Chelonia mydas - Université Paris-Sud

 Università degli Studi di Padova Facoltà di Scienze MM.FF.NN. Laurea Magistrale in Biologia Evoluzionistica Reproductive phenology of Dermochelys coriacea, Lepidochelys olivacea and Chelonia mydas in Suriname and French Guiana Relatori: Marc Girondot, Université de Paris Sud XI, Laboratoire d'Ecologie, Systématique et Evolution Lorenzo Zane, Università degli Studi di Padova, Dipartimento di Biologia A.Vallisneri Laureanda: Anna Rizzo Anno Accademico: 2010/2011 2 Table of contents INTRODUCTION ........................................................................................................................ 5 SEA TURTLES ................................................................................................................................. 5 EVOLUTION AND PHYLOGENY ....................................................................................................................... 5 BIOLOGY OF SEA TURTLES .............................................................................................................................. 7 DISTRIBUTION AND LIFE HISTORY ............................................................................................................. 10 CONSERVATION AND THREATS .................................................................................................................. 13 FRENCH GUIANA AND SURINAME ROOKERY .............................................................................. 16 SEA TURTLE SPECIES NESTING IN FRENCH GUIANA AND SURINAME .................................................. 18 STATISTICAL METHODS FOR POPULATIONS ESTIMATION ......................................................... 27 MARINE TURTLES AT CLIMATE CHANGE .................................................................................... 30 TIMING OF REPRODUCTION ........................................................................................................................ 33 AIMS OF THE RESEARCH .............................................................................................................. 34 MATERIALS AND METHODS .............................................................................................. 37 NESTING COUNTS DATA COLLECTION ......................................................................................... 37 GIRONDOT 2010 NESTING SEASON MODEL ............................................................................... 39 SEASON PARAMETERS ESTIMATION ........................................................................................... 40 DESCRIBING THE SOFTWARE ...................................................................................................... 42 NESTING COUNTS ANALYSIS ........................................................................................................ 43 BUILDING DATABASE ................................................................................................................................... 43 NESTING SEASON FIT ................................................................................................................................... 45 PHENOLOGY ANALYSIS ................................................................................................................ 47 SEA SURFACE TEMPERATURES (SST) ........................................................................................................ 47 NORTH ATLANTIC OSCILLATION (NAO) ................................................................................................. 50 GENERAL LINEAR MODEL (GLM) IMPLEMENTATION .......................................................................... 50 IMPROVING THE MODEL .............................................................................................................. 51 PARAMETERS OPTIMIZATION ..................................................................................................................... 52 RESULTS ................................................................................................................................... 53 NESTING COUNTS ANALYSIS ........................................................................................................ 53 NESTING TRENDS ......................................................................................................................... 58 PHENOLOGY ANALYSIS ................................................................................................................ 61 CLIMATE FACTORS DATA (SST, NAO) ...................................................................................................... 61 GLM MODELS ................................................................................................................................................ 64 IMPROVING THE MODEL .............................................................................................................. 67 DISCUSSION ............................................................................................................................. 69 TURTLE NESTING PHENOLOGY AT GLOBAL WARMING ............................................................... 69 INTRASEASONAL NESTING PERIODICITIES ................................................................................. 75 SUMMARY (ITALIANO) ........................................................................................................ 77 BIBLIOGRAPHY ...................................................................................................................... 89 3 4 Introduction Sea turtles Evolution and Phylogeny Turtles (Class Reptilia, order Testudines) made their first appearance on fossil record some 200 million years ago, during the lower Jurassic, while fully marine forms were thought to have made their first appearance by 150 million years ago (Bowen et al. 1993b). A new first fossil sea turtle record, dating back to 220-­‐
million-­‐years ago was reported from south western China in 2008 (Alroy et al. 2008), but it does not belong to the radiation leading to current species. The oldest fossil of this sea turtle radiation dates back to 110 million years, to the early Cretaceous (Hirayama 1998). The seven species of sea turtles present nowadays, representing the two families of Cheloniidae and Dermochelyidae grouped into the superfamily Chelonioidea, are the only living members from the marine radiation of the suborder Cryptodira, and constitute a monophyletic group. The extent Dermochelyidae only contains the leatherback turtle, Dermochelys coriacea, while Cheloniidae includes six species classified into five genera: the loggerhead (Caretta caretta), olive ridley (Lepidochelys olivacea), Kemp’s ridley (Lepidochelys kempii) and hawksbill (Eretmochelys imbricata) turtle, constituting the tribe Carettini; green (Chelonia mydas) and flatback (Natator depressus) turtle, the tribe Chelonini. The existence of an eighth species, the black turtle or East Pacific green turtle (Chelonia agassizii), has been a matter of debate due to conflicting morphometric and genetic data. It is now generally accepted to be a population or a subspecies of Chelonia mydas. The phylogeny of the group rests on morphological features, fossil evidence and molecular data. Elderly molecular analysis were principally based on cytochrome b (Bowen et al. 1993b) and control region mitochondrial DNA, and ND4-­‐LEU tRNA (Dutton 1995). Recently, given the recognized problems in relying solely on mtDNA, both mitochondrial genes (12S and 16S) and nuclear DNA markers (BDNF, Cmos, R35, Rag1, Rag2), as well as microsatellite loci, were taken into account (Naro-­‐Maciel et al. 2008). Hence, perspectives on several other conflicting hypotheses over general and tribal affinities, as well as species boundaries, have been given new evidence. Among them, the widely recognized distant position of Dermochelys coriacea relative to all other species, the sister-­‐
taxon relationship between flatback and green turtle, and the close affiliation between ridleys (Lepidochelys genus) and the loggerhead turtle. Regarding species boundaries, there is general agreement about grouping L. kempi and L. olivacea as a sister group, while paraphyly of the green turtle seems to be more questionable. Actually, a deep split between Atlantic and Pacific green turtle does exist, with a divergence estimated to have started about 7 million years ago, predating other vicariant events such as the formation of the Isthmus of Panama (3-­‐3,5 mya), and following the closure of the Tethys Sea (14-­‐
5 18 mya), an event preventing the mixing between many tropical marine species of the Atlantic and Indo-­‐Pacific. Moreover, the cooling of southern ocean temperatures from the mid to late Miocene (from 15-­‐17 mya to about 6 mya) could have been at the origin of a split due to cold waters blocking dispersal via southern routes at the tips of South America and South Africa. However, as microsatellite and mtDNA phylogeographic studies support evidence for recent linkages between green turtles from the Atlantic, Indian, and Pacific, it is generally accepted that limited gene flows prevents Atlantic and Pacific Green turtles lineages from being considered separated species (Naro-­‐Maciel et al. 2008). Hence, general agreement has been reached about considering black turtle as a melanistic form of C. mydas, separated only at a population level in the Pacific (Dutton 1995). A summary of phylogenetic relationships among sea turtle species is shown in Figure 1. Figure 1. Phylogenetic relationships among sea turtle species (Naro-­‐Maciel et al. 2008). 6 Biology of sea turtles Figure 2. Sea turtle skeleton. Source: seaturtle.com. Like other Cryptodira, sea turtles are characterized by a peculiar closure of their jaws, obtained by contracting muscles over a cartilage on the otic chamber. Moreover, the head is retracted in a vertical plane and assumes an S-­‐shape between the shoulder girdles (Meylan and Meylan 1999). Compared with other Cryptodira, living sea turtles have a reduced ability to retract their head in the carapace, but they are conferred additional protection by a thick and nearly complete skull roofing. Sea turtles have horny keratin beaks, highly variable shaped among species due to specific food habits: inward pointed in hawksbill, adapted to beat off pieces of sponges; serrated in green turtle, to cut sea grass; sharp both on bottom and top for leatherback, to successfully slice jellyfish; large and thick in loggerhead and ridleys, to break hard shells like the ones of clams and crabs; generalized for flatback, to feed on both soft and hard shelled animals. Marine turtles are considered highly derived morphologically, and developed several adaptations for sea life, common to all species. In comparison with other turtles, their carapace presents a reduced amount of bone, probably an adaptation to gain flexibility when subjected to high water pressure (Spotila 2004). Secondly, the paddle-­‐shaped limbs, in which all movable articulations between the distal bony elements are lost and three or four digits of the hand are markedly elongated. An enlarged shoulder girdle with a markedly elongated coracoid serves as attachment site for the well-­‐developed pectoral muscles that are used for swimming. They are also streamlined to various degrees, which improves their hydrodynamic efficiency. Turtles use their flipper-­‐like limbs simultaneously sweeping front limbs through the water, with a movement quite similar to the one of birds’ wings. The front flippers move up and out and then 7 down and in together in a power stroke that provides propulsion forward and up or down depending upon how the flippers are oriented. The turtles glide forward by momentum as they start to move their flippers, and turn by changing the amount of sweep of one of the front flippers and by rudder-­‐like actions by the hind flippers. Sea turtles are deep divers, and hence they evolved a physiology more similar to the ones of living mammals than other reptiles. A typical sea turtle spends 95 per cent of its time underwater and not more than one hour a day, cumulatively, at the surface. It normally spends 15-­‐20 minutes at a time underwater searching for food, but it can resist without breathing up to 45 minutes. Sea turtles can reach quite deep dive, with leatherbacks retaining the primate, reaching up to 1250 m depth (Houghton 2008). Sea turtles can quickly empty and refill their lungs with just one or few breaths, because their air passages are reinforced with cartilage and surrounded by smooth muscles. During deep dives their lungs collapse and the blood flow to them is restricted; air is left just in the air passages, where there are no blood vessels. Another adaptation to marine life is the presence of wide salt glands behind each eye. These are lacrimal glands remarkably enlarged and modified in order to remove excess salt from body fluids. They both produce a thick, clear, salty mucous which lubrificates the eye during nesting process on land and excrete excess salt. Moreover, sea turtles’ esophagus is adapted to eliminate salt water ingested with food, by expelling the excess water out of the mouth with muscular contraction. There has been much debate on whether marine turtles were hot or cold-­‐ blooded. Actually turtles, particularly leatherbacks, can withstand quite cold water temperatures. Recently it was suggested that they could be homeotherms, due to the difference between their internal temperature and that of the water, which ranges from the 1,2 – 4,3 °C of tropical seas up to the 8,2 °C of northern Atlantic waters (James and Mrosovsky 2004, Southwood et al. 2008). Currently, recent observations have shown that turtles rely on heat generated by their diving activity, retained by their thick shell with a layer of fat, with still a low metabolic rate (Bostrom et al. 2010). This partial endothermy seems to increase with the size, along with increasing body volume vs body surface ratio. This pattern would explain quite well some peculiar turtle habits, like basking on the water surface for a long time with the carapace exposed to the sun, in order to gain warmth. Moreover, turtles of northern latitudes, as the winter occurs, are often affected by what is called “cold stunning”: when temperatures fall to about 7-­‐10 °C, they get too cold to swim and dive, and just float on the surface. Sea turtles are oviparous reptiles. They are iteroparous, nesting on land more than once during the reproductive season, often not in consecutive years. The male developed a longer tail in which the penis is located and is inserted into female’s cloaca during copulation. Females are receptive to mating only during the month before the nesting season and the twelve hours after laying their clutch of eggs. Egg maturation, and hence duration of interesting period, is highly influenced by temperature 8 (Sato et al. 1998 , Hays et al. 2002). Sex of the offspring is determined by temperature during the middle third of embryo development (Yntema and Mrosovsky 1982, Desvages et al. 1993). Pivotal temperature – defined as theoretical temperature that produces 50% of each sex -­‐ is highly variable, on both a species-­‐specific and population-­‐specific basin, and ranges from 28.2 and 30.5 °C (Mrosovsky 1994, Chevalier et al. 1999, Godfrey and Mrosovsky 2006). 9 Distribution and life history Sea turtles are observed on most ocean basins, from the northern and southern limits of the Atlantic, Indian and Pacific oceans to the Tropics and into the Mediterranean Sea. While leatherbacks seem to be the most northern spread, foraging into colder and even polar waters, hawksbills are known as the most tropical ones. All the species are cosmopolitan in their distribution, except for Kemp’s ridley and flatback turtles. While the former is limited to the waters between the Gulf of Mexico and the eastern United States coast, with some individuals occasionally extending as far as the United Kingdom coast and Western Europe, the flatback is endemic only on the Australian continental shelf. General distribution of sea turtle species is illustrated in Figure 3. Figure 3. Overall distribution of sea turtles on a global basin. Source: Defenders of wildlife, www.defenders.org. A generalized history model (Meylan and Meylan 1999) developed with data from the green turtle, and elaborated by other authors, provides a framework to define the life history of all the species of sea turtles. Although each species diverge from the model in significant ways, they share some common aspects, regarding the seasonal and ontogenetic shifts in diet and habitats, which explain much of the observed movements and migrations. After oviposition of clutches in large holes dug in sand beaches, young hatchlings emerge during night-­‐time after a period which can last from 45 days with warmer sand temperatures to up to 65-­‐70 days for cooler ones (Spotila 2004). Upon leaving the nesting beach hatchlings begin a pelagic phase that lasts for several years, a period known as “the lost years” of sea turtles biology, whose duration widely varies among species and populations. The only exception to this pattern seems to be the flatback, which remains in coastal waters and lacks a pelagic phase (Hamann et al. 2011). 10 There are still great uncertainties on these early pelagic phase routes; we have a good knowledge about some species, like loggerhead, while there is little or no knowledge for others, like leatherback and ridleys. Research studies suggest that hatchlings spend the first days of their life swimming out to sea and feeding opportunistically on ctenophores and Figure 4. Green turtle hatchlings heading for the sea. crustacean larval stages. They are often Source: www.susanscott.net. found in association with weed lines or drift lines existing near frontal boundaries of major currents. When they are far offshore, out of the reach of coastal fish and birds predators, they continue to head out into the open ocean and are carried by ocean currents to converging areas, where they find their foraging grounds, like drifting islands of sea weeds, where they feed upon a huge variety of plants, fish larvae, ctenophores, jellyfish, barnacles, crabs and shrimps, as well as insects from nearby land. After the initial lost years, most species of juvenile sea turtles move out of the open ocean and re-­‐enter coastal waters. They occupy areas called “developmental habitats” where immature sea turtles commonly occur but where adults are rarely seen. They use mostly costal feeding grounds where they grow to adult size, entering and departing at predictable sizes. The juvenile turtles have a lower growth rate in respect to hatchlings, also due to less available food supply, and switch to their adult feeding habits. Loggerhead and Kemp’s ridley begin to eat shellfish and crabs; green turtles turn to algae or seagrass, hawksbill to sponges and sea cucumbers, sea anemones and molluscs. Age of maturity occurs between 15 to 50 years or more, depending on the species and geographical area (Meylan and Meylan 1999 , Stewart et al. 2007). Adult turtles spend most of their life inside foraging grounds. These can be fixed in space, like seagrass beds, or transitory, such as ocean areas with seasonal blooms of jellyfish or benthic invertebrates. Sexual maturity makes adults return to nesting grounds. During the reproductive season, both males and females swim up to waters surrounding nesting beaches, where they may remain up to several months. Males begin the migrations first, Figure 5. Green turtles mating, Malaysia. Source: www.allposters.com. reaching the mating grounds and waiting for the females to appear. 11 Mating occurs along the migratory corridor, at courtship or breeding stations, and in the proximity of a nesting beach, the longshore waters called internesting habitat. Reproductive habits of sea turtles are shared by different species. Females usually nest more than once during a single reproductive season, with one or more year internesting intervals which are specie-­‐specific, during which turtles couple and forage (Fossette et al. 2008b, Casey et al. 2010). Others species-­‐
specific differences occur in parameters such as nesting habitat preferences, nesting strategy (aggregated or solitary), size at first reproduction, average clutch size, details of nest size and construction. Nesting behaviour, however, is highly conserved. Typically, solitary females enter nesting beaches during night time, providing any source of disturbance is absent. The only divergent reproductive behaviour is the one frequently observed in Kemp’s and olive ridleys, called mass arrivals or arribadas. When an arribada occurs, huge aggregation of thousands of females gathers on the beach for nesting over a period of a few days. Nesting turtles crawl up the beach for a few minutes, until finding the right spot to dig the nest. After preparing the ground, clearing away any vegetation or debris and digging a body pit more-­‐less of their body size, they start digging the nest with only the hind flippers. When a deep, flask-­‐shaped hole is created, they drop the eggs. When they are all laid, the females start to cover the nest with hind flippers, and when the hole is filled, they continue to tamp down the sand till it is compact, and finally obscure the nest throwing and levelling sand on the whole area. This completed they head for the surf Figure 6. Leatherback turtle laying eggs in a nest. www.cartesfrance.fr. returning to interesting grounds. Source: All sea turtles exhibit migratory behaviour at different times of their lives, both for reproductive and foraging aims. Reproductive migrations between nesting and feeding grounds are the most known due to the facility of tagging females after the nesting effort. They extend over thousands of kilometres, from northern to southern areas. Migration routes obtained by recapture of tagged turtles as well as by satellite telemetry (Troëng et al. 2003, James et al. 2005 , Hays et al. 2006 , James et al. 2007, Fossette et al. 2010a , Fossette et al. 2010c ) integrated with molecular genetics permit the identification of the nesting beach origin of turtles captured at sea. 12 Conservation and threats It is known that several sea turtle populations are currently facing serious problems. Along with the spread and growth of human population all over the world, sea turtles have been exploited more and more. In addition, urbanisation and development of industrial economy has added new sources of damage, like pollutants and waste. As a result, some turtle populations have seen a conspicuous decline. Due to this, in fact all the seven species of marine turtles are considered at risk, and some populations are already locally extinguished. According to IUCN classification, hawksbill turtle, Kemp’s ridley and leatherback turtle are considered Critically Endangered, loggerhead and green are classified as Endangered, and olive ridley is currently Vulnerable (IUCN 2011). For Kemp’s ridley, data are deficient. Causes of decline are multiple, and involve both the action of local communities and the demand from populations of distant metropolis. First of all, the increased request for turtle meat and eggs, which from a limited and local use has turned into a worldwide market. Sea turtle eggs are sold both as a food source or an aphrodisiac. Individuals are taken for their meat, calipee (cartilage) and shell, considered a delicacy. But their bones and skin also become souvenirs, art objects and jewellery, boots, shoes and handbags, or oil. Figure 7. A poacher in Oaxaca, Mexico, takes With mechanization of fishing techniques, eggs from a nesting leatherback. indigenous people could exert a larger impact Source: www.euroturtle.org.
on populations. Even if now harvesting of adult and eggs is illegal in many countries, international concerted effort is still needed to mitigate legal collection and trade, including production of commercial substitutes, opinion pressure, education and enforcement (Hope 2002, Tomillo 2008 ). Another source of threat is still an indirect consequence of food demand, and consists of fishing by-­‐catch. Shrimp trawling, gill netting and long-­‐
line fishing, often occurring on turtle gathering areas, are seriously threatening adult populations. Entangled in a net, a sea turtle is prevented form reaching the surface to Figure 8. "Underwater Sadness" by Ramon Dominguez. A sea turtle breath and could be brought caught in a net in the Sea of Cortez, Mexico. aboard alive, dying or dead. Source: www.telegraph.co.uk. 13 Some solutions have been established to limit this threat. Turtle excluder devices, or TEDs, have been developed for shrimp trawls. They consist of trap doors that permit turtles to swim out, and have managed to reduce turtle mortality by about a half. Smaller nets and their hourly check also reduce mortality. Another solution is to limit trawl location and timing, and to establish protected Figure 9. Turtle Excluder Device (TED). marine reserves to allow population Source: n aturescrusaders.wordpress.com. recovery (Hays et al. 2003 , Pinedo 2003, Lewison 2006, Petersen 2008, Gilman 2009, Dias da Silvaa 2010 , Dono 2010, Varghese 2010). Another menace is constituted by oil spills. Oil often congeals and forms tar that gathers along the ocean’s convergence zones and drift lines, the same where converging of nutrients causes plankton blooms and constitutes a breeding area for hatchlings. Both adult and hatchlings are found dead with signs of oil ingestion. Analysing United States turtles washed up dead, this cause of mortality represents 6 % of cases (Spotila 2004). Also other types of contaminants, like heavy metal and chlorinated compounds, can accumulate in turtles’ tissue and eggs with toxic effects (Frías-­‐
Espericueta , Sakai 2000b, a , Lam 2006, Baribieri 2009, García-­‐Fernández 2009, Guirlet et al. 2010, Jerez 2010, Páez-­‐
Osuna 2010). Plastic bags, however, easy to be mistaken for jellyfish, are the most Figure 10. Juvenile sea turtle recovered from the oil common source of mortality, obstructing spill occurred in Gulf of Mexico in 2010. turtles’ oesophagus and stomach, or Source: www.conserveturtles.org. causing digestive trait disease (Barreiros and Barcelos 2001, Mrosovsky et al. 2009 ). The increasing urbanisation of coastal areas, finally, represents an important risk for hatchling success. The first problem for sea turtles in developed areas is the presence of barriers, concrete or wooden seawalls, rock jetties and sandbag structures, which prevent females from crossing the beach and nesting. Beach nourishment, carried out by mechanically pumping new sand on the beach, alters temperature, gas exchange and water content. Streetlights, lamps and car headlights can also be dangerous for hatchlings, which will be disoriented and walk away from the sea towards artificial light, leading to mortality due to predators, desiccation, or exhaustion (Bourgeois et al. 2009). In conclusion, it seems evident that, in spite of their protected status, sea turtles still need concrete measures on a local, national and international scale. Human induced threats could be reduced by several means. Firstly, assessing capture and mortality rates by fisheries to reduce by-­‐catch and control both legal 14 and illegal fisheries and their practices. Secondly, by reducing poaching of eggs and killing of adults, as well as reducing habitat damage and artificial light pollution. From a scientific perspective, in order to be updated with current population status, it also seems necessary to maintain beach monitoring and demographic studies, as well as expanding research into the effects of environmentally driven changes on reproductive parameters, like clutch frequency and remigration intervals, to improve the interpretation of nesting beach trends. Research should be focused mainly on survivorship of juveniles and subadults. According to Heppel (2000) they constitute one of the main factors driving population dynamics. As they reach maturity in not less than a couple of decades, juvenile mortality severely affects recovery capacity (Spotila 2004). However, huge numbers of juveniles produced and a negative density effect on their growth rate may in some case support a rapid recovery (Bjorndal et al. 2000). Life history of males, which is almost unknown, should be taken into account as well. In a long term perspective, moreover, effort should be made in order to raise awareness among the native human populations and tourists, as well as reinforcing regional cooperation between neighbouring countries (Fossette et al. 2008c). 15 French Guiana and Suriname rookery As sea turtles nesting habitats are constantly disappearing due to urbanisation and human presence, preservation of undisturbed natural rookeries seems to be a matter of greatest importance for the survival of sea turtle populations. Among them, the coast of French Guiana and Suriname, with their 600 km of seashore and 100 km wide continental shelf, currently represent one of the major nesting sites left for sea turtles of the western Atlantic Ocean (Girondot 2012, in press). Leatherback turtles register the most massive presence, but olive ridley and green turtles are also an important one, while hawksbill turtle and loggerhead turtle are only occasionally observed. French Guiana and Suriname are localized on the North East coast of Southern America, bordering on Guyana (W) and Brazil (S and E). Due to its low latitude (5-­‐
6°N), the area is characterised by equatorial climate, and the proximity of the Amazon river, as well as the local Maroni river (Figure 12), which highly affects coastal dynamics, dominated by large scale seashore erosion and accretion, with the disappearance and emergence of new beaches within single years. The seashore, heading in an almost NW-­‐SE direction, consists of long sandy beaches distributed patchily among mangroves and/or coastal rainforests with extensive mudflats, which makes them an ideal landscape for sea turtles, as indicated by the large number of females nesting in the area (Figure 11). Figure 11. General map of Suriname and French Guiana, with nesting sites. Legend: number of females nesting, 25-­‐100, (SWOT). 100-­‐500, 500-­‐1000, > 1000, < 25, unquantified. Source: The State of the World’s Sea Turtles There is no evidence for the presence of nesting leatherback sea turtles before 1950. In the sixties, large numbers of leatherbacks nesting on the beaches of French Guiana were reported. In the eighties, nesting aggregation moved to the beach close to the Yalimapo-­‐Awala villages. That discovery opened the way to 16 the establishment of the Amana Natural Reserve, in 1998, which includes all the nesting beaches historically identified around Yalimapo-­‐Awala, as well as 30 km of marine fringe. Figure 12. Aerial view of Maroni estuary. Source: www.cartesfrance.fr. In addition, in recent years increasing numbers of females are being recorded in other non-­‐protected eastern nesting sites in French Guiana, such as Kourou and Cayenne, the capital city. These sites, however, are subjected to threats and conflict of interests with local politics, as they undergo increasing urbanisation. While in protected sites a remarkably long population monitoring has been carried out, in new urban nesting sites it is difficult to deal with such threats. The first expedition to locate sea turtles nesting sites in Suriname took place in 1963. Since 1964, nest counts have been carried out by the Nature Reserve management organisation. Wia Wia Nature Reserve was implemented in 1961, amended and enlarged in 1966. In 1969, after declaring Maronwinje beaches a sanctuary, the Galibi Nature Reserve was established. Given the periodic phenomenon of seashore modification, it is difficult to accurately assess population trends and implement adequate conservation measures. For a better understanding of these demographic processes, different types of estimates have taken place: from individual tagging, to directly studying population demographic parameters by Capture-­‐Mark-­‐Recapture (CMR) studies of gravid nesting females, as well as direct counting. 17 Sea turtle species nesting in French Guiana and Suriname Leatherback turtle, Dermochelys coriacea (Vandelli, 1761) Kingdom: Animalia Phylum: Chordata Subphylum: Vertebrata Class: Reptilia Order: Testudines Family: Dermochelyidae Genus: Dermochelys Species: Dermochelys coriacea Figure 13. Leatherback turtle swimming. Source: colareboenglish.wordpress.com. The leatherback turtle – named for its shell, covered with skin – is the largest turtle in the world and one of the largest living reptiles. Its mass ranges between 250 to 907 kg, and length spans from 132 to 178 cm, but can occasionally reach up to 213 cm (Spotila 2004). The leatherback has no visible hard shell. It is made up of bones buried into his dark brown or black white spotted skin (carapace), or white dark spotted skin (plastron), with about seven pronounced ridges in its back and five on the underside, which confers a hydrodynamic profile. In comparison with other species, they have remarkably elongated carapace and forelimbs. Hatchlings look mostly white on the underside and black on the back, with white margined flippers and white strips running along the length of their carapace. Their softer, leathery shell is probably an adaptation to great depths, as the leatherback is known to be the deepest of sea turtles divers, reaching up to 1230 m. During deep dives, its carapace compresses and the lung collapses to avoid nitrogen narcosis, leaving air in the respiratory passages (Fossette et Figure 14. Leatherback turtle hatchling. Source: www.frauleindi.com. al. 2010b). Besides being a poikilothermic animal, the leatherback has a limited capacity to maintain a constant body temperature. Its muscle tissue shows a high and fairly constant metabolic rate, which permits the turtle to maintain quite a constant temperature in the much cooler northern waters. Its wide body mass and thick layer of fat under its shell and its shoulder and neck maximizes insulation against heat loss. In addition, it has the capacity to control its blood flow to flippers, dilating and constricting its outer blood vessels. For those reasons, the leatherback is also the most widespread of all turtles: its spatial range goes from tropical waters were it migrates for nesting, to 18 temperate and even subarctic waters (Figure 15). It is found worldwide, in both hemispheres and all oceans with only the exception of Arctic and Antarctic waters. Occasionally individuals swim from the Indian Ocean into the Atlantic (Åkesson et al. 2003). However, genetic differences from populations around the Leatherback Sea Turtle Range
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Figure 15. Leatherback turtle distribution. Source: U.S. National Oceanic & Atmospheric Administration (NOAA). Leatherbacks make the longest migration, indeed, from the equatorial and subtropical latitudes of their nesting sites (Figure 16) up to the northern foraging areas. They forage from the surface down to great depths, feeding mainly on jellyfish, colonial siphonophores like Portuguese man-­‐of-­‐war, tunicates and other soft bodied animals. This specialized diet permit them to be the most fast growing turtles species – the massive adult size is reached in just 7-­‐13 years – and to cover the high energy costs of both long distance migration and high reproductive output. Leatherbacks nest in extremely variable intervals, ranging from 1 to 6-­‐7 years. The majority of females, however, are reported to nest every second year (70 %) and third year (25 %). The number of clutches laid per female in an entire reproductive season ranges from 1 to 9 in 9-­‐10 days intervals, and it is positively related to previous migration duration (Fossette et al. 2008a). It can lead to as many as 14 nests per female during a season. Like other sea turtles, they have temperature sex determination, with pivotal temperature known to occur around 29 °C (Bro et al. 1999). 19 Figure 16. Leatherback turtle worldwide rookeries. Legend: number of females nesting, 500-­‐1000, > 1000, unquantified. Source: The State of the World’s Sea Turtles (SWOT). < 25, 20 25-­‐100, 100-­‐500, Green turtle, Chelonia mydas (Linnaeus, 1758) Kingdom: Animalia Phylum: Chordata Subphylum: Vertebrata Class: Reptilia Order: Testudines Family: Cheloniidae Genus: Chelonia Species: Chelonia mydas Figure 17. Adolescent green turtle resting in Ribbon Reefs, the Great Barrier Reef, Australia. Source: underwaterserver.com. Green turtle – named for the greenish colour of its fat, which is due to its diet of sea grasses and rooted algae – is a species inhabiting tropical and subtropical oceans worldwide. Found in the Atlantic Ocean as far north as Massachusetts and as south as Southern Brazil and South Africa, it is present throughout the Caribbean Sea, the Mediterranean Sea, in wide warmer portions of the Pacific and Indian Oceans (Figure 20). Smaller than the giant leatherback, with a body mass ranging from 65 to 204 kg, and a shell length of 80-­‐122 cm (Spotila 2004). Green turtles have small anteriorly rounded heads, with one pair of prefrontal scales and four pairs of postorbital scales. They have a single – two, in some hatchlings – claw on each flipper. The carapace is broadly oval, with scalloped Figure 18. Green turtle hatchling, Ka'an margins and four pairs of coastal scutes. They Biosphere reserve. Source: animalweb.com. have a very variable colouration: dorsally, it ranges from light to dark brown, plain, streaked or spotted, sometimes shaded with olive, in adults; brown with radiating streaks in immatures; black in hatchlings. The abdomen is white in hatchlings, yellowish in adults. A population variety, black sea turtle or Chelonia mydas agassizii, is found in Eastern Pacific waters, off the coasts of Alaska, through California, down to Chile. It has a dark grey to black narrower and higher carapace and a yellowish-­‐white plastron. The marginal are more constricted over the hind legs, and the post-­‐central laminas are longer Figure 19. Chelonia mydas agassizii, Galapagos relative to their width. Islands, Ecuador. Source: seapics.com. 21 Green Sea Turtle Range
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8,000
NMFS, Office of Protected Resources
March 2009
Figure 20. Green turtle distribution. Source: U.S. National Oceanic & Atmospheric Administration (NOAA). Green turtles migrate extensively to and from their nesting beaches, but spend most of their time in small feeding grounds. Their more important nesting colonies are at Tortuguero, in the Caribbean Coast of Costa Rica (22.500 females per year), Raine Island, on the Great Barrier Reef in Australia (18.000 females per year), (Spotila, 2004). Their large nesting colonies include Oman (6000), the Comoros Islands (5200), the Seychelles (4900), Sabah (3800), Sarawak in Malaysia (2000), Ascension Island (3400) and Trinidad Island in Brazil (3000). Figure 21. Green turtle worldwide rookeries. Legend: number of females nesting 500-­‐1000, > 1000, < 25, 25-­‐100, 100-­‐500, unquantified. Source: The State of the World’s Sea Turtles (SWOT). Female green turtles lay 1 to 7 clutches per nesting season, 3 on average, each clutch containing more or less 110 eggs, in intervals of 12-­‐13 days. Internesting intervals are 4 up to 6 years long (Spotila 2004). When hatchlings emerge, they start to drift off, and float in the sea feeding on an omnivore diet, including snails, algae, ctenophores. At 20-­‐25 cm carapace length 22 they move towards shore and take residence along the coast. From 38-­‐60 cm of carapace lengths, they move to shallow coastal areas and feed on sea grass beds, or on reefs. Juveniles can remain resident on these feeding grounds, until they mature. Their age at maturity depends on composition (quantity of protein) and availability of food: it ranges from the 26-­‐27 years of Costa Rica and Florida, to the 30-­‐40 years of Australia. As adults, they are most strictly herbivorous, but to a lesser extent they are known to feed also on jellyfish, salps, sponges, molluscs, fish, polychaete worms. As leatherbacks, green turtles are threatened worldwide. The decline of the green turtle population is historical, and caused mostly by hunting for consumption and egg collection. By 1900 the worldwide green turtle population was already dramatically reduced, and since then it has declined by another 50-­‐
70 per cent. Nowadays, important conservation initiatives have been established. Despite this, egg poaching still occurs at 45% of nesting beaches, and female harvesting occurs in 27% of them. 47% of populations of juveniles and adults are affected by intentional hunting, and 49% is subjected to by-­‐catch in fishing trawls, nests, and long-­‐lines. Habitat loss, regarding both nesting beaches and sea, affects 25% of populations, while diseases like fibropapillomatosis have an impact on 42% of populations (Spotila 2004). Despite this, they show a quite remarkable recovery capacity, which in some cases has driven to regression even in advanced state (Chaloupka et al. 2009). Devices have been implemented particularly in the United States, Mexico and South America. However, much more needs to be done in order to ensure persistence of green turtle populations. 23 Olive ridley turtles, Lepidochelys olivacea (Eschsholz, 1829) Kingdom: Animalia Phylum: Chordata Subphylum: Vertebrata Class: Reptilia Order: Testudines Family: Cheloniidae Genus: Lepidochelys Species: Lepidochelys olivacea Figure 22. Nesting Olive ridley turtle female. Source: National Geographic. The olive ridley turtle – so called because of its smooth and olive coloured carapace -­‐ is rather easy to distinguish from other species, mainly due to its characteristic coloration. Its carapace, grey in immatures, turns in adulthood to a peculiar mid to dark olive green. The abdomen appears white in immatures and later becomes cream-­‐yellow. It is also smaller in comparison to other species: its carapace length normally ranges from 55 to 76 cm, its mass from 36 to 43 kg (Spotila 2004). The carapace is short and wide, generally smooth and tectiform in adults, interrupted by vertebral projections in juveniles. Five to nine pairs of costal scutes are present, often with asymmetrical configuration, slightly overlapping in juveniles and non-­‐overlapping in adults. Plastron is made up of scutes whose inframarginal ones have characteristic small pores near their rear margin. It has a large and triangular head, with two pairs of prefrontal scales and powerful jaws. Both frontal and hind limbs have two claws, the secondary of which could be lost in some adults. Olive ridleys are typical of tropical and subtropical regions (Figure 24): they are found in the Pacific and Indian Ocean, as well as in the Southern Atlantic, along the west coast of Africa and the north eastern coast of South America. Figure 23. Olive ridley hatchling in Hacienda Baru, Costa Rica. Source: mongabay.com They could also visit northern latitudes Alaska, New Zealand and Chile) in warm periods. Although they have been observed in the open oceans, they mostly live in shallow coastal waters, mainly along drift lines where debris and masses of floating seaweed are concentrated, where they feed on crabs, snails, clams, barnacles encrusted on floating objects, salps and sea squirts, bryozoan, algae, fish, fish eggs and jellyfish. They spend a lot of time basking on the surface, warming in order to speed up their digestion 24 and metabolism (as well as egg maturation) but can be found up to depths of 107 m (Spotila 2004). They conduct long and complex oceanic migrations to their feeding grounds, following water temperature, as they seek ocean features that move such as thermal fronts and edges of cool water masses. They also follow shifting water masses in the course of year. As these locations are not predictable, they wander over vast stretches of the ocean, using both sun Oliveto Ridley
Turtle Range
position and magnetic signals locate. Sea
o·
. Jr
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,
Miles
0
2,000
4,000
6,000
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approximate range of
species.
8,000
NMFS, Office of Protected Resources
March 2009
Figure 24. Olive Ridley Sea Turtle range. Source: U.S. National Oceanic & Atmospheric Administration (NOAA). Feeding conditions have a strong influence on the olive ridley’s reproduction pattern. In years of absence of water mixing and low primary production, (e.g. in El Niño years), food scarcity makes turtles reproduce every 2 or more years. On the contrary, when cool water upwellings and thus high productivity occurs (e.g. in La Niña years), turtles may reproduce in consecutive years. Females lay 2 to 3 clutches of eggs each year, each of which of about 110 eggs (Spotila 2004). The nesting intervals are of about 17 to 45 days. They leave a distinctive track as they use alternate flipper to come quickly up to the beach. They usually nest in night-­‐ time as well as during the day, in the early morning or late afternoon. They can cool more easily than other turtles due to their lighter colour and smaller size, and they choose cloudy Figure 25. Arribadas in Gahirmatha, India. Source: fractalenlightement.com. and windy days. Beside some solitary females, nesting about 2 clutches per season at 14 days intervals, the majority of olive ridleys are mass nesters. They wait from days up to months in shallow waters in front of the coast, gathering to lay their clutches together in thousands for consecutive days, a phenomenon called arribadas (the Spanish for “arrivals”). This reproductive strategy has the goal to reduce 25 predators’ impact, as a single egg or hatchling in a large number has a higher probability to survive. Arribadas females nest approximately 2 clutches per season, at intervals of 28 days, which could be delayed for 6-­‐8 weeks due to environmental conditions. It takes 50 to 65 days for an egg to hatch, depending on nest temperature. The pivotal temperature, determined for Costa Rica population, is 30,4 °C (Hulin et al. 2009). Age at maturity is poorly known, but it is thought to be of 11-­‐16 years (Spotila 2004). Figure 26. Olive ridley turtle worldwide rookeries. Legend: number of females nesting 500-­‐1000, > 1000, < 25, 25-­‐100, 100-­‐500, unquantified. Source: The State of the World’s Sea Turtles (SWOT). Nesting sites occur over the entire range. The major arribada beaches, to which females show high fidelity, are placed in Mexico (La Escobilla), Costa Rica (Ostional, Nancite), India (Gahirmatha) and Suriname (Eilanti). Minor arribada beaches are present in Nicaragua, Panama, India and Mexico, while many others have already disappeared due to adult killing and egg poaching. The most reliable nesting females population estimate, according to Spotila (2004) is of 500.000 – 600.000 in Costa Rica, 450.000 in Messico, 135.000 in India. Other smaller arribadas and solitary nesters could count for 40.000 – 60.000 individuals. From that number, it could be estimated a total number of about 2 millions females, and a total adult population of 4 millions worldwide. Despite this species is undoubtedly the most abundant of the seven marine turtles, it still severely suffers from legal and illegal egg poaching and adult harvest, as well as to killing of adults due to fisheries. Hence, strong beach protection initiatives and the reduction of fisheries impact are still needed to ensure the persistence of the species. 26 Statistical methods for populations estimation Given the critical situation of sea turtles worldwide, detailed assessment of population status and trends on both a local and global scale seems to be necessary, in order to evaluate effects of recovery and conservation activities as well as to set up new appropriate conservation strategies. However, estimating population size through direct counting for these species poses several difficulties. The main challenges are due to cryptic life history stages, trans oceanic dispersal, and non-­‐consecutive annual reproduction (Girondot et al. 2006). Due to unavailability of individuals in the open sea, researchers have traditionally relied on enumerating numbers of nests laid by a population as an index of population size. Identifying a sea turtle nesting crawl is a relatively easy task (Schroeder and Murphy 1999). Females usually leave deep characteristic tracks after nesting, easy to find and identify species by species. Nesting beach surveys, together with studies of nesting females and nest success, can provide reliable information of nests deposited annually, the number of females reproductively active annually, and annual nest productivity. However, there are still some factors that could create a strong bias on the data. First of all, not every crawl of sea turtle results in a nest: if disturbed before oviposition, the turtle may leave the beach without completing its nesting attempt. Nesting and non-­‐nesting tracks are can be confused during monitoring; hence a strong bias of the estimated number of nests can result. Another challenge in counting sea turtles nests is due to the fact that the nesting season spans several months, during which females may change nesting sites, choosing remote unmonitored beaches. This results in temporal or spatial gaps in the monitoring effort. This is particularly true for French Guiana and Suriname rookeries, where coastal dynamics change availability of nesting sites in few or even consecutive years (Fossette et al. 2008c). Another problem occurs if monitoring of a nesting beach is not continuous in time, resulting in temporal gaps. As distribution of nests during a season is known to be bell shaped, correcting these gaps with simple extrapolation from the proportion of the season can lead to false results. The distribution of nests during nesting season is affected by several factors. Among them, the total number of nesting females, the initial date and individual distributions of the nests for each female during a season, quite variable among single individuals. These patterns can also be affected by environmental parameters like water temperatures or trophic local conditions of nesting grounds (Sato et al. 1998 , Weishampel et al. 2004, Fossette et al. 2009 ). Moreover, due to the large amount of field work requested for exhaustive observation and tagging of individuals, many authors have proposed strategies to obtain population size estimates with a strong level of confidence, while reducing field monitoring. 27 Hence, several models have been developed over the years in order to obtain a reliable estimate of the total number of nests during an entire season from partial monitoring data. One category is characterized by a short period of intensive counts (10-­‐14 days) from which overall counts are extracted (Kerr et al. 1999, Jackson et al. 2008 , Sims et al. 2008). However, this strategy has proved ineffective when a lot of sites have to be monitored at the same time, and, as for sea turtles, the peak of nesting could change due to environmental factors. Other methods propose to estimate the change in density of animals from sparse counts at single locations. However, all these methods have been shown to suffer from serious weaknesses (Girondot 2010b). Other two models (Malo 2002, Girondot et al. 2006) have been proposed to deal with this ineffectiveness. However, both present disadvantages. The former seems to be flexible in order to cope with irregular reproductive patterns, such as slow beginning and rapid end, or the inverse. Nevertheless, it imposes a link between changes at the beginning and end. The latter permits us to obtain similar shapes from a wide number of parameters, which compensate each other. On the contrary, for the same reason, single parameters and their standard errors are not easily identifiable, which leads to a lack of information (Godgenger et al. 2009). Other methods (Jenni and Kéry 2003, Gratiot et al. 2006 ) avoid these problems, but impose symmetrical season as a constraint. This is particularly unfeasible for French Guiana and Suriname colonies, where a so-­‐called “small nesting season” is mixed with the end of the main one (Girondot et al. 2007). The method applied in this study, published by Marc Girondot (2010b), uses a parameterized sinusoidal equation to describe asymmetrical nesting season and, at the same time, provides a reliable estimation of a reproductive season’s parameters. It provides a good estimation of population size and its standard deviation with few nesting counts, as well as describing parameters with high biological relevance, such as beginning, peak and ending of nesting season, or maximum number of nests laid. These parameters are a useful tool to conduct environmental analysis, such as describing the possible impact of environmental factors like seawater temperatures, trophic conditions or population density to sea turtles reproductive patterns. The strength of this method is also very easy to use (with the specifically designed MTTS -­‐ Marine Turtles Time Series software) and to allow the simultaneous use of all the information gathered during the monitoring of several beaches, within a season and between seasons. It is also possible to test the effect of different beaches on the phenology of migration. Moreover, the method allows discrimination between missing data and 0 counts. Indeed, the common mistake of non-­‐distinction between 0 track or nest count and a non-­‐monitored night has been proved to produce a strong bias when time series is analysed (Girondot 2010a). 28 In conclusion, the method seems to be efficient in answering biological and conservation questions dealing with migratory species, and could be easily applied to other migratory ones. However, this model has some limitations, too. Temporal patterns of internesting intervals observed within a single nesting season in Yalimapo-­‐Awala beach (French Guiana) for leatherback turtles (Girondot et al. 2006) are known to produce fluctuation in the total distribution of nests, with local maximums in females arrivals. These patterns are of two types. Firstly, they were observed to occur every 9-­‐10 days, a period that constitutes average interesting interval between successive nesting within the reproductive season. Secondly, peaks in nesting activity appear every 14 days, in apparent synchrony with the lunar phase and tide level. These dynamics have been exhaustively described by Girondot et al. (2006) model, and might be integrated in the current one. 29 Marine turtles at Climate Change According to the latest Intergovernmental Panel on Climate Change Assessment Report (IPCC 2007), few doubts remain about actual global warming. Air temperatures have reached levels unequalled since the year 1850, when atmospheric records began, and mean ocean temperatures have seen an increase of about 0,7 °C (Figure 27) in respect to the ones of the last 420.000 years (Hoengh-­‐Guldberg 2007). Figure 27. Three time series of changes in global sea surface temperatures: Hadley Centre SST data set version 2 (HadSST2) analysis, National Climatic Data Centre (NCDC) data (Smith et al., 2005), and Centennial in-­‐situ Observation-­‐Based Estimates of SSTs (COBE-­‐SST) from the Japan Meteorological Agency (JMA), (Ishii et al., 2005). As this trend is expected to accelerate due to the increase of anthropogenic emissions of CO2, marine ecosystems are going to be the ones to pay the highest price, as about 80% of the extra warmth is likely to be absorbed by the oceans (IPCC 2007). This could result in a number of different consequences: thermal expansion, leading up to an 18 to 60 cm sea level rise by 2100; extreme weather events, like colder winters and warmer summers, occurring with wider variability; changes in ocean chemistry, as 0,3 up to 0,5 pH decrease. These changes could severely affect marine organisms, both on global and regional scales (Wilby 2002). Hence, several studies have been conducted to assess the impact of global warming on life cycles, physiology, phenology, demography and behaviour of many marine species (Brown et al. 1999 , Hughes 2000, Walther et al. 2002, Walther , Root et al. 2003 , Allison L. Perry 2005 , Lima 2007 , Reusch 2007). Generally, data demonstrate a coherent response of species’ phenological and spatial shifts to climate change patterns (Sala 2000 , Davis 2001, McCarty 2001 , Visser 2005 , Hickling 2006), from invertebrates (Parmesan et al. 1999 , Warren et al. 2001, Beaugrand 2002 ), to marine mammals (MacLeod 2009, Robinson et 30 al. 2009) and birds (Brown et al. 1999, Winkler 2002, Butler 2003, Gordo 2005, Mills 2005, Springer 2007). Beside occupying an extremely extended living area and relying on a wide range of different habitats throughout their life history, marine turtles are also a group of species of high conservation concerns, being affected by a range of natural and anthropogenic threats. For that reason, evaluating impact of climate change on them seems an issue of greatest importance, which has seen a remarkable increase in recent years (Hawkes et al. 2009). Global climate change threats for marine turtles mostly concern open ocean habitats, coastal habitats and breeding sites. Conditions of open ocean habitats affect both juvenile stages and adults. As hatchlings experience a phase of swim frenzy where they are associated with floating matter at frontal systems of major ocean currents, they are sensible to likely changes in intensity and direction of thermohaline circulation patterns (Rahmstorf 1997 , Stocker and Schmittner 1997), leading to less wide or different dispersing areas (Hamann 2007). Parallely, as spatial distribution of birds and fish species are changing with the climate (Carscadden et al. 1997 , Root et al. 2003, Lehikoinen et al. 2004, Sims et al. 2004), this change could affect the type and intensity of predation. Finally, due to changes in pelagic community induced by climate change (Greve et al. 2001 , Hays 2001, Beaugrand 2002), availability of prey for growing juveniles could change, leading to potential trophic mismatch (Martin Edwards 2004). Sea surface temperature is an important factor for sea turtle distribution (Milton and Lutz 2003) and foraging success, as current patterns influence availability of suitable prey. For narrow dietary species like leatherbacks, which feed mainly on jellyfish, this could be particularly important, as this prey is known to respond sensitively to changes in climate (Edwards and Richardson 2004), and could lead to beneficial effects like the northern expansion of potential range of occupation (James et al. 2006, McMahon and Hays 2006). Changing in coastal in-­‐water habitats, on the contrary, are likely to affect mostly Chelonidae marine turtles, which forage on coastal habitats along the continental shelf (Bjorndal 1997) where optimal conditions of temperatures, surface currents and foraging depth occur, assuring an energetically efficient foraging. These seasonal habitats, however, could become less predictable in time and space due to climate change (Robinson et al. 2009). More foraging specialist species, like herbivorous green turtle or spongivorous hawksbill turtle, might be affected by climate-­‐induced changes in distribution and abundance of their food sources. On the other hand, rising temperatures may actually increase availability of suitable foraging habitat for many species and hence enlarge their total range. Concerning breeding sites, one of the most important factors affecting turtles’ fitness is availability of nesting beaches. Predicted increase of sea level on average by 4,2 mm per year until 2080 (Church et al. 2001 , IPCC 2007) may compromise availability of nesting beaches, especially island and narrow coastal ones (Limpus and Nicholls 1988, Fish et al. 2005, Baker et al. 2006, Jones et al. 31 2007 , Mazaris et al. 2009b) and where urbanisation prevents landward migration (Fish et al. 2008). Sea level rise is also cause of a habit widespread used, the construction of barriers around human areas to protect human settlements, which reduces beach availability (Pilkey and Wright III 1988 , Kraus and McDougal 1996 , Altschul et al. 1997), lessens upper intertidal beach areas (Dugan et al. 2008), as well as leading to entire beach loss (Airoldi et al. , Koike 1996 , Lutcavage 1997 ) and causes phenomena of “renourishment”, where transplanted sand is pumped to replace eroded materials, but providing unsuitable conditions for turtle egg incubation (Crain et al. 1995, Milton et al. 1997 , Rumbold et al. 2001 , Peterson and Bishop 2005). The increase in proportion of extreme weather events like hurricanes or typhoons (Goldenberg et al. 2001, Webster et al. 2005, IPCC 2007) may cause considerable damage to shorelines, leading to loss of nesting beach and decrease hatchling success and hatchling emergence success (Hanson et al. 1998 , Ross 2005 , Pike and Stiner 2007 , Prusty et al. 2007 , Van Houtan and Bass 2007). However, nest fidelity changes among species and population (Pike and Stiner 2007). While species with high site fidelity, like hawksbill turtles (Kamel and Mrosovsky 2005) could be more affected, populations with lower nest fidelity, like leatherback turtles (Witt et al. 2008) can maintain successful nesting even with annual changes in availability of beaches (Girondot and Fretey 1996 , Rivalan et al. 2006, Kelle et al. 2007a) with the potential of colonising new beaches (Encalada et al. 1998 , Mrosovsky 2006, Hamann et al. 2007). Beside the damaging and loss of some beaches, on the other hand, increased air and therefore beach sand temperatures could lead to an increase of thermally suitable nesting habitats, geographically and temporarily, with the colonisation of northern latitudes, as occurred in the past for the expansion of loggerhead turtles in interglacial periods (Bowen et al. 1993a). Actually leatherback turtles in the last decades are known to be nesting at their most northerly (Rabon et al. 2003), but it is still an open question if they could adapt quickly enough to deal with this rapid change. Another issue regarding beach warming is the variation of thermal incubation conditions, with the possible bias of primary sex ratio (Mrosovsky et al. 1984, Janzen 1994 , Davenport 1997 , Glen and Mrosovsky 2004, Hatase and Sakamoto 2004 , Hawkes et al. 2007) as well as survival of clutches (Miller 1985a , Broderick et al. 2001b , Godley et al. 2001a , Hamann et al. 2007 , Hawkes et al. 2007). It is not clear how well turtles would be able to cope behaviourally and physiologically with altered incubation conditions to counter potential feminization. As pivotal temperatures across species and population is relatively conserved (Hawkes et al. 2009), no considerable change is likely to occur at individual physiological level, while behavioural adaptations, like choice of cooler locations or anticipated or delayed nesting, could contribute to maintain production of mixed sex ratio (Doody et al. 2004 , Zbinden et al. 2007, Schwanz and Janzen 2008). On the other hand, the quick adaptation of such a long-­‐lived, late maturing and slow changing group is questionable (Avise et al. 1992, Zug et al. 2002). 32 Timing of reproduction The issue that most concerns this thesis is the potential alteration of turtle reproductive phenology. Phenology, defined as the timing of seasonal activities, has been suggested as an indicator of ecosystem response to global climate change (Parmesan 2006) and hence it has to be taken into serious consideration. Turtles’ phenology can be affected both by foraging grounds and nesting area conditions. As they are capital breeders (Bonnet et al. 1998) turtles reproduction timing is dependent from energy storage accumulated in foraging grounds, with migration to nesting grounds occurring in correspondence to reaching of a definite threshold, with remigration intervals – the period between reproductive years -­‐ spanning from 1 up to 3 years (Rivalan et al. 2005). Even if foraging during nesting season is now known (Fossette et al. 2008b), turtles mostly rely on accumulated energy resources during nesting season, and the difference in observed remigration intervals between males and females (Godley et al. 2002, Hamann et al. 2003, Schroeder et al. 2003) is likely to be explained by different resource requirements. It is clear that environmental conditions of foraging sites can influence prey availability and resource acquisition, together with the decision to breed in a given year and the timing of migration to breeding grounds, as well as the extent of reproductive effort (Kwan 1994 , Miller 1997, Godley et al. 2001b , Solow et al. 2002, Price et al. 2005 , Wallace et al. 2006, Saba et al. 2007, Chaloupka et al. 2008, Reina et al. 2009, Van Houtan and Halley 2011). Moreover, the amount of energy stores from foraging areas could also influence clutch frequency (Solow et al. 2002, Saba et al. 2007, Chaloupka et al. 2008, Van Houtan and Halley 2011) and therefore total duration of individuals nesting season and nesting abundances. Finally, the effects of sea temperatures of foraging areas on reproduction were also evaluated (Mazaris et al. 2009b). The condition of nesting grounds, already mentioned, could also contribute to energy supplies during nesting season, with impacts on duration of nesting season and clutch frequency. However, sea temperatures of nesting grounds could mostly affect intra-­‐annual timing of nesting, with accelerated egg maturation due to warmer water temperatures (Hamann et al. 2003), causing an earlier onset of nesting (Pike et al. 2006, Hawkes et al. 2007, Mazaris et al. 2008), of peak nesting date (Weishampel et al. 2004 , Pike et al. 2006), a decrease in inter nesting intervals (Sato et al. 1998 , Webster 2001, Hays et al. 2002 , Hamel et al. 2008a) and length of nesting season (Pike et al. 2006, Hawkes et al. 2007). 33 Aims of the research Given the Girondot (2010b) model, and being able to rely on a large dataset of nesting counts recorded from 1970 to 2006 in French Guiana and Suriname rookery, research was planned bearing in mind two different objectives. On the one hand, being able to rely on climate and temperature data since 1981 as well, we decided to use the current model to obtain a prospect of sea turtle phenology parameters in French Guiana and Suriname in the period 1979-­‐2006, and to relate it to climate factors. Firstly, an overall prospect of nesting phenology trends of leatherback, olive ridley and green turtle for the period 1979 – 2006 in French Guiana and Suriname has been obtained through the analysis of nesting counts with the Girondot (2010b) model. Then, all parameters obtained were analysed in relation to climatic factors (sea surface temperatures and North Atlantic Oscillation), in order to determine which factors affect reproductive patterns and how they do it, so as to obtain a trust worthy estimation of this possible quantitative relation. Currently, there is no published data concerning local increase of temperatures in French Guiana and Suriname coastal waters, even if there is evidence of a sensible increase in yearly average temperatures in the nearby Caribbean Sea (Winter et al. 1998). To test if a climatic trend was occurring along the rookery, we obtained mean monthly temperatures of the last thirty years up to the present (1981-­‐2011). As shown in the graphics below (from Figure 28 to Figure 30), a clear increasing tendency seems to occur, and it was confirmed by linear regression analysis results (p < 0,01 for January, February, July – December, p < 0,05 for March, April, June; p > 0,05 for May). This evidence attributed further motivation for our work. Secondly, we decided to use the almost complete Yalimapo-­‐Awala 2002 dataset to improve our model, by fitting the nesting counts to the current model with the addition of new parameters. To do that, Girondot et al. (2006) model’s intraseasonal variations formula was summed up to the current model equation, with a search for new parameters by an optimisation method, following Maximum Likelihood criteria. 34 Figure 28. April mean sea surface temperatures of continental shelf area in front of Yalimapo-­‐Awala beach (6-­‐7°N, 53-­‐54° W) for the period 1982-­‐2011. Source: U.S. National Oceanic & Atmospheric Administration (NOAA). Figure 29. August mean sea surface temperatures of continental shelf area in front of Yalimapo-­‐Awala beach (6-­‐7°N, 53-­‐
54° W) for the period 1982-­‐2010. Source: U.S. National Oceanic & Atmospheric Administration (NOAA). Figure 30. December mean sea surface temperatures of continental shelf area in front of Yalimapo-­‐Awala beach (6-­‐7°N, 53-­‐54° W) for the period 1982-­‐2010. Source: U.S. National Oceanic & Atmospheric Administration (NOAA). 35 36 Kolukumbo
Samsambo
Materials and methods Babunsanti
Thomas
1
Nesting counts data Pruimenboom
collection Diana
2 3
4
We consulted a dataset of nesting counts recorded on a series of beaches belonging to Suriname and French Guiana coasts (Figure 31), recorded from 1979 to 2006 for Dermochelys coriacea, and from 2002 to 2006 for Lepidochelys 5 km long (e.g. olivacea and Chelonia mydas. As beaches are several kilometres Yalimapo-­‐Awala beach extends for 4 km), they were often divided into sections roughly equal in distance for a more efficient monitoring (Figure 32). Matapica
Matapica
Maroni estuary
Kourou
Maroni
river
Cayenne
SURINAME
FRENCH
GUIANA
BRAZIL
0
BRAZIL
50 km
Figure 31. Suriname and French Guiana nesting sites. Kolukumbo
Samsambo
Thomas
Babunsanti
Pruimenboom
1
2 3
4
5 km
Diana
Matapica
Figure 32. Nesting beach sections. Maroni estuary
Kourou
Maroni
river
SURINAME
Matapica
37 Cayenne
FRENCH
GUIANA
BRAZIL
Turtles nesting activity was monitored by conducting daily dawn patrols to count all nest tracks made the previous night, and/or by counting individual nesting females at night. For leatherbacks, false crawls are relatively rare and only tracks that reach the upper part of the beach and had signs of post-­‐oviposition camouflage by the female turtle were counted as nests. Whether nesting was observed at night or tracks were detected the following morning, each nest was assigned the date prior to midnight of the night it was laid. Figure 33. Leatherback turtle leaves the beach after laying eggs. Source: www.panoramia.com. 38 Letting t be a day of the year, the number of nests
deposited per night is modeled using the system of
equations below:
of the nesting season; MinB an
when no nests are observed o
P – B = E – P, when the nes
around P; and F = 0, for no fla
(7) if t < B → Min B
The simplest model uses
if t ∈[B, P − F 2] → ((1 + cos(π(P − F 2 − t ) (P − F 2 − B ))) 2)
MinE,
P – B = E – P, Max, F
Girondot (2010b) model to describe the nesting season is based on P,
several 
(
Max
Min
)
Min
−
+
B
B

Gratiot
etyear al. (2006) model.
sinusoidal equations. Knowing t as the numerical value of the day of the 
st Whereas the nesting seas
if
[
,
]
Max
∈
−
+
→
t
P
F
2
P
F
2

(calculated by difference between current day of the year and 1
of January of if t ∈[P + F 2 , E ] → ((1 + cos(π(t − P + F 2) (E − P + F 2))) 2
ment, the formulas developed
the same year), the number of nests deposited per night is estimated using the 
in continuity. The nesting sea
following system of equations: (Max − M i nE ) + MinE

in the interval [B, E ]. Note th
if t > E → MinE
!" ! < ! → !"# ! same definition as that given
!
!
!
most,
who consider
!" !The
∈ !,model
!−
→requires,
1 + cos at
! !
− −7! parameters,
! − − ! which
2 !"# − !"#$
+ !"#$ the range of ti
2
2
2
are defined
graphically
in Fig. 1. The formulas have
mean number of nests at the
!
!
!" ! ∈ ! − , ! +
→ !"# been constructed
to
allow
the
parameters
to
have
son are observed. A conversi
2
2
!
!
!
direct
definition
must be done nume
!" !
∈ ! +biological
, ! → 1interpretations:
+ !"# ! ! − ! +
!−!+
2 !"# − !"#$
+ !"#$ 2
2
2
• MinB is the mean nightly nest number before the
!" ! > ! → !"#$ beginning
of the nesting season;
• MinE
is the mean
nest number
after the
Fitting cr
Where all parameters have dnightly
irect biological interpretations: end
the nesting
season;
• of
MinB is the mean nightly nest number before the beginning of the • Max
is
the
mean
number
of nests at the peak of the
The nest distribution per n
nesting season; nesting
season;
assumed
to be homoscedasti
• MinE is the mean nightly nest number after the end of the nesting • Pseason; is the day of the year on which the nesting seaal. (2006), heteroscedastic Ga
son peaks;
• Max in the mean number of nests at the peak of the nesting (2006)
season; and Poissonian by G
• F is the number of days around the Day P on which
Clearly the homoscedastic
• P is the ordinal date of the year on which the nesting season peaks; the curve of the graph flattens out (Fig. 1);
biases the output (Godgenge
• F is the number of days around the day P on which the curve of the graph • B is the day of the year on which the nesting seato use a truncated Gaussian
flattens out; son begins;
Dahiya 1989) gives very poor
• B is the ordinal date of the year on which the nesting season begins; • E is the day of the year on which the nesting seaparameters (not shown). The
• E is the ordinal date of the year on which the nesting season ends. son ends.
distribution fits the data well
Various constraints can be set up to simplify this
large numbers of nests per nig
The model is represented graphically in Figure 34. Whereas the nesting season is model: MinB = MinE, for the same number of nests out
and the Poissonian distribu
described by segments, the formula allows the segments to be in continuity. beaches with low numbers o
F
genger et al. 2009). When the
Max
ted on high-density beaches,
mean is not large enough, as
are not within the envelope o
mial distribution can be descr
sonian distributions (Lawles
model count data with varyin
sion. The distribution of the
expressed in terms of the me
parameter k, such that the p
non-negative integer x is (An
MinB
Nest number
Girondot 2010 nesting season model
MinE
B
P
Pr(X = x ) =
E
Months
Figure 34. 1.
Graphic representation of Girondot 2010 of
model (Girondot 2010b).used
Fig.
Graphical
representation
the
variables
to describe a nesting season. MinB: mean nightly nest number be fore the beginning of the nesting season;
39 MinE: mean nightly
nest number after the end of the nesting season; Max: mean
number of nests at the peak of the nesting season; P : day of
the year on which the nesting season peaks; F: the number of
days around Day P on which the curve of the graph flattens
(
Γ(k + x ) m
x ! Γ(k ) m + k
)(
x
The negative binomial dist
cations as a model for count d
exhibiting overdispersion (i.e.
ceeding the mean) (Lloyd-Sm
literature, classical uses of the
bution include analysis of para
rence, parasitoid attacks, abun
The model requires at most all seven parameters, but several constraints can be set up to simplify it: -­‐ MinB = MinE, for the same number of nests out of the nesting season; -­‐ MinB e/o MinE = 0, when they are ≈ 0 (e.g. 10-­‐9) when no nests are observed out of the nesting season. -­‐ P – B = E – P, when the nesting season is symmetric around P with L being the length of nesting season then L = E–B thus B = P – L/2 and E = P + L/2; -­‐ F = 0, for no flat portion. The simplest models uses only three parameters: MinB = MinE = 0, P, L with B = P – L/2 and E = P + L/2, Max, F = 0. Season parameters estimation In the Girondot (2010b) model, parameters estimation is obtained through a Maximum Likelihood method. Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, Maximum Likelihood Estimation provides estimates for the model's parameters by maximization of a likelihood function. In statistics, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model given the data. The likelihood of a set of parameter values given some observed outcomes is equal to the probability of those observed outcomes given those parameter values. Given a set of numerical observations and a set of theoretical values obtained through the model, the likelihood function quantifies the likelihood of the model given the data. To calculate the likelihood function, a statistical distribution has to be assumed. For nesting counts data, the statistical distribution chosen is a negative binomial distribution. Negative binomial distribution can be described as a mixture of Poissonian distributions: it is a discrete probability distribution of the number of successes in a sequence of Bernoulli trials -­‐ experiments whose outcome is random and can be of two possible types, "success" and "failure" -­‐ before a fixed number of failure occurs (Cameron and Trivedi 1998). In our case, this kind of distribution is to be preferred for many reasons. Firstly, it represents a discrete, non-­‐negative, heteroskedastic distribution. The heteroskedasticity is particularly important in this case, since the confidence interval is quite variable and has to be taken into account. Secondly, negative binomial allows data to follow an asymmetrical pattern. The negative binomial distribution has broad applications as a model for count data, particularly for data exhibiting overdispersion (e.g. with sample variance exceeding the mean) (Lloyd-­‐Smith 2007). In the biological literature, classical uses of the negative binomial distribution include analysis of parasite loads, species occurrence, parasitoid attacks, abundance samples, and spatial clustering of populations (White & Bennetts 1996). 40 The distribution of the counts X is commonly expressed in terms of the mean m and the dispersion parameter k, such that the probability of observing a non-­‐
negative integer x is (Anscombe 1949): Γ !+!
! !
! !!
!" ! = ! =
1+
, ! > 0, ! > 0 !! Γ ! ! + !
!
For many applications, as here, it is more convenient to work in terms of the natural logarithm of the likelihood function, called the log-­‐likelihood Ln L, than in terms of the likelihood function itself. Since the log function is monotonic increasing, the logarithm of a function achieves its maximum value at the same points as the function itself. Finding the maximum of a function often involves taking the derivative of a function and solving for the parameter being maximized, and this is often easier when the function being maximized is a log-­‐
likelihood rather than the original likelihood function. Hence, the Ln L is calculated for each observation, and the overall value is represented by the sum of all of them. The parameters optimisation consists of various computational attempts in search of the set of parameters, which maximize the maximum likelihood value. In this way, the method selects the set of values of the model parameters that releases the distribution giving the observed data the greatest probability: in other terms, the parameters that maximize the log-­‐likelihood function. In our case, the optimisation was carried out by MTTS software (see following section). In this procedure, two range of values are examined: • -­‐ Ln L, the negative log likelihood function: the lower is the value, the better is the fit; • AIC, the Akaike Information Content: as likelihood tends to increase when new parameters are added, we need another approach that takes into account the number of parameters. AIC is a value expressing the quality of the fit, taking into account both -­‐Ln L and number of parameters: !"# = −2 ∙ !" ! + 2 !"#$%& !" !"#"$%&%#' (Akaike 1974). As for likelihood, the lower the value, the better the fit of the model without overparametrisation. 41 Describing the software MTTS is a simple software tool used for the analysis of nesting counts data with the Girondot (2010b) model. This software takes the nesting counts data as input and through several iterations calculates the best fit between given data and the Girondot (2010b) model, representing the solution graphically. For each solution, it calculates all the seven parameters of the model with their standard error and confidence interval. It also calculates several other parameters, such as: Beginning of season (1st day of data counts), Beginning of nesting season (starting date of nesting season), Peak of nesting season (date of nesting peak), End of nesting season (date of the end of the nesting season), Total number of days during the nesting season, Total number of days patrolled during the nesting season, Total number of nests observed during the nesting season (total number of observed nests from Begin to End), Number of monitored nights, Number of observed nests (total number of observed nests, including the ones before Begin and after End), Total Figure 35. MTTS software interface. number of nests during the nesting season (estimated number of nests from Begin to End), Total number of nests (total estimated number of nests, including the ones before Begin and after End), -­‐Ln L, AIC. The software interface is shown in Figure 35. 42 Nesting counts analysis Building database We used a wide series of databases: each one, in Excel Format or PND Format (database format used for PND X software, another analysis tool using the Girondot et al. (2006) model), contained all available nest counts for a year. The former were transformed into txt files, through BBEdit or Text Edit, the latter were directly used. We obtained series of files, each containing nest counts of all the year, for every year, species and beach section. Files were named in this way: Ex.1 Dc_Luth_Yalimapo_Y1_2006_Nt.txt • Dc: Data of counts (to be distinguished from Data on marking) • Luth: name of the species; (Luth for Dermochelys coriacea; Oliv for Lepidochelys olivacea; Verte for Chelonia mydas). • Yalimapo: name of the nesting beach • Y1: name of beach section • Nt: type of data; (Nf: number of females counted; Nt: number of total turtle tracks, successful nesting occurred tracks + unsuccessful nesting attempts tracks, Dt: unsuccessful nesting attempts tracks). Data were organized into folders, per year and then per species, to obtain a complete database. !"#$%
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Table 1. Suriname beaches (NW-­‐SE direction). 43 !"#$%
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Nesting season fit The first step was to analyse all nest counts with the current Girondot (2010b) model, in order to obtain, for the whole period considered, an estimation of total number of nests and nesting season patterns, useful for further analysis. Analysis was carried out with MTTS software, Version 3.1.6 (224) -­‐ 3.1.9 (227). For Dermochelys coriacea, data were enough to carry out a single fit for each beach section. When lack of data did not permit a trustable fit, a “model” set of parameters was used. As Yalimapo 2002 nesting season was a nearly complete dataset (only 10 missing night counts), with an almost “ideal” distribution, it was adopted as a reference when single parameters were not easy to determine. They occur in the text as Y2GSp (Yalimapo 2002 Grande Saison parameters). The main analysis criterion was to minimize the AIC (Akaike 1974). Figure 36. Yalimapo 2002 nesting season fit. For Lepidochelys olivacea, as well as Chelonia mydas, dataset was not so complete as to allow a single beach section analysis. For that reason, series belonging to each year were analysed comparatively in order to gain information, obtaining a single series of parameters for the year considered. This type of analysis was carried out for Dermochelys coriacea data as well. All datasets were included, taking into account the ones with only 0 counts as well. Software was run several times (n= 1…7) for each series, to ensure that the software found the true minimum –Ln L. Indeed sometimes the fit stopped on local minimum. 45 After the analysis, for all species and years best solutions for each beach were selected, using these criteria aggregated in the AIC (Akaike 1974): 1. maximum likelihood; 2. minimum of parameters. Results were registered in a summarising document for further analysis. Datasets were reported in this order (see Table 1, Table2): • species: Dermochelys coriacea, Lepidochelys olivacea, Chelonia mydas. • years: from 1979 to 2006; • state: Suriname, French Guiana; • beaches and sections; • type of data: Nf, Nt, Dt. Some beaches were not analysed due to lack of data or periodic appearance and disappearance of some of them due to coastal dynamics. 46 Phenology analysis For each beach section, we obtained a profile with the visual graphic representation of the fit, and a prospect with information concerning the nesting season patterns. For Dermochelys coriacea, we obtained such information for all the sections examined, while for Lepidochelys olivacea and Chelonia mydas, as said before, we relied solely on comparative analysis per year. Among the parameters obtained, a phenology study was carried out in order to relate Begin, Peak and End nesting season parameters with climate factors. We attempted to build a General Linear Model (GLM), relating the day of Begin, Peak, End and Length (E-­‐B) of nesting season to two factors: sea surface temperature (sst) and North Atlantic Oscillation (NAO). Sea surface temperatures (sst) Weekly and monthly mean sea surface temperatures the closer to each Suriname and French Guiana beach were obtained from U.S. National Oceanic & Atmospheric Administration (NOAA) web archives. NOAA has been measuring sea surface temperatures from satellites since 1972. Currently, one of the principal sources of infrared data for SST measurement is the Advanced Very High Resolution Radiometer (AVHRR) carried on NOAA POES satellites, beginning in 1978. AVHRR is a broad band, four or five channel -­‐ depending on the model -­‐ scanner, sensing in the visible, near infrared, and thermal infrared portions of the electromagnetic spectrum. The POES satellite system offers the advantage of daily global coverage by using near-­‐polar orbits roughly 14 times daily. In situ SSTs from buoys (drifting and moored) are used operationally to maintain accuracy of satellite SST by removing biases and compiling statistics with time (McClain et al., 1985; Strong, 1991; Montgomery and Strong, 1995; Strong et al., 2000, Reynolds et al, 2002). Spatial temperature range Spatial range of temperatures was chosen in relation to sea turtle species’ specific spatial pattern. The Suriname and French Guiana continental shelf is divided, according to NOAA satellites spatial division, into squared areas corresponding to 1° of latitude and longitude each. After having determined longitude and latitude coordinates of all beaches (using Google Earth), and knowing the spatial migration routes of the three species around the local continental shelf during nesting season, we decided to consider the following areas (Figure 37 -­‐ from upper-­‐left to lower-­‐right): 1) Area 1 (6-­‐7° N, 55-­‐56° W): Matapica; 2) Area 2 (6-­‐7 °N, 54-­‐55° W): Galibi, Babunsanti, Samsambo; 3) Area 3 (6-­‐7°N, 53-­‐54° W): Yalimapo-­‐Awala, Pointe Isère, Farez, Organabo, Ikakumpapi, Irakoubo; 4) Area 4 (5-­‐6° N, 52-­‐53° W): Kourou, Cayenne. 47 emples de trajet de tortues luths dans l’Atlantique Ouest. Carte composite obtenue en combinant les
de Ferraroli et al. (2004) et celles citées à l’adresse http://www.cccturtle.org/sat-wwf-leatherback.htm.
th est présente sur les plages de Guyane française de mars à fin juillet (Girondot & Fretey,
qu’en beaucoup moins
grand nombre en décembre et janvier (Chevalier, Talvy, Lieutenant
Figure 37. Spatial continental shelf division. Source: Google Earth. 9). Ce petit pic de présence
d’animaux en décembre-janvier pourrait correspondre à des
rtant vers le Sud après
la
ponte
en Guyane
(Figure
et revenant
alors du5 Sud
For Dermochelys coriacea, all 3)the area between and (trajet
7° N vert
and 52 and 56 ° W ’Uruguay) (Girondot
et
al.,
2007).
Les
femelles
pondent
jusqu’à
14
fois
(moyenne
5
à
7 the species is was considered. Even if only poor data is available on that issue, nnées) (Rivalan, Pradel, Choquet et al., 2006, Briane, Rivalan & Girondot, 2007, Russo,
supposed to move all around the continental shelf in front of the Suriname and tre ces pontes, elles se dispersent autour de la plage de ponte jusqu’à 140 km des côtes
Guiana au
coast (Girondot al. 2007). The following figure (Figure 38) 2003) et elles restentFrench majoritairement
niveau
du plateauet continental
(Figure
4).
shows the route of turtles between two nesting attempts (Ferraroli 2003). Figure 38. Leatherback turtle spatial patterns between two nesting attempts (Ferraroli 2003). geographic and data counts 2003).
for single beaches were : Utilisation du littoralHence, guyanais since par les femelles
tortuescoordinates luths entre deux
pontes
(Ferraroli,
available, we relied on the temperatures corresponding to the single beach dant possible qu’elles passent par dessus le talus avant de revenir pondre comment cela a
areas. As visible Figure 39, the squares may match with routes en Afrique centralecoordinate (Fossette, Kelle,
Girondot
et al.,in 2008).
individual females make between two consecutive nesting attempts. e des informations disponibles, on peut établir que 5% des femelles passent le talus
lors de leur trajet interponte et qu’elles y restent en moyenne 24 heures sur les 10 jours du
ontes.
48 Figure 39. Overlay of satellite spatial division and leatherback internesting routes around Yalimapo-­‐Awala. For Lepidochelys olivacea and Chelonia mydas, we could rely only on annual estimation obtained analysing all beaches together. Hence, the mean of temperatures of areas 2 and 3 were calculated, as this area is supposed to correspond to the two species interesting spatial range (Girondot et al. 2007). Temporal temperature range As before the beginning of the nesting season turtles are believed to station on the continental shelf in front of the Guianese littoral for a period of around two months (James et al. 2005, James et al. 2007), we chose to consider a period corresponding to a month before the three main patterns of the nesting season: Begin, Peak and End. Time-­‐range of temperatures included the 1st, 2nd, 3rd and 4th weekly mean temperatures of the month before Begin, Peak and End of each turtle species’ nesting season. For testing Length of nesting season, mean monthly temperatures from February to July were also obtained. For Dermochelys coriacea, for testing Begin, Peak and End, all the temperatures (sst1, sst2, sst3, sst4) were considered in the model as single factors with their own coefficient. Interactions between consecutive temperatures (e.g. sst1.sst2, sst2.sst3, sst3.sst4) were also considered. For testing Length of nesting season, monthly mean February, March, April, June, July temperatures (sstF, sstM, sstA, sstJN, sstJL) were considered. Interactions between consecutive temperatures were not considered, due to scarcity of observations. For Lepidochelys olivacea and Chelonia mydas, due to scarcity of data, it was not possible to have such a number of factors. Hence, we chose not to test the duration of nesting season and to use a single temperature factor, Heat Accumulated (ha). 49 Since sea temperatures of local latitudes nearly never fall beyond 25 °C, then considering that a basal temperature, Heat Accumulated (ha) was calculated as ha = (sst1-­‐25) + (sst2-­‐25) + (sst3-­‐25) + (sst4-­‐25) = (sst1 + sst2 + sst3 + sst4) -­‐ 100 A second temperature-­‐dependent factor was considered for all species, temperature variability (sstvb). It consists of the standard deviation between sst1, sst2, sst3 and sst4, and its goal was to measure how much the nesting season parameters were affected by temperature variability. North Atlantic Oscillation (NAO) North Atlantic Oscillation indexes were obtained from the U.S. National Centre for Atmospheric Research (NCAR) website. We took into account the indexes relative to the year before the actual Begin, Peak and End parameters’ one. The North Atlantic oscillation (NAO) is a climatic phenomenon in the North Atlantic Ocean of fluctuations in the difference of atmospheric pressure at sea level between the Icelandic low and the Azores high. Through east-­‐west oscillation motions of the Icelandic low and the Azores high, it controls the strength and direction of westerly winds and storm tracks across the North Atlantic. It is part of the Arctic oscillation, and varies over time with no particular periodicity. As the leatherback is a long distance migrator, which spans from the equatorial and tropical latitudes up to the sub polar ones, while the olive ridley and green turtle area is restricted to subtropical and tropical latitudes, we chose to take into consideration NAO as a factor for just the first one. General Linear Model (GLM) implementation Parameters were estimated by optimization with a backward approach, assuming a normal distribution for the parameters describing phenology (Begin, Peak, End and Length). The software used was GLM Stat X. The year considered (year) was also added as a factor to test for year trend. The general linear models (GLM) for Dermochelys coriacea were the following: a sst1+ b sst2 + c sst3 + d sst4 + e sst1.sst2 + f sst2.sst3 + g sst3.sst4 + h sstvb + j NAO + k Year+ i = Begin, Peak, End a sstF + b sstM + c sstA + d sstJN + e sstJL + f sstvb + g NAO + h year + i = Length The general linear model (GLM) for Lepidochelys olivacea and Chelonia mydas was the following: a ha + b sstvb + c year + d = Begin, Peak, End A weight factor (W) was introduced to individual likelihood by the inverse of the square of standard error, named σ2, for each parameter calculated. The formula for weight was based on the observation that estimators obtained by maximum likelihood are normally distributed (Myung, 2005): 1
! = ! σ
50 Improving the model As we said before, the Girondot (2010b) model efficiently explains the general reproductive dynamics of sea turtle populations. Nevertheless, several different populations of marine turtles display intraseasonal periodic variations, not included in the model. Moreover, the assumption that the temporal nesting distribution of sea turtles is a simple sinusoid has several inherent statistical weaknesses (Girondot et al. 2006). The current version of the model does not give any idea about the possible micro-­‐dynamics that occur during the overall nesting season. Unlike Girondot et al. (2006) model, which provides two types of intraseasonal variations and introduces new parameters to the model, these micro-­‐dynamics still have to be found in the Girondot (2010b) model. Therefore, we tried to introduce periodic variations within the nesting season by incorporating other sinusoid functions and fitting the corresponding parameters against the daily nest counts, in order to find the parameter values which maximized the likelihood by an optimisation search with maximum likelihood criteria. Our goal was to obtain a better data fit as well as new parameters with direct biological meaning. We chose to carry out this optimization search first with Microsoft Excel and then to implement it in R Software, taking the current model as a starting point. The equation of the added sinusoidal equation was taken from Girondot et al. (2006) model and integrated in the current one: !
!! ! = ! ! +
!"# 2!
!!!
!+Δ
Φ
!+!∙! !
!
Where • Φ = period of the sinusoidal pattern; • Δ = phase shift; • α, β and τ govern the amplitude of variation: o if α ≠ 0, β = 0, (τ is set to 1): the amplitude is independent of the number of nests; o if α ≠ 0, β ≠ 0: the amplitude of the fluctuation has two components, one dependent and one independent of the number of nests; τ ≠ 1 renders a non-­‐linear dependent relationship between amplitude of the fluctuations and number of nests. The equation implies that sinusoidal signals are additive, and each sinusoid adds five parameters to the model. The pattern could be simplified setting α = 0, β = 0 or τ = 1, reducing the parameters. 51 Parameters optimization The optimisation was firstly carried out with Microsoft Excel, making use of the Solver function, and then with R Software, writing a script to calculate, as for nesting counts with MTTS software, the best fit for our data with the seven parameter Girondot (2010b) model. As a first step, observed nesting counts of Yalimapo 2002 nesting season dataset were fitted with the expected values calculated with the Girondot (2010b) seven parameters model. The nest distribution per night was a negative binomial distribution, for which both likelihood and AIC factor were calculated. As for nesting counts series with MTTS software, the seven parameters values giving out the best fit for given data were obtained. Secondly, sinusoidal equation parameters (Δ, Φ, α, β, τ) were added to the model, to obtain a first sinusoidal pattern. In this way, a starting set of twelve parameters was calculated. Then, we choose to test the changing of likelihood obtained by different Δ and Φ values, in order to understand if intra-­‐seasonal variations with direct biological meaning could be found. Setting a matrix of changing Δ and Φ values from 0 to 20 by 0.2, we elaborated an R script to calculate likelihood for each of these pairs of fixed values, optimizing the other parameter values at the same time. In this way, we obtained a likelihood map, which showed the changing likelihood in function of Δ and Φ. 52 Results Nesting counts analysis Fit carried out with MTTS Software on data counts of the three species gave following results. Dermochelys coriacea For leatherbacks, we managed to obtain a set of results for all beaches taken one per one. When a parameter had to be imposed due to lack of data, it was not reported. The following tables only show fitted parameters. Begin !"#$
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Table 4. Leatherback turtle ordinal dates of peak of nesting season, 1979-­‐2006. 75675675
54 End !"#$
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9668
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Table 5. Leatherback turtle ordinal dates of end of nesting season, 1979-­‐2006. !?567568
Length !"#$
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Table 6. Leatherback turtle length of nesting season, 1979-­‐2006. 55 -",.)'
!5!
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Lepidochelys olivacea For the olive ridley, as well as for the green turtle, we could only rely on analysis of all the beaches at the same time for each year. Begin !"#$
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Table 7. Olive ridley turtle ordinal dates of begin of nesting season, 2002-­‐2006. Peak !"#$
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!""!
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Table 8. Olive ridley turtle ordinal dates of peak of nesting season, 2002-­‐2006. End !"#$
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Table 9. Olive ridley turtle ordinal dates of end of nesting season, 2002-­‐2006. 56 Chelonia mydas Begin !"#$
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"+$"!$"&
%%$"%$"+
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%'$"%$")
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Table 10. Green turtle ordinal dates of begin of nesting season, 2002-­‐2006. Peak !"#$
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Table 11. Green turtle ordinal dates of peak of nesting season, 2002-­‐2006. End !"#$
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!""$
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Table 12. Green turtle ordinal dates of end of nesting season, 2002-­‐2006. 57 Nesting trends First of all, ordinal date of the year of Begin, Peak, End, and number of days of Length were plotted against the years of data collection in order to detect any possible temporal trend. For Begin, Peak, End, confidence intervals are shown as well. The results are represented below. Dermochelys coriacea The four parameters present quite a complex pattern, with no clear tendency detectable. Figure 40. Leatherback turtle ordinal dates of begin of nesting season, 1979-­‐2006. Figure 41. Leatherback turtle ordinal dates of peak of nesting season, 1979-­‐2006. 58 Figure 42. Leatherback turtle ordinal dates of end of nesting season, 1979-­‐2006. Figure 43. Leatherback turtle length of nesting season, 1979-­‐2006. Comparing Begin, Peak and End, no relation between the three parameters is directly detectable, neither a linear temporal tendency. Figure 44. Leatherback turtle ordinal dates of begin, peak and end of nesting season, 1979-­‐2006. 59 Lepidochelys olivacea Figure 45. Olive ridley turtle ordinal dates of begin, peak and end of nesting season, 2002-­‐2006. Chelonia mydas Figure 46. Green turtle ordinal dates of begin, peak and end of nesting season, 2002-­‐2006. For both the species, variability does not seem to be very wide. However, data were taken into account for further analysis. 60 Phenology analysis Previous data were used to obtain our GLM model through the use of GLM Stat X. Weekly mean temperatures obtained for beach locations and NAO indexes are shown below. As temperature data for the year 1979 were not available, nesting counts data relative to that year have not been taken into account from here on. Climate factors data (sst, NAO) Dermochelys coriacea Weekly mean temperatures Begin !"#$%&
''#&
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Table 13. Weekly mean February sea surface temperatures of French Guiana and Suriname beaches. End !"#$%&
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Table 14. Weekly men June sea surface temperatures of French Guiana and Suriname beaches. 61 Peak !"#$%&
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Table 15. Weekly mean April sea surface temperatures of French Guiana and Suriname beaches. Monthly mean temperatures !"#$%&$'())* +&$,-())*
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Table 16. Monthly mean February – July sea surface temperatures of French Guiana and Suriname beaches. 62 NAO indexes !"#$
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Table 18. Weekly mean March, May and July sea surface temperatures of French Guiana and Suriname beaches, 2002-­‐
2006. Chelonia mydas Weekly mean temperatures !"#$%
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63 GLM models Dermochelys coriacea The GLM results for leatherback showed quite complex results. Begin, Peak and End parameters present different relations with climate factors. Year, sst2, sst3, sst4 and sstvb have shown to have effects in different cases. NAO did not present any significant effect. Begin The selected model is “year + sst2 + sst3”: the pattern presents a marginally significant effect with sst2 (Prob>F = 0,0602), and a significant effect with the year considered (Prob>F = 0,0024) and with sst3 (Prob>F = 0,0219). In fact, a time trend seems to be detectable, as shown in Figure 47. The equation takes this form: Beginleatherback = 1,394 year + 24,64 sst2 -­‐ 37,07 sst3 – 2369 Figure 47. Leatherback turtle ordinal date of begin(start) of nesting season, with trendline, 1987-­‐ 2006. Similarly, a significant fit seems to occur between theoretical ordinal date of begin (calculated with previous equation) and observed data, as shown by the following graph (Figure 48). Figure 48. Fit between observed and theoretical ordinal date of begin of nesting season in leatherback turtle. Peak The selected model is “sst4”: the pattern presents a significant effect only with sst4 (Prob>F = 0,0167). Figure 49 represents the fit between theoretical and observed data for this parameter. The equation takes this form: Peakleatherback = -­‐ 20,35 sst4 + 705,5 Figure 49. Fit between observed and theoretical ordinal date of peak of nesting season in leatherback turtle. End The selected model is “sst3 + sst4 + sstvb”: the pattern presents a marginally significant effect with sstvb (Prob>F = 0,0789), and a significant effect with sst3 (Prob>F = 0,0066) and with sst4 (Prob>F = 0,0244). Fit between observed and theoretical date calculated by above formula is shown in Figure 50. The equation takes this form: Endleatherback = -­‐29,54 sst3 -­‐ 97,60 sst4 -­‐ 97,60 sstvb + 202,0 Figure 50. Fit between observed and theoretical ordinal date of end of nesting season in leatherback turtle. Length No model resulted from testing length of nesting season and climate factors, since any significant relation was found. 65 Lepidochelys olivacea Probably due to scarcity of data, climate factors generally showed no effects on the olive ridley turtle’s phenology. The only exception seems to be the End, where a significant effect for the year considered (Prob>F = 0,0048) and for sstvb (Prob>F = 0,0082) have been found. The match between theoretical and observed dates is shown in Figure 51. The equation takes the following form: Endolivridley = 26,60 year + 544 sstvb -­‐ 5,310e+4 Figure 51. Fit between observed and theoretical ordinal date of end of nesting season in olive ridley turtle. Chelonia mydas No significant effect between phenology parameters and climate factors was obtained for the green turtle. 66 terns, but the discontinuities in the daily nest count datahulst equation [33]
allowsthe beginning
the tools
end of nesting
season show
similar
set that
makes
use of and
these
inappropriate
[35].
that the first-order derivative of
shapes, this can be expressed by setting S1 = -S2 and K1 =
Instead,
a
sinusoid
wasform
incorporated
most reduced
of equation 2into
uses the
only 4
the Richards equation [34]:
K2. Thefunction
The fitted
test for an
parameters
(min = 0, K1 = Kparameters
equation (2) and
the corresponding
2 = 0, S1 = -S2). are
= 0.5 for
asymmetrical
shapeWhen
of the nesting
season
was performed
−1 / e K
against
the
daily
nest
counts.
several
periodic
sigstart
  of
Improving the model S2 = -S1 and K1 = K2.
by forcing
P − d) 
( 1 ) detected, the sum of/sinusoid equations was
 
nals
were
depends
After testing the R script with Yalimapo 2002 dataset, we introduced the new Φ 
and Δ parameters. In order to understand the periodicity and the module of the Equation (2) describes the global shape of the nesting seaused:
negative
potential variation, son.
a list Aof more
values complete
of Φ and Δ from 0,2 to 20 (by incorporate
0,2) was model
could
also
periP is related to the dates
before
creasing
generated, and the overall set of parameters were optimized in relation to these odic variation within the nesting season. Traditionally,
day when there is an
observed
two fixed ones. The result of this search is the likelihood map shown below. A l
season).


d9 +to ∆10 spectral
hasperiod, beenconcurring used τto describe
periodic pat in a analysis
(increase or decrease)
in
nest
k day with the period k
2
π
N ′(variation d) = N(dseems ) + ∑to occur sin
( 3data)
d)the) daily nest count
 (Girondot l. ( 2α006). k + β k ⋅ N(in
but the
discontinuities
elated
to the changefound in slope
of in terns,
by Girondot 2006 e
t a
or
negaφ
k =1 set makes kthe use of these tools inappropriate [35].
s at date P.
simple
Instead, a sinusoid function was incorporated into the
n
K
=
0.
Parameter
φ
defines
the(2)period
the sinusoidal
pattern
equation
and the of
corresponding
parameters
are fitted
rom 0 to 1 with M(d) = 0.5 for
against
theThe
dailyamplitude
nest counts. of
When
several periodic
mber of days since
andthe
∆ isstart
theofphase
shift.
variation
is gov-signals were detected, the sum of/sinusoid equations was
epness of M(d) at P = d depends
was
simerned by α, β and
τ parameters. If the amplitude is indeused:
s increasing when S is negative
of the number of nests, then α ≠ 0 and β = 0 (the
esting season) pendent
and decreasing
l 
t the end of nesting
season).
 β ≠ 0,
d + ∆1).
 to
parameter
τ is not
used and
set
k  If α ≠ 0 τand
N ′(d) = N(d) + ∑  sin  2π
( 3)
α k + β k ⋅ N(d) ) 
(

termined by a positive or negaφk 



k
=
1
then the amplitude(s) of the fluctuations have two comion (1) is reduced to a simple
and the
oneperiod
independent
of the
metrical aroundponents,
P) when K =one
0. dependent
Parameter φ defines
of the sinusoidal
pattern
when
Figure 52. Likelihood map with varying and Δ phase
values. The shift.
red colour represents the minimum a
and the yellow the and
∆of isΦ parameter
the
The
amplitude
ofnon-linear
variation
is govnumber
of nests.
The
τ≠
l renders
maximum likelihood values. flow, the equation (1)
was simerned by α, β and τ parameters. If the amplitude is inde dependent
relationship
between amplitude of the fluctupendent of the number of nests, then α ≠ 0 and β = 0 (the
After that, the script was used to fit Yalimapo 2002 dataset with this new set of ations
number
Equation
(3)
implies
that
twelve and
parameters: Bparameter
, P, E, Mof
inB, τnests.
Mis
inE, F, Mused
ax, Δ, and
Φ, α, set
β, τ. to
The resulting not
1).
If α ≠curve 0 and
β ≠ 0,
> 3 and
is s
hown b
elow (
Figure 5
3). amplitude(s)
of be
the changed
fluctuationsto
have
two comsinusoid signalsthen
arethe
additive.
It can
allow
a

− d)  
 
ponents, one dependent and one independent of the
multiplicative
but this kind of model performs
less

when effect
number of nests. The parameter τ ≠ l renders
a non-linear
well than the additive
andbetween
will not
be discussed
dependentmodel
relationship
amplitude
of the fluctu d−P 
1  Se K  further (data not
ations
and number
of nests.defined
Equation with
(3) implies
shown).
The model
equa-that
son
o ecan
when K > 3 and
signals are additive. It can be changed to allow a
2
tion (3) uses 5l sinusoid
parameters
more than the model defined
multiplicative effect but this kind of model performs less
setting
with equation (2).
(3) can
be and
simplified
well Equation
than the additive
model
will not be
discussed
further (data
The model defined
α = season
0 or βcan
= 0 and/or
τ = l not
(3 shown).
or 4 parameters).
with equaption
(2) of a nesting
slope of
100
80
!
!
!
!
!
!
!
!
!
60
!
!
!
!
!
!
!
!!
!
!!
!!
!
!
!
!
!
40
Nesting counts (number of nests) k
!
!
!
!
!
!
!
!
!
!
! !
!
!!
!
!
!
!
!
!
!
!
!
!
tion (3) uses 5l parameters more than the model
defined
with equation (2). Equation (3) can be simplified setting
fitted
data
by maximum
α = 0 orto
β =experimental
0 and/or τ = l (3 or
4 parameters).
!
!
!
20
!
!
!
!
!
!
! !
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
The curve
halves
(2) was
· (M1 (d)of· M2 (d))
likelihood method using a simplex search [36] followed
between
Ordinal date (days since 01/01) The
curve
was
fitted
to
experimental
data
by maximum
ng the first and second
halves of search [37]. For this purpose, we assumed
by
a
gradient
Figure 53. Graphic curve of Yalimapo 2002 nesting counts fit with twelve parameters model. The green colour represents is
ter:
S
method
using
a simplex
the theoretical values, the blue likelihood
the standard error associated, the red ones the confidence interval. search [36] followed
tively.1The difference
between
the nest distribution
that
for
day
d
is
normally
distributed
minofisthe S parameter: S1 is by a gradient search [37]. For this purpose,
we assumed
er sign
that
the nest distribution
for day d cis+normally
distributed
b) where
a, b
with a standard
deviation
σa = Exp(a.N'(d)
d
P1 <P
min is
son
and
2. The parameter
67 c
with a standard deviation σa = Exp(a.N'(d) + b) where a, b
outside the nesting
and season
c are and
parameters
that are also fitted. This function has
he
maxiand c are parameters that are also fitted. This function has
Note that max is not the maxiadvantage
being
strictly
positive
monotonically
can(Mbe(d) · the
advantage
of being
strictlyand
positive
and monotonically
M (d))
can be ofthe
ause
!
!
!
!
0
!
!
!
!
!
! !
! !
!
!
!
!!
!!
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!
!
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!!!
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!!
0
1
2
100
!
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!!
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200
300
400
68 Discussion Turtle nesting phenology at global warming It is beyond doubt that thermal environmental conditions play an important role in marine turtle biology, ecology and reproduction. Not only climatic conditions of feeding grounds determine the availability of suitable prey during growth and adulthood of individuals, and hence distribution of populations throughout their area (Rahmstorf 1997, Stocker and Schmittner 1997, Hamann et al. 2007), but also reproductive frequency and output (Solow et al. 2002, Saba et al. 2007, Chaloupka et al. 2008, Van Houtan and Halley 2011). As oviparous reptiles with thermal sex determination (Mrosovsky 1994), sand temperatures of nesting beaches have a key-­‐role in setting offspring sex ratio (Glen and Mrosovsky 2004), as well as being an important factor of hatchling success (Miller 1985b, Broderick et al. 2001a, Godley et al. 2001c, Hamann et al. 2007, Hawkes et al. 2007). They affect, therefore, genetic variability and demographic dynamics of future populations. Sea temperature of nesting grounds, in addition, has been shown to influence several aspects of reproductive phenology, such as interesting interval, onset, peak and duration of nesting season, (Weishampel et al. 2004, Pike et al. 2006, Hawkes et al. 2007), probably due to its influence on yolk and egg maturation (Weber et al. 2011). This seems particularly important in a context of global warming, where turtle changes in timing of seasonal activity are likely to influence the spatial-­‐temporal distribution of a number of related species, such as turtle prey, hatchlings seawater predators, as well as marine birds. In our study, a relationship between sea surface temperature of nesting ground areas and turtle reproductive patterns was searched for. Local weekly and monthly mean temperatures of areas in front of nesting beaches, corresponding to sea turtle routes during reproductive periods (Fossette et al. 2009), were considered. For testing begin, peak and end of nesting season, the 1st, 2nd, 3rd, 4th week mean temperature of the month before occurrence of the event was taken into account, while for testing length of nesting season, monthly mean temperatures from February to July were considered. In out case, the use of weekly mean temperatures rather than monthly mean ones seemed to be preferred, as sea surface temperatures of this area are known to have little variation, far lower than the higher one experienced, for example, by loggerhead turtles in the Mediterranean (Mazaris et al. 2008). For the long distance migrating leatherback, North Atlantic Oscillation (NAO), determining climatic conditions of their North Atlantic foraging areas, with wide ecological impact (Hurrell and Van Loon 1997, Ottersen et al. 2001) was also considered, in order to test possible influence of climatic conditions of foraging grounds as well. 69 Dermochelys coriacea Currently, no study on reproductive phenology of the leatherback turtle Dermochelys coriacea in relation to sea temperature exists. Nevertheless, our results seem to be rather coherent with the ones obtained in similar studies on the widely studied loggerhead turtle, Caretta caretta, attesting a strong relation between sea temperatures and nesting trends. For the start of nesting season, a significant positive relation was found between nesting start date and year considered, and a significant negative one with sst3, the sea surface temperature of the 3rd week of February, the month preceding nesting. A marginally significant positive relationship occurs with sst2, the 2nd week sea surface temperature. A kind of homogeneity among different beaches of the same year seems hardly surprising, given the idea, already consistent (Girondot and Fretey 1996, Rivalan 2006, Kelle et al. 2007b), that a single population of nesting leatherbacks moves throughout the whole rookery, choosing from their different beaches and coping with the periodic disappearance of some beaches and appearance of other ones. Leatherback turtle is known to present a briefer interesting interval compared to other species’ ones (Webster 2001, Hays et al. 2002), probably due to its considerably major body mass, a factor that favours health conservation – due to a lower surface-­‐body mass ratio -­‐ accelerating egg maturation (Sato et al. 1998). Its internesting interval, spanning from about 9 to 10 days, was firstly determined for the turtle population of Suriname and French Guiana (Fretey and Girondot 1996). Given such a brief egg maturation period, it is not surprising that the higher environmental thermal influence on the onset of the reproductive season occurs towards the end of the preceding month. The negative correlation between sst3 and date of begin, even if offset by a weak positive relation with sst2, seems to indicate that the two variables are inversely related: the warmer third week of preceding months, the earlier the onset of nesting season. A similar relation was found for loggerhead turtles nesting in Bald Head Island, North Carolina (USA), where a significant warming in sea surface temperatures over a 26-­‐year period (1980-­‐2005) correlates with an earlier onset of the nesting season and with a significant relation between the first egg laid and April and May monthly mean temperatures, May being the month preceding median date of nesting season in loggerhead (Weishampel et al. 2004). Similar results were obtained by Mazaris (2008) in the loggerhead Mediterranean rookery of Zakynthos (Greece), where a significant relation was found between first turtle emergence and April and May monthly mean sst, and between first nest laid and April monthly sst. The use of monthly sea surface temperature, in both cases, seems reasonable, due to the higher temperature variability of these nesting sites compared to the ones of our study. It has to be considered, however, that assuming date of first and last egg laid as indicators of begin and end of nesting season it is not reliable, due to the frequent occurrence of isolated disorientated females. In this way, few individuals, nesting out of the current period, could bias the overall statistic. For peak of nesting season, only sst4, the mean temperature of the 4th week of April (the month preceding the peak of nesting), was significant, with sst4 having 70 a negative correlation to ordinal date of peak. Here, the same inverse relation seems to occur: a warmer temperature of 4th week of preceding month anticipates the occurrence of peak of nesting season, as well as for the onset. These results are partially concurring with the ones obtained for loggerhead turtle on the eastern coast of Florida, USA (Weishampel et al. 2004, Pike et al. 2006), where a slight upward trend in sst over a 15-­‐year study period (1989-­‐
2003) was reported, together with a significant negative relation between earlier median nesting date and May monthly mean sst. Here, equally, the use of median date of nesting as indicator of peak of nesting season is questionable, as it could cause a considerable bias. The end of nesting season, however, shows a more complex dynamic. The end ordinal date of the nesting season is determined by a significant negative relation with sst3, the 3rd weekly mean sst of the month of June, and with sst4, the 4th one. A marginally significant negative relation with sstvb, temperature variability, is also present. Here, we are in front of an addictive effect: the effect of the 3rd weekly mean temperature of the month adds with the one of the 4th, and a weak influence of temperature variability contributes as well, in the same direction. The overall effect, as for onset and peak, is an inverse one: warmer temperatures cause the nesting season to end earlier. Other studies previously considered, on the contrary, found no significant relation between end of season and sea temperatures. In those cases, however, the ending date was counted as the day of the last nest laid, which does not represent, as said before, a reliable estimation. Length of nesting period, in our study, does not show any significant relation to sea temperatures. That is in contrast with the results of previous studies. In Mazaris (2008) the duration between first and last nest laid significantly increased with mean May sst temperature of breeding grounds, and in Hawkes (2007) longer nesting seasons were similarly significantly associated with years with warmer mean sst of April and May and earlier onset of nesting season. On the contrary, Pike (2006) showed how in his study length of nesting season significantly decreased with increasingly May sst and median nesting date occurring earlier. It is difficult to draw a conclusion from such a contradictory prospect. As sea turtles are capital breeders (Bonnet et al. 1998), it seems improbable that nesting season could become indefinitely longer towards the cooler part of the season, since energy reserves limit the number of clutches laid per nesting season (Congdon 1989). However, it is true that warming conditions might have an effect on ecological conditions of breeding grounds, perhaps leading to more abundance of food supply at nesting sites, and hence prolonging the duration of the nesting period. This hypothesis, however, has no evidence up to now, and would require further investigation. It is true that the different methodology used for determining the nesting period in our study (interval between statistically determined end and begin of nesting season), is rather different than the one used in previous studies (interval between last and first nest laid), and this methodological difference might itself explain the difference in results obtained. 71 In all four cases, no significant relation was found for North Atlantic Oscillation (NAO) indexes. This seems to contrast with results showing, for loggerhead turtle reproduction, a strong pattern of dependence of nesting abundance and frequency from the Atlantic Multidecadal Oscillation (AMO) in the Atlantic (Van Houtan and Halley 2011), as well as from El Niño Southern Oscillation (ENSO) (Saba et al. 2007) and Pacific Decadal Oscillation (PDO) in the Pacific (Van Houtan and Halley 2011). A similar relation with North Atlantic Oscillation was likely to be found too, since NAO is known to cause environmental variations that impact primary production (Fromentin and Planque 1996, Planque and Taylor 1998) that filters up trophic levels and affects breeding performances and survival rate in marine top predators (Kitaysky and Golubova 2000, Thompson and Ollason 2001). It is highly probable that NAO, as PDO, ENSO and AMO, does have an influence on food availability, being important for reaching reproductive energy threshold thus determining individuals’ remigration intervals and reproductive output. At our spatial-­‐temporal scale, however, given the actual timing of migration of the leatherback to nesting grounds, estimated at about two months before beginning of nesting season (James et al. 2005), it is not likely that such climatic factors, having their effects on an annual scale and mainly on foraging grounds, may contribute to such a limited timing variability, rather due to earlier onset of vitellogenesis and faster egg maturation times. It is rather probable that NAO impacts nesting patterns in terms of “0 or 1” signal, determining whether a turtle leaves for nesting grounds or not in a definite year, thus influencing the internesting intervals between two reproductive seasons. It could be of interest, however, to analyse this climatic pattern together with thermal conditions of nesting grounds, as undertaken by Van Houtan and Halley (2011). It remains questionable, however, how easily this task could be carried out for the leatherback population nesting in Suriname and French Guiana, having an extremely wide foraging area throughout the overall North Atlantic with highly variable migratory patterns (Girondot 2012, in press). Actually, Mazaris et al (2009a) have already tested the influence of sea surface temperatures of foraging grounds on the onset of nesting season of loggerhead turtles in the Mediterranean rookery of Zakynthos (Greece). In this study, a significant relation was found between the annual sst of three out of five population foraging grounds (located by satellite telemetry) and the date of first adult emergence. However, these results have to be taken with caution. We have already said why first adult emergence and first nest laid both represent unsuitable estimators for the effective start of nesting season. Secondly, the areas considered are only a small part of the potential ones, and the annual time span seems to be quite general for testing this kind of relation. Finally, a correlation already exists between the temperatures of these areas and those of the breeding ones, and the role of the first is much less important in comparison to the second. Therefore, further investigation is needed on this issue. It is clear, anyway, that leatherback population dynamics need specific investigation, as phenology of such different species as the leatherback and the loggerhead are only partially comparable. 72 Lepidochelys olivacea Nesting phenology of the olive ridley turtle Lepidochelys olivacea, on the contrary, showed an even more controversial pattern. No significant relation was found between weekly temperatures, expressed as heat accumulated, and begin and peak of nesting season. On the contrary, a significant relation occurs between ordinal date of end and both temperature variability and year considered, with a direct correlation for both factors. It is possible that annual temperature dynamics, including sst variability, constitute a cause of the protraction of the nesting period. As warmer temperatures normally accelerate egg and yolk maturation, rather than prolonging it, this delay could be due to different food availability, as well as weather conditions. However, there is reason to believe that determination of olive ridley nesting timing is only partially correlated with temperature. We have already said that sea turtles regulate their temperature behaviourally, diving in deepest or shallower waters, or basking at the surface to warm. For olive ridley turtle it is easier to avoid extra warming since it absorbs less solar radiation due to its lighter coloured carapace. In Hamel et al, (2008b), satellite relayed data loggers (SRDLs) were attached to the carapace of four solitary females nesting in the Wessel Islands of Arhem Land in the Northern Territory of Australia. The loggers, among other measures, recorded nesting events and temperature depth profiles of individuals. Data did not reveal any relation between sea temperatures experienced and internesting intervals, with longer periods compared to other species (Sato et al. 1998, Hays et al. 2002 ). Authors suggest that nesting behaviour of the olive ridley could be controlled more by external factors (time and weather) rather than by internal factors (physiology). Actually, even if the study analyses solitary individuals, the olive ridley is the only species, among the three, which is notoriously capable of retaining eggs in the oviduct, an adaptation which enables delayed oviposition when environmental conditions are unsuitable (Plotkin et al. 1995), allowing most populations of the Atlantic, Pacific and Indian Oceans to nest in mass during arribadas on a few selected beaches. As mass nesting olive ridley females are able to lengthen or delay their nesting haul-­‐outs to achieve simultaneous oviposition, solitary nesters like Australian and French Guiana ones could be able to use this ability to delay nesting until optimal conditions prevail, maintaining their metabolic rate independent from water temperature. This slow metabolic rate could both permit an optimal choice of nesting day as well as extended dive duration and depths to exploit a wider range of marine food sources, and hence maximize energy reserves before nesting effort (McMahon and Hays 2006). It is true, however, that as our dataset can count on few observations, and more detailed investigation would be needed to fully understand this peculiar species’ reproductive pattern. 73 Chelonia mydas For green turtle Chelonia mydas, no significant relation with sea surface temperature was found. Actually, only one nesting phenology study on the green turtle exists. Pike (2009) investigated the nesting pattern of the green turtle population of Canaveral National Seashore, Florida (USA), in relation to local sea surface temperatures. Unlike the sympatric loggerhead turtle population, the green turtle nesting season was shown to be unrelated to environmental temperatures at the nesting beach. This pattern contrasts with studies on single diving turtles with micro data loggers (Sato et al. 1998, Hays et al. 2002), which indicate a significant negative correlation between sea temperatures experienced by green turtle females during internesting dives and durations of internesting intervals. It is probable that the slenderness of our dataset did not permit the detection of any significant relation. The contrasting results of other studies, however, suggest that more investigation is needed to deal with this contrasting issue. 74 Intraseasonal nesting periodicities Estimation of nest numbers during the reproductive season represents an indispensable tool for assessing population trends in sea turtle populations. Indeed, direct counting of individuals is extremely difficult due to their wide trans oceanic dispersal during migratory periods, cryptic life history stages and the variable pattern of annual non-­‐consecutive reproduction (Girondot et al. 2006). Through nesting counts, it is possible to investigate population dynamics, evaluate effects of recovery and conservation programs and establish new strategies. Monitoring all beaches of a nesting rookery on a daily basis is, however, not always possible. Choice of new nesting sites or remote unmonitored beaches by nesting females, as well as coastal dynamics which modify the seashore, could result in spatial-­‐temporal gaps in monitoring effort. Similarly, occurrence of extreme weather events or lack of monitoring is a cause of temporal gaps. To cope with these problems, as well as to reduce the amount of work required for monitoring effort, several statistical methods have developed in years to obtain reliable estimates of total number of nests during an entire season from partial monitoring data (Girondot 2010b). The model adopted in this study, developed by Girondot (2010b), uses a parameterized sinusoidal equation to describe nesting season and provide a good estimation of population size with few nesting counts, as well as giving parameters of direct biological relevance. Nevertheless, this method does not provide description of some intraseasonal periodic variations, effectively described by Girondot (2006) in a previous work, which produce fluctuations in the total distribution of nests, with local maximums in females arrivals. These variations consist of sinusoidal patterns with two different periods. One seems to occur on a ~ 10 day basis, a period that constitutes a physiological interval between consecutive nesting events (Girondot and Fretey 1996). The other appears every ~ 15 days, in apparent synchrony with the lunar phase and tide level. In this work, we tried to introduce this intraseasonal variation equation with its set of parameters in the current model, in order to test if the same pattern(s) would have been found. Actually, the first step of this process brought encouraging results. The periodicity of the first pattern found is consistent with the first variation already obtained in the Girondot (2006) model, where a local peak of nesting occurred between 9 and 10 days, which corresponds to the physiological internesting interval of leatherback turtles of French Guiana (Girondot and Fretey 1996). Neverteless, further investigation is suggested in order to verify the presence of other possible intraseasonal dynamics, in order to develop a new and reliable model to optimally describe leatherback turtle reproductive patterns. 75 76 Summary (italiano) Introduzione Le tartarughe marine (superfamiglia Chelonioidea) costituiscono un gruppo di vertebrati molto rilevante nell’ambito dell’ecologia e della biologia della conservazione. Si tratta di un gruppo monofiletico, originatosi dal Triassico superiore e attualmente rappresentato da sette specie: la tartaruga liuto (Dermochelys coriacea), unico costituente della famiglia Dermochelyidae; la tartaruga comune (Caretta caretta), la tartaruga verde o tartaruga franca (Chelonia mydas), la tartaruga embricata (Eretmochelys imbricata), la tartaruga a dorso piatto (Natator depressus), la tartaruga di Kemp o tartaruga bastarda (Lepidochelys kempii) e la tartaruga bastarda olivacea (Lepidochelys olivacea), che insieme costituiscono la famiglia Cheloniidae. Questi animali presentano un ciclo di vita peculiare, che si svolge a cavallo fra la terraferma e il mare aperto. I piccoli nascono da nidi scavati dalle femmine nella sabbia; dopo la schiusa attraversano la spiaggia e prendono la via del mare aperto, per raggiungere dopo alcune settimane di nuoto passivo le aree di foraggiamento, dove stanziano fino alla maturità sessuale. Al sopraggiungere della stagione riproduttiva, maschi e femmine migrano verso le zone di riproduzione per l’accoppiamento e la deposizione delle uova, al termine della quale tornano verso le aree di foraggiamento. La varietà di ambienti che costituiscono il loro habitat e il fatto che svolgano frequenti ed estese migrazioni, però, le espone a numerosi rischi, per lo più di origine antropica. Alla schiusa, alla predazione data da mammiferi, uccelli marini e pesci si è aggiunta quella da parte dei cani randagi, oltre al prelievo di uova da parte delle popolazioni locali. Anche gli adulti sono oggetto di cattura sulle spiagge così come di pesca da parte dell’uomo, poiché in molte regioni il consumo di carne è ancora legale e diffuso. La forma di minaccia maggiore per il gruppo è forse però il by-­‐catch nelle reti da pesca, in particolare quelle da posta, che intrappolano le tartarughe impedendo loro di emergere per respirare. Nonostante la messa a punto di alcuni sistemi, come i Turtle Excluder Devices (TEDs), la pesca costituisce ancora la maggiore causa di morte per le popolazioni di tutto il mondo. Anche gli sversamenti di petrolio, così come l’inquinamento da PCB, metalli pesanti, e l’ingestione di rifiuti, (molto comune è l’ingestione di buste di plastica confuse con le meduse, prede comuni), sono alla base del declino di diverse popolazioni. Alcune patologie, come i tumori della pelle causati dal fibropapilloma virus, costituiscono un’ulteriore minaccia. Non bisogna dimenticare, infine, come il cambiamento climatico, e in particolare il riscaldamento della temperatura media degli oceani, è all’origine di alcune sfide ecologiche non indifferenti. Non solo il riscaldamento delle acque è alla base di cambiamenti nel regime delle correnti oceaniche che possono ripercuotersi nel cambiamento delle rotte per gli stadi giovanili e nella modificazione delle condizioni trofiche delle aree di foraggiamento. L’innalzamento del livello del mare è in grado di compromettere anche l’accessibilità di molte spiagge, così come lo sono le conseguenti fortificazioni spesso costruite dall’uomo a difesa dei litorali. La maggiore insolazione sulla 77 terraferma, insieme a pratiche come il deposito artificiale di sabbia per contrastare l’erosione, potrebbe essere all’origine di profonde modificazioni delle condizioni termiche e fisico-­‐chimiche della sabbia, con possibili ripercussioni sulle condizioni d’incubazione, e determinare quindi un differente successo riproduttivo, così come modificazioni nella sex-­‐ratio della progenie. In ragione del declino subito da numerose popolazioni, le tartarughe marine sono classificate dalla IUCN nella lista delle specie a rischio, secondo diversi gradi: D. coriacea e E. imbricata nella categoria Critically Endangered, C. caretta e C. mydas nella categoria Endangered, e L. olivacea nella categoria Vulnerable (IUCN 2011). Per L. kempii non sono attualmente disponibili sufficienti dati per ottenere una classificazione. Questo ha reso necessaria l’adozione di misure di recupero e la messa a punto di strategie di conservazione in tutte le aree di distribuzione delle specie, così come l’organizzazione di sistemi di monitoraggio a lungo termine. L’estensione del loro habitat e le lunghe migrazioni, però, rendono difficoltoso il monitoraggio diretto di queste popolazioni. Per questa ragione, le indagini di dinamica di popolazione si affidano al conteggio dei nidi deposti durante la stagione riproduttiva quale indicatore del numero di femmine riproduttivamente attive, indice della dimensione delle popolazioni. Individuare un nido è un compito semplice: le femmine di ogni specie, infatti, lasciano sulla spiaggia delle tracce caratteristiche. Il conteggio dei nidi pone però alcune difficoltà. Innanzitutto, non tutte le tracce lasciate dalle femmine indicano la presenza di un nido: se disturbata prima della deposizione, una femmina può abbandonare la spiaggia senza completare il nido. Un altro problema riguarda la possibile presenza di gaps spaziali e temporali nei dati. Nell’arco della stagione riproduttiva, che copre un arco temporale di numerosi mesi, le femmine possono cambiare sito di nidificazione, scegliendo spiagge remote e non monitorate. Questo riguarda in particolare la colonia del Suriname e della Guyana Francese, dove il continuo afflusso di detriti fluviali dal vicino fiume Maroni è all’origine di una dinamica costiera piuttosto variabile, con la comparsa di alcune spiagge e la scomparsa di altre nell’arco di pochi anni. Similmente, se lo sforzo di monitoraggio non è continuo nel tempo, possono prodursi dei gaps temporali. Per ovviare a questo problema, e per ridurre l’entità del lavoro di monitoraggio, molti autori hanno proposto l’adozione di strategie per ottenere stime della dimensione di popolazione con un elevato livello di affidabilità, riducendo al contempo il lavoro sul campo. Dei molti modelli proposti, però, la maggior parte soffre di debolezze metodologiche e statistiche. In questo studio si è scelto di utilizzare il metodo pubblicato da Girondot (2010b), che utilizza un’equazione sinusoidale per descrivere la distribuzione nei nidi durante la stagione riproduttiva e, allo stesso tempo, ricavare una stima di alcuni parametri, come il suo inizio (Begin), il momento di massima deposizione (Peak) e la fine della stagione riproduttiva (End). Anche da conteggi parziali, inoltre, esso è in grado di fornire una stima affidabile del numero complessivo di nidi deposti. Anche questo modello, però, presenta dei margini di perfezionamento. Il precedente modello elaborato da Girondot (2006) prevedeva la presenza di alcune modulazioni periodiche all’interno della macro dinamica principale. Tali 78 variazioni corrispondevano a due tipologie di perturbazioni: in primo luogo, la presenza di massimi locali ogni circa 9-­‐10 giorni, periodo corrispondente all’intervallo fisiologico fra due nidificazioni successive per D. coriacea (Girondot and Fretey 1996); in secondo luogo, una variazione corrispondente ad un periodo di 14 giorni, ovvero l’intervallo fra due massimi di marea consecutivi, determinati dal ciclo lunare. In questo lavoro di tesi, il modello di Girondot (2010b) è stato adottato per due diverse finalità. Da una parte, il modello di base è stato utilizzato per analizzare i dati sulla nidificazione nel periodo 1979-­‐2006 per la colonia del Suriname e della Guyana Francese, una delle maggiori colonie in Atlantico, allo scopo di ricavare la fenologia riproduttiva (data d’inizio, apice e termine della stagione riproduttiva) delle tre specie ivi maggiormente presenti: D. coriacea, L. olivacea e C. mydas. La fenologia è stata poi correlata a due variabili climatiche: la temperatura superficiale dell’acqua per il tratto di oceano antistante la colonia, e l’indice annuale dell’Oscillazione Nord Atlantica, un indicatore delle condizioni climatiche dell’areale di foraggiamento di D. coriacea. Fra le varianti climatiche e i parametri della fenologia riproduttiva è stato costruito un modello lineare per quantificare la possibile relazione fra queste variabili e, di conseguenza, le possibili ripercussioni dell’attuale cambiamento climatico sulla fenologia di queste specie. Dall’altra, ci si è posto l’obiettivo di integrare il modello con l’introduzione delle variazioni presenti in Girondot (2006). Sommando matematicamente queste variazioni al modello preesistente, si è cercato di determinare il valore dei suoi parametri secondo un approccio di Maximum Likelihood. 79 Materiali e metodi Il dataset a nostra disposizione consisteva nell’insieme dei conteggi del numero di nidi deposti giornalmente in una serie di spiagge del Suriname e della Guyana Francese, registrati nel periodo 1979 – 2006 per D. coriacea, e 2002-­‐2006 per L. olivacea e C. mydas. I conteggi sono riferiti non alle spiagge nella loro interezza ma a specifiche sezioni, unità operative nelle quali le singole spiagge, lunghe spesso diversi chilometri, sono state suddivise per un più adeguato monitoraggio. L’attività di nidificazione delle tartarughe è registrata da pattuglie giornaliere che effettuano il conteggio dei nidi deposti la notte precedente e/o delle singole femmine, durante la notte. In entrambi i casi, i nidi sono contrassegnati come deposti nella data antecedente la mezzanotte. Il modello utilizzato da Girondot per descrivere la stagione riproduttiva è basato su una serie di equazioni sinusoidali. Conoscendo t, quale giorno ordinale dell’anno a partire dal 1 gennaio, il numero di nidi deposti per notte è stimato dal seguente sistema di equazioni: !" ! < ! → !"# ! !
!
!" ! ∈ !, ! −
→ 1 + cos ! ! − − !
2
2
!
!
!" ! ∈ ! − , ! +
→ !"# 2
2
!
!
!" ! ∈ ! + , ! → 1 + !"# ! ! − ! +
2
2
!" ! > ! → !"#$ !−
!
−!
2
!−!+
!
2
2
2
!"# − !"#$ + !"#$ !"# − !"#$ + !"#! Tutti questi parametri hanno un diretto significato biologico: • MinB è il numero medio di nidi giornalieri prima dell’inizio della stagione riproduttiva; • MinE è il numero medio di nidi giornalieri dopo il termine della stagione riproduttiva; • Max è il numero medio di nidi all’apice della stagione riproduttiva; • P è la data ordinale dell’anno in cui la stagione riproduttiva ha il suo apice; • F è il numero dei giorni intorno al giorno P in cui la curva del grafico ha un plateau; • B è la data ordinale dell’anno in cui ha inizio la stagione riproduttiva; • E è la data ordinale dell’anno in cui termina la stagione riproduttiva. Per la stima dei parametri, il modello utilizza un approccio di Maximum Likelihood. Si tratta di una metodologia per la stima dei parametri di un modello statistico, attraverso una misura di fit fra il modello e i dati. Dato un modello e un insieme di osservazioni numeriche, la funzione di likelihood quantifica la probabilità che i dati forniti siano il risultato del modello da testare. Stimare i parametri del modello vuol dire trovare tramite ricerca euristica i valori dei parametri che massimizzano questa funzione. Per calcolare la funzione di likelihood è necessario assumere un modello statistico di distribuzione dei dati. Nel nostro caso, la distribuzione di probabilità 80 scelta è una distribuzione binomiale negativa. Essa può essere descritta come un insieme di distribuzioni di Poisson: è la probabilità discreta del numero di successi in una sequenza di prove di Bernoulli, -­‐ esperimenti il cui risultato è random e può essere di due tipi, “successo” o “insuccesso” – avvenuti prima di un numero r d’insuccessi. Questo tipo di distribuzione è adatta per il nostro tipo di analisi poiché è discreta, strettamente positiva, eteroschedastica e asimmetrica. Questo si adatta particolarmente alla nostra tipologia di dati, che presenta un andamento asimmetrico e un intervallo di confidenza estremamente variabile, di cui è necessario tenere conto. In casi come il nostro è inoltre consigliabile lavorare in termini di logaritmo naturale della funzione di likelihood, il log-­‐likelihood Ln L. Poiché il logaritmo è una funzione monotona, il logaritmo della funzione raggiunge il suo massimo valore nello stesso punto della funzione stessa. In compenso, però, è più semplice derivare e risolvere la funzione per massimizzarla, se questa è logaritmica. Il Ln L è calcolato per ogni osservazione; calcolando poi la sommatoria di tutti i valori, otteniamo la Ln L globale. L’ottimizzazione dei parametri consiste quindi nella ricerca euristica del set di parametri che massimizza la Ln L globale. Nel nostro caso, è stato effettuato sia il calcolo del Ln L che il calcolo dell’Akaike Information Criterion (AIC). Si tratta di una misura alternativa per esprimere la qualità del fit di modello e dati. La likelihood tende ad aumentare con il numero dei parametri ma insieme al numero di parametri cresce anche la varianza; per ovviare a questo potenziali bias, questo indice introduce una penalità per la sovra-­‐parametrizzazione. L’indice viene infatti calcolato come: !"# = −2 ∙ !" ! + 2 !"#$%& !" !"#"$%&#' Per entrambi gli indicatori, più basso è il valore, migliore è il fit fra modello e dati. L’analisi dei dati sul conteggio dei nidi è stata effettuata tramite il software MTTS, implementato da Marc Girondot. Si tratta di un software che analizza i dati di nidificazione con il modello di Girondot (2010b), calcolando attraverso numerose iterazioni il fit fra i dati e il modello, fornendone una rappresentazione grafica. Oltre al calcolo dei sette parametri del modello (con il relativo intervallo di confidenza) e gli indici -­‐ Ln L e AIC, l’output del programma comprende anche diversi parametri, quali la data d’inizio, apice e termine della stagione riproduttiva, durata della stagione riproduttiva, numero totale di nidi osservati, di giorni di monitoraggio e stima del numero totale di nidi durante la stagione riproduttiva. Il dataset è stato suddiviso in file .txt, ognuno relativo ai conteggi di una specifica sezione, per ciascuno degli anni del periodo in esame. Ogni singolo dataset è stata analizzato con il software MTTS, allo scopo di ottenere delle stime relative al numero di nidi deposti e ai parametri della fenologia riproduttiva (Begin, Peak e End), nel periodo considerato. Per D. coriacea, in presenza di un gap nei dati che impedivano di determinare alcuni parametri (es. una mancanza di dati nei primi mesi dell’anno impediva di determinare il parametro Begin) è stato utilizzato un set di parametri “modello”, ricavati dall’analisi del dataset di Yalimapo-­‐Awala 2002, che presenta una distribuzione “ideale” e un monitoraggio pressoché completo durante l’anno, 81 con solo una decina di giorni di mancato monitoraggio. Questa strategia ha permesso di analizzare singolarmente tutti i singoli dataset, anche quelli parzialmente incompleti. Da questi risultati sono state poi selezionate le soluzioni ottimali per ciascuna spiaggia in ciascun anno, sulla base dell’AIC ottenuto. Per L. olivacea e C. mydas, invece, non è stato possibile disporre di un dataset “modello”. Per ovviare alla presenza di gap nei dati, è stata effettuata l’analisi comparativa di tutti i dataset relativi a ciascun anno. In questo modo si è ottenuta una stima affidabile della fenologia, pur potendo contare su di un solo set annuale di parametri. Fenologia riproduttiva Da questi dati, un primo obiettivo è stato quello di indagare la possibile correlazione dei parametri della fenologia riproduttiva (Begin, Peak, End e Length, durata della stagione riproduttiva) con alcune variabili climatiche, con l’obiettivo di costruire un modello lineare (General Linear Model o GLM) che ne esprimesse la possibile relazione. I fattori climatici presi in esame sono stati la temperatura superficiale dell’acqua e l’indice dell’Oscillazione Nord Atlantica (North Atlantic Oscillation o NAO). Le medie mensili e settimanali delle temperature superficiali dell’acqua sono state ottenute dagli archivi web della NOAA (National Oceanic & Atmospheric Administration). Si tratta di dati ottenuti attraverso registrazione tramite il satellite NOAA POES, provvisto di un radiometro ad alta risoluzione, l’Advance Very Hig Resolution Radiometer (AVHRR). Si tratta di uno scanner a banda larga, sensibile alle radiazioni dello spettro visibile, nel vicino infrarosso e nell’infrarosso termico. Le registrazioni giornaliere del satellite sono integrate con quelle di sensori poste su delle boe, che ne riducono i potenziali bias. Per ottenere le temperature di nostro interesse, la piattaforma continentale antistante il Suriname e la Guyana Francese è stata divisa in aree quadrate ciascuna corrispondente ad 1° di latitudine e longitudine, corrispondenti alla griglia di analisi utilizzata dalla NOAA per la rilevazione delle temperature. Dopo aver determinato le coordinate geografiche di ciascuna delle spiagge del nostro dataset, e conoscendo (da studi basati su marcatura degli individui con sonde satellitari) alcune delle rotte compiute dagli individui durante la stagione riproduttiva, abbiamo individuato quattro aree chiave, i quadrati corrispondenti ai tratti di mare antistanti le spiagge di nostro interesse, estraendo i valori di temperature corrispondenti. Sono state ottenute, per ciascuna specie e ciascuna spiaggia, la temperatura media settimanale della prima (sst1), la seconda (sst2), la terza (sst3) e la quarta (sst4) settimana del mese antecedente la data d’inizio, apice e termine della stagione riproduttiva, oltre che la temperatura media mensile a partire dal mese di febbraio fino al mese di luglio. Un altro fattore, la deviazione standard sulle quattro temperature medie settimanali (sstvb) è stato adottato quale indicatore di variabilità termica. Anche l’anno (year) è stato considerato come fattore nel modello. 82 L’oscillazione Nord Atlantica è un pattern di circolazione atmosferica determinato dalla fluttuazione ciclica della differenza di pressione a livello del mare fra l’Islanda e le Azzorre. Essa determina il flusso zonale (correnti di direzione ovest-­‐est) nell’Atlantico occidentale e la direzione delle perturbazioni lungo l'Atlantico settentrionale. Gli indici sono stati ottenuti dall’archivio web del U.S. National Center for Atmospheric Research (NCAR). Per ciascun anno, è stato utilizzato l’indice annuale relativo all’anno precedente. Abbiamo testato la NAO solo per D. coriacea, essendo l’unica specie a coprire l’intero Atlantico con le sue rotte di migrazione. Abbiamo quindi costruito un modello lineare, correlando gli indicatori climatici ai parametri fenologici ottenuti. Per D. coriacea, sono stati costruiti i seguenti modelli: a sst1+ b sst2 + c sst3 + d sst4 + e sst1.sst2 + f sst2.sst3 + g sst3.sst4 + h sstvb + j NAO + k Year+ i = Begin, Peak, End a sstF + b sstM + c sstA + d sstJN + e sstJL + f sstvb + g NAO + h year + i = Length Per L. olivacea e C. mydas, l’esiguità delle osservazioni non permetteva di testare una grande quantità di parametri. Si è quindi scelto di non testare la durata della stagione riproduttiva, e di utilizzare un unico parametro per la temperatura, heat accumulated o ha, con la seguente formula: ha = (sst1-­‐25) + (sst2-­‐25) + (sst3-­‐25) + (sst4-­‐25) = (sst1 + sst2 + sst3 + sst4) -­‐ 100 Il modello adottato è stato il seguente: a ha + b sstvb + c year + d = Begin, Peak, End Il modello lineare è stato testato attraverso il software Glm Stat X con un approccio backward, tramite cioè l’eliminazione progressiva dei fattori non statisticamente significativi, massimizzando la funzione di likelihood. Variazioni al modello Un ulteriore obiettivo del lavoro era quello di sommare alcune variazioni periodiche, rilevate e già introdotte nel precedente modello elaborato da Girondot (Girondot et al. 2006), nel modello corrente (Girondot 2010b). É stato dapprima implementato uno script in R che calcolasse il fit fra il modello corrente e i dati. Lo script è stato testato utilizzando il dataset di Yalimapo-­‐Awala 2002. Successivamente, è stata sommata matematicamente, per ogni diversa sezione della sinusoide, l’equazione corrispondente alle micro dinamiche presenti in Girondot (Girondot et al. 2006), introducendo due nuovi parametri: Φ, il periodo della variazione, e Δ, il modulo della variazione. La presenza di eventuali micro variazioni al modello principale è stata verificata in due modi. In primo luogo, è stata generata una matrice contenente i valori di Φ (in ascissa) e Δ (in ordinata). I valori di questa matrice sono stati mantenuti fissi, e per ogni casella della matrice è stata calcolata la likelihood tramite il modello, mantenendo fissi Δ e Φ e ottimizzando i restanti parametri, ottenendo così una mappa di likelihood. Questi valori sono stati poi inseriti nell’equazione, e il nuovo modello è stato nuovamente testato con il dataset Yalimapo-­‐Awala 2002. 83 Risultati Fenologia riproduttiva I dati riguardanti l’andamento delle temperature di superficie del tratto di oceano antistante la colonia di Suriname e Guyana Francese mostrano, come prevedibile, un andamento piuttosto omogeneo, che varia fra i 24,5 e i 27,5 °C. Per quanto riguarda l’andamento dei parametri fenologici, i dati mostrano un andamento piuttosto costante per L. olivacea e C. mydas, mentre per D. coriacea è evidente una dinamica non lineare, di non immediata interpretazione. Attraverso la correlazione dei parametri fenologici ottenuti (Begin, Peak e End) con gli indicatori climatici (sst1, sst2, sst3, sst4, sstvb, ha, NAO) in GLM Stat X, si sono ottenute alcune relazioni significative. Per D. coriacea, è stata riscontrata una correlazione significativa fra il parametro Begin e year, (Prob>F = 0,0024), sst3 (Prob>F = 0,0219), mentre è presente una correlazione marginalmente significativa con sst2 (Prob>F = 0,0602). Il modello ottenuto è il seguente: Beginleatherback = 1,394 year + 24,64 sst2 -­‐ 37,07 sst3 – 2369 Per quanto riguarda il parametro Peak, una relazione significativa è stata individuata in merito a sst4 (Prob>F = 0,0167). Il modello risultante è il seguente: Peakleatherback = -­‐ 20,35 sst4 + 705,5 Il termine della stagione riproduttiva sembra invece essere correlato significativamente a sst3 (Prob>F = 0,0066) e sst4 (Prob>F = 0,0244), mentre una relazione debolmente significativa lo lega a sstvb (Prob>F = 0,0789). Il modello che ne risulta è il seguente: Endleatherback = -­‐29,54 sst3 -­‐ 97,60 sst4 -­‐ 97,60 sstvb + 202,0 I dati di L. olivacea non mostrano alcuna relazione con le variabili climatiche, con l’eccezione di End, che mostra significativa correlazione con Year (Prob>F = 0,0048) e sstvb (Prob>F = 0,0082), nel seguente modello: Endolivridley = 26,60 year + 544 sstvb -­‐ 5,310e+4 Nessuna relazione significativa, invece, è stata riscontrata per C.mydas. Variazioni al modello Dopo aver testato lo script relativo al modello con il dataset di Yalimapo-­‐Awala del 2002, il modello è stato modificato con l’introduzione dei parametri Δ e Φ. Generando una matrice di valori compresi fra 0,2 e 20 a intervalli di 0,2, e, per ogni combinazione di valori, tenendo fissi questi ultimi e ottimizzando i parametri rimanenti, è stata ottenuta una mappa di likelihood che mette in relazione questi due parametri con la likelihood globale. La mappa mostra chiaramente un minimo di – Ln L in corrispondenza di valori di Φ compresi fra 9 e 10. Inserendo questi parametri nel modello e ottenendo il fit fra questo e i dati di Yalimapo-­‐Awala nel 2002, è possibile visualizzare chiaramente queste variazioni periodiche, cui è associato un decremento di – Ln L. 84 Discussione Le condizioni termiche ambientali giocano un ruolo importante nella biologia, ecologia e riproduzione delle tartarughe marine. La temperatura, infatti, influenza le condizioni dei luoghi di foraggiamento, e quindi la disponibilità di risorse alimentari, con un impatto diretto sulla distribuzione delle popolazioni, la frequenza e l’entità dello sforzo riproduttivo. Le condizioni termiche dei luoghi di foraggiamento determinano la sex ratio della progenie e influenzano il successo riproduttivo degli individui, oltre che determinare in molti casi la fenologia riproduttiva, le date di inizio, apice, termine e durata della stagione riproduttiva. La sensibilità di tali specie alla temperatura, nel contesto del corrente cambiamento climatico, è senza dubbio da tenere in seria considerazione, poiché l’ecologia delle tartarughe marine influenza tutta una serie di specie ad essa legate, prede e predatori, mammiferi, pesci e uccelli marini. Nel nostro studio, i parametri della fenologia riproduttiva (inizio, apice, fine e durata della stagione riproduttiva) sono stati messi in relazione con alcune variabili climatiche, le temperature superficiali dell’acqua nella zona di mare antistante la colonia riproduttiva di Suriname e Guyana Francese, e l’Oscillazione Nord Atlantica, indicatore delle condizioni climatiche nei luoghi di foraggiamento. Per D. coriacea non esiste alcuno studio precedente riguardante gli effetti della temperature dell’acqua sulla fenologia riproduttiva. Alcune delle relazioni riscontate, però, sono coerenti con i risultati ottenuti in simili studi per la tartaruga comune Caretta caretta che, dopo la tartaruga liuto, presenta in estensione e latitudine il secondo areale più esteso fra le sette specie di tartarughe marine. La data ordinale d’inizio della stagione riproduttiva sembra essere influenzata dall’anno considerato, implicando una certa omogeneità fra spiagge dello stesso anno, oltre che un possibile aumento lineare legato al cambiamento climatico; è inversamente correlata alla temperatura media della terza settimana antecedente l’inizio della stagione, e direttamente, pur se in modo marginalmente significativo, dalla temperatura media della seconda. Un anticipo dell’inizio della stagione riproduttiva in relazione all’incremento della temperature è stato analogamente riscontrato per C. caretta a Bald Head Island (North Carolina, USA) e a Zakyntos (Grecia), prendendo in considerazione le temperature medie mensili di aprile e maggio. La data di picco della stagione riproduttiva sembra altresì inversamente correlata alla temperatura media della quarta settimana del mese precedente. Questi risultati sono coerenti con quelli ottenuti per C. caretta in alcuni siti posti sulla costa occidentale della Florida (USA), utilizzando le temperature medie del mese di maggio. Anche il termine della stagione riproduttiva, per D. coriacea, è inversamente correlato alla temperatura, con un effetto additivo della temperature della terza e quarta settimana del mese precedente, oltre che della variabilità termica. Questo contrasta con i risultati degli studi sopracitati su C. caretta, che invece non riscontrano alcuna relazione significativa. 85 La durata della stagione riproduttiva, invece, non mostra alcuna relazione significativa con le variabili climatiche. Gli studi condotti su C. caretta, invece, mostrano risultati contrastanti. Se la temperatura media del mese di maggio ha l’effetto di prolungare la durata della stagione riproduttiva alla colonia di Zakyntos e a Bald Head Island (NC, USA), nella costa occidentale della Florida al contrario una più elevata temperatura media del mese di maggio tende a ridurre la durata della stagione riproduttiva. Una relazione inversa, dunque, sembra esistere fra la temperatura dell’acqua e la dinamica riproduttiva di questa specie, causando un vero e proprio shift temporale. Questo sembra essere coerente con la fisiologia delle tartarughe marine, che incubano le proprie uova con tempi di maturazione per i quali, data l’ectotermia di questi animali, le temperature ambientali giocherebbero un ruolo fondamentale. Esperimenti condotti su C. caretta e C. mydas, marcando gli individui con dei sensori e registrando le temperature ambientali sperimentate dagli individui, hanno mostrato una relazione inversa fra queste e gli intervalli fra due nidificazioni successive. Poiché le tartarughe marine sono capital breeders, il cui sforzo riproduttivo dipende cioè in larga parte dalle risorse energetiche accumulate nel tempo nelle aree di foraggiamento, sembra improbabile che la stagione riproduttiva possa protrarsi nel tempo solo in risposta alle condizioni termiche, poiché il numero di uova da poter produrre è limitato soprattutto da constraint di tipo energetico. I risultati ottenuti per C. caretta, la più studiata fra le diverse specie di tartarughe marine, confermano questi risultati per quanto concerne inizio e apice della stagione, ma non per la sua fine e la sua durata. Bisogna sottolineare, comunque, che si tratta di studi condotti con una metodologia diversa da quella adottata in questo studio: inizio e fine della stagione sono determinate dalla data del primo e ultimo nido deposto, e questo, in presenza di singoli individui disorientati che nidificano con tempistiche anomale, può creare un notevole bias nei risultati ottenuti. Non è stata riscontrata alcuna relazione con l’indice di Oscillazione Nord Atlantica. Questo ci lascia presumere che le condizioni climatiche dei luoghi di foraggiamento non influenzino direttamente la fenologia riproduttiva. Similmente è possibile che la relazione che lega queste variabili sia troppo complessa, o mascherata da altri fattori, per essere quantificabile con il semplice indice NAO, ma che necessiti più approfondite indagini. Per L. olivacea non è stata riscontrata alcuna relazione significativa con le variabili termiche, ad eccezione della data di termine della stagione riproduttiva, positivamente correlata con l’anno corrente e la variabilità termica. Questo suggerisce un generale effetto della temperature e della sua variabilità che, in una specie a distribuzione rigorosamente tropicale e subtropicale e quindi adattata a condizione termiche stabili, possono avere l’effetto di ritardare il termine della stagione riproduttiva. L’influenza termica sulla fenologia di questa specie, però, resta controversa. Uno studio condotto su questa specie in Australia (Hamel et al. 2008b), tramite sonde che registravano le temperature ambientali incontrate da alcuni individui durante gli intervalli fra una nidificazione e l’altra, non rilevano alcuna influenza della temperature sulla 86 durata di questi intervalli, a differenza di quanto osservato in quelli condotti su C. caretta e C. mydas. L’ipotesi degli autori è che queste tartarughe, a causa della loro esclusiva capacità di nidificare in massa durante le arribadas, siano in grado di controllare maggiormente rispetto ad altre specie il loro tasso metabolico e i tempi di maturazione delle uova, prolungandoli in attesa delle condizioni metereologiche ottimali pe la deposizione. Vista l’esiguità del nostro dataset, comunque, è auspicabile un maggiore approfondimento sull’argomento. I risultati ottenuti per C.mydas sono forse di ancor più complessa interpretazione. Nessuna relazione significativa è stata riscontrata nel nostro studio, e questo potrebbe essere imputabile sia all’esiguità del nostro dataset che a una reale influenza della temperature sulla fenologia. Un analogo studio svolto a Canaveral National Seashore (Florida, USA), mostra analoghi risultati, non avendo riscontrato alcuna relazione di questo tipo. Questi risultati, però, sono in contrasto con i due studi condotti registrando con sonde termiche la temperatura sperimentata dagli individui fra due nidificazioni successive, che invece mostrano una significativa e inversa relazione fra le temperature ambientali e la durata di questi intervalli. Per questi motivi, suggeriamo anche in quest’ambito ulteriori approfondimenti. Per quanto riguarda le variazioni inter stagionali al modello di Girondot (2010b), le osservazioni sperimentali confermano quanto ottenuto da precedenti studi. La mappa di likelihood mostra chiaramente un minimo in corrispondenza di valori di Φ compresi fra 9 e 10. Questo valore indica la periodicità delle variazioni rilevate: si tratta dell’intervallo fra due nidificazioni successive individuato per D. coriacea nella colonia del Suriname e della Guyana Francese (Girondot and Fretey 1996), e corrisponde allo stesso periodo determinate per la prima serie di variazioni riscontrate nel precedente modello (Girondot et al. 2006). I massimi locali riscontrati, quindi, sembrano effettivamente essere il risultato di constraint fisiologici. Resta naturalmente da verificare la presenza della seconda tipologia di perturbazioni individuata nel precedente studio del 2006, quella con periodo 14 giorni, legata al ciclo lunare. É quindi auspicabile proseguire l’indagine in questa direzione. 87 88 Bibliography Aas, E., J. Beyer, and A. Goksøyr. 2000. 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