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Fish and Fisheries. 2019;00:1–12. wileyonlinelibrary.com/journal/faf
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1© 2019 John Wiley & Sons Ltd
Received: 20 November 2019
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Accepted: 22 November 2019
DOI : 10.1111/faf.124 32
ORIGINAL ARTICLE
Abundance and distribution of the white shark in the
Mediterranean Sea
Stefano Moro1,2 | Giovanna Jona-Lasinio2 | Barbara Block3 | Fiorenza Micheli3,4 |
Giulio De Leo3 | Fabrizio Serena5 | Massimiliano Bottaro6 | Umberto Scacco7 |
Francesco Ferretti8
1Department of Environmental Biology,
Sapienza University of Rome, Rome, Italy
2Department of Statistical Sciences,
Sapienza University of Rome, Rome, Italy
3Hopkins Marine Station of Stanford
University, Pacific Grove, CA, USA
4Stanford Center for Ocean Solutions,
Pacific Grove, CA, USA
5Institute for Biological Resources and
Marine Biotechnology, National Research
Council (CNR-IRBIM), Mazara del Vallo, Italy
6Stazione Zoologica Anton Dohrn, Naples,
Italy
7Institute for Environmental Protection and
Research (ISPR A), Rome, Italy
8Department of Fish and Wildlife
Conser vation, Virginia Tech, Blackburg, VA,
USA
Correspondence
Stefano Moro, Department of Environmental
Biology, Sapienza University of Rome,
Piazzale Aldo Moro 5, 00185 Rome, Italy.
Email: stefano.moro@uniroma1.it
Funding information
MIUR (Italian Ministr y of Education,
University and Scientific Research),
Grant/Award Number: 20154X8K23-
SH3; Lenfest Ocean Program; Bertarelli
Foundation; Schmidt Technology Partners;
MedReAct; Regione Lazio with the Grant
Programme “Torno Subito 2017”
Abstract
Conservation of apex predators is a key challenge both in marine and terrestrial eco-
systems. The white shark is a rare but persistent inhabitant of the Mediterranean
Sea and it is currently assessed as “critically endangered” in the region. However, the
population trends and dynamics of this species in the area are still unknown. Little is
known about white shark distribution, habitat use and population abundance trends,
aspects that are critical for conservation and management. In this study, we built the
most comprehensive database of white shark occurrence records in the region. We
collected 773 different records from different sources and used them to character-
ize the spatial and temporal patterns of abundance of Mediterranean white sharks
bet ween 1860 and 2016. We analysed these data by using generalized additive mod-
els and used spatially disaggregated information on human population abundance as
a proxy of observation effort. Our results suggest a complex trajectory of popula-
tion change characterized by a historical increase and a more recent reduction (61%,
range 58%–72%) since the second half of the 20th century. In particular, analyses
reveal a 52% (range 37%–88%) to 96% (range 92%–100%) overall decline in different
Mediterranean sectors and a contraction in spatial distribution. Here, we provide the
first reconstruction of abundance trends and offer new hypotheses regarding the
drivers of change of white sharks in the Mediterranean. Our approach can be broadly
applied to data-poor contexts to reconstruct change and inform the conservation of
endangered top predators in the Mediterranean Sea and other intensely used marine
regions.
KEY WORDS
Mediterranean Sea, observation effort, opportunistic and sparse data, spatio-temporal
patterns, standardized trends, white shark
1 | INTRODUCTION
The loss of top predators is one of the most challenging forms of
global environmental change, with still unrecognized and not well-
understood ecosystem effects (Estes et al., 2011). The decline of
large predators on land started approximately 50 kya with the great
demographic and geographic human expansion (Dirzo et al., 2014;
Ripple et al., 2014). In the oceans, recent increases in resource ex-
ploitation by high seas industrial fishers as well as artisanal fleets,
habitat degradation and climate change pose unprecedented chal-
lenges also to marine predators (Worm & Tittensor, 2011). In the
Mediterranean Sea, predator loss is more severe than other ocean
2
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MORO et al.
sectors due to thousands of years of human impact on marine com-
munities (Coll et al., 2010) and currently high cumulative human
pressure on marine ecosystems (Micheli et al., 2013). However, it is
only in the last 50 years that systematic data started to be gathered
to evaluate the impact of fishing on exploited marine populations
and ecosystems (Ferretti, Jorgensen, Chapple, Leo, & Micheli, 2015;
McClenachan, Ferretti, & Baum, 2012). This lack of historical base-
lines hampers our ability to fully understand the ecology of habitats
and species, and consequently, our ability to evaluate the conserva-
tion status of Mediterranean ecosystems and marine populations.
Among marine animals, sharks are one of the marine taxa with
the highest percentage of threatened species (Dulvy et al., 2014).
They are generally vulnerable animals, with very low resilience and
biological productivity, strongly susceptible to fishing pressure
(Ferretti, Worm, Britten, Heithaus, & Lotze, 2010; Queiroz et al.,
2019), though examples of sustainable shark fishery also exist (see
for details Simpfen dorfer & Dulvy, 2017). Because of these features,
in the past decades, many shark populations showed rapid declines
in multiple marine regions mostly because of the direct effect of tar-
geted and bycatch fisheries (Baum & Blanchard, 2010). An among
global ocean sectors, the Mediterranean Sea showed the worst
population declines and conservation statuses for many populations
(Cavanagh & Gibson, 2007; Ferretti, Myers, Serena, & Lotze, 20 08).
In 2016, the International Union for Conservation of Nature (IUCN)
compiled the second regional assessment of sharks and rays in the
Mediterranean Sea, reporting that after ten years the situation had
not improved and the conservation status of these species remained
probably the worst in the world (Dulvy, Allen, Ralph, & Walls, 2016).
In fact, 39 of the 73 assessed species are still threatened and 13
are still considered Data Deficient (Dulvy et al., 2016). Moreover,
for most non-data-deficient species, the risk assessment was based
on suspected trends and not adequately supported by quantitative
analyses. For instance, the conservation status of the white shark
(Carcharodon carcharias, Lamnidae) was raised from Endangered to
Critically Endangered in both Mediterranean and European Regional
Red Lists (Dulvy et al., 2016; Nieto et al., 2015) solely on the ground
of its current sporadic occurrence in the region and suspected
declines.
The white shark is one of the largest and most widespread top
predators in the ocean. It is broadly distributed between the sub-
polar and subtropics in both hemispheres, with major coastal ag-
gregation sites in temperate latitudes (Compagno, 2001). Most of
the biological and ecological research on white sharks have been
carried out in three coastal aggregation sites (Huveneers et al.,
2018): California (Chapple et al., 2011; Tinker, Hatfield, Harris, &
Ames, 2016), southern Australia (Bruce & Bradford, 2012) and South
Africa (Kock et al., 2013). Here, satellite and acoustic tagging, ge-
netics and isotopes analyses have deepened our ecological under-
standing of the species’ migrations, preferred habitats, population
structure, abundance and distribution patterns (Bruce & Bradford,
2012; Carlisle et al., 2012). Even if a general assessment of the
white shark abundance trends is challenging to achieve, there have
been several attempts in different areas to assess the status of the
sharks. Locally, different data sources and modelling approaches
have been adopted (Christiansen et al., 2014; Curtis et al., 2014;
Dudley & Simpfendorfer, 2006; Lowe et al., 2012; Reid, Robbins, &
Peddemors, 2011). Most of these attempts showed a slow increase
in white shark populations or subpopulations relative abundance in
recent years due to the presence of local protecting measures (Curtis
et al., 2014; Dudley & Simpfendorfer, 2006; Lowe et al., 2012; Reid
et al., 2011). By contrast, relative declines have been detected in
regions lacking these measures (Christiansen et al., 2014). Despite
both the protecting provisions existing and the actual conservation
status, no white shark population trend analyses have been carried
out in the Mediterranean basin, so far.
In the Mediterranean Sea, white sharks have been sporadi-
cally but regularly detected throughout history. Several authors
(Fergusson, 1996; Gubili et al., 2011) hypothesized the existence of
a distinct Mediterranean population, and phylogeographic analyses
showed a large genetic distance and scarce genetic flow between
the Mediterranean and north-western Atlantic white sharks (Gubili
et al., 2011, 2015). However, little is known about the ecology and
biology of the Mediterranean population. Studying white sharks in
the Mediterranean Sea is challenging because of the low population
density and the absence of conventional aggregation sites, such as
around pinnipeds colonies (Klimley & Anderson, 1996). This makes
it challenging to conduct monitoring studies with the use of elec-
tronic tagging, and limits the scope of genetic and isotopic analyses
(Gubili et al., 2011). However, opportunistic occurrence records exist
(Boldrocchi et al., 2017; De Maddalena & Heim, 2012; Fergusson,
1996; Serena, Mancusi, & Barone, 2008). Regional reviews of these
data generated interesting hypotheses on patterns in population
structure, movements and abundance. For example, several catches
1. INTRODUCTION 1
2. METHODS 3
2.1. Shark database construction and exploratory
analysis
3
2.2. Model framework 3
2.3. Collecting observation effort data 4
2.4. Estimating standardized shark sighting trends 4
2.5. Testing for spatial range contraction 5
3. RESULTS 5
3.1. Explorator y data analysis 5
3.2. Model fitting results 7
3.2.1. Temporal trends 7
3.2.2. Spatial patterns and distribution shrinkage 7
4. DISCUSSION 7
ACKNOWLEDGEMENTS 10
DATA AVAILABILITY STATEMENT 10
REFERENCES 10
SUPPORTING INFORMATION 12
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MORO et al .
of young-of-the-year white sharks reported for the Sicilian Channel
suggested the presence of a nursery area in this sector (Fergusson,
1996, 2002). The reduction in white shark sightings through the
years from areas with a parallel strong decline of tuna populations,
combined with recent and historical sightings in and around farm
pens and tuna traps (Galaz & De Maddalena, 2004; Storai et al.,
2011), suggested a close relationship between Atlantic Bluefin tuna
(Thunnus thynnus, Scombridae) and white shark occurrence in the
Mediterranean Sea (Boldrocchi et al., 2017; De Maddalena & Heim,
2012; Kabasakal, 2016). Yet, more research is needed to understand
population structure, size, movements and distribution within the
area and their relations with environmental and biological variables.
Mediterranean white shark observation records are opportu-
nistic because they are often collected from fishers’ anecdotes or
newspaper articles (Pearce & Boyce, 2006), and, hence, obtained
without a specific and systematic sampling effort. Nevertheless, in
the absence of systematic surveys, opportunistic data provide valu-
able insights on species distribution, habitat requirements and pop-
ulation trends (Christiansen et al., 2014; Curtis et al., 2014; Ferretti
et al., 2015). For example, McPherson and Myers (2009) used white
shark sightings collected between 1868 and 2005 to infer popula-
tion changes in the Adriatic Sea. They estimated temporal trends of
occurrence records under different scenarios of observation effort
and estimated an 84% decline (CI: +27% and −98%). These estimates
were highly uncertain as the authors had no direct information on
observation effort but provided a first quantitative estimate of white
shark population change in a sector of the Mediterranean Sea. Here,
we expand on this approach to produce the first regional assessment
at the Mediterranean scale. We assembled the most comprehensive
database of white shark's occurrence records currently available for
the Mediterranean Sea and estimated trends controlling for obser-
vation effort, which was directly estimated with long-term trend
models of coastal human population censuses in the region. We used
these standardized records to infer trends in population abundance
and asked whether, how and how much the white shark population
has changed in abundance and spatial distribution over the last two
centuries.
2 | METHODS
2.1 | Shark database construction and exploratory
analysis
We built a database containing all the white shark records in the
Mediterranean Sea using different sources and multiple search strat-
egies (Supporting Information), also including existing institutional
databases, such as MEDLEM (Mediterranean Large Elasmobranchs
Monitoring) currently under the auspices of the General Fisheries
Commission for the Mediterranean Sea (GFCM, Serena et al.,
2008). We followed through citations in the listed references to
delineate the history of each account and to determine whether
the record was original or redundant (reporting records from other
publications) (Ferretti, Morey Verd, Seret, Sulić Šprem, & Micheli,
2016). For each observation we recorded: date and location; total
length; weight; sex; age (adapted from Bruce & Bradford, 2012;
Supporting Information); record type (stranding, catch, sighting,
signs of predation on other marine animals); stomach contents; fish-
ing gear involved in the capture; and bibliographic reference for pub-
lished accounts. Then, we performed an exploratory data analysis in
order to evaluate the most immediate ecological information, such
as the sightings' temporal and spatial distribution, length frequency,
length–weight relationship and a qualitative stomach content analy-
sis (Supporting Information).
2.2 | Model framework
We stratified the Mediterranean sea in spatio-temporal statistical
units of different resolution (e.g. FAO or GFCM statistical sectors
and year) and following McPherson and Myers (2009)'s approach,
we assumed that the expected number of sightings per statistical
unit (year t and geographic se c tor s) was related to the fo llowi ng va ri-
ables: number of possible observers (observation effort), Ots, their
propensity to report a sighting, Pts, white sharks population abun-
dance, Nts, and shark detectability, Dts. In systematic and dedicated
surveys, these factors can be controlled. Conversely, this information
is incomplete for opportunistic data (McPherson & Myers, 2009). In
this work, we chose the human population size (Hts) as a proxy of ob-
servation effort. Therefore, we treated Ots and Pts as a joint process,
essentially assuming that all observers had a constant probability to
report a record (McPherson & Myers, 2009) and that the number of
observers is proportional to the human population size (Ots = cHts,
where c is constant). Hence, we assumed that detectability, Dts, has
a proportional relationship with abundance (Dts = kNts), assuming, in
other words, that a specific amount of observation effort is needed
to detect a fixed proportion of the individuals present in the study
area (McPherson & Myers, 2009). This represents the simplest pos-
sible scenario to infer standardized abundance trends of the shark
population. In fact, under these assumptions, the probability of a
recorded sighting depends only on Hts and Nts. We tested deviations
from such an assumption in the following inferential analysis.
Given that Mediterranean white shark sightings are rare and dis-
crete events, we assumed that the exp ec te d number of sharks per sta-
tistical unit follows either Poisson or Negative Binomial (NB, in case of
overdispersed data) distributions where their means are a function of
predictors describing Nts and Hts. However, it is important to note that
factors other than Hts can affect the obser vation effor t variabilit y over
space and time. For example, the marine area covered by the potential
observers and the technological innovations in fisheries and boating
may have played a pivotal role in changing the sightings probability
(fisheries have expanded in distance from shore and overall range over
time; Rousseau, Watson, Blanchard, & Fulton, 2019). So, the sighting
probability may have increased during the 20th century, but testing
this hypothesis was not possible with the available data. Therefore,
since this issue arises when interpreting the frequency of occurrence
4
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MORO et al.
of many other Mediterranean species, we explored the performance
of a single, readily available and systematic proxy of observation effort.
2.3 | Collecting observation effort data
In order to investigate trends in the spatio-temporal distribution
of our proxy of observation effort, we divided the Mediterranean
coastline into 202 coastal regions, belonging to 23 different coun-
tries and retrieved the human population in each coastal region
(Supporting Information) (Figure 1a,b). Historical time series of
the human population were collected for each coastal region over
a time range of 156 years (1860–2016). Multiple sources, such
as national census reports or international databases (Eurostat,
World Bank), were consulted to rebuild each human population
time series (Supporting Information). As not all considered regions
had annual census estimates for the whole time period, we inter-
polated missing years with a regression approach. Annual popula-
tion size estimates for each region were obtained by fitting GAMs
(Wood, 2011) and log-scale regressions to the historical popula-
tion censuses data. Then, we selected estimates according to the
best fitting model in each region (details are in the Supporting
Information).
2.4 | Estimating standardized shark sighting trends
In order to identify the spatial and temporal patterns characteriz-
ing the sighting data, we considered a time range of 156 years (be-
tween 1860 and 2016) and different levels of resolution for spatial
strata. We chose 1860 as our initial observation year because ear-
lier sightings were scarce and previous human population data in
most Mediterranean countries were unavailable. Observation ef-
fort and shark occurrences were spatially aggregated using both the
GFCM's Mediterranean Geographic Sub-area stratification (GSAs,
http://www.fao.org/gfcm/data/map-geogr aphic al-subar eas/en/)
(Figure 2a) and the coarser FAO Major Fishing Area 37's stratification
of eight divisions (http://www.fao.org/gfcm/data/map-geogr aphic al-
subar eas/en/) (Figure 3b). In order to find the best fitting model, we
tested multiple model structures (Supporting Information) with vari-
ous levels of temporal aggregation, functional relationships between
response and predictors (e.g. linear or more complex with polynomi-
als and splines) and the two statistical distributions of the response
variable (NB and Poisson). GAM s wi th 1-year time bins res ul te d as the
best model class in terms of AIC, fitting deviance and residuals analy-
sis. Model fits were performed by using the R package mgcv (Wood,
2011). We fitted a GAM with a Negative Binomial Distribution and a
log link function to the annual number of shark occurrences recorded
in each GSA , using the observation effort (annual number of peo-
ple for that GSA) as an offset term and the GSAs as spatial sectors
(hereby referred as GSA model). The model structure was
where zij is the ith observed number of sharks in year yi (i = 1, …, 156)
and GSA j, f is a smooth function estimated using penalized likelihood
maximization (with a smooth parameter estimated by Restricted
Maximum Likelihood) (Wood, 2011), [GSA]j is a factor with 27 levels
(j = 1, …, 27), corresponding to the GSAs), log (Hij) is the offset term and
𝜀ij
is the error term for ith observation in GSA j, assu me d to be normally
distributed around 0 and with variance to estimate.
In our modelling exercises, we faced several issues. First, in order
to verify the assumption of a linear relationship between the re-
sponse variable and the offset term, we fitted a parallel model with
a spline on the human population abundance and compared the two
model's prediction errors through RMSEs (root mean square error,
Supporting Information). Second, although we could expect the
observation effort to increase with time, we supposed there could
be stages in our observation period where sighting effort changed
abruptly (i.e. star t of ocean use for bathing, intere st in marine scie nce,
conflicts and epidemics). Therefore, we tested for the effect of these
discrete important events dividing our temporal range in bins char-
acterized by different hypothetic sighting effort regimes (Supporting
Information). Finally, there was a trade-off between spatial and tem-
poral resolution. Because of the limited number of sightings from
specific Mediterranean sectors, the use of a complex spatial strati-
fication, such as the GSA scheme, with a high temporal resolution,
allowed us only to detect a common temporal trend throughout the
basin and a sector-specific spatial effect on shark's abundance. It was
where all terms are the same as in the GSA model except for fj, which
here is sector-specific ([FAO Division]j, with j = 1, …, 8). In this way, we ob-
tained subregional tempora l trends , thou gh with a lower spatial resolution.
However, this kind of model parametrization assigns the same number of
knots to each sub-regional curve via REML (restricted maximum likeli-
hood). In this way, the trajectories estimated for well-represented sectors
(with a high number of records) could have leverage on the others with
fewer occu rre nces reco rde d. Thus , in order to valida te the cur ves obtained
with the FAO model and to avoid the presence of artefacts related to the
sparse nature of data, we chose to fit, parallelly, a single-sector model for
each division (Supporting Information). Finally, we predicted the expected
number of sharks in each spatio-temporal model unit considering a fixed
amount of observation effort (five million people), in order to standardize
the shark abundance trend on easily interpretable values.
2.5 | Testing for spatial range contraction
Given its “Critically Endangered” status, it is expected the species
went through a range contraction together with a decline in popula-
tion abundance (Worm & Tittensor, 2011). To test for this scenario,
we aggregated the data in two main periods (1945–1975, 1976–2016)
deemed to have the most comparable regimes of observation effort.
We excluded from the analysis all the sightings from before 1945 in
order to minimize the bias linked to unaccountable variations of the
(1)
log (
z
ij)
=f(y
i
)+[GSA]
j
+log(H
ij
)+𝜀
ij
(2)
log(zij)
=
fj(yi)
+
[FAODivision]j
+
log(Hij)
+𝜀
ij
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5
MORO et al .
observation effort, such as the two World Wars presence. In addi-
tion, our aim was to test for a relatively recent decline associated
with a spatial range contraction. Hence, we fitted a negative binomial
generalized linear model (GLM) (R package MASS, Venables & Ripley,
2002) to the annual number of shark occurrences (considered as rep-
licates) for each time bin in each FAO division, still maintaining the
observation effort as an offset term. The model structure was.
where zij is the observ ed numb er of shark s in per iod Ti and FAO di visio n j,
β0 is the intercept, β
1–2 are regression coefficients, Ti is the time bin, [FAO
Division]j is a factor with eight levels (j = 1, …, 8, corresponding to the
spatial sectors), log (Hij) is the offset term and
𝜀ij
is the error term for ith
observation in each FAO division j. Hence, we predicted the expected
number of sharks in each FAO division for each period considering a
fixed value of observation effort (five million people) and compared this
index for each sector between the two different time bins.
3 | RESULTS
3.1 | Exploratory data analysis
We identified a total of 773 white shark records within the
Mediterranean Sea, spanning from the end of the Middle Ages
(1453) to 2016. However, 93% (718) of these occurred after 1860,
which is the period when we had the most reliable data and, con-
sequently, was used for our trend analyses. Fisheries catches
accounted for 66% of the records, 48% coming from tuna traps, fol-
lowed by gillnets (23.0%), hand lines (8%), harpoons (6.4%), purse
seines (5.6%) and longlines (5.2%) (Figure 1e). The remaining por-
tions came from strandings, sightings, recorded predation events
and bites to humans. Records were mainly distributed in the western
Mediterranean Sea, in particular, the Northern Adriatic sea (GSA 17,
20.9%), Ligurian and North Tyrrhenian Sea (GSA 9, 13.8%), Southern
Sicily (GSA 16, 9.5%) and off the Balearic Islands (GSA 5, 8.5%); and
pertained mainly to adults (42.8%), subadults (10.5%) and juveniles
(9.6%) (adapted from Bruce & Bradford, 2012) with the majority of
individuals of being from 4 to 6 m long (Figure 1b). However, it is im-
portant to emphasize that more than a third (32.8%) of records were
lacking length information and, consequently, we could not address
the age class. Sex ratio was biased towards women (64.1% of the
142 records having sex), and the individuals that had also informa-
tion on stomach contents (n = 122) suggested that bony fish were
the main prey (27.3%), followed by odontocetes (25.0%), scavenging
carcasses of other animals (principally farm animals, pets—5.7% and
humans—10.2%) and chelonians (6.8%) (Figure 1d).
Coastal human population increased six-fold since 1860, rising
from 29.6 million to 183.8 million in 2016, though this increase was
geographically heterogeneous (Figure 1a). The highest increases were
detected in the eastern basin sectors, with the maximum rise around
(3)
log(
zij)=𝛽0+𝛽1
(
Ti
)
∙𝛽2[FAODivision]
j
+log(Hij)+𝜀
ij
FIGURE 1 Observation effort and exploratory analysis. (a) Absolute variation of coastal regions human population size calculated
between 1860 and 2016 and expressed in logarithmic scale. The red dots correspond to the white sharks sighting locations. (b)
Length frequency. The absolute frequency is reported over each bar. (c) Sex distribution within age classes (YOY = young-of-the-year,
JUV = Juveniles, SUB = Subadults, ADL = Adults, UND = Undetermined). (d) Diet composition. The absolute frequency is reported over each
bar. (e) No. of specimens caught by each fishing gear category per age class
20
30
40
50
Latitude
7
50 50
65
145
128
53
21
(b)
0.0
0.1
0.2
0.3
0−1 m1−2 m2−3 m3−4 m4−5 m5−6 m6−7 m>7m
Total length (m)
Relative frequency
(c)
0
20
40
60
YOYJUV SUBADL UND
Age class
N° of specimens
Female, n = 91
Male, n = 51
25
12
3
18 20
26
44
42
48
10
(d)
0.0
0.1
0.2
0.3
Batoids
Bird
Chelonians
Empty
Humans
Inedible
objects
Mollusc
Mysticetes
Odontocets
Other
sharks
Pinnipeds
Teleost
Terrestrial
mammal
Trophic category
Relative frequency
(e)
0
25
50
75
GillnetHarpoon LineLongline Others Purse seineTrawl Tuna trap
Fishing gear
N° of specimen
s
YOY [n = 14]
JUV [n = 27]
SUB [n = 26]
ADL [n = 148]
UND [n = 30]
–10010 3020 40
Log (absolute
rate of increase)
6
4
2
0
(a)
Longitude
6
|
MORO et al.
FIGURE 2 Temporal and spatial changes of sighting rate estimated by the GSA model. (a) Temporal effect: expected shark sighting rate
between 1860 and 2016 predicted by using a fixed observation effort of five million people. Magnitude of the detected decline, starting
year and p-value for the smooth term is shown in the top-right corner. The red line is the overall mean sighting rate. (b) Spatial effect: dots
are the average variations in mean no. of shark detected in each GSA. Point colour matches colours in the map. Segments indicate the
confidence boundaries. (c) Spatial unit used in the model: GFCM Geographic Sub-Areas (as indicated in the Res. GFCM/33/2009/2). The
green dots in the map correspond to the white sharks sighting locations extracted from a variety of source observations
0.0
0.5
1.0
Year
Nº of sharks per 5 million people
(b)
0.0
0.3
0.6
0.9
1860 1875 1890 1905 1920 1935 1950 1965 1980 1995 2010
134567891011.21213141516171819202122232425262728
GSAs
Mean nº of sharks
20
30
40
50
–100 10 20 30 40
Longitude
Latitude
(c)
(a)
p−value = 2.47e−05
1975 − 61.5%
FIGURE 3 FAO model. (a) Mediterranean white shark temporal abundance trend (1860–2016) predicted by the FAO model in each FAO
division, considering a fixed observation effort value (five million people). The black dots represent the actual sighting rates (expressed as no.
of sightings per five million people) (b) Spatial unit used in the model: FAO Major Fishing Area 37 (Mediterranean and Black Sea) divisions.
The green dots in the map correspond to the white sharks sighting locations
2.2 − Ionian 3.1 − Aegean 3.2 − Levant 4.1 − Marmara Sea
1.1 − Balearic1.2 − Gulf of Lions 1.3 − Sardinia 2.1 − Adriatic
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 19201950 1980 2010
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010 1860 1890 19201950 1980 2010
0.0
0.5
1.0
1.5
2.0
0.0
0.5
1.0
1.5
2.0
0
1
2
3
0.00
0.05
0.10
0.15
0.20
0.0
0.3
0.6
0.9
0.0
0.2
0.4
0.6
0.0
0.5
1.0
0.0
0.4
0.8
1.2
Year
Nº of sharks per 5 millions people
20
30
40
50
–100 10 20 30 40
Longitude
Latitude
(a) (b)
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MORO et al .
the Marmara Sea (319.4-fold). By contrast, the lowest rates of change
were observed in the western and central Mediterranean sectors,
such as the Northern Adriatic, Southern Sicily and Corsica, where the
human population doubled throughout the same period (Figure 1a).
3.2 | Model fitting results
3.2.1 | Temporal trends
Shark observations increased throughout the period (Figure S2), but
when we controlled for changes in the potential number of observ-
ers (Figure 2a), we detected an initial increase, characterized by two
peaks in the 1880s and a higher one in the 1980s, followed by a 61%
(range 58%–72%) decline between 1975 and 2016. Similarly, to the
changes in the human population, this trajector y was not homogene-
ous throughout the basin. At the FAO divisions' level, the non-linear
smoothing term for year (Figure 3a–c) was significant for seven of
the eight considered sectors (five with α = 0.05 and two considering
α = 0.1) and five of these six significant trajectories ended in recent
declines (Figure 3b). These declines began earlier and were more
intense in the peripheral sectors, such as the Marmara Sea (1961 –
96.0%, range 92%–100%), Adriatic Sea (1883 – 94.1%, range 90%–
100%) and the Balearic (1954 – 82.5%, range 76%–98%) than central
Mediterranean sectors (Ionian from 1988 a 52.1% decline, range
37%–8 8% ; and Sardinia from 198 0 a 76.4% decl ine, ra nge 75%–91%).
3.2.2 | Spatial patterns and distribution shrinkage
Our standardized indices of shark abundance identified heteroge-
neous spatial distribution landscapes. The main hot spots were lo-
cated in the western Mediterranean sectors (Figure 2b), especially
in the Balearic Islands (0.73, CI 0.51–1.05), Maltese waters (0.37,
CI 0.21–0.64) and Corsica (0.34, CI 0.18–0.65). Shark abundance
cold spots were in all Eastern Mediterranean GSAs, except for the
Marma ra and Aege an Sea . When we ag gregated the rec ord s in t wo
time bins (1945–1980; 1980–2016), we detected a significant con-
traction of the species’ spatial distribution. All the Mediterranean
Sea peripheral sectors recorded a decrease in shark abundance
in the second period (Figure 4c), with the highest difference de-
tected in the Marmara Sea (−96.6%, CI −95.2% to −<99.9%), fol-
lowed by the Balearic (−73.1%, CI −72.5% to −74.7%) and the Gulf
of Lions (−38.0%, CI −41.4% to −18.3%). All the central sectors, in-
stead, highlighted an increase in the shark abundance (Figure 4c),
with the highest value detected in the Ionian division (+222.6%, CI
+191.6% to +322.4%).
4 | DISCUSSION
Conservation actions and recovery plans for threatened and en-
dangered marine top predators are broadly limited by a lack of
information on the population status and trends at the scale of
whole ecor egions and over multi-deca dal time scales. By ana lysing
156 years of white shark records in the Mediterranean Sea, we
were able, for the first time, to estimate large-scale and long-term
trajectories of white shark abundance indices across the entire re-
gion. The use of all the available sources of information, integrated
with a proxy controlling for the observation effort change within
space and time, permitted us to standardize our trends. These
standardized indices of population abundance suggested that the
species went through a complex trajectory of change, character-
ized by an increasing phase followed by a sharp decline since the
198 0s. The re cent decl ine , toge t her wit h a dete c ted rang e contr ac-
ti on in the sp ati al dist r ibu t ion of recor ds ( Wo rm & Titten sor, 2011 ),
and stronger and more prolonged declines estimated in peripheral
regions compared to central sectors, suggest an overall rapid de-
cline of the white shark population in the region in the last 3–4
decades. Our results are in contrast with population abundance
increases inferred in other regions, such as California (Lowe et al.,
2012), north-western Atlantic (Curtis et al., 2014), South Africa
(Dudley & Simpfendorfer, 2006) and Australia (Reid et al., 2011).
Conversely, they are in line with regions where the white shark
occurrence data are sparse and infrequent, such as the Northwest
Pacific Ocean (Christiansen et al., 2014). These results confirmed
earlier evidence of regional declines provided by McPherson and
Myer s (20 09), Bol dro cchi et al., (2017 ) and Fer ret ti et al. (20 0 8) for
a larger taxonomic group, but scaled-down recent Red List assess-
ments carried out by the IUCN, which classified the white shark
as critically endangered in the Mediterranean Sea and European
waters (Dulvy et al., 2016; Nieto et al., 2015). Our results suggest
instead an overall decline of 61.5% over the last 10 years or three
generations, which would classify the species as endangered (EN)
“if the reduction causes may not have ceased or well understood,”
as stated in IUCN Criteria Version 3.1 (A2-bc). Taken together,
our results and those of studies conducted elsewhere highlight
the importance of regional analyses and the risk of extrapolating
trends across different geographies. It is easily perceivable that
each region has peculiar characteristics, history of human impact
and drivers of change. An informative regional assessment would
prevent wastage of both conservation efforts and resources.
Similarly, we confirmed previous evidence of the prevalence
of Mediterranean white sharks in western sectors (Boldrocchi
et al., 2017), characterized by distinct bioecological and physical
oceanographic characteristics from the Eastern Mediterranean.
The west–east temperature (Bosc, Bricaud, & Antoine, 2004) and
productivity (Coll et al., 2010) gradients would make the warmer
Eastern Mediterranean waters a suboptimal habitat for adult en-
dothermic white sharks (Carey et al., 1982). By contrast, the colder
and productive western sectors could represent resource hot
spots for the species. These are in fact important breeding and
feeding ground for bluefin tunas (Cermeño et al., 2015) and small
cetaceans (Gnone et al., 2011; Lauriano, Pierantonio, Donovan,
& Panigada, 2014), which are important food items for the white
sharks (Figure 1d, Boldrocchi et al., 2017). This result highlights
8
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MORO et al.
the critical importance of the western sectors for the persistence
of white shark populations.
Th e whi t e shark eco l ogy in the Medi ter r anean Se a is stil l po orly
characterized and these analyses are a step forward addressing
this important issue. Among the multiple hypotheses that may ex-
plain the estimated trajectories of change, we highlight three po-
tential drivers. First, over the last 200 years, coastal fishing in the
Mediterranean Sea has notably increased and expanded through-
out the region (Piroddi et al., 2015) impacting both juvenile and
adult white sharks, but contemporarily increasing the number of
occurrence records. Young-of-the-year and juvenile white sharks
are vulnerable to inshore gears such as trammel or gill nets (Bruce
& Bradford, 2012; Curtis et al., 2014; Lowe et al., 2012), which
have been massively used all along the Mediterranean shores also
for targeting sharks (Ferretti, Osio, Jenkins, Rosenberg, & Lotze,
2013). Adult individuals were frequently reported in tuna traps
(>50% of catches on record, Figure 1e), which were fixed gears
historically used to catch bluefin tunas on their migratory routes
in the Mediterranean Sea (Bombace & Lucchetti, 2011) and could
have represented a source of mortality for white sharks for centu-
ries. Since the 1960s, tuna traps ceased to be profitable and most
have been closed as an effect of tuna overexploitation by indus-
trial purse seining and other pelagic fisheries (Fromentin & Powers,
2005; ICCAT, 2017; Rouyer et al., 2018). This may have reduced
the impact on adult white sharks as well as the number of catches
we had on record. Meanwhile, white sharks began to be exposed
to offshore fishing, especially tuna and swordfish longlining, which
greatly escalated in the region during the last 50 years (Ferretti
et al., 2008). In this period, white sharks were exposed to both
inshore and offshore fishing and could not benefit from sheltering
offshore which was practically unexploited historically (Ferretti
et al., 2008). Similar patterns have been observed in South Africa
and Australia (Dudley & Si mpfendor fer, 2006; Ferretti et al., 2010 ;
Reid et al., 2011).
It is also possible that Mediterranean white sharks have fol-
lowed the population trajectory of Bluefin tuna, one of their most
frequent prey in the region (Boldrocchi et al., 2017; De Maddalena
& Heim, 2012; Kabasakal, 2016). In our data, 27.3% of the white
sharks with stomach content data ate bony fish and 47% of these
fishes were tunas (Figure 1d, Table S6). Tunas are suitable prey for
white sharks (Hussey et al., 2012) and the bluefin tuna's overex-
ploitation in the last 50 years may have reduced one of the most
FIGURE 4 Spatial range contraction. Predicted mean annual no. of sharks every five million people for each FAO division in time
intervals: (a) 1945–1975, (b) 1976–2016. The red dots in the maps show the white sharks sighting locations in each period. (c) Variation
between 1945–1975 and 1976–2016 time bins. Red sectors correspond to abundance rise, while blue sectors correspond to abundance
decline
30
35
40
45
0102030
Longitude
Latitude
0.2
0.4
0.6
Mean
n°of sharks
30
35
40
45
0102
03
0
Longitude
Latitude
0.2
0.4
0.6
Mean
n°of sharks
30
35
40
45
0 10 20 30
Longitude
Latitude
–0.75
–0.50
–0.25
0.00
0.25
Change in the
number of
sharks records
(a) (b)
(c)
|
9
MORO et al .
imp ort ant prey resources for this species in the area (ICCAT, 2017;
Rouyer et al., 2018). The long-ter m trajec tory we estimated for the
white shark records has a temporal phase similar to the tim e series
of Mediterranean bluefin tuna abundance estimated from centu-
ries of tuna trap data (Ravier & Fromentin, 2001). The bluefin tuna
decline detected in recent decades (Fromentin & Powers, 2005;
Rouyer et al., 2018) coincides with the recent decline of the white
shark sighting rate, supporting the plausibility of a predator–prey
dynamic between the two species. Yet tuna overexploitation also
caused the end of the tuna trap fishery. Therefore, it is unclear
whether such a contemporar y decline in sighting rate has been
caused by the end of an important source of white shark mortality
in the region (i.e. decline in catch records from tuna traps), or by
an underlining population decline through indirect bottom-up ef-
fect s (be cause of the declin e of an important prey), or both fa ctors
combined. However, no differences in GAM’s trajectories were
detected by fitting the FAO model with and without the tuna trap
catches (Supporting Information, Figure S9) in all sectors but the
Balearic (Division 1.1), a piece of evidence against the decline in
catch record hypothesis. Whereas, the predator–prey hypothesis is
cor ro borate d by the fact that adult white sharks’ preferential prey,
such as pinnipeds and whale carcasses (Hussey et al., 2012), have
been much scarcer or essentially absent in the Mediterranean Sea
for most of the period considered in this analysis. The only pinni-
ped in the region, the monk seal (Monachus monachus, Phocidae),
has small remnant populations only in the Eastern Mediterranean
sectors (Karamanlidis & Dendrinos, 2015) and was considered
rare (heavily depleted by centuries of overhunting) in most of the
Mediterranean Sea by the 18th centur y (Johnson, 2004). Whale
abundance is also much lower than in other ocean sectors where
white sharks occur (Notarbartolo di Sciara, 2002). It is, therefore,
possible that adult white sharks adapted to feed mainly on tunas
in the Mediterranean Sea; a hypothesis that would make this pop-
ulation unique respec t other global populations and should be for-
mally tested in future research.
The above explanations are confounded by the change in ob-
servation effort expected from the spatial and temporal expan-
sions of fisheries and other factors affecting the probability to
detect records. We used trajectories of human population change
along the Mediterranean coasts as a single and practical proxy of
observation effort, but human population abundance is one of its
multiple components. For example, linguistic barriers and politi-
cal instability may have limited the number of records we found
in North African and Middle Eastern regions, as well as the two
World Wars and the 1918 Flu Pandemic may have acted simi-
larly in Europe during these periods (D’Ancona, 1949; Thurstan,
Brock i n g ton, & Ro ber ts , 20 10). In ad dit i o n , epis o d i c event s , su ch as
a 19th-cen tur y rew ard pro gram me iss ued by th e Imper ial Ma riti me
Austrian Government to cull white sharks in the Adriatic Sea (De
Marchesetti, 1882), could have boosted the probability to have
occurrence records independent of human population changes.
Similarly, the expansion of the use of the Internet and social net-
works in the last 20 years has likely increased the probability that
a record of a white shark capture is reported. International (CITES,
CSM, Barcelona and Bern Conventions) and national legislation (in
Malta, Israel, Croatia, Montenegro and Slovenia) to protect this
species may have deterred Mediterranean fishermen in reporting
catches, fearing disruptive or legal consequences for their activ-
ities. Although these factors may have acted in different direc-
tion (i.e. biasing upward or downward the estimated trends), the
probabilistic distribution we used to handle the response variable
allows clustered observations, quantifying these sources of bias
is now a top priority to further explaining and reducing uncer-
tainty of the general large-scale spatial and temporal patterns we
identified. Nevertheless, our modelling approach represents an
innovation in analysing opportunistic data, by testing observation
effort regimes that are not simulated, as done so far (Christiansen
et al., 2014; Curtis et al., 2014; McPherson & Myers, 2009), but
quantitatively estimated through the use of a proper observation
effort proxy. Indeed, most of the cited features affecting obser-
vation effort are in some ways related to demographical changes
of the human population (i.e. fishing pressure, technological de-
velopment). Hence, adopting an observation effort proportional
to the human population mitigates the confusing effects of the
mentioned factors.
Reconstruction of the Mediterranean white shark spatio-tem-
poral patterns of abundance, obtained by using all available occur-
rence records, generated new hypotheses on the species’ population
structure and predator–prey dynamics in the region. Testing these
hypotheses with further dedicated research will further contribute
to reconstruct population baselines of this species and deepen our
understanding of its life history, ecology and biogeography. These
aspects are crucial to ensure the conservation of white sharks in
the region and across the planet. We also identified occurrence hot
spots that would represent important sampling locations for collect-
ing high-quality biological data, including tracking data to directly
assess distribution, foraging and habitat use. These field studies are
expensive and require careful planning on where and when white
sharks are most likely detected.
Globally, there are multiple species of conservation concern
with a similar scantiness of abundance data that would benefit from
our approach of combining all available occurrence data. Our study
shows that a careful examination of these data, even if opportunis-
tic, can reveal important ecological patterns, particularly regarding
trends in abundance and spatial distribution, that are critical to in-
form adequate conser vation actions and science-based recovery
plans.
ACKNOWLEDGEMENTS
This work is part of the Global Shark Abundance Baselines funded
by the Lenfest Ocean Program. Dr. Stefano Moro was partially
supported by the PRIN2015 supported-project "Environmental
processes and human activities: capturing their interactions via sta-
tistical methods (EPHAStat)" funded by MIUR (Italian Ministr y of
Education, University and Scientific Research) (20154X8K23-SH3).
Additional funders are as follows: Lenfest Ocean Program, Bertarelli
10
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MORO et al.
Foundation, Schmidt Technology Partners, MedReAct and Regione
Lazio with the Grant Programme “Torno Subito 2017”. We Thank
Annie Adelson for help in bulding the sighting database.
DATA AVA ILAB ILITY STATE MEN T
The data that support the findings of this study are available from
the corresponding and senior authors (stefano.moro@uniroma1.
it and ferretti@vt.edu) upon reasonable request.
ORCID
Stefano Moro https://orcid.org/0000-0001-7424-1382
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Moro S, Jona-Lasinio G, Block B,
et al. Abundance and distribution of the white shark in the
Mediterranean Sea. Fish Fish. 2019;00:1–12. h t t p s : / / d o i .
org /10.1111/f af.12432
