pease 1999

A spatially oriented analysis of estuaries and their associated

commercial sheries in New South Wales, Australia

B.C. Pease*

Fisheries Research Institute, NSW Fisheries, PO Box 21, Cronulla, NSW 2230, Australia

Received 30 December 1997; received in revised form 12 March 1999; accepted 2 April 1999

Abstract

Management restrictions, in the form of input controls, on the complex commercial wild-capture sheries within 53 estuaries

in New South Wales (NSW) are currently applied to individual estuaries. The objective of this study was to determine whether

the estuarine commercial sheries of NSW can be grouped into larger, logically related spatial management units than

individual estuaries, based on the similarity of shared environmental and sheries attributes. Although there have been many

attempts to delineate marine biogeographic regions around Australia, there has been no attempt to relate commercial sheries

to bioregions or use them directly in sheries management schemes. In this study, multivariate techniques were used to

analyse spatial relationships among 53 estuaries, based on 8 environmental, 22 commercial shing method and 81 taxonomic

attributes. Principal components analysis of the environmental attributes indicated the presence of three latitudinal estuarine

regions (north, central and south), corresponding closely to the coastal inshore regions previously delineated by studies based

primarily on oceanographic attributes. After stratifying estuaries by water area, multivariate analysis of the sheries attributes

veried the presence of these same three latitudinal regions. Water area and latitude were the primary physical attributes of the

estuaries which were correlated with the delineation of these three regions based on sheries attributes. The management

implications of the results are discussed. Because the regions are delimited by attributes of the commercial sheries, they

provide a useful framework for future research on and management of estuarine sheries in NSW. The method of applying

multivariate analysis simultaneously to attributes of the physical environment and commercial sheries, as described in this

paper, may be useful for identifying regions in other multi-species sheries with complex shing area and effort components.

# 1999 Elsevier Science B.V. All rights reserved.

Keywords: Multivariate analysis; Classication; Regionalisation; Estuarine sheries; Australian estuaries

1. Introduction

The commercial, wild-capture, estuarine sheries

of New South Wales (NSW), Australia are not only

productive, they are also very diverse and complex. In

recent years recorded landings of these estuarine sh-

eries have comprised up to 127 taxa, caught with over

20 shing methods. Estuarine sheries contribute

approximately 20% of the total commercial, wild-

capture, sheries harvest (30 00034 000 tonnes) from

NSW waters (Pease and Scribner, 1993, 1994; Scrib-

ner and Kathuria, 1996). Commercial catches have

been taken from over 90 estuaries in NSW since 1940

Fisheries Research 42 (1999) 6786

*Tel.: +61-2-9527-8411; fax: +61-2-9527-8576; e-mail:

[email protected]

0165-7836/99/$ see front matter # 1999 Elsevier Science B.V. All rights reserved.PII: S 0 1 6 5 - 7 8 3 6 ( 9 9 ) 0 0 0 3 5 - 1

Fig. 1. Map showing the 53 study estuaries on the coast of New South Wales, Australia. Dashed lines show proposed regional boundaries.

68 B.C. Pease / Fisheries Research 42 (1999) 6786

(Pease and Grinberg, 1995) and over 50% of the total

commercial harvest (including aquaculture) from

NSW waters is comprised of species that are estuarine

dependent (Pollard, 1976).

Management restrictions on these estuarine sh-

eries consist of a complex set of input controls which

are currently applied to individual estuaries. These

restrictions vary considerably among estuaries along

the 1900 km NSW coastline (Fig. 1). The objective of

this study was to determine whether the commercial

estuarine sheries of NSW can be grouped into larger,

logically related, spatial management units than indi-

vidual estuaries, based on the similarity of attributes of

the physical environment and commercial sheries.

There have been many attempts to delineate marine

biogeographic regions around Australia (Hedley,

1926; Whitley, 1932; Bennett and Pope, 1953; Knox,

1963; Wilson and Allen, 1987). These early bioregio-

nalisations were based on subjective evaluations of

environmental characteristics and the distribution pat-

terns of selected taxonomic groups. They are useful

for taxonomic studies, but the spatial scales are too

large for use with localised sheries. More recent

bioregionalisation studies by Hayden et al. (1984),

the CSIRO Divisions of Fisheries and Oceanography

(1997), the Australian and New Zealand Environment

and Conservation Council (1998), and Pollard et al.

(1998) have been aimed at implementing a system of

representative marine protected areas. Multivariate

techniques have been used in some of these recent

studies to quantitatively summarise the presence/

absence distribution patterns of marine and estuarine

species in a number of taxonomic groups simulta-

neously. The scale of these recent studies in Australia

is more useful than earlier work but the patterns

may be dominated by marine shelf-dwelling species

that are not generally found in estuaries. None of

the bioregionalisation work in Australia has been

based on attributes of commercial sheries or subse-

quently used for dening or managing commercial

sheries.

Bioregions have been dened for estuarine shes on

the west coast of the United States (Horn and Allen,

1976; Monaco et al., 1992) using multivariate analysis

of presence/absence species distributions. These stu-

dies focused on species distribution patterns and

regression analysis of the relationships between spe-

cies diversity and environmental parameters. How-

ever, the analyses were not related to the commercial

sheries in these estuaries.

Stergiou, 1988, 1989 and Stergiou and Pollard

(1994) recently used multivariate techniques in a

spatial analysis of the commercial sheries catches

from Greek waters and discussed the management

implications of the resulting regionalisation. Because

of the complexity of the estuarine sheries in NSW, I

have expanded on the multivariate techniques of

Stergiou and Pollard (1994) to include simultaneous

multivariate analyses of attributes of the physical

environment, commercial shing methods and com-

mercial catch by taxon.

2. Methods

2.1. Study areas

There are over 900 water bodies along the coast of

NSW which are permanently or periodically open to

the Pacic Ocean (Williams et al., 1998). Most of

these water bodies are small, ephemeral creeks and

drains. In this study an estuary is dened as: ` a

partially enclosed coastal body of water which is

either permanently or periodically open to the sea

and within which there is a measurable variation of

salinity due to the mixture of sea water with fresh

water derived from land drainage'' (Day, 1981). For

the purposes of this study an estuary is considered to

have a single opening to the ocean and includes all

arms, branches, basins and lagoons which are con-

nected to this water body.

During the ve-year period from 1991 through 1995

commercial sh and shellsh catches were consis-

tently reported (i.e. catches greater than zero reported

each year) from the 53 estuaries shown in Fig. 1.

These 53 estuaries were used as the primary spatial

units for this study.

2.2. Physical/environmental attributes

Eight physical/environmental attributes (Table 1)

were used for the initial spatial classication of the

53 estuaries. These attributes were chosen because

they are readily quantiable attributes of the physical

environment that are known to be linked with estuar-

ine species distribution and diversity (Horn and Allen,

B.C. Pease / Fisheries Research 42 (1999) 6786 69

1976; Pease et al., 1981; Bell and Pollard, 1989;

Monaco et al., 1992; Pollard, 1994; Gilmore, 1995).

Geomorphology is the only attribute that has not been

quantied in previous studies. I have assigned ordinal

numbers to the three estuary types dened by Roy

(1984). A value of one was assigned to ` saline coastal

lagoons'' which have entrances that are closed under

most conditions. A value of two was assigned to

` barrier estuaries'' which have narrow, restricted

entrance channels in which tides are attenuated. A

value of three was assigned to ` drowned river valley

estuaries'' which have deep, marine dominated

entrance regions with full tidal exchange.

A matrix comprised of the natural log-transformed

physical/environmental value for each attribute for

each estuary was constructed. From this matrix, a

triangular matrix of similarities between all pairs of

estuaries was calculated using normalised Euclidean

distance. This similarity matrix was then subjected to

cluster analysis by group-average linking to construct

a hierarchical agglomerative dendrogram for delineat-

ing spatial groups. The matrix of transformed physi-

cal/environmental attributes was then ordinated using

principal components analysis (PCA) to view the

spatial relationships. Log transformation was selected

for attributes in each type of multivariate analysis after

plotting the distribution of each attribute and obser-

ving the effects of the most common transformation

algorithms (Clarke and Warwick, 1994). All multi-

variate analyses were carried out on a personal com-

puter using PRIMER version 3.1b and the

implementation of appropriate clustering and ordina-

tion techniques was based on the recommendations of

Clarke and Warwick (1994).

2.3. Commercial fisheries attributes

The commercial sheries catch and effort data for

this study were compiled from the mandatory monthly

catch returns which have been submitted to the NSW

Department of Fisheries by all licensed commercial

shers in NSW since 1940. Since 1990, shers in the

estuarine sheries have been required to submit a

separate monthly return for each estuary shed during

each month. They are also required to list the number

of days shed using each shing method and the

weight of each species or dened species group

caught, along with other information about shing

vessel, crew and catch disposal. For detailed informa-

tion about the catch statistics collection and storage

system used see Pease and Grinberg (1995).

The annual mean number of days shed per sher

using each of 22 gear types (Table 2) and the annual

mean catch per sher of 81 taxa (Table 3) from each of

the 53 study estuaries during the ve year period

19911995 were used to conduct the spatial analysis

of the commercial shing method and catch data.

Therefore, the sampling unit for these attributes in

each estuary during this period was an approximation

of the average individual commercial sher.

Matrices of log transformed values of days shed by

each shing method for each estuary were compiled

along with matrices of log transformed values of catch

by each of the taxa for each estuary. Triangular

matrices of similarities between all pairs of estuaries

were computed using the BrayCurtis coefcient

(Bray and Curtis, 1957) for effort and catch separately.

The effort and catch similarity matrices were sub-

jected to cluster analysis using group-average linking

Table 1

Physical/environmental attributes used in the principal components analysis

Attributes Units Sources

Latitude Degrees and minutes Hydrographic charts

Geomorphological type 1. Saline coastal lagoon Roy, 1984

2. Barrier estuary

3. Drowned river valley estuary

Catchment area Square kilometres Bell and Edwards, 1980

Water area Square kilometres West et al., 1985

Entrance depth Metres Hydrographic charts; Lucas, 1976; West et al., 1985

Entrance width Metres Hydrographic charts; Lucas, 1976; West et al., 1985

Average annual rainfall Millimetres Bell and Edwards, 1980

Seagrass area Square kilometres West et al., 1985


to construct hierarchical agglomerative dendrograms

for delineating spatial groups. Signicance of spatial

boundaries was tested with one way analysis of simi-

larities (ANOSIM as described by Clarke and War-

wick, 1994). Diversity characteristics of methods and

taxa within groups was summarised with the

DIVERSE program (Clarke and Warwick, 1994)

which calculates total abundance, occurrence, Marga-

lef's richness index and Pielou's evenness index at

each site (estuary). Methods and taxa discriminating

spatial groups were identied using the SIMPER

program (Clarke and Warwick, 1994). For each

method or taxon this program calculates the ratio of

average contribution to similarity between groups to

the standard deviation of similarity between groups.

This ratio will be referred to as the ` discrimination

index'' because a high value reects greater usefulness

for discrimination than lower values. Average abun-

dance of each method or taxon within each group is

also summarised by the SIMPER program.

In order to view spatial relationships, the similarity

matrices were ordinated using non-metric multidi-

mensional scaling (MDS as described by Kruskal,

1964). Similarity of estuaries is inferred from their

proximity in these two-dimensional plots. Finally, the

BIO-ENV procedure (Clarke and Ainsworth, 1993)

was used to analyse the correlation between spatial

patterns associated with physical/environmental attri-

butes and the spatial patterns associated with catch by

taxa. This was done by selecting the subset of abiotic

variables which maximise the Spearman rank correla-

tion between abiotic and biotic similarity matrices.

Table 2

Summary of commercial fishing methods used in spatial analysis (mean days fished and standard deviation are the mean and standard

deviation of days fished per year by commercial fishers in all 53 study estuaries during the period 19911995)

Method Mean days fished Standard deviation

Nets

Seine, baitfish 686 223

Seine, garfish (bullringing) 754 263

Seine, beach (fish hauling) 11 035 836

Hoop or lift 114 84

Gill net, bottom set 4604 3169

Gill net, flathead 3337 1633

Gill net, splash/retrieve 14 304 11 144

Gill net, top set 25 479 16 559

Prawn beach seine (hauling) 4460 576

Prawn wall net (running) 4176 1608

Prawn danish seine 4284 1266

Prawn set pocket 4032 1127

Prawn trawl 17 616 1767

Traps

Crab 17 319 1909

Eel 6606 2030

Fish 5781 1767

Lobster 1111 410

Lines

Handline 2644 488

Jigging 36 29

Set line 42 21

Others

Hand gathering 760 140

Skindiving 146 98


Table 3

Summary of commercial fisheries taxa used in spatial analysis (mean catch and standard deviation are the mean and standard deviation of the

annual commercial catch (kg) from 53 study estuaries during the period 19911995)

Common name Scientific name Mean catch (kg) Standard deviation

Finfish Classes Chondrichthys and Osteichthys

Anchovy Engraulis australis 8299 4653

Biddy, silver Gerres subfasciatus 152 855 28 427

Bream, black and yellowfin Acanthopagrus butcheri and A. australis 342 148 58 732

Catfish, estuary Cnidoglanis macrocephalus 1370 1226

Catfish, forktailed Arius graffei 1149 1703

Catfish, unspecified Families Plotosidae and Ariidae 16 981 5606

Dory, John Zeus faber 639 600

Drummer Girella elevata 649 824

Eel, pike Murainesox bagio 2018 2024

Eel, river Anguilla australis and A. reinhardtii 182 064 42 386

Fish, unspecified estuary Classes Chondrichthys and Osteichthys 70 142 16 298

Flathead, dusky Platycephalus fuscus 165 550 10 360

Flathead, sand Platycephalus caeruleopunctatus and P. bassensis 3040 1519

Flounder, unspecified Family Pleuronectidae 2064 590

Garfish, no bill Arrhamphus sclerolepis 8955 3375

Garfish, river Hyorhamphus regularis 26 482 11 360

Garfish, sea Hyporhamphus melanochir 11 201 8075

Goatfish Family Mullidae 2990 5775

Hairtail Trichiurus coxii 25 707 28 770

Hardyhead Family Atherinidae 145 235

Kingfish, yellowtail Seriola lalandi 4304 4882

Leatherjacket, unspecified Family Monacanthidae 21 098 4562

Longtom Family Belonidae 1163 377

Luderick Girella tricuspidata 383 187 45 698

Mackerel, blue Scomber australasicus 5785 5968

Mackerel, unspecified Family Scombridae 63 68

Morwong, red Cheilodactylus fuscus 201 176

Morwong, unspecified Family Cheilodactylidae 48 19

Mullet, flattail Liza argentea 110 844 29 210

Mullet, pink-eye Myxus petardi 4098 2438

Mullet, sand Myxus elongatus 17 182 12 991

Mullet, sea Mugil cephalus 1 804 851 155 777

Mulloway Argyrosomus hololepidotus 57 187 10 131

Nanata Class Osteichthys 627 1144

Old maid Selenotoca multifasciatus 9066 3755

Pike Dinolestes lewini 3211 1701

Pilchard Family Clupeidae 11 182 4131

Salmon, Australian Arripis trutta 2092 1225

Shark, bull Carcharhinus leucas 7626 4718

Shark, carpet Orectolobus ornatus 555 301

Shark, fiddler Trygonorrhina spp. 923 347

Shark, hammerhead Family Sphyrnidae 167 56

Shark, shovelnose Aptychotrema rostrata 45 35

Shark, unspecified Class Chondrichthys 4339 1875

Snapper Pagrus auratus 3783 387

Sole, black Synaptura nigra 510 156

Sole, lemon Paraplagusia unicolor 72 40

Stingray Families Urolophidae and Dasyatidae 1552 819

Sweep Scorpis lineolatus 385 292

Tailor Pomatomus saltatrix 43 694 8565

Tarwhine Rhabdosargus sarba 29 239 16 799


3. Results

3.1. Physical/environmental attributes

Cluster analysis of the physical/environmental attri-

butes (Fig. 2(a)) indicated that, at the normalised

Euclidean distance of 4.0, there were three latitudinal

groups of estuaries. A northern group included only

estuaries located between the northern border of NSW

and 328S (Fig. 1). A central group contained most ofthe estuaries located between 328 and 358100S, as wellas the Clarence and Camden Haven Rivers from north

of 328S and the Clyde River, Moruya River and TurossLake from south of 358100S. A southern group con-tained most of the estuaries between 358100S and thesouthern border of NSW, as well as Wollumboola and

Smiths Lakes from the central region.

The ordination of physical factors by principal

components analysis (Fig. 2(b)) shows the spatial

integrity of the three groups dened by the previous

cluster analysis. Information about the rst ve prin-

cipal components is summarised in Table 4. The rst

two principal components accounted for most (76%)

of the variance in the data set. Principal component 1

Table 3 (Continued )

Common name Scientific name Mean catch (kg) Standard deviation

Trevally, black Siganus fuscesens 7631 2026

Trevally, silver Pseudocaranx dentex 82 449 11 313

Trumpeter Latridopsis forsteri 2704 1348

Trumpeter, unspecified Family Latrididae 2664 1725

Whitebait Class Osteichthys 36 292 12 867

Whiting, sand Sillago ciliata 137 926 19 891

Whiting, school Sillago bassensis and S. flindersi 387 711

Whiting, trumpeter Sillago maculata 37 735 11 785

Whiting, unspecified Family Sillaginidae 748 361

Yellowtail Trachurus novaezelandiae 32 477 8500

Crustaceans Phylum Arthropoda

Crab, blue swimmer Portunus pelagicus 152 602 26 196

Crab, mud Scylla serrata 109 770 17 833

Crab, unspecified Section Brachyura 299 195

Lobster, eastern rock Jasus verreauxi 2970 1737

Prawn, eastern king Penaeus plebejus 83 714 20 081

Prawn, greasyback Metapenaeus bennettae 37 452 17 106

Prawn, school Metapenaeus macleavi 587 816 147 738

Prawn, tiger Penaeus esculentus P. semisulcatus and P. monodon 2888 2465

Prawn, unspecified Family Penaeidae 72 778 26 846

Shrimp, mantis Order Stomatopoda 570 215

Molluscs Phylum Mollusca

Calamari, southern Sepioteuthis australis 738 652

Cockle Anadara trapezia 50 403 16 793

Cuttlefish Sepia spp. 1346 1344

Octopus Octopus spp. 16 814 6261

Pipi Family Donacidae 2974 2468

Scallop Pecten fumatus 120 214

Squid Photololigo spp. 49 937 6898

Other shellfish Phyla other than Chordata

Beachworms Phylum Annelida, family Nereidae 119 56

Shellfish, unspecified Phyla other than Chordata 3671 1690


Fig. 2. (a) Dendrogram showing group average clustering of 53 estuaries based on eight physical/environmental attributes. Shading shows

groups delineated at a normalised Euclidean distance of 4.00; (b) Ordination of 53 estuaries by PCA of physical/environmental attributes.

Boundaries and shaded fill delineated by cluster analysis in Part a. Nestuaries north of 328, Cestuaries south of 328 and north of 358 100 andSestuaries south of 358 100.


explained 57% of the variance in the data set and was

strongly associated with the physical factors related to

size of the estuary (Table 4). In decreasing order of

signicance these attributes were water area, entrance

depth, entrance width, and geomorphological type

(type 1 saline lagoons included the smallest estuaries

and type 3 drowned river valleys included the largest

estuaries). Principal component 2 explained 19% of

the variance and was strongly associated with physical

factors related to latitude, such as rainfall. The vectors

for estuary size and latitude are shown on the ordina-

tion (Fig. 2). Principal components 35 explained

only 20% of the variance and were primarily asso-

ciated with seagrass area, catchment area and geo-

morphological type, respectively.

A summary of the distribution of each physical

factor with respect to the regions delineated in

Fig. 2 provides an insight into the way these factors

inuence the multivariate regionalisation process.

Fig. 3 summarises the distribution of geomorpholo-

gical type among regions. All of the northern estuaries

are riverine barrier estuaries. The central region is

dominated by large lagoon-type barrier estuaries and

drowned river valley estuaries. The southern region

contains most of the small intermittently opening

saline coastal lagoons.

Fig. 4 summarises the distribution of the other

physical factors among estuaries within the three

regions. The lowest values for all of the physical

attributes were generally found in the small estuaries

of the southern region. The highest values for all of the

physical attributes directly related to estuary size

(water area, entrance depth and entrance width) were

generally found in the central region and the values for

these attributes in the northern region were intermedi-

ate to the values in the other two regions. Catchment

area is less closely related to estuary size and the

highest values were found in both the northern and

central regions. Average annual rainfall was inversely

related to latitude, with the highest rainfall in the

northern region. Seagrass area followed a similar

regional pattern to the attributes related to estuary size.

3.2. Commercial fishing method attributes

Two levels of cluster analysis were applied to the

shing method attributes before a regional pattern was

detected. Analysis of attributes for all estuaries

revealed a grouping pattern based primarily on water

area. Further cluster analysis of the group composed of

` large'' estuaries, resulted in the delineation of two

latitudinal groups with a single boundary at 358100S.

Table 4

Coefficients in the linear combinations of physical/environmental attributes making up the principal components (also shown are the

percentage variations explained by the first five principal components)

Variable PC1 PC2 PC3 PC4 PC5

Latitude 0.291 0.576 0.141 0.376 0.166Geomorphological type 0.400 0.117 0.277 0.280 0.728Catchment area 0.401 0.099 0.109 0.650 0.225Water area 0.411 0.061 0.400 0.144 0.110Entrance depth 0.405 0.228 0.216 0.146 0.566Entrance width 0.404 0.176 0.235 0.381 0.222Mean annual rainfall 0.139 0.747 0.054 0.366 0.036Seagrass area 0.279 0.015 0.792 0.192 0.094% Variation explained 57 19 12 5 3

Fig. 3. Distribution of 53 estuaries by geomorphological type

based on Roy (1984) and by region delineated in Fig. 2. Northern

region n12 estuaries, central region n13 estuaries and southernregion n28 estuaries.


Cluster analysis of the shing method data for all

estuaries (Fig. 5(a)) indicated that, at a BrayCurtis

similarity of 40% there were three groups of estuaries

which were primarily separated by magnitude of water

area. The group labelled ` large'' contained most of

the estuaries that were generally larger than approxi-

Fig. 4. Distribution of catchment area, water area, entrance depth, entrance width, average annual rainfall and seagrass area by estuary along

an increasing latitudinal gradient from the northern-most estuary at the origin. Shaded regions are those delineated in Fig. 2.


Fig. 5. (a) Dendrogram showing group average clustering of 53 estuaries based on 22 fishing method attributes. Shading shows groups

delineated at a similarity of 40%; (b) ordination of 53 estuaries by MDS (stress0.10) based on 22 fishing method attributes. Boundaries andshaded fill delineated by cluster analysis in Part a. Sestuaries smaller than 4 km2 and Lestuaries larger than 4 km2. Dashed line shows theapproximate boundary between estuaries smaller than 4 km2 and those larger than 4 km2.


mately 4 km2 in water area (only 8 of the 37 estuaries

in this group were smaller) and were located in all

three of the regional groups dened by the previous

analysis of physical attributes. The two estuaries

labelled ` small north'' were less than 4 km2 in water

area and were located in the northern region. The third

group, labelled ` small south'' comprised estuaries

generally less than 4 km2 in water area and located

in the southern region, except for Lake Wollumboola

which is slightly larger than 4 km2 and is located near

the southern boundary of the central region.

The ordination of the shing method data by MDS

(Fig. 5(b)) shows the spatial integrity of the three

groups dened by the previous cluster analysis. Estu-

ary size generally increases from left to right. The

dashed line divides estuaries into those smaller than

4 km2 on the right (except for Swan and Wollumboola

Lakes, which are slightly larger) from those larger

than 4 km2 on the left (except for Brou Lake and Bega

River, which are smaller). A low stress value (0.10) for

the MDS implies that this two-dimensional ordination

provides a relatively good spatial representation of

similarity.

ANOSIM showed that shing effort per sher in

estuaries greater than 4 km2 was signicantly different

(P4 km2) within

the three latitudinal regions dened by the previous

analysis of physical attributes. Fishing effort in large

estuaries in the northern region was not signicantly

different (P>0.05) from the effort in large estuaries in

the central region, but shing effort from large estu-

aries in the southern region was signicantly different

(P4 km2

NorthCentral 418 16 2.46 0.84South 108 12 2.31 0.83

a For the 30 large (>4 km2) and 23 small (4 km2) estuaries. In both a and b: daystotal days fished,methodsnumber of fishing methods, richnessMargalefs indexand evennessPielou's index.


Fig. 6. (a) Dendrogram showing group average clustering by 22 commercial fishing method attributes of 37 estuaries designated as ` large'' in

Fig. 5(a). Shading shows groups delineated at a similarity of 56%; (b) ordination of these 37 ` large'' estuaries by MDS (stress0.13) based on22 fishing method attributes. Boundaries and shaded fill delineated by cluster analysis in Part a. Nestuaries north of 328, Cestuaries southof 328 and north of 358 100 and Sestuaries south of 358 100.


3.3. Commercial catch by taxa attributes

Analysis of the catch attributes provided results that

were very similar to those previously shown for the

analysis of commercial shing method attributes.

Again, two levels of cluster analysis were applied

before a regional pattern was detected. Analysis of

attributes for all estuaries revealed a grouping pattern

based primarily on water area. Further cluster analysis

of the group composed of ` large'' estuaries resulted in

the delineation of three latitudinal groups with the

same boundaries as those delineated by the PCA of

physical attributes.

Cluster analysis of the catch by taxa data for all

estuaries (Fig. 7(a)) and the resulting ordination

(Fig. 7(b)) revealed grouping patterns similar to those

observed for the shing method attributes; however,

Fig. 7(b) illustrates that the groups which were based

on cluster analysis of catch attributes showed an even

more distinct separation at the 4 km2 boundary than

those based on shing method attributes. The only

exceptions to the 4 km2 boundary were Swan and

Wollumboola Lakes in the ` small'' estuary group,

which were slightly larger than 4 km2, and Pambula

Lake and Bega River in the ` large'' group, which were

slightly smaller than 4 km2. The lower stress value

(0.09) for this MDS implies that this two-dimensional

ordination provides a slightly better spatial represen-

tation of similarity than the MDS of shing method

attributes (stress0.10).ANOSIM showed that catch compositions from

estuaries greater than 4 km2 in area were signicantly

different (P

(Fig. 8(b)) conrms that all three groups are spatially

distinct, except for the inclusion of Clarence River in

the central region. The low stress value (0.11) for this

MDS implies that this two-dimensional ordination is a

good spatial representation of similarity.

ANOSIM was used to test the null hypothesis that

there was no signicant difference in catches from

large estuaries (>4 km2) among the three latitudinal

regions dened by the previous analysis of physical

attributes. Catches from each region were found to be

signicantly different (P4 km2) with the PCA ordination

of physical attributes in the large estuaries. The high-

est Spearman rank correlation coefcient of 0.58

resulted from the combination of latitude and water

area, which indicates that these are the primary

Table 6

Mean diversity attributes of catch per estuarya,b

Estuaries Catch Taxa Richness Evenness

a. All estuaries

Large 11 635 49 5.17 0.65

Small 1547 18 2.40 0.61

Correlation 0.90 0.94 0.90 0.21

b. Estuaries >4 km2

North 11 639 48 5.04 0.58

Central 16 555 59 5.99 0.68

South 3638 34 4.00 0.70

a From 30 large (>4 km2) and 23 small (4 km2) estuaries. In both

a and b: catchtotal catch, taxanumber of taxa, rich-nessMargalef's index and evennessPielou's index.

Fig. 8. (a) Dendrogram showing group average clustering by 81

catch by taxa attributes of 30 estuaries designated as ` large'' in

Fig. 7(a). Shading shows groups delineated at a similarity of 62%;

(b) ordination of these 30 ` large'' estuaries by MDS (stress0.11)based on 81 catch by taxa attributes. Boundaries and shaded fill

delineated by cluster analysis in Part a. Nestuaries north of 328,Cestuaries south of 328 and north of 358 100 and Sestuariessouth of 358 100.


physical factors explaining the shared relationship

between the physical and biological variables.

4. Discussion

Multivariate analysis of the abiotic and biotic vari-

ables in this study indicates that the estuaries of NSW

can be grouped into three latitudinal regions, with

boundaries at 328 and 358100S. The physical factorsthat contribute most to this regional structure appear to

be those related to estuary size and latitude. It is

difcult to assign a more specic role to physical

and environmental factors because they tend to be

highly intercorrelated and cause/effect relationships

are poorly understood (Horn and Allen, 1976; Monaco

et al., 1992).

Estuary size is a primary factor in the delineation of

regional distribution patterns of estuaries in NSW, as

well as determining the nature of the commercial

sheries within them. The rst principal component

of the PCA (Table 4) was dominated by ve variables

associated with estuary size: water area, entrance

depth, entrance width, catchment area and geomor-

phological type. The distribution pattern of these

variables (Figs. 3 and 4) indicates that most of the

largest estuaries, which are typically drowned river

valleys and large barrier lagoon estuaries, are found in

the central region. Most of the smallest estuaries,

many of which are saline coastal lagoons, are located

in the southern region. All of the medium to large

sized estuaries in the northern region are riverine

barrier estuaries.

The initial classication of all estuaries by shing

method and catch by taxa also resulted in a size-based

clustering pattern (Figs. 5 and 7). This pattern in the

spatial distribution of sheries variables is caused

primarily by the fact that shers use signicantly

fewer shing methods in estuaries smaller than

approximately 4 km2 (Table 5). The pattern is

enhanced by the fact that beach seining is not gen-

erally used in these small estuaries, whereas the largest

estuarine catches of many sh species are obtained by

this method. Analysis of the sheries variables in

estuaries larger than 4 km2 (Figs. 6 and 8) indicates

that the number of shing methods also plays an

important role in separating the sheries of the rela-

tively small estuaries in the southern region from those

in the other regions. However, the signicant differ-

ence of catch by taxa between the large estuaries of the

northern and central regions was not apparently linked

to a signicant difference in shing effort.

The high correlation between richness of taxa and

water area (Table 6), along with the BIO-ENV corre-

lation between water area and catch by taxa, indicate

that estuary size is a primary factor delineating the

Table 7

Comparison of regions by top four discriminating taxa based on SIMPER analysis of species data from large (>4 km2) estuariesa

Common name Scientific name Discrimination index Average catch (kg)

North Central South

Silver biddy Gerres subfasciatus 2.17 33 1155

Tarwhine Rhabdosargus sarba 2.08 2 207

Squid Photololigo spp. 1.95 1 192

Yellowtail Trachurus novaezelandiae 1.88 1 435

Sea mullet Mugil cephalus 3.19 3830 586

Mud crab Scylla serrata 2.61 413 9

Bull shark Carcharhinus leucas 2.24 46 0

Old maid Selenotoca inultifasciatus 2.01 39 0

Sea mullet Mugil cephalus 2.06 2569 586

Trumpeter whiting Sillago maculata 1.97 254 8

Blue swimmer crab Portunus pelagicus 1.82 447 9

Squid Photololigo spp. 1.82 192 2

aDiscrimination indexratio of average dissimilarity for this taxon to standard deviation of dissimilarity for the taxon. Average catch is theaverage catch per fisher in each of the two regions being compared.


catch composition of estuarine sheries. The mechan-

ism behind this relationship is complex and undoubt-

edly involves many abiotic and biotic variables.

Structure and complexity of shing effort is one

important factor confounded with many environmen-

tal factors. Horn and Allen (1976) and Monaco et al.

(1992) also found a signicant correlation between

species richness (presence/absence) of shes and

estuary size on the west coast of the United States.

Using multiple regression techniques, Horn and Allen

(1976) identied estuary mouth width as the only

signicant predictor of species number, while Monaco

et al. (1992) found mouth depth to be the best pre-

dictor. In the current NSW study, both mouth depth

and width had high coefcients in the rst principal

component of the PCA (Table 4). Mouth depth and

width generally increase with increasing estuary size.

A larger entrance also indicates a greater degree of

marine inuence with greater access to and from the

marine environment, which is generally associated

with higher species richness than euryhaline estuarine

environments. Larger estuaries tend to have greater

heterogeneity of habitats, which also leads to

increased species richness (Gilmore, 1995). Sea-

grasses provide a particularly complex habitat which

has important nursery functions for many commer-

cially important species (Pease et al., 1981; Bell and

Pollard, 1989). Seagrass area in the estuaries of this

study was regionally distributed similarly to the vari-

ables related to estuary size (Fig. 4).

Estuary size is also linked to geomorphology, runoff

and associated entrance opening regimes. Hurrell and

Webb (1993) found a linear relationship between

water area and catchment area of estuaries in NSW.

They showed that the smallest estuaries, closest to the

origin of the relationship, tend to be closed for longer

periods than they are open because runoff is directly

related to catchment area. Estuaries of an intermediate

size are intermittently closed, but remain open most of

the time and the largest estuaries all remain perma-

nently open. Comparisons of the sh communities in

intermittently open and nearby permanently open

estuaries in Australia (Pollard, 1994; Potter and

Hyndes, 1994) and South Africa (Bennett, 1989) have

shown that fewer sh species generally occur in the

intermittently opening estuaries. Therefore, another

factor explaining the signicantly lower species rich-

ness in estuaries smaller than 4 km2 is the fact that

65% of these are only intermittently open (West et al.,

1985). It is also worth noting that 80% of the inter-

mittently opening estuaries occur in the taxonomically

depauperate southern region. Furthermore, both cen-

tral coast estuaries which the PCA associated with the

southern region (Smiths and Wollumboola Lakes) are

intermittently opening.

Most of the regional outliers or exceptions in the

analysis of physical/environmental and catch attri-

butes are probably related to variablility in estuary

size within latitudinal regions. The Clarence River

is located in the northern region but the PCA of

physical attributes and MDS of catch attributes both

put this estuary into the central group. Clarence

River is the largest estuary in the northern region

and the fourth largest estuary in NSW. The PCA also

put the two largest estuaries in the southern region

(Clyde River and Tuross Lake) into the central group

and the two smallest estuaries in the central region

(Smiths and Wollumboola Lakes) into the southern

group.

Latitude is the other general physical variable that

appears to play an important role in the classication

of estuaries and the sheries within them. However, as

discussed above, this role is apparently secondary to

that of estuary size. Latitude provides a simple, spa-

tially quantiable variable; however, it is autocorre-

lated with a large array of environmental variables

such as temperature, rainfall and wind patterns,

including those variables which may actually deter-

mine estuary size. Latitude and latitudinally distrib-

uted average annual rainfall (Fig. 4) were the primary

coefcients contributing to the second principal com-

ponent of the PCA (Table 4) and provide the spatial

orientation and consistency of the classication into

northern, central and southern regions. Bucher and

Saenger (1994) also found that average annual rainfall

played a key role in classifying tropical and subtro-

pical Australian estuaries.

The BIO-ENV correlation between latitude and

catch by taxon indicates that latitude is another pri-

mary factor delineating the catch composition of

estuarine sheries. However, Table 6 illustrates that

this correlation is not derived from a direct relation-

ship between latitude and species richness. Horn and

Allen (1976) and Monaco et al. (1992) also found that

cluster analysis of sh species (presence/absence) in

estuaries on the west coast of the United States


resulted in latitudinally oriented groups. However,

multiple regression analysis showed that latitude

was not a signicant predictor of species richness.

These results support the ndings of Rohde et al.

(1993), that Rapoport's rule (species richness gener-

ally increases with decreasing latitude) cannot be

generally applied to estuarine or marine teleost shes.

Horn and Allen (1976, 1978) indicated that the

latitudinal structure of estuarine and coastal sh fauna

along the California coast is primarily related to water

temperature and oceanographic boundaries. Recent

coastal bioregionalisation studies in NSW by Ortiz

(1994) and Pollard et al. (1998) relate the inuence of

the East Australian Current (EAC) to multivariate

analysis of coastal marine sh distributions (recorded

presence/absence). They found that the inuence of

this warm, western boundary current (Cresswell,

1987) divides the coast of NSW into three regions

which correspond well with those delineated in the

present study. The EAC ows southward from the

Coral Sea, and typically remains close inshore on the

continental shelf until it reaches an easterly coastal

protrusion, such as Cape Byron (288390S), SmokyCape (308570S) or Sugarloaf Point (328260S), whereit diverges from the coast and forms large eddies. Ortiz

(1994) found that the EAC separates from the con-

tinental shelf most frequently at Sugarloaf Point and

provides a sub-tropical inuence on the continental

shelf waters north of Sugarloaf Point 90% of the time.

On the continental shelf southwards of Sugarloaf Point

to Beecroft Head (358S), the warm temperate watersof the northern Tasman Sea are inuenced around 50%

of the time by sub-tropical water in eddies from the

EAC that impinge on the coast. South of Beecroft

Point, coastal trapped waves hold cold temperate

water from the southern Tasman Sea inshore and warm

water from the EAC only impinges around 10% of the

time. Using multivariate techniques, Ortiz (1994) and

Pollard et al. (1998) found that coastal sh species

distributions also t into this model of northern,

central and southern regions with similar regional

boundaries.

Recent multivariate analysis of oceanographic data

for the marine surface (150 m) waters around Aus-

tralia by the CSIRO Divisions of Fisheries and Ocea-

nography (1997) also resulted in a very similar pattern

of northern, central and southern regions adjacent to

the NSW coast, with boundaries at 328 and 358S.

Temperature, salinity, nitrate, silicate and dissolved

oxygen measurements collected by research vessels,

satellites and surface drifters were used in the classi-

cation analysis and the resulting groups were ordi-

nated on two-dimensional maps. The resulting map

provides a visual summary of the EAC processes

discussed by Ortiz (1994).

The multivariate techniques used in the current

analysis result in a bioregional pattern for the estuarine

sheries of NSW that is consistent with the ndings of

other bioregional studies in Australia and the United

States. As early as 1953, Bennett and Pope recognised

that intertidal ora and fauna along the coast of NSW

was associated with three biogeographical provinces

(Solanderian, Peronian and Maugean). More recently,

Pollard et al. (1998) identied three ` major coastal

biophysical regions'' in NSW with similar boundaries

to those identied in the current study. These studies

show general agreement despite the fact that different

techniques were used in each study and sampling bias

occurs in all such bioregionalisation studies. Biologi-

cal attributes used in the current study are based on the

commercial sher as a sampling unit and incorporate

both quantity and taxonomic composition of the aver-

age catch. It is understood that shers introduce bias

by sampling non-randomly with a range of selective

gears (Hilborn and Walters, 1992; Stergiou and Pol-

lard, 1994) and that non-biological, socio-economic

factors such as local traditions, market dynamics and

management strategies inuence a sher's sampling

activity. The other studies mentioned used presence/

absence data collected primarily by researchers. Pre-

ndergast et al. (1993) demonstrated that presence/

absence data from large-scale faunal surveys are often

subject to bias caused by variation in recording inten-

sity, which is undoubtedly magnied for highly

mobile marine species. The method of applying multi-

variate analysis simultaneously to attributes of the

physical environment and commercial sheries, as

described in this paper, provides a useful means of

identifying regions in multi-species sheries with

complex shing area and effort components. These

techniques can be easily applied to other complex

sheries.

The main purpose of this study was to dene and

describe spatial patterns in the estuarine sheries of

NSW. The primary strength of this multivariate

approach is that it incorporates a wide range of


physical/environmental, shing effort and catch attri-

butes in the descriptive process. Analysis of the shing

effort data indicates that the sheries attributes are

subject to variability associated with estuary size,

which confounds relationships between sheries attri-

butes and environmental factors. However, along with

the more general regionalisation results, the enhanced

description of shing effort and catch characteristics

of the complex estuarine sheries provides informa-

tion which is potentially very useful for future man-

agement.

With signicantly lower commercial catch and

effort, those estuaries less than 4 km2 in area should

be considered for closure as estuarine harvest refugia.

Because of their small size these estuaries are less

accessible for commercial shing activity than larger

estuaries but more vulnerable to overshing, potential

ecosystem damage and social conict with residents

and recreational users. Pollard (1994) showed that

small intermittently opening lagoons support a lower

diversity of non-commercial sh species than larger,

permanently opening lagoons, but also demonstrated

that they may support signicant quantities of com-

mercially and recreationally important estuarine sh

species. The distribution of these smaller estuaries, as

potential harvest refugia along the coastline, should

provide enhanced recruitment opportunities for many

inshore coastal species (Pollard, 1976). Recent unpub-

lished tagging studies show extensive movement of

species such as yellown bream and blacksh between

estuaries in NSW, indicating that such small estuarine

refugia could potentially enhance stocks of these

species in the surrounding larger estuaries.

The three latitudinal regions which have been

identied by this study should be considered as poten-

tial ` management units''. The development of man-

agement plans for the State's estuarine sheries should

be structured around a recognition of regional factors.

In fact, the Management Advisory Committee (MAC)

for the estuarine sheries of NSW is currently using

these three ` bioregions'' in a proposed zoning policy

(Zantiotis-Linton, 1998) to reduce social conict in

the shery by restricting the activity of individual

estuarine shers to a single bioregion. Regional fac-

tors should also be considered when reviewing and

restructuring input controls. Another recent proposal

by the estuarine sheries MAC to standardise and

simplify the seasonal regulation of gill net soak times

in this shery also employs these bioregions, recog-

nising the regional variability in seasonal water tem-

peratures. The three estuarine bioregions described

may also provide a useful framework for future stock

assessment and monitoring of commercially and

recreationally important sh and shellsh species,

recognising that factors such as shing effort and

growth rates vary regionally.

Acknowledgements

The author wishes to thank Dr. John Glaister and

Kevin Rowling for their interest and support, without

which this work would not have reached fruition. I

also wish to thank Trudy Walford for her help with

graphics, Rossana Silveira for her assistance with the

PRIMER software and Christine Allen, Dr. Rick

Fletcher, Dr. Dave Pollard, Dennis Reid, Kevin Rowl-

ing and Rob Williams for reviewing the paper and

providing helpful comments.

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