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Submitted 31 May 2017
Accepted 23 November 2017
Published 2 January 2018
Corresponding author
Christoph A. Rohner,
chris@marinemegafauna.org
Academic editor
David Johnston
Additional Information and
Declarations can be found on
page 17
DOI 10.7717/peerj.4161
Copyright
2018 Rohner et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Satellite tagging highlights the importance
of productive Mozambican coastal waters
to the ecology and conservation of whale
sharks
Christoph A. Rohner1, Anthony J. Richardson2,3, Fabrice R. A. Jaine1,4,5,
Michael B. Bennett6, Scarla J. Weeks7, Geremy Cliff8,9, David P. Robinson10,
Katie E. Reeve-Arnold11 and Simon J. Pierce1
1Manta Ray & Whale Shark Research Centre, Marine Megafauna Foundation, Praia do Tofo, Mozambique
2CSIRO Oceans and Atmosphere, Dutton Park, QLD, Australia
3Centre for Applications in Natural Resource Mathematics (CARM), School of Mathematics and Physics,
The University of Queensland, St Lucia, QLD, Australia
4Sydney Institute of Marine Science, Mosman, NSW, Australia
5Department of Biological Sciences, Macquarie University, North Ryde, NSW, Australia
6School of Biomedical Sciences, The University of Queensland, St Lucia, QLD, Australia
7Biophysical Oceanography Group, School of Geography, Planning and Environmental Management,
The University of Queensland, St Lucia, QLD, Australia
8Kwa-Zulu Natal Sharks Board, Umhlanga, KZN, South Africa
9Biomedical Resource Unit, University of KwaZulu-Natal, Durban, KZN, South Africa
10 Shark Watch Arabia, Dubai, United Arab Emirates
11 All Out Africa Marine Research Centre, Praia do Tofo, Inhambane, Mozambique
ABSTRACT
The whale shark Rhincodon typus is an endangered, highly migratory species with a wide,
albeit patchy, distribution through tropical oceans. Ten aerial survey flights along the
southern Mozambican coast, conducted between 2004–2008, documented a relatively
high density of whale sharks along a 200 km stretch of the Inhambane Province,
with a pronounced hotspot adjacent to Praia do Tofo. To examine the residency and
movement of whale sharks in coastal areas around Praia do Tofo, where they may be
more susceptible to gill net entanglement, we tagged 15 juveniles with SPOT5 satellite
tags and tracked them for 2–88 days (mean =27 days) as they dispersed from this
area. Sharks travelled between 10 and 2,737 km (mean =738 km) at a mean horizontal
speed of 28 ±17.1 SD km day−1. While several individuals left shelf waters and travelled
across international boundaries, most sharks stayed in Mozambican coastal waters over
the tracking period. We tested for whale shark habitat preferences, using sea surface
temperature, chlorophyll-aconcentration and water depth as variables, by computing
100 random model tracks for each real shark based on their empirical movement
characteristics. Whale sharks spent significantly more time in cooler, shallower water
with higher chlorophyll-aconcentrations than model sharks, suggesting that feeding in
productive coastal waters is an important driver of their movements. To investigate
what this coastal habitat choice means for their conservation in Mozambique, we
mapped gill nets during two dedicated aerial surveys along the Inhambane coast and
counted gill nets in 1,323 boat-based surveys near Praia do Tofo. Our results show
that, while whale sharks are capable of long-distance oceanic movements, they can
How to cite this article Rohner et al. (2018), Satellite tagging highlights the importance of productive Mozambican coastal waters to the
ecology and conservation of whale sharks. PeerJ 6:e4161; DOI 10.7717/peerj.4161
spend a disproportionate amount of time in specific areas, such as along the southern
Mozambique coast. The increasing use of drifting gill nets in this coastal hotspot for
whale sharks is likely to be a threat to regional populations of this iconic species.
Subjects Aquaculture, Fisheries and Fish Science, Conservation Biology, Ecology, Marine Biology,
Natural Resource Management
Keywords Rhincodon typus, Biotelemetry, Movement ecology, Oceanography, Fishing pressure
INTRODUCTION
Knowledge of the movements of a species in space and time improves understanding of
its habitat use and ecology, can enhance conservation management, and allows prediction
of the species’ response to changing conditions (Sims, 2010;Block et al., 2011;Hays et
al., 2016). It can, however, be technologically and logistically challenging to study the
movements of difficult-to-access species, such as wide-ranging marine fishes. Recent
improvements in the equipment available for marine animal tracking, coupled with refined
analytical techniques (Nathan et al., 2008;Block et al., 2011;Costa, Breed & Robinson,
2012), have made it easier to interpret both the movements and motivation underpinning
the spatial ecology of even highly-mobile species (Sims et al., 2006).
Whale sharks Rhincodon typus move thousands of kilometres horizontally (Hueter,
Tyminski & De la Parra, 2013;Berumen et al., 2014;Hearn et al., 2016) and perform vertical
dives to >1,900 m depth (Tyminski et al., 2015). Although they actively move and do not
simply follow surface ocean currents (Sleeman et al., 2010), ecological drivers of their
movements are poorly understood. As coastal aggregations of whale sharks, including
our study population off Mozambique, comprise mostly juveniles (Rohner et al., 2015b),
reproduction is not likely to influence their movements during this life stage. Avoiding
predation is also not a likely factor driving the movements of these large (>4 m in
length) sharks that have few natural predators (Rowat & Brooks, 2012). Rather, prey search
behaviour is likely to be the major driver of their movement, as zooplankton, the primary
prey of whale sharks, are patchily distributed (Lalli & Parsons, 1997) throughout the species’
tropical to warm temperate distribution (Rowat & Brooks, 2012).
Whale sharks are sighted off Praia do Tofo in southern Mozambique throughout the year
(Rohner et al., 2013b;Haskell et al., 2015). Although some inter-annual site fidelity has been
observed (Rohner et al., 2015b), photo-identification data suggest a short mean residency
time (9 days) for this stretch of coast (C Prebble et al., 2017, unpublished data). Where they
go, and the underlying drivers of this rapid turnover, remain uncertain. Although whale
sharks are also seen in nearby Tanzania, Seychelles and Djibouti, photo-identification
has shown limited connectivity among those sites (Norman et al., 2017;Brooks et al., 2010;
Andrzejaczek et al., 2016). Despite their well-documented ability to move long distances
(Hueter, Tyminski & De la Parra, 2013;Hearn et al., 2016), including from Praia do Tofo
(Brunnschweiler et al., 2009), in the Indian Ocean there have been few examples of whale
sharks being re-sighted outside the geographic region where they were first identified
(Norman et al., 2017). As most photo-identification and tag deployment has taken place
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 2/24
at aggregation sites dominated by juvenile males, limited inference can be made about
the behavior of the broader whale shark population (Rohner et al., 2015b). Mature whale
sharks (>800–900 cm long; Acuña Marrero et al., 2014;Rohner et al., 2015b) may range
further, and are likely to be more oceanic, as few have been sighted at coastal aggregation
sites (Hearn et al., 2016;Robinson et al., 2016;Ramírez-Macías et al., 2017).
There is a clear conservation imperative to understand the movement ecology of whale
sharks in southern Mozambique. Whale shark sightings at Praia do Tofo decreased by 79%
between 2005 and 2011 with local environmental parameters taken into consideration
(Rohner et al., 2013b), a trend that has continued following the conclusion of that study
(Pierce & Norman, 2016). In the northern Mozambique Channel, following a slight increase
in sightings from the tuna purse-seine fleet between 1991–2000, there was a decrease from
2000–2007 (Sequeira et al., 2013). In absolute terms, 600 sightings were reported from
1990s, decreasing to ∼200 from 2000–2007 (Sequeira et al., 2014), and peak monthly
sightings decreased by ∼50% (Sequeira et al., 2014). While large-scale oceanographic
mechanisms may influence sightings (Rohner et al., 2013b), there are also fisheries-related
captures and mortalities of whale sharks in the region (Jonahson & Harding, 2007;Capietto
et al., 2014;Everett et al., 2015).
Mozambique ranks low on the global Human Development Index: 0.418 =181 of
188 countries (United Nations Development Programme, 2016). With over two thirds of
Mozambique’s population living within 150 km of the coast, ∼50% of their protein
intake comes from fish (Hara, Deru & Pitamber, 2007). Gill net use has been increasing
in Mozambique since the cessation of conflict in 1992 (WWF Eastern African Marine
Ecoregion, 2004), and nets have been actively distributed by fisheries officials in some
areas of the country to move fishing effort away from sensitive inshore nursery habitats
(Leeney, 2017). Floating gill nets, extending from the beach to ∼200 m offshore, pose a
threat to marine megafauna species swimming along this coast. While few formal data are
available, these gill nets are routinely used off the Inhambane coast. At least two whale
shark mortalities have been observed in this area, both sighted opportunistically (S Pierce,
pers. obs., 2015), and entanglements are commonly reported (Speed et al., 2008; S Pierce,
2017, unpublished data). Whale sharks are a valuable focal species in marine tourism
off Praia do Tofo and adjacent areas (Pierce et al., 2010;Tibiri¸
cá et al., 2011;Haskell et al.,
2015). The species received formal protection in Mozambique and, separately, were listed
on Appendix I of the Convention of Migratory Species—which requires prohibition of
take by signatory countries (which includes Mozambique)—during 2017.
Here we examine the regional movements and underlying environmental drivers
of whale shark activity in Mozambique. We use aerial surveys, satellite telemetry and
randomised model shark tracks to establish their activity hotspots in this region, and test
the hypothesis that they preferentially spend most of their time in shallow coastal waters.
With the limited data available, we also assess the potential for interaction with the coastal
gill net fishery along the Inhambane coast.
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 3/24
Figure 1 Whale shark and gill net locations from aerial surveys (conducted in 2004–2008 and in 2016,
respectively). Density of whale shark sightings along (A) the northern and (B) southern stretch of the
southern Mozambique coast and (C) along the northern South Africa coast. The red line shows the flight
path of whale shark surveys and a cross indicates gill nets in use.
Full-size DOI: 10.7717/peerj.4161/fig-1
MATERIALS AND METHODS
Aerial surveys for whale sharks
Data on the spatial distribution of whale sharks in southern Mozambique were acquired
from aerial survey flights conducted by the KwaZulu-Natal Sharks Board in a top wing
aircraft, flown 305 m (1,000 ft) above sea level at 184 km h−1(100 knots) (Fig. 1). Two
observers recorded time and GPS coordinates for each whale shark within ∼750 m of the
coast during 10 regional flights between 2004 and 2008 in February and March. Flights
were conducted when viewing conditions were optimal, characterised by light winds
and minimal cloud (see full methods in (Cliff et al., 2007)). For aggregations of multiple
individuals, central coordinates were used when only the start and end GPS position were
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 4/24
recorded. Aerial surveys have the limitations that whale sharks can only be seen by observers
in surface waters, but the species also occupies deeper habitats in which they would not be
able to be sighted. Logistical and cost constraints also meant that a relatively small number
of aerial surveys were available for this study. Aerial survey data did not temporally match
satellite tagging data. Spatial data were mapped in ArcGIS 10.2.1 in 1 km2grids and whale
shark numbers expressed per km2.
Study area and whale shark tagging
Fifteen juvenile whale sharks, comprising 12 males and 3 females ranging from 540–865 cm
total length (TL), were equipped with Smart Position or Temperature Transmitting
(SPOT5) tags from Wildlife Computers, and tracked between November 2010 and January
2012. All tagged sharks were photographically identified based on their spot pattern
posterior to the gills and matched on, or added to, the Wildbook for Whale Sharks global
whale shark database (http://www.whaleshark.org;Arzoumanian, Holmberg & Norman,
2005). Sex was determined based on the presence (male) or absence (female) of claspers.
Male maturity status was assigned according to clasper length and thickness (Rohner et
al., 2015b). Longer-term (pre- and post-tagging) site fidelity of these sharks was assessed
through to the end of 2016 via photo-identification submissions to the Wildbook database.
Length estimates were derived from laser photogrammetry and visual size assessments, with
an estimated error of ±50 cm (Rohner et al., 2011). All tags were deployed immediately off
Praia do Tofo in southern Mozambique (23.85◦S, 35.54◦E). The tag’s float was covered with
dark antifouling paint to minimise bio-fouling and make it less obvious to predatory fishes.
The tag was connected to a ∼5 cm titanium dart (Wildlife Computers) via a ∼180 cm
tether. The first five tags had a stainless steel game-fishing swivel 30 cm from the dart,
before it became evident from retrieval of shed tags that the swivel was a weak point and
was therefore not used in later deployments. The first three tags used stainless steel wire
as a short tether connecting the dart with the swivel; the remainder of the tether (and the
entire tether in later deployments) comprised Dyneema braid. The dart was inserted into
the skin at the posterior base of the 1st dorsal fin for the first three tags, using a 200 cm
hand spear. Tag retention was improved on subsequent deployments by implanting the
dart slightly further anteriorly, so that the tag floated adjacent to the 1st dorsal fin. No
animal was restrained, caught or removed from its natural habitat for the purpose of this
study. Whale shark tagging was compliant with ethics guidelines from the University of
Queensland’s Animal Ethics Committee and was conducted under their approval certificate
GPEM/186/10/MMF/WCS/SF.
SPOT5 tags are positively buoyant and communicate with the ARGOS system
(http://www.argos-system.org) when the wet/dry sensor is exposed to air. Tags were
programmed for a daily limit of 300 transmissions to save battery power in case of extended
tag retention. Transmitted data included tag location and accuracy (location classes 3, 2, 1,
0, A, B, Z), as well as sea surface temperature (SST) at the time of transmission. We used
standard methods by Hearn et al. (2013; time of transmissions and time-at-temperature
data) to determine when a tag detached from the shark, and removed the floating portion
of the tracks before analyses were conducted. We only used location classes 3, 2 and 1
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 5/24
for further analyses. Estimated precision for location classes 3, 2 and 1 are theoretically
0.15, 0.35 and 1.00 km (ARGOS), but are larger when the tag is deployed on an animal
at sea, with mean errors of 0.49, 0.94 and 1.10 km, respectively (Costa et al., 2010). More
than half of all transmissions (n=1,930) were characterised by ARGOS location classes
3, 2 and 1 and allowed accurate position estimation. Track distance was measured as the
sum of the straight-line distances between two adjacent locations. Nine tags also recorded
the proportion of time spent in 12 pre-defined temperature bins during 1, 5 or 6 h time
intervals with data recorded at 05:00 h, 06:00 h, 11:00 h, 17:00 h, 18:00 h and 23:00 h.
These time-at-temperature (TAT) data are limited to a period preceding a transmission
via satellite, and hence do not reflect the full temperature range experienced by the tagged
whale sharks. Available TAT data ranged from 36–100% of tracking days for individual
sharks (mean =81%) and 173 of 262 days in total for all sharks combined. SST and
chlorophyll-aconcentration (Chl-a) data were derived from the Moderate Resolution
Imaging Spectroradiometer website (MODIS; modis.gsfc.nasa.gov) to produce monthly
day- and night-merged SST and Chl-atime series at 1 km2spatial resolution for the period
sharks were tagged. Chl-awas used as a proxy for zooplankton availability. Despite a possible
lag in zooplankton abundance in response to a phytoplankton bloom (Plourde & Runge,
1993;Flagg, Wirick & Smith, 1994), phyto- and zooplankton abundance is often correlated
(Hutchinson, 1967;Richardson & Schoeman, 2004;Ware & Thomson, 2005) and has been
used similarly in previous studies on planktivorous elasmobranchs (Sims et al., 2003;
Sleeman et al., 2007;Graham et al., 2012). To investigate drivers of coastal occurrences
of whale sharks, SST values were extracted for one coastal location near Praia do Tofo
(23.85◦S, 35.62◦E, 36 m depth) and one further offshore (23.85◦S, 36.00◦E, 988 m depth,
∼45 km from the coast). SST and Chl-avalues were also extracted for all positions with a
location class 3, 2 or 1 from tracked whale sharks and for all positions from random model
sharks (see below). A nine-month mean was produced for SST and Chl-a, encompassing all
months when tagged sharks were tracked. Bathymetric data were derived from the NOAA
ETOPO2 dataset at a ∼1 km resolution.
Random model sharks
We generated random model tracks (‘model sharks’) for each tagged shark (‘real sharks’)
based on characteristics of the real tracks, similar to analyses conducted on basking
sharks Cetorhinus maximus by Sims et al. (2006). Input data for this analysis were observed
locations with accuracy classes 3, 2 and 1, and a step was defined as the most direct, straight
line between successive locations. Each model shark had the same starting location, overall
track distance, and step-length frequencies as the real whale shark, but the order of steps
was randomised. Real whale sharks often swam along the coast (Fig. S1), but as we had no
a priori expectation whether sharks would move north or south or offshore, our random
sharks took a random angle between steps while constraining the total length of the track
to that of the real sharks. For a step that crossed land, or extended beyond the study area
boundary (20–30◦S, 31–40◦E), another random turning angle was taken. The simulation
was run in R (R Development Core Team, 2008) and sets of 100 model shark tracks were
generated for each whale shark (Fig. S2). The aim of the model sharks was not to mimic
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 6/24
the real sharks, but to test whether the real sharks had a preference for locations on the
regional shelf (0–200 m depth, 22.17◦S–24.51◦S), or for certain SST or chl-aconditions.
Kernel density estimation analysis
All transmitted tag locations and modelled shark locations were input to ArcGIS 10.2.1.
The ‘‘kernel density tool’’ was used to calculate percentile kernels of location density.
Kernel density estimates were produced following MacLeod (2013), with a search radius
of 5 km and the outlying locations falling into the 2.5% kernel removed. Kernel density
estimation analysis is based on transmitted locations and cannot consider the periods of
the overall tracking duration when no locations were transmitted, which equaled 183 of
403 days in our dataset.
Gill nets
Gill nets in the study area were set and drifting at the surface perpendicular to the beach. Net
dimensions varied among fishing communities in the region, but were typically 20–200 m
long, 5–8 m deep, and had a mesh size of 5–20 cm. Nets were made from monofilament or
thin rope. Whale sharks are not specifically targeted in Mozambique, but nets with a larger
mesh size present an entanglement risk. Locations of these gill nets along the ∼200 km of
coastline between Zàvora to Pomene were recorded with a GPS during two aerial survey
flights in May 2016. A transect was flown along the coast in a Bat Hawk LSA at 244 m
(800 ft) above sea level at 60 knots and ∼300–500 m from the beach. To assess the trend in
gill net use over time, we used survey data off the Praia do Tofo area itself. We conducted
1,323 boat-based surveys from 2012 to 2015, during which gill nets were counted on the
way to dive sites located along a 40 km stretch of coast. Surveys were on average 21.3 km
long, but survey design was influenced by which sites the dive company accessed at the
time. We calculated the number of gill nets per 1,000 km of survey track for each year
over the 4-year period. The gill net surveys did not temporally match with the whale shark
tracking data, as pre-2012 gill nets were not counted because they were rarely in use around
Praia do Tofo.
RESULTS
Whale shark aggregation
Flight observers recorded a total of 202 whale sharks in southern Mozambique during the
10 aerial survey transects between 2004 and 2008, with a mean of 3.4 individuals 100 km−1.
The focal area of whale shark sightings was the 200 km stretch of coastline between Zàvora
and Pomene, with the peak at Praia do Tofo (Fig. 1). Several large aggregations were
observed near Praia do Tofo, with the largest being 51 individuals sighted on 1 March
2005.
Gill nets were recorded during aerial surveys in the same region where whale shark
sightings were highest between Zàvora and Pomene (Fig. 1). In the immediate area around
Praia do Tofo, boat-based surveys showed that gill net usage increased ∼7 times from 0.95
to 6.44 nets per 1,000 km survey track from 2012 to 2015.
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 7/24
Table 1 Track details of 15 whale sharks equipped with SPOT5 tags, with track number, shark ID on the Wildbook for Whale Sharks global
database, sex, total length (TL), track start and end date and track duration. Track distance is measured as the sum of the straight-line distances
between two adjacent locations, only including locations of ARGOS class (LC) 3, 2 and 1.
# ID Sex TL (cm) Start date End date Days Track
distance
(km)
Speed (km
day−1)
No. of fixes
(Pos. day−1)
Number of
fixes (LC
3,2,1 day−1)
Days with
locations
(% of total
tracking
days)
1 MZ-421 M 560 11-Nov-10 14-Nov-10 4 66.6 16.7 8.7 6.7 4 (100%)
2 MZ-562 M 540 02-Feb-11 05-Feb-11 4 280.3 70.1 9.7 4.7 3 (75%)
3 MZ-286 F 550 19-Jul-11 28-Jul-11 10 261.5 26.1 6.9 4.2 8 (80%)
4 MZ-275 M 745 22-Jul-11 25-Jul-11 4 10.4 2.6 6.0 2.3 2 (50%)
5 MZ-418 M 700 09-Aug-11 18-Aug-11 10 325.5 32.6 7.1 2.6 10 (100%)
6 MZ-238 M 600 09-Aug-11 24-Aug-11 16 412.7 25.8 5.4 2.0 10 (63%)
7 MZ-241 M 630 10-Aug-11 03-Sep-11 25 814.6 32.6 5.4 2.9 23 (92%)
8 MZ-463 M 635 11-Aug-11 21-Aug-11 11 457.1 41.6 8.4 5.6 6 (55%)
9 MZ-606 M 550 26-Aug-11 20-Sep-11 26 668.0 25.7 7.8 3.8 21 (81%)
10 MZ-607 M 865 11-Aug-11 05-Oct-11 56 204.5 3.7 1.0 0.3 8 (14%)
11 MZ-600 M 600 23-Jul-11 18-Oct-11 88 2,446.8 27.8 5.1 3.2 38 (43%)
12 MZ-614 M 600 12-Oct-11 08-Nov-11 28 677.0 24.2 8.6 3.6 24 (86%)
13 MZ-615 F 650 26-Oct-11 17-Jan-12 84 2,736.7 32.6 3.7 1.6 38 (45)
14 MZ-165 M 670 25-Nov-11 26-Nov-11 2 23.9 11.9 12.0 6.0 2 (100%)
15 MZ-471 M 820 28-Nov-11 01-Jan-12 35 1,687.0 48.2 6.0 3.7 23 (66%)
Maximum 865 88 2,737 70.1 12.0 6.7 100%
Minimum 540 2 10 2.6 1.0 0.3 14%
Mean 648 26.9 738 28.1 5.0 2.6 55%
Horizontal movements, tag retention and transmissions
SPOT5 tags remained on the sharks for 2–88 days (mean ±SD =27 ±28.1 d) and
transmitted locations on 55% of days of the combined tracking duration (Table 1).
Whale sharks travelled at a mean speed of 28 km day−1(median =26.1 km day−1,
range =2.6–70.1 km day−1), similar to whale sharks tracked elsewhere (Table 2). The
longest straight-line, along-track distances were 2,737 km over 84 days, and 2,447 km
over 88 days (Table 1). All sharks remained within the southern Mozambique Channel
and eastern South African waters while tagged (Fig. 2). Seven sharks (47%) moved
offshore for at least part of their track, while the other eight (53%) remained on the
shelf near the coast. Tracking duration did not influence whether sharks went offshore
or stayed coastal (t= −1.11, df =11.4, p=0.29). Season may have played a role, with
a greater proportion of sharks moving offshore in summer (three out of three), less in
winter (three of five), and a lower proportion again in spring (two of seven), although
numbers were too small to be conclusive (Fig. 2). Whale sharks travelling away from
the coast swam significantly further (mean =1,137 vs. 282 km) and faster (mean
=43 vs. 20 km day−1) than those that stayed in coastal waters (t=2.29, df =8.3,
p=0.05, and t=2.46, df =11.1, p=0.031, respectively). Of the five sharks tagged
within a short time period (9–11 July 2011), one initially swam northward along the
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 8/24
Table 2 Published whale shark tagging study information, with tag type; N, number of tracked sharks; M, males; F, females; mean total length
and range in brackets (cm); mean (±SD) total distance travelled; tag attachment duration and mean (±SD) daily speed. Failed tags are not in-
cluded in the analysis.
Location Tag type N (M, F) Total length
(cm)
Distance
(km)
Duration
(days)
Speed
(km d−1)
Reference
Mozambique Real-time 15 (12, 3) 648 (540–865) 738 (±861.7) 26 (±28.0) 29 (±30.7) This study
Qatar Real-time 28 (17, 11) 704 (500–900) 378 (±546.3) 69 (±60.7) 7 (±13.5) Robinson et al. (2017)
Ecuador Mix 26 (0, 26) 1047 (400–1,310) 2,273 (±1,933.6) 62 (±50.6) 41 (±25.5) Hearn et al. (2016)
Saudi Arabia Archival 47 (14, 16) 391 (300–700) 502 (±613.4) 146 (±80.3) 4 (±4.9) Berumen et al. (2014)
Mexico Archival 28 (10, 18) 738 (500–900) 699 (±1,322.8) 68.4 (±54.5) 9 (±11.0) Hueter, Tyminski & De la Parra (2013)
Mozambique Archival 2 (1, 1) 725 (650–800) 607 (±838.6)*47 (±56.6) 8 (±8.3) Brunnschweiler et al. (2009)
Seychelles Real-time 3 (1, −) 617 (500–700) 1,769 (±1,471.2) 42 (±20.8) 43 (±70.6) Rowat & Gore (2007)
Taiwan Real-time 3 (3, 0) 423 (400–450) 4,250 (±1,458.1) 143 (±56.1) 30 (±26.0) Hsu et al. (2007)
Australia Archival 10 (1, 7) 715 (470–1,100) 581 (±544.8)*92 (±88.9) 6 (±6.1) Wilson et al. (2006)
SE Asia Real-time 6 (−,−) 567 (300–700) 890 (±1,284.1) 35 (±48.9) 25 (±26.2) Eckert et al. (2002)
Mexico Real-time 14 (−, 7) 643 (300–1,800) 1,812 (±3,749.4) 149 (±334.6) 12 (±11.2) Eckert & Stewart (2001)**
Notes.
*Indicates straight-line distances from tagging to pop-up location.
**A record of a >13,000 km track from this paper is now broadly considered to be from a floating tag (Andrzejaczek et al., 2016).
coast and four swam southward. Apart from MZ-463, which travelled to northern South
Africa, these sharks stayed in coastal waters and swam past Praia do Tofo again after
3–13 days.
Home range and random model sharks
The kernel density estimation analysis of whale shark tracks showed that the main hotspot
of whale shark activity was between Zàvora and Praia do Tofo, with a second, less intense
hotspot around the Pomene headland, 100 km north of Praia do Tofo (Fig. 3A). High-use
areas were on the continental shelf. By contrast, model sharks spread from Praia do Tofo
and their high activity zone included areas off the continental shelf (Fig. 3B). Overall, whale
sharks spent significantly more time on the regional shelf (85%) than model sharks (15%;
χ2=1239.6, df =15, p<0.001). An example is shark MZ-241, which swam north along
the coast, then briefly headed offshore, before returning to coastal waters south of Praia do
Tofo (Fig. S2). This was one of 10 sharks that spent more time on the shelf than any of the
corresponding 100 model tracks for each real shark. Only MZ-562 (8% of a 3-day track)
and MZ-463 (26% of a 10-day track) spent less time on the regional shelf than half of the
model sharks.
Tagged sharks transmitted their position on 30 separate days while they were in the
immediate whale shark search area off Tofo (23.85◦S–23.93◦S), excluding detections from
the day of tag deployment. Only two sharks, on two separate days, were re-sighted in
regular visual surveys using photo-identification during the period of tag deployment. One
of these had its tag entangled in a fishing line, causing the tag to sit under the shark’s body
and preventing it from breaking the surface to transmit, so we removed the tag and line.
Photo-identification data indicated that most of the tagged sharks (67%) returned to the
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 9/24
31˚E 32˚E 33˚E 34˚E 35˚E 36˚E 37˚E 38˚E 39˚E 40˚E
30˚S
29˚S
28˚S
27˚S
26˚S
25˚S
24˚S
23˚S
22˚S
21˚S
-10000 -7500 -5000 -2500 0
Bathymetry m
A
35˚E 36˚E
24˚40’S
24˚30’S
24˚ 20’S
24˚ 10’S
23˚50’S
23˚40’S
23˚30’S
23˚20’S
23˚ 10’S
22˚ 50’S
36˚ 20’E
23˚ 00’S
24˚ 00’S
Praia do Tofo
200 m
1000 m
35˚ 20’E 35˚ 40’E
B
200 m
1000 m
200 km 50 km
MZ-238
MZ-463
MZ-286
MZ-615
Legend
Praia do Tofo
MZ-471
MZ-418
MZ-606
MZ-275
MZ-607
MZ-165
MZ-421
Legend
MZ-562
MZ-241
MZ-600
MZ-614
Maputo
Zàvora
Pomene
Zàvora
Pomene
Figure 2 Whale shark tracks in the southern Mozambique Channel. Bathymetry maps showing the
movements of satellite-tagged sharks. (A) Sharks that included large-scale movement off the continental
shelf (n=8). (B) All sharks that remained locally on the continental shelf (n=7). Circle, winter; triangle,
spring; square, summer deployments.
Full-size DOI: 10.7717/peerj.4161/fig-2
Figure 3 Kernel density maps. Kernel density estimations from all satellite tag locations for (A) tracked
whale sharks and (B) random model sharks.
Full-size DOI: 10.7717/peerj.4161/fig-3
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 10/24
A
B
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Number of transmissions
0 200 400 600 800 1000
Sea surface temperature (°C)
20 22 24 26 28 30
18−19
19−20
20−21
21−22
22−23
23−24
24−25
25−26
26−27
27−28
28−29
29−30
Sea surface temperature (°C)
Number of transmissions
0 200 400 600 800 1000
Figure 4 Sea surface temperature preferences. (A) Number of tag transmissions in each sea surface tem-
perature bin, showing a wide temperature distribution and an affinity for surface temperatures of 22–
26 ◦C. (B) Number of transmissions made by the tags in each month, with mean monthly sea surface tem-
perature plotted for Praia do Tofo (square; 23.85◦S, 35.62◦E) and 45 km directly offshore (circle; 23.85◦S,
36.00◦E).
Full-size DOI: 10.7717/peerj.4161/fig-4
region after losing their tag, with these sharks being sighted on 2–11 unique days (mean
=4.8 ±2.6 days) over 1–6 unique calendar years between 2005 and 2016 (mean =3.2 ±
1.4 years).
Temperature and chlorophyll-a distributions
Tag-derived temperature data showed whale sharks moved through surface temperatures
between 18.5–29.7 ◦C, with a mean of 23.9 ±1.51 ◦C. Half of all transmissions were from
a narrow range of 22–24 ◦C waters, and >95% were from 21–27 ◦C waters (Fig. 4A). This
temperature distribution is at least partly a result of the seasonal bias in tagging, with most
transmissions in winter and spring when coastal and offshore temperatures were relatively
cool (Fig. 4B).
Whale sharks spent more time in cooler water with higher Chl-athan model sharks
(Figs. 5A and 5B). Mean Chl-awas significantly higher for whale sharks (mean =1.18 ±
2.74 mg m−3) than model sharks (mean =0.27 ±0.79 mg m−3;t= −9.38, df =803.3,
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 11/24
> 0.1
0.2−0.3
0.4−0.5
1−5
10−50
0.3−0.4
5-10
0.5-1
0.1−0.2
Chlorophyll-a concentration (mg m-3)
Whale shark tracks
Frequency (%)
Modelled shark tracks
010203040 40302010
Whale shark tracks Modelled shark tracks
> 20
21-22
24-25
26-27
28-29
22-23
27-28
25-26
20-21
29-30
23-24
Sea surface temperature (°C)
010203050 40 5040302010
B
A
CD
34˚E 36˚E 38˚E32˚E
22˚S
24˚S
26˚S
28˚S
10.0
1.0
0.1
0.01
Praia do Tofo
D
34˚E 36˚E 38˚E 29
28
27
26
25
24
23
22
21
20
19
Praia do Tofo
32˚E
22˚S
24˚S
26˚S
28˚S
C
mean = 1.18
SD = 2.74
mean = 0.27
SD = 0.79
mean = 24.2
SD = 1.59
mean = 24.5
SD = 1.62
Figure 5 Real vs. random tracks. Distributions for all locations of real tracks (‘‘whale shark tracks’’,
white) and for all locations of 100 random tracks per real shark (‘‘modelled shark tracks’’, grey) of
satellite-derived (A) sea surface temperature (SST) and (B) chlorophyll-aconcentration (Chl-a). Nine-
month mean images of (C) SST and (D) Chl-ashowing their respective mean regional distributions for
the study period.
Full-size DOI: 10.7717/peerj.4161/fig-5
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 12/24
2.1−5.0
5.1−10.0
10.1−15.0
15.1−17.5
17.6−20.0
20.1−22.5
22.6−25.0
25.1−27.5
27.6−29.0
29.1−31.0
Proportion of time
020406080
2.1−5.0
5.1−10.0
10.1−15.0
15.1−17.5
17.6−20.0
20.1−22.5
22.6−25.0
25.1−27.5
27.6−29.0
29.1−31.0
20406080
Sea surface temperature Tag temperature data
A
B
Figure 6 Sea surface vs. vertically-integrated temperatures. Proportion of time spent in each temper-
ature bin for sea surface temperature of all locations (‘‘Sea surface temperature’’) and for tag-recorded,
time-integrated temperature (‘‘Tag temperature data’’) for locations (A) on the shelf and (B) off the shelf
for all tags.
Full-size DOI: 10.7717/peerj.4161/fig-6
p<0.001). Mean satellite-derived SST was significantly cooler for whale shark locations
(mean =24.23 ±1.59 ◦C) than for model sharks (24.49 ±1.62 ◦C; t=4.28, df =679.4,
p<0.001; Fig. 5B). Chl-aand SST distributions were also significantly different between
whale sharks and model sharks (χ2=549.1, df =8, <0.0001 and χ2=297.5, df =10,
p<0.0001, respectively). Coastal shelf waters had higher Chl-a(Fig. 5C) and were cooler
(Fig. 5D) than offshore waters over the 9-month duration of this study.
Vertical movement (inferred from temperature-at-depth)
Temperatures recorded in binned intervals of up to 24 h prior to each transmission indicated
that some of the tagged sharks made pronounced vertical movements. Combining data
from all tags, the temperature bin extremes ranged from 5.1–10 ◦C up to 27.6–29 ◦C. The
largest proportion of time (64%) was spent in 22.6–25 ◦C water. Overall, whale sharks
experienced a wider temperature range when they were off the continental shelf as opposed
to inshore (Fig. 6). When on the shelf, they spent the majority of time (76%) in 22.6–25 ◦C
water, while the coldest temperatures recorded from shelf waters were in the 15.1–17.5 ◦C
bin (0.1% of time). By contrast, when off the shelf, sharks spent the most time in warmer
25.1–27.5 ◦C water, while the coldest offshore temperatures were in the 5.1–10.0 ◦C (0.3%
of time) and in the 10.1–15.0 ◦C bins (7.9%).
DISCUSSION
Whale sharks tagged at Praia do Tofo moved widely in southern Mozambican and eastern
South African waters. Although the duration of tag transmission was relatively short for
most sharks, they spent a disproportionately high amount of time in regional shelf waters
between Zàvora and Pomene. This is of concern for regional whale shark conservation, as
gill net use is rapidly increasing in the same coastal area where tagged whale sharks spent
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 13/24
a lot of time, leading to a higher chance of net entanglement and mortality. Whale sharks
moved through water with higher Chl-athan simulated model sharks, suggesting that
foraging is a major driver of their movements in this region.
The coastal whale shark hotspot in southern Mozambique
The primary activity hotspot for tagged whale sharks was a ∼200 km stretch of shelf waters
along the coast from Zàvora to Praia do Tofo, and also around Pomene. This agrees with
our aerial survey data from 2004–2008, despite the temporal mismatch of the two datasets,
which strengthens the importance of this area for whale sharks. One caveat is that both
technologies require the sharks to be in surface waters to be detected, and whale sharks may
also be abundant elsewhere in deeper water but remain undetected. The observed hotspot
was not the result of random movement, or a bias due to the tagging site, as model sharks
spent significantly less time on the continental shelf than real whale sharks. In addition,
long-term whale shark sightings at Praia do Tofo fluctuated, but did not have a seasonal
trend (Rohner et al., 2013b). Hence, while our tracks were relatively short and did not span
the whole year, the general pattern may apply throughout the year. The narrow shelf waters
around Praia do Tofo were a preferred habitat for whale sharks in the region in our study,
which is further corroborated by photo-identification and tourism studies (Pierce et al.,
2010;Haskell et al., 2015;Rohner et al., 2015b). However, our tagging data also show that
the core use area for whale sharks in Mozambique is larger than previously reported, and
larger than in some other, more defined whale shark aggregations that exploit specific
and localised ephemeral prey sources or biological events (Heyman et al., 2001;Robinson
et al., 2013;Rohner et al., 2015a). For example, the 50% kernel densities covered 185 km2
in Mozambique compared to just 66 km2in Qatar (Robinson et al., 2017).
Eight whale sharks (53% of those tagged) returned to the tagging site during tag
attachment after significant initial (>50 km) movement away from the site, mostly along
the coast. Only two of these individuals were photographically recaptured, despite close to
daily survey effort in good conditions for potential resightings (S Pierce, 2012, unpublished
data). This further stresses the importance of sightings-independent methods for assessing
whale shark residency, as detectability can be low, even when regular visual surveys are
performed (Cagua et al., 2015;Andrzejaczek et al., 2016). Eight of the 15 tagged whale
sharks were photographically re-sighted at Praia do Tofo after losing their tags, indicating
some degree of site fidelity. Elsewhere, whale sharks also return to other aggregation sites,
as determined by photo-ID techniques (Holmberg, Norman & Arzoumanian, 2009;Rowat
et al., 2011), and their site fidelity may be more prevalent than expected from sightings
data (Cagua et al., 2015).
Preference for shelf waters
During the 8 months of the year (Jul–Feb) that whale sharks were tracked, over a combined
duration of 403 days, whale sharks actively chose continental shelf waters that were cooler
and had higher Chl-athan the modelled sharks that moved randomly. While shallower,
cooler water and higher Chl-aco-vary in our study region, the bigger difference in Chl-a
between real and model sharks indicated that they mostly selected Chl-a. Their preference
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 14/24
for cooler shelf waters with higher Chl-ais thus likely to be related to foraging activities.
Even though whale sharks do not directly feed on phytoplankton, and there is often a
lag between the timing of phytoplankton and zooplankton blooms (Plourde & Runge,
1993;Flagg, Wirick & Smith, 1994), high phytoplankton biomass is often indicative of high
zooplankton densities (Hutchinson, 1967;Richardson & Schoeman, 2004;Ware & Thomson,
2005). Whale shark sightings (Sleeman et al., 2007) and the abundance of other large marine
animals have previously been correlated with Chl-a(Zagaglia, Lorenzzetti & Stech, 2004;
Block et al., 2011;Graham et al., 2012;Jaine et al., 2012). We suggest that the juvenile whale
sharks at Praia do Tofo that stay on the shelf do so to take advantage of high local food
availability. Whale sharks off Praia do Tofo have been seen feeding ∼20% of their time
during daylight hours (Pierce et al., 2010). Stomach contents of whale sharks from southern
Mozambique and northern South Africa were dominated by mysids, a group of demersal
zooplankton that emerge into surface waters at night (Rohner et al., 2013a). Shallow coastal
waters also have a high abundance of other demersal zooplankton (Alldredge & King, 1977;
Ohlhorst, 1982). This suggests that Mozambican coastal waters are important foraging
grounds for these juvenile whale sharks, perhaps more at night than during the day.
Tag-recorded temperature data further support the hypothesis that whale sharks often
remain in shelf waters to exploit foraging opportunities. When off the shelf, in deeper
waters, whale sharks experienced a broader temperature range that extended to cooler
temperatures than those recorded from the surface. By contrast, the temperature range
recorded for locations on the shelf were similar to surface water temperatures. This indicated
that little diving behaviour took place, as shelf waters in the Mozambique Channel get
significantly cooler at depth (Lamont et al., 2010;Malauene et al., 2014;Rohner et al., 2017).
This suggested that whale sharks increased their vertical movement when off the shelf.
Whale sharks dive to bathypelagic depths (>1,000 m), as has been demonstrated with
pressure-recording tags (Brunnschweiler et al., 2009;Tyminski et al., 2015). One whale
shark tagged near Praia do Tofo undertook most deep dives in the southern Mozambique
Channel during the day, when zooplankton is often found at depth (Loose & Dawidowicz,
1994), suggesting that these dives might have been related to foraging (Brunnschweiler
et al., 2009). Results from biochemical dietary studies have suggested that whale sharks
may feed on meso- and bathypelagic crustaceans and fishes, among other prey (Rohner
et al., 2013a). Since temperatures of 4.2 ◦C, 5.5 ◦C and 9.2 ◦C were recorded at 1,264 m,
1,092 m and 1,087 m depth respectively (Brunnschweiler et al., 2009), one of our tagged
sharks, MZ-463, may have dived to depths of around 1,000 m (5.1–10 ◦C bin), potentially
to feed.
Whale sharks swam at a mean speed of ∼28 km d−1which is within the large range
of swimming speeds reported in previous studies. Larger sharks (>900 cm TL) tagged
in other locations exhibited similar speeds to juveniles (Wilson et al., 2006;Hearn et al.,
2016), and the difference in distance covered per day among studies is likely to be primarily
influenced by the sharks’ behaviour (feeding vs. migrating) rather than their size, at least
for sharks >400 cm TL. Similarly, total mean track distance in different studies is likely to
be influenced by both tracking duration and whale shark behaviour.
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 15/24
Conservation and management implications
This study supports the results from other tracking studies that show whale sharks routinely
swim long distances and cross international boundaries. Offshore areas were used by some
of the tagged individuals and may be important habitats for the species, particularly
large, mature animals (Hearn et al., 2016) that are seldom seen at coastal aggregations
(Rowat & Brooks, 2012;Rohner et al., 2015b;Ramírez-Macías et al., 2017). Results of this
study indicate that southern Mozambican whale sharks routinely cross into South African
waters, in addition to some interchange with Madagascar (Brunnschweiler et al., 2009), the
Seychelles (Andrzejaczek et al., 2016) and Tanzania (Norman et al., 2017). A coordinated
regional approach to managing the species’ conservation in the Western Indian Ocean is
therefore of importance, given the transnational boundaries crossed by individual sharks,
and their occupancy of international waters.
That notwithstanding, these juvenile whale sharks spent a large proportion of their
time on the shelf adjacent to Praia do Tofo, indicating that this is a particularly important
habitat within the region. Drifting gill nets are set in the same areas where the whale
shark activity hotspot was recorded. Furthermore, their use in the Praia do Tofo area
has increased over recent years. While the satellite tracking dataset (2010–2012) does not
temporally match with the gill net abundance dataset (2012–2015), we suggest that the
spatial overlap of the whale shark hotspot and the increasing gill net use in the area raises
concerns, especially considering the regular north-south movement of whale sharks close
to the coast that is likely to bring them in contact with these nets. However, concomitant
data on gill net numbers and locations and the distribution of whale sharks would be
needed to quantify the risk to whale sharks. Other threatened species, such as manta
rays, may also be affected by this fishery (Rohner et al., 2017). There are few available
data on catch and injury rates along this remote coast, although multiple mortalities
from gill nets and injuries characteristic of net entanglement have been reported from the
Inhambane Province (Speed et al., 2008, S Pierce, 2015, unpublished data). Interview-based
surveys with fishing communities are presently underway to provide more information
on catches. Whale sharks within the Indian Ocean are listed as ‘Endangered’ on the IUCN
Red List of Threatened Species (Pierce & Norman, 2016), and they are locally important
to a burgeoning marine tourism industry (Pierce et al., 2010;Tibiri¸
cá et al., 2011;Haskell
et al., 2015). The lack of habitat-level protection, coupled with poor regulation of inshore
fisheries in Mozambique, is a clear threat to this population.
ACKNOWLEDGEMENTS
We thank Clare Prebble and Peter Bassett, along with other volunteers from the Marine
Megafauna Foundation (MMF) for their assistance in the field. We thank the people who
found and returned some of the tags. We gratefully acknowledge the NASA Ocean Biology
Processing Group for provision of Moderate Resolution Imaging Spectroradiometer
satellite data. Janneman Conradie and Joshua Axford from MMF conducted the gill
net aerial surveys and Ross Newbigging (All Out Africa) and Jessica Williams (Moz
Turtles) helped compile the gill net visual survey data. We thank David Johnston,
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 16/24
Jeremy Kiszka and one anonymous reviewer for their constructive comments on our
submitted manuscript. Casa Barry Lodge and Peri-Peri Divers provided logistics field
support. This research has made use of data and software tools provided by Wildbook for
Whale Sharks, an online mark-recapture database operated by the non-profit scientific
organisation Wild Me with support from public donations and the Qatar Whale Shark
Research Project. Some maps were created using ArcGIS software by Esri, please visit
http://www.esri.com. We acknowledge the use of free vector and raster map data sourced
from http://www.naturalearthdata.com.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
Field work was supported by the Shark Foundation, Aqua-Firma, Waterlust, a Rufford Small
Grant and the PADI Foundation. Christoph Rohner and Simon Pierce were supported by
two private trusts. Anthony Richardson was supported by the Australian Research Council
Future Fellowship FT0991722. The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Shark Foundation.
Aqua-Firma.
Waterlust.
PADI Foundation.
Australian Research Council Future Fellowship: FT0991722.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•Christoph A. Rohner conceived and designed the experiments, performed the
experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed
drafts of the paper.
•Anthony J. Richardson conceived and designed the experiments, analyzed the data,
contributed reagents/materials/analysis tools, prepared figures and/or tables, reviewed
drafts of the paper.
•Fabrice R.A Jaine analyzed the data, reviewed drafts of the paper.
•Michael B. Bennett and Geremy Cliff contributed reagents/materials/analysis tools,
reviewed drafts of the paper.
•Scarla J. Weeks conceived and designed the experiments, contributed reagents/materi-
als/analysis tools, reviewed drafts of the paper.
•David P. Robinson analyzed the data, prepared figures and/or tables, reviewed drafts of
the paper.
Rohner et al. (2018), PeerJ , DOI 10.7717/peerj.4161 17/24
•Katie E. Reeve-Arnold performed the experiments, reviewed drafts of the paper.
•Simon J. Pierce conceived and designed the experiments, performed the experiments,
wrote the paper, reviewed drafts of the paper.
Animal Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
Whale shark tagging was compliant with ethics guidelines from the University of
Queensland’s Animal Ethics Committee and was conducted under their approval certificate
GPEM/186/10/MMF/ WCS/SF.
Data Availability
The following information was supplied regarding data availability:
The raw data has been provided as a Supplemental File.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.4161#supplemental-information.
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