Download Sociometric analysis of the role of penaeids in the continental shelf

Fisheries Research 87 (2007) 46–57
Sociometric analysis of the role of penaeids in the continental shelf
food web off Veracruz, Mexico based on by-catch
L.G. Abarca-Arenas a,∗ , J. Franco-Lopez b , M.S. Peterson c ,
N.J. Brown-Peterson c , E. Valero-Pacheco d
a
Instituto de Investigaciones Biológicas, Universidad Veracruzana, Dr. Luis Castelazo s/n, Col. Industrial Animas, Xalapa, Veracruz, México, CP 91190, Mexico
b Laboratorio de Ecologı́a, FES-Iztcala-UNAM. Av. De los Barrios s/n, Los Reyes Iztacala, Tlalnepantla, Edo. de México, México CP 54090, Mexico
c Department of Coastal Sciences, The University of Southern Mississippi, 703 East Beach Drive, Ocean Springs, MS 39564, USA
d Posgrado en Neuroetologı́a, Instituto de Neuroetologı́a, Universidad Veracruzana, Apartado Postal 566, Xalapa 91001, Veracruz, Mexico
Abstract
Continental shelf macrofauna are impacted by trawling and associated by-catch activities. These activities have been predicted to change the
macrofaunal community structure and the resulting food web. Four food webs (three seasonal and one pooled) were constructed using shrimp
by-catch samples from 21 surveys from 1991 to 1994. Network sociometric analyses were performed on the webs in order to explore structural
characteristics focusing on the role of penaeids. The number of nodes varied from 43 to 73. The out-degree centrality value for the combined web
was the highest. For the penaeid node, the windy season centrality was the highest (47%) and the rainy season the lowest (11%). The number
of cutpoints by season was: windy four, dry two, and three for the combined web. Using the lambda sets analysis; penaeid is a key node for the
structural cohesion of the trophic network. The rainy season food web is the most homogeneous and penaeid is an important node such that its
elimination would cause a major structural and functional disruption to the network. These modeling exercises suggest over-fishing by trawlers
would impact the continental shelf community structure and thus significantly change the existing trophic relationships.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Centrality; Food webs; Gulf of Mexico; Networks; In-degree; Out-degree; Trophic relations
1. Introduction
Coastal fisheries are of great benefit to the economy and
the society in Mexico. Total annual landings of coastal finfish and shellfish in 1997 in Mexico were around 1.5 million
tonnes (CONAPESCA, 2003). Furthermore, the shrimp fishery has been one of the most important extractive industries
on both Mexican coasts. During the last 5 years, a decrease in
shrimp captures has been reported mainly in the Campeche Bank
zone (CONAPESCA, 2003). Additionally, trawling for shrimp
has been demonstrated as a destructive method not only for the
benthic fauna but for many fish species (Yañez-Arancibia and
Sánchez-Gil, 1988; Hall, 1999; Keller, 2005). Worldwide, the
portion of the total by-catch that is returned to the sea (i.e. global
discards) was estimated in 1992 as 11.2 million tonnes for the
shrimp fishery (Hall, 1999; Jennings et al., 2001), and the US
Gulf of Mexico shrimp fishery discards 10.3 kg fish of for each
∗
Corresponding author. Tel.: +52 228 841 8910.
E-mail address: [email protected] (L.G. Abarca-Arenas).
0165-7836/$ – see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.fishres.2007.06.019
kilogram of shrimp captured (Alverson et al., 1994). For the
Mexican portion of the Gulf of Mexico, the latest estimate was
12 kg of by-catch for each kilogram of shrimp (Yañez-Arancibia
and Sánchez-Gil, 1988).
Trawl fisheries for shrimp and demersal finfish account for
over 50% of total estimated discards, from this, tropical shrimp
trawl fisheries have the highest discard rate accounting for
around 27% of total estimated discards (Keller, 2005). In recent
years, a reduction in discards has been observed, due to the
promotion of using more selective fishing gears and discard
regulations. However this proportion is higher for the finfish
fisheries (40%) than for the shrimp fisheries in the case of the
Gulf of Mexico (Keller, 2005). The high by-catch numbers represent a problem for the fishery managers since most of the
time this capture is not reported, and the data are not taken into
account during fisheries assessments (Hall, 1999).
From an ecological perspective, the damage to the seafloor
and the associated fauna due to trawling has been well documented (Albert and Bergstad, 1993; Diamond et al., 1999;
Norse and Watling, 1999; Bianchi et al., 2000; Diamond et
al., 2000; Jennings et al., 2001; Sánchez and Olaso, 2004).
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
Furthermore, there are also negative impacts on the pelagic
fauna (Sainsbury, 1987, 1988; Sainsbury et al., 1997). Few of
the studies devoted to fish community structure deal with the
trophic relationships of the community components (Albert and
Bergstad, 1993; Stobutzki et al., 2001; Abarca-Arenas et al.,
2004). Studies at the ecosystem hierarchical level are needed in
order to better understand both the target species role and the
overall effect of by-catch removal on the whole system. This
approach to studying the fishery would produce the possibility
of sounder management (EPAB, 1999; Pikitch et al., 2004) as
well as a better understanding of communities and ecosystems
related to the fisheries.
Food web analysis has been a well-documented tool to
achieve a fisheries ecosystem approach as well as to understand the ecosystem under various scenarios (Arreguı́n-Sánchez
et al., 1993a, 1993b; Pauly et al., 1998, 2000). One advantage of
this type of analysis is the parallel understanding of the ecosystem under different scenarios. Although published food webs
do not provide a true representation of all interacting species,
a good amount of information on the structure and function of
ecosystems has been obtained. Among this is the possibility of
extracting the information necessary to identify those species
that play an important role in the ecosystem, also known as keystone species (e.g. Dunne et al., 2002; Jordán and Scheuring,
2002; Luczkovich et al., 2003).
Although shrimp utilize the productive estuarine and coastal
nursery habitat as post-larvae and juveniles, their importance to
fisheries occurs once they return to the open ocean as adults.
However, little is known regarding the ecological role of shrimp
on the open ocean food web. This is an important aspect considering the decline that the shrimp population density has had over
the years. Understanding the shrimp’s role in trophic interactions
could help fisheries managers recommend sound exploitation
policies that will take into account the interaction of shrimp
with other species of the ecosystem. The aim of this work was
to define the structural role of shrimp in four food webs, three
seasonal and one for the pooled data set. We used the centrality, betweenness and lambda sets indices as proposed by the
social sciences (Wasserman and Faust, 1994). This was done
with the idea to explore the application of methods from other
disciplines in resolving ecological problems. We found that the
application of sociometric indices resulted in an important tool
for the structural and functional analysis of trophic networks.
2. Area description
Predator and prey samples were taken offshore of the
Alvarado lagoon system (between 18◦ 45 and 19◦ 00 N and
95◦ 40 and 85◦ 57 W, located NE of the Campeche Bank), an
important region for the shrimp fishery. The region has three
seasonal periods: “nortes” or windy, from October to February
with occasional high winds (up to 100 km/h) and some rain; the
dry season, from March to June, and the rainy season from July
to September. A significant amount of freshwater comes to the
area during the rainy season, mainly from the Papaloapan River
watershed adjacent to the zone.
47
3. Data source
The food webs analyzed were assembled from the stomach
contents of fishes captured as by-catch during shrimp fishing
off the Alvarado coast. The capture methods, as well as the
methods used to describe and analyze the stomach contents of
all species, are considered in greater detail in Abarca-Arenas
et al. (2004). Briefly, fishes were collected for stomach content
analysis on 21 sampling dates between May 1991 and November
1994 from commercial shrimp trawling boats. Two-hour nightly
cruises were conducted twice during each sampling time. Six
sampling dates corresponded to the rainy season, six to the dry
season, and nine to the windy season. More than 6000 fishes
were analyzed from the entire collection, representing 10% of
the total by-catch captured from the shrimp trawling. From the
total of 159 fish species collected, those representing ≥10% of
the abundance were used for the present work, totaling 51 fish
species for all seasons.
After seasonal tables of fish species and their food items were
assembled, it was noticed that some of the prey were identified
at too general a level, i.e. crustaceans. In order to eliminate these
kinds of items, and reduce the loss of data, we assigned these prey
items proportionally to their respective type (Abarca-Arenas et
al., 2004). That is, if “crustacean pieces” was an item, then that
percentage was assigned proportionally to other crustaceans in
the diet like shrimp, crab, lobster, etc. This method eliminates
a possible bias when statistical analyses were performed, as it
reduces the variability in prey items introduced by non-specific
identifications. Once the food items were ordered, the feeding
preferences of the fish prey were tabulated from the literature
(Abarca-Arenas and Valero-Pacheco, 1993; Arreguı́n-Sánchez
et al., 1993a, 1993b; Browder, 1993; Chávez et al., 1993; VegaCendejas et al., 1993).
With these data, predator–prey matrices were assembled seasonally and for all data pooled across seasons. For each matrix,
the rows are the predators and the columns are the prey. A number one indicates that a predator on row i preys on a species in
column j, and is called an adjacency matrix. Adjacency matrices
were used for all the network analyses as described below. Each
matrix can also be represented as a graph where each species
represents a node that is linked to other species (see Fig. 1). The
link between two nodes or species is called an edge and can be
either undirected or directed. In the former case, the relationship between the two nodes involved has no inherent direction,
such that the adjacency matrix is perfectly symmetric. That is,
the relation between species A and B is the same as the relation between species B and A. The latter case is represented by
an arrow pointing from the prey to its predator. This kind of
adjacency matrix is, in general, asymmetric because the relation between species A and B might not be the same as the one
between B and A. For example, if species A preys on species B
the opposite does not necessarily happen (see Fig. 1a and b).
4. Network analysis
Species involved extensively in relationships with others in
these food webs are called prominent, because they are embed-
48
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
Fig. 1. (a) Directed network showing the interaction between five species, A–E. A non-directed network will show no arrows between the species. (b) Adjacency
matrix showing the relationship between species of a five-species network. (c) In-degree centrality (IDC) and out-degree centrality (ODC) for the network in (a) and
(b) and their normalized values computed by dividing the IDC and ODC by n − 1.
ded in the network. In order to measure which species are
prominent, several indices have been proposed that measure how
centrally located a species is within the network. Some indices
are better suited for non-directed networks, others are better
for directed networks, and some other indices are adequate for
both kinds. Independently of the kind of network, the centrality
index of each node is calculated and they are used to calculate
the network centrality. These calculations are used to identify
the existence of one or more species with a high centrality value.
If the network has one or more species with high centrality, it is
heterogeneous while a network with no such species is homogeneous. The variance of the individual centrality is another
measure commonly used, and some authors prefer this method
to measure centrality.
Centrality indices are based, in part, on the number of nodal
ties in a network. A network in which all nodes point to and
are connected to only one node is called a “star” network
(Hanneman, 2001; Watts et al., 2002) (Fig. 2a). This particular network has a centrality degree of one, and the “star” node
has a degree of the number of species, minus one. Thus, for any
other kind of network, the centrality value would be standardized on the degree of a “star” network with the same number
of nodes. An extreme of this kind of network is a circular one
(Fig. 2b) where a node is connected to the adjacent one, which is
then connected to another, and so on. The last node is connected
to the first node.
For undirected graphs, counting the links is enough. But for
directed graphs, where a node may link to the outside or have
links entering the network from the outside, it is possible to calculate a centrality value for these cases and these are termed indegree centrality (IDC) and out-degree centrality (ODC), respectively (see Fig. 1c). In this case, the adjacency matrix is asymmetric. In the case of the food webs, those links leaving a node represent being eaten by another node or species while an incoming
link represent preying on a second node or species. In most of the
cases, the values for the IDC and ODC are different, and the net-
Fig. 2. Examples of two extreme types of networks. (a) Star network, node one
is connected to all other nodes and these to no other and (b) circle network where
one node is connected to the adjacent and so on.
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
work centrality will be different as well. Finally, for asymmetric
adjacency matrices, it is possible to symmetrize it and calculate
an index for undirected graphs. All indices were calculated using
the program UCINET 6 for Windows (Borgatti et al., 2002).
4.1. Degree centrality
For undirected graphs the following index was used:
CD (ni ) =
S
xij =
j=1
S
xji ,
(1)
j=1
where CD is the Centrality Index for species ni , and Xij , Xji , are
the links that represent the relation between species i and j.
Because this value depends on the number of nodes or species
of the network, a standardized version was used as follows:
CD (ni )
CD
(ni ) =
,
S−1
(2)
where S is the number of species in the network; this index is
independent of the number of species, and thus can be compared
across networks of different sizes. A node with a large value is
in direct contact or adjacent to many other nodes.
For directed graphs both equations are used twice, one for
the output links, the ODC, of each node and one for the input
links, the IDC.
Taking into account the individual species centrality degrees
of the network, it is possible to calculate group centralization.
When a large value of group centralization is obtained, there is
an indication that a single species is central. Thus, the group centralization measures how variable or heterogeneous the species
centralities are. It can be considered as a measure of dispersion
or variability.
Considering CD (n*) as the maximum observed value of centrality in the network, the group centrality is calculated by
S
CD =
i=1 [CD (n
∗) − C
D (ni )]
(S − 1)(S − 2)
2
i=1 (CD (ni ) − C̄D )
S
This index measures the extent to which a particular species
lies between two or more species of the network. That is, a
species has a central position in the network if it is between
several species. In a food web, an intermediary species could
represent an important position in the flow of matter from one
species to another or from one trophic level to the next, even if
it is not connected to many other species. If gjk is the number of
geodesic (i.e. the number of species from j to k) linking species
j and k containing species i, with one species acting as the intermediary, then the betweenness index for species ni is the sum of
all pairs of species not including the ith species:
j<k gjk (ni )
CB (ni ) =
.
(5)
gjk
In this equation, CB is the betweenness centrality index for
species i distinct from j and k. If the value of this index is zero
then species ni falls on no geodesics, i.e. it is not an intermediary. The maximum value for this index is (S − 1)(S − 2)/2. The
standardized formula for the betweenness centrality is
CB (ni )
CB (ni ) = 2
,
(6)
(S − 1)(S − 2)/2
resulting in values between 0 for no betweenness of the species
and 1 for a species which is between all pairs of species.
If CB (n*) is the largest realized species betweenness index
for the web analyzed, then the food web betweenness centrality
index is
2 Si=1 [CB (n∗ ) − CB (ni )]
,
(7)
CB =
(S − 1)2 (S − 2)
with a minimum value of 0 when all species have exactly the
same value of the betweenness index. A high value of this index
will indicate that few of the species in the food web are between
many others, or, in another sense, few species are intermediary
in the matter flow between species of the trophic network.
4.3. Block analysis
S
=
4.2. Betweenness centrality
(3)
where n* is the value of the maximum centrality for species n.
The limits of the group centrality are 0 and 1. The former
value indicates that all species have the same centrality (circular
network Fig. 2b), and the food web is relatively homogeneous.
In contrast, a value of 1 represents one species dominating all
others in the network and represents a network where one node
is central to all others as in a star network (Fig. 2a).
Another statistical value commonly used is the variance of
the species’ degree:
SD2
49
,
(4)
where C̄D is the mean centrality value over all the species; the
variance of the individual centrality values can be used as a
measure of the network’s homogeneity (Wasserman and Faust,
1994).
In an attempt to reduce the dimensionality of the food webs, a
block model was performed on each of the networks (Borgatti et
al., 2002). A block model is a simplified version of the multirelational network that represents the general feature of the food
web’s structure. The final result is a matrix containing species
that are structurally similar. Blocking a network carries with it
the discovery of cutpoints. A cutpoint is a node (a species in this
web), that, if removed, will result in the network being divided
into two or more other networks. It is possible for a network to
have more than one cutpoint or to have no cutpoints.
4.4. Lambda sets
A network can be divided into different number of sets or
subgraphs, each with a certain number of lines connecting each
node. Each of these subgraphs is termed LS Sets. Within each of
50
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
the LS Sets there is a certain amount of connectivity and there is
also an amount of connectivity between the LS Sets that shape
the whole network. Considering this last property, it is possible
to measure the strength of connectivity between the various sets
which is called the Lambda Sets. The line connectivity of nodes
i and j, denoted as λ(i,j) if i and j belong to different sets, is
equal to the minimum number of lines that must be removed
from the graph in order to leave no path between the two nodes.
The smaller the value of λ(i,j) the more vulnerable are nodes i
and j to be disconnected from the graph by removing the lines.
The higher the number of λ(i,j) the higher the number of lines
that should be removed in order to disconnect i and j. In the case
of the food webs, a large value of the lambda index will suggest
a species with a high connectivity between the set to which it
belongs and other sets.
5. Results
Of the 159 fish species captured during the 4-year period, 27
species were used for the dry season, 28 for the rainy season, and
44 for the windy season. For the pooled data, 51 fish species were
analyzed (Table 1 ). From these species, a total of 21 different
composite food items were identified (Table 1).
For the rainy season, a total of 46 nodes or species (predator
and prey) were used for the food web analysis (Table 1). A total
of 133 links between the nodes were recorded, representing a
mean value of 2.89 links per node. Of all species, two (plant
remains and algae) were basal, meaning they prey on no other
species. A total of 21 top species were obtained, representing
fish that were not preyed upon by any other species. The final
23 species were intermediary, being both predators and prey.
For the 63 predators and prey species in the windy season
food web (Table 1), a total of 205 links were observed with a
mean value of 3.26 links per species. Of all the species 2 were
basal, 37 top, and 24 intermediate. The dry season food web
(Table 1) was composed of 43 predator and prey species with a
total of 156 links, and a mean value of 3.63 links per species. Of
all the species 2 were basal, 20 were top, and 21 species were
intermediate. The overall food web was composed by a total of
73 predator and prey species with a total of 369 links, resulting
in 5.05 links per species as a mean. Of all species 2 were basal,
36 intermediate and 35 top.
5.1. Centrality
Our results showed shrimp as the species with the highest
number of output links. This is represented as a high value for the
ODC index of this species, second after detritus for each season
(Table 2). For the pooled data matrix, shrimp fell to the fourth
place, after squid, caridean shrimp and detritus. These results
are an indication of the high pressure that predators have on
shrimp. This pressure is apparent after observing an increase of
the ODC when the number of species of the food web increases,
i.e. the windy season centrality value and number of species is
the highest. On the other hand, the IDC for shrimp was very low
for all seasons (Table 2), mainly due to their highly specialized
feeding habits.
Table 1
List of species and other items used in the food webs for the Alvarado, Veracruz
coastal shelf
Season
Code
Name
D, R
D, W
D, R, W
D, R
W
R
D
D, R, W
D, R, W
R, W
D, R, W
D, R, W
D, R, W
W
R, W
W
D, R, W
W
W
D, R, W
R
D, R, W
D, W
D, W
W
W
D, R, W
W
W
W
D, R, W
R, W
D, R, W
D, W
D, R
R, W
D, W
R
R, W
D
D, R, W
R, W
W
R, W
W
W
D, R, W
D, W
D, R, W
R, W
D, R, W
D, R, W
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
Achirus lineatus
Anchoa hepsetus
Anchoa mitchilli
Anchovia rostralis
Caranx hippos
Cetengraulis edentulus
Citharinchthys spilopterus
Chloroscombrus chrysurus
Conodon nobilis
Cyclopsetta chittendeni
Cynoscion arenarius
Cynoscion nothus
Diapterus auratus
Diapterus rhombeus
Diplectrum bivittatum
Diplectrum formosun
Engyophrys senta
Eucinostomus melanopterus
Gymnotorax nigromarginatus
Haemulon aureolineatum
Harengula clupeola
Harengula jaguana
Hoplunnis macrura
L. graellsi
Lobotes surinamensis
Lutjanus campechanus
Micropogonias furnieri
Myrophys puntatus
Ophidion welshi
Polydactilus octonemus
Porichthys porosissimus
Prionotus rubio
Pristipomoides aquilonaris
Sardinella aurita
Saurida brassiliensis
Scorpaena plumeri
Selar crumenophthalmus
Selene setapinnis
Selene spixii
Selene vomer
Sphyraena guachancho
Stellifer lanceolatus
Stenotomus caprinus
Syacium gunteri
Syacium papillosum
Symphurus plagiusa
Synodus foetens
Trachurus lathami
Trichiurus lepturus
Umbrina coroides
Upeneus parvus
B. cantori
Prey items
D, R, W
R, W
D, R, W
D, R, W
R, W
W
W
D, R, W
D, R, W
53
54
55
55
57
58
59
60
61
Copepods
Plankton
Squid
Penaeid shrimp
Amphipods
Tanaids
Isopods
Stomatopods
Polychaetes
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
Table 3
Food web and shrimp betweenness index for each season and seasons pooled
Table 1 ( Continued )
Season
D, R, W
R, W
D, R, W
D
R, W
D, R
D, R, W
D, W
D, R
D, R, W
D, R, W
D, R, W
51
Code
Name
62
63
64
65
66
67
68
69
70
72
73
74
Mysidacean
Brachiura
Portunus spinicarpus
Caridean shrimp
Fish larvae
Fish remains
Mollusks
Bivalves
Gasteropods
Detritus
Plant remains
Algae
The list represents all species used for the pooled food web; species occurring
during each season are indicated with D: dry season, R: rainy season, and W:
windy season.
Histograms for each season’s IDC and ODC are presented in
figures three to four. It is clear a fast decay of the ODC for the
all season but steeper for the windy one. The IDC values drop
slower and several of the species present similar values.
The food web with the highest centrality was the one for the
windy season (43.8%; Table 2). The centrality values for the
other two seasons were similar to each other but lower than the
windy season. Species like shrimp and fish larvae were dominant
as food sources, based on their high centrality, during the windy
season. That is, these two species presented the highest ODC
values. The remaining two seasons presented a different picture.
Several species with similar ODC values were present, although
none had values as high as that of shrimp and fish larvae for the
windy season food web.
The centrality index for the pooled food web was relatively
high, more than 56% (Table 2). This value was affected by the
detritus and squid high centrality values. Interestingly, shrimp
do not appear to have a central role when all food webs are
pooled; they are replaced in the rank list by squid.
Examination of the variance of the centrality values showed
that the windy season food web was the most heterogeneous with
Rainy
Windy
Dry
Pooled
Food web (%)
Shrimp
12.22
11.40
12.60
11.89
3.531
4.448
3.257
2.376
a relatively high variance (39%; Table 2). The centrality variances were low (<18%) for the other seasons, from which it may
be concluded that the food web is relatively homogeneous with
respect to the ODC and IDC values. A large difference is evident
when comparing the seasonal centrality variances with that of the
pooled food web (Table 2). The value for the variance suggests
that the pooled food web is highly heterogeneous, primarily due
to the high difference of centrality values for a few species (e.g.
detritus, squid) compared to the rest of the species. That is, a
few species are central while the rest are peripheral species.
For the betweenness index (the amount a species falls as an
intermediary between two or more other species), the values
obtained were small, ranging from 11% to 13% (Table 3).
Since the models represent food webs, and the links connecting
each species represent flow of matter, the small values of
betweenness represent an almost direct relation between prey
and its predator. The smaller value for the shrimp node suggests
an almost direct linkage between the food taken by the shrimps
and their predators.
5.2. Blocking
For the rainy season, no blocks were found, meaning that the
food web cannot be divided into different sub-webs. Thus, no
cutpoints were computed for this season (Table 4). For the windy
season food web, seven blocks were identified (Table 4), and
four species were considered as cutpoints (copepods, penaeids,
fish larvae, and algae). The dry season food web blocking
resulted in four blocks and two cutpoints (penaeids and fish
remains; Table 4). The pooled data food web presented five
Table 2
Centrality values and variance for each food web by season and pooled
S2
Fish larvae
Squid
DET
Penaeid shrimp
Caridean shrimp
ODC (%)
IDC (%)
ODC
IDC
ODC
IDC
ODC
IDC
ODC
IDC
ODC
IDC
ODC
IDC
Rainy
Windy
Dry
Pooled
29.43
8.99
17.34
2.04
8.89
4.44
11.11
2.22
35.56
0
33.33
6.67
–
–
43.84
14.33
39.96
5.04
48.38
4.84
14.51
1.61
29.03
14.51
46.77
4.83
–
–
29.59
14.97
14.49
3.56
–
–
9.52
2.38
35.71
21.43
26.19
7.14
7.14
11.9
56.03
26.27
113.07
21.04
5.56
6.94
51.39
2.78
41.67
0
41.66
12.5
45.83
6.94
Overall network centrality values (%), as well as those for key species such as penaeids, fish larvae, squid, detritus and caridean shrimp are shown.
ODC: out-degree centrality; IDC: in-degree centrality; S2 : variance; DET: detritus: not present.
52
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
Table 4
Number of blocks and cutpoints of the food web for each season and seasons
pooled, including the species identified as cutpoints
Season
Blocks
Cutpoints
Rainy
Windy
Dry
Pooled
0
7
4
5
Copepods, penaeids, fish larvae, and algae
Penaeids, and fish remains
B. cantori, squids, and caridean shrimps
blocks and three cutpoints (Bregmaceros cantori, squids, and
caridean shrimp; Table 4). The blocking algorithm is a similar
technique to the cluster analysis widely used in ecology, which
represents a high degree of complexity as the number of clusters increases. The resulting block analysis for the food webs
showed the windy season as the most complex of all due to
the number of blocks discovered, representing a higher level
of hierarchization within the windy season food web as compared to the other two seasons as well as to the pooled data
(Figs. 3–5).
In order to exemplify the effect of eliminating one of the
species marked as cutpoint, figures six and seven represent the
food web for the dry season as an example. The original ODC
food web (Fig. 6a), is compared to the food web (Fig. 6b) after
deleting the node representing the penaeid shrimps. For the IDC
Fig. 7a represents the original food web while Fig. 7b the food
web after the penaeid shrimps deletion. The species Lepophidium graellsi (node 24) is completely disconnected from the rest
of the food web as a consequence of the shrimp deletion. On the
other hand, the number of links and the position of other species
in the ODC and IDC ranking changes as well.
5.3. Lambda sets
During the rainy season, the relationship between penaeids
and detritus was most important for maintaining the network
structure, with detritus having a secondary importance (Table 5).
For the windy season food web, fish larvae and penaeids were
the nodes with the most important relationships. The dry
season food web showed the most important relationship
was between fish remains and detritus, followed closely by
penaeids (Table 5). In the pooled food web, the most important
relationship was between squid and detritus followed by
penaeid and caridean shrimp.
Table 5
Lambda sets for each season and for seasons pooled
Season
Rainy
Windy
Dry
Pooled data
Lambda sets
Penaeids and detritus
Plant remains
Fish larvae and penaeids
Fish remains and detritus
Penaeids
Squids and detritus
Penaeids and caridean shrimp
Fig. 3. Out- and in-degree centrality distribution for the species of the windy
season. Species codes are as in Table 1. Notice that not all species codes are
presented for clarity.
6. Discussion
Centrality is a major component in different kinds of network analysis approaches such as social networks (Wasserman
and Faust, 1994) and the World Wide Web (WWW) (Barabási et
al., 2000; Albert et al., 1999). The importance of centrality lays
in identifying the principal components within a social group
with which individual and/or group behavior is explained. In
the case of the WWW, the study of centrality resulted in the
analysis of hubs (computers with a high degree of traffic) and
subsequently to the fragility of the WWW (Albert et al., 2000)
in case of attacks from hackers. The possibility of characterizing hubs within a system of computers allows the possibility to
prepare the WWW from different attacks.
In the case of food webs, centrality has been related to the
concept of keystone species (Solé and Montoya, 2001; Dunne
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
53
Fig. 4. Out- and in-degree centrality distribution for the species of the rainy
season. Species codes are as in Table 1. Notice that not all species codes are
presented for clarity.
Fig. 5. Out- and in-degree centrality distribution for the species of the dry season.
Species codes are as in Table 1. Notice that not all species codes are presented
for clarity.
et al., 2002; Girvan and Newman, 2002). In this approach, the
suggestion is that those species with a high number of links (i.e.,
high ODC and IDC) could be considered as a keystone species
of the community (Albert et al., 2000). But this brings an interpretation problem: should a keystone species be the one with a
high ODC or the one with a high IDC? This issue has been considered by Albert et al. (2000; see also Jordán and Scheuring,
2002), who proposed that a keystone species should be the one
with the highest connectivity, thus representing the most important species in the community. On the other hand, Jordán et al.
(1999) and Jordán (2001) proposed a method to identify a keystone species using weighed trophic networks and not solely
the presence or absence of a link between them. Furthermore,
Allesina and Bodini (2004) show that species connectivity not
necessarily indicates its importance as a structural node in the
food web.
Our results showed a relatively high value of the ODC index
for shrimp in every food web analyzed. However, this was not
the case for the IDC value. Detritus was dominant for the IDC
index; representing the importance that organic recycling has on
the ecosystem. However, an elevated out-degree value also represents the high pressure on the shrimp population from their
predators. The windy season was the season with the highest ODC value for penaeids, as well as the season with the
greatest number of fishes. These two values are related to each
other considering that fish species are mostly opportunistic in
their feeding habits. Thus, an increase in the shrimp population
will trigger an increase in their mortality by fish predation as
noted for the windy season compared with the other two seasons. Interestingly, the food web for the pooled data presented
a smaller value for the penaeid ODC than that of the windy
season. That is, the overall population mortality for shrimp
54
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
Fig. 6. Food web representation for the dry season for the out-degree centrality: (a) with and (b) without the penaeids shrimps. Nodes are arranged from the lowest
(top) to the highest (bottom) values of the degree. The species codes are as in Table 1.
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
55
Fig. 7. Food web representation for the dry season for the in-degree centrality (a) with and (b) without penaeids shrimps. Nodes are arranged from the lowest (top)
to the highest (bottom) values of the degree. The species codes are as in Table 1 .
could be greater during the windy season than on a yearly
basis.
Relating network centrality with the homogeneity of the
species relationships using the variance results (Wasserman and
Faust, 1994), the rainy and dry seasons were the most homogeneous of all food webs analyzed. The windy season was
heterogeneous in this respect mainly due to the dominance of
penaeids as prey of many species. The dominance by penaeids
56
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
based on the ODC index produced a bias on the food web index,
thus inflating the value. The pooled food web had by far the
highest value of ODC variance, denoting a high dominance by
few species. In particular, detritus and squid dominated the food
web structure on an annual basis.
The relatively high homogeneity of the food webs is closely
related to the low betweenness values found for each food web.
Although penaeids presented a high centrality, their position in
the food web does not make them an important intermediary of
the matter flow. This might be true as well for the other relatively
highly central species, since the overall food web betweenness
values were low. The low betweenness values also indicate that
the food chains are short, which agrees with other studies on
food webs (e.g. Williams et al., 2002). It is expected that if a food
web has several central species these will act as intermediaries
to others, increasing the betweenness values (Hanneman, 2001)
and thus the chain length. The food webs analyzed here were
low in their centrality properties and thus by consequence the
betweenness values were also low.
The number of blocks found in a food web is an indication of
the structural homogeneity of that web. As the number of blocks
increases in a network, the relationship between its components
becomes more complicated and heterogeneous. Our data showed
the rainy season, with a low S2 , had no blocks and no cutpoints.
That is, the food web for this season is very homogeneous and
the species relationships are structurally compact. In contrast,
the windy season presented the highest number of blocks and
species acting as cutpoints. This web had the most potential
subgroups of any of the webs analyzed. When data for all seasons was aggregated, an intermediate number of blocks were
recorded, and penaeids were not a cutpoint, contrary to results
in the windy and dry seasons.
These blocking and cutpoint results resemble what was found
by Abarca-Arenas and Ulanowicz (2002; see also Bersier and
Sugihara, 1997; Krause et al., 2003) after aggregating species for
the Chesapeake Bay food web. Their results showed that, as the
species were combined into single trophic species, the amount
of information decreased, as the theory dictates. However, certain combinations of species aggregations actually increased the
information. That is, the quality of species lumping is an important factor in order to define the structure and functionality of
a food web. The increase in the number of species, as in the
pooled data food web, did not increase the blocking or number
of cutpoints as could be expected. The windy season food web,
with less species, was more susceptible to division than the one
with higher species number.
Analyzing the resulting lambda sets of species for each food
web we found that the above analysis is congruent. Several of the
species responsible for maintaining the structural characteristics
were also found as key in the relational structure. This means
that species like squid and penaeids seem to be responsible for
the structural and functional cohesion of the food webs.
The values obtained by the various centrality indices indicated an important position of penaeids within all food webs
analyzed. Penaeids as a food source for fish is an important item
in every season of the year. The food web dynamics showed a
variable structure among the different seasons of the year. The
results showed that a yearly-based food web does not resemble
what is happening each season with the species relationships
and hence with the food web structure.
This is also reflected, after the penaeid shrimps deletion in
one season as an example, resulted in a depletion of the number
of links between species, and the isolation of one of the species.
This results on a change in the structural properties of the food
web and most probably on its functioning.
Harvey et al. (2003) found that an understanding of species
interactions as influenced by intensive fishing is a key to responsible management. Unfortunately, this is not an easy task. The
more we try to understand the species interactions within an
ecosystem, more questions are proposed. Topological structure
of complex networks is a key factor in understanding species
interactions (Arii and Parrott, 2004). Our results showed the
intricate relationships existing within the coastal shelf community as well as their temporal variation. We discovered that
shrimp, besides being an important human resource, are an
important link in the trophic network of the area. We showed
that depletion of shrimp biomass could negatively impact the
overall community structure and function of the area. With this
in mind, people responsible for the fishery policies should take
into account the important role that shrimp has in the ecosystem.
Further work needs to be done to determine quantitatively the
role of shrimp in the community food web. A keystone species
is not only the species with the highest number of predators; the
biomass of the species is a factor that should also be taken into
consideration. Furthermore, weak interactions might be as or
more important than strong ones in defining the food web structure and its dynamics. Currently, the food webs examined here
are being analyzed using the amounts of matter flowing from
species to species. The combination of this ongoing analysis and
the analysis presented here will result in a better understanding
of the role of penaeids in the trophic network.
Finally, the application of sociometric indices resulted in
an important tool for the structural and functional analysis of
trophic networks. Of the whole universe of tools used by sociologist to analyze social networks, just a handful of them were
applied. A continuous exploration on these tools certainly will
produce sound ecological hypotheses that will help us understand the structure, functioning, and evolution of ecosystems.
Acknowledgments
We appreciate the help of the crew of the CETMAR-Alvarado
vessel during the sampling. Travel expenses were paid in part
by the Grant No 00-06-010v from SIGOLFO-CONACyT to
LGAA.
References
Abarca-Arenas, L.G., Ulanowicz, R.E., 2002. Effects of taxonomic aggregation
on network analysis. Ecol. Model. 149, 285–296.
Abarca-Arenas, L.G., Valero-Pacheco, E., 1993. Toward a trophic model of
Tamiahua, a coastal lagoon in Mexico. In: Christensen, V., Pauly, D. (Eds.),
Trophic models of aquatic ecosystems. ICLARM Conference Proceedings
26, pp. 1181–1185.
L.G. Abarca-Arenas et al. / Fisheries Research 87 (2007) 46–57
Abarca-Arenas, L.G., Franco-López, J., Chávez-López, R., Arceo-Carranza, D.,
Morán-Silva, A., 2004. Trophic analysis of the fish community taken as
bycatch of shrimp trawls of the coast of Alvarado, Mexico. Proceedings of
the Gulf and Caribbean Fisheries Institute 55, 384–394.
Albert, A., Jeong, H., Barabási, A.L., 1999. Diameter of the World Wide Web.
Nature 401, 130–131.
Albert, R., Jeong, H., Barabási, A.L., 2000. Error and attack tolerance of complex
networks. Nature 406, 378–381.
Albert, O.T., Bergstad, O.A., 1993. Temporal and spatial variation in the species
composition of trawl samples from a demersal fish community. J. Fish Biol.
43(A), 209–222.
Allesina, S., Bodini, A., 2004. Who dominates whom in the ecosystem? Energy
flow bottlenecks and cascading extinctions. J. Theor. Biol. 230 (3), 351–358.
Alverson, D.L., Freeberg, M.H., Murawski, S.A., Pope, J.G., 1994. A global
assessment of bycatch and discards. FAO Fisheries Technical Paper, # 339,
p. 233.
Arii, K., Parrott, L., 2004. Emergence of non-random structure in local food
webs generated from randomly structured regional webs. J. Theor. Biol.
227, 327–333.
Arreguı́n-Sánchez, F., Seijo, J.C., Valero-Pacheco, E., 1993a. An application of
ECOPATH II to the north continental shelf ecosystem of Yucatan, Mexico.
In: Christensen, V., Pauly, D. (Eds.), Trophic Models of Aquatic Ecosystems.
ICLARM Conference Proceedings 26, pp. 269–278.
Arreguı́n-Sánchez, F., Valero-Pacheco, E., Chávez, E.A., 1993b. A trophic box
model of the coastal fish communities of the southwestern Gulf of Mexico.
In: Christensen, V., Pauly, D. (Eds.), Trophic models of aquatic ecosystems.
ICLARM Conference Proceedings 26, pp. 197–205.
Barabási, A.L., Albert, R.A., Jeong, H., 2000. Scale-free characteristics of random networks: the topology of the World Wide Web. Physica A 281, 69–77.
Bianchi, G.H., Gislason, K., Graham, L., Hill, X., Jin, K., Koranteng, S.,
Manickchand-Heileman, I., Payaı̌, Sainsbury, K., Sanchez, F., Zwanenburg,
K., 2000. Impact of fish on size composition and diversity of demersal fish
communities. ICES J. Mar. Sci. 57, 558–571.
Bersier, L.F., Sugihara, G., 1997. Scaling regions for food web properties. Proc.
Natl. Acad. Sci. 94, 1247–1251.
Borgatti, S.P., Everett, M.G., Freeman, L.C., 2002. UCINET 6 for Windows.
Software for Social Network Analysis. Analytical Technologies, Harvard.
Browder, J.A., 1993. A pilot model of the Gulf of Mexico continental shelf.
In: Christensen, V., Pauly, D. (Eds.), Trophic models of aquatic ecosystems.
ICLARM Conference Proceedings 26, pp. 279–284.
Chávez, E.A., Garduño, M., Arreguı́n-Sánchez, F., 1993. Trophic structure of
Celestun Lagoon, Southern Gulf of Mexico. In: Christensen, V., Pauly, D.
(Eds.), Trophic models of aquatic ecosystems. ICLARM Conference Proceedings 26, pp. 186–192.
CONAPESCA. 2003. Anuario estadı́stico de Pesca 2001. Secretarı́a
de Agricultura, Ganaderı́a, Recursos Naturales y Pesca. México.
http://www.sagarpa.gob.mx/conapesca/planeacion/anuario/anuario2001.zip.
Diamond, S.L., Conwell, L.G., Crowder, B., 1999. Catch and bycatch: the qualitative effects of fisheries on population vital rates of Atlantic Croaker. Trans.
Am. Fish. Soc. 128, 1085–1105.
Diamond, S.L., Conwell, L.G., Crowder, B., 2000. Population effects of shrimp
trawl bycatch on Atlantic Croaker. Can. J. Fish. Aquat. Sci. 57, 2010–2021.
Dunne, J.A., Williams, R.J., Martinez, N.D., 2002. Network structure and biodiversity loss in food webs: Robustness increases with connectance. Ecol.
Lett. 5, 558–567.
EPAB, 1999. Ecosystem-based Fishery Management. Report to Congress by
the Ecosystem Principles Advisory Board. U.S. Department of Commerce,
Washington, DC.
Girvan, M., Newman, M.E.J., 2002. Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99, 7821–7826.
Hall, S.J., 1999. The Effects of Fishing on Marine Ecosystems and Communities.
Blackwell Science, Malden, MA, USA.
Hanneman, R.A., 2001. Introduction to Social Network Methods. University of
California, Riverside.
57
Harvey, C.J., Cox, S.P., Essington, T.E., Hansson, S., Kitchell, J.F., 2003. An
ecosystem model of food web and fisheries interactions in the Baltic Sea.
ICES J. Mar. Sci. 60, 939–950.
Keller, K., 2005. Discard in the world’s marine fisheries. An update. FAO Fish.
Technical Paper No. 470. FAO Rome, Italy.
Jennings, S., Pinnegar, J.K., Polunin, N.V.C., Warr, K.J., 2001. Impacts of trawling disturbance on the trophic structure of benthic invertebrate communities.
Mar. Ecol. Prog. Ser. 213, 127–142.
Jordán, F., 2001. Seasonal changes in the positional importance of components
in the trophic flow network of the Chesapeake Bay. J. Marine Syst. 27,
289–300.
Jordán, F., Scheuring, I., 2002. Searching for keystones in ecological networks.
Oikos 99, 607–612.
Jordán, F., Takács-Sánta, A., Molnár, I., 1999. A reliability theoretical quest for
keystones. Oikos 86, 453–462.
Krause, A.E., Frank, K.A., Mason, D.M., Ulanowicz, R.E., Taylor, W.W., 2003.
Compartments revealed in food web structure. Nature 426, 282–285.
Luczkovich, J.J., Borgatti, S.P., Johnson, J.C., Everett, M.G., 2003. Defining and
measuring trophic role similarity in food web using regular equivalence. J.
Theor. Biol. 220, 303–321.
Norse, E.A., Watling, L., 1999. Impacts of mobile fishing gear: the biodiversity
perspective. Am. Fish. Soc. Symp. 22, 31–40.
Pauly, D., Christensen, V., Dalsgaard, J., Froese, R., Torres Jr., F., 1998. Fishing
down marine food webs. Science 279, 860–863.
Pauly, D., Christensen, V., Walters, C., 2000. Ecopath, Ecosim, and Ecospace
as tools for evaluating ecosystem impact of fisheries. ICES J. Mar. Sci. 57,
597–706.
Pikitch, E.K., Santora, C., Babcock, E.A., Bakun, A., Bonfil, R., Conover,
D.O., Dayton, P., Doukakis, P., Fluharty, D., Heneman, D., Houde, E.D.,
Link, J., Livingston, P.A., Mangel, M., McAllister, M.K., Pope, J., Sainsbury, K.J., 2004. Ecosystem-based fishery management. Science 305,
346–347.
Sainsbury, K.J., 1987. Assessment and management on the demersal fishery of
the continental shelf of northwestern Australia. In: Polovina, J.J., Ralston, S.
(Eds.), Tropical Snappers and Groupers: Biology and Fisheries Management.
Westview Press, Boulder, CO, pp. 465–503.
Sainsbury, K.J., 1988. The ecological basis of multispecies fisheries management
of a demersal fishery in tropical Australia. In: Gulland, J.A. (Ed.), Fish
Population Dynamics. John Wiley, Chichester, pp. 349–382.
Sainsbury, K.J., Campbell, R.A., Lindholm, R., Whitelaw, A.W., 1997. Experimental management of an Australian multispecies fishery: examining the
possibility of trawl-induced habitat modification. In: Pikitch, K., Huppert,
D.D., Sissenwine, M.P. (Eds.), Global Trends: Fisheries Management. American Fisheries Society, Bethesda, Maryland, pp. 107–112.
Sánchez, F., Olaso, I., 2004. Effects of fisheries on the Cantabrian Sea shelf
ecosystem. Ecol. Model. 172, 151–174.
Stobutzki, I., Miller, M., Brewer, D., 2001. Sustainability of fishery bycatch: a
process for assessing highly diverse and numerous bycatch. Environ. Concer.
28, 167–181.
Solé, R.V., Montoya, J.M., 2001. Complexity and fragility in ecological networks. Proc. Roy. Soc. B 268, 2039–2045.
Wasserman, S., Faust, K., 1994. Social Network Analysis. Methods and Applications. Cambridge University Press, Cambridge, UK.
Watts, D.J., Dodds, P.S., Newman, M.E.J., 2002. Identity and search in social
networks. Science. 296, 1302–1305.
Williams, R.J., Berlow, E.L., Dunne, J.A., Barabási, A.L., Martinez, N.D., 2002.
Two degrees of separation in complex food webs. Proc. Natl. Acad. Sci. 99,
12913–12916.
Vega-Cendejas, M.E., Arreguı́n-Sánchez, F., Hernández, M., 1993. Trophic
fluxes on the Campeche Bank, Mexico. In: Christensen, V., Pauly, D. (Eds.),
Trophic models of aquatic ecosystems. ICLARM Conference Proceedings
26, pp. 206–213390.
Yañez-Arancibia, A., Sánchez-Gil, P., 1988. Ecologı́a de los recursos demersales
marinos. AGT Editor, S.A., México.