Environmental Factors Influencing Whale Shark Occurrence and Movements at Mafia Island, Tanzania
ENVIRONMENTAL FACTORS INFLUENCING WHALE SHARK OCCURRENCE AND MOVEMENTS AT MAFIA ISLAND, TANZANIA
Christoph A. Rohner & Simon J. Pierce | Marine Megafauna Foundation Michael Berumen, Jesse Cochran & Fernando Cagua | KAUST University Mathias Igulu & Baraka Kuguru | Tanzanian Fisheries Research Institute
Jason Rubens | World Wide Fund for Nature
WWF Project Report
Environmental factors influencing whale shark occurrence & movements at Mafia Island, Tanzania
Christoph A. Rohner 1 & Simon J. Pierce 1,2 Michael Berumen3,4, Jesse Cochran3 & Fernando Cagua3 Mathias Igulu5 & Baraka Kuguru5 Jason Rubens6
1 Manta Ray and Whale Shark Research Centre, Marine Megafauna Foundation, Praia do Tofo, Inhambane, Mozambique 2 Wild Me, Praia do Tofo, Inhambane, Mozambique 3 Coral Reef Ecology Laboratory, Red Sea Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia 4 Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, United States of America 5 Tanzania Fisheries Research Institute (TAFIRI), Dar es Salaam, Tanzania 6 World Wide Fund for Nature, Dar es Salaam, Tanzania
Chris Rohner: [email protected] +255 (0)76 490 0044 Simon Pierce: [email protected] +44 74 288 39945 Michael Berumen: [email protected] +966 54 470 0019 Mathias Igulu: [email protected] +255 (0)78 684 4878 Jason Rubens: [email protected] +255 (0)75 422 9450
WWF – Mafia Island whale shark study 1. Executive Summary
The whale shark Rhincodon typus is the largest fish in the world. Its size, gentle nature and tendency to swim at the surface in predictable coastal aggregation sites means that it can be a focal species for marine tourists. Whale sharks are categorised on the IUCN Red List as ‘Vulnerable’ to extinction following fishery-induced declines in some parts of the world. Research on this species has been conducted mainly at coastal aggregation sites, where scientists have access to an otherwise oceanic, migratory shark. Mafia Island off the coast of Tanzania is one of these coastal aggregation sites.
The present whale shark study expanded upon an initial WWF-funded study during 2007-09 that described this aggregation site. Here, we examined the influence of environmental variables on whale shark presence and sightings at Mafia Island. To accomplish this, we measured local variables (e.g. with a weather station), and investigated regional oceanographic processes, as well as examining shark population structure, local movements, feeding ecology, interactions with fishers and the human threats present around Mafia Island. Active fieldwork by the project team was conducted between October 2012 and March 2013, with some aspects of the study continuing to June 2013.
Compared to reports from previous years, we saw more individual sharks and had more encounters during the 2012/13 season. We photographically identified 87 different whale sharks and had a sightings success rate per boat trip of 72%, with an average of 4.8 individuals seen per trip. The highest number of sharks per trip was seen in November (6.1) and Dec (7.8), while the activity peak (sum of acoustic detections of 30 tagged sharks) was in January. This was earlier than expected from previous reports, which described a peak in sightings from December to March. In the present study, while boat-based sightings ceased in March, some acoustic detections continued into May. These data, and additional sighting reports from local fishers, suggest that at least some whale sharks are likely to be present off Mafia Island over longer periods than previously recognized. Acoustic detections after March 2013 were mostly offshore and from sharks swimming deeper in the water column, accounting for the reduced sightings by surface- based observers. Continuation of this study into 2014 will improve our understanding of year- round site use by the sharks.
The population structure of Mafia Island whale sharks was broadly similar to other aggregations in the Indian Ocean, comprising mostly male (83%) and juvenile (97%) sharks. The sharks ranged from 415–917 cm in total length as determined with laser photogrammetry. Of the 27 sharks photographed in Mafia prior to our study, 18 were seen again this season, indicating a high degree of site fidelity. Another shark was previously identified in Mozambique indicating there is at least some limited connectivity between these two aggregation sites.
An array of 19 acoustic receivers was positioned around Kilindoni Bay to detect 29 whale sharks that were equipped with acoustic transmitter tags. Over 54,000 detections were recorded from November 2012 to May 2013, showing that whale sharks were highly active in the area. The acoustic receiver range at which 50% of signals were detected was 367 m. Acoustic activity continued throughout the study in contrast to whale shark sightings, which ceased in March. The likely cause for the discrepancy is a shift in whale shark habitat selection toward deeper, offshore areas.
WWF – Mafia Island whale shark study Apart from showing high-use of Kilindoni Bay, whale sharks also had a long average residency time in the immediate area; 42 days determined by photo-ID and 73 days determined by acoustic telemetry. They likely stayed close to Mafia Island for a long time because of the high prey availability. Whale sharks were usually sighted while they were feeding at the surface. Zooplankton analysis revealed that the most important prey taxon was the sergestid shrimp Lucifer hanseni, which comprised almost 50% of identified items from plankton samples collected near feeding whale sharks. By contrast, copepods dominated the plankton from samples taken in locations where whale sharks were not feeding. Sergestids are larger than copepods and thus the plankton biomass was 10 times higher during feeding events than when sharks were not feeding. Furthermore, the sardines (dagaa) targeted by the local ring net fishery were frequently seen feeding on the same sergestid shrimps, often associated with whale sharks. This co-feeding behaviour leads to conflict between whale sharks and fishers.
Fisher observations showed that whale sharks were seen mainly in two locations: in Kilindoni Bay, where this ecology study was situated, and near Koma Island which is located close to the mainland 50km north-west of Kilindoni. This secondary hotspot for sightings may warrant further investigation in future seasons. Of the 147 fishing trips where fishers recorded whale shark presence/absence, sharks interfered with fishing operations on 18 occasions and were trapped inside the ring net 12 times. Fishers tried to let the sharks escape, but sometimes their fishing gear (and potentially the shark) was damaged in the process. No direct whale shark mortality was reported, although apparently this has occurred on a small number of occasions in the past (L. Mokoki pers. comm.).
Whale sharks at Mafia Island are subject to anthropogenic pressures that may affect their survival, or at least their normal behaviour. We investigated this by looking at the scars on the bodies of surviving sharks during routine photo-ID activities. The majority of whale sharks had a scar (75%) and, although most scars were minor, this indicates that collisions with boats are common. Scarring was more prevalent here than in whale sharks at the Seychelles (67%), Mozambique (37%) and Australia (27%) (Speed et al. 2008). Abrasions on the dorsal ridge and fins were likely caused by contact with a boat hull, while engine propellers likely inflicted lacerations. Most scars appeared to be caused by small boats, such as the tourist boats that engage in whale shark viewing and by dagaa fishing boats. Improved adherence to the code of conduct by tourism vessels could reduce this threat, and we recommend a second skipper/guide workshop at the start of the next season. Continuation of the dialogue with fishers is also recommended, both for obtaining data and disseminating information.
We used a modelling approach to investigate potential variables influencing whale shark sightings and presence at Mafia Island. Local factors were important drivers of whale shark presence off Mafia Island, explaining a large amount of the variance in the generalised linear models (sightings model = 84%; acoustic model =71%). The most important factors driving whale shark sightings at the surface were the month, and the proportion of whale sharks that were observed feeding. Month was also the most important predictor in the acoustic model. Many of the significant factors appeared to indirectly affect whale shark presence through influencing the availability of their macrozooplankton prey.
Overall, we found the Mafia Island whale shark aggregation to be composed of mostly juvenile, male sharks and with a clear seasonal peak in sightings, similar to other aggregations in the Western Indian Ocean. The sharks were mostly seen at the surface when feeding, and the feeding zooplankton samples indicated that sergestid shrimps were the main component of their day-time
WWF – Mafia Island whale shark study diet. Other environmental variables are likely to impact the position of the sergestids in the water column, and thus the visibility of feeding whale sharks. While the origin of the productivity in Kilindoni Bay remains to be determined, it appeared that regional-scale oceanography does not play a major role. The Rufiji river outflow on the mainland side of the bay could be important, however the seasonality of its peak flow (and thus nutrient input) and high whale shark sightings do not overlap.
2. Acknowledgements
This project was funded by WWF Switzerland, our thanks to Doris Calegari for her support. In-kind contribution in the form of acoustic equipment and personnel time was provided by KAUST. Project management support was provided by WWF Tanzania: we thank Haji Machano, Kennedy Mawole, Jacqueline Ngowi, Victor Myovela, Zainab Ismail, Emiliana Semuguruka and all WWF-TZ staff for their help and support of our project. On Mafia we thank Liberatus Mokoki for sharing his local knowledge, and Gregory, Matthew, Hamisi and Afro for captaining our boats. We are grateful to Jean and Anne de Villiers for supporting us in their research camp and to Swaumu for looking after us during excursions to Utende. We thank Danielle Keates and David von Helldorff at Sea Point for providing us with dive gear throughout our work. Thanks also to Clare Prebble and Tom Horton from MMF who assisted with processing photographic identifications of sharks, and volunteer assistants at the field site over the course of the study.
WWF – Mafia Island whale shark study 3. Contents
1. Executive summary ii 2. Acknowledgements iv 3. Contents v 4. Introduction 1 Whale sharks 1 Mafia Island 1 Aims 3 5. Sub-components (A) Population structure of whale sharks at Mafia Island 4 Methods 4 Observational trips 4 Whale shark searches 4 Whale shark encounters 4 Results 5 Discussion 8
(B) Why do whale sharks aggregate at Mafia Island? 9 Methods 9 Results and Discussion 9 Meteorology of coastal East Africa 9 Oceanography of the Western Indian Ocean 9 Regional-scale oceanography for the whale shark season 11 Discussion 12
(C) Feeding ecology 13 Methods 13 Zooplankton collection and biomass estimation 13 Taxonomic composition 13 Results 13 Feeding vs. non-feeding biomass 13 Feeding vs. non-feeding taxonomic composition 14 Discussion 17 (D) Movements of whale sharks at Mafia Island 18 Methods 18 Array design 18 Acoustic array 18 Transmitter deployment 18 Range test 19 Acoustic monitoring 19 Results and Discussions 19 Transmitter deployment 19 Range test 19 Acoustic monitoring 20 Issues 26 Conclusions 26
(E) Fisher – whale shark interactions 28
WWF – Mafia Island whale shark study Methods 28 Study area 28 Data collection 28 Results and Discussions 29 Interactions between fishers and whale sharks 29 Fishers’ whale shark sightings 29 General observations 32 Challenges 32
(F) Local threats 33 Methods 33 Scarring 33 Boat traffic 33 Tourism and fishing 33 Results and Discussions 33 Scars 33 Boat traffic 36 Tourism and fishing 36
(G) Environmental influences on whale sharks 38 Methods 38 Model data 38 Models 38 Results 41 Sightings model 41 Acoustic model 43 Discussion 44
6. Recommendations 46 7. Conclusions 47 8. List of References 49 9. Appendices 53
WWF – Mafia Island whale shark study 4. Introduction
Whale sharks The whale shark Rhincodon typus is the largest fish in the world, growing to 20 m in total length (TL) and weighing up to 34 t (Chen et al. 1997). Whale sharks are distributed around the world in all tropical to subtropical seas (Compagno 2001, Jaffa & Taher 2007), with seasonal records from temperate regions such as New Zealand (Duffy 2002) and Canadian waters (Turnbull & Randell 2006).
Despite their large size and circumglobal distribution, little was known about their biology and ecology until the mid-1990s, with most earlier publications referring solely to the locality of sightings, strandings or captures (e.g. Wolfson 1986). The reproductive mode of whale sharks was uncertain until Joung et al. (1996) described them as lecithotrophic livebearers, whereby embryos hatch from egg cases in uteri. In the following years, whale shark research expanded rapidly, leading to the first international whale shark conference in 2005 and an associated special issue in Fisheries Research in 2007, where various aspects from satellite tracking to foraging ecology were discussed (Irvine & Keesing 2007). The body of whale shark literature has since grown further (reviewed by Rowat & Brooks 2012), driven mostly by research at key aggregation sites, such as at Ningaloo Reef in Australia, Mahé Island in the Seychelles and Praia do Tofo in Mozambique.
A whale shark aggregation at Mafia Island in Tanzania has recently become the subject of scientific study. The sharks started to attract the interest of local tourism operators on Mafia from around 2005. This prompted the World Wide Fund for Nature (WWF) to commission initial studies on population dynamics in partnership with the US Shark Research Institute (SRI) from 2006 to 2009 (Potenski 2009) and on local community knowledge (de Villiers 2007). Whale sharks were generally seen at the site between October and March. Sightings fluctuated, but the drivers behind this variation remained uncertain. A follow-up study (reported herein) was commissioned by WWF and launched over the 2012/13 season with the specific objective of gaining a better understanding of the factors driving both seasonal and shorter-term timing of whale shark presence and sightings.
Mafia Island Mafia Island lies off the central coast of Tanzania in East Africa, directly offshore of the Rufiji River Delta (Fig. 1). The island is roughly 50 km long and 15 km wide, comprising an area of ~500 km2. Its main town, Kilindoni, is on the south-west coast of the Island (7.92°S, 39.65°E, Fig. 2). The Mafia Island Marine Park encompasses the area from just south of Kilindoni around the southern end of the island to 7.75°S, including Boydu, Jibondo, Juani and Chole Islands. The park was gazetted in April 1995 and includes areas of varying levels of protection.
Two monsoonal seasons characterise the weather conditions in coastal Tanzania. The northeast monsoon prevails from Nov-Feb and brings calm winds and high temperatures. The southeast monsoon prevails from Mar–Oct, with highest rainfall in Apr–May (Jiddawi & Öhman 2002, Lugomela et al. 2002). Ocean currents on the offshore side of Mafia Island flow northward throughout the year, driven by the East African Coastal Current (Swallow et al. 1991).
Most previous observations of whale sharks have taken place outside the marine park, in Kilindoni Bay (Potenski 2009). This bay extends from Ras Kisimani in the south to Ras Mbisi to the north of Kilindoni and is largely shallow, not exceeding 30 m depth. The intertidal zone is up to ~1 km wide and mangroves line
WWF – Mafia Island whale shark study 1 the entire bay excepting the area immediately off Kilindoni town. The substrate in the bay is mostly sand, with a few dispersed and damaged reef areas and some mud and sea grass close to the coast.
Fig. 1. Tanzania’s coastline, with Mafia Island and major locations indicated.
WWF – Mafia Island whale shark study 2 Fig. 2. Mafia Island, with the coastline, 10 m depth contour and Marine Park boundary indicated. S1–4 = sampling stations.
Aims The previous WWF/SRI whale shark study at Mafia Island from 2007-09 has provided a baseline on which this study could build. The main findings were:
(1) Whale sharks were mostly seen from Dec–Mar (sightings made on 81% of trips), although a lesser number of sightings were also made throughout the year. (2) 585 encounters were recorded on 237 trips, a mean of 2.5 sharks per trip. (3) 21 individual whale sharks were photographically identified, with 53 sharks distinguished through photos and tagging. (4) 78% of encounters were males and visual size estimates ranged 1.5–7.5 m, with one 13 m TL individual sighted. (5) Satellite tagging indicated some dispersal away from Mafia Island.
Following and expanding on the recommendations from this previous work, the main aim of the present study was to assess which environmental variables influence whale shark presence and sightings at Mafia Island. To do this, we had several objectives: (1) Describe the population structure of whale sharks at Mafia Island. (2) Investigate seasonality in shark presence. (3) Consider oceanographic influences on whale sharks at Mafia Island (4) Investigate whale shark feeding ecology. (5) Assess whale shark movements on a local level. (6) Examine the interactions between fishers and whale sharks.Identify threats to whale sharks at Mafia and their potential solutions.
WWF – Mafia Island whale shark study 3 5. Sub-components: Methods, Results & Discussion
(A) Population structure of whale sharks at Mafia Island
Methods Observational trips We undertook boat trips on 103 days between 17 Oct 2012 and 15 Mar 2012. Each trip started and finished at our research camp near Kilindoni (Fig. 2). Different boats were used depending on availability from the Whale Safari operators. These included the main boat, a ~7m long fiberglass single-hull, a ~7m long trimaran and a ~6m long wooden boat. On 85 trips a 15 hp engine was used, while a 40/45 hp engine was used on the remaining 17 trips. Trip distance and duration were measured with a hand-held GPS unit (Garmin Etrex 10). GPS data were not complete for 16 (distance) and 13 (duration) trips, respectively, and these values were estimated based on logbook notes.
Average trip duration was 238 min (± 76 min SD, range = 47–540 min) and average trip distance was 32 km (± 9 km SD, range = 7–51 km). Data were summarised as whale sharks seen per trip, because trip distance and duration depended on the location of sharks and the range of tasks (plankton collection, acoustic station deployments or retrievals etc.) scheduled for a given research trip.
Whale shark searches At least one observer was searching for whale sharks at all times. Almost all whale sharks were spotted when their 1st dorsal fin, upper jaw or upper caudal lobe broke the surface, while sometimes birds diving into the water indicated the presence of whale sharks, or fishers informed us of sightings. Searches usually included stations 1-4 (see below Environmental sampling), but routes varied depending on (1) whale shark locations on the previous day, (2) weather conditions, and (3) the location of the dagaa fishers.
Whale shark encounters When a whale shark was seen, a swimmer with a camera entered the water to collect data on the individual. Where possible, each whale shark was identified based on a photograph of their spot pattern posterior to the gills (Arzoumanian et al. 2005) and a unique encounter number was assigned in the global whale shark database (ECOCEAN 2012). Matches were assigned to previously identified sharks, while new sharks were assigned a new number based on the format TZ-XXX, with TZ = Tanzania and XXX = a 3-digit number starting at 001. As it was not possible to photo-identify every shark that was sighted to avoid any potential for double-counting individuals, we use the number of photographically identified whale sharks in all further analyses. This means that results presented here represent the minimum number of sharks observed.
The sex of each shark was determined visually by examining the pelvic fins for the presence of claspers (external, paired reproductive organs) in males or their absence in females. Maturity in male whale sharks was assessed using external clasper morphology (Norman & Stevens 2007). Immature sharks had relatively small, uncalcified claspers, whereas mature sharks had thick, calcified claspers that extended past the pelvic fins. Their total length (TL) was visually estimated and their associated fauna, scarring and tags were recorded. A laser photogrammetry system mounted on a housed digital camera (Canon G12), as described in Rohner et al. (2011), was used to measure TL and inner clasper lengths. Biopsies of the skin and sub- dermal tissue were collected from 52 male and 8 female sharks for collaborative genetic and biochemical projects. Samples were extracted from ventral to the 1st dorsal fin using a hand-spear with a modified tip. Samples were taken from the left side of the shark until 12th Jan 2013, after which the right side was sampled, including re-sampling of some sharks.
WWF – Mafia Island whale shark study 4
Results We recorded 520 encounters with 87 different whale sharks that could be photographically identified. Removing duplicates within each day, we had 489 whale shark encounters on 103 trips, with a mean ± SD of 4.8 ± 4.85 individuals and a range of 0–21 individuals per day. The highest mean number of individual whale sharks was recorded in December (7.8 sharks per trip), followed by November (6.1) and January (5.0; Table 1). Whale sharks were encountered on 72% of survey trips, and no sharks were seen in March.
A significant sex bias was observed, with 69 males (83.1%), 14 females (16.9%) and 4 sharks of unknown sex (Fig. 3; ɍ2 = 23.1439, df = 1, p < 0.0001). Sharks were sighted on a mean of 5.97 ± 5.0 different days, ranging from once (17 sharks) to 20 times (TZ-048). The difference between males (6.5 times) and females (4.9) was not significant (t = 1.2264, df = 22.429, p = 0.2328). The mean duration between first and last sighting was 42 ± 36.5 days, with differences based on sex not significant (male = 46.4, female = 32.6; t = 1.2947, df = 18.436, p = 0.2114).
The visually-estimated mean total length of all sharks was 571 ± 95 cm with a range between 350–900 cm (Fig. 4). Total length of males (577 cm) and females (542 cm) was not significantly different (t = 1.6128, df = 25.941, p = 0.1189). We also measured 45 individuals with laser photogrammetry. Mean TL of these sharks was 640 ± 136 cm, ranging between 415–971 cm. Males were significantly larger than females (642 cm and 564 cm, respectively; t = 2.7982, df = 20.595, p = 0.0109). Inner clasper length ranged 29–87 cm (mean ± SD = 49.9 ± 16.8 cm) and 2 of these sharks, plus 1 whose claspers were not measured, were considered mature. The majority of males were immature (97%).
Of the 27 whale sharks photographically identified from Mafia Island before our study, 18 (67%) were seen again this season. These sharks were first identified from May 2007–November 2011 and had a mean interval between first and last sighting of 1453 ± 596 days (range = 357–1995 days). One whale shark has previously been identified outside the Mafia Island area: MZ-136 was first recorded at Praia do Tofo, Mozambique, in November 2006, almost six years before it was seen at Mafia Island.
WWF – Mafia Island whale shark study 5 Table 1. Boat trips and individual whale sharks identified per month during our survey season 2012/13.
Month Trips %Trips with sharks Number of sharks Sharks per trip October 12 83.3 (10) 50 4.2 (±3.4) November 21 85.7 (18) 127 6.1 (±5.1) December 24 87.5 (21) 187 7.8 (±5.8) January 20 75.0 (15) 99 5.0 (±3.9) February 19 52.6 (10) 26 1.4 (±1.6) March 7 0.0 (0) 0 0.0 (±0.0) Total 103 71.8 (74) 489 4.8 (±4.9)
Fig. 3. Whale shark size range, mean total length (TL) and percentage of males at global aggregation sites (GoC = Gulf of California) (Pravin 2000, Graham & Roberts 2007, Norman & Stevens 2007, Hobbs et al. 2009, Brooks et al. 2010, Riley et al. 2010, Rowat et al. 2011, Hsu et al. 2012, Ramírez-Macías et al. 2012a, Ramírez-Macías et al. 2012b, Rohner 2012). The maturity size is based on data from Mozambique and South Africa (Rohner 2012) and the birth size is based on data from Taiwan (Joung et al. 1996).
WWF – Mafia Island whale shark study 6
Fig. 4. Size structure of whale sharks (black = males, grey = females) identified at Mafia Island with (A) visual estimates and (B) laser photogrammetry estimates.
WWF – Mafia Island whale shark study 7
Discussion We encountered 87 different whale sharks during 520 encounters at Mafia Island over the study period. Removing the individuals identified twice in the same day, we saw 4.8 sharks per trip and observed whale sharks on 72% of research trips. Compared to the previous report by Potenski (2009), we had a higher frequency of shark sightings (72% vs. 64%) and saw almost twice as many sharks per trip (4.75 vs. 2.5). Our data are, however, from 103 trips over 5 intensive months during the peak season, compared to 237 trips over 27 months spanning all seasons. Nevertheless, our survey apparently coincided with an exceptionally good whale shark season, which was also the perception of experienced local tourism operators. Most whale sharks were seen in Nov and Dec, which was different from the Dec–Mar high season reported from previous years (Potenski 2009).
The population and size structure of whale sharks at Mafia Island was broadly similar to those at other aggregation sites in the Western Indian Ocean, being comprised of 83% males and dominated by immature sharks ranging ~400–900 cm TL. The Seychelles, Maldives and Ningaloo Reef aggregations had wider size ranges (Norman & Stevens 2007, Riley et al. 2010, Rowat et al. 2011), although this difference may be the result of different size estimation techniques used (visual vs. laser photogrammetry). Considering the whole size spectrum of the species (Fig. 3), the Mafia Island aggregation does not contain many very young or large, sexually mature individuals.
Individual whale sharks were resighted 6 times on average and their mean residency time (i.e. days between first and last sighting within a season) was 42 days, indicating that they spent a considerable amount of time in the Mafia Island area. This is different from some other aggregation sites, such as in Mozambique where whale sharks are seen for 2–3 days before leaving the area again (Rohner et al. 2013). This suggests the food environment at Mafia Island is rich and consistent for relatively long periods, supported by the observations that whale sharks were mostly seen feeding during the present study. Many whale sharks (67%) identified from previous years returned to Mafia Island during the 2012/13 season. Four sharks, TZ-010, TZ-011, TZ-012 and TZ-017, were seen in 3 calendar years. The high proportion of returning sharks indicates a degree of site fidelity, likely driven by the seasonally high food availability.
The sighting of MZ-136 is the first record of a whale shark having visited both Mafia Island in Tanzania and Praia do Tofo in Mozambique, an along-the-coast distance of over 2000 km. This is the furthest recorded movement in the Western Indian Ocean to date; previous records include one shark travelling 1200 km from Praia do Tofo to Madagascar (Brunnschweiler et al. 2009) and several others swimming 500 km from Praia do Tofo to northern South Africa (Rohner 2012).
WWF – Mafia Island whale shark study 8 (B) Why do whale sharks visit Mafia Island?
Methods Oceanography The oceanographic setting of the region around Mafia Island was characterised through a literature search. Satellite-derived data were then used to compare the 2012/13 season’s conditions with the long-term understanding of the regional dynamics. We used weekly merged-mission sea surface height (SSH) data and weekly mean sea surface temperature (SST) data from AVHRR Pathfinder v.4.1 and GAC, provided by NOAA on www.oceanwatch.org.
Results Meteorology of coastal East Africa Seasonal changes in coastal East Africa are largely influenced by climatic conditions. The Inter-Tropical Convergence Zone (ITCZ) migrates annually, which creates the two monsoonal seasons experienced in the region: the northeast monsoon, which prevails from Oct–Mar, and the southeast monsoon, which prevails from Mar–Oct. The NE monsoon generally brings dry and warm air to the coast, while the SE monsoon is characterised by high cloud cover, rain and cooler air (McClanahan 1988). In coastal Tanzania, the NE monsoon (kazkazi) prevails from Nov–Feb, when the winds are calmer and temperatures higher than during the SE monsoon (kusi) (Jiddawi & Öhman 2002). The highest rainfall occurs from Apr–May, while Jun-Aug is the driest period (Lugomela et al. 2002).
Oceanography of the Western Indian Ocean The oceanography of the southern Indian Ocean broadly consists of a large anticyclonic gyre. Its northern extent is formed by the South Equatorial Current (SEC) moving from Australia to Africa at ~10°S (Wyrtki 1973). The SEC bifurcates at Madagascar into a southern limb flowing along the subcontinent and a northern limb continuing westward to the African mainland (Fig. 5; Swallow et al. 1988). There, the SEC bifurcates again at ~11°S into a southern part going into the Mozambique Channel and the northward- flowing East African Coastal Current (EACC; Swallow et al. 1991). The EACC is defined as the part of the eastern boundary current that flows northward throughout the year. This is opposed to the more complex Somali Current System that is heavily influenced by the monsoon (Schott & Fischer 2000). The Somali Current flows northward during the SE monsoon (e.g. most pronounced during Jun/Jul), but reverses during the NE monsoon (Fig. 6). During the NE monsoon, the EACC thus meets an opposing Somali Current, which results in the South Equatorial Counter Current that moves eastward (Schott & McCreary 2001). The northern extent of the EACC is somewhat disputed, but conservatively set at ~3°S meaning that Mafia Island (7.5–8°S) is well within the influence of the EACC (Schott & McCreary 2001). Oceanic currents in the Mafia Island area thus flow northward along the shelf independent of season.
The EACC creates downwelling off the Tanzanian coast, leading to nutrient-poor waters. This downwelling is strongest during the SE monsoon that is characterised by high current speeds (McClanahan 1988). Broadly-speaking, the northern part of Eastern Africa, where upwelling occurs, is more productive than the Tanzanian coastal waters. Off Tanzania, nutrient concentrations are likely to be higher when coastal runoff and water column mixing is highest during rainy and windy periods (Newell 1959).
WWF – Mafia Island whale shark study 9
Fig. 5. The oceanography of the western Indian Ocean during the northeast monsoon. SEC = South Equatorial Current; EACC = East African Coastal Current; SECC = South
WWF – Mafia Island whale shark study 10 Equatorial Counter Current. Regional-scale oceanography for the whale shark season Sea surface height: Weekly merged SSH data showed the SEC reaching the African coastline at the Tanzania/Mozambique border and the EACC consistently flowed past Mafia Island over this whale shark season (Fig. 6). Comparing monthly merged SSH data from this season with corresponding data from previous whale shark observations revealed no obvious feature, current strength or direction that consistently influence sightings at Mafia Island.
Fig. 6. 2-weekly composites of sea surface height and geostrophic current data for (A) the NE monsoon 10-16 Jan 2013 and (B) SE monsoon 7-13 Jun 2012.
Sea surface temperature: Weekly mean SST data showed two broad-scale oceanographic features of the Western Indian Ocean. First, the comparatively cool northern limb of the SEC flows past the tip of Madagascar, bifurcates at ~11°S and then flows as the EACC northwards past Mafia Island (Fig. 7). Second, the influence of the reversed Somali Current on SST off Kenya and Somalia lasted 5 weeks past its subsidence as indicated by SSH data. On a smaller scale, SST in the area between Mafia Island and the mainland was usually warmer than surface water off the shelf in Oct–Dec, while in March it was cooler. Exact local patterns could not be determined, however, with satellite-derived data.
WWF – Mafia Island whale shark study 11 Fig. 7. Sea surface temperature data for the week of (A) 11-17 Oct 2012, showing the cool SEC and the EACC; and (B) 14-20 Feb 2013, showing the reversed Somali Current and the start of the SECC. Note the different scale bars.
Discussion Satellite-derived sea surface temperature and sea surface height data have shown the main large-scale features described in the oceanographic literature: (1) The northern limb of the SEC reaches the African mainland, with the northern part forming the EACC that flows past Mafia Island. (2) The Somali Current that flows southward during the NE monsoon, leading to cool water off the coast of Somalia and Kenya and forming the SECC when it hits the opposing EACC off southern Kenya. These features could potentially influence large-scale whale shark movement in the region. Whale sharks sometimes swim from Kenya to Tanzania and vice versa (Potenski 2009), and such small-scale movement could conceivably occur on or along the continental shelf, perhaps with northward movement being supported by the EACC. The productive upwelling waters off Somalia during the NE monsoon could be good foraging grounds for whale sharks, however, NE monsoon is also high season for whale sharks at Mafia Island. Finally, eastward movement to the Seychelles aggregation (Rowat et al. 2011) could be supported by the SECC that was prominent at the end of this Mafia Island whale shark season.
Regional scale oceanographic conditions did not reveal clear drivers of high productivity and subsequent whale shark sightings at Mafia Island during Nov–Feb. Highest productivity can be expected during the rainy season when nutrient input from the Rufiji River would be highest. At present, we lack nutrient data over time and plankton data from Mar–Oct to characterise the nutrient/prey availability over the seasons.
The major observation we made in the field was that whale sharks were feeding most of the time (73% of encounters). They appeared to feed on the same macrozooplankton throughout the season. Combined with the many re-sightings within the season and a mean residency time (photo-derived) of 42 days, this indicated that local zooplankton is the most important driver. The re-sightings of individuals from previous years suggested that whale sharks regularly return to Mafia Island, likely to take advantage of the high zooplankton abundance. Oceanographic features, such as eddies, thermal fronts or upwelling are unlikely to play a major role and future studies should therefore concentrate on the origin, abundance and population structure of the local macrozooplankton.
WWF – Mafia Island whale shark study 12 (C) Feeding ecology
Methods Zooplankton collection and biomass calculations Plankton was collected using a 200 ʅm mesh size, 50 cm diameter net with a mechanical flowmeter, towed at the surface ~15 m behind the boat. Samples were immediately fixed in a 5% formaldehyde solution. Plankton tows at stations 1–4 were consistent in starting location, direction and duration (Station 1: - 7.888865545 °S, 39.64251054 °E; Station 2: 7.87222269°S, 39.64985233 °E; Station 3: 7.838845029 °S, 39.60276238 °E; Station 4: 7.905833367 °S, 39.63769446 °E). Whale shark feeding tows varied in location and were aimed to collect plankton as close to feeding whale sharks as possible. We collected 50, 52, 53, 47 and 20 samples from stations 1, 2, 3, 4 and whale shark feeding samples, respectively. GPS positions were recorded for start and end location of plankton tows, as well as for current measurement start and end locations whereby we measured the distance, bearing and time a surface drone (usually an orange, passion fruit or round tomato) floated over a ~3–5 min period. The flowmeter did not record filter volume consistently and we thus calculated the filtered volume using the distance (ZPDist) and direction (ZPDir) of the plankton tow, the distance (CDist) and direction (CDir) of the surface current and the radius (R) of the net:
Filtered volume = ʋ* R2 * (ZPDist + CDist * cos(ʋ /180 *(ZPDir – Cdir)))
In the laboratory, the formalin solution was removed, samples rinsed in freshwater and then split into two equal parts with a Folsom plankton splitter. One half was placed in a pre-weighed glass petri dish, dried for 24 h at 60 °C and weighed again. Zooplankton biomass was expressed in mg m-3 as dry mass per filtered water volume. The other half of the samples was stored in formalin and later used for taxonomic examination.
Taxonomic composition We analysed 21 samples for their taxonomic composition. Ten samples were plankton tows next to feeding whale sharks and 11 were background tows from the same day (except for the comparison sample to the 7 Jan 2013 feeding tow, which was one day later) and in the same vicinity. Samples were diluted and subsamples analysed under a stereo-microscope until at least 100 specimens were counted (mean ± SD = 231.7 ± 115.5, range = 101–512). Counts per taxon were then expressed as percentage and means calculated for background samples and for whale shark feeding samples. A multi-dimensional scaling (MDS) analysis was performed in Primer (v. 6.1.6, Primer-E) using 21 taxonomic categories and lumping the least-common taxa into “others”. No transformation was applied to percentage data prior to MDS calculations, with 1000 restarts and a minimum stress of 0.01. A one-way analysis of similarity (ANOSIM) with 9999 permutations was performed to test significance between the two groups.
Results Feeding vs. non-feeding biomass Mean zooplankton biomass from all samples at stations 1–4 was 2.6 mg m-3, with the lowest biomass just off the research camp at station 1 (mean ± SD = 1.4 ± 1.9 mg m-3) and the highest off Kwabibi at station 4 (3.7 ± 5.3 mg m-3). Mean zooplankton biomass in whale shark feeding samples (25.2 ± 22.8 mg m-3) was almost 10 times higher than the mean background biomass (Table 2). This difference was even higher (13 times) among the samples from the nine days, where plankton was collected from feeding whale sharks and from a nearby background station. On 16 Nov 2012, the biomass at station 4 was almost as high as the
WWF – Mafia Island whale shark study 13 mean whale shark feeding biomass (19.7 mg m-3), however, all whale sharks were observed further offshore where the biomass was still 3 times higher. Table 2. Zooplankton biomass (mg m-3) at background stations and whale shark feeding samples.
Station Mean biomass (±SD) N 1 1.4 ± 1.8 50 2 3.0 ± 10.3 52 3 2.2 ± 2.8 53 4 3.6 ± 5.3 47 All background stations 2.5 ± 6.1 Whale shark feeding 25.2 ± 22.8 20
Feeding vs non-feeding taxonomic composition The taxonomic composition of samples from whale shark feeding events and from corresponding same- day background samples differed significantly (Fig. 8; ANOSIM r<0.01)). Background samples contained mostly copepods (67% of counted specimens). In contrast, feeding samples were dominated by non- copepod taxa (68%).
Half of all specimens identified from background samples were calanoid copepods, with 26% Acartia spp. and 25% juvenile calanoids (Table 3). Other important copepods were the poecilostomatids Coryaceus spp. 9% and Oncea spp. 3%. Of the non-copepod taxa, eggs were by far the most common (17%), followed by gastropod shells (4%), brachyuran larvae (3%) and fish eggs (3%). No sergestids were found in the background samples.
By contrast, almost half of all specimens counted in whale shark feeding samples were one species of macrozooplankton - the sergestid Lucifer hanseni - with 30% of the total counts being adult females and 17% adult males (Table 3, Fig. 9). Other important taxa were juvenile calanoids (17%), Corycaeus spp. (6%), the cyclopoid Oithona spp. (5%) and oiklopleurids (5%).
WWF – Mafia Island whale shark study 14 Fig. 8. PCA plot of taxonomic composition from feeding (orange) and comparison (blue) samples, with 40% similarity indicated in dashed lines.
Table 3. Percentages of taxonomic counts from background and whale shark feeding plankton samples. Only taxa with >1% are shown here, also found (<1%) were: harpacticoid copepods (Microsetella spp. and Macrosetella spp.), Sapphirina spp., fish larvae, bivalves, polychaete larvae, stomatopod larvae, Lucifer hanseni mysis larvae, ostracods, calycophorans, fish scales, isopods, amphipods, mysids and scyphozoans.
Taxon Background samples Whale shark feeding Copepods 67.3 32.2 Calanoids 50.2 17.7 Acartia spp. 25.5 <1 Calanoid juvenile 24.8 16.8 Poecilostomatoids 12.3 6.8 Corycaeus spp. 8.8 6.3 Oncea spp. 3.4 <1 Cyclopoids 2.0 4.7 Oithona spp. 2.0 4.7 Copepod nauplii 2.6 2.8
Non-copepods 32.7 67.8 Sergestids 0 47.8 Lucifer hanseni f 0 29.9 Lucifer hanseni m 0 17.2 Oiklopleurids 1.6 4.9 Eggs 17.4 3.9 Cladocerans <1 3.0 Fish eggs 2.5 2.9 Gastropod shells 3.5 2.1 Brachyuran larvae 3.1 1.9 Decapod larvae 1.1 <1 Chaetognaths 1.0 <1
WWF – Mafia Island whale shark study 15
Fig. 9. Lucifer hanseni from whale shark feeding tows; (A) An adult female (top) and male (bottom), scale = 5 mm; (B) a close-up of male copulatory organ on pereiopod, scale = 0.5 mm; (C) a close-up of the last
WWF – Mafia Island whale shark study 16 abdominal segment in a male, with the arrows indicating the two pointy processes that distinguish the males from females, scale = 0.5 mm; (D) stalked eye with the short rostrum, scale = 0.5 mm. Discussion Whale sharks were feeding mostly in dense patches of the sergestid shrimp Lucifer hanseni. Sergestids also dominated one of the three whale shark stomach contents examined from Mozambique (Rohner et al. in press) and they were a major taxon identified from whale shark feeding events in Mexico (Motta et al. 2010). However, whale sharks feed on a variety of prey taxa in various locations, including chaetognaths in Djibouti (Rowat et al. 2011), Acartia spp. copepods in the Baja California, Mexico (Nelson & Eckert 2007), fish eggs off Qatar (Robinson et al. 2013) or brachyuran eggs off Christmas Island (Meekan et al. 2009). All of these taxa were present in our samples as well, but they were apparently not available in sufficient quantities for the whale sharks to focus on those taxa. Instead, L. hanseni dominated the plankton during feeding events across the entire observation season.
Lucifer shrimps are generally part of the pelagic macrozooplankton and are widely distributed in tropical and subtropical waters (Antony 2005). The abundance, biology or ecology of Lucifer species in tropical waters is virtually unstudied. As an example from the subtropics, L. faxoni has a relatively short life span of 30–40 days and reaches maturity after 19 days at 30 °C (Lee et al. 1992). The same species undertakes daily vertical migration from close to the sediment into the water column and to the surface. This can be driven by tidal fluctuations and/or by solar cycles, and the shrimp can adjust their position when moving into the water column at different tidal phases (Woodmansee 1966). Lucifer hanseni also vertically migrates, and off Japan their emergence was mostly driven by tides, not time of day (Oishi & Saigusa 1997). In the subtropics of China, Lucifer shrimps are most abundant in summer and their increase in occurrence over the past decades has been attributed to an increase in water temperature (Ma et al. 2009, Xu 2010). It is thus possible that L. hanseni off Mafia Island is also most abundant during the NE monsoon season when whale sharks are mostly seen feeding at the surface.
The biomass from feeding events was almost 10 times higher than from background samples. Although difficult to compare with wet mass reported in Motta et al. (2010), our difference in biomass between feeding and background samples was higher than the 2.5 times observed off Mexico. Lucifer hanseni was also mainly responsible for the difference in biomass between the whale shark feeding samples and the background samples. The shrimps are larger than other taxa, such as copepods or fish eggs, but are also very dense, leading to a high dry weight per filtered volume of water.
WWF – Mafia Island whale shark study 17 (D) Local movements of whale sharks at Mafia Island
Methods Array design Based on whale shark sightings from 2006–2010 (Fig. 10 and feedback provided by tour operators, we designed an array that aimed to investigate the local movements of whale sharks. Most previous sightings were concentrated in a 5 km radius from Kilindoni town, though this could have been due to restricted survey effort. We decided to concentrate about half of the stations in this area of apparent high shark activity. The remaining stations were scattered in other areas based on our own sightings and anecdotal evidence from local sources. One limiting factor that restricted receiver placement was depth. There are more than 100 previous sightings in near-shore waters shallower than 5 m. However, since Kilindoni Bay is heavily fished with drag-nets for sardines and mackerel, we limited the depth of the stations to water deeper than 10 m chart datum.
Fig. 10. Map showing the distribution of whale shark sightings from 2006 to 2010 (black dots) and the locations of the installed stations (red dots).
Acoustic array Initially, 19 receivers were deployed (Appendix 1, Table 2) on mooring stations. KAUST provided 20 receivers, but one was damaged during transport and could not be used. A diver secured a receiver to each mooring using cable ties. At the time of the final download in May 2013, 14 receiver stations were still active (Appendix 1, Table 3).
Transmitter deployment In total 30 sharks were tagged: 15 sharks were tagged in October–November 2013 with tags with a pressure sensor, and another 15 were tagged in the second half of December with tags without a pressure sensor (Appendix 1, Table 4). Tags were attached to free-swimming sharks using a Hawaiian pole spear
WWF – Mafia Island whale shark study 18 with a special application needle. The needle inserted a titanium anchor into the shark’s skin, with the tag being secured to the anchor by stainless steel cable.
Range test Two one-week range tests were performed. The locations were chosen to be representative of inshore and offshore stations (Appendix 1 Table 5). In both tests V16 control tags were deployed at 0, 200, 400, 600 and 800 m from the receiver. The range was calculated using a generalized linear mixed model (GLMM) with a binomial distribution (Venables & Ripley 2002). The initial model contained distance and depth as fixed effects and the receiver-transmitter combinations as random effects.
Acoustic monitoring We used the daily sum of detections across the receiver array as an indicator of relative shark activity. Daily detections were standardised using the proportion of sharks detected by the array relative to the number of sharks tagged to that date.
The spectral density estimate is a measure of how much of the energy of a signal is distributed on different frequencies/periods, i.e. it characterises the frequency content of the signal. When the spectral density is plotted against period, a spike in the energy plot indicates that there is a cyclic pattern at that particular period that dominates the signal. We calculated the spectral density of the sum of detections across the whole array in one-hour bins in order to identify periodicity and other temporal patterns in the shark behaviour (periodogram; Brockwell & Davis 1991). To test if there were significant differences between different times of the day, we used a GLMM with a Poisson distribution and random effects at the observation level to account for over-dispersion.
Principal Component Analysis is an eigenvector method used to reduce dimensionality on data; it converts a multivariate dataset to a two-dimensional space in which the variability of the response variable is the largest. This enables the identification of groups within the response variable.. To identify movement patterns within the array, we grouped the acoustic stations into different categories using a principal component analysis (PCA; Mardia et al. 1979). For the PCA, stations were defined by the total sum of monthly detections. We then visually compared the contribution of each group along time using a smoothed series of daily detections by each group (GLM Poisson). We also examined the effect of shark sex and total length on the residency times within the array. All analyses were performed using R 3.0.1 (R Development Core Team 2011).
In order to detect entry-exit pathways on the array that were too subtle to be detected by the Principal Component Analysis, we calculated centers of activity for each of the tagged sharks in one-week bins (Simpendorfer 2008).
Results and Discussion Transmitter deployment We tried to balance the shark sex distribution by tagging a higher proportion of females (28%) relative to males than the proportion of females in the sighted population (17%). Based on laser measurements, tagged sharks were 5.9 ± 1.2 m (mean ± SD) in total length.
Range test
The range of the acoustic array (R50% or distance corresponding to a 0.5 probability) in Mafia was 367 ± 141 m (mean ± SD, Fig. 11). The maximum range (R10%) was 663 ± 142 m. The effect of depth was not significant WWF – Mafia Island whale shark study 19 at the 5% level, which means that sharks are equally likely to be detected by an inshore or by an offshore station. Estimated average ranges represent a worst-case scenario: range test tags were installed close to the sea bottom, while by contrast sharks will be more likely to swim in the water column which positively affects the detection range (Cagua et al. 2013).
6. Fig. 11. Relationship between detection probability and distance from the receiver. The black line represents the average detection range. The grey area shows variability of the detection probability and the range (plus-minus one standard deviation).
Acoustic monitoring Over the seven months of the study, the array has accumulated over 54000 detections from 29 whale sharks. The season peaked unimodally in January, when about 40% of the total detections were obtained. The least active month, May, still had significant whale shark activity with 865 detections from 20 different individuals (Fig. 12).
Fig. 12. Number of daily shark detections standardised by the number of sharks detected relative to the number of sharks tagged (Fig 13). The red line represents a ‘GLM’ smoother of degree 5 (log transformed).
The number of sharks detected relative to the number of sharks tagged decreased with time, as expected (Fig. 13). In the last weeks of February and the first week of March, only a small proportion of sharks were detected, however sharks returned to the array after the second half of March and then left again after the second week of May.
WWF – Mafia Island whale shark study 20
Fig. 13 Proportion of sharks detected by the array relative to the number of sharks tagged. The red line represents a ‘GLM’ smoother of degree 5 (logit transformed). Grey area illustrates the standard error (95% confidence interval).
Half of the deployed tags included pressure sensors with a precision of 4.1 ± 31 m. The mean depth was markedly larger during March, April and May than in the preceding months. This coincided with the increase of detections in the offshore stations, which likely explains why the boat based sightings dropped to zero over that period (Fig. 14).
Fig. 14. Depth of tags with pressure sensors. The red line shows a GAM smoother for the data. Although data shows that there were tags at depths > 30 m, it is important to remember that the pressure sensors have a precision of ± 34 m.
Sharks had an average residency time (defined as the time difference between the last and the first detection) of 73.1 ± 7.3 days (mean ± SE, not corrected for tagging date). There was no significant difference between male and female sharks (t-test, p=0.6). However, larger sharks had a marginal tendency to leave the array sooner when compared with smaller sharks (t-test, p=0.08).
Based on monthly number of detections at each station, the Principal Component Analysis (PCA) suggested classifying them into four distinctive groups: Kwabibi (1 receiver), Tirene-Inshore (3 receivers), Deep-North (2 receivers) and Other (10–15 receivers) stations (Fig. 15 and Fig. 16). Kwabibi was identified as a hotspot for shark activity during the second half of December and the beginning of January. It is remarkable that this group – composed of only one station – accounted for ~30% of the detections at its peak (Fig. 17).
WWF – Mafia Island whale shark study 21
Fig. 15. Principal Component Analysis classification of the monthly activity in the acoustic stations.
Fig. 16. Stations grouped by affinity in monthly activity. Blue for Tirene-Inshore stations, green for Kwabibi, magenta for North Corridor stations and red for Other stations.
WWF – Mafia Island whale shark study 22
Fig. 17. Relative smoothed contribution to daily detections of the four station categories. Magenta for the North-Corridor, blue for Tirene-Inshore, green for Kwabibi and red for Intermediate stations.
The grouping clearly highlights hotspots of activity during the study. Stations where the sharks were not spending time, but rather passing by, compose the ‘intermediate’ group. Those stations are presumably located in areas where sharks were feeding was limited and are not necessarily in between other stations. The Tirene-Inshore stations north of Kilindoni were the main centre of activity during most of the study, accounting for ~ 60% of the total detections. They were most important at the beginning of the season (early November) and again in February during the main period of shark activity. The offshore receivers, particularly stations 16 and 17, showed a small peak in the second half of November. Then they proportionally dominated since March. In February, these two receivers combined for only 2% of detections, in March they increased to ~40%, and during both April and May they recorded over ~80% (Fig. 17, Fig. 18). Interestingly, there was a peak in the Other stations group in the second half of February. Presumably, it was an intermediate step between the inshore and the offshore stations.
WWF – Mafia Island whale shark study 23 Fig. 18. Smoothed daily detections in each of the four stations groups.
The centers of activity for each individual sharks were plotted as well as the number of daily detections on each receiver (Fig. 19A,B and Appendix Fig 1). The graphs offer evidence that is in accordance with the PCA analysis. Most of the sharks show a marked utilization of the Deep-Offshore area at the end of the season, especially receivers K16 and K17, and K 18 to a lesser extent. Because some of the sharks were detected on the last week before data was downloaded from the receivers, it is not possible to be completely sure that this corridor actually correspond to an exit pathway, however this is possible given that the detection rate decreased thereafter and sharks were also seen in Koma. None of the sharks appears to have exited the array through the Ras Kisimani – Boydu corridor.
Because sharks were all tagged when they already were on the study area, it is not possible to determine entry pathways. However this information should be available if the tagged sharks return for the next season.
WWF – Mafia Island whale shark study 24 Fig. 19A. Centers of activity of 15 tagged sharks over time. Location of the detection stations (receivers) is indicated by a black dot. The color of the line reflects the date of the activity center (see color scale in Fig. 19B)
Fig 19B. Centers of activity of 14 tagged sharks over time. Location of the detection stations (receivers) is indicated by a black dot. The color of the line reflects the date of the activity center.
The spectral density has a distinctive peak at a 24-hour period (Fig. 20). This peak indicates that there is a dominant cycle of 24 hours in the detection data i.e. there is evidence of a diel pattern in the shark behaviour. Additionally, the energy content is large in periods over 90 days. This suggests that a high amount of the temporal variability might be induced by seasonal patterns, with a frequency larger than the duration of this experiment. To investigate the diel pattern we categorised the combined hourly detections into day, night, sunrise and sunset and tested its effect with a GLMM-Poison. Sunrise and sunset were not significantly different to day or night. The amount of detections was significantly lower during nighttime than during daytime (GLMM Poisson, t=12.5, p<0.001, Fig. E21). Presumably, sharks stay longer in small feeding areas during daytime.
WWF – Mafia Island whale shark study 25
Fig. 21. Spectral density–periodogram of the amount of shark detections aggregated in one-hour bins. The purpose of this analysis is to detect any periodicities in the data by observing peaks at the frequencies corresponding to those periods.
Fig. 21. Average detections per hour during the daytime vs. nighttime. Values were calculated using a GLMM-Poisson (count data). Confidence intervals are quite narrow because the random-effect, which was evaluated at the observation level, accounted for most of the variability.
Issues On November 26, the shark tagged with 15370 was re-sighted; the tag was lost leaving behind a ~10 cm open cut in the skin of the shark. Due to the loss of the entire tag rig (anchor, tether, and tag) the loss is believed to have been caused by anchor failure. This tag may have been pulled out due to entanglement or due to a person pulling it off the shark. Six receivers were lost or damaged over the course of the study. In some cases, this appears to have been caused by extreme sedimentation burying the entire mooring. Theft was indicated in two cases where the moorings remained but all four cable ties were severed.
Conclusions & Recommendations Year round residency of the same whale sharks has not previously been documented from any aggregation. The relatively high degree of whale shark activity observed throughout the study suggests that a proportion of the sharks visiting Mafia may be an exception to this generalisation. Results from the fisher–whale shark interaction component further support that whale sharks may be present throughout the year. Monitoring would have to continue at least until the end of October 2013 to confirm this, and further monitoring afterwards is recommended. The sightings data for this study – taken alone – would WWF – Mafia Island whale shark study 26 support a conclusion of only seasonal use. However, the acoustic detections revealed a seasonal shift in habitat use into deeper, offshore areas. This behavior reduces the visibility of whale sharks at the usual monitoring sites. This is further evidence that acoustic telemetry is a useful independent means of determining local movement patterns.
The acoustic data suggest that the plankton analysis stations could be rearranged to reflect more the shark hotspots. For example, K17 could be the used as Station 3 instead of K19 from now on. The benefit of consistency however, outweighs this change for now.
Acoustic data suggest heavy utilisation of the area close to the new pier that is being constructed in Kilindoni. Management measures such as maximum speeds, obligatory spotters to avoid collisions with sharks and propeller guards for tourist and dagaa boats should be considered.
WWF – Mafia Island whale shark study 27 (E) Fisher – whale shark interactions
3 Mate rial a nd Meth od
Methods Study area Sampling was conducted between November 2012 and July 2013 at Kilindoni, Mafia District. The study site was selected based on previous studies (Potenski 2009) that revealed the area to be frequently visited by whale sharks (Fig. 22). The ecological condition of Mafia Island has been previously identified as favourable habitat for both juvenile and adult whale sharks (Potenski 2009).
Fig. 22. Location of the main fishing sites (grounds) for ring-net fishers around Mafia Island. Black circle indicate fishing grounds visited by ring-net fishers.
Data collection A sampling form was developed that covered the key questions to explore on the interaction between ring net fishers and the whale sharks. Monthly data were collected by 10 boat captains from local dagaa (Swahili word for sardine/anchovy) fishing vessels, all captains were residents of Kilindoni, Mafia. The names of the boats and captains were identified through consultation with two experts (Mr. Liberatus Mokoki and Masoud Kipanga) and Mafia district fisheries officers who had in depth knowledge on the general issues related to the local fishery. The main selection criteria were that the boat had to be active throughout the year, registered and permanently based in Mafia. Two groups of ring-net fishers were identified; one operating during the day and the other operating during the night. A total of 10 GPS units were provided, 8 units for night fishers and 2 units for day fishers. The number of night fishers is relatively larger compared to day fishers and sardine are mostly attracted by light during dark nights. The GPS units were to be used for recording geographical location of whale shark sightings, fishing ground and navigating to and from the fishing grounds. Prior to data collection, boat captains were trained in the use of GPS units and how to mark and record geo-locations. Thereafter, we had regular meetings at least every month (November 2012 to July 2013) to (i) get consensus feedback information from fishers on the shark sightings, behaviour, location and catch records, and (ii) monitor the data recording and collection (i.e., downloading GPS data from each captain and assess the quality of the data collected). This exercise opened up a dialogue and helped establish trust between fishers and researchers, in turn yielding additional qualitative information. The end target was to obtain high-
WWF – Mafia Island whale shark study 28 quality data on fisher–whale shark interactions and to further understand any interference caused by whale sharks to fishers during fishing operations.
Fishers were asked to report the total catch per fishing trip. Total catch is normally estimated by the number of buckets per fishing day/trip. A bucket weighs approximate 20 kg. Therefore, to get an estimate on the total catch (kg) per fishing trip, the number of buckets was multiplied by 20.
Results and Discussion Whale shark sightings The results indicated the presence of whale sharks at both of the main two fishing grounds frequently visited by fishers (Fig. 23). One area is close to Mafia Island, to the north of Kilindoni, and the other is almost 50 km to the northwest, near to Koma Island and the mainland. Whether these are different groups or one group that was observed at two different places remains to be investigated. Considering the distance between the sites, it is mostly likely to be the same group of whale sharks, as such short distances can be covered by sharks in one night.
Fig. 23. Location and number of whale sharks sightings from October 2012 to January 2013. Size of the circle indicates frequency of sightings.
Observations from fishers suggested the presence of whale sharks throughout the year, with a marked peak in November, corresponding with the start of the NE monsoon. Numbers of whale shark sightings per trip were lowest in the months of December, January and April (Fig. 24). The low records for December and January corresponded with strong NE monsoon winds when fewer fishing trips were made, while in April they corresponded with heavy rainfall.
WWF – Mafia Island whale shark study 29
Fig. 24. Number of whale sharks sightings per trips made for each month from November 2012 to July 2013.
Whale sharks seemed to move further offshore between February and July 2013, with more sightings made close to Koma Island (Fig. 25) during that period. This is consistent with results from the acoustic telemetry study in this project.
Fig. 25. Location and average number of whale shark sightings during Feb–Jul 2013. Size of the circle indicates the frequency of sightings.
The consent observation from fishers suggest that most fishing trips were concentrated few kilometres from Mafia Island, however findings suggest that at certain time fishers are likely to have a larger catch if they ventured to Koma Island (Fig. 26). Fishers claimed that they could venture to Koma during the transition period of monsoon winds, particularly when winds change from SE to NE (October/November). During this period, sea conditions are relatively calm and fish
WWF – Mafia Island whale shark study 30 move away from the coast of Kilindoni towards areas near to Koma. Fishers obtain higher catches during this period (Fig 27).
Fig. 26. Location of dagaa catches (mean catches/trip) from November 2012 to July 2013. Size of the circle indicates the magnitude of the catch as recorded by fishers.
Fishers also claimed from their experience that during the NE to SE monsoon transition period (March/April) most fishes are concentrated towards the coast of Kilindoni. The western side of Mafia Island seems to be protected from open ocean influences during SE winds, which could be a potential driving factor contributing to the observed variations in whale shark sightings and fish catches (Figs. 25 & 27). Low capital and time spent to get to Koma Island seems to be the hindering factor for fishing around that location, apart from monsoon wind patterns.
Fig 27. Monthly mean dagaa catch. Error bar = Standard deviation from the mean value.
WWF – Mafia Island whale shark study 31 Interactions between fishers and whale sharks A total of 147 fishing trips were made from October 2012 to June 2013. Out of 147 trips there were 18 occasions where whale sharks interfered with fishing operations. In 12 (67%) out of 18 encounters, sharks became entangled in the net and in most cases they were set free. In some occasions (4 out of 12) the fishers’ nets were damaged.
Findings showed two species of dagaa - (Stolephorus indicus and S. commersonnii) and the Indian mackerel (Rastrelliger kanagurta) were the dominant catch during the current study. The two species of dagaa are closely related and are commonly found in Tanzania coastal waters (Bianchi 1985, Khan et al. 2010). Observations from fishers suggested that whale sharks were mostly associated with dagaa and they hypothesised that whale sharks were feeding on dagaa.
Because fishers’ nets were damaged when whale sharks were entangled in the net, fishers tended to cancel fishing operations in that particular area and move to another area when they noticed there was a group of whale sharks. However, the consensus response from fishers claimed that whenever they saw a school of dagaa they also saw a group of whale sharks. It is most probable that the feeding ecology of dagaa and whale sharks are overlapping. While dagaa are known to feed on copepods (Hajisamae et al. 2004), at Mafia Island they have been observed feeding on sergestid shrimps alongside whale sharks. This in-part may be the reason why whale sharks are often sighted swimming with schools of dagaa
General observations The geo-referenced information observed from fishing sites, seasonal sightings and catch data suggest that there are two ecological habitats preferred by whale sharks, one located opposite the Rufiji Delta at Koma Island and the other at Kilindoni bay. This observation has added extra information, as fishers tend to cover large area compared to our own group boat observations and, more importantly, most of these observations were made during the night where there is less activity and disturbance as compared to daytime observations.
Challenges Changing of boat captains affected collection of quality data. New captains had no training on the use of the GPS units, thus a monthly meeting is crucial to ensure continuous collection of high- quality, reliable data.
WWF – Mafia Island whale shark study 32 (F) Local threats
Methods Scarring We recorded visible scars after having taken the identification photographs and tagged and biopsied the shark. Scars were classified according to Speed et al. (2008) who distinguished seven categories: (1) abrasions, (2) lacerations, (3) nicks, (4) bites, (5) blunt trauma, (6) amputations and (7) others. We added (8) old tag tethers as a further category. Superficial scars were classified as “minor”, including most abrasions, shallow lacerations, amputations of fin tips and all nicks and old tethers. More severe scars were classified as “major”, which were deemed to be potentially life-threatening or causing significant impairment. These included amputations of fins, deep lacerations and blunt trauma to the head.
Boat traffic During research trips, we recorded the number of tourist boats that we encountered. This number is not necessarily the same as the total number of tourist boats on the water on a given day, considering that we spent only a portion of the day on the water. However, we expect our counts to be similar to total tourist boat traffic because the tour operators generally sent their boats out at the same time as we did our research trips. We also recorded the number of dagaa fishing boats seen during research trips. This number is more difficult to use because much of the dagaa fishery occurs at night and we do not expect our numbers to correspond to actual fishing boat traffic.
Tourism and fishing The fisher – whale shark interaction component of this study deals with issues concerning local fishers in detail. Here we simply report our underwater observations. The tourism aspect was not investigated in detail during our study, but we raise some apparent issues here.
Results and Discussion Scars We observed a variety of scars on whale sharks at Mafia Island, with 75% of individuals bearing a scar. Of the 118 scars recorded, 110 (93.2%) were classified as minor and only 6.8% were major scars, which likely affect the shark’s survival chances (Table 4). These included four amputations of fins, three lacerations penetrating through the dermal layer and one blunt trauma on the head (Fig. 27). Abrasions were the most common scar type (33.1%), followed by lacerations (17.8%), nicks (17.8%) and amputations (12.7%; Table 4). Abrasions were mostly on the leading edge of the 1st dorsal fin and the on the dorsal ridge. Most lacerations appeared to be cuts inflicted by boat propellers, while amputations were suggestive of fishing gear entanglement or propeller strikes.
While direct fishing and by-catch mortality can be a major threat for whale sharks, scars on survivors can be used to detect other possible threats. The vast majority (93.2%) of scars recorded on Mafia Island whale sharks were minor in nature, but they can still indicate potential sources of mortality. They also suggest a degree of anthropogenic disturbance to normal behavior of the sharks is present at Mafia Island. Most prominent were abrasions on the dorsal ridge and 1st dorsal fin. These minor scars were most likely inflicted when the shark contacted a boat hull near
WWF – Mafia Island whale shark study 33 the surface. In some cases, we could see boat paint on those scars. One third of all sharks had such abrasions, which indicates that boat strike is a regular event. Lacerations and amputations, both often inflicted by a propeller strike, were also prominent and further corroborate this concern. Abrasions were the most common scar in whale sharks in Mafia Island and Mozambique, while bite wounds and lacerations were most prominent in sharks at Ningaloo Reef and in Seychelles, respectively (Speed et al. 2008). The almost complete lack of bite wounds in Mafia Island sharks suggests that they do not encounter as many large predatory sharks as whale sharks in other areas of the Indian Ocean. Some of the scars, such as when both lobes of the caudal fin were amputated, suggest net entanglement or perhaps active removal of the fins by fishers while trying to rescue their fishing net. Overall, the scars show that contact with boat hulls and propellers are common. This issue can to some extent be addressed by training skippers of tourist boat to go slow and carefully around whale sharks, and by discussing these findings with dagaa fishers.
Table 4. Percentages of different whale shark scar types, with counts in brackets.
Scar type Total Major Minor Abrasions 33.1 (39) 0 (0) 35.5 (39) Lacerations 17.8 (21) 37.5 (3) 16.4 (18) Nicks 17.8 (21) 0 (0) 19.1 (21) Bites 2.5 (3) 0 (0) 2.7 (3) Blunt trauma 4.2 (5) 12.7 (1) 3.6 (4) Amputations 12.7 (15) 50 (4) 10.0 (11) Old tether 7.6 (9) 0 (0) 8.2 (9) Other 4.2 (5) 0 (0) 4.5 (5) ALL 100 (118) 100 (8) 100 (110)
WWF – Mafia Island whale shark study 34
Fig. 27. Examples of whale sharks scars at Mafia Island. (A) minor lacerations on 1st dorsal of TZ- 025; (B) major amputations on TZ-081; (C) major lacerations on TZ-039; (D) abrasions and nicks on TZ-041; (E) blunt trauma on head of TZ-041; (F) amputation of pectoral fin on TZ-032; (G) old tether on TZ-001; (H) bite scar on lower caudal lobe of TZ-042.
WWF – Mafia Island whale shark study 35 Boat traffic We encountered an average of 2.1 tourist boats per trip (± 1.7 SD, range = 0–7). Summing up the tourist boats to weekly traffic resulted in 9.5 boats per week (± 7.3 SD, range = 0–31). Tourist boat numbers fluctuated over the season, with more boats searching for whale sharks during the busy Dec–Jan period. The highest number of tourist boats coincided with the most whale sharks seen during the week starting 27 Dec 2012 (Fig. 28A). Dagaa fishing boats were more numerous than tourist boats, averaging at 8.3 boats seen per trip (± 5.2 SD, range = 0–27) and 36.3 boats per week (± 20.1 SD, range = 2–88). Their numbers also fluctuated over the season, but in contrast, their peak was in Nov–Dec (Fig. 28B).
Different boat types frequent Kilindoni Bay. During our fieldwork period, the largest active boats were barges and tow boats involved in the jetty construction. Ferries that connect to the mainland are the other large, motorised boats. Occasionally, we received information from ferry captains on the whereabouts of whale sharks. Collisions with ferries are likely to have major impact on whale shark survival. The Kilindoni jetty is situated in a high activity space for whale sharks and a go-slow area around the jetty would be a useful consideration for managers.
Scars indicated that small boats and their outboard propellers are often responsible for damaging whale sharks. Dagaa fishing boats and tourists boats fall into this category, and we showed that there is a high amount of traffic from these boats around whale sharks. High tourist boat traffic often coincided with high whale shark sightings, likely because the operators get more tourists when sightings seem very likely. Dagaa fishers on the other hand mostly depend on calm conditions to spot and catch their fishes. The best conditions for fishing were early in the whale shark season, while it was often windy and choppy in January despite many shark sightings made then.
Tourism and fishing We received several reports from tourists indicating that tour operators did not always adhere to a code of conduct agreed between them around 2008-09. Complaints included (1) that boats were driven too fast and striking sharks, (2) too many snorkellers were crowding the shark and that (3) guides did not provide a thorough briefing. There was one report of a tourist “riding” a whale shark by hanging on to its 1st dorsal fin. Dagaa fishers often assisted us in finding whale sharks and sometimes allowed us to swim around their boat while they pulled in their nets. Whale sharks were sometimes trapped in the ring nets but, during our observations, the sharks always got out unharmed. We saw four sharks that had a fishing hook stuck in their tail or mouth.
Apart from boat strikes, whale sharks are also threatened by entanglement in fishing gear, and unregulated tourism also has the potential for negative impact. While we did not witness whale sharks entangled in ring nets, this could certainly be an issue during the night when fishers cannot see (and release) whale sharks in their nets. Anecdotal evidence from previous years suggest that whale shark mortality in fishing nets has occurred at Mafia Island. Crowding or touching whale sharks by tourists or chasing them at high speed by boat often leads to the shark swimming away or diving to avoid an encounter, which leaves both the tourists and probably the sharks unhappy. Operators have recognised this issue and are discussing possible measures. Our skipper/guide training workshop in February was attended by almost all staff involved with whale shark viewing and was well received. We suggest a repetition of this course at the start of the next season.
WWF – Mafia Island whale shark study 36
Fig. 28. Weekly sums of (A) tourist boats, (B) dagaa fishing boats, and (C) whale sharks encountered on research trips.
WWF – Mafia Island whale shark study 37 (G) Environmental influences on whale sharks
Methods Model data We used two different response variables in two models: (1) Individual whale sharks sighted per trip during research boat excursions (“sightings model”) and total acoustic detections relative to tagged sharks per day (“acoustic model”). Whale sharks sighted per trip (1) was used in preference over whale sharks per unit time or per unit distance because our trip distance and duration were influenced more by the activities carried out that day and the location of the whale sharks, rather than representing standardised search effort. We then used a suite of predictor variables in the models, as listed in Table 5. Temporal predictors were month and time of day of observation. Time of day referred to mean time of whale shark sightings, or the time at the middle of the trip in cases where no sharks were seen. We included total track distance in the model. As a measure of whale shark behaviour, we included the proportion of identified whale sharks that were feeding. Hourly weather data from a station installed specifically for this study at Ngombeni, on the southwestern side of Mafia Island (Fig. 2) were calculated as daily mean values for wind speed, air temperature, barometric pressure and humidity, with wind direction expressed as the daily mode (most common direction) and rainfall and solar radiation expressed as daily sums. Beaufort sea state was assessed during each boat trip. Tide data were downloaded from Mr. Tides 3 (Hahn Software 2007) for Dar es Salaam, adding 20 min to obtain times for Kilindoni harbour. Tidal range was the difference between the lowest and highest tides in a day. Tidal phase was determined based on the time difference between the nearest high tide and the mean time of whale shark observation (or the middle of trip when no shark was seen). “High tide” was defined as -1.49 to +1.5 h from high tide, “outgoing” was from -4.49 to -1.5 h, “low tide” was <-4.5 and >4.5, and “incoming” was from 1.51 – 4.5 h. Moon illumination data were downloaded from NASA’s Horizons website (http://ssd.jpl.nasa.gov/horizons.cgi) for Mafia Island. Number of dagaa fishing boats and tourist boats counted during research trips were also used as predictors. We also attempted to measure water temperature and salinity, but had equipment failure or loss with these loggers.
Models We used a modeling approach to investigate environmental influences on whale shark sightings and acoustic detections. First, we explored the relationships in a generalised additive model (GAM) to optimise the input into a generalised linear model (GLM). A negative binomial GLM with natural splines was constructed using the statistical software R (v.2.13.0; www.r-project.org), with whale shark sightings set as the response and the variables listed below as predictors (Table 5). Based on the GAM results, we conservatively set the degrees of freedom (df) = 3 for the smooth terms. The best model was assessed using a stepwise Akaike’s Information Criterion (AIC) function with a penalty parameter k = 2. The AIC test retains only important predictors in the model and deletes the non-significant ones. The significance of each retained predictor was assessed with a ʖ2 test (Venables & Ripley 2002).
A second model was constructed with acoustic detections per tagged shark for that date as the response and most of the same predictors as those used in the sightings model. In the model output figures, the y-axis is a relative scale, so that a y-value of zero is the mean effect of the adjusted predictor on the response, a positive y-value indicates a positive effect on the response, and a negative y-value indicates a negative effect on the response. If a horizontal line can be placed between the 95% confidence limits (dotted lines), this implies that the relationship
WWF – Mafia Island whale shark study 38 between the response and the predictor is not significant. These lines tend to diverge near the extremes of the range for continuous predictors as a consequence of fewer observations.
WWF – Mafia Island whale shark study 39 Table 5. Predictors used in the generalised linear models for whale shark sightings and acoustic detections.
Model Predictors Type Description Mean ± SD (range) Both Month Categorical Calender month of sighting/detection Levels: Oct - Mar Sightings Time of day Continuous Mean time of shark sightings or mean trip time in absence of sharks 10.2 ± 2.5 (7.0 - 17.8) Sightings Track length Continuous Total distance of the survey trip (km) 32.2 ± 8.8 (7.1 - 51.3) Sightings Feeding proportion Continuous Proportion of whale sharks feeding 0.65 ± 0.34 (0 - 1) Both Beaufort Categorical Sea state on the Beaufort scale (Levels: 0, 1, 2, 3, 4) 1.55 ± 1.06 (0 - 4) Both Wind direction Continuous Modal daily wind direction (°) 88.2 ± 68.3 (0 - 337.5) Both Wind speed Continuous Daily mean wind speed (km h-1) 4.16 ± 1.95 (0.83 - 8.94) Both Solar radiation Continuous Solar radiation (MJ/m2/h) 19.7 ± 5.7 (6.8 - 27.8) Both Rain Continuous Daily sum of rainfall (mm) 4.6 ± 13.0 (0 - 98.6) Both Air temperature Continuous Daily mean air temperature (°C) 27.3 ± 0.8 (25.6 - 28.5) Both Barometer Continuous Daily mean barometer pressure (hPa) 1010.6 ± 1.4 (1007 - 1013.6) Both Humidity Continuous Daily mean relative humidity (%) 84.9 ± 3.8 (75.8 - 94.5) Both Tidal range Continuous Daily tidal range (m) 2.6 ± 0.9 (0.5 - 4.1) Sightings Tidal phase Categorical Tidal phase of mean shark sighting time or mean trip time with no sharks Levels: High, outgoing, low, incoming Both Moon illumination Continuous Percentage of moon disc illuminated (%) 51.2 ± 36.6 (0.2 - 100) Both Fishing boats Continuous Number of fishing boats spotted per trip 8.3 ± 5.1 (0 - 27) Both Tourist boats Continuous Number of tourist boats spotted per trip 2.1 ± 1.7 (0 - 7)
WWF – Mafia Island whale shark study 40 Results Sightings model The GLM with whale shark sightings as a response explained 84% of the variance (Table 6). A dropterm AIC evaluation of the full model retained 12 significant predictors. This model had a lower AIC score than the full model (AIC = 408.7, res. dev. = 82.7, 12 predictors vs. AIC = 421.7, res. dev. = 67.66, 18 predictors). The most important predictors were month and feeding proportion (variance = 33% and 30.1%, respectively), followed by wind speed (5.8%) and tidal phase (4.8%). Other strongly significant predictors (r2 <0.001) were barometric pressure and wind direction (Table 6).
Table 6. Percentage of variance explained by the GLMs and significance values of a ʖ2 test performed on the AIC-supported models
Sightings model . Acoustic model . Predictor Variance ʖ2 p Variance ʖ2 p Month 33.0 49.9 <0.001 34.0 44.1 <0.001 Feeding proportion 30.1 49.5 <0.001 - - - Wind speed 5.8 7.0 0.072 13.5 38.8 <0.001 Tidal phase 4.8 24.7 <0.001 - - - Beaufort 2.6 18.4 0.001 - - - Barometric pressure 2.5 37.1 <0.001 5.8 20.1 <0.001 Humidity 1.6 13.3 0.004 - - - Moon 1.3 17.5 0.001 7.7 16.4 <0.001 Dagaa boats 1.2 14.3 0.003 2.7 12.9 0.005 Wind direction 1.0 26.2 <0.001 1.5 12.4 0.006 Track length 0.1 4.3 0.037 - - - Air temperature 0.1 15.2 0.002 - - - Tourist boats - - - 5.3 20.3 <0.001 Sum 84.2 70.5
With all other variables taken into account, whale shark sightings increased over the first half of the study and decreased afterwards, with no sharks seen in March. More whale sharks were seen when a higher proportion were observed feeding. The most significant increase occurred between 0 and ~40% of individuals feeding. Wind speeds above 5 km h-1 had a negative effect on shark sightings, but wind direction, although strongly significant, and Beaufort sea state did not show a clear influence. Whale shark sightings decreased with barometric pressures above 1011 hPa and slightly decreased with warmer air temperatures. Humidity did not have a strong influence over the range where most observations were made. Moon illumination had an incoherent effect, with a trend for more sightings during 20–40% of the moon illuminated. Fewer whale sharks were seen during high tide compared to low, incoming or outgoing tides. There was a trend for fewer whale sharks sighted when more dagaa fishing boats were counted. Track length did not show a significant relationship with whale shark sightings (Fig. 5).
WWF – Mafia Island whale shark study 41
Fig. 29. Model output showing the relationship between whale shark sightings and the various predictors. The rug plot along the x-axis indicates sampling effort and dotted lines mark the 95% confidence intervals. We used a common scale bar (-3 to 2) for all plots except month. * = the March value was at ~-35 outside the scale, also resulting in CI outside the scale.
WWF – Mafia Island whale shark study 42 Acoustic model The AIC-supported model (AIC = 443.1, res. dev. = 122.9) retained half of the predictors from the full model (AIC = 458.4, res. dev. = 124.1, 14 predictors) and explained 70.5% of the variance. The most important predictors were month (34% of variance explained) and wind speed (13.5%). Other strongly significant predictors (r2 <0.001) were moon illumination, barometric pressure and number of tourist boats (Table 6).
The month with the lowest whale shark detections was March, similar to the sightings model results. Wind speeds above 4 km h-1 had a negative influence on shark detections, as did barometric pressure above 1011 hPa. Wind direction did not have an obvious influence on detections. More sharks were detected during 20–40 % of the moon illuminated compared to 50– 90 %. More dagaa fishing boats appeared to relate to fewer shark detections, although sample size at the high end of the scale was too low to be confident in this trend. More sharks were detected when more tourist boats were spotted (Fig. 30).
Fig. 30. Model output showing the relationship between daily acoustic detections per tagged shark and the various predictors. The rug plot along the x-axis indicates sampling effort and dotted lines mark the 95% confidence intervals. We used a common scale bar (-3 to 1) for all plots except month. * = the March value was at ~-40 outside the scale, also resulting in CI outside the scale.
WWF – Mafia Island whale shark study 43 Discussion The sightings- and acoustic detection GLMs both explained a lot of the variance, with 84% and 71% respectively. This was an increase in model performance over similar studies on whale shark abundance using GLMs in Mozambique (24%; Rohner et al. 2013) and Western Australia (25%; Sleeman et al. 2010), and indicates that local factors included in the model have a strong influence on whale shark presence. The likely reason for this difference to other aggregations is that whale sharks off Mafia Island appear to be resident for long periods, perhaps even throughout the year, while for example off Mozambique they are only present for 2–3 days at a time (Rohner et al. 2013).
The month factor was important in both models and indicated a peak in the NE monsoon months, as was expected. The same result was seen in the “raw” total daily shark detections (Fig. 12) over a slightly longer time period. However, only re-analysis with a longer data set will show the true importance of season on whale shark sightings. A defined peak in shark presence is also seen at many of the other whale shark aggregation sites around the world and is often thought to coincide with a burst in productivity (Rowat & Brooks 2012). In the absence of year-round plankton data from Mafia Island at this stage, we can only speculate that such a peak in zooplankton abundance is responsible for the seasonality in whale sharks here. This is a likely explanation, considering the dense patches of sergestids we observed (see below).
The second-most important predictor in the sightings GLM was the proportion of whale sharks that were feeding, with more sightings at higher proportions. This corroborated our observational evidence of large feeding groups of whale sharks. At least during the NE monsoon season, feeding is therefore an important motivator for shark presence at the surface. The underlying drivers of the high zooplankton abundance should be a focus of further studies, not least because the dagaa, which support the local fishery, feed on the same macrozooplankton. Due to difficulties with the plankton collection equipment and resulting gaps in the logbook data, we could not include zooplankton biomass as a predictor in the model. With more samples collected during the off-season and another NE monsoon season, we will be able to rectify this and expect to show that plankton biomass is an important driver.
Some of the other significant predictors, such as wind direction, wind speed and tidal phase likely indirectly influenced whale shark presence via zooplankton. High wind speeds also result in choppier surface conditions, potentially reducing sightability (Rowat et al. 2009, Rohner et al. 2013). We could not verify this, however, as we found a lack of a clear relationship with Beaufort sea state. Acoustic detections were also lower at higher wind speeds meaning that wind speed affected whale shark presence rather than just sightings. Wind direction was not an important driver, most likely because our sampling period coincided with the NE monsoon and we thus do not have a lot of observations from a different wind regime. Continuation of the project over the whole calendar year will allow us to better investigate this relationship.
We saw fewer whale sharks at high tide compared to low, incoming and outgoing tides. It may be that zooplankton schools expand onto shallow intertidal flats during high tide, resulting in fewer feeding opportunities for the sharks. However, tidal phase did not have a strong influence (4.8% of variance) on sightings and tidal range was not supported in either model. This stands in contrast to studies on whale sharks in the Maldives (Riley 2006), and reef manta rays Manta alfredi in Indonesia (Dewar et al. 2008) and Australia (Jaine et al. 2012), where animals were sighted more during strong tidal fluxes. The likely difference is that in reef environments, tidal currents can be strong, concentrating plankton into dense patches in predictable locations (Jaine et al. 2012),
WWF – Mafia Island whale shark study 44 while tide-related currents are not as strong over the sand substrate of Kilindoni Bay. Moon illumination was retained in both models, with more sharks present about one week after new moon. The link between schooling of macrozooplankton and moon illumination was not investigated here, but the lunar phase could have an effect on the availability of whale shark prey.
Whale shark presence significantly decreased between 1011 and 1013 hPa barometric pressure in both models, while the initial increasing trend up to 1010 hPa had wide confidence intervals and low sample sizes. A higher barometric pressure anecdotally results in fishes becoming less active, however, whether a similar effect exists on large, wide-ranging, oceanic species such as the whale shark remains unlikely and unstudied. Similarly, the significant decrease in sightings with increasing air temperature or the relationship with relative humidity is unlikely to be of biological significance to whale sharks. However, these factors could drive surface swarming of macrozooplankton and thus indirectly affect whale shark behaviour.
We found a trend for slightly fewer sharks spotted when more fishing boats were counted. The same factor was not retained in the acoustic model. Since we often encountered whale sharks and dagaa feeding on the same macrozooplankton, it appears that whale sharks swim deeper when fishing boats are near and are thus not sighted but still detected acoustically. Conversely, number of tourist boats was positively correlated with shark detections, but the factor was not retained in the sightings model. It is a relatively weak trend that is likely caused by more boats going to see the sharks when the sharks are around, rather than a response to the boats by whale sharks.
WWF – Mafia Island whale shark study 45 7. Recommendations
6.1 Continuation of the project We recommend a continuation of the project to cover a whole calendar year and a second peak whale shark season, at least up to end of March 2014. This will allow us to investigate the year- round presence of whale sharks off Mafia Island with acoustic telemetry and describe the varying levels of activity over the seasons. It would also cover a wider variety of conditions (e.g. wind direction) and provide more data points that will improve the modelling outputs. A second whale shark season might also show different results, especially because the 2012/13 season was exceptionally good for spotting whale sharks according to local sources.
6.2 Detailed plankton study We also recommend considering a more detailed investigation into the drivers of the high macrozooplankton abundance off Mafia Island. Plankton in the tropics is generally very poorly understood and thus inferences from the literature are difficult to make. The dense patches of sergestid shrimps described here are important not only for whale sharks but also for the dagaa on which the local fishery depends. A more detailed understanding of the macrozooplankton dynamics would thus be important to explain fluctuations in sightings or catches and in predicting potential future shifts.
6.3 Upgrade of the acoustic array On a practical note, we recommend replacing the cable ties of the acoustic receivers with stainless steel hose clamps to reduce potential theft and accidental failure. Moorings in areas of very dynamic sedimentation (e.g. K10, K13, K19) should be relocated or redesigned with attachment poles longer than 1 m. An electronic flowmeter for further plankton studies is also recommended over the previous mechanical one that often failed.
6.4 Create a whale shark management plan The ongoing construction of the Mafia jetty is expected to increase human activities in Kilindoni bay (specifically boat traffic) and this might have negative impact on the occurrence of whale sharks and the tourism business. Acoustic data showed the inshore area immediately around the jetty to be a high activity space for whale sharks. There is thus an immediate need to think about protection measures and to develop a whale shark management and conservation plan. Measures such as a go-slow zone around the jetty could be considered.
6.5 Skipper/guide workshop To further address the immediate threat of boat strikes, another skipper/guide workshop is recommended at the start of the next whale shark tourism season. This should help reiterate the code of conduct and increase compliance. Similarly, a compliance survey about the code of conduct by tourism operators could improve both whale shark protection and the running of these operations.
6.6 Economic survey We suggest to conduct an economic survey in collaboration with the tourism operators to quantify the benefits from whale shark tourism to the local economy. This will help in future discussions with government parties and help leverage management/protection initiatives.
WWF – Mafia Island whale shark study 46 8. Conclusion
The whale shark aggregation at Mafia Island, Tanzania is similar to other locations in the Indian Ocean where whale sharks are seen surface feeding in coastal areas. The Mafia Island whale sharks also have a similar size and sex structure to these other aggregations, in that they consist of mostly immature male individuals. It appears that whale sharks, like other shark species, spatially segregate based on sex and size. Intriguingly, the major habitats for female, very young and mature individuals remain unknown at present.
We did not see any whale sharks for the last month of our boat-based survey, which indicated a seasonal aggregation as is the case in most other whale shark aggregations (Rowat & Brooks 2012). Reports from fishers showed, however, that whale sharks were still present up to the end of May at lower numbers and further offshore. Acoustic data similarly showed that they spent much more time in offshore areas late in the season. Additionally, these data indicated that the sharks were swimming at a deeper mean depth and may thus be out of sight for surface-based observers. Whether the sharks disappear from Mafia Island over the SE monsoon months or whether they stay remains to be seen from our continuation of this study.
Close to 100 individual whale sharks have been identified from Mafia Island. We saw the same sharks multiple times over the season, and their residency time was relatively long. We also encountered 67% of sharks that were photographically identified from Mafia Island in previous years. This indicates that a relatively small number of whale sharks use this site extensively. The high sighting potential is great for tourists who visit the island to swim with the sharks. The fact that the same few sharks frequent the area also means that it is important to minimise anthropogenic stress on their survival and behavior – if they get harassed by tourists or fishers and leave, there will not simply be new sharks moving to Mafia the next week. The high level of boat- inflicted scars is thus a significant threat to the whale shark’s presence.
Feeding was the most important behaviour seen during our observations. While whale sharks feed on a variety of prey in different locations (Rohner et al. in press), they do require a high biomass of prey to make feeding energetically profitable. We found the sergestid shrimp Lucifer hanseni to be the major prey at Mafia Island during our observational study. As whale sharks were often seen feeding alongside dagaa (the main target of the local ring net fishery), we suggest that these sergestids play a crucial role in the food web of Kilindoni Bay. However, they are a poorly-studied group of zooplankton and any data on them beyond our report is currently lacking from Mafia Island, and indeed from tropical bays in general.
Whale sharks interfered with dagaa fishing operations around 10% of the time. This is cause for concern for the sharks, which might get entangled in fishing gear. It is also a concern for the fishers as their nets can get damaged and they themselves could be injured by a trapped shark. Perhaps the best thing for Mafia Island in general could be to avoid setting the nets around whale sharks, although this may be difficult to achieve during night-time fishing. This would certainly benefit the sharks, which in turn are the basis of a growing local tourism enterprise.
Finally, whale shark sightings were driven largely by local variables such as month, the proportion of sharks feeding, wind speed, tidal phase etc. These variables accounted for 84% of the variance in the sightings dataset – a strong explanatory power. This also meant that regional-scale oceanographic patterns did not play an immediate role in whale shark sightings at Mafia Island. Larger-scale drivers could still influence this aggregation indirectly, but we did not find such a
WWF – Mafia Island whale shark study 47 relationship. On a local scale, the month of observation had the biggest influence on sightings. This showed that conditions bringing whale sharks to the surface, where they are seen, vary over time. In the 2012/13 season the best conditions were in December for sightings and in January for acoustic detections. The second-most important predictor for sightings was whether the sharks were feeding or not. We saw more sharks when they were surface feeding than when they were not feeding. This again indicated the importance of the sergestid shrimps identified as their major prey item. The drivers of plankton availability at the surface close to Kilindoni remain to be studied in detail. We show here that atmospheric, tidal and lunar predictors are likely to play a role and thus influence whale shark sightings off Mafia Island.
WWF – Mafia Island whale shark study 48 9. List of References
Antony G (2005) Occurrence and distribution of the planktonic shrimps of the genus Lucifer in the EEZ of India. Journal of the Marine Biological Association of India 47:20-30 Arzoumanian Z, Holmberg J, Norman B (2005) An astronomical pattern-matching algorithm for computer aided identification of whale sharks, Rhincodon typus. Journal of Applied Ecology 42:999-1011 Bianchi G (1985) FAO species identification sheets for fishery purposes. Field guide to the commercial marine and brackish-water species of Tanzania. Prepared and published with the support of TCP/URT/4406 and FAO (FIRM) Regular Programme., Vol. FAO, Rome Brockwell PJ, Davis RA (1991) Time Series: Theory and Methods, Vol. Springer Brooks K, Rowat D, Pierce SJ, Jouannet D, Vely M (2010) Seeing spots: Photo-identification as a regional tool for whale shark identification. WIOMSA Journal of Marine Science 9:185-194 Brunnschweiler JM, Baensch H, Pierce SJ, Sims DW (2009) Deep-diving behaviour of a whale shark, Rhincodon typus, during long-distance movement in the western Indian Ocean. Journal of Fish Biology 74:706-714 Cagua EF, Berumen ML, Tyler EHM (2013) Topography and biological noise determine acoustic detectability on coral reefs. Coral Reefs:1-12 Chen CT, Liu KM, Joung SJ (1997) Preliminary report on Taiwan's whale shark fishery. TRAFFIC Bulletin 17:53-57 Compagno LJV (2001) FAO Species Catalogue for Fishery Purposes No. 1. Sharks of the World: An Annotated and Illustrated Catalogue of Shark Species Known to Date, vol. 2: Bullhead, Mackerel and Carpet Sharks (Heterodontiformes, Lamniformes and Orectolobiformes), Vol. FAO, Rome de Villiers A (2007) Final report: Survey of indigenous knowledge on whale sharks in Mafia Island, Tanzania. In. WWF and WHASCOS, Tanzania Dewar H, Mous P, Domeier M, Muljadi A, Pet J, Whitty J (2008) Movements and site fidelity of the giant manta ray, Manta birostris; in the Komodo Marine Park, Indonesia. Marine Biology 155:121-133 Duffy CAJ (2002) Distribution, seasonality, lengths, and feeding behaviour of whale sharks (Rhincodon typus) observed in New Zealand waters. New Zealand Journal of Marine and Freshwater Research 36:565-570 ECOCEAN (2012) Global whale shark photo-ID library. In. Available at http://www.whaleshark.org (accessed 2 Aug 2012), ECOCEAN Graham RT, Roberts CM (2007) Assessing the size, growth rate and structure of a seasonal population of whale sharks (Rhincodon typus Smith 1828) using conventional tagging and photo identification. Fisheries Research 84:71-80 Hajisamae S, Chou LM, Ibrahim S (2004) Feeding habits and trophic relationships of fishes utilizing an impacted coastal habitat, Singapore. Hydrobiologia 520:61-71 Hobbs J-PA, Frisch AJ, Hamanaka T, McDonald CA, Gilligan JJ, Neilson J (2009) Seasonal aggregation of juvenile whale sharks (Rhincodon typus) at Christmas Island, Indian Ocean. Coral Reefs 28:577 Hsu HH, Joung SJ, Liu KM (2012) Fisheries, management and conservation of the whale shark Rhincodon typus in Taiwan. Journal of Fish Biology 80:1595-1607 Irvine TR, Keesing JK (2007) International collaboration in science, conservation and sustainable tourism of whale sharks. Fisheries Research 84:1-3 Jaffa K-Sv, Taher NABA (2007) Whale sharks in Palestinian waters: a whale shark (Rhincodon typus, Smith 1828) rescued near the Tantura Beach, Carmel Coast, North Palestine. The first
WWF – Mafia Island whale shark study 49 record from the Palestinian Mediterranean Coast. Gazelle: The Palestinian Biological Bulletin 71:22–23 Jaine FRA, Couturier LIE, Weeks SJ, Townsend KA, Bennett MB, Fiora K, Richardson AJ (2012) When giants turn up: Sighting trends, environmental influences and habitat use of the manta ray Manta alfredi at a coral reef. PLoS ONE 7:e46170 Jiddawi NS, Öhman MC (2002) Marine fisheries in Tanzania. AMBIO: A Journal of the Human Environment 31:518-527 Joung SJ, Chen CT, Clark E, Uchida S, Huang WYP (1996) The whale shark, Rhincodon typus, is a livebearer: 300 embryos found in one 'megamamma' supreme. Environmental Biology of Fishes 46:219-223 Khan SA, Lyla PS, John BA, Kuamr CP, Murugan S, Jalal KCA (2010) DNA barcoding of Stolephorus indicus, Stolephorus commersonnii and Terapon jarbua of Parangipettai coastal waters. Biotechnology 9:373-377 Lee WY, Omori M, Peck RW (1992) Growth, reproduction and feeding behavior of the planktonic shrimp, Lucifer faxoni Borradaile, off the Texas coast. Journal of Plankton Research 14:61- 69 Lugomela C, Lyimo T, Bryceson I, Semesi A, Bergman B (2002) Trichodesmium in coastal waters of Tanzania: diversity, seasonality, nitrogen and carbon fixation. Hydrobiologia 477:1-13 Ma Z, Xu Z, Zhou J (2009) Effect of global warming on the distribution of Lucifer intermedius and L. hanseni (Decapoda) in the Changjiang estuary. Progress in Natural Science 19:1389-1395 Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis, Vol. Academic Press McClanahan TR (1988) Seasonality in East Africa's coastal waters Marine Ecology Progress Series 44:191-199 Meekan MG, Jarman SN, McLean C, Schultz MB (2009) DNA evidence of whale sharks (Rhincodon typus) feeding on red crab (Gecarcoidea natalis) larvae at Christmas Island, Australia. Marine and Freshwater Research 60:607-609 Motta PJ, Maslanka M, Hueter RE, Davis RL, de la Parra R, Mulvany SL, Habegger ML, Strother JA, Mara KR, Gardiner JM, Tyminski JP, Zeigler LD (2010) Feeding anatomy, filter-feeding rate, and diet of whale sharks Rhincodon typus during surface ram filter feeding off the Yucatan Peninsula, Mexico. Zoology 113:199-212 Nelson JD, Eckert SA (2007) Foraging ecology of whale sharks (Rhincodon typus) within Bahia de Los Angeles, Baja California Norte, Mexico. Fisheries Research 84:47-64 Newell BS (1959) The hydrography of British East African coastal waters. II. Fishery Publications 12:1-18 Norman BM, Stevens JD (2007) Size and maturity status of the whale shark (Rhincodon typus) at Ningaloo Reef in Western Australia. Fisheries Research 84:81-86 Oishi K, Saigusa M (1997) Nighttime emergence patterns of planktonic and benthic crustaceans in a shallow subtidal environment. Journal of Oceanography 53:611-621 Potenski MD (2009) Final report: Whale shark population assessment, behaviour & movements at Mafia Island, Tanzania. In. WWF and Shark Research Institute, Tanzania Pravin P (2000) Whale shark in the Indian coast - Need for conservation. Current Science 79:310- 315 R Development Core Team (2011) R: A Language and Environment for Statistical Computing. In: R Foundation for Statistical Computing (ed), Vienna, Austria Ramírez-Macías D, Meekan M, De La Parra-Venegas R, Remolina-Suárez F, Trigo-Mendoza M, Vázquez-Juárez R (2012a) Patterns in composition, abundance and scarring of whale sharks Rhincodon typus near Holbox Island, Mexico. Journal of Fish Biology 80:1401-1416
WWF – Mafia Island whale shark study 50 Ramírez-Macías D, Vázquez-Haikin A, Vázquez-Juárez R (2012b) Whale shark Rhincodon typus populations along the west coast of the Gulf of California and implications for management. Endangered Species Research 18:115-128 Riley MJ (2006) Site fidelity of Rhincodon typus to the Republic of Maldives. In. Royal Geographical Society, Framfield Riley MJ, Hale MS, Harman A, Rees RG (2010) Analysis of whale shark Rhincodon typus aggregations near South Ari Atoll, Maldives Archipelago. Aquatic Biology 8:145-150 Robinson DP, Jaidah MY, Jabado RW, Lee-Brooks K, El-Din NMN, Al Malki AA, Elmeer K, McCormick PA, Henderson AC, Pierce SJ, Ormond RFG (2013) Whale sharks, Rhincodon typus, aggregate around offshore platforms in Qatari waters of the Arabian Gulf to feed on fish spawn. PLoS ONE 8 Rohner CA (2012) A global whale shark hotspot in southern Mozambique: population structure, feeding ecology, movements and environmental drivers. PhD, The University of Queensland, St Lucia Rohner CA, Couturier LIE, Richardson AJ, Pierce SJ, Prebble C, Gibbons MJ, Nichols PD (in press) Diet of whale sharks Rhincodon typus inferred from stomach content and signature fatty acid analyses. Marine Ecology Progress Series Rohner CA, Pierce SJ, Marshall AD, Weeks SJ, Bennett MB, Richardson AJ (2013) Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks. Marine Ecology Progress Series 482:153-168 Rohner CA, Richardson AJ, Marshall AD, Weeks SJ, Pierce SJ (2011) How large is the world's largest fish? Measuring whale sharks Rhincodon typus with laser photogrammetry. Journal of Fish Biology 78:378-385 Rowat D, Brooks K, March A, McCarten C, Jouannet D, Riley L, Jeffreys G, Perri M, Vely M, Pardigon B (2011) Long-term membership of whale sharks (Rhincodon typus) in coastal aggregations in Seychelles and Djibouti. Marine and Freshwater Research 62:621-627 Rowat D, Brooks KS (2012) A review of the biology, fisheries and conservation of the whale shark Rhincodon typus. Journal of Fish Biology 80:1019-1056 Rowat D, Gore M, Meekan MG, Lawler IR, Bradshaw CJA (2009) Aerial survey as a tool to estimate whale shark abundance trends. Journal of Experimental Marine Biology and Ecology 368:1- 8 Schott FA, Fischer J (2000) Winter monsoon circulation of the northern Arabian Sea and Somali Current. Journal of Geophysical Research: Oceans 105:6359-6376 Schott FA, McCreary JP (2001) The monsoon circulation of the Indian Ocean. Progress in Oceanography 51:1-123 Sleeman JC, Meekan MG, Fitzpatrick BJ, Steinberg CR, Ancel R, Bradshaw CJA (2010) Oceanographic and atmospheric phenomena inŇuence the abundance of whale sharks at Ningaloo Reef, Western Australia. Journal of Experimental Biology and Ecology 382: 77-81 Speed CW, Meekan MG, Rowat D, Pierce SJ, Marshall AD, Bradshaw CJA (2008) Scarring patterns and relative mortality rates of Indian Ocean whale sharks. Journal of Fish Biology 72:1488- 1503 Swallow J, Fieux M, Schott F (1988) The boundary currents east and north of Madagascar: 1. Geostrophic currents and transports. Journal of Geophysical Research: Oceans 93:4951- 4962 Swallow JC, Schott F, Fieux M (1991) Structure and transport of the East African Coastal Current. Journal of Geophysical Research: Oceans 96:22245-22257 Turnbull SD, Randell JE (2006) Rare occurrence of a Rhincodon typus (Whale shark) in the Bay of Fundy, Canada. Northeastern Naturalist 13:57-58
WWF – Mafia Island whale shark study 51 Venables WN, Ripley BD (2002) Modern Applied Statistics with S (4th Edition), Vol. Springer, New York Wolfson FH (1986) Occurrences of the whale shark Rhincodon typus Smith. In: Uyeno T, Arai R, Taniuchi T, Matsuura K (eds) Indo-Pacific Fish Biology: Proceedings of the Second International Conference on Indo-Pacific Fishes, Ichthyological Society of Japan, Tokyo Woodmansee RA (1966) Daily vertical migration of Lucifer, planktonic numbers in relation to solar and tidal cycles. Ecology 47:847-850 Wyrtki K (1973) Physical Oceanography of the Indian Ocean. In: Zeitzschel B, Gerlach SA (eds) The Biology of the Indian Ocean, Book 3. Springer Berlin Heidelberg Xu ZL (2010) Determining optimal temperature and salinity of Lucifer (Dendrobranchiata: Sergestoidea: Luciferidae) based on field data from the East China Sea. Plankton and Benthos Research 5:136-143
WWF – Mafia Island whale shark study 52 10. Appendices
Table 1. Location, installation date and dpth (m) of the monitoring stations. Name Latitude Longitude Depth K01 -7.918277811 39.64080557 6 K02 -7.905833367 39.63769446 12 K03 -7.897604518 39.64419572 15.7 K04 -7.888865545 39.64251054 15.5 K05 -7.891088258 39.6514231 14.5 K06 -7.88167865 39.64997311 17.5 K07 -7.884154664 39.65988798 15 K08 -7.875462379 39.66744762 14 K09 -7.87222269 39.64985233 13 K10 -7.912494466 39.59866379 12 K11 -7.838845029 39.60276238 25 K12 -7.853195602 39.66511779 13 K13 -7.823583679 39.6744046 15 K14 -7.853736151 39.62334489 17 K15 -7.841655063 39.64212748 25 K16 -7.82786 39.63046 27 K17 -7.814116236 39.64745116 23 K18 -7.784940507 39.66014246 19 K19 -7.883608248 39.60796938 13 K20 -7.942599412 39.57420263 25
WWF – Mafia Island whale shark study 53 Table 2. Initial receiver array in October 2012 Install Station Receiver Date Time K01 113043 25-Oct-12 09:42 K02 113028 25-Oct-12 09:30 K03 109037 24-Oct-12 12:00 K04 109038 23-Oct-12 09:50 K05 104848 23-Oct-12 10:10 K06 104861 23-Oct-12 10:30 K07 113484 23-Oct-12 10:45 K08 109044 25-Oct-12 13:10 K09 113477 25-Oct-12 12:10 K10 104849 25-Oct-12 10:20 K11 104857 25-Oct-12 11:05 K13 113031 25-Oct-12 11:45 K14 103918 27-Oct-12 12:02 K15 104856 27-Oct-12 12:15 K16 109041 27-Oct-12 12:28 K17 104845 27-Oct-12 12:41 K18 109039 27-Oct-12 13:01 K19 113042 28-Oct-12 13:00 K20 113486 28-Oct-12 12:40
Table 3. Receiver array remaining in tact at last download in May 2013 Station Receiver Date Time K03 VR2W-113028 22-Jun-13 16:00 K04 VR2W-109037 22-Jun-13 15:38 K05 VR2W-104848 22-Jun-13 15:01 K06 VR2W-113031 22-Jun-13 14:25 K07 VR2W-113484 22-Jun-13 13:45 K08 VR2W-109044 22-Jun-13 13:11 K09 VR2W-113043 22-Jun-13 12:45 K11 VR2W-104861 23-Jun-13 12:18 K12 VR2W-104847 23-Jun-13 08:52 K14 VR2W-103918 23-Jun-13 12:50 K16 VR2W-109041 23-Jun-13 11:25 K17 VR2W-104845 23-Jun-13 10:30 K18 VR2W-109039 23-Jun-13 09:50 K20 VR2W-113486 22-Jun-13 10:14
WWF – Mafia Island whale shark study 54
Table 4. Tag ID, Size, sex and tagging date of the tagged individuals Tag Shark 15369 4.5m M - 22 Oct 2012 15373 7m M - 23 Oct 2012 15367 5m M - 24 Oct 2012 15370 7m M - 24 Oct 2012 15365 6m M - 27 Oct 2012 15799 5m F - 29 Oct 2012 15372 2m M - 29 Oct 2012 15801 5m M - 29 Oct 2012 15778 6m M - 29 Oct 2012 15803 4.5m M - 29 Oct 2012 15371 6m M - 30 Oct 2012 15366 5m M - 30 Oct 2012 15368 7m F - 30 Oct 2012 15805 5m F - 7 Nov 2012 15807 5m F - 10 Nov 2012 28059 6m F - 15 Dec 2012 28062 6.5m M - 15 Dec 2012 28060 5m M - 15 Dec 2012 28058 8.5m M - 15 Dec 2012 28056 8m M - 16 Dec 2012 28057 5m M - 16 Dec 2012 28061 4.5m M - 22 Dec 2012 28082 7m M - 22 Dec 2012 28063 6m M - 22 Dec 2012 28079 4.5m M - 23 Dec 2012 28077 6.5m M - 23 Dec 2012 28064 5m F - 25 Dec 2012 28078 5m F - 25 Dec 2012 28080 6m F - 29 Dec 2012 28081 9m M - 29 Dec 2012
Table 5. Location, depth and dates of the range tests Test Station Start End Depth 1 K05 28 Nov 2012 08 Dec 2012 15 2 K11 21 Jan 2013 05 Feb 2013 25
WWF – Mafia Island whale shark study 55
Fig 1. Daily detections on each of the listening stations.
WWF – Mafia Island whale shark study 56