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RESEARCH ARTICLE
Population structure, distribution and habitat use of the
Critically Endangered Angelshark, Squatina squatina, in the
Canary Islands
Eva K. M. Meyers
1
|Fernando Tuya
2
|Joanna Barker
3
|David Jiménez Alvarado
2
|
José Juan Castro‐Hernández
2
|Ricardo Haroun
2
|Dennis Rödder
1
1
Zoologisches Forschungsmuseum Alexander
Koenig, Bonn, Germany
2
Biodiversity and Conservation Research
Group, Universidad de Las Palmas de Gran
Canaria, Telde, Spain
3
Zoological Society of London, London, UK
Correspondence
E. Meyers, Zoologisches Forschungsmuseum
Alexander Koenig, Adenauerallee 160, 53111
Bonn, Germany.
Email: e.k.m.meyers@gmail.com
Abstract
1. Angel sharks are among the most threatened fish worldwide, facing regional and global extinc-
tion. In Europe, populations of the three Critically Endangered angel sharks (Squatina aculeata,
Squatina oculata and Squatina squatina) have been severely depleted.
2. Taking advantage of the last global ‘hotspot’of the angelshark, Squatina squatina, this study
gathered data through a citizen science programme to describe the occurrence of this shark
in the coastal waters of the Canary Islands. Specifically, this study described (1) the population
structure, and (2) habitat use of this species, which was used in a Species Distribution Model to
(3) examine realized and potential distribution patterns, and to (4) determine the relative
importance of environmental predictors on the occurrence of S. squatina.
3. Over the 12 months sampling period (April 2014 –March 2015), 678 sightings were reported.
Individuals ranged from 20 to 200 cm (total length). Larger sightings of both females and neo-
nates occurred mostly in April to July, i.e. during the pupping season. Males were significantly
more frequent in November to January, i.e. during the mating season. Angelsharks were
encountered at depths from <1 m to a maximum of 45 m. Small‐sized individuals (i.e. neonates)
exclusively occurred in shallow water (0–25 m). Most sharks occurred on sandy bottoms
adjacent to reefs.
4. Even though sightings were recorded at all seven islands in the archipelago, there were fewer
encounters in the western than the eastern islands.
5. The Species Distribution Model indicated that the probability of occurrence mainly correlated
with sea surface temperature, chlorophyll, salinity and depth. Areas with the greatest habitat
suitability were in shallow water.
6. The angelshark displayed spatial (vertical and horizontal) and temporal segregation by size and
sex. This information is vital to inform conservation of this Critically Endangered shark in its
last stronghold.
KEYWORDS
coastal, distribution, endangered species, fish, modelling, ocean
1|INTRODUCTION
Chondrichthyans are key to the world's oceans as they play a signifi-
cant functional role as predators (Heithaus, Wirsing, & Dill, 2012),
and have gained important value in the wildlife tourism sector (Vianna,
Meekan, Pannell, Marsh, & Meeuwig, 2012). However, one‐quarter of
all chondrichthyan species are threatened with extinction, with angel
sharks found to be the second most threatened family of elasmo-
branchs in the world (Dulvy et al.,2014). Several populations of
sawfishes, skates and angel sharks have been locally and/or regionally
Received: 3 May 2016 Revised: 19 January 2017 Accepted: 12 February 2017
DOI: 10.1002/aqc.2769
Aquatic Conserv: Mar Freshw Ecosyst. 2017;1–12. Copyright © 2017 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/aqc 1
extirpated (Dulvy & Forrest, 2010; Dulvy, Sadovy, & Reynolds, 2003)
across their distribution ranges, or have not been reported for many
decades, highlighting the necessity of urgent conservation and
management (Dulvy et al.,2014).
Research efforts to assess the status of species and, in particular,
to identify those that are at risk are essential in any conservation plan-
ning (Simpfendorfer, Heupel, White, & Dulvy, 2011). It is particularly
challenging to develop conservation and recovery strategies for spe-
cies with limited scientific data on their basic ecology and distribution,
especially for those species that already have reduced population sizes
and are rarely caught or reported in fisheries. Considering that many
studies on elasmobranch trends have used data from fisheries, there
is a greater challenge to collect scientific data on rare or declining
species with no commercial interest. One approach to tackle data gaps
is to include information from alternative sources, such as those pro-
vided through citizen science programmes, an approach increasingly
used for marine and coastal conservation worldwide (Cigliano et al.,
2015). Data provided by observers and volunteers has often been
highlighted as an adequate alternative (Delaney, Sperling, Adams, &
Leung, 2007; Goffredo et al., 2010). In particular, this approach is a
cost‐effective solution to obtain large datasets covering wide geo-
graphic areas (Bernard, Götz, Kerwath, & Wilke, 2013; Cohn, 2008;
Newman, Buesching, & Macdonald, 2003; Pattengill‐Semmens &
Semmens, 2003). This strategy has been useful, for example, to gather
information on the distribution, abundance, habitat use and population
structure of elasmobranchs such as grey reef sharks, Carcharhinus
amblyrhynchos (Hussey, Stroh, Klaus, Chekchak, & Kessel, 2013);
manta rays, Manta alfredii (Jaine et al.,2012) and Manta birostris (Luiz,
Balboni, Kodja, Andrade, & Marum, 2009); yellow stingrays, Ueobatis
jamaicensis (Ward‐Paige, Pattengill‐Semmens, Myers, & Lotze, 2011);
smalltooth sawfish, Pristis pectinata (Waters et al.,2014; Wiley &
Simpfendorfer, 2010); wobbegong sharks, Orectolobus spp.
(Huveneers, Luo, Otway, & Harcourt, 2009); and whale sharks,
Rhincodon typus (Graham & Roberts, 2007; Meekan et al., 2006). The
efficacy of citizen science as a tool to monitor shark populations has
also been validated in comparative studies. For example, data gathered
by experienced dive guides to estimate the abundance of reef sharks in
Palau was consistent with a comparative study using long‐term telem-
etry data to estimate abundance of the same population (Vianna,
Meekan, Bornovski, & Meeuwig, 2014). Moreover, public sightings
data to monitor the smalltooth sawfish population in the south‐east
USA provided important information on the current distribution of
the species and additional valuable information used for conservation
efforts (Wiley & Simpfendorfer, 2010). However, it is recognized that
there are a variety of concerns with the quality of datasets provided
by recreational divers (Ward‐Paige & Lotze, 2011). For example,
underwater surveys conducted by divers are limited by diving
conditions (e.g. wave action, accessibility, turbidity) and the limits of
recreational diving depths.
Understanding distribution patterns of species and their habitat
use is crucial for many aspects of their conservation and environmental
management (Brooks, Sloman, Sims, & Danylchuk, 2011; Colton &
Swearer, 2010; Franklin, 2009). Species Distribution Models (SDMs),
also known as ecological niche models, link species occurrences or
abundances at a range of sites with environmental drivers and/or
spatial characteristics of the sites (Franklin, 2009). Species Distribution
Models can be applied on varying scales, ranging from continental to
microhabitats. However, when using SDMs to estimate the potential
distribution of a species within a subset of its range, extrapolation
beyond this range may be associated with high uncertainty. In marine
systems, SDMs have become particularly important in terms of conser-
vation planning (Robinson et al., 2011). For example in the design of
marine protected areas; the implementation of certain habitat conser-
vation strategies; understanding fisheries interactions; and predicting
impacts of climate change and exotic species invasions (Embling
et al., 2010; Hannah et al.,2007; Maxwell, Stelzenmuller, Eastwood,
& Rogers, 2009; Sequeira, Mellin, Fordham, Meekan, & Bradshaw,
2014; Sundblad, Bergström, & Sandström, 2011). Species Distribution
Models have now been applied successfully to a wide range of marine
species, including seaweeds (Martinez, Viejo, Carreno, & Aranda,
2012), seabirds (Oppel et al.,2011), reef fishes (Mellin, Bradshaw,
Meekan, & Caley, 2010), whales (Druon et al., 2012) and sharks
(McKinney, Fulford, Wu, Hoffmayer, & Hendon, 2012; Sequeira,
Mellin, Rowat, Meekan, & Bradshaw, 2012; Sequeira et al., 2014).
The angelshark, Squatina squatina (Linnaeus, 1758), is a dorso‐
ventrally flattened, bottom‐dwelling, shark listed as Critically
Endangered by the IUCN Red List of Threatened Species in 2006
and 2015 (Ferretti et al.,2015). This species inhabits continental
shelves down to 200 m depth (Compagno, Dando, & Fowler, 2005)
and can also be found in estuaries and brackish waters (OSPAR
Commission, 2010). The angelshark was historically distributed from
Norway to the West Sahara and the Canary Islands, including the
Baltic, Mediterranean and Black seas (Compagno et al., 2005).
Although there are no data on current or historical abundance, histor-
ical records from fisheries landings and research survey data have
revealed that S. squatina was very abundant throughout its entire
distribution range, including the North Sea, the English Channel
(Day, 1880) and the Mediterranean Sea (Psomadakis, Maio, & Vacchi,
2009). However, in the past 50 to 100 years, it has suffered severe
population declines and is currently absent from research vessel sur-
veys and fisheries landings throughout its entire range (International
Council for the Exploration of the Sea, ICES, 2014; OSPAR, 2010).
Recent studies have reported occasional sightings in the Black Sea,
the Sea of Marmara, the Aegean Sea, the Levantine Sea and the
Northern Adriatic Sea (Bilecenoğlu, Kaya, Cihangir, & Çiçek, 2014;
Fortibuoni, Borme, Franceschini, Giovanardi, & Raicevich, 2016;
Kabasakal & Kabasakal, 2014). Importantly, to date, there are no
population estimates for this species. As a result, baseline information
on the current spatial distribution patterns, habitat use, abundance and
population structure of the angelshark are lacking, which are necessary
to promote urgent conservation policies. Seasonal coastal migrations
linked to warming water temperature have been described in the
northern part of its range (Compagno et al.,2005; Wheeler, Blacker,
& Pirie, 1975), which fits observations for other species within the
same genus, e.g. Squatina californica (Compagno et al.,2005;
Eschmeyer, Herald, & Hammann, 1983; Kato, Springer, & Wagner,
1967; Natanson & Cailliet, 1986). A tagging study conducted in Irish
waters reported that S. squatina undertakes seasonal migrations into
deeper waters during a certain season and may return to the same
area, or be resident, for a certain period of time (Green, 2007).
2MEYERS ET AL.
However, data on the migratory behaviour of angelsharks are very
scarce and should be further investigated.
In the Canary Islands, benthic elasmobranch species are often
sighted and the angelshark is, in particular, one of the most commonly
encountered species by recreational scuba divers (Narvaez, 2013;
Tuya, Sanchez‐Jerez, Dempster, Boyra, & Haroun, 2006). This repre-
sents a unique opportunity to gain vital biological and ecological data
on this Critically Endangered species. Diving tourism is an important
industry in the Canary Islands; the large number of scuba diving
operators who regularly visit the same sites throughout the entire year
represents an important opportunity to gather information on
angelsharks. In this sense, the aims of this study were to identify and
describe the spatio‐temporal distribution patterns of juvenile and adult
angelsharks in the coastal waters of the Canary Islands. Specifically,
data were obtained from recreational scuba divers through a citizen
science programme, and through parallel underwater visual surveys
to validate sightings and gather extra information, to (1) describe the
population structure, (2) identify the habitat use, (3) examine real-
ized and potential distribution patterns, and (4) determine the
relative importance of environmental predictors on the occurrence
of S. squatina in the Canary Islands.
2|METHODS
2.1 |Study region
The Canary Island archipelago comprises seven main islands and four
islets (Chinijo Archipelago) that have emerged after successive volcanic
events from the ocean basin. All together, the islands have a surface
area of 7490 km
2
and a coastline covering 1501 km. They are located
west of the African coast, situated between latitude 27.68–29.58 N
and longitude 18.28–14.58 E. The easternmost part of the archipelago
(Fuerteventura) lies only 90 km away from the shore of the African
mainland, while La Palma island is almost 400 km from the African
coast (Fernández‐Palacios & Martín Esquivel, 2001). The Canary
Islands are a very popular tourist destination (12 million tourists in
2015) particularly for scuba divers. There are 84 official diving centres
distributed across the archipelago. The most popular islands for diving
are El Hierro, Fuerteventura, Gran Canaria, Lanzarote and Tenerife.
Divers reach dive sites either from land or by boat, but due to the
exposure and hydrodynamics of the islands, most dive sites are located
in eastern and southern regions of each island.
2.2 |Angelshark presence data
Data on angelshark encounters were obtained through a citizen
science tool, POSEIDON (www.programaposeidon.eu), which was
initiated by the Universidad de Las Palmas de Gran Canaria to monitor
marine biodiversity in the Canary Islands. Within this ongoing
programme a specific online tool was developed for the Angel Shark
Project, to compile data of angelshark encounters. The project and its
database were promoted through available social media (Facebook,
Twitter), media releases (local newspaper, local TV), a website (www.
angelsharkproject.com) and educational materials that were distrib-
uted to dive centres. Dive schools, centres and shops in all seven
islands were also individually approached, or contacted via email, to
encourage their participation in the project and to receive training
and educational material on the database and on angelsharks.
Recreational scuba divers were encouraged to use the database to
register the exact location on a map (latitude and longitude) of their
angelshark encounters within the archipelago and record specific data
on: date and time of encounter; number of sharks; estimated total
length (categorized as: < 30 cm, 30–100 cm, >100 cm); sex of the
shark, if able to identify (male/female/unknown); habitat type where
the shark was seen (categorized as sand, reef, rock and sand and
seagrass); water depth (m); water temperature (°C); behaviour of
the shark at the time of the encounter; and the total diving time of
the observer (Figure S1, Supporting information). To submit data into
the database, observers had to register as a user and fill out a ‘profile’
with contact data, age, occupation, level of diving experience, diving
certification and affiliations to any diving club/centre/school. In
addition, participating diving centres submitted a record of the number
of dives per month, which was used as a measure of diving effort. The
effort was quantified as the average number of dives that were under-
taken by each diving centre per month, as well as the number of partic-
ipating diving centres per island. This information was subsequently
used to standardize the number of sightings per island, to account
for varying sampling effort between islands.
The reliability of supplied data was validated in the following ways:
(i) personal contact with observers to evaluate their level of expertise
and reliability; (ii) level of diving experience of the observer; (iii) regular
scientific dive surveys to locations where encounters were registered
to confirm the presence of angelsharks.
Scientific diving surveys to areas of predicted occurrence of
angelsharks, or to exact locations indicated in the database by
observers, were carried out to validate the citizen science data. The
survey consisted of a visual exploration of the area during a 60 minute
dive. During these surveys, more detailed information was collected
on the habitat types, population structure and site affinity in ‘hotspots’
(i.e. areas with a high sighting frequency) that were identified via the
database.
2.3 |Data processing
Only validated records based on the previously described criteria were
used for the analysis. Effort‐based analyses, as described earlier, were
used to avoid an overestimation or underestimation of sightings
according to varying sampling effort among islands and seasons. To
describe the population structure, size estimates were classified
into three body length categories: neonates: <30 cm, pre‐adults:
30–100 cm and adults: > 100 cm, and were distinguished between
males, females and unknown sex. Deviation from an expected 1:1
sex ratio for the overall study was tested through a chi square test.
Correlation between sightings of females and neonates was calculated
with a Pearson product moment correlation. To investigate the habitat
use of angelsharks, a correlation analysis between the body size of
each individual and the depth of the sighting was carried out by means
of a Pearson product moment correlation. Furthermore, a factorial
one‐way Analysis of Variance (ANOVA) was used to test for significant
differences in the overall number of sightings (standardized by the
MEYERS ET AL.3
number of participating diving centres per island) between three
groups of islands distributed along an east to west gradient through
the archipelago: the eastern (Lanzarote and Fuerteventura), central
(Gran Canaria and Tenerife) and western islands (La Gomera, La Palma
and El Hierro). Data were square‐root transformed to achieve
parametric assumptions (homogeneous variances and normality).
2.4 |Environmental data
To determine the potential distribution of angelshark according to
environmental drivers,monthly level‐3 pre‐processed environmental
data were acquired from Aqua‐MODIS (http://oceancolor.gsfc.nasa.
gov/) at 4 km resolution (2.5 arc min) with a temporal coverage of
12 months (September 2013 to October 2014). In addition, environ-
mental data were derived from the Bio‐ORACLE online platform
(http://www.oracle.ugent.be/), a climate dataset designed for marine
species distribution modelling at a spatial resolution of 9.2 km (5 arc
min) (Tyberghein et al.,2012). Bathymetry data were derived from
the General Bathymetric Chart of the Oceans (GEBCO). Spatial data
(environmental predictor variables) were resampled to the same cell
size using ArcToolBox functions from ArcGIS 10.2.2 (ESRI
Corporation).
The monthly sets of the following variables were used for SDM
development (see Table S1 for further information on the source of
each variable): chlorophyll aconcentration (Chl), cloud cover, diffuse
attenuation, dissolved oxygen (dissox), nitrate concentration, photo-
synthetically active radiation (PAR), pH, phosphate, particulate organic
carbon (POC), salinity, silicate, sea surface temperature (SST) (daytime
and night‐time) and bathymetry. In order to extract a subset of
variables covering the most important environmental variations, all
(monthly) variables were summarized by a Principal Component
Analysis (PCA) using Cran R3.2.2. Ten principal components (PCs) with
eigenvalues greater than 1 were used as predictors for the SDM.
All environmental variables were selected based on their direct or
indirect (e.g. because they serve as a proxy for prey availability) influ-
ence on elasmobranch distribution and movement. For example a
study conducted by Vögler, Milessi, and Quiñones (2008), identified
that temperature, salinity and bathymetry were the environmental
variables that affected the distribution of Squatina guggenheim, in the
south‐west Atlantic. For whale sharks, Chl,bathymetry and SST were
the main drivers for habitat suitability (McKinney et al.,2012; Sequeira
et al.,2014). Diffuse attenuation, PAR and Chl were also used because
they characterize areas of high productivity (Jaud, Dragon, Garcia, &
Guinet, 2012; Sequeira et al.,2012), which are preferentially occupied
by many marine species (Block et al.,2011).
2.5 |Modelling approach
Ideally, species distribution data should be generated from entirely
randomized data collection surveys; however, such data may be
difficult to obtain in many cases, for example through citizen science
programmes. Hence, this study used a maximum entropy approach,
via Maxent 3.3.2 (Phillips, Anderson, & Schapire, 2006; http://www.
cr.princeton.edu/~shapire/Maxent), to calculate a species distribution
model. Maxent is a programme for modelling potential distributions
from presence‐only data and random background data (Elith et al.,
2011). This method estimates the probability that the environmental
conditions are suitable for a targeted species at a given site
(Elith et al.,2011; Phillips et al.,2006) and is particularly suitable for
presence‐only data of species where systematic survey data are
limited (Elith et al.,2011).
Applying the default settings, the species records were 100 times
randomly split into 70% training and 30% testing subsets. The area
under the receiver operating characteristic curve (AUC) was used to
measure model performance (Fielding & Bell, 1997; Swets, 1988).
AUC values above 0.7 indicate that the performance of the model is
acceptable (Phillips et al.,2006). Variable importance was estimated
using a permutation approach, as well as a jack‐knife test, implemented
in Maxent. A map showing the potential distribution of angelsharks
was generated applying the 10% training presence logistic threshold
as a non‐fixed presence–absence threshold. In order to derive a biolog-
ically meaningful training background for the SDM, a bathymetry layer
of the Canary Islands was re‐classed in DIVA‐GIS 5.4 to indicate areas
up to 200 m depth, highlighting the areas with the maximum depth at
which angelsharks occur (Compagno et al.,2005).
3|RESULTS
Over the 12 months sampling period (April 2014 –March 2015), 678
sightings of S. squatina were registered through the citizen science
programme. In total, 39 citizen scientists submitted their sightings to
the POSEIDON database, corresponding to 22 independent scuba
diving centres located at Lanzarote (n= 6), Fuerteventura (n= 4), Gran
Canaria (n= 7), Tenerife (n= 2), La Gomera (n= 1), La Palma (n= 1) and
El Hierro (n= 1). There was not an equal number of sightings from each
island, nor from each diving centre. The average number of dives that
were undertaken by each diving centre per month during the study
period was 60.5 dives per month.
In total, 40 scientific dive surveys were conducted across the
archipelago (except La Gomera), in those spots where S. squatina were
either encountered, as indicated by the POSEIDON database users, or
where the habitat requirements (according to our predictions) seemed
to be suitable. Sharks were encountered in 55% of these scientific dive
surveys (n= 22). Most encounters and aggregations (for this study,
more than two sharks reported in one particular area were considered
as an aggregation) of angelsharks occurred during night dives.
3.1 |Population structure
Over the study period, June was the month with the highest number of
encounters (n= 143; 25% of total encounters), followed by May
(n= 65; 12% of total encounters), December (n= 62; 11% of total
encounters) and April (n= 43; 8% of total encounters). Taking into
account the diving effort (average number of dives per month), there
are two peaks in encounters, one in spring/early summer (April–July)
and one in winter (December–February) (Figure 1). September was
the month with the least reported sightings (n= 13; 2% of total
encounters), despite repeated dives (at day and night) in spots where
encounters have been commonly reported during other months. Most
4MEYERS ET AL.
neonates were sighted between April and July (n= 70 out of 76), par-
ticularly in June and July. Sightings of females and neonates correlated
positively (r
s
= 0.73, P< 0.01), with a greater number recorded during
those months (Figure 1).
Sex was identified in n= 170 cases; there were significantly more
females than males (χ
2
= 4.840, df = 1, P= 0.0278). Male sharks
were encountered in lower numbers throughout the study (39.3%),
with a peak in June (early summer) and between November and
January (winter, Figure 1). Male observations were significantly more
frequent in winter (n= 44) than in summer (n= 24) (χ
2
= 5.882, df = 1,
P= 0.015). Moreover, except for two sightings, all male sharks were
>100 cm TL.
Overall, angelsharks ranged in size from 20 to 200 cm (total length
(TL)). The most common reported sizes were >100 cm (n= 237). The
estimated TL of the largest individual was a 200 cm female. Maturity
data for angelsharks (Osaer, Narváez, Pajuelo, & Lorenzo, 2015)
suggests that 53% of encountered sharks had not yet reached sexual
maturity, while 47% were considered to be sexually mature. Two
females were observed giving birth in June, while 12 gravid females
were registered (three in February, two in March, five in June/July
and two in December), some with mating scars.
The total number of encounters per island (standardized according
to sampling effort per island) showed a clear gradient from the eastern
to the western islands; the overall number of sightings in the eastern
and central islands (Lanzarote, Fuerteventura, Gran Canaria and
Tenerife) was significantly larger than those at the western islands
(La Gomera, La Palma and El Hierro) (Figure 2, one‐way ANOVA,
p= 0.02). The number of neonates was particularly elevated in
Tenerife (36 out of a total of 69), at a nursery area that is
currently being monitored in an ongoing study. Another possible
FIGURE 1 Overall number of sightings per month (April 2014 –March 2015) of the angelshark, Squatina squatina, in the Canary Islands; data
were standardized by the average number of dives per month of the 22 participating diving centres. Sightings are categorized according to
neonates(< 30 cm TL), juveniles (30–100 cm TL), adult males and females (> 100 cm) and unknown estimated total length
FIGURE 2 Number of sightings of the angelshark, Squatina squatina, at each of the seven islands (shown in west to east order) from April 2014 to
March 2015; data were pooled through time and standardized by the number of participating diving centres per island. Sightings are categorized
according to neonates (< 30 cm TL), juveniles (30–100 cm TL), adult males and females (> 100 cm) and unknown estimated total length
MEYERS ET AL.5
nursery area was identified in Lanzarote, where 21 neonates were
reported.
Angelsharks were encountered in water depths from <1 m to a
maximum of 45 m; most encounters occurred between 10 and 15 m
depth (n= 189) (Figure 3a). Neonate and juvenile angelsharks were
encountered only within the first 25 m. Sharks observed in deeper
areas (>15 m) increased in TL (Figure 3a; r
s
= 0.79, P< 0.01). Most
sharks (88.9%) were observed associated with sandy bottoms, either
close to reefs or strips of sand. A small proportion of individuals were
also encountered on reefs and seagrass beds (Figure 3b). Angelshark
presence was reported to the database at temperatures between 18°
C and 22°C.
3.2 |Species distribution modelling (SDM)
Among a set of 10 PCs, representing 119 environmental variables, two
PCs (PC4 and PC5) were the most influential set of variables character-
izing the potential spatial distribution of S. squatina in the Canary
Islands (Table S2). PC4 was positively correlated with SST, salinity
and pH, but negatively correlated with Chl, POC, diffuse attenuation
coefficient (kd490) and bathymetry. PC5 was positively correlated
with cloud cover, dissolved oxygen, phosphate, bathymetry and
negatively correlated with Chl,diffuse attenuation coefficient, SST,
PAR, POC (Table S3). In addition, the Maxent model's jackknife test
of variable importance also showed that these variables accounted
for most variation to explain the potential distribution of angelsharks.
The remaining PCs are not discussed further, as their contribution
was negligible (< 5%).
Maxent's response curves showed how each PC influenced the
prediction of the model. Curves showed that the logistic prediction
change as each PC varied, leaving all other PCs at their average sample
value (Figure S2). Analysis of the PC4 response curve suggested that
the probability of species presence was affected particularly by the
SST, salinity, and pH, as well as Chl, POC, diffuse attenuation coeffi-
cient (kd490) and bathymetry, with a truncated response curve. The
opposite is true for PC5, which showed a bell‐shaped response curve,
indicating reduced suitability after a certain threshold of the environ-
mental predictors. On the other hand, the response curves indicated
a Maxent model using only the corresponding PCs. The response curve
using only the PC4 showed that the variables which correlated with
this principal component increase or decrease the probability of occur-
rence beyond a threshold. The PC5, however, has a similar bell‐shaped
FIGURE 3 (a) Number of sightings of the angelshark, Squatina squatina at different water depths. Sightings are categorized according to neonates
(< 30 cm TL), juveniles (30–100 cm TL), adult males and females (> 100 cm) and unknown estimated total length. (b) Percentage of sightings of the
angelshark, Squatina squatina, associated with four different habitat types
6MEYERS ET AL.
response curve, indicating, again, a threshold at which the environmen-
tal variables of this PC become a limiting factor on angelshark
occurrence (Figure S3).
The SDM has a strong predictive power with an AUC value of
0.973 and a Test AUC of 0.961. The minimum training presence
logistic threshold of the SDM was <0.1 (0.0217), while the value for
the 10 percentile training presence logistic threshold was 0.4559.
The 10 percentile training presence threshold indicates the probability
value where 10% of the presence points do not fall in the potential
area.
Sightings of angelshark were recorded at all seven islands
(Figure 4a). The potential distribution map denotes suitable habitat
at all seven islands, with a higher suitability around the easternmost
(Lanzarote, Fuerteventura) and central islands (Gran Canaria and
Tenerife). Areas with the largest habitat suitability were at shallow
depth, close to the shore. For example, on the north‐eastern side of
Gran Canaria, the bay of Sardina was particularly highlighted. In
Lanzarote, the northern Chinijo archipelago and almost the entire
eastern coast, particularly the area of ‘Puerto del Carmen’, was pre-
dicted as a highly suitable habitat. There were also areas with high
suitability in the south‐east of Fuerteventura. A high frequency of
sightings per cell was observed at four sites: in ‘Las Teresitas’
(Tenerife), in ‘Sardina del Norte’and in ‘El Cabrón’(Gran Canaria)
and in ‘Puerto del Carmen’(Lanzarote) (Figure 4b). Three of these sites
have also been identified as potential nursery areas, due to the ele-
vated number of sightings of juvenile sharks.
FIGURE 4 Habitat suitability map, from the maximum entropy model, for the angelshark, Squatina squatina. (a) Frequencies of sightings per cell are
indicated as unfilled circles; (b) sightings are shown as black dots. Warmer colours indicate higher suitability areas; light grey colour denotes areas
up to 200 m depth
MEYERS ET AL.7
4|DISCUSSION
This study is the first to analyse the spatio‐temporal occurrence
patterns, population structure, distribution and habitat use of the
angelshark, Squatina squatina, in the Canary Islands. The results of
the study demonstrated that angelsharks exhibit highly structured
and reproductively active populations, distributed along coastal
shallow areas in the entire archipelago. Angelsharks were encountered
throughout the entire year, but not consistently over different months.
Almost the full range of sizes (20 to 200 cmTL; i.e. neonates, juve-
niles and adult sharks) were reported. Adult sharks have been observed
all year‐round; however, the occurrence of very large‐sized individuals
(> 200 cm) was rare. According to the literature, angelsharks can reach
up to 244 cm TL (Capapé, Quignard, & Mellinger, 1990; Compagno,
1984; Quigley, 2006). The largest shark encountered in this study
was, however, a female of 200 cm. This could be due to either an
underestimation of sizes, or because large sharks remain absent from
these areas or move to deeper areas. Small to middle sized sharks that
may have not yet reached sexual maturity, i.e. between 30 and 100 cm
TL (Compagno, 1984; Osaer et al., 2015; Tonachella, 2010) were
reported throughout the entire study period. There were two peaks
in the number of sightings, corresponding to summer (June–July) and
winter (December–February). The majority of neonate encounters
occurred between April and July, which corresponded with an
increased number of female sightings. This suggests that the pupping
season starts in early spring (April) and reaches its peak at the end of
summer (July). Moreover, this hypothesis is strengthened by the obser-
vation of seven gravid females during this period, which fits previous
observations between 2006 and 2008 at Gran Canaria Island (Narvaez,
2013). The second peak in sightings was in winter (December–
February), which may correspond to the mating season. This hypothe-
sis is strengthened, as mating, mating scars and gravid females were
observed or reported during this period. In addition, large‐sized male
sharks were significantly more frequent in winter, thus it is plausible
that during summer, male sharks either undertake horizontal or vertical
migrations to different areas (deeper or offshore), which could explain
the significantly greater proportion of female sightings. Despite the
reproductive behaviour of the angelshark being poorly understood,
Narvaez (2013) and Osaer (2009) have also indicated that active males
were predominantly found during winter and that the pupping season
started in spring at Gran Canaria Island, as has been demonstrated
here. Sexual segregation, either seasonal or spatial, has also been
observed for other species of the same genus, including Squatina
californica (Pittenger, 1984), Squatina tergocellata (Bridge, Mackay, &
Newton, 1998) and Squatina guggenheim (Awruch, Nostro, Somoza, &
Di Giacomo, 2008). For example, similar to S. squatina, adult S.
guggenheim females migrate to shallow coastal areas (< 40 m depth)
to breed (Vooren & Da Silva, 1991). The fact that 79 neonates and
12 gravid females were sighted clearly demonstrates that the
population still remains reproductively active.
The angelshark is currently distributed along the entire coastline
of the Canary Islands. However, the occurrence of angelsharks signifi-
cantly decreased from the easternmost towards the westernmost
islands, i.e. angelsharks were observed predominantly in the
central (Gran Canaria, Tenerife) and easternmost (Lanzarote and
Fuerteventura) islands. This was further corroborated by the SDM, as
S. squatina exhibited a high predicted probability of occurrence
towards the easternmost islands. The presence of angelsharks around
the westernmost islands (La Palma, El Hierro and La Gomera) was rare,
and not confounded by varying sampling effort. For example, despite
the elevated number of diving centres in El Hierro Island (nine diving
companies and >20 000 divers per year), angelsharks have been only
infrequently encountered there. Differences in the composition and
abundance of marine species across the Canary Island archipelago
have been previously detected for fishes (Tuya, Boyra, Sanchez‐Jerez,
Barbera, & Haroun, 2004) and macroalgae (Tuya & Haroun, 2009). It is
thought that this is a result of large‐scale oceanographic variability
associated with the proximity of the Canary Islands to the continental
shore of Africa. The eastern side of the Canary Islands is routinely
influenced by the seasonal coastal upwelling off the African coast,
while the western part of the archipelago is situated towards the oligo-
trophic ‘open’ocean (Davenport, Never, Helmke, Pérez‐Moreno, &
Llinas, 2002). Thus, the easternmost islands (Lanzarote and
Fuerteventura) are regularly influenced by cooler sea water that results
in higher primary productivity (Chl), while the westernmost islands
(La Palma and El Hierro) often have a higher sea surface temperature
(by c. 2°C). This oceanographic gradient seems to be important to
explain the inter‐island distribution patterns of angelsharks and the
contrasting number of sightings between the easternmost and central,
and westernmost islands. Sea temperature among other abiotic fac-
tors, has been successfully used to predict occurrences and influence
movement of many sharks and rays (McKinney et al., 2012; Sequeira
et al., 2014; Vaudo & Heithaus, 2009; Vooren & Da Silva, 1991).
For S. squatina, temperature and light dependent variables such as
SST, Chl, kd400 and PAR were predicted to have the greatest effect on
its occurrence. Similarly, Narvaez (2013) found a seasonal variance,
possibly related to sea temperature, for the number of angelshark
observations at a diving spot (‘El Cabrón’) in Gran Canaria. These
results revealed more sightings during winter and spring, correspond-
ing to a sea temperature between 17°C and 21°C. Despite occurring
mainly in temperate waters, thermal tolerance and related ecological
adaptations of S. squatina have not been studied in detail. A study
conducted on the impact of climate change on threatened species
(including S. squatina) in UK waters using a set of environmental
variables (e.g. temperature) revealed that the potential distribution of
S. squatina in the North Sea would not be severely affected by climate
change (Jones et al.,2013). Thus, this may indicate that angelsharks are
able to tolerate temperature fluctuations and, more likely, also cope
with other environmental factors. In the marine system, however,
abiotic factors such as water temperature, salinity and nutrient con-
centration may be inter‐correlated and do not act in isolation (Schlaff,
Heupel, & Simpfendorfer, 2014). Thus, determining the main drivers of
distribution may be more complex and requires careful consideration
of abiotic and biotic factors such as food, shelter and predator
avoidance.
In addition to the hypothesis that varying oceanographic patterns
across the archipelago influence angelshark distribution, there are
three alternative hypotheses for this change. First, proximity to the
African coast per se could also have influenced past and present colo-
nization events and, therefore, provide an alternative/complementary
8MEYERS ET AL.
explanation for the decrease of angelshark occurrences towards the
westernmost islands. Second, the older islands are located in the east-
ern and central part of the archipelago; these islands have therefore
wider continental platforms compared with the most recent islands
(La Palma and El Hierro, in particular) (Mitchell, Dade, & Masson,
2003). This suggests that, potentially, there is more suitable habitat
for angelsharks in the eastern and central relative to the western
islands. Third, abyssal barriers between adjacent islands, except
between Lanzarote and Fuerteventura, may also constrain connectiv-
ity between islands. It still remains unclear whether angelsharks
possess the biological adaptations to undertake large migrations
through the water column, and so whether the populations among
the Canary Islands and the rest of the distribution range are connected.
The SDMs highlighted a high frequency of sightings and a high
predictive occurrence of sharks at two particular sites (Sardina del
Norte in Gran Canaria and Puerto del Carmen in Lanzarote), which
correspond with the most popular diving spots in the Canary Islands.
Two additional localities, one at Tenerife (Las Teresitas) and the other
at Gran Canaria (El Cabrón) also included a high frequency of sightings
per grid cell. Sightings are lacking in some suitable areas predicted by
the SDM, such as those located off the north and west coasts of most
islands. These areas are not frequented by recreational scuba divers
because of rough sea conditions (wave action and restricted accessibil-
ity) and hence remain mostly unexplored. In general, it was not possible
to validate the predictive map, by comparing the sighting frequency
per cell with the probability of occurrence per cell. This resulted from
an uneven survey effort among the islands.
The distribution of angelsharks is connected with a number of
habitat features, including the bathymetry and the type of substrate.
The angelshark predominantly uses areas composed of sand strips, in
most cases adjacent to reefs, but it was also observed directly resting
on reefs and within seagrass meadows. Angelsharks typically prefer
soft substrates, which is associated with their behaviour of burying in
sand for camouflage (Compagno, 1984; Compagno et al.,2005). The
SDM predicted that the likelihood of angelshark occurrence was
driven by a set of environmental variables, which may encapsulate
the ecological niche of this shark.Our data showed that the habitat
use of S. squatina changes according to their body size and, subse-
quently, their sex. Deeper waters were exclusively inhabited by large
sharks (> 100 cm TL), while neonate and juvenile sharks exclusively
occurred in shallow waters (< 25 m depth). This may indicate that
small‐sized sharks are subjected to predation and, therefore, choose
the safety of shallow water habitats to evade predators. A similar
behaviour has been observed for other shark species (DeAngelis,
McCandless, Kohler, Recksiek, & Skomal, 2008; Heupel, Carlson, &
Simpfendorfer, 2007). However, predator avoidance may not be the
only factor causing segregation; for example, the availability of prey
may also have an influence on their preference for shallow habitats.
Vooren and Da Silva (1991) revealed that juvenile Squatina guggenheim
of both sexes occurred close to shore, while adult sharks were distrib-
uted offshore up to 100 m depth. Vögler et al. (2008) also found that
the different habitat preferences of S. guggenheim are linked to an
increase in body size. For other sharks (e.g. Port Jackson sharks, nurse
sharks and reef sharks), particularly for those that are coastal residents,
segregation along depth gradients was linked to temperature
tolerances (Speed, Field, Meekan, & Bradshaw, 2010), foraging
behaviour (Sims et al.,2008) or predator avoidance (Dicken, Smale, &
Booth, 2006). Shallow coastal areas are linked to more elevated tem-
peratures, compared with deeper areas. It is likely that angelsharks,
similar to leopard sharks (Hight & Lowe, 2007; Nosal, Caillat, Kisfaludy,
Royer, & Wegner, 2014; Nosal et al.,2013), may take advantage of
warm shallow areas to speed the development of their offspring. How-
ever, more research is needed to further investigate the influence of
elevated temperatures in shallow habitats on angelshark physiology.
Mindful of the caveats of citizen science data (Ward‐Paige &
Lotze, 2011), this study has demonstrated that using scuba divers to
report sightings is an effective method to obtain valuable data on
angelsharks in the Canary Islands. It should be noted that data were
collected in a depth range between 0 and 45 m depth, however, similar
to other marine systems (Robinson et al.,2011), sampling effort was
biased towards shallow sites close to the coast. Because of sampling
limitations, certain assumptions can only be made for areas below
45 m depth. In this sense, it is necessary to conduct further long‐term
studies (e.g. acoustic telemetry) to validate citizen science data and to
explore the distribution and the migratory behaviour of angelsharks,
including areas greater than 40 m in depth.
5|CONCLUSIONS: IMPLICATIONS FOR
CONSERVATION
Understanding life‐history strategies of sharks has important implica-
tions for their conservation (Simpfendorfer et al.,2011). This study
demonstrated that angelsharks display spatial (vertical and horizontal)
and temporal segregation by size and sex. Angelshark distribution is
driven by a combination of factors, including environmental and
geographical/geological variation across the archipelago as well as
biotic factors, e.g. reproductive behaviour, prey abundance and
predator avoidance, which require further investigation.
This information has recently been used to develop the
Angelshark Action Plan for the Canary Islands (Barker et al.,2016).
Improved understanding of the spatial distribution of angelsharks, in
particular through the identification of critical habitats, can be used
to design spatial protective measures, taking into account segregation
by size and sex. Effective protective measures for the angelshark can
be developed through the understanding of timing of key life‐history
events, e.g. pupping and mating. For example, the results of this study
could be used to limit recreational and commercial fishing in certain
shallow waters during the mating (winter) and pupping (spring/sum-
mer) seasons. Similarly, this knowledge can be used to better target
public awareness raising campaigns, e.g. when angelsharks are more
vulnerable to disturbance, for divers or beach users during the pupping
season, or in areas where angelsharks are seen more often.
This study identified three potential nursery areas. It is clear that
(1) long‐term monitoring studies to determine whether these areas
are used as nursery areas for angelsharks, according to Heupel et al.
(2007), should be conducted here. In addition, other key research areas
to prioritize include: (2) long‐term tagging and acoustic monitoring of
sharks to understand their vertical and horizontal habitat use, taking
into account different life stages, sex and seasons; (3) identification
MEYERS ET AL.9
of critical habitats in nearshore areas in terms of mating and pupping;
(4) movement and connectivity of angelsharks at multiple scales; and
(5) estimates of relative abundance. Prioritizing research based on
conservation needs both optimizes capacity and available resources,
as well as ensuring that conservation strategies/action plans are
implemented effectively to minimize the threats to angelsharks in the
Canary Islands.
ACKNOWLEDGEMENTS
We thank all the diving centres, Non‐Governmental Organizations
(NGOs) and citizen scientists in the Canary Islands that have provided
information regarding their angelshark sightings and have continued to
support the conservation work on this species. In particular we thank
David Thompson (La Santa Diving), Carlos Suarez and Anna Clavero
(Océanos de Fuego), Domingo Castro (Pozo Scuba) and Javier
Bermudez (Buceo La Graciosa) for providing an outstanding amount
of information and support during surveys. Special thanks are due to
Tony Sanchez Deniz for sharing his extensive knowledge and help
during the surveys. Thanks to Francisco Otero for his expert advice
and to Felipe Ravina Olivares, Jacobo Marrero, Alejandro Escánez,
Ana Crespo, Michael Sealey and Belen Caro who provided local
support and collaborated with the Angel Shark Project to better
understand and conserve angelsharks. We thank Yaiza Fernandez
Palacios and Alejandro Garcia for managing the POSEIDON database.
We also thank two anonymous reviewers for their contributions to
the improvement of the manuscript. This work was partly made
possible through the generous financial support of the Save our Seas
Foundation.
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SUPPORTING INFORMATION
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How to cite this article: Meyers EKM, Tuya F, Barker J, et al.
Population structure, distribution and habitat use of the Criti-
cally Endangered Angelshark, Squatina squatina, in the Canary
Islands. Aquatic Conserv: Mar Freshw Ecosyst. 2017. https://
doi.org/10.1002/aqc.2769
12 MEYERS ET AL.
