Impacts of water quality on the harvest of school prawn (Metapenaeus macleayi) in a peri-urban river system.
Article Type: Report
Subject: Shrimps (Research)
Water quality (Research)
Authors: Pinto, U.
Maheshwari, B.L.
Pub Date: 08/01/2012
Publication: Name: Journal of Shellfish Research Publisher: National Shellfisheries Association, Inc. Audience: Academic Format: Magazine/Journal Subject: Biological sciences; Zoology and wildlife conservation Copyright: COPYRIGHT 2012 National Shellfisheries Association, Inc. ISSN: 0730-8000
Issue: Date: August, 2012 Source Volume: 31 Source Issue: 3
Topic: Event Code: 310 Science & research
Product: Product Code: 0913080 Shrimp NAICS Code: 114112 Shellfish Fishing SIC Code: 0913 Shellfish
Geographic: Geographic Scope: Australia Geographic Code: 8AUST Australia
Accession Number: 303011411
Full Text: ABSTRACT The school prawn (Metapenaeus macleayi) is important among commercial prawn trawler operators, but its harvest is affected in a complex way by a number of interacting water quality, and other variables. In this study, using the Hawkesbury-Nepean River system as a case study, we use Pearson correlation and hierarchical agglomerative cluster analysis (HACA) to assess the influence of the selected water quality (n = 7), quantity (n = 1), and weather (n = 2) parameters on the prawn harvest. Using data records (n = 104) collected over a 9-y period, we found water temperature (r = 0.63, P < 0.01), dissolved oxygen (r = -0.59, P < 0.01), and rainfall (r = 0.26, P < 0.01) to be significantly correlated variables with prawn harvest. The HACA produced 3 distinct clusters of variables nutrient availability for prawns (the total nitrogen, the total phosphorus, reactive silicate, turbidity, and suspended solids), the physical river environment (temperature, rainfall, and river flow), and the biochemical river environment (dissolved oxygen and chlorophyll a). The study revealed that 2 key variables, viz., temperature and rainfall, representing the physical river environment are statistically significant in affecting prawn harvest in the study area. Therefore, from fishing industry point of view, the future river management need to focus on strategies that will improve the physical river environment, particularly to cope with the impacts of future peri-urban development and climate change scenarios.

KEY WORDS: prawn, Metapenaeus macleayi, correlation analysis, temperature, chlorophyll a, Hawkesbury River

INTRODUCTION

The fishing industry based on prawns and shrimp is important in many tropical and subtropical regions of the world. In Australia, school prawn (Metapenaeus macleayi, Haswell) occurs along the east coast of Australia. It is one of the key species that inhabits estuarine environments from Tin Can Bay, Queensland, to Corner Inlet, Victoria (Ruello 1973b, Glaister 1978a). Estuarine prawn trawling is restricted to 3 regions in New South Wales (NSW): the Clarence, the Hunter, and the Hawkesbury Rivers (Broadhurst & Kennelly 1994, Montgomery et al. 2012). Of the numerous other aquatic species caught in the Hawkesbury River, M. macleayi has been the most popular type of seafood among Sydney fish consumers, and their catch area falls mostly within the lower estuarine reaches of the Hawkesbury-Nepean River (HNR) system (Hawkesbury Trawl Association 2001, Howard & Howard 2005).

Many prawn species migrate seasonally from shallow coastal waters into the deeper freshwaters to complete their life cycle. The spawning of M. macleayi occurs mainly in the estuaries, the postlarvae then move upstream where salinity is less than 20%, and mature individuals return to the ocean for breeding (Ruello 1973b, Rowling et al. 2008-2009). Based on a number of studies, Ruello (1973b) suggests that the main-food source of M. macleayi includes chitinous remains of crustaceans, annelid worms, algae, and diatoms. Opportunistic school prawns bury themselves slightly under the sediments and use chelipeds to transfer food particles into their mouthparts (Ruello 1973a). Adults are markedly more present in turbid coastal waters than in clean waters (Ruello 1973a). In the Hawkesbury River, prawn trawling is allowed in reaches downstream of Lower Portland, near the confluence of Colo River (Fig. 1A). The prawn catches consist predominantly of M. macleayi (40%) and Eastern King prawn (Penaeus plebejus; 2%) (Howard & Howard 2005).

During the past decade, the peri-urban zones of the Hawkesbury-Nepean catchment have been affected significantly by land use changes, largely related to housing development, to accommodate the increasing population. The current Metropolitan Plan for Sydney proposes 770,000 homes in addition to the existing 1.68 million households to accommodate a forecast of 6 million extra residents in Sydney by 2036 (NSW Department of Planning 2010). Commercial fishers in the region are concerned that future urbanization will result in changes to the natural pattern of river flow, presence of excessive algae growth in upstream reaches, and increased discharge of treated effluent, and will have negative consequences on their fish catch (Hawkesbury Trawl Association 2001). Although the effects of rainfall (Ruello 1973b), ecological interactions with the sediment bed (Ruello 1973a), migration habits (Ruello 1977), effects of river discharge (Glaister 1978b), and mortality patterns (Montgomery et al. 2012) of school prawns have been well studied in some detail, there has been less attention given to understanding the influence of water quality on prawn harvest.

The main objective of this study was to examine how the variables, water quality and weather, influence the harvest of school prawns (M. macleayi; hereafter simply referred to as prawns) in a peri-urban river system by using Pearson correlation and hierarchical agglomerative cluster analysis (HACA). A reach along the HNR system is used as a field site for this study. Considering the overwhelming land use changes resulting from urbanization in the Hawkesbury-Nepean catchment during the past 3 decades, this study will not only increase our current understanding of prawn harvest, but will also help in ranking river management decisions to cope with future impacts of urbanization and to balance the interests of different river users.

[FIGURE 1 OMITTED]

MATERIALS AND METHODS

Study Area

The HNR (33[degrees]34' 14.72"S, 151[degrees]20' 16.36"E to 34[degrees]11' 31.59"S, 150[degrees]43'11.57"E) is the major source of potable water in Western Sydney, and provides 80% of potable water for the region, including the Sydney Metropolitan area (Fig. 1A, B). The HNR is a combination of 2 major rivers, the Nepean River (155 km) and the Hawkesbury River (145 km), which join at the Grose River confluence near the rural town of Yarramundi, NSW (Markich & Brown 1998). From the head waters at the Woronora Plateau southwest of Sydney through to the Pacific Ocean at Broken Bay 30 km north of Sydney, the fiver system passes through extensive peri-urban landscapes over its 300 km length (Howell & Benson 2000). There are numerous point and diffuse sources of anthropogenic pollution that originate from peri-urban, agricultural, and industrial activities. Point-source pollution is attributed to sewage treatment plants (STPs), mining activities, and discharged industrial effluent, whereas diffuse sources of pollution are related to urban runoff and agricultural activities associated with farms and market gardens. Unlike other natural rivers, in which flow is dominated by rainfall events, the flow regime of the HNR is highly regulated by impoundments and treated effluent discharge from STPs. There are 22 dams, 15 weirs, and 18 STPs situated along the HNR system. The major dam on this river is at Warragamba, which holds about 2.031 x [10.sup.9] [m.sup.3] of water captured from a 9,051-[km.sub.2] catchment area (Turner & Erskine 2005). The lower Hawkesbury River downstream from Lower Portland is the most popular among the commercial and amateur anglers and prawn trawler operators in the region.

Data Collection

For the purpose of this study, we obtained prawn catch, water quality, quantity, and weather data of the study area from 3 government agencies: the NSW Department of Primary Industries, the Sydney Catchment Authority, and the Australian of Bureau of Meteorology. Prawn harvest (measured in kilogram) was based on the information provided by registered anglers and prawn trawler operators to the NSW Department of Primary Industries. The date and location where the prawns were caught include the month of the year and the river reach between Lower Portland and the river mouth. Prawn harvest weight is the sum of all reported gross landings (measured in kilograms) in a month using different fishing gear for harvest. The water quality data, chlorophyll a (measured in micrograms per liter), temperature (measured in degrees Centigrade) dissolved oxygen (DO-measured in milligrams per liter), total nitrogen (measured in milligrams per liter), total phosphorus (measured in milligrams per liter), turbidity (NTU), silicate reactive (measured as milligrams per liter) and suspended solids (measured in milligrams per liter) values were obtained from a routine government water-quality monitoring program conducted by the Sydney Catchment Authority. Collection and analysis of these data was done in accordance with the American Public Health Association's Standard Methods for the Examination of Water and Wastewater (Eaton and Franson 2005).

We included the total monthly flows (measured in megaliters) recorded at Penrith weir in this study because these flows have a major influence on the downstream macrophyte community, fish life, and water quality (Healthy Rivers Commission of New South Wales 1998). Monthly rainfall data (measured in millimeters) at Richmond was selected because of the continuity in data collection. This information was obtained from the Australian Bureau of Metrology (Bureau of Meteorology 2011).

Statistical Analysis

The raw data set (n = 104) includes monthly water-quality, quantity, and rainfall data collected between 2000 and 2009. Pearson correlation coefficients among the variables were calculated using raw data records whereas z-scale transformed data was used for the HACA and line graphs. We used the squared Euclidean distance as a distance measure, and Ward's method as a linkage method in HACA. The use of the squared Euclidean distance measure has been widely adopted for multivariate surface water quality classifications because it is capable of placing a greater weight progressively on variables that are further apart (Alberto et al. 2001, Shrestha & Kazama 2007). The actual distances among variables were further rescaled and presented as a number between 0 and 25 in the dendrogram.

RESULTS

The descriptive statistics of the data set used in this study are shown in Table 1. The water temperature varied from 11-29[degree]C, depending on the season, and indicated the highest significant correlation with the prawn harvest (r = 0.63, P < 0.01; Table 2). DO also indicated a significantly negative correlation with prawn harvests between 2000 and 2009 (r = -0.59, P < 0.01). The correlation between rainfall and prawn harvest was low, although significant (r = 0.26, P < 0.01). All other variables indicated a considerably low correlation with prawn harvest (Table 1). The maximum rainfall in the study area was 239 mm in March 2002.

The dendrogram indicates the variables considered in the study and can be divided into 3 distinguishable clusters of variables based on their characteristics at a rescaled distance unit of 15 (Fig. 2). The first cluster combines variables related to nutrient availability for prawns: total nitrogen, total phosphorus, reactive silicate, turbidity, and suspended solids. The second cluster relates to the physical fiver environment for prawns and includes 3 variables: temperature, rainfall, and river flow. The final cluster relates to the biochemical river environment as indicated indirectly by chlorophyll a and DO levels of the river water. It should be noted that the variable prawn harvest is part of the second cluster (i.e., the physical river environment), and it suggests that the temporal variation of prawn harvest over the years tends to follow the trends observed for temperature, rainfall, and flow. Prawn harvest is associated with trends in water temperature (r = 0.63, P < 0.01), DO (r = -0.59, P < 0.01), and rainfall (r = 0.26, P < 0.01).

The fluctuation of significantly correlated variables--water temperature and rainfall with prawn harvest in standardized units is presented in Figure 3A and B. The prawn season of the HNR is between September and June, and often an increased harvest is recorded around December. Thus, the consistent low prawn harvest readings coincide with the small number of prawn trawler operators reporting the catch data between June and August. During the prawn season, the catch weights follow seasonal temperature fluctuations closely (Fig. 3A). DO is always high in colder months (i.e., July to August; Fig. 3C), which is the result of the effects of water temperature and increased oxygen levels produced by algae. Rainfall indicated at least I distinct peak during each prawn season. High rainfall events are rare during colder months when prawn trawling does not commonly occur.

DISCUSSION

Water Temperature and Prawn Harvest

Commercially important prawn species inhabit, for a major proportion of their life cycle in estuarine regions of river systems, which are often prone to temperature and salinity fluctuations that occur as a result of tidal and freshwater mixing (Aziz & Greenwood 1981). In this study, we observed temperature as an important variable determining the catch of prawns in the HNR.

A significant correlation observed in the current study agrees with previously published temperature effects on Penaeus vannamei (Pacific white shrimp) and Metapenaeus bennettae (Aziz & Greenwood 1981, Wyban et al. 1995). Based on outdoor ponds in Hawaii, water temperature was observed to correlate significantly with the growth of P. vannamei, suggesting that shrimp weighing less than 10 g are more likely to grow faster in waters as warm as 30[degrees]C (Wyban et al. 1995). Leung and Hochman (1990) incorporated this relationship into an economic model for aquaculture farmers. Based on laboratory experiments, Aziz and Greenwood (1981) reported the survivability of juvenile M. bennettae in a wide temperature range of 8.1-32.9[degrees]C, and demonstrated how their lower and upper lethal temperature levels could be altered by exposing them to varying acclimation temperatures. The current climatic predictions for the Western Sydney region implies an increase in atmospheric temperature (17-29[degrees]C) by 1.6[degrees]C by 2030, and 4.8[degrees]C by 2070 (CSIRO 2007). If the water temperature increases in the future as predicted, the current study indicates that the climate change may impact prawn harvest while prawns adapt to new temperature regimes and cope with changes in DO levels and other factors. As such, the future change in temperature regime will be an important management consideration for many prawn trawler operators to sustain their livelihood.

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Because of the high correlation between prawn harvest and water temperature, as shown in this study, it may be possible to develop a rapid assessment tool to predict prawn harvest using water temperature and some easy-to-measure water-quality variables. The availability of such a tool could help prawn trawler operators in the region to plan and manage their business more objectively. Currently, some prawn trawler operators use the clarity of water to obtain some indication of their prawn harvest (M. Howard, 2011, pers. comm.), but this is often too subjective.

DO and Prawn Harvest

DO is the next important variable after water temperature, and it plays a key role in the life of prawns. However, the value of DO is affected by temperature, shade, nutrient richness, turbulence, decomposition of large volumes of algal blooms, and organic matter (Davis 1975, Kramer 1987). We observed a significantly negative correlation (r = -0.59, P < 0.01) between prawn harvest and DO levels. Furthermore, DO is affected by water temperature and chlorophyll a-bearing phytoplankton, as evident from cluster 3 (Fig. 2). The prawn harvest season is usually in the warmer months, when the fiver system is naturally low in DO levels, and DO could be further reduced when phytoplankton biomasses are present in abundance either in live or dead form. This seasonal variability makes the interaction of the variables affecting prawn harvest along with DO quite complex.

Pinto and Maheshwari (2011) reported anaerobic fermentation occurring in the lower reach of the HNR system that led to the depletion of available oxygen for fish life, mostly a result of the peri-urban development occurring in the region. The lowest value recorded for DO in the current data set is 4 mg/L, which is well above the lethal level reported for most penaeid prawn species (0.2-1.0 mg/L) (Allan & Maguire 1991). This lowering of DO indicates that there will be a considerable reduction in prawn catches in downstream reaches if the phytoplankton blooms prevail in the upstream reaches and organic matter continues to accumulate in the lower reaches of the river system.

Prawn Harvest and Other Variables

We observed a low but statistically significant positive correlation between prawn harvest and rainfall (r = 0.26, P < 0.01). The prawn season of the HNR is usually the wet season of the year for this region, thus, an increase in river flow resulting from rainfall events is likely to be beneficial in terms of prawn harvest. Although not significant, the HACA suggested that the patterns of variation of river flow and rainfall tend to be similar to prawn catches (Fig. 2). This observation is consistent with some published literature relating to hydrological regimes and their influence on the growth of prawns and other aquatic living beings (Hildebrand & Gunter 1953, Subrahmanyam 1964, Glaister 1978b, Gillson 2011). Heavy rainfall events often carry large amounts of plant detritus, sediment, and organic food scraps that benefit immensely the survival and growth of juvenile M. macleayi. Hildebrand and Gunter (1953) observed how the fluctuations in Penaeus setiferus is related to the salinity levels influenced by rainfall, whereas Subrahmanyam (1964) showed how the larval recruitment of Penaeus monodon is stimulated by rainfall in the Godavari estuary, although the reason for the observed pattern was not stipulated.

In Australia, significant emphasis has also been given to studies relating to the impacts of fiver flow on downstream fisheries productivity in coastal systems. A study conducted in the Logan River system in Queensland indicated how the increased seasonal pattern of river flow discharges influences positively the production of commercial and recreational coastal fisheries (Loneragan 1999, Gillson 2011). In particular, Glaister (1978b), reported a direct relationship between river discharge and the adult M. macleayi caught in the Clarence River, NSW. The current study suggests the catch of M. macleayi is affected by upstream flows regardless of their origin (i.e., rainfall or flow releases) in the Hawkesbury River. Thus, the findings of the present study are important for prawn trawler operators because the river system is often subject to variable flow regimes by river management authorities resulting from heavy rainfall events (Ball & Keane 2006, Krogh et al. 2008). It is also evident from the current study that natural rainfall events are more beneficial, yielding an increased harvest compared with human-controlled flow events.

The nutrient dynamics in the diet of M. macleayi in the Hawkesbury River have not been reported previously. We noted a considerably low correlation between the total phosphorus and prawn harvest (r = 0.06). The low value of the correlation may be a result of the feeding habits and nutrient requirements of prawns and shrimp, and is somewhat different from the findings of previous studies (Kitabayashi et al. 1971, Ambasankar et al. 2011). However, further research is warranted to verify this fact and to assess the degree of beneficial effects of total phosphorus for the phytoplankton and fish communities.

Implications for the Prawn Industry

The study revealed that 2 key variables--temperature and rainfall (representing the physical river environment)--are statistically significant to affect prawn harvest in the study area. It is now increasingly believed that rapid urbanization is causing warming of urban areas along with global warming (Kataoka et al. 2009). Urbanization results in substantial changes to land surfaces, especially increases in areas with concrete, roads, and tiled roofs. These changes disturb the local environment through changes in heat absorption, storage, and radiation, leading to an increase in local temperature. This warming is referred to as the urban heat island effect. Therefore, from the fishing industry point of view, future river management needs to focus on strategies that will improve the physical river environment, particularly the aspects that affect temperature and rainfall through future peri-urban development and climate change.

The increased water demand for drinking and industrial use, and production of large volumes of nutrient-rich surface run off and domestic effluent from peri-urban landscapes are also important for river management, especially for the protection of aquatic life. The current study highlighted that prawn harvest is impacted in a complex manner by a combination of interrelated variables and factors (including the possibility of climate change) that need careful attention and in-depth understanding to manage the river system in the long run to sustain the fishing industry.

It is important to note that the results of this study could have been improved by incorporating the confounded flow effects, tidal inflows at downstream reaches, lunar periodicity, and interspecies competition that may possibly influence prawn harvest data. However, determination of the river flow at lower reaches of the HNR is an extremely difficult task because the river system receives variable inflows (considerably affected by STPs) from tributaries and tidal inflows. Although, in practice, it is difficult to conduct large fisheries surveys, these data (even at monthly intervals) provided sufficient evidence to understand relationships between prawn harvest and water-quality and weather variables that can be used to design appropriate river management strategies.

CONCLUDING REMARKS

In this study, we analyzed the effects of water-quality, quantity, and weather variables on the harvest of M. macleayi in a peri-urban river system. The analysis indicated that water temperature, DO, and rainfall were variables that correlated significantly with prawn harvest. Furthermore, our analysis indicated that the variables considered can be grouped into 3 distinct clusters: nutrient availability for prawns (total nitrogen, total phosphorus, reactive silicate, turbidity, and suspended solids), the physical river environment (temperature, rainfall, and river flow), and the biochemical river environment (DO and chlorophyll a). Water temperature and amount of rainfall received by upstream reaches (representing the physical river environment) are the key variables which significantly affected the downstream prawn harvest. Therefore, from the fishing industry point of view, future river management needs to focus on strategies that improve the physical river environment, particularly the aspects of future peri-urban development and climate change.

ACKNOWLEDGMENT

We acknowledge the NSW Department of Primary Industries, the Sydney Catchment Authority, and the Bureau of Meteorology, Australia, for providing long-term data sets for use in this study.

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U. PINTO AND B. L. MAHESHWARI *

School of Science and Health, Hawkesbury Campus, University of Western Sydney, Locked Bag 1797, Penrith, NSW 2751, Australia

* Corresponding author. E-mail: b.maheshwari@uws.edu.au

DOI: 10.2983/035.031.0332
TABLE 1.
Descriptive statistics of the data set.

                              Minimum         Maximum

Prawn, school (kg)            91           23,172.2
Flows at Penrith             880.9        150,065.1
  Weir (ML)
Rainfall, Richmond             0.4            255.6
  (mm)
Chlorophyll a ([micro]g/L)     1.3             67
Temperature ([degrees]C)      11.4             29
Nitrogen total                 0.1              1.56
  (mg/L)
Phosphorus total               0.01             0.087
  (mg/L)
Turbidity (NTU)                3               51.1
Dissolved oxygen               4.1             12.6
  (mg/L)
Suspended solids               3               35
  (mg/L)
Silicate reactive              0.08             6
  (Si[O.sub.2]; mg/L)

                                Mean            SD

Prawn, school (kg)           6,981.85      5,796.10
Flows at Penrith             9,579.62     20,932.70
  Weir (ML)
Rainfall, Richmond              60.21         55.80
  (mm)
Chlorophyll a ([micro]g/L)      20.16         11.52
 Temperature ([degrees]C)        20.41          5.06
Nitrogen total                   0.52          0.23
  (mg/L)
Phosphorus total                 0.03          0.02
  (mg/L)
Turbidity (NTU)                 10.63          7.23
Dissolved oxygen                 8.74          1.71
  (mg/L)
Suspended solids                 9.92          5.94
  (mg/L)
Silicate reactive                1.57          1.46
  (Si[O.sub.2]; mg/L)

TABLE 2.
Pearson correlations between prawn harvest, flow, rainfall,
chlorophyll a (Chi a), temperature (Temp), total nitrogen (TN),
total phosphorus JP), turbidity (Turb), dissolved oxygen (DO),
suspended solids (SS), and reactive silicates (SIL).

            Prawns      Flow       Rainfall     Chl a

Prawns      1.00
Flow        0.03        1.00
Rainfall    0.26# **    0.63 **     1.00
Chl a       0.11       -0.12        0.13        1.00
Temp        0.63# **    0.08        0.35 **     0.34 **
TN         -0.12        0.15        0.10       -0.07
TP          0.06        0.22 *      0.21 *     -0.03
Turb        0.14        0.41 **     0.25 *      0.02
DO         -0.59# **   -0.24 *     -0.39 **     0.11
SS          0.07       -0.03        0.05        0.38 **
SIL         0.08        0.29 **     0.19       -0.44 **

             Temp         TN          TP         Turb

Prawns
Flow
Rainfall
Chl a
Temp        1.00
TN         -0.16        1.00
TP          0.15        0.61 **     1.00
Turb        0.19        0.49 **     0.65 **     1.00
DO         -0.77 **    -0.07       -0.33 **    -0.30 **
SS          0.17        0.20 *      0.48 **     0.51 **
SIL        -0.12        0.53 **     0.48 **     0.43 **

              DO          SS          Sil

Prawns
Flow
Rainfall
Chl a
Temp
TN
TP
Turb
DO          1.00
SS         -0.08       1.00
SIL        -0.28 **    0.02        1.00

* Correlation is significant at the 0.05 level (2-tailed).

** Correlation is significant at the 0.01 level (2-tailed).

The numbers in bold indicate the highly correlated variables with
prawns.

Note: The highly correlated variables with prawns
are indicated with #.
Gale Copyright: Copyright 2012 Gale, Cengage Learning. All rights reserved.