Results
The Celtic Sea is characterized by a “slow-fast” continuum of life
history, from small, short-lived taxa producing small offspring to
large, long-lived taxa with large offspring (Pianka 1970; Beukhofet al. 2019b). Large, long-lived species with low reproductive
output are typically the most sensitive to fishing pressure (Winemiller
& Rose 1992; Le Quesne & Jennings 2012; Wiedmann et al. 2014).
The most sensitive taxa to fishing are mostly elasmobranchs: sharks,
spurdog Squalus acanthias , tope shark Galeorhinus galeusand smooth hound Mustelus sp. followed by rays, cuckoo rayLeucoraja naevus , thornback ray Raja clavata , blonde rayRaja brachyura and small-eyed ray Raja microocellata . Some
large fishes also show high values of sensitivity such as European
conger Conger conger and ling Molva molva (Fig. 1, axis
1).
Highest values of eigenvector centrality (hereafter called centrality
for simplicity) characterize highly connected taxa linked to taxa that
are themselves highly connected. In the Celtic Sea, these are large
piscivorous fishes, namely whiting Merlangius merlangus , megrimLepidorhombus whiffiagonis , cod Gadus morhua , hakeMerluccius merluccius , turbot Scophthalmus maximus , and
squid Loligo sp. . In our case, the most central species are not
the most sensitive (Fig. 2). Notwithstanding this observation, taxa at
high trophic levels tend to be more sensitive to fishing and more
central than other species. Indeed, sensitivity tends to increase toward
the top of the network (higher values of δ15N) and centrality increases
with trophic levels (Appendix, Fig. S1).
Vulnerable taxa are defined as both sensitive and exposed to fishing. In
the Celtic Sea, we found no highly vulnerable taxon, i.e. no taxa in the
top right corner (Fig. 3). However, some taxa had medium-high values of
vulnerability: cod, edible crab Cancer pagurus , smooth-hound, and
to a lesser extent hake, anglerfish Lophius piscatorius , European
conger, European plaice Pleuronectes platessa , blackbellied
anglerfish Lophius budegassa and ling (Fig. 3). In addition,
three of these vulnerable taxa (cod, hake and anglerfish) have high
values of centrality. These taxa, despite being central, are not
accounting for a large proportion of the total biomass (Fig. 3), which
suggests that whether these taxa are affected or favored by an external
factor (i.e. environmental conditions or human pressures), it would have
a low impact on the total biomass of the Celtic Sea.
Simulating scenarios of species extinction sequences, we found that
connectance (defined as the number of realized interactions in the
network divided by the potential ones) is decreasing the fastest when
the taxa are sequentially removed according to their number of preys
(Preys removal sequence) and their influence (Centrality removal
sequence) (Fig. 4A.). These removal scenarios are also responsible for
the fastest collapse of the network (the remaining taxa are not linked
together) after simulating the extinction of respectively 60% and 75%
of the taxa of the network. These scenarios of taxa extinctions lead to
a network with a lower connectance than if the taxa were deleted
following a random sequence (Fig. 4A.). For these two removal sequences,
values of modularity show the largest increase and values of nestedness
the largest decrease (Fig. S2). Sequentially removing taxa with the
highest number of predators (Predators removal sequence) provokes a less
steady decrease of the connectance, but still with values lower than the
model of random extinctions. The network collapses after the removal of
75% of the taxa. Conversely, the removal of only the 7% of the taxa
that are the most exposed to fishing (Exposure removal sequence) leads
to an increase in connectance, with values higher than the random model.
Removing the taxa most sensitive to fishing (Sensitivity removal
sequence) does not lead to variations in connectance different from the
random model and causes the later collapse of the network, after
removing 93% of the taxa (Fig. 4A.).
The removal of the first 7% of the most exposed taxa to fishing
(Exposure removal sequence) causes the largest number of secondary
extinctions (Fig. 4B.). Then, the simulated extinctions of taxa with the
largest number of predators (Predators removal sequence) leads to the
highest and fastest rate of secondary extinctions, higher than the null
model, after the removal of 19% of the taxa. Removing taxa from the
most to the least central (Centrality removal sequence) produces
secondary extinctions comparable to the random model (Fig. 4B.).
Finally, removing the taxa with the largest number of preys (Preys
removal sequence) and the most sensitive taxa (Sensitivity removal
sequence) leads to the lowest number of accumulated secondary
extinctions, even lower than the null model (Fig. 4B.).
A network is the most robust to node loss when the removal of taxa
(primary removal) does not lead to secondary extinctions. The R50
robustness (Dunne et al. 2004) is defined as the proportion of
taxa that has to be removed to reach the loss of ≥50% of the taxa in
the original network. The larger the R50 is (maximum value of 50%), the
more robust the network is. Here, the Sensitivity and Preys removal
sequences lead to the most robust network (R50=50%), followed by the
random model (46%), the Centrality removal sequence (46%), the
exposure removal sequence (45%) and the Predator removal sequence
(39%).