Thermocline
influence on
vertical behaviour
of marine
predators
Ivo Matias Lopes da Costa
Mestrado em Biodiversidade, Genética e Evolução
Departamento de Biologia2018
Orientador
Nuno Miguel Cabral Queiroz, Investigador Pós-Doutoral, CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos
© S h ark Co n se rvati o n R e se arch @ UM
Todas as correções determinadas pelo júri, e só essas, foram efetuadas. O Presidente do Júri,
Agradecimentos
Ao meu orientador, Doutor Nuno Queiroz, pelo incentivo e amizade demonstrados e por me ter possibilitado estudar aquilo que realmente gosto. Pela paciência que tiveste comigo em todos estes anos (já lá vão cinco desde que te fui pedir para trabalhar contigo!), e por teres partilhado não só todo o teu conhecimento científico, mas também grandes qualidades humanas, tornando te no melhor orientador que poderia ter. Às minhas colegas de trabalho, Marisa Vedor e Ana Couto, pela paciência nas dúvidas com o R, pela amizade e porque acima de tudo, me terem ajudado sempre que precisei. A vocês devo muito do êxito conseguido.
Ao Gonzalo, pelo trabalho de campo intensivo que desempenhou durante vários meses nos barcos. És parte integrante e fundamental no nosso trabalho.
A todos os meus colegas do grupo AGE, pela ajuda na discussão de ideias e pela amizade demonstrada desde o momento em que comecei a estudar no CIBIO.
A todos os meus amigos e família, que sempre estiveram do meu lado e me apoiaram. Um obrigado infinito aos meus pais por todo o apoio durante todos estes anos. Porque me ensinaram a nunca desistir e por me fazerem acreditar que podia ser sempre melhor. Porque são parte de mim.
À minha irmã pelo seu apoio incondicional, e por ser um exemplo de perseverança, que me deixa muito orgulhoso.
À Diana, por estar sempre a meu lado, por todo o seu amor, paciência e compreensão. Por ser a fonte de inspiração e motivação que me faz continuar. Porque sem ti não teria conseguido.
Resumo
Os predadores marinhos desempenham um papel fundamental nos ecossistemas marinhos. Infelizmente, observamos a um nível global declínios acentuados em várias espécies marinhas, pois muitas destas são suscetíveis a pesca acessória ou sobre- exploradas. Desde o aparecimento dos transmissores por satélite, os cientistas focaram-se em tentar perceber o movimento e o comportamento dos peixes em relação a determinadas condições ambientais. Em particular, as frentes térmicas e a termoclina que são caracterizados por processos de convergência, onde o nível de produtividade primaria é elevado sendo por isso capaz de suportar produtores secundários e consequentemente atrair grandes predadores marinhos. Para além disso, é expectável que os predadores marinhos se movam no ambiente de modo a maximizar a taxa de encontro com as suas presas. Os voos de Lévy são uma classe especial de passeios aleatórios que podem representar uma solução ótima na procura em cenários onde as presas são escassas e estão distribuídas aleatoriamente. Ainda assim, o potencial adaptativo desta estratégia em relação a determinados fatores ambientais não é conhecido. O principal objetivo deste estudo foi tentar clarificar como os gradientes da termoclina (intensidade, localização) afetam os movimentos, em particular, o comportamento de forrageamento (forrageamento ótimo e os movimentos curvos) de predadores marinhos. Neste trabalho encontramos fortes evidências de que a termoclina influencia o movimento de quatro espécies de predadores de topo marinhos (tubarões e atuns). Resultados mostram que os movimentos curvos aparecem associados a termoclina em diversos indivíduos. Contudo, a esperada correlação negativa entre a distância entre os movimentos de curva e a termoclina com a sua intensidade não é evidente em todas as espécies analisadas. Esta padrão só foi demonstrado para tubarões azuis e tubarões anequim. Além disso, os movimentos semelhantes a voos de Lévy são predominantes nos predadores pelágicos marcados, onde o aumento do expoente de Lévy está associado ao aumento da intensidade da termoclina para tubarões azuis e atuns albacora. Estes resultados sugerem a termoclina como uma importante zona de forrageamento com grande disponibilidade de presas onde os predadores marinhos parecem mostrar comportamentos semelhantes com movimentos de forrageamento em torno de um ponto central.
PALAVRAS CHAVE: Predadores marinhos, termoclina, voos de Lévy, movimentos de curva, modelos lineares mistos, movimentos de forrageamento em torno de um ponto central
Abstract
Marine predators play a vital role in the functioning of marine ecosystems. Unfortunately, rapid declines in several marine species are occurring worldwide since they are also very susceptible to by-catch and to overfishing. Since the advent of satellite tagging, scientists focused in understanding the reason why animals move and behave in a certain way when facing distinct environmental conditions. In particular, thermal fronts and the thermocline, are characterized by convergence processes with high levels of primary production which are able to sustain secondary producers and therefore attract large aggregations of marine predators. Pelagic predators are also expected to move through the environment in a manner that maximises encounter rates with prey patches. Lévy flights are a special class of random walk that may represent an optimal solution to the biological search problem in complex landscapes where preys are sparsely and randomly distributed. Nevertheless, the adaptive significance of this strategy in response to specific environmental features in the ocean is unclear. The main objective of this study was to clarify how changes in thermocline gradients (intensity, location) affect the foraging behavior (optimal foraging and turning movements) of marine top predators. We found a strong support that thermocline drives the movement of four species of top predatory fish (sharks and tunas). Results showed that turning points movements appeared associated with thermocline location in several individuals. However, the expected correlation where the turning points distance to the thermocline decreased with increasing its intensity is not evident for all species. This pattern only appeared for blue sharks and mako sharks. Furthermore, Lévy-like movements are prevalent in pelagic predators and the increase of Levy exponent is associated with increases in thermocline strength. Results suggest the thermocline is an important foraging area of increased prey availability where pelagic predators appeared to exhibit central place foraging-like behavior.
KEYWORDS: Marine predators, Thermocline, Lévy flights, Turning points, Linear mixed models, Central place foraging-like behaviour
List of contents
Agradecimentos ………... i Resumo ……… Palavras-chave ………... ii ii Abstract ……… Keywords ………. iii iii List of contents ……… iv List of tables ……… v List of figures ………... vi List of abbreviations ………... x Introduction ……….. Tracking marine species: an historical perspective……..………. Movements and environment associations ……… Optimal foraging ………. General objectives ………. 1 1 4 7 8 Materials and methods ……….. Study animals and tagging ………... Processing of archival data ……….. Thermocline detection ………... Vertical movement analysis ……….. Turning points calculation ………. Optimal foraging analysis ………. Linear mixed modelling ………. 9 9 11 11 12 13 14 17 Results ………. Thermocline features ………. Vertical movements ………... Turn-over movements and central place foraging ……… Optimal foraging behaviour ……….. 19 19 22 30 38 Discussion ………... Fisheries and conservation ……….. 42 47 References ………... 49List of tables
Table 1 – Information about the habitat, distribution, tagging region (ATL – Atlantic Ocean; PAC – Pacific Ocean) and total number of tags used for the 10 species tracked with electronic tags in this study ………... 10 Table 2 – Example of a trimmed time series dataset format for one blue shark. Dashed box contains the two variables (depth and temperature) that were used to create the Matlab
input file.………... 11
Table 3 – Log-likelihood equations for each distribution used in this study (truncated power
law and exponential) ………... 15
Table 4 – Summarised combination of wAIC values and the corresponding result. Note the ambiguous wAIC combination that ends with a GOF verdict or a mixed model ……… 17 Table 5 – Total number of sections, detections and no detections for the Matlab algorithm. In bold is species with percentages of no detection over 75 per cent ……… 21 Table 6 – Linear model results for all sharks and for each species, individually. β –
correlation value; sd- standard deviation of correlation value; p - p value ……….. 41 Table S1 – Summary information for turning points and thermocline variables ………. 58 Table S2 – Summary information for Lévy distribution and thermocline variables ………. 70
List of figures
Figure 1 – Pop-off satellite transmitter tag (a). Satellite tagging deployment on a blue shark with PSAT in the Atlantic (b).………... 9 Figure 2 – Schematic representation of thermocline. High gradients are within thermocline
and low gradients are above and below it ………. 12
Figure 3 – Dive analysis output. Pattern of dive time series of a blue shark (lower part) with a small section containing three turning points (blue circle - ascend turning point; red circle
- descend turning point) ………... 14
Figure 4 – Examples of a truncated power law (left) - Lévy behaviour - and an exponential (right) - Brownian motion – distribution. Note a mixture between smalls steps and long
reorientation jumps in Lévy pattern (left).……… 17
Figure 5 – Map showing the general movement patterns of 44 geo-located sharks used in
this study overlaid on bathymetry ……… 19
Figure 6 – Thermocline output from the Matlab algorithm for one yellowfin tuna (yft 90). (a) Temperature profile of thermocline with its depth limits. Dashed line represents the upper / lower limit of thermocline while the full line is the depth where its temperature gradient was maximum. (b) Depth of thermocline (red crosses) and the range of its amplitude (upper-lower limit) (blue lines). (c) Temperature maximum gradient (thermocline strength) ………... 20 Figure 7 – Mean thermocline depth (a) and strength (b) for each species analysed in this work ………
21
Figure 8 – Mean depth by two day section for each species (left) and corresponding text reference and species for nine individuals (right) ……….. 22 Figure 9 – Depth and temperature profile for shark 1. (a) Hourly mean depth (blue line) for each individual was plotted against thermocline depth (dark points) and strength (colored squares). Note that white spaces in a strength gradient indicate possible algorithm failure in detecting the thermocline. Red line sets a smooth line for thermocline depth calculated based in a linear model with y ~ poly(x,10) formula. (b) temperature-at-depth profile for the corresponding individual. Black and white dashed line correspond to mean night and day depth. Grey line sets a smooth line for thermocline depth calculated based in a linear model with y ~ poly(x,10) formula with 95 percent confidence interval ……… 23
Figure 10 – Depth and temperature profile for shark 2 (a and b) and 3 (c and d),
respectively………. 24
Figure 11 – Depth (a) and temperature (b) profile for shark 4 ……… 25 Figure 12 – Depth and temperature profile for shark 5 (a and b) and 6 (c and d),
respectively………. 26
Figure 13 – Depth and temperature profiles for yellowfin tunas, yft95 (a and b) and yft138
(c and d), respectively ……….. 28
Figure 14 – Depth (a) and temperature (b) profile for one bugeye tuna (bet26) ……… 29 Figure 15 – Time-at-depth plots (TAD) for shark 4 (top) and shark 5 (down). Black dashed
line represents thermocline depth ………... 29
Figure 16 – Turning-points plot for shark 2: (a) Mean turning depth by hour. Black points correspond to turning points depth. (b) frequency of turning points by day (green points) with a smooth line. (c) turning point kernel density plot by day. Black points correspond to raw thermocline depths; grey full line in border plots is a smooth line calculated using a linear model with y ~ poly(x,10) formula with 95 percent confidence interval. Bar on top
represent thermocline strength ………... 31
Figure 17 – Turning-points plot for shark 3: (a) Mean turning depth by hour. Black points correspond to turning points depth. (b) frequency of turning points by day (green points) with a smooth line. (c) turning point kernel density plot by day. Black points correspond to raw thermocline depths; grey full line in border plots is a smooth line calculated using a linear model with y ~ poly(x,10) formula with 95 percent confidence interval. Bar on top
represent thermocline strength ………... 32
Figure 18 – Turning-points plot for shark 4: (a) Mean turning depth by hour. Black points correspond to turning points depth. (b) frequency of turning points by day (green points) with a smooth line. (c) turning point kernel density plot by day. Black points correspond to raw thermocline depths; grey full line in border plots is a smooth line calculated using a linear model with y ~ poly(x,10) formula with 95 percent confidence interval. Bar on top
represent thermocline strength ………... 33
Figure 19 – Turning-points plot for the mako shark 5: (a) Mean turning depth by hour. Black points correspond to turning points depth. (b) frequency of turning points by day (green points) with a smooth line. (c) turning point kernel density plot by day. Black points correspond to raw thermocline depths; grey full line in border plots is a smooth line calculated using a linear model with y ~ poly(x,10) formula with 95 percent confidence interval. Bar on top represent thermocline strength………... 34
Figure 20 – Mean turning depth by hour for yft95 (a), yft138 (b) and bet26 (c). Black points correspond to turning points depth. Grey full line is a smooth line calculated using a linear model with y ~ poly(x,10) formula with 95 percent confidence interval. Bar on top represent thermocline strength ……... 35 Figure 21 – Turning point kernel density plot by day for yft95 (a) and yft138 (b). Black points correspond to raw thermocline depths; grey full line in border plots is a smooth line calculated using a linear model with y ~ poly(x,10) formula with 95 percent confidence interval. Bar on top represent thermocline strength ………. 36 Figure 22 – Thermocline as a central place for foraging in shark 5. Grey boxplots for ascend turning points depth by each two day overlapping the corresponding temperature profile.
Red line is the thermocline depth ……… 37
Figure 23 – Best fit distribution plot for each species and by two thermoregulatory groups. Black bars represent the truncated lévy behaviour; dark grey bars the exponential distribution and light grey bars the unclassified sections ………. 38 Figure 24 – Best fit distribution plots for each strength quartile. Black bars represent the truncated lévy behaviour; dark grey bars represent exponential distribution and light grey bars are the unclassified sections. Quartile 1 (strength<0.17), quartile 2 (strength<0.26), quartile 3 (strength<0.42), quartile 4 (strength<3.99). Colour intensity represent intensity of
thermocline ……… 39
Figure 25 – Upper panel: Examples of good fits to truncated power-law distributions (Lévy behaviour) in blue (a), mako (b) and tiger (c) shark. Middle panel: Depth profile plot for blue shark (shark 4) with red lines indicating the separation between two sections of different thermal habitats, specifically, between thermocline with low intensity (d) and high intensity (f). Lower panel: MLE analysis result (log rank plots) for the two sections (g, h) with corresponding µ values (Lévy exponent). Outline circles show observed step lengths, red lines the best fit truncated Pareto distribution, and blue lines the best fit for exponential
distribution ………. 40
Figure S1 – Map showing the general movement patterns of 44 geo-located sharks used in this study overlaid on (a) bathymetry, (b) sea surface temperature and (c) mixed layer depth. The yellow polygon represents the area of tagging for Pacific bigeye and yellowfin
Figure S2 – Schematic representation of the decision process for model selection used in this work. The process starts at question (A) with green lines being followed for positive responses and red dashed lines for negative responses. A truncated power law (TP) fit is only concluded when there is considerable certainty that it is the correct conclusion. OOM signifies orders of magnitude. In each sector (A, B, C, D, E, F, G) the decision is determined for the following questions: (A) Does wAIC support the fitted TP over the alternate exponential; (B) Does wAIC support the fitted exponential over the alternate TP; (C) Does the adjusted GOF support exponential over TP; (D) Does wAIC reject the fitted exponential in favour of the alternate TP; (E) Does the adjusted GOF reject exponential in favour of TP; (F) Is the TP exponent in the range 1.0 to 3.0; (G) Does the fitted TP range (i.e. xmin to xmax) span at least 1.5 orders of magnitude ………... 56 Figure S3 – Central place foraging-like behaviour in shark 4. Blue boxplots represent ascend turning points depth by two-day sections. Dashed line represents shark’s max
List of abbreviations
ATL Atlantic Ocean
AUV Autonomous underwater vehicle
av Average
DA Dive Analysis
DVM Diel vertical migration
EAC East Australian Current
et al And others
e.g. For example
GF Gulf Stream
GOF Goodness of fit
GPS Global positioning system
ID Individual identification
IUCN International Union for the Conservation of Nature
i.e. That is
KS Kolmogorov-Smirnov
LFF Lévy flight foraging
LLH log-likelihoods
LMM Linear mixed models
m
Med
Meters
Mediterranean Sea
min Minutes
MLE Maximum likelihood analysis
PAC Pacific Ocean
PSAT Pop-off satellite archival transmitters
Sec Seconds
SEC South Equatorial Current
TAD Time-at-depth
TP Truncated Pareto distribution
wAIC Akaike Information Criteria weights
xmax Maximum value
Introduction
Tracking marine species: an historical perspective
Marine predators play a key role in marine ecosystems. As apex predators, they are important for the functioning of the ecosystems, more precisely in top down processes 1
and providing an indication of the ocean state 2. Unfortunately, rapid declines in several
marine species are occurring worldwide since they are also very susceptible to by-catch and to overfishing 3–5. By-catch fisheries have been improving for at least the past century, mostly in the Atlantic Ocean, which leads to a depletion of marine predator’s populations below the sustainable levels where the recovery may be difficult or very low even if the fishing pressure is removed 6. For example, during the twentieth century,
populations of skates were fished and completely eradicated from some areas and have not returned, leading to a regional extinction of these species 7. Concerning to sharks,
since they have slow growth rates, late age at sexual maturity and low fecundity, and even though there is a particular concern for bycatch fisheries, sharks’ populations are still declining mainly because the recovery is very slow compared to the number of catches 8. Moreover, tunas have been also overexploited due to their commercial
importance and recent catches continue to exceed historical levels 9. To face this severe
scenario, biologists have completed over the last years several studies with fish tracking and movement analysis focusing in the understanding of animal behaviour and ecology that have created the basis for marine fisheries management and species conservation. However, catches of many highly migratory fishes including oceanic sharks and tunas remain largely unregulated with poor monitoring and data reporting 10.
A major problem when we study management and conservation measures for mobile wide-ranging species is that their movements and distribution pattern remain mysterious for most of the species. Moreover, we cannot observe these species easily or follow their movements since they spend most of the time submerged. Thus, it is extremely difficult to determine their habitat occupancy, both horizontal and vertical, population trends and how they will respond to future environmental changes. Nevertheless, the advent of electronic devices capable of tracking the movements of marine predators was essential to step towards most of these problems, allowing the study of habitat selection processes, distribution patterns and spatial dynamics 11,12.
Conventional tagging based on methods known as capture-mark-recapture have been used for centuries as markers on marine and freshwater fishes 13. The concept of this
method is marking a large number of fish so they are individually identifiable, release back to their natural habitat and then during ordinary fisheries operations recapture the
individuals released. This provided valuable data on stock structure, distribution of life history intervals and level of resource exploitation, yet no information regarding individuals journey masked important components of life history that could hold key answers why the fish population was already declining 14. Moreover, conventional
tagging needs a high amount of time for data accumulation, no information about vertical movements is obtained and more important, it is completely dependent of fisheries 3,15,16.
During sixties, the first studies using alternative methods, such as focal acoustic telemetry, started to appear to study individual behaviour in the natural environment. These devices capable of emitting or transponding sound energy were first used to track individual sharks 17. The acoustic tags could be coupled with a wide array of sensors that
transmitted data about water temperature, swimming depth, fish muscle temperature, cranial temperature, swim speed, tail beat frequency and heart rate 18. However, to
obtain this information it is necessary that a vessel followed the moves made by the transmitter, and hence, each individual fish, which became expensive for studies with highly mobile species that move extensive areas per day. Despite the fact that studies with focal acoustic telemetry are limited to a few days 19,20, they provided relevant
information. For example, during the seventies and eighties, the blue shark Prionace glauca was one of the most tracked species using these tags, yielding insights about its vertical movements. They discovered that blue sharks displayed seasonal depth oscillations, with sharks during late summer, autumn and winter making regular vertical oscillations from the surface to 400 meters (m) at daylight and moving in the first 100 m at night. During summer, this pattern of large oscillations was ceased 21. Tiger sharks
Galeocerdo cuvier tracked from Oahu, Hawaii, to Penguin Banks with acoustic tags, did not show diel behaviour (differences in day night depths) maintaining generally the same depth preference 22. Another example with one white shark Carcharadon carcharias that
was tagged feeding near a whale carcass in Long Island, New York, revealed that this individual continued feeding in areas close to the carcass and eventually moved southwest following the 25 m isobar 23. Acoustic tagging using stationary recording
receiver arrays is another example with acoustic tags that have been used since the eighties until now. Currently, several studies continue to use these devices for long-term monitoring of fish behaviour, where individuals marked with acoustic tags send a signal every time they pass close to one static receiver. One study with hammerhead sharks Sphyrna lewini used acoustic tags to monitoring individuals in the eastern Pacific 24.
Hammerhead sharks gathered in the seamount during the day, moved to more pelagic areas during the night and then returned again to the same area at dawn. Another study, with young bull sharks Carcharhinus leucas, evaluated the distribution and space-use of individuals in relation to environmental features in Caloosahatchee River estuary in
Southwest Florida 25. As a major conclusion of this study, the researchers found that
salinity has a great influence on the shark distribution, while the temperature did not perform such a relevant role. Young bull sharks showed preference by salinities between seven and 20, avoiding areas with salinities above seven. In a different study 26, young
blacktip sharks Carcharhinus limbatus were monitored using also acoustic telemetry to determine mortality estimates in the nursery area, suggested that young sharks are most vulnerable to all types of mortality during the first 15 weeks.
During the nineties, data loggers were small enough to not obstruct the normal fish movements and were still equipped with powerful batteries capable of storing high-quality data. However, such studies based on archival transmitters are dependent on the tag retrieval that is very low (recovery rate of 5-10% of the total individuals tagged; 27.
The advent of electronic tags capable of relaying stored data via satellites allowed scientists to obtain easier ecological information without dependence on fisheries returns. Termed pop-off satellite archival transmitters (PSAT) are attached externally to the fish and released (‘pop-off’) at a pre-programmed time to float to the surface and transmit the data continuously to Argos satellite receivers 28. The first generation of these
tags provided useful information, mostly about the characterization of the thermal habitat used by the fish, however, in terms of horizontal movements these tags essentially provided the same information of mark-recapture-studies (only tagging site were known). Then, later generations of PSAT allowed the storage of depth and light levels that had an extreme importance and transformed our understanding of large fish behaviour. Light level incorporation permitted to estimate longitude, by comparing the time of local midnight or midday with that at Greenwich, and latitude from the day length 29. Using this
information, it was possible to determine geolocations and therefore to reconstruct the movement track of the fish. Studies with bluefin tuna, Thunnus thynnus, and basking shark, Cetorhinus maximus, were among the first that used reconstructed tracks using light levels. In the former study, scientists concluded that tunas make transatlantic migrations moving between foraging grounds in the western Atlantic and spawning grounds in the Mediterranean Sea or Gulf of Mexico 30. Concerning basking shark, the
world’s second largest fish, a study conducted in two areas (English Channel, Plymouth and in the Clyde Sea, Scotland) demonstrated that this species is active in the winter and do not hibernate, as was once supposed. Basking sharks displayed extensive horizontal and vertical movements following productive continental-shelf and shelf-edge habitats during summer, autumn and winter 31. As impressive as these findings
were, extensive submergence periods and tag failure problems persist in satellite–linked tags, decreasing the accuracy of horizontal trajectories (more temporal gaps). Moreover,
studies based on long-term attachments are difficult because premature releases of the tag are common 32,33. To increase the accuracy and obtain time data series without
temporal gaps, several studies applied new methodologies. For example, Argos system started to use GPS constellation of satellites to obtain movement tracks with greater accuracy in turtles, pinnipeds and seabirds 34,35. More recent, Fastloc GPS receivers
were used to study the long-term behaviour of ocean sunfish Mola mola in Portugal 36.
These receivers allowed the fast and more accurate acquisition of the satellite constellation for location fixing with the data being transmitted via Argos. Still, the problem with temporal gaps in the track still remain and since some movement analysis require continuous-time tracks without temporal jumps, Argos and GPS tags are not recommended. To overcome the temporal gaps problem and to construct movement tracks with more accuracy, new methods have been used, such us tags that incorporate tri-axial accelerometer sensors 37,38. Tracking the movements of marine fauna is
important, however, it is crucial to link this information with the feeding patterns to capture a more genuine picture of the fish behaviour. This can be achieved using another device, such as video cameras that can record the microhabitat use which is important for testing hypothesis about drivers of habitat use 39.
Movements and environment associations
Understanding the behaviour of large pelagic fish along with several ecological levels and scales has been the major goal of a growing number of studies in the last years
4,36,40–44. Differences in distribution patterns and movements, both horizontal and vertical,
of pelagic species (e.g. sharks, tunas) have been documented as a result of sexual activity, prey distribution and type, and habitat characteristics. Males and females are differentially distributed among different habitat, in the same or different areas, which leads to a segregation of sex and size at a regional scale 45. For example, studies with
white sharks have shown that their seasonal movement pattern are influenced by physiological needs (e.g. mating or parturition) 46, with sharks moving between coastal
areas to a common offshore region, with males returning to their original site in the same year while sexually mature females typically return in alternate years 47. Movements of
large pelagic fish may also reflect changes in prey type or prey distribution. Spatial and temporal coherence between predators and prey must exist for predators to survive, and they should adopt certain movement strategies to optimise prey patch encounter rates
48,49. Differences in the foraging trips duration between two colonies of Magellanic
availability, with short trips occurring in areas with high prey availability near the breeding grounds 50. Changes in behaviour associated with prey type were also observed in blue
sharks tagged in North Atlantic 4. In this study, vertical shifts in depths were linked to a
change in prey type as the sharks moved from coastal to offshore regions. Moreover, several species are known to display deep diving behaviour during daylight and spending night time at shallower depths. This oscillatory behaviour has been recorded in juvenile mako shark Isurus oxyrinchus 51, bigeye tuna Thunnus obesus 52 and school shark
Galeorhinus galeus 53 with individuals diving to greater depths to follow prey (using
extensive vertical movement increase the probability of encounter new prey patches). However, this pattern might be also related to a potential role in navigation through the detection of magnetic fields or sensing different water masses, or it could also be linked with thermoregulation. For example, tunas, as endothermic animals, can maintain an internal body temperature above that of the surrounding water improving their muscular efficiency that allows themselves to swim at burst speeds in cold water 54. Still, individuals
must come to the surface in order to reheat their bodies before diving again to colder water to capture prey with rapid accelerations 19,55. Endothermy is also found in lamnid
sharks, such as white and mako shark, that have the capacity to conserve metabolic heat via vascular counter current heat exchangers thereby maintaining a steady-state body temperature above ambient water temperature. Thus, endothermic fish generally have an extensive habitat distribution since they can inhabit cold waters (e.g. salmon shark Lamna ditropis 56,57). On the other hand, ectothermic organisms are frequently
adapted to live within a limited range of temperatures, which comprises a thermal optimum that maximizes their physiological performance. Therefore, when conditions move away from this optimum, an organism experiences reduced growth, reproduction, foraging, or competitiveness 58,59.
A very important subject to consider in behavioural ecology is the reason why animals move and behave in a certain way when facing distinct environmental conditions, which lead to different habitat use patterns, both at horizontal and vertical scales, in response to several oceanographic features (e.g., fronts, eddies and stratification). An oceanic front is a narrow layer of enhanced horizontal gradients of water properties (temperature, salinity, oxygen) that separate broader areas with different water masses or different vertical structure 60–62. Such physical discontinuities are characterised by convergence processes that contribute to increase primary production at fronts and create hotspot of marine life from phytoplankton to apex predators. Therefore, for pelagic fish, fronts represent regions of forage accumulation or nursing areas and are known to influence movements and distribution of marine
predators 41,63. Studies with blue sharks 4 and basking sharks 64 demonstrate the role of
thermal fronts in shaping behaviour patterns of large predators. In the former species, patterns of movement were assessed in northeast Atlantic where sharks were observed spending more time in highly productive regions characterised by the presence of oceanic front. Basking sharks were observed not only foraging at thermal fronts regions but also using such areas for annual courtship and breeding in the North East Atlantic. A similar understanding of vertical movements of predatory pelagic predators in relation to oceanographic features is much needed. Similarly, to fronts, the thermocline is expected to have an important role in migrations patterns of marine pelagic species. This oceanic layer, where temperature decreases quickly with increasing depth, works like a barrier that confines prey 65 and may explain changes in dives and distribution of top
predators. For example, in Paul Island, seals Callorhinus ursinus that hunt along the bottom preferred areas with deepest thermoclines that confined prey below, and shallower thermoclines were used by seals that fed during the night because they have easy access to migrating prey closer to the surface 66. Vertical movement of Pacific
bluefin tuna was also linked to the thermocline, with individuals staying in shallower depths and performing dives through it for maximizing the success of encountering prey
67. The distribution and abundance of marine pelagic fishes are usually driven by
variations in water temperature as a result of different thermal responses among species, therefore, the thermocline may play a key function in shaping vertical habitat use patterns. Another study with the common dentex Dentex dentex, a coastal apex predator monitored the vertical movement activity of individuals in relation to the oscillations of the seasonal thermocline during two summer periods in the Medes Island. During the summer, day and night vertical movements were associated with the warm suprathermoclinal layer, with individuals adjusting their vertical movements following the depth changes of the thermocline 58. In addition, rhythmic movement patterns, such as
diel vertical migration (DVM), also occur as a response to environmental conditions 68.
Active pelagic predators perform these daily large-scale vertical movements presumably to pursuit migrating prey, however, these patterns are very complex with some pelagic organisms either not performing DVM or performing reverse DVM 69,70. These patterns
can be considered deterministic where the animal undertakes predictable patterns of movement in response to predictable environmental changes, however, intrinsic to these movements are other behaviours patterns. While the overall diel pattern has been well studied, where and when other movements, such as searching and foraging, occur and the motivations underlying these behaviours are poorly understood 71.
Optimal foraging
A central issue for behavioral ecology is to understand how organisms search for resources within heterogeneous natural environments. Organisms move through an environment in a manner that increases the encounter of resource targets, as prey, potential mates or preferred refuging locations 12. A predator can search for prey guided
only by external cues, using cognitive (memory) and detective (olfaction and vision) skills. However, in some cases the movement is not-oriented, and random search patterns define encountering rates in environments where prey distribution is complex and follows stochastic dynamics. Since all animals face the same problem, the possibility that a general rule for optimizing search patterns emerged by natural selection has been suggested. Several studies in optimal foraging have demonstrate that scyphozoan jellyfish 72, aquatic birds 73,74, sharks, tunas as well ocean sunfish 49 show an optimal
search pattern (Lévy flights) which optimise encounters with prey that are sparsely distributed. Lévy flights are special random walks that comprise “walk clusters” of relatively short step lengths, or flights intervals (distances between turns), connected by longer movements “jumps” between them, with this pattern repeated across all scales resulting in a scale invariant or fractal patterns. Step lengths in a Lévy flight are chosen from a probability distribution with a power-law tail, resulting in step lengths with no characteristic scale: P (lj) ∼ lj−μ, with 1 < μ ≤ 3 where lj is the flight length (step length of move j) and μ is the power-law exponent 12,49,75–78. In theory, Lévy flight increases the
probability of encountering prey in sparse environments and predators adopt this search strategy when prey is sparse and unpredictable distributed. On the other hand, Brownian motion is characterised by an exponential distribution that is optimal near abundant, predictable sources of prey 74. Additionally, Lévy flight foraging hypothesis (LFF)
proposes that animals naturally evolved to display movements characterised by power tail distribution because of increasesed chances of encountering new prey patches. For example, white sharks exhibit patterns of movement consistent with Lévy flights in areas with low prey concentrations such us open continental shelf, shelf edge and open, whereas near seal colonies they display Brownian-type vertical movements 79. Similarly,
tagged wandering albatrosses Diomedea exulans had shown, not only habitat dependent foraging patterns predicted by LFF hypothesis, but also high-energy gains in resource sparse habitats 74. Clear signals of Lévy behaviour were also found in blue
sharks, with individuals showing a prevalent Lévy distribution in less productive areas whereas movement patterns approximated a Brownian motion in more productive waters
49. Moreover, despite the differences in scale, T cells hunt like aquatic marine predators.
pathogen-infected targets in the central nervous system is aided by a generalized Lévy walk search strategy 80. Such movement patterns have also been seen in trace fossils. Preserved
movement patterns of extinct creatures fall in the domain of Lévy random walks which could suggest that Lévy-like behavior has been used by foragers since at least the Eocene. In fact, these patterns can have a more ancient origin, which might explain recent widespread observations of such patterns among modern taxa 81. Currently, the
main challenge is to associate Lévy walk movements with a specific context by formulating appropriate hypotheses, which means that we must go beyond characterizations of movement patterns exclusively in terms of step-length distributions and make an effort to understand what is behind its emergence 82.
General objectives
In this study we use ten species of top marine predators (Table 1) listed as “near threatened”, “vulnerable” and “endangered”, underlining the need for new information on their movements with regard to environmental gradients (e.g. thermocline) in order to strengthen the foundation for the management and conservation of marine ecosystems. Thus, in this context the specific objectives were:
1. Describe how sharks diving behaviour is affected by thermocline structure, evaluating the frequency and the distribution of turning points (turn-over movements) in relation to thermocline intensity and location.
2. Understand the fine-scale movements of sharks and tunas in the context of LFF hypothesis. Thus, we will evaluate not only the presence of Lévy walks (representative of optimal foraging) in tagged fish and evaluate the relationship between the truncated power law exponent (μ value) and thermocline structure.
Material and methods
Study animals and tagging
PSAT (Fig.1, b) were attached to ten species of marine predators including sharks, tunas and devil ray in Atlantic and Pacific oceans (see Table 1 for species description). Sharks and the one devil ray were captured using baited longlines or rod-and-line angling and the tags were attached to the dorsal fin or to the musculature tissue. Concerning tunas, tags were surgically implanted in the peritoneal cavity that was essential for these tags to remain attached to the tunas for prolonged periods. All tagging procedures were approved by institutional ethical review committees and were made by trained personnel, with the fish being released after few minutes without any apparent adverse effects. Data from archival transmitters comprised time series of depth (pressure), temperature and light, sampled at several time intervals (one to ten seconds[sec], 12 sec, 20 sec, 30 sec, 32 sec, one minute [min] and two min).
Figure 1. Pop-off satellite transmitter tag (a). Satellite tagging deployment on a blue shark with PSAT in the Atlantic (b). b
Table 1. Information about the habitat, distribution, tagging region (ATL – Atlantic Ocean; PAC – Pacific Ocean) and the total number of electronic tags used for the ten species tracked in this study.
1 Habitat and distribution information were accessed from the website of the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species (<http://www.iucnredlist.org/>;
downloaded May 2018).
Taxonomic order Family Common name Scientific name 1Habitat 1Distribution Tagging region Total tagged
Carcharhiniformes Carcharhinidae Oceanic whitetip Carcharhinus longimanus
Oceanic - epipelagic, mesopelagic
Subtropical; Tropical ATL 16
Carcharhiniformes Carcharhinidae Tiger Galeocerdo cuvier Neritic -pelagic; Oceanic - epipelagic, mesopelagic
Temperate; Tropical ATL, PAC 5
Carcharhiniformes Carcharhinidae Blue Prionace glauca Oceanic - epipelagic, mesopelagic, bathypelagic; Neritic - pelagic
Temperate; Tropical ATL 8
Hexanchiformes Hexanchidae Broadnose sevengill Notorhynchus cerepedianus
Neritic - benthopelagic Temperate; Subtropical PAC 2
Lamniformes Lamnidae White Carcharodon
carcharias
Oceanic - epipelagic, mesopelagic. Neritic - pelagic
Cold and warm temperate; Tropical
PAC 12
Lamniformes Lamnidae Shortfin mako Isurus oxyrinchus Oceanic - epipelagic, mesopelagic, bathypelagic. Neritic - pelagic
Temperate; Tropical ATL 2
Lamniformes Lamnidae Porbeagle Lamna nasus Oceanic - epipelagic,
mesopelagic. Neritic - pelagic
Cold temperate; subtropical
ATL, PAC 6
Rajiformes Mobulidae Devil ray Mobula mobular Oceanic - epipelagic,
mesopelagic. Neritic - pelagic
Subtropical Med. 1
Perciformes Scombridae Bigeye Thunnus obesus Oceanic - epipelagic,
mesopelagic
Temperate; Tropical PAC 96
Perciformes Scombridae Yellowfin Thunnus albacares Oceanic - epipelagic Subtropical; Tropical PAC 236
Processing of archival data
Since data from archival transmitters comprised several time intervals, we divided all the datasets into three groups with the best time interval possible (datasets with time intervals between one and ten sec were set to ten sec, between 12 sec and one min were set to one min and the remaining were set to two min). Moreover, datasets of yellowfin tunas were also corrected for temporal gaps present. Finally, from these time series datasets (see Table 2 for an example) we created depth-temperature profiles with Matlab format (.mat) for each individual. Therefore, the data processing of archival tags generated two separate sets of data for each fish to be used in the next analysis. The depth-temperature profiles were used in thermocline analysis with Mares algorithm in Matlab, and time series datasets (containing date, depth, temperature and light) were suitable for Dive Analysis (DA) program 74 for turning points calculation and Maximum
likelihood analysis (MLE).
Table 2. Example of a trimmed time series dataset format for one blue shark. Dashed box contains the two variables (depth and temperature) that were used to create the Matlab input file.
Date Depth Temperature Light
2007-08-24 10:39:00 7.00 16.45 182.00 2007-08-24 10:39:10 11.50 16.20 178.00 2007-08-24 10:39:20 15.50 16.15 193.50 2007-08-24 10:39:30 20.50 16.15 169.25 2007-08-24 10:39:40 25.00 16.00 193.50 2007-08-24 10:39:50 28.50 14.75 189.50
Thermocline detection
In order to investigate the influence of the thermal structures on the vertical behaviour and foraging movements of tagged fish, we calculated gradients of the thermocline (Fig. 2). The method was based on the analysis of depth-temperature profiles (.mat) with an algorithm in Matlab previously used in an autonomous underwater vehicle (AUV) for ocean sampling 83. This method was based on adaptive sampling concept, meaning that
the robot processes the oceanographic data in real time and decide autonomously the best trajectory to follow. In the ocean, there are many environmental variables that can be mapped using this type of methods, mainly if they can be identified by strict boundaries (e.g. fronts and thermocline). This method allows to detect fine-scale intense vertical gradients and to infer different thermoclines variables 83,84. Concisely, during a
fish dive, the algorithm evaluates and compares the vertical temperature gradient with a given threshold to define the upper and lower limit of the thermocline. After defined these
limits, thermocline characteristics were extracted from the previous vertical profile (in particular, the maximum gradient and the thermocline depth and its limits). This information was used to adapt the thresholds for the next vertical profile allowing to temporal and spatial variations of thermocline parameters. To access the temperature gradients, the algorithm clustered the data points into bins and extracted the differences of averaged values. These bins were adjusted automatically with thermocline thickness, the vertical velocity of the fish and the sampling rate, with estimation of a potential error. Because the bin size could influence the detection of gradients (small bin size could lead to large errors in gradient estimation while larger bins may increase the smooth of temperature variations) several bin sizes were tested in order to find the correct interval for estimation of thermocline gradients. Thus, the algorithm ran with at least five to eight bins spanning the depths of the thermocline. Finally, thermocline gradients were accessed by two-day section of the complete time series dataset.
Figure 2. Schematic representation of thermocline 84. High gradients are within thermocline and low gradients are above
and below it.
Vertical movement analysis
The time series dataset comprised data at very high resolution (as previously mentioned, ten sec, one min, two min) providing not only the ability to analyse the spatiotemporal patterns for each individual at a very fine scale but also to understand possible differentiated patterns of vertical habitat use between species. We computed time-at-depth (TAD) and temperature-at-time-at-depth matrices with five m time-at-depth bins and one hour time bins for each individual dataset, using an interpolation to obtain a smoother contour plot color-coded by the proportion of time/average temperature. The interpolation was completed using loess () filter function from R package “stats” (R Core Team) for TAD profiles and na.approx function from R package “zoo” 85 for temperature matrix profiles.
Additionally, thermocline depth was also plotted with TAD and TAT matrices, to investigate possible changes in behaviour related with thermocline and check the
performance of thermocline algorithm (observe if the strict boundaries in temperature profile coincide with detected thermocline depth), respectively.
Turning points calculation
Prior to estimate the turning points depth, all the time series datasets were transformed into move step lengths by calculating vertical movement deltas between successive pairs of data. As part of the procedure, two causes of possible calculation error were evaluated. Primarily, high temporal resolution of datasets (e.g. one sec) can be insufficient time to record movement deltas greater than the resolution of the tag which could lead to considerable step-wise alternating values. As mentioned before, time series datasets were under-sampled to at least ten sec which was found to be sufficient time for the fish to make moves significantly greater than the tag depth resolution. Secondly, in all datasets, sampling artifacts were introduced when the fish made long movements with a temporal interval that exceeded the sampling interval. To have this into account we coalesced steps that were part of a single movement (i.e. where the trend of consecutive steps was either a continuously increasing or decreasing depth) into a single step rather than many smaller steps. Only step-lengths greater than ten m were used.
Datasets with move step-lengths for each individual were analysed using DA in order to calculate the depth of turning points (upward and downward movements). For diving time series data, these points can be identified as the intermediate depths between move steps (Fig.3). Mean distance for each two-day section was calculated aggregating distances between all turning points and corresponding thermocline depth. The same procedure was implemented using only ascend or descend turning points, where each point was classified according to the former movement of the fish (e.g. before an ascend turning points the fish performed an ascend movement). To better understand the influence of thermocline gradients on turning movements of sharks, we created three types of plots: (1) turning points depth profile plot; (2) turning points frequency plot and (3) turning points kernel density plot. For the kernel density plot, we created a matrix with the number of turning points by 5m depth bins and by one-day time bins. To this matrix, we applied a kernel density smooth using image.smooth function from R package “fields”
86. Moreover, for both turning points depth profile and kernel density plot we transformed
Figure 3. Dive analysis output. Pattern of dive time series of a blue shark (lower part) with a small section containing three turning points (blue circle - ascend turning point; red circle - descend turning point).
Optimal foraging analysis
Several time series datasets were very long and comprise a very broad range of behaviours in terms of space use and step lengths. To increase the reliability of the analysis, time series datasets were divided into shorter two-days sections in order to capture movements that were behaviourally more consistent than the time series as a whole. As described for the turning point analysis, the time series datasets were converted into move step-lengths to consider the potential errors. Prior to performing Maximum Likelihood Estimation (MLE) analysis, it was important to know that, regardless of the computations performed to determine the best fitting distribution, it was essential to select candidate distributions that are meaningful for the hypothesis being tested in this study. This study was based on the LFF hypothesis, where three models are most relevant: power law, truncated power law (truncated Pareto) and exponential. Exponential distributions produce move step lengths with normal diffusion (i.e. Brownian movements), whereas power laws produce super-diffusive movements that predators adapt to increases the probability of encountering prey in sparse environments 12.
However, since pure power law fits are rare in nature (natural movement data is inevitably bounded), in this study only truncated power law and exponential distributions were considered (Fig.4). Other distributions may provide better fits to the data, but since we were only interested in the distributions that were consistent with the hypothesis being tested these possible distributions were not considered.
MLE methods 87 were then used to define which of the two models (truncated
power law or exponential) best fitted the observed move step-length frequency distribution for each two-day sections. Concisely, the appropriate MLE equation was
used to derive an exponent with the initial xmin parameter set to the minimum value found in the dataset. A best fit dataset was generated with the estimated parameters and a Kolmogorov-Smirnov (KS) test was used to determine the goodness of fit (GOF). To determine the best fit value for the xmin parameter, we repeated the calculation with increasing values for xmin taken from the dataset with the value that resulted in the best (lowest) KS-D statistic being retained as the best fit value. When fitting a truncated Pareto distribution (TP), the method was repeated to also derive a best fit value for the xmax parameter, so for the truncated Pareto distribution both the xmin and xmax parameters were fitted in the same way. There were two departures from the method as implemented in the programming code given in 87. First, once values for xmin and xmax
had been derived, the dataset was reduced to include only values between those lower and upper bounds. The resulting dataset, therefore, contained only the step lengths fitting the proposed distribution and it was this that was used to produce plots of log10 rank vs log10 step-length; Secondly, rather than test all values in the dataset as possible candidates for xmin or xmax, the iterative search routine was halted once five consecutive worse fits had been found to avoid the problem of fitting to a very small sub-set of the data, a problem exacerbated by complex biological data and exponent estimation. The aim of fitting the lower and upper bounds was to find the distribution that best fit most of the data, rather than select a small sub-set of the data that was a very good fit to a particular distribution.
Akaike Information Criteria weights (wAIC) were then used as a model selection and for that it was necessary to compute log-likelihoods (LLH) for both of the competing distributions. The LLH equations for distributions used in this study are given in Table 3.
Table 3.Log-likelihood equations for each distribution used in this study (truncated power law and exponential). Truncated power law
Exponential
The procedure used to estimate the exponents and parameters of these two distributions normally created different xmin and xmax values, thus resulting in fitting the two distributions to different ranges of the original data. Therefore, it was not possible to compare LLHs from the same fitted distribution. To overcome this problem, we divide the analysis into two stages, whereby LLHs and Akaike weights for both distributions are
computed from each fitted dataset. First, we computed the LLH for the fitted TP distribution and using the same dataset (defined by the xmin and xmax parameters) compute the LLH for the competing exponential distribution fitted to that same data. Using these LLH we calculated wAIC that could be compared in the usual way to perform the first stage of model selection (e.g. in the first stage TP distribution was favoured over the competing exponential distribution for that range of the data). In the second stage, we repeated the calculations for the reverse scenario: for this, we calculated the best fit exponential distribution to the original data, then proceeded to fit the competing TP distribution to that data range over which the exponential was best fitted. In this way, we obtained a second comparison of wAIC that could be used to confidently rule out the exponential distribution if it was not selected over the TP distribution during the first or second stages. Hence, using this two stage method we only considered a dataset to be fitted by the TP distribution if wAIC favoured the fitted TP over the competing exponential (stage 1), and favoured the competing TP over the fitted exponential (stage 2). Occasionally, this two stage method was not enough (results of wAIC analysis could be ambiguous). In other words, the distributions could be fitted to slightly different ranges of the data resulting in conflicting wAIC, whereby the TP fitting favours the competing exponential and vice versa. To resolve these, we used the (GOF) of the two fitted distributions. Because the KS test was used as part of the fitting process we made use of the D statistic as a measure of goodness of fit. Moreover, in this study the GOF was adjusted to account for how much of the original dataset was fitted by the distribution, using the equation Dadj = D * (log (Total Steps) / log(Fitted Steps)) where D is the KS-D
statistic. Taking the log of total and fitted steps served to decrease the impact of a difference of just a few points. If the fit was to the entire dataset then the result was D; hence, the less of the data fitted by the best fit distribution the poorer (larger) the resulting GOF value became. This adjustment of D statistic permitted a better comparison between distributions fitted to different ranges of the dataset (Table 4). Finally, datasets where the wAIC decision was classed as a best fit TP, there was the additional requirements that the estimated exponent μ falls within the Lévy range (1 < μ ≤ 3), and that the range of data fitted (i.e. xmax – xmin) should span at least 1.5 orders of magnitude. If TP datasets failed these requirements might represent more complex behaviour patterns (e.g. a mixture of Lévy and Brownian strategies), thus, were allocated to the group of mixed models (see the model selection in Fig. S2 in Supplementary materials). After all two-day section being classified, for sections described as truncated power law TP exponent (lévy exponent) was extracted.
Table 4. Summarised combination of wAIC values and the corresponding result. Note the ambiguous wAIC combination that ends with a GOF verdict or a mixed model.
Fitted TP Competing exponential Fitted exponential Competing TP
Truncated power law 1 0 0 1
Exponential 0 1 1 0
GOF decision 1 0 1 0
Mixed model 0 1 0 1
Figure 4. Examples of a truncated power law (left) - Lévy behaviour - and an exponential (right) - Brownian motion – distribution. Note a mixture between smalls steps and long reorientation jumps in Lévy pattern (left).
Linear mixed modelling
To model shark behaviour in relation to thermocline gradients, data from turning points and optimal foraging analysis were combined with thermocline gradients for each two-day section. Then, we developed a set of linear mixed models (LMM), with all species and separately for each species, using the function “lme” from the R package “nlme” 88.
The response variable was the turning points distance and levy exponent. Thermocline strength was included as a fixed variable, and ID was included as a random variable. An autocorrelation structure was also included in the model to account for the temporal correlation in the dataset. Additionally, the same procedure was also applied for turning points depth as response variables and thermocline depth as a fixed factor.
Briefly, the selection approach for linear mixed effects model used in this study followed a protocol consisting of ten steps 89. Prior to performing LMMs, we focused on
x y
the data exploration that contains key concepts that are particularly important before any model analysis 90. Therefore, for each individual model we checked for outliers in X and
Y, evaluated collinearity between explanatory and the relationship between X and Y, and also confirmed the normality and independence of the data. Note that running over these concepts could lead to an inappropriate analysis. The first step of protocol consisted in applied a simple linear model and evaluate if the residuals were normally distributed (QQ plot or histogram of residuals) and if the homogeneity assumption was valid (check by plotting the standardised residuals versus fitted values and by plotting the standardised residuals versus the explanatory variables, in this case, thermocline strength or depth). In this part, we also evaluated the presence of points with high Cook’s distance that could be influential to the analysis. The second step of the protocol consisted of fit the model using gls function 88. In this way, we could compare (using AIC values) a linear regression
model with a linear mixed effect model that was calculated with using the lme function
88. Generally, we saw a clear pattern of heterogeneity indicating that term ID should be
included in the model. Thus, LMM included not only response and explanatory but also a random factor. In the third step, we defined the optimal variance structure, testing different random structures and multiple variances for residuals. In the fourth and fifth step, we fit the new model and compared with old gls model using AIC values (basically, we evaluate if the new model with optimal random effects included the variance associated to ID). If the new model was better, we inspected the normality and homogeneity of residuals using the same plotting methods of step one (step six). At this point, we found the optimal residual variance structure of the model. The next steps (seven, eight, nine, ten) of the protocol evaluate different models and try to find the optimal fixed structure. Since we only used one explanatory variable these steps were not necessary. Additionally, we evaluated the presence of temporal autocorrelation in the data using acf function from package stats. To deal with this violation of independence we included and tested several correlation structures in the model using AIC values. After all of this process, the final model was also inspected for homogeneity and normality of residuals.
Results
Archival transmitters were attached to 384 pelagic predators including 51 sharks, 332 tunas and one devil ray in Atlantic and Pacific Oceans (Table 1). Our study included ten species of marine predators that occupied oceanic and/or neritic habitats spanning broad distributions from temperate to tropical waters, inhabiting areas with different thermoclines depth (Table 1; Fig.S1). Only, 44 geo-located tracks (latitude-longitude positions) were successfully obtained for blue, mako, porbeagle, tiger, sevengill, white and oceanic whitetip sharks, and consequently mapped in overlay with bathymetry, sea surface temperature and thermocline depth (Fig.5; Fig. S1). Movements from these geo-located sharks exhibited co-occurrence pattern between species in the same areas within oceans. In general, individuals in the Atlantic and Pacific Ocean showed movement patterns associated with the Gulf Stream (GF) current and with the East Australian Current (EAC), respectively.
Figure 5. Map showing the general movement patterns of 44 geo-located sharks used in this study overlaid on (a) bathymetry
Thermocline features
To characterize the stratification of water temperature (Fig.6; a) the Matlab algorithm estimated thermocline amplitude, depth and strength (Fig. 6; b) by each two day-section. Thermocline analysis was completed for 278 individuals from the total of tags deployed. The inadequate data sets held tag errors and/or high sampling intervals (> two min) that lead to the failure of the thermocline algorithm. Moreover, to increase the robustness of our analysis we only used datasets with a percentage of no detections lesser than 75 per cent (Table 5). Therefore, from the total of tags and after these filter process the
analysis focused only in six species (blue, mako, tiger and porbeagle sharks; bigeye and yellowfin tunas).
Figure 6. Thermocline output from the Matlab algorithm for one yellowfin tuna (yft 90). (a) Temperature profile of thermocline with its depth limits. Dashed line represents the upper / lower limit of thermocline while the full line is the depth where its temperature gradient was maximum. (b) Depth of thermocline (red crosses) and the range of its amplitude (upper-lower limit) (blue lines). (c) Temperature maximum gradient (thermocline strength).
a
Table 5. Total number of sections, detections and no detections for the Matlab algorithm. In bold are species with percentages of no detection over 75 per cent.
Species Total number
of sections Number os detections Number of no detections Percentage of no detections (%) Oceanic whitetip 1388 278 1110 79.9 Tiger 153 70 83 54.2 Blue 200 173 27 13.5 Broadnose sevengill 130 1 129 99.2 White 852 99 753 88.4 Shortfin mako 64 56 8 12.5 Porbeagle 317 142 175 55.2 Devil ray 40 9 31 77.5 Bigeye 5759 1667 4092 71.0 Yellowfin 19499 5295 14204 72.8
For the total number of sections, the thermocline depth ranged between 3.8 and 146,7 m (av. = 67,5 m), and thermocline strength varied from 0.02 to 3.99°C/m (av. = 0,32 °C/m). Concerning mean the depth of thermocline, tiger sharks preferred areas with deeper thermoclines, whereas mako shark revealed a preference for thermoclines close to the surface (Fig.7). With regards to mean thermocline strength, porbeagle shark move through the strongest thermoclines detected in our study, however, with the highest range of strength used (s.d. = 0,46; Fig.7, Table S1 and S2 in Supplementary materials). Additionally, blue shark preferred areas with the weaker thermoclines. We also observed differences for these two thermal gradients when we compared ectothermic and endothermic species. Ectothermic species (blue and tiger sharks) used areas with deep and weak thermoclines, while endothermic species (mako, porbeagle and tunas) preferred shallow and strong thermoclines.