xiii

BEBERAPA ASPEK EKOLOGI Plicofollis argyropleuron (SILURIFORMES: ARIIDAE) DI SUNGAI MERBOK, KEDAH

ABSTRAK

Taburan dan kelimpahan, biologi pembiakan, corak pertumbuhan dan hubungan dengan parameter fizikokimia telah dikaji ke atas Plicofollis argyropleuron yang juga dikenali dengan nama tempatannya, Goh. Kajian ini dijalankan di Sungai Merbok, Kedah.

Sampel ikan diambil daripada hasil tangkapan nelayan tempatan dari Jan-10 sehingga Jan-

11 dan dianalisis. Taburan ikan dan kekerapan ikan muncul adalah paling tinggi pada Feb-

10 dan hubungannya dengan parameter fizikokimia telah dikaji. Secara amnya, taburan hujan menjadi faktor utama dalam mempengaruhi taburan dan kekerapan kemunculan spesis ini. Sebanyak 30-50 ekor ikan diambil setiap bulan dan pelbagai saiz diperolehi dari

162mm hingga 336mm. Tahap kematangan ikan jantan dan betina dikategorikan kepada lima peringkat iaitu tidak matang, sedang matang, matang, matang sepenuhnya dan selepas matang mengikut ciri-ciri luaran gonad dan melalui kaedah histologi. Musim mengawan ikan ini ialah pada Jan-10 dan Apr-10 untuk ikan jantan dan bagi ikan betina ialah pada

Mar-10 dan Aug-10. Ini menunjukkan spesis ini mempunyai kaedah pembiakan tidak serentak. Nilai relatif faktor keadaan, K setiap bulan ialah lebih dari 1 menunjukkan tahap kesihatan ikan, keadaan persekitaran dan bekalan makanan adalah dalam keadaan optimum untuk pembiakan spesis ini di Sungai Merbok. Corak pertumbuhan ikan ini adalah sama seperti corak pertumbuhan untuk ikan jenis tropika. Hasil kajian penyelidikan ini sangat berguna dalam mengenal pasti pengaruh dan kesan parameter persekitaran terhadap spesis ikan estuari.

xiv

SOME ECOLOGICAL ASPECTS OF Plicofollis argyropleuron

(SILURIFORMES: ARIIDAE) IN THE MERBOK ESTUARY OF

KEDAH

ABSTRACT

Distribution and abundance, reproductive biology, growth pattern and their relationship to physicochemical parameters have been studied on long snouted catfish,

Plicofollis argyropleuron, locally known as Goh. This study was conducted in Merbok estuary, Kedah. The fish samples were analysed from the catches of artisanal fishermen collected from Jan-10 to Jan-11. Fish are most abundant and frequently occurred in Feb-10 and their relationship with physicochemical parameters were observed. Generally, rainfall tends to be the main factor that influences their abundance and occurrence. Monthly samples from 30 to 50 of Plicofollis argyropleuron were collected with the size varied from 162 mm to 336 mm. The maturity stages of male and female were classified into five different stages namely immature, maturing, mature, ripe and spent according to physical appearances of the gonad and histological method. Their spawning season is Jan-10 and

Apr-10 ( in males) and Mar-10 and Aug-10 (in females) which suggested that this species exhibit asynchronous reproductive behavior. Relative condition factor value, K for all months is more than 1 indicates the level of fish health, environmental conditions and nutrient supply are at the optimum level for this species reproduction in Merbok estuary.

The growth pattern of this species is concurrent with the tropical fish growth pattern. The

xv findings of this study will be beneficial in inferring the affects of environmental parameters and their impacts to the estuarine fish species.

xvi

CHAPTER 1

GENERAL INTRODUCTION

1.1 INTRODUCTION

Recent investigations have shown that anthropogenic disturbances to natural ecosystems

often results in the simplification of ecosystems and diversity loss which can have a significant

effect on the ecosystem functioning. However, due to the lacking of baseline studies and habitat

monitoring, the impacts of anthropogenic disturbances on the ecosystem functioning were often

undocumented, resulting in little understanding on how the natural ecosystems respond to

increasing losses of species (Naeem et al., 1994, 1996; Tilman et al., 1996; Symstad and Tilman,

2001).

Ecology or ecological science is the study related to the distribution and abundance of living organisms and how the distribution and abundance are affected by the interactions between organisms and their environment (Jody, 2005). The environment of an organism may include the physical surrounding which can be described as the sum of local abiotic factors such as insolation

(light), climate and geology as well as the other organisms that share the habitat. Ecology is usually considered a branch of biology that studied living organisms. Organisms can be studied at many different levels from protein and nucleic acids (in biochemistry and molecular biology) to cells (in cellular biology) to individuals (in botany, zoology and other similar disciplines) and finally at the

level of populations, communities and ecosystems to the biosphere as a whole; these latter strata are the primary subjects of ecological inquiries.

Estuary water functions as an important nursery habitat for many juvenile marine fishes that are often assumed to dwell in estuaries during their early life. This assumption is based on the occurrence of juveniles and also by the dogma of estuaries as vital nursery habitats (Able and

Fahay, 1998; Able, 2005; Ray, 2005). However, the possibility of the juvenile’s ability to recruit and utilize the environments as nursery remains largely unknown. Separate metrics such as occurrence, density, growth and mortality are often used as an index of juvenile production (nursery value) and often compared between putative nursery habitats to evaluate their relational values

(Able, 1999). These surrogates (especially growth rate) have often been compared between microhabitats within and among the estuaries (Sogard, 1992; Gibson, 1994; Phelam et al., 2000;

Ross, 2003). However, due to the lack of comparative data on habitat use by fishes in the estuary

(Able, 2005), the overall value of a particular nursery habitat is intangible and difficult to measure

(Wilson et al., 2005) although the theoretical foundation of the relative value of the nursery habitat is simple (Beck et al., 2001; Kraus and Secor, 2005).

Study on fish community or species assemblages includes species richness, diversity, morphological, physiological attributes and trophic structures (Zarul Hazrin, 2006). Fish assemblages represent a variety of trophic levels (omnivores, herbivores, insectivores, planktivores, piscivores) tend to integrate effects of lower trophic levels (Garcia-Lopez et al., 2006) and depending on species and life stages (Gorman and Karr, 1978; Welcomme, 1985). Knowledge on

the fish assemblages in estuary is essential to understand the functioning of these systems. The

studies on the interactions between the fish and its habitat are determined by the relationship

between aquatic and terrestrial habitats as fish usually consumed terrestrial sources such as insects

and fruits provided useful information for conservation purposes (Lagler et al., 1977; Ponton and

Copp, 1997; Fialho et al., 2007).

Fish distribution is a result of the interaction between fishes and their chemical, physical

and biological surroundings (Lagler et al., 1977; Gordon et al., 1996; Bistoni and Hued, 2002). A number of factors affect the abundance, distribution and productivity of fish which include space competition, predation, water quality, nutrient supplies and flow variability (Gorman and Karr,

1978; Zakaria-Ismail and Sabariah, 1994; Gordon et al., 1996; Cassati et al., 2006; Andrus, 2008).

In addition, the presence of riffles and in-stream woody debris that forms heterogeneous habitats influences the fish assemblages (Angermeier and Karr, 1983; Platts et al., 1983; Benke et al., 1985;

Bisson et al., 1987). Short-term changes in fish abundance may also occur due to disturbances such as flash floods or droughts (Zarul Hazrin, 2006). Fish needs suitable water quality, migration routes, spawning grounds, feeding sites, shelter from predator and disturbances since they spend their entire life in the same habitat (Angermeier and Karr, 1983; Cowx and Welcomme, 1998).

Dudgeon (1992) pointed out that Asian river ecosystem degradations were related to human activities. Overfishing contributes more in mortality rate of marine species than freshwater species

(Kottelat and Whitten, 1996; Coates et al., 2003). Human’s land activities will have direct or indirect impact on fish diversity (Nguyen and De Silva, 2006). The impacts were often measured by

looking at fish assemblages, their presence or absence and their abundance (Bojsen and Barriga,

2002; Diana et al., 2006; Di Prinzio et al., 2009). In addition, the study of functional properties of fish such as feeding habit, reproductive biology and growth may provide details on the possible effects of deforestation (Vila-Gispert et al., 2000; Bojsen, 2005) especially in tropical ecosystem where terrestrial invertebrates are an important food sources for fish (Angermeier and Karr, 1983).

At present, very limited studies concerning fish ecology have been conducted in the estuaries and coastal areas of Malaysia. Thus, a precise identification and stock description on the fish is required. The information from the study would be very useful in determining and evaluating the degree of fish population changes in regard to human activities and environmental changes in an area. Such knowledge would be invaluable for the sustainable exploitation and management of fish resources in both estuary and coastal area ecosystems.

Therefore, this research was undertaken at Merbok estuary, Kedah with specific reference to

Plicofollis argyropleuron. This study was separated into three different chapters and was mainly aimed to:

1) Study the distributions and abundances of the Plicofollis argyropleuron in a relationship

with physical parameters.

2) Study the reproductive biology of the species.

3) Study the growth, mortality and recruitment patterns of the species.

CHAPTER 2

LITERATURE REVIEW

2.1 Estuary ecosystem

Estuary is one of the most important ecosystems in the world due to the existence of its diverse biological entities. Estuary is defined as a partially surrounded coastal body of water that

has an open connection with the ocean at the lower parts where freshwater will be flushed off from

inland and mixed with the saltwater that came from the open sea (Velaqzuez et al., 2008). This unique ecosystem makes estuary more productive and abiotically variable yet a rigorous and stressful habitat for certain species.

There are three main processes in estuarine environment. One of the processes is the physical process by hydrological factors such as water quality modifications caused by climate changes (Roessig et al., 2004). This includes salinity, rainfall, pH, turbidity, conductivity, water depth and temperature. Identification of significant association between fish species and habitat conditions was the first step to incorporate the environmental information with fish abundance.

Beside physical and chemical aspects, biological factor such as fish assemblage make a good sense to perceive the health of the estuaries. The assemblage of estuarine fishes quickly respond to the fluctuation of environmental characteristics allowing them to be recognized as 5

sensitive indicators of habitat degradation, environmental contamination and overall system productivity (Ecoutin et al., 2005; Qadir et al., 2005).

The study of estuaries has been split into two categories of climate, the tropical and temperate. Due to the complex scenario presented on tropical estuaries including the relationship between environmental factors within estuaries, spatial and temporal patterns in composition, abundance and distribution in fish assemblages, this ecosystem received considerable attentions

(Pombo et al., 2005).

2.2 Merbok estuary

Merbok estuary is situated in the north-west Peninsular Malaysia at 5o30’N 100o25’E. Here, the Merbok river flows through paddy field with alluvium soils to the mangrove area on its estuarine part into the Straits of Malacca. The length of the river is about 35 kilometres and 3 to 5 metres in depth with a few 20 metres deep holes in tributaries associate with Merbok river. The freshwater part of the river consist of only a few kilometres long as the seawater intrudes until 30 kilometres of its length (±86% of the river part) that tidal occurs at most part of the river (Ong et al., 1991).

Merbok river brings discharge water from the surrounding catchment area together with alluvium deposit and mudstone with a few scattered outcrops of granite and quartz. The catchment area around the Merbok estuary was estimated about 550 km2. This estuary was connected with the

Sungai Muda through a channel at the south part of the river and covered by about 50 km2 of

mangrove vegetation on the estuarine part (Ong et al., 1991). According to Ong et al. (1980), the mangrove is dominated by Rhizophora apiculata and Bruguiera plaviflora that can grow up to 30 metres and high in productivity.

2.3 General information of fish

Fish are cold-blooded vertebrates with gills, fins as they depend on water as a medium to live (Lagler et al., 1977). Their feeding, digestion, assimilation, growth, responses to stimuli and reproduction depend on the water conditions (Lagler et al., 1977). Fishes are the most diverse among the vertebrate groups with 57 orders of living fishes and 482 families, contrast to number of orders and families of amphibians (8,27), reptiles (4,49), birds (29,165) and mammals (23,122)

(Matthews, 1998). Lowe-McConnell (1987) reported that the Amazon river basin has the world’s richest fish fauna with more than 1300 species. Froese et al. (1999) estimated that 7000 fish species that are consumed by humans for food, sports and the aquarium trade were threatened by environmental degradation. Only less than 2000 species were known for their life history parameters such as growth and length at first maturity which was important for fishery management.

2.4 Studied species

According to Marceniuk and Menezes (2007), there are 29 genera in the Ariidae family.

Ariids are found worldwide in tropical to warm temperate zones. In Malaysia, there are 25 species from 11 genera recorded (Mansor et al., 2010). Ariids, live primarily in the sea unlike the majorities of catfish families that are restricted in the freshwater and have little tolerance for brackish or

marine conditions. Ariids catfish are found in shallow temperate and tropical seas around the

coastline of North and South America, Africa, Asia and Australia. They are absent in Europe and

Antarctica (Velasco and Oddone, 2004). In general, members of Ariidae family attain large sizes,

long living, slow growing, low fecundity and mouthbreed their eggs. The members of this family

have a deeply forked caudal fin. There are usually three pairs of barbels. They possess some bony

plates on their head and near their dorsal fins. Some species have venomous spines in their dorsal

and pectoral fins (Mansor et al., 2010).

Kingdom : Animalia (C. Linnaeus, 1758)

Phylum : Chordata (Bateson, 1885)

Subphylum : Vertebrata (Cuvier, 1812)

Class : Osteichthyes (Huxley, 1880)

Order : Siluriformes

Family : Ariidae

Genus : Plicofollis

Species : Plicofollis argyropleuron (Valenciennes, 1840)

(Source: Global Biodiversity Information Facility (GBIF) Data Portal, 2008)

FAO/English name : Longsnouted catfish

Vernacular/Local name : Goh

Fish identifications : This species has a greyish-blue body. It has an adipose fin pale blotch, anal soft rays in between 14-21, a depressed and elongated head, 27-36% (mean

32%) of SL, low set eyes and a small mouth with width of 24-40% (mean 31%) of HL. Gill rakers are absent on hind aspect of first 2 gill arches.

Habitat, Biology and Fisheries : This species is a demersal-type fish. It lives in brackish and marine water. They usually occur in inshore waters over soft bottoms. They feed on detritus,

prawns, soft-bodied organisms and mud. Commonly, they are marketed fresh, salted or dried.

Figure 2.1 Upper tooth patches of Plicofollis argyropleuron

Figure 2.2 Dorsal view of head (left side) and skull (right side) of Plicofollis argyropleuron

Conand et al. (1995) reported that sea catfish were caught throughout the year mostly near the coast during rainy season and in deeper water during dry season. Juveniles and small individuals are found in large numbers in area adjacent to the coast while larger fish occurs in deeper water. Most of the catch is made by boats with lines or gill nets and occasionally by trawlers. This fish is usually marketed fresh, dried or salted (Mansor et al., 1998). Some of the species are targeted by industrial and artisanal fisheries which significantly affecting the total regional production (Velasco and Oddone, 2004).

2.5 Fish as bioindicator

Markert et al. (2003) defined bioindicator as an organism that contains information on the

quality of the environment where a sensitive species which lead to a change in biodiversity can be

taken as surrogates for larger communities and act as an indicator for the condition of habitat or

ecosystem thus providing a cost- and time-efﬁcient mean to assess the impacts of environmental

disturbances on an ecosystem. The use of bioindicator has evolved substantially and is frequently

been incorporated into policies and regulations in order to monitor the ecological integrity of

watersheds (Moyle and Randall, 1998), lakes (Karr, 1981; Harig and Bain, 1998), semi-natural

pastures (Part and Soderstrom, 1999b), rangelands (Bradford et al., 1998) and forests (Brooks et al.,

1998). Bioindicators are also use as the veriﬁcation of the compliance of industries to speciﬁc anti- pollution laws (MacDonald and Smart, 1993) and as the assessment of habitat quality (Powell and

Powell, 1986; Canterbury et al., 2000).

Diatoms and benthic invertebrates are among the organisms that have been used as a bioindicator. However, due to lack in the life-history information, specialized taxonomists, difficult and time consuming sampling, sorting and identifying, these organisms are less preferred as

bioindicator (Karr, 1981). Simon (1999) proposed that fish is one of the best bioindicators and

remained as an important part of aquatic study to evaluate water quality.

Many researcher in this field such as Karr (1981), Leonard and Orth (1986), Hughes and

Noss (1992), Barbour et al. (1999) and Simon (1999) have listed the advantages of using fish as bioindicator. They mentioned that:

1) Fish are relatively long-lived and mobile so they will be a good indicator for long term

effects (several years) and broad habitat conditions.

2) Fish tops the aquatic food web and is consumed by humans making them important for

assessing contamination.

3) Fish are easy to collect, sort and identify to the species level even at the field allowing them

to be released unharmed.

4) Environmental requirements of most fish are relatively well-known. Life history

information is extensive for most fish species.

5) Fish is a migratory organism make them as suitable indicators for habitat connectivity.

6) Their economic and aesthetic values help to raise the awareness of the aquatic systems

conservation.

7) While assessing the environmental quality by fish assemblages, the stock assessment can

also be determined for a sustainable harvest resource.

8) Generally fish assemblages represent a variety of trophic levels (planktivores, herbivores,

omnivores, insectivores, piscivores) which they tend to integrate effects of lower trophic

levels.

2.6 Fish assemblages

The relationship of habitat structure and physiochemical quality of stream with the fish assemblages has been widely used (Casatti et al., 2006; Qadir et al., 2009). Akin et al. (2005)

reported that abiotic factors (E.g. salinity, temperature, dissolved oxygen (DO), freshwater inflow,

structural attributed of habitat, depth, geographic distance from the estuary mouth and hydrograph)

affect the occurrence of fish within the estuaries. The large scale (in kilometres) patterns of fish

distribution are the outcome of species response to their physical environment (Remmert, 1983).

Abiotic factors operating over large spatial scale were believed to determine coarse community

structure whereas biotic interactions define species abundance and distribution patterns within

structure (Akin et al., 2005).

Fish species richness is one of the basic multimetric biological monitoring indices that

frequently used to measure stream health (Karr and Chu, 1999; Daniel, 2006). Zaret and Rand’s

(1971) studied the fish assemblage in a small stream was perhaps the first to examine the effect of

season on the patterns of niche segregation. They found that most fishes expanded their resources

use during the wet seasons as most resources are abundant during this time while showed a trend to niche compression and segregation during dry season (Winemiller et al., 2000).

2.7 Factors influencing the distribution of fish

It is important to understand the relationship between the biotic community structure and the physical habitat (Martin-Smith, 1998) because every fish species has different habitat preferences (Fialho et al., 2007). Habitat diversity, the biomass, richness, mean fish size and density of fish were correlated with,

1) Water depth and velocity (Mendelson, 1975; Schlosser, 1985; Ali et al., 1988; Meffe and

Sheldon, 1988; Sheldon and Meffe, 1995)

2) Substrate type, aquatic vegetation and bank cover (Gorman and Karr, 1978; Bishop and

Harland, 1982; Schlosser, 1982; Rakocinski, 1988; Bishop and Forbes, 1991)

Sheldon (1968), Moyle and Vondracek (1985), Bain et al., (1988) and Koehn (1992) found that water depth and current velocity were the most important variables influencing fish distribution. The selection of fish in the physical habitat depends on the geological, morphological and hydrological processes (Cowx and Welcomme, 1998).

2.7.1 Water temperature

The presence of riparian vegetation influenced the water temperature. The vegetation shaded most of the water surfaces preventing excessive warming (Allan and Castillo, 2007) and provide inputs of allochthonous organic materials for the biological production in small stream

ecosystems (Rohasliney and Jackson, 2009). Since fish are cold-blooded, the increase in water temperature will influence their body temperature, growth rate, food consumption and feed conversion (Gadowaski and Caddel, 1991; Kausar and Salim, 2006). Fish growth and liveability are optimum at certain temperature range (Gadowaski and Caddel, 1991). As for example, Afzal Khan

et al. (2004) reported that the optimum growing temperature for warm water fish ranged from 25-

30oC. An increase in temperature may accelerate the digestion of nutrients due to the increase in

enzyme activities (Shcherbina and Kazlauskene, 1971; Gordon et al., 1996). High temperatures

may lead to disease outbreaks inhibited the fish growth (Platts et al., 1983).

2.7.2 Salinity

Salinity is the concentration of ions dissolved in water consisting of sodium, magnesium, calcium, chloride, sulphate, carbonate and bicarbonate. Gordon et al., (1996) stated that salts enter a stream through saline groundwater, sea salts dissolved in the rainwater and agricultural runoff. The gas solubility in water was reduced as salinity increased (Helfman, 2009).

The loss of water molecules during evaporation increased the conductivity, total dissolved solids (TDS) and salinity by leaving dissolved minerals in the water system (Jacobsen, 2008). TDS contains of all organic and inorganic substance in water including sodium, calcium, magnesium and chloride (Gordon et al., 1996). The ability of the ions to conduct an electrical current increased the conductivity. Since salinity and conductivity were positively correlated (Jacobsen, 2008), the increasing salinity will increase the conductivity as well.

2.7.3 Conductivity

Conductivity defined as a measure of dissolved salt in the water or the total amount of

dissolved ions in the water (Michaud, 1991). As flowing water picks up salts from the sediment bed

(rocks and soils), conductivity will increase in the downstream direction. When the water reaches the estuary, conductivity rises very sharply as freshwater mixed with the high salt content of sea water. During storms, a high water level will decrease the conductivity since rainwater has a low dissolved salt content. Any soluble salts on the ground at the beginning of a large storm are quickly picked-up in the surface flow and become diluted by the rain.

2.7.4 Water depth

Gordon et al. (1996) stated that the variations in water depth are created by variations in

channel from pools and riffles. Water depth can be categorized as very shallow (0-5m), shallow (5-

20m), moderate (20-50m) and deep (>50m) (Gorman and Karr, 1978). Compared to riffles, deeper

pool and run habitats are generally more complex due to the presence of debris, roots or group of

boulders with extensive space in between (Martin-Smith, 1998). Previous studies reported positive

correlation between pool depth and the fish size (Power, 1987; Harvey and Stewart, 1991) and

species richness (Mendelson, 1975; Meffe and Sheldon, 1988). Fish that lives in pools tend to be

larger in size while small fish tends to live in shallow water (Gordon et al., 1996).

2.7.5 Turbidity

Turbidity is a measurement of water clarity and how easily light penetrates into the water. In

a presence of other particles like sediments and suspended solid in the water, the amount of light

that can pass through the water will be low. Thus, the cloudier the water, the greater the turbidity is.

Aquatic organisms such as aquatic plants that carry out photosynthesis and some species of fish that

use light for protection purpose can be affected by the changes in turbidity. Turbidity preference of

fishes is a species-specific response and correlates with the age of the fish (Cyrus and Blaber, 1987;

Blabber and Cyrus, 1983).

2.7.6 Rainfall

In most part of the world, the rain is the main source of freshwater that provides suitable

conditions for diverse ecosystems as well as water for hydroelectric power plants and crop

irrigations. Precipitation is a key component of water cycle and it is accountable for depositing most of the fresh water on the planet. An increase in temperature will lead to an increase in evaporation that induces extra precipitation. There has been an upsurge in the amount of heavy

precipitation events in most areas during the past century as well as an increment since the 1970s in

the prevalence of droughts especially in the tropics and subtropics (Climate Change Division,

2008).

2.7.7 pH

pH is a measure of how acidic or alkalic a solution is which range value from 0 to 14.

Solution is acidic if pH < 7, alkalic if pH > 7 and neutral at pH >7<. pH can be used as a measure to verify estuary water quality. Normally, the pH of coastal waters vary from pH 7-8 while wetlands, swamps and other forested areas will have lower pH levels. Changes in water pH value can be caused by several factors such as photosynthetic uptake of carbon dioxide by aquatic plants, deficiency of oxygen in waters, high rainfall and inflow of contaminants. Most aquatic organisms are well-adapted living in solution with pH between 5 and 9. In estuary, the pH tends to remain constant because the chemical components in seawater act as buffer to resist large changes of pH.

However, biological activity might significantly alter the pH in the estuary. For an example, an excess presence of algae (algal bloom) may results an increase in pH levels in the estuary and become potentially harmful to aquatic organisms (Huet, 1994).

2.8 Sex ratio

Sex ratio indicates the composition of males and females in a population. It is calculated by expressing the respective numbers of males and females as a percentage of adult examined. This method is said to be more preferable when involving a direct ratio between numbers of males and females as it does not consider data where all the specimens may be of one sex (Beumer, 1979).

Klingbeil (1978) reported that sex ratios calculated for an entire commercial season are based on an

estimated total numbers of males and females landed. These ratios require correction for any

differences in average weight of the sexes.

2.9 Fish breeding strategy

Three key strategies in fish breeding pointed by Wallace and Selman (1981) and De

Vlamming et al. (1982) are:

1) Development of oocyte simultaneously (synchronous)

This type of breeding strategy occurred when sperm and egg produced and fertilized once

before the fish dies. Fish in this category will only be experiencing its breeding cycle once during its lifetime.

2) Development of oocyte in grouping and simultaneously (grouping synchronous)

Fish that experience this type of strategy release sperm and egg that will form a few development cycles and fertilized for a few times. This cycle occurs in one year period or only a few weeks with many fertilization times (during particular season).

3) Development of oocyte non-simultaneously (asynchronous)

The development of oocyte and fertilization occurred continuously and the reproductive cycle can be repeated during the fish lifetime. For some species, each individual are able to fertilize a few eggs every day during the breeding season.

A breeding strategy can be influenced by a lot of factors. In order to perform a perfect breeding cycle and ensure all eggs are produced safely before and after been fertilized, certain elements need to be taken into consideration such as environment suitability, the presence of an enough food supply, reduction of predator amount and water quality level.

2.10 Reproductive biology

Barr (1968) and Crim and Gleb (1991) pointed that ovary cycle can be divided into four levels. For testis cycle, Grier (1981) and Sukumasavin (2001) proposed that it can be divided into five levels. The levels are:

Ovary cycle:

1) Oogonium replication through mitosis division. Oogonium is small and round in shape

and present either in single or in grouping.

2) Transformation of oogonium to oocyte through meiosis. Primary oocyte would develop

into critical stage and will respond on stimulation of gonadtrophin hormone.

3) Development of oocyte and vitellogenic process occurs actively and correlated with

gonadtrophin hormone. 21

4) Maturity of oocyte and ovulation occurs when fertile oocytes are released into the water.

Testis cycle:

1) Testosterone hormone stimulates spematogonia (2n) to undergo mitosis and become

primary spermatocyte.

2) Primary spermatocyte will grow and reduce the number of chromosomes to half and

become secondary spermatocyte.

3) At the end of meiosis, secondary spermatocyte will become spermatid.

4) Ketotestosteron hormone will stimulate spermatid to produce tail to become

spermatozoa. This process is called spermatogenesis. Spermatozoa is a mature sperm in

the testis lumen.

5) Spermatozoa will further dissolve in seminal liquid and produces semen. This process is

called spermiation. Male fish is considered mature at this time (functional maturity).

Testis lumen will be filled with sperm and semen will be released when the abdomen

being pressed

2.11 Gonadosomatic Index (GSI)

Gonadosomatic index, abbreviated as GSI is a tool of measuring the sexual maturity of animals in relation to ovary and testis development (Barber and Blaker, 2006). The calculation for

GSI is as follows,

GSI = Wet gonad weight x 100

Wet body weight

Payne (1975), Htun-han (1978) and Delahunty and de Vlaming (1980) reported that

reproductive cycle involving apparent changes to gonad weight, heart and somatic tissue. Changes

in the gonad maturity can be observed during the changes of gonad weight in the reproductive cycle.

2.12 Spawning season

According to De Vlaming (1972), the right spawning time is very important for the species survival. Previous study has showed that tropical fish spawn year round (Aizam et al., 1983) with peak period during rainy season. Either in sea or river, heavy rainfall would result in a rising of the

water level in littoral zone and further improving the quantities of food such as phytoplankton and

zooplankton (Bagenal, 1967). The presence of food would naturally stimulate the fish to spawn and

would ensure the survival of larval and juvenile fish.

2.13 Fecundity

Fecundity is the actual reproductive rate of an organism or population measured by the

number of gametes (eggs), seed set or asexual propagules (Etienne, 2010). Marked differences in

fecundity among species often reflect different reproductive strategies (Pitcher and Hart, 1982;

Wootton, 1984; Helfman et al., 1997; Murua and Saborido-Rey, 2003). Within a given species,

fecundity may vary as a result of different adaptations to environmental habitats (Witthames et al.,

1995). Even within a stock, fecundity is known to vary annually, undergo long term changes

(Horwood et al., 1986; Rijnsdorp, 1991; Kjesbu et al., 1998) and has been shown to be proportional

to fish size, age and condition. Larger fish produces more eggs, both in absolute and in relative

terms to body mass. For a given size, females are in better condition to exhibit higher fecundity

(Kjesbu et al., 1991).

2.14 Length at first maturity

Fish becomes sexually mature for the first time at a size that is rather constant to the

proportion for their final length. This value is given as close to 2/3 of the final body length. The

length at which 50% of the population is found to be matured (M50) is calculated by using a graph

paper (Wooton, 1998).

2.15 Growth in fish

Growth can be defined as a gradual increase or development in term of size and number of the living organisms over time (Pauly, 1984; Sparre and Venema, 1998). According to Jobling

(2002), the changes in either length or weight or both as the fish grows up defined growth. Growth is expressed in a growth curve with sigmoid form. The growth rates can be slow due to the competition for foods in a crowded situation within the fish population (Gulland and Holt, 1959).

Conversely, thinning population due to fishing activities or intensive predation will result in faster

growth rates of individual fish as abundance food is available (Luff and Bailey, 2000).

The study of fish growth can be beneficial to distinguish population, stocks by habitat,

longevity, fish stock assessments and fisheries management. Fish population size, structure, and

distribution fluctuate in response to environmental variations (Lett and Doubleday, 1976; McRae and Diana 2005). Natural disturbances (floods, droughts, fires) and anthropogenic changes (new

fishing technologies, regulation changes or non-native fish introductions) can alter the fish

populations. Thus, status and trends in size, age structure, abundance, maturity schedules or

fecundity of fish in a population are the key to informed decision making (Ault and Olson, 1996;

Post et al., 2003). Several methods can be used in growth study such as hard-part (scales, otoliths

and vertebrae) analysis, mark-recapture (tagging) experiments and length measurements method

(King, 1995).

2.16 Length-weight relationship

The study of length and weight data is very helpful in producing a standard outcome of fish

sampling programs. By estimating the length-weight relationship, the fish population can be

examined (Sparre and Venema, 1992; 1998). Length-weight regressions have been broadly used to determine the weight from the length due to the technical difficulties and the amount of time consumed to record the weight at the sampling site (Morato et al., 2001). Koutrakis and Tsikliras

(2003) reported that in fisheries biology, length-weight relationship are useful in determining the

weight and biomass when only length measurements are available and allowing comparison of species growth between different regions.

The importances of length-weight relationship have been highlighted in previous studies such as Diaz et al. (2000), Luff and Bailey (2000), Ayoade and Ikulala (2007) and Samat et al.

(2008). They pointed out that,

1) Data on length-weight can give crucial clues about climatic and environmental changes and

eventually about changes in human subsistence practices.

2) It act as a measure for the availability of food sources and the degree of competition for

food since the size attained by the organisms may vary due to the variations in the supply of

nutrients or due to competition factor.

3) Length and weight data can be used to estimate growth parameters of the species as well as

to estimate the mortality rate of the species available in fish stock assessments.

4) Length-weight relationship can be used as an indicator of the condition factor, the general

well-being of fish populations.

5) Length and weight data can be applied to estimate the standing stock biomass through

length-frequency.

Calculation of length-weight relationship can be conducted by measuring the length and weight of fish sample at certain time because the length and weight of fish gradually changes (Le

Cren, 1951; Jones et al., 1999; Cherif et al., 2008). The relationship of W=aLb is used to describe

the change in weight of a fish where,

W = observed fish weight

a = the intercept of the regression

b = the regression coefficient/the slope (fish growth rate)

According to Rosa et al. (2006), in the length-weight relationship, parameters a and b were

estimated by linear regression analysis based on logarithms,

log (W) = log a + log b

Generally, the parameter b does not fluctuate significantly throughout the year, while parameter a may vary daily, seasonally and between habitats (Goncalves et al., 1997). Jones et al.

(1999) said that the b value can show whether the growth is either a somatic growth, allometric growth or isometric growth. If the b value is:

< 3 = negative allometric growth (fish become less rounded as the length increases)

Equal to 3 = isometric growth (length and weight of the fish increases proportionally)

>3 = positive aloometric growth (fish become more rounded as the length increases)

The degree of fitness between length and weight was determined by the correlation

coefficient, r² (Mansor et al., 2001).

2.17 Mortality

Mortality rate is a measure of the number of deaths in a population, scaled to the size of that

population per unit time (Sparre and Venema, 1992; 1998). Fish mortality is a common term that widely used in fisheries science that denotes the removal of fish from a stock through death.

Generally, there are two types of fish mortality; the natural mortality and fishing mortality. The definitions of these two types of fish mortality are,

Natural mortality = The removal of fish from the stock due to natural causes such as disease, competition, old age, predation, cannibalism, starvation and lethal environmental conditions and not related with fishing activities.

Fishing mortality = A loss of fish from the stock due to fishing activities using any fishing gears

According to Sparre and Venema (1998), natural mortality is denoted by ‘M’ while fishing

mortality is denoted by ‘F’ in fisheries models. They also said that, fish mortality is very important

in estimating the trend of a population. Moreover, for determining year-class strength, the high

mortality rate during early life of fish (larval stage) is generally considered as an influential factor

(Ronald and Charles, 1986).

2.18 Recruitment

Recruitment is a number of individuals that reach a specific stage of the life cycle such as metamorphosis, settlement or joining the fishery (Beverton and Holt, 1957; Gulland, 1969; Pauly et al., 1986). The recruitment process involved two phases which are the pre-recruits (eggs, larvae, juvenile) and post-recruits (exploited phase of the population). Recruitment involves migration from the nursery areas to the fishing areas or habitat change from pelagic to demersal especially larvae of many species or from artisanal fisheries in lagoon to commercial fisheries in the open or coastal areas (Werner and Gilliam, 1984; Keast, 1985). Habitat use and mortality rate during first year of life can affect and create the variations in recruitment patterns of the species (Spencer and

Lars, 1999).

CHAPTER 3

DISTRIBUTIONS AND ABUNDANCES OF Plicofollis argyropleuron AND THEIR

RELATIONSHIP WITH PHYSICAL PARAMETERS IN MERBOK ESTUARY, KEDAH

3.1 INTRODUCTION

Due to the complex scenario presented on tropical estuaries and the relationship between environmental factors within estuaries and fish assemblages, this type environmental have received considerable attentions (Pombo et al., 2005). However, distribution and abundance of species diversity in estuarine system in north-west Peninsular Malaysia have been scarcely studied.

Knowledge on how environmental variables influence the spatial and temporal structure of a species assemblage in the estuary water is also scarce.

Several physical and biological factors such as climate stability, spatial heterogeneity, competition, predation and primary productivity have influence on the fish assemblages (Martin-

Smirth and Laird, 1998; Galacatos et al., 2004; Andrus 2008; Kadye et al., 2008). Love and May

(2007) reported, habitats used for spawning, foraging and shelter are usually high in species diversity.

Fish assemblages are recognized as sensitive indicators of habitat degradation, environmental contamination and overall system productivity (Ecoutin et al., 2005; Qadir et al.,

2005). The relationship between fish assemblages and environmental factors has been documented by several studies (Martin-Smith, 1998; Beamish et al., 2003; Love and May, 2007; Kadye et al.,

2008). Most of the studies found that water temperature, salinity and water depth affected the fish assemblages.

An understanding on the distributions of fish throughout the stream and river networks and the factors that responsible for those distributions are essential in determining the patterns of fish assemblages which can be used as an indicator of the ecosystem healthiness. This chapter will provide information on the distributions and abundances of Plicofollis argyropleuron in the Merbok estuary and their relationship with physical parameters.

3.2 OBJECTIVES

1) To study the composition, distributions and abundances of Plicofollis argyropleuron.

2) To determine the relationship between fish assemblage and physical parameters.

3) To clarify seasonal changes (dry and rainy season) as an indicator of fish assemblage.

3.3 MATERIALS AND METHODS

3.3.1 Study area

Merbok estuary is located in the north-west of Peninsular Malaysia (5o20’N; 100o25’E and

5o30’N; 100o26’E) [Figure 3.1]. The river is about 35 kilometres long with majority comprise of estuary water since seawater intrudes up to about 30 kilometres of the river with only a few kilometres of freshwater in the upper reach (Ong et al., 1991). The river depth varies from 3 to 5 metres with a few 20 metres deep holes at entry point of tributaries joining the Merbok river.

Freshwater flows into the estuary through several small streams, tributaries and creeks in as well as through ground runoff (sheet flow) during heavy rainfall. The estuary is connected with Sungai

Muda via a channel at the southern part. Sungai Muda is the major river with an average discharge of about 100m3 sec-1. The drainage system was formed by a network of tributaries: Sungai Keluang,

Sungai Gelam, Sungai Petani and Sungai Terus.

The regional climate of the estuary is dominated by the north-eastern monsoon from

November to March and the south-western monsoon from May to Septermber (Nur Fadhilah,

2011). The monsoons are not severe because of shielding by the peninsular mountain range during northeast monsoon and by the island of Sumatra during the southwest monsoon. Therefore, wind has little effect on the estuarine characteristics of the Merbok river.

(A) (B)

Figure 3.1 (A) Location of sampling site, Merbok estuary in the northern of Peninsular Malaysia (in square). (B) A few tributaries (in blue circles) that connected with Merbok estuary (in red square)

3.3.2 Sampling technique

Catch per unit effort (CPUE) is the measurement of fish abundance (weight) that was taken from the fishermen catches. At the site, when the catches received, the fishes were weighed, identified and grouped in-situ under its own grade by the fishermen. If the haul exceeded 10000g, only 7000 to 10000g from the total amount of catches used as the samples. Our species was taken randomly from the fisherman boxes which contain other species of fishes, prawn, crab and squid regardless of their sizes in order to avoid bias or less precise data.

The fishing gear used was synthetic barrier net with the size of 100 to 200m length, 3 to 5m

width and the mesh size of 2.54cm. The net was bound with several bamboos and long woody stick

and was set like a fence along the inner estuary banks and some was installed partially and

alternately across the estuary. This kind of gear operates in respond to the tidal movement. The fast

moving water current created by tidal changes caused the inflow of drifted ichthyofauna and

crustacean species caught onto the net. The harvest period was 12 hours after the net was set up.

Estuary physical parameters were important especially in fish study because these

parameters have influence on the population of fish as well as other lives. In this study, these parameters were measured to describe estuary condition of Merbok. In situ physical readings were recorded monthly from Jan-10 until Jan-11. Measurements were taken during high tide. Due to the time limitation of the tidal fluctuation, some of the readings have to be taken during the low tide.

Seven parameters were measured; water temperature (oC), salinity (ppt), conductivity, turbidity

(cm), water depth (m), pH and rainfall.

SCT meter was used to record the water temperature, salinity and conductivity. Water

turbidity was measured using 30cm (diameter) of Secchi disk with a rope that has been marked in

metres, water depth was measured using water sampler as the ballast and rope marked in metres as

a marker and pH levels were measured using pH meter.

3.3.3 Data analysis

The dominance patterns of the fish assemblages were assessed from the combination of frequency of occurrence and catch per unit effort (CPUE) expressed in percentage (%FO and

%CPUE) (Garcia-Lopez et al., 2006).

Frequency of occurrence (FO) was calculated as below:

The number of occurrence of a species

Duration of sampling (month)

Monthly percentage of FO (%FO) was calculated as below:

The FO of the species for each month x 100

Total FO of the species for one year

One of the best indices to assess the abundance of a species is catch per unit effort (CPUE)

(Sparre and Venema, 1992; Abowei, 2009).

Catch per unit effort (CPUE) was calculated as below:

The total number of individuals captured from each site

Total effort/trip

Monthly percentage of catch per unit effort (%CPUE) was calculated as below:

The CPUE of the species for each month x 100

Total CPUE of the species for one year

According to Garcia-Lopez et al. (2006), species with %FO ≥ average, %FO was classified as frequent fish whereas those with %FO < average, %FO was considered as rare fish. In term of abundance, a similar method applied to %CPUE for the classification. %CPUE ≥ average, %CPUE resulting in high abundance and %CPUE < average, %CPUE resulting in low abundance. The combinations of %FO and %CPUE were used to classify fish into four groups as their relative importance:

1) High abundance and frequent (≥%CPUE, ≥%FO)

2) High abundance and rare (≥%CPUE, <%FO)

3) Low abundance and frequent (<%CPUE, ≥%FO)

4) Low abundance and rare (<%CPUE, <%FO)

3.4 RESULTS

A total number 488 individual of fish were sampled during this study. Samples were

collected monthly over one year period from Jan-10 to Jan-11. The samples number ranged from 27 to 70 individuals. The highest number was in Feb-10 while the lowest was in Jan-11. In the beginning of year, samples number fluctuated up and down but towards the end of the year, the samples number slowly decreased [Figure 3.2]. The highest fish abundance was recorded in Feb-10

with 16.99% of total catch and May-10 seems to be the month with the lowest abundance recorded,

2.72% [Figure 3.3]. High fish frequency occurred during Feb-10 with 14.34% and rare in Jan-11,

(5.53%) [Figure 3.4]. The total number of fish, their abundance and frequency of occurrence were

summarized in Table 3.1.

Figure 3.2 Monthly total number of individuals collected in Merbok estuary, Kedah

Figure 3.3 The percentage of fish abundances (%CPUE) by month collected in Merbok estuary, Kedah

Figure 3.4 The percentage of frequency of occurrences (%FO) by month collected in Merbok estuary, Kedah

Table 3.1 Relative importance based on percentage abundance (%CPUE) and percentage of frequency of occurrence (%FO) of fishes captured during the period of Jan-10 to Jan-11 in Merbok estuary, Kedah, Malaysia. Group: 1 = High abundance and frequent, 2 = High abundance and rare, 3 = Low abundance and frequent and 4 = Low abundance and rare. N = Total number of individuals

Month % CPUE % FO Group N Jan-10 12.4 10.04 1 49 Feb-10 16.99 14.34 1 70 Mar-10 9.47 13.54 1 66 Apr-10 3.16 6.35 4 31 May-10 2.72 7.99 3 39 Jun-10 10.26 5.77 2 28 Jul-10 0 0 - 0 Aug-10 2.9 6.97 4 34 Sep-10 7.89 9.02 1 44 Oct-10 4.14 7.99 3 39 Nov-10 13.59 6.35 2 31 Dec-10 8.64 6.15 2 30 Jan-11 7.39 5.53 4 27

Most of the months were categorized into Group 1 where the species was high in abundance

and frequently (%CPUE ≥ average %CPUE and %FO ≥ average %FO). Those months were Jan-10,

Feb-10, Mar-10 and Sep-10. In month of Jun-10, Nov-10 and Dec-10, fishes were also high in abundance although they were rarely occurred. P. argyropleuron was more common in wet season since heavy rainfall distributions were received during these months (www.met.gov.my). In May-

10 and Oct-10, although the numbers of P. argyropleuron collected were very much less, the

frequency was relatively stable. Apr-10, Aug-10 and Jan-11 are months with low fish abundance

and they rarely occurred. The %CPUE and %FO was significantly correlated at P < 0.01.

Table 3.2 Catch per unit effort (CPUE) and mean ± standard deviation of physical parameters in Merbok estuary, Kedah from Jan-10 until Jan-11. Parameters observed: CPUE (g/boat/trip), water temperature (◦C), water depth (m), turbidity (cm), pH (pH), salinity (ppt), conductivity (µmhos/cm) and rainfall (mm)

Months CPUE Water Water Turbidity pH Salinity Conductivity Rainfall (g/boat/trip) temperature(◦C) depth (m) (cm) (pH) (ppt) (µmhos/cm) (mm) Jan-10 17200 28.73±0.41 4.08±3.46 5.88±1.68 6.95±0.28 25.71±5.89 29283.33±5512.52 0.6±1.38 Feb-10 23570 30.76±0.38 3.41±2.06 7.17±1.93 7.6±0.09 25.88±1.58 32245.83±2317.96 2.8±5.45 Mar-10 13140 30.76±1.07 3.38±1.28 9.83±4.17 7.18±0.46 22.42±3.06 32208.33±3501.49 4.4±15.12 Apr-10 4390 31.56±0.61 3.23±1.04 9.4±4.51 6.81±0.44 16.93±6.49 25900±9291.46 8.3±15.56 May-10 3770 31.88±0.43 4.43±1.64 14.67±5.49 7.18±0.15 22.26±3.31 39497.5±7034.07 6.4±10.91 Jun-10 14240 31.07±0.47 3.90±0.89 6.67±1.29 7.28±0.24 19.16±4.32 36831±4486.46 6.4±11.45 Jul-10 620 30.02±0.08 2.77±0.25 9.25±2.75 10.73±0.05 25.73±1.42 33066.67±7468.66 3.2±5.11 Aug-10 4030 30.33±0.12 4.63±0.91 6.13±1.65 7.49±0.5 24.5±5.04 34212.5±6508.38 7.8±15.2 Sept-10 10940 29.03±0.89 2.43±1.42 7.7±1.86 6.88±0.25 11.68±4.49 15837.5±10092.19 12.8±18.64 Oct-10 5750 30.29±0.22 6.36±1.97 11.8±2.95 6.9±0.09 18.1±3.07 26300±4188.53 2.3±5.93 Nov-10 18850 29.7±0.39 3.1±0.86 12.3±4.77 7.06±0.29 20.5±2.69 27250±3182.14 12.3±17.6 Dec-10 11990 28.8±0.37 2.83±0.41 16.67±6.68 7.08±0.35 13.04±7.19 14833.33±10147.25 5.3±9.28 Jan-11 10250 27.51±0.14 2.14±0.95 170.76±24.32 6.78±0.34 11.71±2.74 19387.5±4331.43 3.1±6.74

Referring to Table 3.2, CPUE ranged from 620 to 23570g/boat/trip, rainfall (0.6 to

12.8mm), water temperature (25.51 to 31.88oC), water depth (2.43 to 6.36m), salinity (11.68 to

25.88ppt), conductivity (14833.33 to 39497.5µmhos/cm), turbidity or transparency (5.88 to

170.76cm) and pH between 6.78 and 10.73. The highest CPUE recorded was in Feb-10

(23570g/boat/trip) and lowest in Jul-10 (620g/boat/trip). The mean rainfall was lowest in Jan-10

(0.6mm) and highest in Sept-10 (12.8mm). Water temperature differed markedly between

months, with low value (27.51oC) in Jan-11 and higher value (31.88oC) in May-10. The water depth fluctuated from 2.43m in Sep-10 to 6.36m in Oct-10. Salinity values were varied monthly with the lowest mean value of 11.68 ± 4.49ppt in Sep-10 and the highest, 25.88 ± 1.58ppt in Feb-

10. The highest conductivity mean value was recorded in May-10 (39497.5µmhos/cm ±

7034.07µmhos/cm) and the lowest in Dec-10 (14833.33µmhos/cm ± 10147.25µmhos/cm). Jan-

10 seems to be the month with the lowest turbidity value recorded, 5.88cm and the highest turbidity value recorded was in Jan-11, 170.76cm. pH was found to be varied significantly across months with an explicit fluctuation of mean value in Feb-10 and Jul-10. pH showed good relationships over broad salinity regimes with average and minimum indices and even tighter correlations with low salinity. At low salinity, average pH ranged from 6.88 to 7.28 and at high salinity, the average pH was at 7.49 or greater. Their correlations were analyzed using Pearson correlation (ρ) and presented in Table 3.3. All parameters were correlated at P < 0.01 except for turbidity-conductivity (P < 0.05). These correlations were probably influenced by the rainfall distribution. The lowest values were recorded under low rainfall (dry season) and the higher values were observed during the high rainfall (wet season).

Table 3.3 Pearson correlation (ρ) of physical parameters and their relationship with fish abundance in Merbok estuary, Kedah. Parameters observed: water temperature (◦C), water depth (m), turbidity (cm), pH (pH), salinity (ppt) and conductivity (µmhos/cm).

Temperature Water depth Turbidity pH Salinity Conductivity CPUE

Temperature 1 0.400** -0.519** 0.526** 0.454** 0.692** -0.413**

Water depth 0.400** 1 -0.176 0.468** 0.485** 0.502** -0.157

Turbidity -0.519** -0.176 1 -0.182 -0.378** -0.259* 0.562** pH 0.526** 0.468** -0.182 1 0.645** 0.639** -0.288**

Salinity 0.454** 0.485** -0.378 0.645** 1 0.821** -0.639**

Conductivity 0.692** 0.502** -0.259** 0.639** 0.821** 1 -0.483**

CPUE -0.413** - 0.157 0.562** -0.288** -0.639** -0.483** 1

* significant at P=0.05 ** significant at P=0.01

3.5 DISCUSSION

Estuaries are transitional systems where seasonally fluctuating freshwater river flows meet the daily fluctuating marine tides to create condition of highly variable salinity and other environmental factors that influence the fish assemblage structure (Haedrich, 1983; Whitfield,

1999; Blaber, 2000). Based on the dynamics of these two variables, three distinct estuarine zones have been established for the estuary systems: a riparian zone (upper estuary) in the upper limit of tidal influence, an intermediate mixing zone (middle estuary) with features constantly change due to the different characteristics of water and a coastal zone (lower estuary) with the estuarine plume (Kjerfve, 1987). Although some species may occupy all three zones at times, many different fish species tend to prefer a particular estuary zone there by forming or changing assemblage structure throughout the longitudinal estuarine extent according to the environmental conditions in each zone. The variations in physical conditions (Horwitz, 1978; Matthews and

Hill, 1979; Reash and Pigg, 1990; Poff and Allen, 1995; Smale and Rabeni, 1995, Barletta et al.,

2005; Ramos et al., 2006b) act as one of the crucial determinants in fish assemblage structure.

Estuarine margins lead to high structural complexity and spatial heterogeneity when occupied by aquatic macrophytes and wood debris (Keefer et al., 2008). Estuaries with habitat heterogeneity usually have high species richness than the homogenous one (Gorman and Karr,

1978; Schlosser, 1982; Whitfield, 1983). Mangroves are dominant habitats in tropical estuaries and their structural complexity provides shelter and food as well as decreased the predation risk for fish making it an ideal rearing ground for juvenile fishes (Laedsgaard and Johnson, 2001).

Protected areas in the estuarine zone features particular fish assemblages and serve as rearing

grounds (Beck et al., 2001; Lazzari et al., 2003). The relationship between environmental

variables and the distribution of organisms within estuaries has been studied in the estuaries

exposed to human activities (Marshall and Elliot, 1998; Whitfield, 1999; Akin et al., 2005).

Fish occurrences and distributions in an estuary vary according to environmental changes like precipitation regime, the intensity and distance of the salt wedge inversion which were determined by the estuary morphology, current velocity and the availability of food resources

(Camargo and Isaac, 2003; Re, 2005). Temperature and salinity are the important variables influencing the occurrence, density and growth of eggs and larval fish in the estuarine region

(Faria et al., 2006; Ramos et al., 2006a). Fish assemblage may also be seasonally influenced in

an estuarine region (Harris and Cyrus, 2000) and seasonal variations have been well documented

(Morais and Morais, 1994; Barletta-Bergan et al., 2002; Re, 2005).

Abiotic variables are more important than biotic factors (e.g. competition, predation) in

structuring fish assemblages (Tonn and Magnuson, 1982; Rodriguez and Lewis, 1997; Jackson et

al., 2001; Quist and Hubert, 2005). Simultaneously, other researchers hypothesized that

favourable abiotic conditions will be superseded by biotic variables when competitor or predator

densities are high (Sumari, 1971; Persson, 1997; Quist et al., 2003). However, it is likely that

environmental factors interact to some degree (Hinch, 1991; Rodriguez and Lewis, 1997;

Jackson et al., 2001) and the importance of biotic variables may be habitat specific (Hinch, 1991;

Quist et al., 2003). Most tropical regions experience two distinct seasons (dry and wet) and the

majority of the water bodies depend on seasonal changes to activate and deactivate

environmental parameters (Fialho et al., 2007).

The precision and accuracy of physical parameters estimate for a fish population was

likely to be related with biological characteristics of the fish population, sampling gear used,

sampling method and number of samples obtained. Even though the effects of sampling biases

on the accuracy of parameters estimates have received some attention (Andrew and Mapstone,

1987; Downing 1989, Goodyear, 1995; Garner, 1997), the precision is still less frequently

considered. Methods exist for determining requisite precision and associated sample size for

certain objectives such as testing for specific differences between populations or treatments

(Desu and Raghavarao, 1990). However, the point is to be effectively arbitrary and unlikely

related to biological importance (Johnson, 1999). Downing (1989) stressed that any selected

target precision is essentially arbitrary unless the project is specific-objective and real sampling costs were defined and related to precision.

Abundance is possibly the least studied of the fish ‘’indices’’ that are related to environmental factors due to the difficulty in obtaining accurate estimates (Marshall and Ryan,

1987; Hinch and Collins, 1993). Changes in fish distribution and abundance will undoubtedly affect human communities who harvest these stocks (Roessig et al., 2004). Factors such as calm water and food availability were suggested to affect the distribution and abundance of the fish

(Cyrus and Blaber, 1992). The effects of climate change (Roessig et al., 2004) on estuarine fish

individuals, populations, communities and assemblages have been widely addressed (Gibbs,

2006).

In many estuaries, the declining abundances of species that spend all or part of their life

cycle in the estuaries was mainly caused by illegal fishing (Secor and Waldman, 1999; Lotze et

al., 2006) and this recruitment of fishes from estuaries were strongly affecting the marine

population dynamics (Elliot and Taylor, 1989). It was believed that large-scale patterns in the

distribution of organisms resulted primarily from the species responses to their physical

environmental parameters as dominant abiotic variables were thought to act like physiological

sieve that plays a vital role in structuring a community. Abiotic factors may set up the

community framework but the biotic interactions refine the species distribution patterns within

this structure. Previous studies have shown the associations between environmental variables and

fish assemblage structure. Assemblage structure has been linked to temperature (Rakocinski et

al., 1992; Arceo-Carranza and Vega-Cendejas, 2009), pH (Rahel and Magnuson, 1983; Rahel,

1984; Rago and Weiner, 1986; Persson, 1997; Jackson et al., 2001), conductivity (Persson,

1997), Secchi depth (Rodriguez and Lewis, 1997; Tejerina et al., 1998), salinity (Peterson and

Ross, 1991; Szedlmayer and Able, 1996; Whitfield, 1999; Arceo-Carranza and Vega-Cendejas,

2009), turbidity (Peterson and Ross, 1991), water depth (Keskin, 2007) and hydrology patterns

(Pritchett and Pyron, 2011).

In this study, a total number of 488 individuals of Plicofollis argyropleuron were

sampled. Samples were collected monthly and each sample consists of 27 to 70 individuals. The

highest number of individual collected was in Feb-10 and the lowest was in Jan-11 [Figure 3.2].

The number of individuals and biomass caught in rainy seasons were more than those in dry

seasons [Table 3.2]. This is to be expected since the high water season is the main feeding and

growing period for nearly all species in the seasonal flood plain estuaries of the tropics (Lowe-

McConnell, 1975; Welcomme, 1979; Ikomi et al., 1997; Tejerina et al., 2010). During dry months, the nutrients depleted and the water level falls at the end of the rainy month causing fish to move seawards (Lowe-McConnell, 1975; Idodo-Umeh, 1987; Arimoro et al., 2006).

Moreover, there is a limited accessibility into the water body for the fisherman during dry season due to reduced water volume (Allison et al., 1997).

Fish was most abundant in Feb-10 and less abundant in May-10 [Figure 3.3]. Merbok estuary experienced the primary maximum rainfall in Sep-10 to Nov-10 while the secondary maximum generally in Mac-10 to May-10; the primary minimum occurred in Jan-10 to Feb-10 with the secondary minimum in Jun-10 to Jul-10 [Table 3.2]. The wet season (rainy season) started in Feb-10 since high rainfall received during this month and May-10 marked the start of the dry season which explains the fish abundance patterns. Roessig et al. (2004) described seasonal rainfall as the main factor that affects the strategies of the life cycle of fish including their movement, feeding, growth and spawning. Seasonal variations in rainfall create and/or eliminate micro-habitats which are important for fish (Olukolajo and Oluwaseun, 2008). In addition, precipitation promotes alterations in species abundance and richness over a large spatial scale as well as small spatial scale such as in small creeks (Grossman et al., 1985).

The highest CPUE recorded was in Feb-10, 23570g/boat/trip while the lowest was

620g/boat/trip recorded in Jul-10 [Table 3.2]. Many factors such as season, temperature, habitat and depth are known to influence CPUE and the variances in the data for the species (Harden and Connor, 1992; Justus, 1996; Cunningham, 2000). CPUE was positively correlated with

turbidity (r = 0.562) and pH (r = 0.288) while negatively correlated with temperature (r = -

0.413), salinity (r = -0.639) and conductivity (r = -0.483) at P < 0.01 [Table 3.3].

Water temperature is a very important parameter influencing the biota in a water body by

affecting activities such as behaviour, respiration and metabolism of aquatic organisms. It is

necessary to study the temperature variations in water body, in animal ecophysiological and toxicological aspects as the water density and oxygen content are temperature-related, hence

indirectly affects osmoregulation and respiration of the animal (De A.K, 2002; Dam and

Aurangabad, 2011). Water temperature plays an important role in structuring fish communities in

mangroves, estuaries and coastal areas (Whitfield, 1999; Blaber et al. 2000). Temperature can

limit the presence of fauna and have a selective effect on the composition of the microbial community (Karl, 1985; 1995). It may also be considered as a quasi-conservative tracer (Johnson et al., 1988a). A relatively small temperature variation may affect the distribution and abundance of fish as recorded in the present study and this was in agreement with the results of Nip and

Wong (2010). The large inter- and intra-month variation in water temperature was due to the southwest monsoon season in March-May and the northeast monsoon season in September-

February and tidal fluctuation in the estuary with cold incoming seawater and warm outgoing

freshwater (Mansor et al., 2012a).

As in Table 3.2, CPUE was high when water temperature ranged between 28oC-31oC.

However, as the water temperature increase, CPUE became lower. This explained why %CPUE

is high in Feb-10 as recorded water temperature was at 30.76oC but in May-10, water

temperature started to increase beyond 31.88oC which resulted in low %CPUE in that month

[Figure 3.3]. As the water temperature increase, the dissolved oxygen decrease since hot water hold less oxygen compared to cold water (Helfman et al., 2009). Probably, due to the insufficient of oxygen during the high temperature, fish might not able to survive which affected the fish abundance (%CPUE). CPUE was negatively correlated with temperature at r = -0.413 (P < 0.01).

As mentioned above, increasing water temperature will reduce dissolved oxygen as hot water hold less oxygen compared to cold water. Generally, fish will have higher metabolic rate at high temperature therefore requires more oxygen (Helfman et al., 2009). To reduce the metabolic rates and the uptake of oxygen in the water during high temperature, fish reduce its swimming activity (Kramer, 1987; Haney and Walsh, 2003). This reason makes it clear why %FO was low when water temperature is high since fish rarely occur in order to reduce its swimming activity.

Salinity is regarded as a variable that influences the occurrence of some species (Akin et al., 2005). Euryhalinity and stenohalinity of teleosts are primarily defined by their ability and inability to maintain blood osmolality with narrow physiological ranges under different salinity conditions (Kaneko et al., 2008). As a marine stenohaline, this species cannot tolerate a wide fluctuation of salinity. This explained why %CPUE decreased from Mar-10 to May-10 [Figure

3.3] since the salinity change sharply from 22.42ppt to 16.93ppt and up to 22.26ppt [Table 3.2] which caused blood osmolality to fluctuate up and down within the tolerable level for P. argyropleuron. CPUE was negatively correlated with salinity at r = -0.639 (P < 0.01).

This present study in the Merbok estuary showed that salinity was the strongest parameter to influence Merbok estuary fish assemblages, r = -0.639 at P < 0.01 [Table 3.3].

During the rainy season, freshwater runoff increases, leading to a decrease in salinity. However, during dry season, high salinity triggers the entry of some marine species into the estuary due to availability of food and shelter from predators (Blaber, 1997; Marshall and Elliot, 1998). This created a competitive situation among the migrated species and the resident species which sparked the schooling behaviour causing them to hover at the bottom of the estuary and cannot be seen (bring to a decline in catch rate). This observation was supported by the fluctuation in

CPUE in this study and the negative correlation between CPUE and salinity.

Conductivity is defined as the ability of water to conduct electric current which increases with the increasing concentrations of total dissolved solids (TDS) (Lind, 1979). Toxicity of TDS to aquatic life depends on the combinations and concentrations of the ions in solution which may have additives or synergistic properties and is not predictable from TDS concentration alone

(Chapman et al., 2000). Black (1977) recommended a maximum of 400mg/L for diverse fish populations. Conductivity range in this study was above this chronic toxicity range [Table 3.2] and P. argyropleuron exhibited marked sensitivity to this form of water quality degradation

(Kennedy et al., 2003) thus elucidate why fish occurrence was negatively correlated with conductivity.

An obvious %CPUE changes can be seen from Mar-10 to May-10 and from Aug-10 to

Oct-10 [Figure 3.3] resulted from a shifting conductivity during that period [Table 3.2]. In an aquatic environment, conductivity is a very important element. Fish are very sensitive to

conductivity since it is strictly related to the amount of osmotic pressure exerted on their cellular

membranes. As in most other vertebrate species, teleosts maintain the osmolality of the body

fluid at constant levels of approximately one-third of seawater osmolality. Osmoregulation in adult teleosts is largely the result of integrated ion and water transport activities of the gills, kidney and intestine (Bentley, 2002; Evans et al., 2005; Marshall and Grosell, 2006).

Conductivity is directly proportional with salinity. Thus, an increase in salinity increases the

conductivity due to the presence of salt or mineral content. This will create a hypertonic

condition to the fish cellular membranes. In a vice versa case, it will turn into a hypotonic

condition. This uncertain condition due to conductivity fluctuation was not good for P.

argyropleuron. Conductivity was positively correlated with salinity (r = 0.821, P < 0.01),

concurrent with the previous finding by Ladipo et al. (2011).

The conductivity values ranged between 14833.33µmhos/cm to 39497.5µmhos/cm

[Table 3.2]. The highest mean value was recorded in May-10 (39497.5µmhos/cm ±

7034.07µmhos/cm) and the lowest was in Dec-10 (14833.33µmhos/cm ± 10147.25µmhos/cm)

[Figure 3.5]. The conductivity values differed significantly across months (p < 0.05). The

variation was affected by continuous flush-off-effluent during the primary rainy season which

became lesser towards the end of the year, as demonstrated by the lower value in Dec-10. The

conductivity of most water ranges from 10 to 1000µmhos/cm but may exceed 1000µmhos/cm in

polluted waters or those receiving large quantities of land runoff (Water Quality Assessments,

1996). Estuary supporting good mixed fisheries ranged between 150 and 500µmhos/cm.

Conductivity outside this range could indicate that the water is not suitable for certain species of

fish or macro invertebrates. Industrial waters discharge can range as high as 10000µmhos/cm

(water.epa.gov). Squire and Moller (1982) found that the conductivity affects both signal intensity and receptor sensitivity of the fish. According to their study, receptor sensitivity decreased at higher conductivity levels. However, Gerhard (1993) came out with different findings where receptor sensitivity increased at higher conductivity levels which in line with the present study. An increase in species receptor sensitivity might reduce the efficiency of fishing methods by the fishermen and indirectly influence the catch rate (result in low CPUE). CPUE was negatively correlated with conductivity at r = -0.483 (P < 0.01).

Turbidity is the measure of cloudiness in water. The more turbid the water, the murkier it is. Suspended solids in turbid water can reduce the amount of light penetration into the water, clog fish gills, decreases the fish resistance to disease, reducing the food availability and reduces the growth rate (Minnesota Pollution Control Agency, 2008). This statement tells us that as the turbidity increase, the catch rate would decrease. However, CPUE was positively correlated with turbidity at r = 0.562 (P < 0.01) in this study. This might due to the presence of its barbels, P. argyropleuron managed to rely heavily on it to get foods (Piet and Guruge, 1997) and survive thus the fish catch rate was not much affected.

Turbidity was always a determinant factor in fish abundance (Whitfield, 1994; Laroche et al., 1997; Strydom et al., 2002). Blaber et al. (2000) suggested that turbidity had positive effect on fish abundance. For effective catching (high catch rate), the nets must not be visible (high turbidity) to the fish in the water (Klust, 1982). Although fish are known to be myopic, they can see up to 10m distance in 20m depth water (Tasdemir, 1997). Visibility of the nets is affected by weather conditions, turbidity, water flow, water depth and etc. The turbidity can vary according

to the seasonal variations. The fish in clear water (low turbidity) may recognise the nets more easily (resulting in low catch rate) which also possibly explained the positive correlation between

CPUE and turbidity.

In systems with high levels of algae productivity and high flow of terrestrial inputs (e.g.

estuaries), turbidity levels are naturally high (Johnson and Hines, 1999; Meager, 2003). In such

areas, turbidity can have a positive effect in reducing predation as visual is limited. This explains

why %FO had a positive correlation with turbidity as fish tends to occur frequently during this time to avoid predators. However, at extremely high levels of turbidity, fish growth and survival may be negatively affected (Besh et al., 2001) explaining the reason why fish caught was less

present in Jan-11 [Figure 3.4] as drastic rises in turbidity (170.76cm) was observed [Table 3.2].

Jan-10 seems to be the month with the lowest turbidity value recorded (5.88cm) and the

highest turbidity value was recorded in Jan-11 (170.76cm) [Table 3.2]. Turbidity and suspended

solids are the important variables relative to transport and bioavailability of contaminants

(Castane et al., 2006). Turbidity resulted from the scattering and absorption of incident light by particulate matter in the water and can often be related to solids (Chapman and Kimstach, 1992).

A high turbidity value indicates a high concentration of total suspended solids (TSS) (Parr et al.,

1998; Johnson and Hines, 1999; Meager, 2003). The higher TSS concentrations may be

attributed by the accidental discharges entering the estuary from local drainages. The level of

suspended solids may also be enhanced by anthropogenic activities in the estuary (Parr et al.,

1998). Higher suspended solids decreased the passage of light through water causing slow

photosynthesis by aquatic plants which in turn reducing the dissolved oxygen content of the

water. It has also been reported that high amount of TSS will cause water to heat up more rapidly and hold more heat (temperature increase), which adversely affect the aquatic life that are more adapted to a lower temperature regime (Chagas and Suzuki, 2005). However, our result was rather contradicted since it was found that as turbidity increased, water temperature decreased

(vice versa). This was probably due to the lower atmospheric temperature during the rainy reason that indirectly decreased the water temperature. Similar result was reported by Dam and

Aurangabad (2011).

An acceptable pH range for an estuary is between 7 to 9 according to the Australian and

New Zealand Environment and Conservation Council (ANZECC) guidelines. In this study, pH on monthly basis is almost constant at pH 7 except a pronounced shift in Jul-10 [Table 3.2].

During this month, pH reached almost 11. The changing is too drastic for the species to tolerate which explains why %CPUE decline sharply in Jul-10 where zero catch was observed [Figure

3.3]. CPUE was negatively correlated with pH at r = -0.288 (P < 0.01). This result was supported by rainfall distribution as in Jul-10, minimum amount of rainfall was received [Table 3.2].

Interannual changes in freshwater flow and boundary concentrations are the driving forces in the whole estuarine pH. pH tends to increase with a decrease in the freshwater flow (Hofmann et al.,

2008b).

The level of pH affects the toxicity of other compounds in the water. For example, as pH

increases, the toxicity of ammonia will increase too. An increase in ammonia level will create an

ammonia stress that directly affects the fish and caused harmful side effects such as increased

disease susceptibility and organ failure. Ammonia stress will also create a displacement of

oxygen by ammonia in the water that may indirectly cause damage on the gill structure of the fish. This condition will cause the fish to hover at the bottom of the water (Toole, 2010), appearing less in the water. %FO was negatively correlated with pH.

pH was the most robust parameter, showing good relationships over broad salinity regimes with average and minimum indices; and yet tighter correlations with low salinity. At low salinity, average pH ranged from 6.88 to 7.28 while at high salinity, the average pH was 7.49 or greater [Table 3.2]. These seconded by the positive correlation between pH and salinity (r =

0.645, P < 0.01). The pH was found to be varied significantly across months (p < 0.05) with an explicit fluctuating mean value in Feb-10 and Jul-10 [Table 3.2]. The result of the present study showed that the fluctuation of pH followed the same trend as salinity which in agreement with

Lakshman and Durga (2005).

pH may vary on a daily basis due to photosynthetic and metabolic processes, as well as with the tidal cycles. Although salinity changes and low dissolved oxygen are regarded as two of the most significant factors affecting the distribution and success of organisms, pH is another factor that may be more important than has been generally realized (Amy and Charles, 2002).

Due to high concentrations of bicarbonate, calcium and other ions in high-salinity seawater that provide substantial buffering capacity against pH shift, pH is often discarded as an issue for concern (Knezovich, 1994). However, even the actual magnitude of the fluctuation may seem small, it is important to remember that pH is based on a log scale, so even a change of 0.2–

0.5units can have enormous physiological ramifications (Amy and Charles, 2002).

The effect of water depth on CPUE was not analysed in details as the vertical distribution of the fish was unknown. The exact depth of the fish within the water column could not be

identify thus the results from a depth analysis would only be speculative. Furthermore, the

sampling area was not distributed among depths in a way that would be appropriate for examining this relationship. This could possibly explain the reason why there is no correlation between water depth and CPUE in this study. In this study, all parameters were correlated with each other at P < 0.01 (except for turbidity-conductivity, P < 0.05) [Table 3.3].

CHAPTER 4

REPRODUCTIVE BIOLOGY OF Plicofollis argyropleuron IN MERBOK ESTUARY,

KEDAH

4.1 INTRODUCTION

Fish have great nutritious value that serve as an important alternative to other protein sources. Therefore, in order to protect and better appreciate these available living resource, fish reproduction time and condition should be determined (Begum et al., 2010). The reproductive biology of fish and their population dynamics are of great importance for efficient fisheries management practices (Hoenig and Gruber, 1990). Fish can also act as a biological indicator to determine environmental stability (Grabarkiewicz and Davis, 2008). In relation to these, Froese and Binohlan (2000) developed a simple method to evaluate the link between fish size at maturity and the status of the population using length-frequency data analysis.

Fecundity, one of the most important biological aspects of fish, plays a significant role to evaluate the commercial potentialities of fish stock. Fecundity value must be known to effectively assessing the abundance and reproductive potential of a fish stock (Das et al., 1989).

It is of prime important to know the fecundity of a fish species for successful fish culture and effective management practises (Miah and Dewan, 1984).

Studies on the population biology of marine catfish in Malaysian waters are still scarce

and little is known about the reproductive biology of the Ariidae fish species. However, studies

on the reproductive biology and the stomach content of Arius maculatus from the Matang mangrove reserves in Perak have been conducted by Mazlan et al. (2008). A study on morphometric and meristic characteristics of five Arius spp. from the coastal area of Kedah was conducted by Mansor et al. (2012e) and a study on population dynamics of Osteogeneiosus militaris off the coastal waters of Penang has been done by Mansor et al. (2012). Beyond this, no similar studies have been conducted on the reproductive biology of Plicofollis argyropleuron in

Malaysia or elsewhere.

4.2 OBJECTIVES

1) To study the monthly pattern of gonadosomatic index (GSI), condition factor (K) of

Plicofollis argyropleuron and their relationship.

2) To determine the spawning season of the P. argyropleuron.

3) To observe the gonad stage development visually and confirm its maturity stages using

histological technique.

4) To estimate fecundity of the P. argyropleuron.

5) To estimate length at first maturity and their relationship with total length, body weight

and gonad weight.

4.3 MATERIALS AND METHODS

The study area has been described in details in Chapter 3.

4.3.1 Sampling technique

Monthly sampling was carried out from Jan-10 to Jan-11. At the sampling site (fish

landing site), P. argyropleuron were collected as the samples for that month from the fisherman

catches. All of the samples were preserved in an ice chest during transportation to the laboratory.

4.3.2 Laboratory works

In the laboratory, the number of fish was recorded. The total length, body length and

standard length were measured to the nearest millimetre (mm) by using measuring board, both

body and gonad were weight to the nearest gram (g) by using an electronic scale and maturity

status of the gonad was identified. The data was recorded and rounded at two decimal places.

Fish was measured on their left side (Frota et al., 2004) [Figure 4.1]. Total length was measured from anterior mouth to the tip of the longest caudal fin rays. Measure from anterior mouth part to the end of caudal peduncle was taken for a standard length [Figure 4.2].

Figure 4.1 Measuring steps and devices

Figure 4.2 A fish morphological features and measurement of total length and standard length

Figure 4.3 The location of fish gonad (red circle) in P. argyropleuron

After all measurements were recorded, the fish samples were dissected using the dissecting kits. The abdomen was split opened to take out the gonads and weighed out. The fish gonad was located below the intestine and near the backbone base of the fish [Figure 4.3]. The maturity status of the gonad was identified based on macroscopic observations. Each gonad was assigned as immature (stage I), maturing (stage II), matured (stage III), ripe (stage IV) or spent

(stage V) following Gomes and Araujo (2004). Gonad that will be used for further microscopic observations (histological analysis) was preserved in 10% formalin solution. According to

Humason (1972), the preservation bottles need to be shaken from time to time so that sample will not stick at the bottom of the bottle and allowing the preservation solution to cover the entire surface of the sample. On the other hand, matured gonads that were needed for the fecundity

study were preserved in Gilson’s solution in order to loosen the tissue surrounding the eggs.

Fecundity was estimated by counting the total number of mature eggs in both ovaries (Nikolsky

et al., 1973; Nikolsky, 1974). The relationship between oocyte number with total length, body

weight and gonad weight was estimated as recommended by Lampert et al. (2004) and Mesa et

al. (2007).

4.3.3 Histological analysis

Through histological analysis, five maturity stages were microscopicly identified

according to the vascular irrigation intensity, colour and percent volume of abdominal cavity

occupied by the gonads. Initially, gonads were fixed in 10% formalin for 24 hours. They were

then cut into a small pieces of tissue (longitudinal or cross-sections) and were fixed again in 10% formalin for another 24 hours. After the fixation, the gonads were dehydrated, cleared with the xylene and embedded in paraffin wax. The gonad was further trimmed and sectioned using the microtome before staining. The longitudinal or cross-sections of the gonad tissues were stained with haematoxylin-eosin. Oocytes were classified according to their morphology, affinity for the dyes used and the presence of specific inclusions (lipid droplets, yolk granules and yolk vesicles). Histological identification of the various maturity stages was determined based on the development of the germinate cells in the ovary and testis as well as by the presence or absence of different types of oocytes (i.e. whether organized by ovarian lamellae or not) and spermatocytes with descriptions according to Narahara (1991).

4.4. RESULTS

A total number of 488 individuals of Plicofollis argyropleuron were collected during the sampling period from Jan-10 until Jan-11. The total comprises of 177 males and 311 females of

P. argyropleuron. Female predominates male almost every month except in Jun-10 and Dec-10.

The monthly ratio of male:female showed higher number of females throughout the year with the highest male:female ratio at 1:10.3 [Table 4.1].

Table 4.1 The number and sex ratio of Plicofollis argyropleuron sampled from Merbok estuary, Kedah

Month Total (N) Male (N) Female (N) Sex ratio

Jan-10 49 14 35 1:2.5

Feb-10 70 21 49 1:2.3

Mar-10 66 16 50 1:3.1

Apr-10 31 14 17 1:1.2

May-10 39 13 26 1:2.0

Jun-10 28 15 13 1:0.9

Aug-10 34 3 31 1:10.3

Sep-10 44 22 22 1:1.0

Oct-10 39 15 24 1:1.6

Nov-10 31 15 16 1:1.1

Dec-10 30 18 12 1:0.7

Jan-11 27 11 16 1:1.5

The temporal changes in the GSI of P. argyropleuron males and females are shown in

Figure 4.4. Both sexes had two pronoun peak of GSI mean in this study. In male, they occurred

in Jan-10 (0.62±1.76) and Apr-10 (0.89±1.09) while in female, were in Mar-10 (1.81±2.65) and

Aug-10 (3.24±3.84). This indicated that the spawning season occurs during Jan-10 and Apr-10

for male while for female, Mar-10 and Aug-10, suggesting that this species exhibited asynchronous reproductive behaviour.

(a) Male (b) Female

Figure 4.4 Monthly gonadosomatic index (GSI mean ± standard deviation) of male and female of Plicofollis argyropleuron in Merbok estuary, Kedah

The condition factor, K, ranged from 1.029 (Nov-10) to 1.2154 (Feb-10) in males and

from 1.0269 (Dec-10) to 1.208 (May-10) in females. The K values for both males and females

showed a declining trend towards their spawning period, Jan-10 and Apr-10 (in males) and Mar-

10 and Aug-10 (in females) respectively. After the spawning peak, there was a fluctuation in their condition factors trend onwards [Figure 4.5].

Figure 4.5 Monthly condition factor in Plicofollis argyropleuron in Merbok estuary, Kedah

Figure 4.6 shows a correlation trend between GSI mean and condition factor. The trend was inversely proportional regardless in male or female. As GSI mean was high, condition factor became low. However, there was a direct proportional relationship between GSI mean and condition factor during the spawning season of Apr-10 (males) and Aug-10 (female) suggesting that they might affect or influence each other in certain way.

(a) Male (b) Female

Figure 4.6 Relationship between GSI mean and condition factor of Plicofollis argyropleuron in Merbok estuary, Kedah

The monthly percentage of gonad maturity stages in male and female were presented in

Figure 4.7. In male, the highest percentage recorded for stage I was in Aug-10, stage II in Jun-10, stage III in Apr-10, stage IV in Jan-10 and stage V in Jan-11. In female, the highest percentage recorded for stage I was in Mar-10, stage II in Apr-10, stage III in Jun-10, stage IV in Aug-10 and stage V in Jan-11. The occurrences of mature males and females fluctuated throughout the year.

(a) Male (b) Female

Figure 4.7 Monthly gonad maturity stages in percentage (%) of Plicofollis argyropleuron in Merbok estuary, Kedah

These stages were identified based on macroscopic and microscopic observations which were briefly described in Table 4.2. They were differentiated based on the gonad colour, size of gonad in body cavity and their physical appearances. Histology was used for confirmation.

Microscopic observations were clearly observed and shown in Plate 4.1.

Table 4.2 Gonad descriptions for macroscopic and microscopic observations

(a) Male

Maturity stages Macroscopic Microscopic Stage I: Immature The size is small. Whitish-cream Spermatogonia not organized in tubules (Plate 4.1a) in colour. Sac looks transparent

Stage II: Maturing The size occupies ¼ of the body Spermatogonias, spermatocytes and spermatids cavity. Whitish in colour. Eggs arranged in tubules (Plate 4.1b) present in the sac but in a smaller size

Stage III: Matured The size occupies ½ of the body Highest concentration of spermatids in seminiferous cavity. Whitish-milky in colour. tubules (Plate 4.1c) Blood vessel presents. Eggs become bigger in size and compact in the sac

Stage IV: Ripe The size occupies ¾ of the body Seminiferous tubules filled with spermatozoa (Plate 4.1d) cavity. Blood vessel becomes very clear

Stage V: Spent The size started to reduce due to Testis in regression. Cells appear fused. Higher frequency post-spawning mode. Sac flattered of primary and secondary spermatocytes (Plate 4.1e)

(b) Female

Maturity stages Macroscopic Microscopic

Stage I: Immature The size is small. Whitish-pinkish Oogonia in a serial of ovarian lamellae. Oocytes in in colour. Sac looks transparent nucleolar chromatin. Perinucleolar stages with higher frequency (Plate 4.2a)

Stage II: Maturing The size occupies ¼ of the body cavity. Ovarian lamellae and oocytes from nucleolar chromatin Yellowish colour started to be seen. turn into cortical alveoli oocytes. Zona radiate and yolk Eggs present in sac but in a smaller size vesicle presents (Plate 4.2b)

Stage III: Matured The size occupies ½ of the body cavity. Higher concentration of cortical alveoli oocytes. Presence Yellow in colour. Blood vessel presents. of oocytes of all anterior stages. Zone radiate and yolk Eggs become bigger in size and compact vesicle in bigger size (Plate 4.2c) in the sac

Stage IV: Ripe The size occupies ¾ of the body cavity. Presence of vitellogenic oocytes and higher concentration Blood vessel becomes very clear. Part of of ripe oocytes. Yolk granule presents. Oil drop can the sac busted with some eggs out of the be observed (Plate 4.2d) sac. Eggs tend to slip out if the sac gently present

Stage V: Spent The size started to reduce due to post- Post-ovulatory follicle in higher concentration. Atresic spawning mode. A number of eggs oocytes present remaining in sac. Sac becomes flattered

Plate 4.1 Testis of Plicofollis argyropleuron showing the immature, maturing, matured, ripe and spent

(a) Stage I: Immature (b) Stage II: Maturing

(e) Stage V: Spent

Plate 4.2 Ovary of Plicofollis argyropleuron showing the immature, maturing, matured and ripe

(a) Stage I: Immature (b) Stage II: Maturing

A total of 31 samples of Plicofollis argyropleuron at stage III and IV were examined.

The number of eggs ranged from 16 to 66. They were linearly regressed with the fish total length, body weight and gonad weight. The correlation between fecundity and total length was positively related at regression coefficient, R2 = 0.752, between fecundity and body

weight at R2 = 0.916 and between fecundity and gonad weight at R2 = 0.895 [Figure 4.8].

(a) (b)

N = 31 N = 31

(c)

N = 31

Figure 4.8 Relationship between fecundity with (a) total length, (b) body weight and (c) gonad weight of Plicofollis argyropleuron from Merbok estuary, Kedah

From the total of 488 fish examined, 24 were matured males and 110 were matured females. The length at first maturity (L50) was slightly larger in females (259.8mm) than in males (249.5mm) indicating that males reached maturity earlier than females for this species

[Figure 4.9].

(a) Male (b) Female

Figure 4.9 Length at first maturity of (a) male and (b) female Plicofollis argyropleuron from Merbok estuary, Kedah

4.5 DISCUSSION

Information on the reproductive strategy and fecundity were necessary to evaluate the reproductive potential of a species (Murua et al., 2003). The description of the phases of gonadal development was of great importance for understanding the dynamics of the gonad and assessing the reproductive mechanisms of a species. Gonadosomatic index has been used as an indicator of the spawning period in teleosts (Arruda et al., 1993; Santos et al., 2006) and the use in reproductive biology has been considered more appropriate when associated with other indicators of reproduction. Macroscopic examination of the gonads of fish revealed that during the maturation processes, the ovaries and testis undergo gradual macroscopic changes (De Martini and Lau, 1999). The present study on P. argyropleuron from the estuaries of northern part of Peninsular Malaysia was the first attempt to estimate the reproductive potential of P. argyropleuron including the size structure, establishment of gonadal stages, spawning periods, fecundity and length at first maturity.

The proportion of females was found to be greater compare to the males in this study.

This inequality may correspond to behavioural differences between the sexes (Vazzoler,

1996). Moreover, males practising parental care during the reproductive season (Chaves,

1994; Lima et al., 2013) have been proposed as a possible explanation for the lower number of males (Mazzoni and Caramachi, 1995).

GSI has been successfully used to determine the reproductive season (Kanabashira et al., 1980). In the present study, the spawning periods appeared to coincide with the transition periods between the rainy and dry seasons. In the rainy season, Merbok estuary became enriched with nutrients coming from tributaries. However, in the dry season, the estuary

system receives more seawater. In both scenarios, higher productivity in the estuary is

triggered by the abundance of food organisms for juvenile fish and resulting in higher fish

survival (Lowe-McConnell, 1987). Spawning season took place during the month of Jan-10

and Apr-10 (in males), as well as Mar-10 and Aug-10 (in females) [Figure 4.4]. According to

Chaves (1991), females are the better indicators of spawning period than males since males stay in the mature stage for a longer period of time. Due to this reason, the spawning season between male and female for this species was not synchronized. This finding is concurrent with Mansor et al. (2012b).

The condition factor is used to measure various ecological and biological factors such as the degree of fatness, gonad development and the suitability of the environment with regard to feeding conditions (Mac Gregoer, 1959). Anderson and Neumann (1983) reported that the condition factor was a relative indicator of fish health and the degree of sustainable environments where variations in the relative condition factor is linked to the sexual maturity and the degree of nutrient supply within the environment. Fish condition reflects the state of well-being of a fish or its population (Welcomme, 2001). Koops et al. (2004) define condition as a measure of the energy available for allocation to life-history decisions such as reproduction, growth or migration.

According to Chellapa et al. (1995), fish with high condition factor are relatively heavy for their length while fish with a low condition factor are too light for their length.

Koops et al. (2004) stated that whether an individual is heavy or light for its length is not about being fat or thin but about whether the individual has an excess or limitation of energy available to invest in the life-history decisions. At high K values, excess energy results in the accumulation of fat and oil reserves and little demand on protein for energy production. At 75

low K values, the metabolic energy demands have exhausted the lipid reserves and proceed

with the catabolism of proteins. Prior to spawning, the K value (especially if calculated to

include gonad weight) will generally increase as the gonads matured and enlarged (Htun-han,

1978). After spawning, the K rapidly declines with the shedding of gametes. It is assumed that food intake is sufficient to allow reserves to increase between spawning. If not, the fecundity, egg size or yolk content will be reduced in the subsequent spawning. This is also generally the period of most rapid somatic growth (Chellapa et al., 1995).

In this study, the K values of both sexes demonstrated a similar trend. The values decreased while approaching the spawning periods, increased during spawning (Jan-10, Apr-

10 in male and Mar-10, Aug-10 in female) and reduced after spawning [Figure 4.5]. These

trends are most likely coincided with the transition of rainy and dry periods that triggered

higher productivity in the area. They were also affected by the abundance of nutrients that

enriched the productivity and feeding intensity during the rainy season (Mar-10 to May-10

and Sep-10 to Nov-10) which enhances spawning strategy. However, during the dry season

(Jan-10 to Feb-10 and Jun-10 to Jul-10), the productivity of the estuarine system was

triggered by a high salinity influx from the coastal margin (Mansor et al., 2012a) which

promotes better conditions for the fish grow.

The condition of fish was believed to be influenced by the seasonal changes of gonads

and the feeding intensity (Papageorgiou, 1979). Changes in condition factor may be caused

by an active resource transfers during the gonads development cycle during the reproductive

periods (El-Agami, 1988; Hadi, 2008; Shallow and Salama, 2008; Sugilar et al., 2012).

Environmental conditions such as rainfall and productivity of the ecosystem has also been

reported to affect the condition factor (Abowei, 2010). In Figure 4.6, male and female 76

showed inverse relationship. As the GSI increase, the condition factor lowered. However,

during spawning season, both parameters increased simultaneously. In this study, maximum

rainfall received from Mac-10 to May-10 and Sept-10 to Nov-10. The spawning season for P. argyropleuron falls within these ranges explaining even when resource transfer to the gonads occurs during reproductive periods which supposes to cause condition factor to decline, the high availability of food resources overcome this energy-loss in fish thus maintain its condition factor. Roessig et al. (2004) pointed out that seasonal rainfall was the main factor affecting the strategies of the life cycle of fish such as their feeding.

As previously mentioned, GSI peaks were pronounced in Jan-10 and Apr-10 (males) and in Mar-10 and Aug-10 (females) in Figure 4.4. The percentage occurrence of gonads in different stages of maturity in different months was displayed in Figure 4.7. Males and females of stage IV were abundantly found in Jan-10 (67%) and Aug-10 (29%) respectively indicating the spawning period of P. argyropleuron from Jan-10 to Aug-10. This evidence was further supported by the occurrence of spent stage in fish (in Oct-10 to Jan-11) in large number right after the spawning period. The occurrences of stage V male (13%) and female

(33%) were mostly recorded in Jan-11. According to Chaves (1991), females are better indicators of spawning period since males stay in mature stage for a longer period of time.

Mature females of P. argyropleuron were found in high percentage (23%) in Aug-10 and simultaneously spawned within the same month. While in males, the mature stage was frequently reported in Apr-10 (29%) and maintained in this stage longer during this period since little number of ripe males observed after Apr-10. Short breeding season is expected for species with total spawning and the extended breeding period is expected for species with partial spawning (Wallace and Selman, 1981).

The macroscopic examination and histological analysis of gonad of P. argyropleuron revealed that during the maturation process the ovaries and testis underwent gradual macroscopic modifications. These microscopic characteristics define the stages. Gonadal maturation was classified in according to the macroscopic and microscopic observations of five phases: immature, maturing, matured, ripe and spent. Such phases were based on the scale of maturity defined by (Vazzoler, 1981; 1996) and were adapted from marine catfish

[Table 4.2] in agreement with the histological microscopic aspects of the testicles [Plate 4.1] and ovaries [Plate 4.2].

The testis and ovaries of the P. argyropleuron were located under the gas bladder and attached to the body cavity by mesenteric tissues. The testis were connected to the body cavity by mesorchia tissue and appeared like a cylindrical sac. Five gonadal stages were described based on gonad form, size, weight, colour and oocyte diameter. The same classification used by Mansor et al. (2012b).

The testis did not show accentuated differences in size and form, being prolonged and filiform. The spermatogenesis, on the other hand did not present the remarkable variations over the development process compared to the oogenesis, with gradual decrease from spermatogonia to spermatozoa [Table 4.2]. The increase of testis volume in the mature phase corresponds is contributed by the seminiferous tubules, where cells of different spermatogenic lineage, mainly spermatozoa are contained in the seminal fluid (Hoar and

Randall, 1969).

In Plate 4.1, the spermatogenesis process of P. argyropleuron male was observed.

During the immature stage (stage I), the spermatogonia was not well-organized in the tubules.

As they matured, spermatogonia underwent repeated mitotic division which forms primary

spermatocytes which then underwent reduction division (meiotic division) to form secondary

spermatocytes. The division of the secondary spermatocytes produces spermatids. All of

them are arranged in the testis tubules (stage II). In stage III, spermatids appeared in the

highest concentration which indicates the gonad has fully matured. The spermatids then

metamorphosed into motile and potentially functional gametes known as spermatozoa in

stage IV. Spermatozoa, the smallest germinative cells regardless the length of its tail, ready

for spawning (Gomes and Araujo, 2004). In the spent stage, testis is in regression and cells appear fused. The unfertilized spermatozoas remain in the testis and the spermatogonia starts to reappear signifying the initiation of the spermatogenesis cycle (stage V).

As ovaries developed, they show accentuated differences in size and form [Plate 4.2].

The mature stage of ovaries is well marked by their largest volume, corresponding to increasing size of cells of the germinative lineage. Variations in this form occur starting from the appearance of the filiform in the immature stage changed into lobular along the maturation process and resulting in wrinkled after spawning. Afterward, they became turgid and lobuled, characterizing the starting of the recovering stage. The origin of yolk-vesicle formation and the mechanisms of its formation were observed in this stage. As this type of cell developed, the yolk-vesicle shows a migration toward the cytoplasm to the zone radiate, breaks up during the fertilization for the liberation of its content between the zone radiate and the cytoplasm surface, avoiding the polysperm (Hoar and Randall, 1969).

Various stages of oocytes development have been classified into a number of phases.

The growth is comparatively slow with few cytoplasm changes. The vitellogenic phase characterized by faster growth and the depositional of large amounts of yolk in ooplasma.

The terminology proposed by West (1990) was used. The microscopic observation for female maturity stages for this species was presented in Plate 4.2. During their immature stage (stage

I), oogonia present in a serial of ovarian lamellae. Oocytes, similar to oogonia although somewhat larger appear in nucleolar chromatin. Early of stage I, oocytes are in chromatin nuclear stage. Late of stage I, perinucleolar stage occurs. The only difference between these two phases is the cytoplasm’s affinity towards the dyes used (Gomes and Araujo, 2004). The differences in size and form could only be recognize in stage II where size of gonads are much bigger and occupy almost one fourth of the abdominal cavity. In stage II, ovarian lamellae and oocytes from nucleolar chromatin turn into cortical alveoli oocytes. Zona radiate and yolk vesicle started to appear in this stage (Selman et al., 1988). Ovaries of stage II changed their colour from white to light yellow with translucent granules and the shape of eggs were irregular compared to ovaries of stage III where the colour turns from yellow to orange with regular shape of eggs granule and the wall of the ovary was surrounded by the blood vessel. During the mature stage (stage III), cortical alveoli oocytes were highly concentrated. Oocytes appear in all anterior stages. Zone radiate and yolk vesicle become bigger. As the ovary developed, the zona radiate became thicker. The yolk granule or yolk globule and oil drop present. Zona radiate was dyed with eosin. This stage is classified as stage IV and the fish ready to spawn during this ripe stage (Hunter et al., 1986). The final stage (spent stage) is concentrated with post-ovulatory follicle and the empty space caused by mature oocytes that were forced into the lumen of the ovary and extruded from the follicular layers. Presence of atresic oocytes can be observed in stage V.

Fecundity plays a significant role in evaluating the commercial potentialities of fish

stock. It must be known to assess the abundance and reproductive potential of a fish stock

(Das et al., 1989). It is of prime importance to know the fecundity of a fish species for

efficient fish culture and effective management practices (Miah and Dewan, 1984). Marked

differences in fecundity among species often reflect different reproductive strategies (Pitcher

and Hart, 1982; Wootton, 1984; Helfman et al., 1997; Murua and Saborido-Rey, 2003).

Within a given species, fecundity may vary as a result of different adaptations to habitat environment (Witthames et al., 1995). Even within a stock, fecundity is known to vary annually, undergoing long-term changes (Horwood et al., 1986; Rijnsdorp, 1991; Kjesbu et al., 1998) and shown to be proportional to fish size and condition. Larger fish produce more eggs, both in absolute and in relative terms to their body mass. For a given size, female is in better condition to exhibit higher fecundity (Kjesbu et al., 1991).

Fish size and condition are the key parameters to properly assess fecundity at the population level. In heavily exploited populations, large old fish will be eliminated more rapidly because they are exposed to size-selective fishing mortality (Trippel, 1999). In this situation, population fecundity not only declines as a consequence of the reduced abundance of spawners but also due to the disproportionate reduction in large, highly fecund females.

Values of condition indices vary among individuals and may vary annually within individuals. Changes in environmental factors such as temperature may affect the condition by influencing fish behaviour and metabolism as well as food availability. Declines in fecundity due to reduced condition can be reflected in a lower number of oocytes that develop in a given breeding season or through atresia. In extreme cases, low condition can induce reproductive failure and lead to the skipping of spawning seasons (Bell et al., 1992;

Livingston et al., 1997). Fecundity and atresia can also be affected by environmental 81

pollution (Johnson et al., 1998). Methods to estimate annual fecundity in relation to

reproductive strategy were described by Murua and Saborido-Rey (2003).

In this study, P. argyropleuron demonstrated positive correlation between fecundity and total length, fecundity and body weight as well as fecundity and gonad weight [Figure

4.8]. Fecundity was found to vary from 16 (in fish of 154mm total length, body weight of

109.7g and gonad weight of 0.18g) to 66 (in fish of 320mm total length, body weight of

441.42g and gonad weight of 27.88g). This result indicated that smaller fish had smaller number of eggs and bigger size fish contributed higher number of fecundity. The linear relationship observed in P. argyropleuron seems to represent a general pattern among Ariidae fish species in agreement to Coates (1988) and Pinheiro et al. (2006). In fact, Ariidae produce one of the largest eggs among teleosts (Wallace and Selman, 1981).

Although gonad development and subsequent spawning may depend on various environmental stimuli, individuals must reach a certain age or size before they are capable of spawning (King, 2001). Dependency of maturation to the age or length is regulated by the temperature regime of the water and strongly linked to the fish growth (Moyle and Cech,

2000; Welcomme, 2001). Length at first maturity (L50) is the length at which 50% of the fish

have reached maturity (Mahomoud et al., 2011).

In this study, males P. argyropleuron reached sexual maturity at slightly smaller size

(249.5mm) than female (259.8mm) [Figure 4.9]. Since matured individuals were found in smaller male, this suggested that males matured earlier than females. Males may mature at smaller size either because they grow slower or because they mature at younger age (Mansor et al., 2012b). These differences in length at first maturity may be attributed to the differences 82

in environmental conditions such as food supply, population density and changes in temperature and salinity (Bardacki and Tanyoloc, 1990; Unlo and Balci, 1993). Life history theory predicts that size at maturity is linked to adult lifespan where species that have short lifespan mature early (Wootton, 1990) suggesting that males of P. argyropleuron have a short lifespan since they mature earlier compare to females. The adult lifespan of P. argyropleuron was investigated and presented in the next working chapter.

CHAPTER 5

GROWTH, MORTALITY AND RECRUITMENT PATTERN OF Plicofollis

argyropleuron IN MERBOK ESTUARY, KEDAH

5.1 INTRODUCTION

An optimum fish growth depends on the suitability of an aquatic habitat which is

determined by the condition factor (K) value. A higher K value (K>1) indicates the habitat is

suitable for fish growth in term of food availability and other requirements (Samat et al.,

2008). The condition factor can also be affected by the gonadal maturation cycle (Froese,

2006). During the reproduction period, condition factor value tends to be low (K<1) as fish

loses some weights after the spawning period.

The length-weight relationship (LWR) is a key factor in investigation of biology and

management of fish species (Odat, 2003; Thomas et al., 2003; Frota et al., 2004;

Abdurahiman et al., 2004; Golam and Tawfeequa, 2006; Ayoade and Ikulala, 2007; Samat et al., 2008, Jamabo et al., 2009; Offem et al., 2009). This information is important for the

evaluation of general health parameters of fish species such as fatness, breeding, feeding

states and their suitability in an environment (Schneider et al., 2000; Gonzalez-Gandara et al., 2003; Farzana and Saira, 2008). LWR also provides clues to any environmental changes as well as sustainable management of fish stock (Samat et al., 2008; Efitre et al., 2009). Frota et al. (2004) reported that the b value from the growth equation (W=aLb) indicates the growth

rate of the species.

Fish physiology and growth can be affected directly or indirectly by the water quality.

High water pH causes alkalosis, skin damaging, browning of gills areas and increasing mucus

production. Excessive ammonia increases the toxicity of water causing ammonia poisoning to the fish which may cause symptoms such as red streaking on the body and paler gills. These types of diseases reduce or inhibit fish growth, affecting the value of growth coefficient, b

(Sachidanandamurthy and Yajurvedi, 2008).

Despite the study on fish distribution and reproductive biology, little is known about

the ecology and exploitation levels of fishes in the Merbok estuary. Population parameters

estimation including length-weight relationship, growth and mortality generated from the

length-frequency data are among of the best ways to estimate the well-being of fish species as

well as providing sustainable use of stock (Kolaneci et al., 2010). Thus, this chapter

attempted to provide baseline information for the growth, mortality and recruitment patterns

of Plicofollis argyropleuron for future research as well as making comparison between years

and locations.

5.2 OBJECTIVES

1) To identify the suitable environmental condition for the Plicofollis argyropleuron.

2) To study the length-weight relationship of the P. argyropleuron and their relationship

with the environment’s suitability.

3) To obtain the growth and population parameters of the species.

4) To obtain the recruitment pattern of this species.

5.3 MATERIALS AND METHODS

The study area has been described in details in Chapter 3 and the sampling technique and laboratory works were described in details in Chapter 4.

5.3.1 Data analysis

5.3.1.1 Condition factor (K)

The condition factor was used in order to compare the ‘condition’, ‘fatness’ or well- being of fish. It was based on the hypothesis that heavier fish of a particular length are in a better physiological condition (Bagenal, 1978). Condition factor is also a useful index for the monitoring of feeding intensity, age and growth rates in fish (Oni et al., 1983). Condition factor was calculated from the expression as below:

K = 100 W

Where W is the whole body weight in grams and L is the total length in centimetres.

(Fulton, 1904; Bagenal, 1978; Pauly, 1983)

5.3.1.2 Bhattacharya’s Plot

Bhattacharya’s plot is a method used to separate group or cohort from composite distribution when several groups (cohorts) of fish are available in the same sample (Gayanilo and Pauly, 1997). This method basically involves the removal of normal distributions, each representing a cohort of fish, from a mixture of distribution, starting on the left-hand side of the overall distribution. Due to the difficulty in determining the age of tropical fish by using

hard parts such as otoliths, the separation of length-frequency distribution into cohorts will

help to assign the age to each of those cohorts (Sparre and Venema, 1998).

5.3.1.3 Length-weight relationship (LWR)

The length-weight relationship was estimated using the equation,

W=aLb

Where a is the intercept and b is the slope (fish growth rate) (Cherif et al., 2008). A power

curve or any non-linear curve is more difficult to fit than the straight line considered in

equation W=aLb. In many cases, more complex curve can be transformed into a linear form

by the use of natural logarithms (symbolized as ln). The equation transformed into linear

form is:

ln W = ln a + b (ln L)

The equation is the same form as the linear equation Y = a + bX. The values of ln W

and ln L equal to Y and X respectively while ln a and b equal to the intercept and the slope

respectively. Thus, the data was treated as a linear regression by plotting ln W against ln L.

The degree of fitness between length and weight was determined by the correlation of

coefficient, r2 (Mansor et al., 2001).

Value of b is an index of growth type of fish (isometric or allometric) and used in fish classification (Smith, 1996). Isometric growth is when the fish has an equal increment of both length and weight parameters (b = 3), negative allometric growth or the light group (b < 3) and positive allometric growth or heavy group with b > 3 (Smith, 1996; Odat, 2003; Thomas

et al., 2003; Ayoade and Ikulala, 2007; Samat et al., 2008; Jamabo et al., 2009; Laghari et al.,

2009; Offem et al., 2009).

5.3.1.4 Length-frequency data

Analysis of length-frequency data was carried out using a FISAT II software

(Gayanilo et al., 2005) to estimate growth and mortality parameters.

a) Growth parameter estimation

I. ELEFAN I (incorporated in FISAT) was used to sequentially arrange and

restructure the monthly length-frequency data set (Uneke et al., 2010) to

obtain asymptotic length (L∞) and curvature parameter (K) (Kleiber and

Pauly, 1991). The growth in length was described by the von Bertalanffy

-K(t-to) Growth Function (vBGF) formula, Lt = L∞ x (1-e ) where Lt is the mean

total length (cm) of the fish at age t, L∞ is the mean asymptotic length (cm), K

-1 is a growth constant (year ), t is the age of the fish and to is the age of the fish

at zero length (Vakily and Cham, 2003). Rn is a goodness of fit index that

select the best combination of L∞ and K in length-frequency data set. The

highest value of Rn indicates the best combination of L∞ and K (Abowei et

al., 2009)

II. A growth performance index, φ’ was calculated as φ’ = log10K + 2log10L∞

where the parameters K and L∞ are the parameters from the vBGF

III. A potential longevity, tmax was calculated to estimate fish life-span with

formula of tmax= 3/K

b) Mortality parameter estimation

I. Length-converted catch curve was used to convert length-frequency data into

age-frequency data (Pauly et al., 1995). Total mortality (Z) was estimated

from the length-converted catch curve with assumption that the collected

length-frequency data represent a steady-state population (Vakily and Cham,

2003). Z was estimated by means of linear regression of the form ln (Ni / ti) =

a + bti where Ni is the number of fish in length class I, ti = ( 1 / K ln [(L∞-L1) /

(L∞-L2)] ) is time needed for the fish to grow through length class I, ti = (1/K)

ln [ 1- (Lt/L∞) ] is relative age corresponding to the class mid-point of length

class I and b with sign changed gives Z without seasonality

II. Natural mortality (M) was estimated using Pauly’s M equation; Log M = -

0.0066 – 0.279 log L∞ + 0.6543 log K + 0.4634 log T; where T is the mean

annual surface water temperature in the river

III. The estimation of fishing mortality (F) was calculated by subtracting M from

IV. The exploitation rate (E) was computed using Gulland’ expression (Gulland,

1971) as E = F/Z

V. Biological reference point was calculated using Patterson (1992) formula, Fopt

= 0.5M and Flimit = 2/3M

5.4 RESULTS

Table 5.1 Condition factor (K) of Plicofollis argyropleuron on monthly basis

Month Total (N) Male Female

Jan-10 49 1.15 1.12

Feb-10 70 1.22 1.18

Mar-10 66 1.15 1.15

Apr-10 31 1.19 1.13

May-10 39 1.17 1.21

Jun-10 28 1.06 1.12

Aug-10 34 1.15 1.14

Sep-10 44 1.05 1.12

Oct-10 39 1.05 1.05

Nov-10 31 1.03 1.16

Dec-10 30 1.06 1.03

Jan-11 27 1.11 1.18

The condition factor (K) of a fish reflects any physical and biological circumstances

and fluctuates by the interactions among feeding conditions, parasitic infections and

physiological factors. As mentioned above, the condition factor observed in the present study

ranges from 1.03 to 1.22 for P. argyropleuron implicating that the fish were in good condition (K > 1) which indicates the suitability of the habitat for the growth of this species.

(a) Male

(b) Female

Figure 5.1 Length-weight relationship of Plicofollis argyropleuron in a linear form (left side) and power form (right side)

The linear and non-linear regression analysis of length-weight relationship in P. argyropleuron male and female showed that the relationship between length and weight was positively correlated. The b value was 3.048 for male while 3.112 for female. These values were significantly higher than 3 indicating positive allometric growth of this species.

Table 5.2 Estimated growth parameters of Plicofollis argyropleuron in FiSAT II 1.2.2

Methods L∞ K R2 φ’

ELEFAN:

K-Scan 36.23 0.72 0.192 2.98

Response surface 35.00 1.01 0.145 3.09

Auto search 35.73 1.00 0.169 3.11

Ford-Walford 29.43 0.79 0.757 2.84

Mean 34.10 0.88 0.316 3.01

Table 5.3 Comparative growth parameters (L∞ and K) and indices of growth performance (φ’) of Plicofollis argyropleuron in Merbok estuary, Kedah with other locations

Species Location Country L∞ K φ’ Reference

Netuma barba Patos Lagoon Brazil 63.8cm 0.13 2.72 Reis

Estuary (1986)

Arius heudeloti Coastal water Guinea 70.0cm 0.14 2.84 Conand et al.

(1995)

Arius latiscutatus Coastal water Guinea 65.0cm 0.15 2.80 Conand et al.

(1995)

Arius parkii Coastal water Guinea 61.2cm 0.17 2.80 Conand et al.

(1995)

Geniden barbus Patos Lagoon Brazil 90.0cm 0.07 2.78 Velasco et al.

Estuary (2006)

Plicofollis argyropleuron Merbok Malaysia 34.1cm 0.88 3.01 Present study

Estuary (2013)

Figure 5.2 Probability of capture of each length class of Plicofollis argyropleuron (L25 =

19.44 cm, L50 = 20.49 cm, L75 = 20.87 cm, N = 488)

Figure 5.3 Length-converted catch curve for Plicofollis argyropleuron. Regression statistic: y-intercept, a = 4.73; slope, b = -1.18; r = 0.998; N = 488

Table 5.4 Estimated population parameters of Plicofollis argyropleuron

Growth parameters Estimates

to -0.18

Asymptotic growth, L∞ (cm) 34.10

Growth rate (K/year) 0.88

Growth performance index (φ’) 3.01

Length at first capture, Lc (cm) 20.49

Total mortality (Z/year) 1.18

Natural mortality (M/year) 1.74

Fishing mortality (F/year) -0.56

Exploitation rate (F/Z) -0.47

Longevity (Tmax) 3.41

The population estimation (Table 5.4) indicated that the value of to was -0.18, growth

performance index was 3.01, the asymptotic growth was 34.10 cm and the growth rate was

0.88/year. The annual instantaneous rate of total mortality (Z), natural mortality (M) and

fishing mortality (F) which were derived from the length-converted catch curve [Figure 5.3] were 1.18/year, 1.74/year and -0.56/year, respectively. The annual instantaneous fishing

mortality rate was lower than the biological reference points derived from Patterson (1992)

equation (Fopt = 0.87 and Flimit = 1.16), suggesting that P. arygropleuron was underexploited

in Merbok estuary. Exploitation rate (E) was calculated as -0.47 and the life span of P.

argyropleuron in its natural habitat (potential longevity) was computed at 3.41 years. E

corresponded to the changing levels of F/Z. The length at first capture was 20.49 cm. The

growth curves produced with the parameters in Table 5.4 were shown over its restructured

length distribution in Figure 5.4.

Figure 5.4 Growth curve of Plicofollis argyropleuron by ELEFAN I superimposed on the restructured length-frequency diagram (L∞ = 34.10cm, K = 0.88 year-1)

Figure 5.5 Recruitment pattern of Plicofollis argyropleuron (L∞ = 34.10 cm, K = 0.88 per year, to = -0.18)

5.5 DISCUSSION

A number of criteria used to assess the suitability of the length-frequency data for the

estimation of growth in Plicofollis argyropleuron. First, the modal groups should be

discernible from raw data (Wolff, 1989) with an obvious shift in the modal length over time

(Pauly, 1984). Second, Pauly (1984) proposed that the data collected over a period of six

months and above is suitable for the growth estimation study. However, Hoenig et al. (1987) and Gulland and Rosenberg (1992) specifically suggested that samples collected between 12 and 14 months are sufficient enough. The data used in this study met these criteria as individuals were collected during a period of 12 months (one-year study).

Growth, recruitment and mortality are the primary phases of population dynamics

(often termed rate functions) influencing the harvestable segment of a fish population (Brown and Guy, 2007). Assessing the population dynamics of fish is best achieved by collecting long-term data using standardized methods as the biotic and abiotic influences on population dynamics typically vary from year to year. Unfortunately, such data sets are rare as the data collection is costly. Instead of long-term data, other methods have been developed to estimate

the population dynamics (Kevin et al., 2010). Population parameters estimation generated from length-frequency data is one of the best ways to estimate the well-being of fish species

(Kolaneci et al., 2010).

Growth is part of fish life cycle. As the fish grows up, it undergoes different growth stanzas and length-weight relationship. Length-weight relationship has been applied as a basic in fish stock assessment (Ricker, 1968). Length-weight relationship is a key factor in the investigation of the biology and management of fish species (Odat, 2003; Thomas et al.,

2003; Frota et al., 2004; Abdurahiman et al., 2004; Golam and Tawfeequa, 2006; Ayoade and Ikulala, 2007; Samat et al., 2008; Jamabo et al., 2009; Offem et al., 2009). Generally in fish, size factor is more biologically relevant compared to age as some of the ecological and physiological factors are more size-dependent than age-dependent. Consequently, length- weight regressions were frequently used to estimate the weight from length because direct weight measurements can be time-consuming in the field (Sinovcic et al., 2004).

Length-weight relationship determines the growth rate of fish with b value used as the index (Carlander, 1969; Royce, 1972; Lagler et al., 1977). In general, b value of fish is closer to 3, despite the many variations of fish forms (Carlander, 1969; Cinco, 1982; King 1996).

According to Jones (1976), the length-weight relationship may change seasonally thus the length-weight parameters presented should be considered as average values. This was seconded by Biswas (1993) who reported that the b value may vary seasonally in response to seasonal variations in environmental condition and changes in the fish’s well-being. He also pointed out that the b value could be an indicator of the physiological condition of the fish.

The length-weight relationship and the b value can be influenced by fishing pressure that excessively crops the adult. Under unstressed condition, the length-weight relationship of this species may differ from that recorded in this study. The b value can also be used to compare the condition of fish between temporal and spatial level (Froese, 2006). The lotic and lentic environment or polluted and non-polluted environment would also determine the condition of the fish. According to Mansor et al. (2010), fish tend to be heavier in the lotic than in the lentic environment.

High values of regression coefficient (R2) from the result indicated the correlation

between length and weight of fish was strong (Ahemad, 2004; Ahemad and Irman, 2005). As

reported by Ayoade and Ikulala (2007) and Jamabo et al. (2009), the strong correlation of

these two parameters in which the increase in weight was directly proportionate to an

increase in length for a normal fish and vice versa. It is a common thing for males and

females in a population to mature at different schedules or to have different growth rates

(Brown et al., 2006; Coelho and Erzini, 2006). Most fish scored b value from 2 to 4 (Hadil,

2004; Pervin and Mortuza, 2008; Samat et al., 2008; Jamabo et al., 2009). The value of b

greater than 3 indicates that fish become plump as they increase in length while the b value

lower than 3 shows that the fish gets slimmer with increasing length (Jobling, 2002). The b values, 3.048 (male) and 3.112 (female) in this study were in the normal range for the growth of most fishes [Figure 5.1] indicating P. argyropleuron experienced a positive allometric growth (b>3). This type of growth suggests that the species lives in a healthy environmental

(condition factor, K > 1).

A positive allometric growth suggests the space area and food supply in the habitat were sufficient throughout the year (Farzana and Saira, 2008; Samat et al., 2008). The b value is also related to the ecological and biological factors, food supply, spawning conditions and other factors such as sex and age of fish (Shukor et al., 2008; Offem et al.,

2009). Generally, suitable range of water pH and others physical parameters provided suitable habitats for all fish species that is optimum for fish growths. As mentioned by

Sachidanandamurthy and Yajurvedi (2008), deterioration in water quality either directly or indirectly affects fish physiology and growth. Such stress reduces or inhibits fish growth thus affecting the value of growth coefficients (b).

The estimated growth parameters of P. argyropleuron were presented in Table 5.2.

L∞ was 34.10cm, K was 0.88 and φ’ was 3.01. L∞ represented the maximum length of an infinitely old fish of the given stock. K is called the ‘curvature parameter’ which determines how fast the growth relative to L∞ (how fast the fish reaches its maximum size). According to Sparre and Venema (1992), K = 1.0 indicating fast growth, K = 0.5, medium growth and K

= 0.2, slow growth.

Comparison of growth parameters (L∞ and K) and indices of growth performance

(φ’) of P. argyropleuron in Merbok estuary, Kedah with other locations (different species with same family) were shown in Table 5.3. Pauly and Munro (1984) suggested the use of φ’ values for growth parameters (L∞ and K) to compare different methods and authors with the assumption that even if the species was taken from a different area, it should have similar value of φ’ when the estimation of growth parameters is reliable and accurate (Bellido et al.,

2000). The growth between species was often compared by directly comparing the L∞ and K

(Cailliet and Goldman, 2004). Species within the same family are expected to have similar φ’ values (Moreau et al., 1986). According to Sparre and Venema (1992), a high value of L∞ combines with a low value of K and vice versa. This statement was in agreement with the comparisons shown in Table 5.3. The difference between recorded L∞ and K was probably influenced by environmental conditions (Abowei, 2010) such as ecological characteristics thus affecting the population size and adaptation patterns of their life cycle (Hashemi et al.,

2010). Gertjan and Mark (2005) reported that a higher value of L∞ increases the variance of the environmental variables.

The growth parameter, K is related to fish metabolic rate. Pelagic species are often

more active than demersal species resulted in a higher K value. According to Sparre and

Venema (1992), metabolic rate is also a function of temperature which means tropical fishes

have higher K value than their cold-water counterparts. This statement was in line with current study. Up to date, there was no record of the population parameters for P. argyropleuron in Malaysia. The high value of growth performance index (φ’), 3.01 indicated that this species had a good growth in Merbok estuary. Most tropical marine fishes exhibit fast growth (Sparre and Venema, 1998; Afraei Bandpei et al., 2010).

The population parameters were summarized in Table 5.4. Life history characteristics can be used to classify the vulnerability of a species to fishing pressure and the level of productivity within a population (Schaefer, 1996; Musick, 1999). Natural mortality (M) is attributable to natural processes such as old age, predation, competition, starvation and disease or those altered by human activities (e.g. habitat degradation or loss or population isolation) while fishing mortality (F) is attributable to harvest or handling by recreational or commercial fishers (Colvin, 1991; Reed and Davies, 1991; Radomski, 2003). Natural mortality (M) and fishing mortality (F) contribute to the total mortality (Z). In addition, growth and mortality are antagonistic. According to Barry and Tegner (1989), the predominance of growth on mortality can be perceived by the ratio Z/K being less than 1; a ratio greater than 1 means that the stock is collapsing; if the ratio is equal to 1, the population is in a steady state; if this proportion is much higher than 2, the stock is over-exploited. In this present study, the Z/K ratio was 1.34 which was greater than 1, suggesting that the stock of this species was collapsing.

100

The estimated annual instantaneous rate of mortality for P. argyropleuron was relatively low [Table 5.4]. The rates were 1.18 (total mortality, Z), 1.74 (natural mortality, M) and -0.56 (fishing mortality, F). The natural mortality (M) was higher than the fishing mortality (F) in this fish suggesting that fishing activities has lesser influence on the fish populations. The decreasing population was probably due to predation, disease and death for old ages (Sparre and Venema, 1998; Abowei, 2010).

Population assessments occasionally rely on a length-at-age index as a proxy for

growth rates of fish (Purchase et al., 2005). Age at zero length (to) was calculated at -0.18

[Table 5.4]. With negative to values, juveniles grow more quickly than the predicted growth

curve for adults; with positive to values, juveniles grow more slowly (King, 1997).

Aforementioned in Chapter 3, there was high food availability in the estuary that will ensure

an accelerated growth rate of the juveniles. As they were recruited into adult stock, the food

became limited in supply causing competition for food intensified resulting in the slowing

down of growth.

Growth is strongly dependent on food availability thus creates the variation in

individual growth in fish population. Numerous studies reported that the energetic of fish

have quantified relationship between food availability (related to density) and individual

growth (Webb, 1978; Brett, 1995; Jobling, 2002). The effects of food for the growth of

individual fish have also been known by fish culturists for centuries. Ivley (1961) constructed

models to describe the relationship between the amount of food consumed and food

concentration. Since that, a lot of empirical parameterized models have been developed to

describe individual consumption (linked to growth) as a function of food ration (Webb, 1978;

Elliot, 1994; Jobling, 2002). 101

Age structures were often used as an estimation of fish population dynamics (Everhart and Youngs, 1981; Isely and Grabowski, 2007). In order to gain an accurate estimation of age structure, biologists must obtain a random sample of the population. It is important to use the standard techniques in fish aging (Beamish and McFarlane, 1983). The aging techniques have been validated quite for long times (DeVries and Frie, 1996). An estimate of population dynamics will be correct if the fish were aged correctly (Marzolf, 1955), allowing wise management and resource allocation decisions (Isely and Grabowski, 2007). Determining the age of fish takes considerably more effort than measuring and weighing fish but usually warranted during population assessments (Kevin et al., 2010).

The amount of fishing mortality attributed to fishermen who harvest what they catch is called exploitation (Malvestuto, 1996). Generally, fish is optimally exploited when the rate of fishing mortality is equal to the rate of natural mortality (F = M or E = 0.5) according to

Gulland, 1971 and Hashemi et al., 2010. Estimation of absolute abundance and a harvest estimate based on creel surveys can be used as to calculate exploitation (Malvestuto, 1996).

As in Table 5.4, the estimated exploitation rate was -0.47 (E<0.5) suggesting P. argyropleuron was under-exploited which further lend credence to the fact that the growth of this species is maintained all the ways uninterrupted.

Fish growth rates in a population are intricately linked to the recruitment and mortality rates. Growth rate influences survival and age at sexual maturity. Various coefficients of the von Bertalanffy Growth Function are commonly indexed to growth of fish which was widely used to describe the lifetime pattern of somatic growth of organisms such as fish with indeterminate growth (Ricker, 1975). In addition, specific growth rate (the change of the logarithm of weight or length per unit time), relative growth rate (the relative 102

change of the weight or length per unit time) and length at age either measured at time of

capture or back-calculated from hard part of the species are also used to index the growth

(Gompertz, 1825; Richards, 1959).

Many populations of fish exhibit variable recruitment therefore the mean age of fish

and the number of year-classes in a sample obtained from a population may act as useful

indices. For example, a population of long-lived species with only younger age-groups present in random samples could be experiencing high exploitation or environmental stress.

Similarly, another population that had several missing age-groups in a random sample could be facing poor or failed recruitment in the other way round (Guy and Willis, 1995).

Pauly et al. (1988) used von Bertalanffy Growth Function (vBGF) curve to compare the overall growth performance. Some researchers used vBGF as a simulation model for the rearing of certain fish species (Springborn et al., 1992; De Graaf, 2004). vBGF plot is a more robust method as it gives a reasonable estimate of L∞ and K (Sparre and Venema, 1992). The restructured vBGF curves indicated that major spawning for P. argyropleuron was in March

[Figure 5.4].

Recruitment is defined as the process in which young fish enter the exploited area and become liable with the fishing gear (Beverton and Holt, 1957; Gulland, 1969). Knowledge on the recruitment patterns is a requisite in modern fisheries management (Gideon et al., 2011).

Annual recruitment is typically the most variable factor affecting the dynamics of fish populations yet can provide substantial insight into the reason why the fish population vary in size and structure (Gulland, 1982; Allen and Pine, 2000; Maceina and Pereira, 2007).

Variability in recruitment of fish into the harvestable population can be estimated with the 103

recruitment variability index (Guy and Wilis, 1995) or with the coefficient of determination

2 (r ) resulted from simple linear regression of loge (catch at age) on age (Isermann et al.,

2002). In Figure 5.5, P. argyropleuron showed a single annual peak recruitment per year.

104

CHAPTER 6

CONCLUSION AND RECOMMENDATION

6.1 Conclusion

Estuaries, frequently referred as fish nursery areas (Franco-Gordo et al., 2003;

Berasategui et al., 2004) sustain a number of resident and non-resident species of commercially important fish species, crustaceans and seagrass communities (Sasekumar,

1992; Elliot and Dewailly, 1995; Laegdsgaard and Johnson, 1995; Vasconcelos et al., 2010) that are mainly represented by larvae and juveniles (Duffy-Anderson et al., 2003; Castro et al., 2005). The estuaries are known to be impacted by several biotic and abiotic factors

(Weinstein and Heck, 1979). The influences of numerous intrinsic and extrinsic factors to the species were reflected in the structure of fish assemblages (Gorman and Karr, 1978;

Matthews, 1998). For many years, intrinsic factors such as competition and predation have been the focus of studies seeking to explain the structure of fish assemblages (Schlosser,

1995). Somehow, the emphasis on the role of extrinsic factors had increased as numerous studies demonstrated the non-equilibrium nature of most communities (Grossman et al.,

1990; Sale et al., 1994) or the importance of contextual environmental factors (Schlosser,

1987, 1991). The importances of extrinsic factors in influencing local assemblage structure

have been varied geographically (Nash, 1988; Drake, 1990) as a function of predictability or

the harshness of the environment (Peckarsky, 1983; Ward and Stanford, 1983; Poff and

Allen, 1995) and with the community’s composition itself (Detenbeck et al., 1992; Tokeshi,

1993; Power et al., 1996; Sale, 1996). An understanding of the role of extrinsic factors in structuring specific assemblages is critical for the conservation and maintenance of diverse natural assemblages (Marsh-Matthews and Matthews, 2000). 105

However, little attentions and efforts have been made to address estuarine protection

despite the obvious ecological and economic importances that estuaries have to offer (Barbier

et al., 2011). The challenges for estuarine protection are presumed to be greater where

activities such as port and shipping, tourism and recreation, industrial development, artisanal

fisheries and increasing coastal population are more prominent (Hanna, 1999; Gregory and

Wellman, 2001; Schneider et al., 2003; Lubell, 2004; Safford et al., 2009). The terrestrial and

aquatic resource governing agencies also frequently overlap in their jurisdictions at the land-

sea interface which confounded effective coastal protection planning (Schneider et al., 2003;

Safford et al., 2009).

Researches on fish assemblages in the estuaries have shown that salinity plays a major

role in shaping the fish assemblage structure (Theil et al., 1995; Maes et al., 1998; Marshall and Elliott, 1998; Wagner and Austin, 1999; Whitfield, 1999; Camargo and Isaac, 2003;

Martino and Able, 2003; Berasategui et al., 2004; Barletta et al., 2005; Castro et al., 2005;

Selleslagh and Amara, 2008). It was reported that salinity is the best predictor of fish richness

while temperature indicates fish abundance (Thiel et al., 1995; Leonardo et al., 2010). Even

though estuarine fishes are able to cope with salinity fluctuation, their ability to do so varies

since the influence of salinity on fish is often due to the tolerance and preference of species

for this variable (Elliott et al., 1990; Blaber, 2000). The distribution of fish species within

estuarine grounds is resulted from the dynamic responses of the individuals to multiple

environmental variables such as water temperature, salinity, food availability or sediment

type (Stoner et al., 2001; Selleslagh et al., 2009) as can be observed in Merbok estuary. In the

presence of environmental stress such as low dissolved oxygen, high temperature, high

content of ammonia (Boyd, 1981), organisms’ ability to maintain its internal environment

(metabolism, catabolism and reproduction) is reduced (Ezra and Nwankwo, 2001). In view of 106

this, monitoring of water quality which focused on determination of optimal, sub-lethal and

lethal values of physical parameters such as water temperature, water depth, pH,

conductivity, salinity and turbidity should be embraced (Gerking, 1949; Sheldon, 1968; Boyd and Lichtkoppler, 1985; Schlosser, 1987; Capone and Kushlan, 1991; Taylor et al., 1993).

Several of these physical parameters have been studied on indigenous habitats (Boyd, 1981;

APHA, 1991; King, 1998; Ezra and Nwankwo, 2001).

Other environmental parameters such as temperature, water depth and turbidity also play important roles in determining fish assemblages (Blaber and Blaber, 1980; Blaber,

2002). Usually, tropical estuaries are high in turbidity (Blaber, 2000; Leonardo et al., 2010), an important characteristic of rearing grounds for juvenile fishes (Robertson and Blaber,

1992) since predators visual is less effective in low light levels (Blaber and Blaber, 1980;

Seehausen and van Alphen, 1998). Turbidity may also affect the mate choice through the cost of mate sampling and assessment. If the costs are high, females can become less discriminating (Kodric-Brown, 1990). For example, in the presence of predators, females are less choosy (Forsgren, 1992; Berglund, 1993) and also when they have to expend more energy on mate sampling (Milinski and Bakker, 1992). A lot of biological, chemical and physical factors cause stress that lead to the change in their behaviour. It is possible that due to low visibility, mechanical irritation or other reasons, the abundance of planktonic algae induce stress in females causing them to be less selective (Jarvenpaa and Lindstrom, 2004).

Temperature is seldom a structuring factor in tropical areas as it remains relatively stable throughout the year whereas oxygen may restrict the distribution and movement of fishes

(Araujo et al., 1998; 2002). Low dissolved oxygen values contributed to the impoverished fish fauna in the estuary (Blaber et al., 1984). Assemblage structure is also depends on water

107

depth (Araujo et al., 2002) which correlated to the substrate types (Home and Campana,

1989).

There are three hypotheses that are commonly related to the fluctuated population that

create differences in species abundance: (i) species interactions (e.g. predator-prey

interactions or disease) (Rosenzweig and MacArthur, 1963; Kareiva, 1987; Hudson et al.,

1992) (ii) nonlinearity in single-species dynamics (May, 1974; Hassell et al., 1976; Turchin

and Taylor, 1992) and (iii) variations in the environment that affect vital rates such as

survival and growth (Moran, 1953; Fieberg and Ellner, 2001). Seasonal changes in the catch

rates of tropical communities were ascribed mainly to the reproductive patterns and the

increasing recruitment (Darracott, 1977; Wright, 1988). Most tropical fish are able to survive

and reproduce in harsh and fluctuating environments which make them tolerant to a wide

range of conditions (Matthews, 1988; Brown and Matthews, 1995; Matthews, 1998). Small

seasonal changes in environmental parameters minimally influence the fish assemblage and

the seasonal changes in fish assemblage were limited to changes in the occurrence of species

(Sanvivente-Anorve et al., 2000; Leonardo et al., 2010). Such shifts could be linked to the processes in the fish life history associated with reproductive seasons, growth and recruitment patterns (Robert et al., 2007; Mendoza-Carranza and Vieira, 2008; Sanchez-Gil et al., 2008).

The lack of seasonal changes in fish assemblages can be attributed to dominant resident species that have long recruitment season with batch spawning and able to tolerate a broad range of environmental conditions (Leonardo et al., 2010). Gerking et al. (1979) suggested that fish prefers warmer temperatures as an optimum temperature particularly for reproduction.

108

Fish breeding systems are affected by various environmental factors (Parissh et al.,

1983; Bakun, 1996). Resource distribution has been shown to be especially important determinant of mating systems (Emlen and Oring, 1977; Lindstrom and Seppa, 1996).

Human-induced changes on the environment can have strong effects on the fish reproduction, hence their breeding systems (Guillette et al., 1994). As a result of human activity, many aquatic environments are becoming increasingly eutrophic, leading to increase growth of planktonic algae. As a consequence, both turbidity and organic material silting increases. In addition, the breakdown of organic material consumes oxygen in the water column (Larsson et al., 1985). Increased silting, low oxygen concentrations and altered light conditions have been shown to affect the reproductive behaviour of fishes (Potts et al., 1988; Seehausen et al.,

1997; Jones and Reynolds, 1999a; Cobcroft et al., 2001; Utne-Palm, 2002; Hale et al., 2003;

Lissaker et al., 2003).

Descriptions of reproductive strategies and the assessment of fecundity are fundamental topics in the study of biology and population dynamics of fish species (Hunter et al., 1992). Studies on the reproduction including the assessment of size at maturity, fecundity, duration of reproductive season, daily spawning behaviour and spawning fraction, permit quantification of the reproductive capacity of an individual fish. This information in combination with estimation of eggs production enables the assessment of spawning stock biomass (Saville, 1964; Parker, 1980; Lasker, 1985). The increasing knowledge on the state of a stock improves standard assessments of many commercially valuable fish species.

Moreover, the establishment of extensive data bases on the reproductive parameters corresponding on abiotic factors enables the study of casual relationships between reproductive potentials and environmental variations. This leads to a better understanding of

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observed fluctuations in reproductive output and enhances our ability to estimate recruitment

(Kraus et al., 2002).

Fish growth depends on water quality in order to boost up its production (Ugwumba

and Ugwumba, 1993). Adequate knowledge on hydrological conditions of any body of water permits a better understanding of the population and life cycle of fish community (Adebisi,

1981; Ayodele and Ajani, 1999). Dhawan and Kaur (2002) observed that feeding and fertilization work together to induce an increase in fish production. A complex way of feeding (food chain) resulted from the close association of various organisms that live, grow and multiply in water determines the optimum quality of fish produced (Boyd and

Lichtkoppler, 1985).

In this study, a number of physical parameters recorded were favourable for fish growth and fell within the standard ranges that have been previously documented. Water temperature of 27.4oC to 32.35oC recorded agreed with the ranges recorded by Hassan (1974)

and Ugwumba and Ugwumba (1993). According to Lin (1951), temperature ranges in

between 27oC-32oC allow tropical fish to eat more and grow faster. Temperature controls

growth rates in marine species influencing the duration of larval stage and the survival rate.

Subtle interdecadal changes in temperature resulted in growth variations that might produce

important fluctuation in fish survival (Takasuka et al., 2007). Mean turbidity values of 2.5cm

to 120cm shows that Merbok estuary water was fairly turbid. Turbidity is inversely

proportional to the abundance of most plankton hence an increase in plankton will reduce the

turbidity of water (Larsson et al., 1985; Dhawan and Kaur, 2002). Consequently, an increase

in plankton will provide ample of food for the fish. The average pH values of 6.35–8.15

110

recorded in this study were within pH values of 6.0 - 9.0 documented by Swingle (1961) and

Boyd (1985), most suitable for maximum productivity in terms of fish growth.

Consistent food availability for the fish species throughout the study period maintains

the fish diversity and fishery resources in Merbok estuary. The relationship between fish growth and water parameters showed that no single parameter can be singled out. These parameters must be kept at optimal levels to guarantee optimum fish growth and survival rate. An optimal physical variable values encourages good feeding, food utilization and high stocking density of fish eggs, larvae and adults (Alabaster, 1982).

Conclusively, all the physical parameters studied and documented gave an average

requirement for all the parameters needed for fish reproduction, growth and survival. The

rainfall is the most influencing environmental factor governing fish community structure in

estuarine systems as it influences the changes in water temperature, water depth, turbidity,

pH, salinity and conductivity. These factors have significant effects on the temporal fluctuation of fish abundance. The smaller catches of fish species, decreasing size of commercially important fishes, decreasing diversity and abundance of fish species coupled with the uncertain fluctuation levels of the water body parameters are indicators of the healthiness level of the estuary and cannot be ignored (Gamito and Cabral, 2003).

Since similar studies on Plicofollis argyropleuron have not been conducted

elsewhere, it was not possible to compare the results obtained in this study. Histological

analysis of the gonads supports the observations of morphological analysis and interpretation

of spawning seasons. The spawning season of P. argyropleuron occurs in Jan-10 and Apr-10

(males) and Mar-10 and Aug-10 (females) which coincides with the transition between the 111

rainy and dry seasons. A higher level of nutrients is flushed into the estuary system during this transition causing high abundance of food that triggers the fish to spawn. Males mature at a smaller size compared with females which indicated that males mature earlier than females for this species.

The conditions of the estuary in this study played an important role in determining the growth type for the fish and their adaptations towards the environment they live in. The population parameters estimation for Plicofollis argyropleuron was recorded for the first time and can be used as a base for comparisons with other population studies in tropical environments to bridge the information gap about tropical fish.

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6.2 Recommendation

In an estuary ecosystem, the priority goes to managing the estuary habitats before the

fish species as there is paucity information on the biology of the individual species. By

having a lot of information of recognizable habitats that support distinct fish communities,

managing the estuary is an indirect way of maintaining the communities. The presence of

mangrove in the estuary is crucial for the fish growth as it provides more available niches of

the estuary. Reducing in-point and non-point resource pollutions are also essential in the conservation strategies for estuary.

Existing survey data such as fish identification, population and distribution need to be verified and updated while the database across regions should be compiled for analysis of new impacts and management needs. Extensive ecological fish studies such as life cycle, age and growth in Malaysia would give more information on the biology of fishes in Malaysia.

There are several recommendations that might be useful in order to achieve this:

1) Fluctuations in the size of fish stocks are not only caused by fisheries. Changes in

environmental conditions also affect the stocks. Water temperature and other

environmental parameters fluctuate over time which caused fish conditions change

more or less regularly. Therefore, it would be necessary for related authorities to

perform an inspection regarding the environmental conditions from time to time to

monitor the health of fish stock. The authorities may use the collective data that was

obtained from any related researches.

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2) Human’s demand on fish stock is exceeding the available stock resulting in stock

declension. Knowledge on spawning season of the species would be necessary since

during the spawning season, a lot of eggs will be released out. To allow these

potential eggs survive to become adult fish regulations will have to be implemented

by banning fishing activity during spawning season.

3) While fishing, fishermen tend to get various sizes of adult or juvenile fish. Juveniles

should be released back into the habitat. This will allow of the juvenile to grow up to

become adult fish and maintaining the availability of fish stock. Information on length

at first maturity will be useful to implement the size regulation for fishing which

restrict the fishermen from catching immature fish. This indirectly involves the use

appropriate mesh size of their fishing nets.

4) Information on length-weight relationship and condition factor would be valuable

since they provide us with the b value to indicate the fish growth rate and K value that

describes the environment healthiness and suitability for the fish. These values can be

applied as an indicator values in preservation and conservation strategies.

5) A variety of parameters, indices and models are available for assessing and

monitoring fish populations. Each provides some insight about fish population and the

decision making in management strategies. Assessing and monitoring fish populations

may be costly and time consuming but should not be neglected. Rather, these costs

represent the reality that science-based fisheries management requires a significant

investment of resources. It is necessary for the authorities to gather the best

information possible and base management decisions on sound science. The wise use

114

of appropriate indices will be an important component of fish population assessments in turn become an important component for fisheries management.

115

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