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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:
peaseb@fisheries.nsw.gov.au
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
70 B.C. Pease / Fisheries Research 42 (1999) 6786
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
B.C. Pease / Fisheries Research 42 (1999) 6786 71
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
72 B.C. Pease / Fisheries Research 42 (1999) 6786
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
B.C. Pease / Fisheries Research 42 (1999) 6786 73
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.
74 B.C. Pease / Fisheries Research 42 (1999) 6786
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.
B.C. Pease / Fisheries Research 42 (1999) 6786 75
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.
76 B.C. Pease / Fisheries Research 42 (1999) 6786
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.
B.C. Pease / Fisheries Research 42 (1999) 6786 77
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.
78 B.C. Pease / Fisheries Research 42 (1999) 6786
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.
B.C. Pease / Fisheries Research 42 (1999) 6786 79
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.
B.C. Pease / Fisheries Research 42 (1999) 6786 81
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.
82 B.C. Pease / Fisheries Research 42 (1999) 6786
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
B.C. Pease / Fisheries Research 42 (1999) 6786 83
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
84 B.C. Pease / Fisheries Research 42 (1999) 6786
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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