12 resultados para Fastloc


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Background: ARGOS satellite telemetry is one of the most widely used methods to track the movements of free-ranging marine and terrestrial animals and is fundamental to studies of foraging ecology, migratory behavior and habitat-use. ARGOS location estimates do not include complete error estimations, and for many marine organisms, the most commonly acquired locations (Location Class 0, A, B, or Z) are provided with no declared error estimate.
Methodology/Principal Findings: We compared the accuracy of ARGOS locations to those obtained using Fastloc GPS from the same electronic tags on five species of pinnipeds: 9 California sea lions (Zalophus californianus), 4 Galapagos sea lions (Zalophus wollebaeki), 6 Cape fur seals (Arctocephalus pusillus pusillus), 3 Australian fur seals (A. p. doriferus) and 5 northern elephant seals (Mirounga angustirostris). These species encompass a range of marine habitats (highly pelagic vs coastal), diving behaviors (mean dive durations 2–21 min) and range of latitudes (equator to temperate). A total of 7,318 ARGOS positions and 27,046 GPS positions were collected. Of these, 1,105 ARGOS positions were obtained within five minutes of a GPS position and were used for comparison. The 68th percentile ARGOS location errors as measured in this study were LC-3
0.49 km, LC-2 1.01 km, LC-1 1.20 km, LC-0 4.18 km, LC-A 6.19 km, LC-B 10.28 km.
Conclusions/Significance: The ARGOS errors measured here are greater than those provided by ARGOS, but within the range of other studies. The error was non-normally distributed with each LC highly right-skewed. Locations of species that make short duration dives and spend extended periods on the surface (sea lions and fur seals) had less error than species like elephant seals that spend more time underwater and have shorter surface intervals. Supplemental data (S1) are provided allowing the creation of density distributions that can be used in a variety of filtering algorithms to improve the quality of ARGOS tracking data.

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Over recent years, a major breakthrough in marine animal tracking has occurred with the advent of Fastloc-GPS that provides highly accurate location data even for animals that only surface briefly such as sea turtles, marine mammals and penguins. We assessed the accuracy of Fastloc-GPS locations using fixed trials of tags in which >45 000 locations were obtained. Procedures for determining the speed of travel and heading were developed by simulating tracks and then adding Fastloc-GPS location errors. The levels of detail achievable for speed and heading estimates were illustrated by using empirical Fastloc-GPS data for a green turtle (Chelonia mydas, Linnaeus, 1758) travelling over 3000 km across the Indian Ocean. The accuracy of Fastloc-GPS locations varied as a function of the number of GPS satellites used in the location calculation. For example, when Fastloc-GPS locations were calculated using 4 GPS satellites, 50% of locations were within 36 m and 95% within 724 m of the true position. These values improved to 18 and 70 m, respectively, when 6 satellites were used. Simulations indicated that for animals travelling around 2·5 km h-1 (e.g. turtles, penguins and seals) and depending on the number of satellites used in the location calculation, robust speed and heading estimates would usually be obtained for locations only 1-6 h apart. Fastloc-GPS accuracy is several orders of magnitude better that conventional Argos tracking or light-based geolocation and consequently will allow new insights into small-scale movement patterns of marine animals.

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1. Some animals migrate huge distances in search of resources with locomotory mode (flying/swimming/walking) thought to drive the upper ceilings on migration distance. Yet in cross-taxa comparisons, upper ceilings on migration distance have been ignored for one important group, sea turtles. 2. Using migration distances recorded for 407 adult and 4715 juvenile sea turtles across five species, we show that for adult cheloniid turtles, the upper ceiling on species migration distances between breeding and foraging habitats (1050–2850 km across species) is similar to that predicted for equivalent-sized marine mammals and fish. 3. In contrast, by feeding in the open ocean, adult leatherback turtles (Dermochelys coriacea) and juveniles of all turtle species can travel around 12 000 km from their natal regions, travelling across the widest ocean basins. For juvenile turtles, this puts their maximum migration distances well beyond those expected for equivalent-sized marine mammals and fish, but not those found in some similar sized birds. 4. Post-hatchling turtles perform these long-distance migrations to juvenile foraging sites only once in their lifetime, while adult turtles return to their breeding sites every few (generally ?2) years. Our results highlight the important roles migration periodicity and foraging mode can play in driving the longest migrations, and the implications for Marine Protected Area planning are considered in terms of sea turtle conservation.

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Long distance migration occurs in a wide variety of taxa including birds, insects, fishes, mammals and reptiles. Here, we provide evidence for a new paradigm for the determinants of migration destination. As adults, sea turtles show fidelity to their natal nesting areas and then at the end of the breeding season may migrate to distant foraging sites. For a major rookery in the Mediterranean, we simulated hatchling drift by releasing 288 000 numerical particles in an area close to the nesting beaches. We show that the pattern of adult dispersion from the breeding area reflects the extent of passive dispersion that would be experienced by hatchlings. Hence, the prevailing oceanography around nesting areas may be crucial to the selection of foraging sites used by adult sea turtles. This environmental forcing may allow the rapid evolution of new migration destinations if ocean currents alter with climate change.

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Aim  Resources can shape patterns of habitat utilization. Recently a broad foraging dichotomy between oceanic and coastal sites has been revealed for loggerhead sea turtles (Caretta caretta). Since oceanic and coastal foraging sites differ in prey availability, we might expect a gross difference in home-range size across these habitats. We tested this hypothesis by equipping nine adult male loggerhead sea turtles with GPS tracking devices. Location  National Marine Park of Zakynthos (NMPZ) Greece, central and eastern Mediterranean (Adriatic, Ionian and Aegean seas). Methods  In 2007, 2008 and 2009, Fastloc GPS-Argos transmitters were attached to nine male loggerheads. In addition, a Sirtrack PTT unit was attached to one male in 2007. Four of the turtles were tracked on successive years. We filtered the GPS data to ensure comparable data volumes. Route consistency between breeding and foraging sites of the four re-tracked turtles was conducted. Foraging site home range areas and within site movement patterns were investigated by the fixed kernel density method. Results  Foraging home range size ranged between circa 10 km2 at neritic habitats (coastal and open-sea on the continental shelf) to circa 1000 km2 at oceanic sites (using 90% kernel estimates), the latter most probably reflecting sparsely distributed oceanic prey. Across different years individuals did not follow exactly the same migration routes, but did show fidelity to their previous foraging sites, whether oceanic or neritic, with accurate homing in the final stages of migration. Main conclusions  The broad distribution and diverse life-history strategies of this population could complicate the identification of priority marine protected areas beyond the core breeding site.

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The implementation of appropriate protection measures for endangered species in protected areas requires knowledge of their fine-scale habitat use. In May and June of 2006 and 2007, we used GPS loggers (some linked to the Argos system) and a conventional Argos transmitter to track male and female loggerhead turtles Caretta caretta in the vicinity of the breeding area of Laganas Bay within the National Marine Park of Zakynthos, Greece. We obtained (1) 9681 useable locations (mean: 1383 locations ind.–1; range: 519 to 2198 locations) from Tracktag GPS loggers attached to 7 females for a mean duration of 34 d (range: 17 to 52 d); (2) 1245 useable locations (mean: 311 locations ind.–1; range: 38 to 1110 locations) from 4 males fitted with Fastloc Argos tags for a mean duration of 29 d (range: 3 to 51 d) and (3) 100 locations from 1 male fitted with a conventional Argos satellite tag tracked for 128 d. GPS data indicated that before the onset of nesting, both males and females primarily used an area within 500 m of the shore along a core 9 km stretch of coastline, where existing protective legislation requires strengthening. Our observations suggest that a 76.7% female-biased operational sex ratio, measured previously from in-water surveys, may represent a realistic sex ratio estimate in the period before nesting starts. In the first month following the onset of nesting, female spatial distribution remained similar, whereas most males departed for distant areas presumably to forage. Our study provides quantitative evidence of the need to improve the management planning and conservation measures to protect sea turtles in a coastal breeding area, and new insights on male turtle migration.

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The term ‘biologging’ refers to the use of miniaturized animal-attached tags for logging and/or relaying of data about an animal's movements, behaviour, physiology and/or environment. Biologging technology substantially extends our abilities to observe, and take measurements from, free-ranging, undisturbed subjects, providing much scope for advancing both basic and applied biological research. Here, we review highlights from the third international conference on biologging science, which was held in California, USA, from 1 to 5 September 2008. Over the last few years, considerable progress has been made with a range of recording technologies as well as with the management, visualization, integration and analysis of increasingly large and complex biologging datasets. Researchers use these techniques to study animal biology with an unprecedented level of detail and across the full range of ecological scales—from the split-second decision making of individuals to the long-term dynamics of populations, and even entire communities. We conclude our report by suggesting some directions for future research.

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Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy μ, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of ≥10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD ≥ 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.

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Phenotypic variation and individual experience can create behavioural and/or dietary variation within a population. This may reduce intra-specific competition, creating a buffer to environmental change. This study examined how intrinsic variation affects foraging behaviour of Australian fur seals. Foraging movements of 29 female Australian fur seals were recorded using FastLoc GPS and dive behaviour recorders. For each individual, body mass, flipper length and axis length were recorded, a tooth was sampled to determine age, and milk was collected for diet analysis. Clustering of fatty acid dietary analysis revealed 5 distinct groups in the population. Behaviour was described using 19 indices, which were then reduced to 7 principal components (>80% of the behavioural variation). Bayesian mixed effect models were developed to describe the relationship between these components and intrinsic variation. No association was found between diet and age or body shape; however, age had a negative relationship with component 1 (27% of variation). Older females spent less time at-sea and foraged nearer to the colony. Age had an effect on component 5 (7% of variation), which represented haul-outs and dive depth; older females made fewer visits to haul-out sites and dived deeper to the benthos. This suggests that as animals age they are able to utilise prior knowledge to exploit nearby foraging sites that younger animals are either unaware of, or have yet to gain the experience required to efficiently utilise. Mass had a negative effect on components representing the directedness of a foraging trip, suggesting heavier individuals were more likely to travel directly to a foraging site.

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Within a heterogeneous environment, animals must efficiently locate and utilise foraging patches. One way animals can achieve this is by increasing residency times in areas where foraging success is highest (area-restricted search). For air-breathing diving predators, increased patch residency times can be achieved by altering both surface movements and diving patterns. The current study aimed to spatially identify the areas where female Australian fur seals allocated the most foraging effort, while simultaneously determining the behavioural changes that occur when they increase their foraging intensity. To achieve this, foraging behaviour was successfully recorded with a FastLoc GPS logger and dive behaviour recorder from 29 individual females provisioning pups. Females travelled an average of 118 ± 50 km from their colony during foraging trips that lasted 7.3 ± 3.4 days. Comparison of two methods for calculating foraging intensity (first-passage time and first-passage time modified to include diving behaviour) determined that, due to extended surface intervals where individuals did not travel, inclusion of diving behaviour into foraging analyses was important for this species. Foraging intensity 'hot spots' were found to exist in a mosaic of patches within the Bass Basin, primarily to the south-west of the colony. However, the composition of benthic habitat being targeted remains unclear. When increasing their foraging intensity, individuals tended to perform dives around 148 s or greater, with descent/ascent rates of approximately 1.9 m•s-1 or greater and reduced postdive durations. This suggests individuals were maximising their time within the benthic foraging zone. Furthermore, individuals increased tortuosity and decreased travel speeds while at the surface to maximise their time within a foraging location. These results suggest Australian fur seals will modify both surface movements and diving behaviour to maximise their time within a foraging patch.