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Estimated 95% home range sizes derived from different location qualities for a green turtle tracked for 14 months in the Chagos Archipelago, western Indian Ocean (a), another tracked for 3 months in Shark Bay, Western Australia (b), and a third tracked for 5 months in Bonaire, Caribbean Netherlands. For (a) and (b), the dashed line with triangles represents home range estimates based on all available data (1 location per day) per location class, while the solid line with circles represents the mean (±SE) estimate based on sub-sampled data to standardize data volume across location classes (see “Materials and Methods”). For the Chagos turtle, the estimate for Argos location classes 1–3 is a single value based on all available locations due to low sample size
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The advent of Fastloc-GPS is helping to transform marine animal tracking by allowing the collection of high-quality location data for species that surface only briefly. We show how the improved location accuracy of Fastloc-GPS compared to Argos tracking is expected to lead to far more accurate home range estimates, particularly for animals moving o...
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In recent years, scientists have recognized the need to conduct in-water studies to better understand the biology of juvenile sea turtles. Little is known regarding the seasonal abundance of juvenile populations in temperate developmental areas such as those in the Gulf of Mexico. It is likely that the Gulf of Mexico supports a year-round populatio...
Identification of high-use foraging sites where imperiled sea turtles are resident remains a globally-recognized conservation priority. In the biodiverse Gulf of Mexico (GoM), recent telemetry studies highlighted post-nesting foraging sites for federally threatened loggerhead turtles (Caretta caretta). Our aim here was to discern loggerhead use of...
Abstract Background A better understanding of sea turtle spatial ecology is critical for the continued conservation of imperiled sea turtles and their habitats. For resource managers to develop the most effective conservation strategies, it is especially important to examine how turtles use and select for habitats within their developmental foragin...
Citations
... In sum, mini-GPS data had to be cleaned and by using 95% MCP excluding obvious outliers, we obtained useable range data. Another aspect that must be accounted for in studies using GPS dataloggers is the location error (Thomson et al. 2017;Fleming et al. 2021). Every GPS has some inaccuracy associated with it and the location error is the distance between the GPS-generated location and the true location (Frair et al. 2010;McMahon et al. 2017 and references within). ...
Home ranges of free-living mammals have typically been studied via radio-tracking to understand how individuals use their environment. Recently, GPS collars have become popular in large mammals. However, GPS collars are rarely used in small mammals, as they are too heavy, especially when needing coating to protect against gnawing. Here we test the efficiency of mini-GPS collars to measure range estimates compared to the use of radio-collars in a small rodent of 100 g body mass. We equipped 20 bush Karoo rats with mini-GPS loggers and thereafter with radio-transmitters to determine ranges. We validated the accuracy of the mini-GPS loggers by comparing them with the fixes from a handheld GPS and found both to be similar. We estimated range sizes using both traditional methods of Kernel and minimum convex polygon estimates as well as modern methods from movement ecology taking the location error of the mini-GPS into account. Using modern methods led to smaller range estimates, but results were in so far consistent that daily ranges for bush Karoo rats determined using mini-GPS were much larger than home range estimates from radio tracking. Using radio-tracking enabled us to establish the central shelter, while the mini-GPS revealed areas where rats had been observed foraging. We found a distinct location error and therefore suggest using modern approaches from movement ecology which can take this error into account. In sum, mini-GPS revealed more accurate estimates of the ranges than radio-tracking in a small rodent of 100 g body mass.
... This presents several challenges, particularly for analyses of habitat use. Home range size, for example, is highly sensitive to location error [23][24][25], yet SSM-improved Argos locations are often used directly in home range estimations [26][27][28]. Even errorinformed approaches to estimating home ranges may assume a circular error distribution around locations [29], calling into question their compatibility with Argos tracking data [30]. ...
Background.
Due to the large errors in Argos Doppler location estimates, Argos-based satellite transmitter data are rarely used in studies of fine-scale habitat selection by animals. Novel state-space models (SSMs) for path reconstruction from animal movement data improve location estimates, delivering refined estimations of an animal’s most likely path and, also, re-estimating the uncertainties for each location. However, the SSM-refined uncertainties are still relatively large and the true locations of animals tracked with PTTs (Platform terminal transmitters) remain impossible to determine. We suggest an approach that uses the SSM-refined location uncertainties to quantify the probabilities of an animal’s occurrence in each habitat and infer which of the habitats it most likely visited.
Methods.
We test the performance of our approach against habitat use assays based on most likely locations from raw Argos Doppler estimates and Argos Doppler estimates refined with an SSM. For this, we combine a GPS tracking dataset (2214 location fixes) from one individual and an Argos-PTT tracking dataset (1708 location points) from 14 individual Continental Black-tailed Godwits (Limosa limosa limosa) breeding in agricultural grasslands in The Netherlands utilizing both simulations and empirical data to assess habitat use.
Results.
The approach that accounted for location uncertainties on top of a state-space model improved habitat assignments in the simulation study by 5% compared with only the SSM-refined Argos location points and by 23% compared with the raw Argos locations. We provide working code in R that can be reproduced for the analysis of habitat selection of animals followed with PTTs.
Conclusions.
Low-precision tracking data may be suitable to study habitat selection if location uncertainties are taken into account. The approach presented here has the potential to considerably improve the validity of such analyses, opening up new opportunities for the use of Argos Doppler data in analyses of habitat selection by animals. Since Argos Doppler location uncertainty parameters are required for the inference of the most likely used habitat, it is crucial that users acquire this information from Collecte Localisation Satellites (CLS) when initiating a new study.
... Satellite and archival tags were deployed on 59 female green turtles during the 2018, 2019 and 2020 breeding seasons, between August 13 and November 8. In 2018, 20 turtles were equipped with SPOT-375B tags (99 × 55 × 21 mm, 152 g, ® Wildlife Computers), which rely on the Argos satellite system only and have an accuracy of hundreds of meters to >1 km (Thomson et al. 2017). In 2019 and 2020, FastGPS tags (F6G 376B, 115 × 64 × 43.5 mm, 220 g, ® Lotek), which provide both Argos and GPS locations (mean ± SD accuracy of fast-acquisition GPS ranging from 172.0 ± 317.5 m for a minimum of 4 satellites to 26.0 ± 19.2 m for a maximum of 8 satellites; Hazel 2009), were de ployed on 9 and 15 females, respectively. ...
... The Argos tracking data were first filtered by removing the class Z locations, corresponding to the lowest location class provided by the Argos service (considered as error locations; Witt et al. 2010, Thomson et al. 2017). All GPS locations were obtained from at least 4 satellites. ...
... Yet, the fact that we did not detect a year effect when comparing only 2019 (14 406 nests) with 2020 (59 676 nests) suggests that competition for resting sites is not noticeable in the waters surrounding Poilão Island, or perhaps the difference in abundance was not sufficient to lead turtles to explore larger areas to find suitable resting sites. Alternatively, the observed year effect may potentially be influenced by the use of the Argos system in 2018, which is less accurate and can lead to larger home ranges (Thomson et al. 2017). Further deployments using the same tag type across more years may elucidate this matter. ...
Understanding the spatial distribution of wildlife is fundamental to establish effective conservation measures. Tracking has been key to assess movement patterns and connectivity of sea turtles, yet some regions of great significance are largely understudied. We tracked 44 green turtles from the largest rookery in the Eastern Atlantic, on Poilão Island, Guinea-Bissau, during the 2018 through 2020, to assess their inter-nesting movements, connectivity with nearby islands and fidelity to inter-nesting sites. Additionally, we investigated individual and environmental factors that may guide inter-nesting distribution and assessed the adequacy of a marine protected area to support this population during the breeding period. Green turtles had an overall home range of 124.45 km2, mostly occupying a restricted area around Poilão Island, with 52% of this home range falling within the no-take zone of the João Vieira-Poilão Marine National Park. Turtles exhibited strong fidelity to inter-nesting sites, likely as a strategy to save energy. Only two turtles performed significant excursions out of the Park, and connectivity between Poilão and nearby islands within the Park was limited. Larger turtles and turtles tagged later in the nesting season tended to have smaller core areas and home ranges, thus, experience may potentially benefit energy saving. This study highlights the importance of a marine protected area for the conservation of one of the largest green turtle breeding populations globally, and hints on ways to further increase its effectiveness.
... Restricted movement patterns have been commonly reported from foraging hawksbill turtles tracked in other areas [27][28][29][30]. The animals tracked in the present study exhibited similar restricted movements (Additional file 1: Table S1). ...
... The animals tracked in the present study exhibited similar restricted movements (Additional file 1: Table S1). The Red Sea hawksbill turtles exhibited similarly sized home range and core use areas as from other regions, with some smaller and some larger (Additional file 1: Table S1) [27][28][29][30]. Home ranges may simply reflect the physical constraints where each animal resides. ...
... Our results showed more than a tenfold difference in the home range and core area estimations when using GPS-derived locations compared to the Argos-derived locations (see also [27]). While home range estimations from both data types indicated highly constrained foraging grounds when compared to the available habitat, the difference in area estimation had a pronounced effect on finer-scale analyses, such as the benthic habitat classification (see Additional file 1: Fig. S1). ...
Background
Hawksbill turtles ( Eretmochelys imbricata ) are Critically Endangered throughout their global range, and concerningly little is known about this species in the Red Sea. With large-scale coastal development projects underway in the northern Red Sea, it is critical to understand the movement and habitat use patterns of hawksbill turtles in this environmentally unique region, so that effective conservation strategies can be implemented. We satellite tagged three hawksbill turtles, one 63 cm curved carapace length adult male captured near Wahlei Island, one 55 cm turtle captured in the Gulf of Aqaba, and one 56 cm turtle suffering from a floating syndrome which was captured at Waqqadi Island, rehabilitated, and released at Waqqadi Island. Turtles were tracked for 156, 199, and 372 days between October 2020 and November 2021.
Results
We calculated the home ranges and core use areas of hawksbill turtles using kernel-density estimations and found that each turtle showed high fidelity to their foraging sites. Home ranges calculated with GPS-derived locations ranged between 13.6 and 2.86 km 2, whereas home ranges calculated with Argos-derived locations ranged from 38.98 to 286.45 km ² . GPS-derived locations also revealed a higher proportion of time spent in coral and rock habitats compared to Argos, based on location overlap with the Allen Coral Reef Atlas. We also found that turtles were making shallow dives, usually remaining between 0 and 5 m.
Conclusions
While the number of tracked turtles in this study was small, it represents an important contribution to the current understanding of spatial ecology among foraging hawksbill turtles globally, and provides the first-ever reported hawksbill turtle tracking data from the Red Sea. Our results suggest that protecting coral reef habitats and implementing boating speed limits near reefs could be effective conservation measures for foraging hawksbill turtles in the face of rapid coastal development.
... However, a significant advantage of Fastloc GPS devices is that they acquire GPS ephemeris data in 10 s of milliseconds, compared to 5-12 s for SWIFT GPS devices, and will therefore be more suitable for briefly surfacing marine animals. This is a domain where Fastloc GPS devices have been widely applied with great success [48,49], and where SWIFT GPS devices will have little utility. Fastloc devices also process and compress signals on-board the tag, which can then be transmitted over the Argos network [50]-which is not currently a feature for SWIFT GPS devices. ...
The remote collection of animal location data has proliferated in recent decades, and higher-frequency data are increasingly available with battery-saving optimisations such as ‘snapshot’ algorithms that acquire GPS satellite data and post-process locations off-board. This is the first study to assess the effects of vegetation and topography on the fix success rate and location error of global positioning system (GPS) devices that use the SWIFT fix algorithm, developed by Lotek. To assess fix success rate (FSR—the proportion of successful fixes compared to the total number of attempts) and location error (LE), we conducted a stationary test at a predominately forested site on the South Island of New Zealand. The overall FSR was 83% (± 15.3% SD), which was affected strongly by canopy closure above 90%. Half of the locations were within 8.65 m of the true location, 79.7% were within 30 m, and 95% of locations were within 271 m. When 6 or more satellites were used, this reduced to 4.92 m and 18.6 m for 50% and 95%, respectively. Horizontal dilution of precision (HDOP), the number of satellites, and canopy closure all influenced location error. To field test the fix success rate of SWIFT GPS devices, we deployed them on forest-dwelling parrots with 2 and 3-h fix intervals, which showed similar FSR results to the stationary test when cavity-nesting individuals were removed (FSR mean ± SD = 81.6 ± 5.0%). The devices lasted an average of 147 days before depleting the battery, resulting in an average of 1087 successful fixes per individual at an average time of 9.38 s to acquire the GPS ephemeris, resulting in an average of 3.73 attempted locations per mAh of battery for PinPoint 350 devices. Our study provides a baseline for fix success rates and location errors under forested conditions that can be used for future SWIFT GPS tracking studies.
... Titanium and Inconel flipper tags have enabled the tracking of movements and habitat use with individual marine turtles across decades (Limpus and Limpus 2003) while current satellite telemetry technology is limited to tracking turtles over short durations, often less than a year, as in the current study. Over time, advances in satellite tag technology, such as the advent of Fastloc-Global Positioning System (GPS) that has allowed for more accurate locations and can receive signals in just milliseconds, have facilitated the tracking of air-breathing marine organisms that may only surface briefly (Dujon et al. 2014; Thomson et al. 2017). Collectively, the information derived from either CMR or telemetry studies has been used for analysis of fine-scale movements and behaviours of marine turtles during migration, and identification of foraging sites (Limpus and Limpus 2001;Troëng et al. 2005;Shimada et al. 2020). ...
Marine turtles encounter different threats during various life-history stages. Therefore, understanding their movements and spatial distribution is crucial for effectively managing these long-lived migratory organisms. This study combines satellite telemetry data with long-term capture-mark-recapture data derived from flipper tag studies to determine distribution patterns of endangered loggerhead turtles ( Caretta caretta ) during post-nesting migrations from different eastern Australian nesting sites. Individuals from the K’gari-Fraser Island and Great Barrier Reef island rookeries typically migrated northward, whereas individuals from mainland rookeries migrated equally northward and southward. Despite this difference in foraging distribution, loggerheads from the different rookeries did not differ substantially in their migration duration or distance travelled. The foraging distribution identified from successful satellite tag deployments represented 50% of the foraging distribution identified from a large long-term flipper tag recovery database. However, these satellite telemetry results have identified new migration and foraging habitats not previously recognised for loggerhead turtles nesting in eastern Australia. Additionally, they support the conclusion from a past study using flipper tag recovery data that the mainland nesting turtles migrate to different foraging grounds than the turtles nesting on Great Barrier Reef islands. Collectively, the two data sources provide valuable data on the migration route, habitat distribution and ecological range for a threatened genetic stock of loggerhead turtles.
... Upon surfacing, the SPLASH10-F-321A satellite tags transmitted location data, including both ARGOS and Fastloc GPS locations. For subsequent analyses, we only report on GPS positions based on their higher accuracy for estimating home range and fine-scale habitat use patterns (Dujon et al., 2014;Thomson et al., 2017). Additionally, the satellite transmitters were programmed to record and archive dive-depth, light levels, and ambient sea temperature. ...
The behaviour and spatial use patterns of juvenile manta rays within their critical nursery habitats remain largely undocumented. Here, we report on the horizontal movements and residency of juvenile reef manta rays (Mobula alfredi) at a recently discovered nursery site in the Wayag lagoon, Raja Ampat, Indonesia. Using a multi-disciplinary approach, we provide further corroborative evidence that the lagoon serves as an important M. alfredi nursery. A total of 34 juvenile rays were photo-identified from 47 sightings in the sheltered nursery between 2013–2021. Five (14.7%) of these individuals were resighted for at least 486 days (~1.3 years), including two juveniles resighted after 641 and 649 days (~1.7 years), still using the nursery. Visually estimated (n=34) disc widths (DW) of juveniles using the nursery site ranged from 150–240 cm (mean ± SD: 199 ± 19), and the DW of two juveniles measured using drones were 218 and 219 cm. Five juveniles were tracked using GPS-enabled satellite transmitters for 12–69 days (mean ± SD: 37 ± 22) in 2015 and 2017, and nine juveniles were tracked using passive acoustic transmitters for 69–439 days (mean ± SD: 182 ± 109) from May 2019–September 2021. Satellite-tracked individuals exhibited restricted movements within Wayag lagoon. The minimum core activity space (50% Utilisation Distribution-UD) estimated for these five individuals ranged from 1.1–181.8 km2 and the extent of activity space (95% UD) between 5.3–1,195.4 km2 in area. All acoustically tagged individuals displayed high residency within the nursery area, with no acoustic detections recorded outside the lagoon in the broader Raja Ampat region. These juveniles were detected by receivers in the lagoon throughout the 24 h diel cycle, with more detections recorded at night and different patterns of spatial use of the lagoon between day and night. The observed long-term residency of juvenile M. alfredi provides further compelling evidence that the Wayag lagoon is an important nursery area for this globally vulnerable species. These important findings have been used to underpin the formulation of management strategies to specifically protect the Wayag lagoon, which will be instrumental for the survival and recovery of M. alfredi populations in Raja Ampat region.
... We found that comparisons of quantitative measures of home-range area, core-use areas, and even movement rates across sirenian species or studies need to be interpreted with caution due to the differing data collection or analytical methods employed. Home-range estimates can vary greatly with the location accuracy of the tracking method (e.g., Thomson et al. 2017), with the smoothing parameter or bandwidth used in kernel density analyses (Kie 2013), and with the tracking duration of the animal. An individual's home range is often estimated as the 95% utilization distribution of its sightings or PTT/GPS locations; this area can encompass large areas that extend several kilometers beyond any actual locations (e.g., de Iongh et al. 1998). ...
The coastal marine and inland freshwater environments inhabited by manatees and dugongs around the world are spatially heterogeneous and highly dynamic over a range of time scales, often aligned with predictable geophysical cycles
(tidal, diel, seasonal). Central to sirenian adaptations for meeting these varied ecological challenges is plasticity in their movement behavior
, which allows them to find and utilize resources that are key to their survival and reproduction, to escape risks posed by predators and humans, and to leave habitats that become inhospitable. The development and deployment of animal-borne GPS tags have tremendously advanced our knowledge in the domain of small spatio-temporal scales by providing highly accurate locations many times per day. The recent addition of multi-sensor biologgers is further deepening our understanding of the connections between fine-scale behavioral changes and environmental features experienced by the animal. Individual sirenians generally show strong site fidelity within a season to one or a small number of high-use core areas within their home range. Manatees and dugongs usually move at a leisurely pace within and between habitats that provide forage, shelter, thermal refuge, and (for coastal manatees) fresh water. As marathon swimmers, sirenians can sustain a cruising speed of ~2 to 4 km/h for lengthy periods, but when threatened, they can briefly sprint at speeds up to 30 km/h. A common theme across species, ecosystems, and spatio-temporal scales is that access to forage is often constrained due to environmental fluctuations, including tidal cycles
in coastal systems, seasonal
water level
cycles in flood-pulse river systems, and seasonal temperature
changes in higher-latitude regions. Sirenians negotiate trade-offs among key activities within these fluctuating environments while apparently minimizing exposure to predators and other threats through their movement behavior
. There is an increasing body of evidence suggesting that many sirenian populations predominantly forage at night, plausibly as an adaptation to reduce risk of falling victim to hunters or, possibly, boat strikes. Sexual selection has also shaped the behavioral ecology of sirenian movements, as mature males are frequently on the move in search of estrous females during the breeding season
. Mating and parturition can alter female movements and habitat selection for brief periods, but otherwise reproductive status does not appear to strongly affect female movement behavior
over large or small scales. Further research is warranted on most sirenian populations to confirm these conclusions. Continued technological and analytical advancements promise to reveal more secrets of these fascinating and cryptic creatures.KeywordsBehavioral thermoregulationBiologgerCentral place foragingDiel movementsHome rangeMovement rateSatellite trackingSeasonal rangeSex differencesSireniansSite fidelitySmall-scaleTravel corridors
... However, the potential effects of survey frequency and sample size have yet to be explored for UASs. This directly contrasts with the wealth of studies exploring the effects of the number of animals and sampling frequency for remote tracking datasets, despite the potential for similar issues with frequency and sample size effects (Thomson et al., 2017;Sequeira et al., 2019;Shimada et al., 2020). For instance, home range size estimates are particularly impacted by sample size and bias towards individuals (Borger et al., 2006;Plotz et al., 2016;Thomson et al., 2017), therefore, it is essential to identify key parameters impacting the interpretation of UAS surveys. ...
... This directly contrasts with the wealth of studies exploring the effects of the number of animals and sampling frequency for remote tracking datasets, despite the potential for similar issues with frequency and sample size effects (Thomson et al., 2017;Sequeira et al., 2019;Shimada et al., 2020). For instance, home range size estimates are particularly impacted by sample size and bias towards individuals (Borger et al., 2006;Plotz et al., 2016;Thomson et al., 2017), therefore, it is essential to identify key parameters impacting the interpretation of UAS surveys. ...
... As a case in point, 1000s of sea turtles have been individually GPS-tracked at breeding and foraging grounds globally (Hays and Hawkes, 2018). However, at given sites, only small numbers of animals (10s) are typically tracked, with a very strong bias towards adult females (Thomson et al., 2017;Lamont and Iverson, 2018). Yet, there is clear evidence that males and females use breeding sites differently and, often, dynamically (James et al., 2005;Arendt et al., 2011b;Schofield et al., 2013). ...
Quantifying how animals use key habitats and resources for their survival allows managers to optimise conservation planning; however, obtaining representative sample sizes of wildlife distributions in both time and space is challenging, particularly in the marine environment. Here, we used unoccupied aircraft systems (UASs) to evaluate temporal and spatial variation in the distribution of loggerhead sea turtles (Caretta caretta) at two high-density breeding aggregations in the Mediterranean, and the effect of varying sample size and survey frequency. In May–June of 2017 to 2019, we conducted 69 surveys, assimilating 10,075 inwater turtle records at the two sites. Optimal time interval between surveys to capture the dynamics of aggregations over the breeding period was <2-week intervals and >500 turtles (from the combined surveys). This minimum threshold was attributed to the core-area use of female turtles shifting across surveys in relation to wind direction to access warmer nearshore waters and male presence. Males were more widely distributed within aggregations than females, particularly in May when mating encounters were high. Most males were recorded swimming and oriented parallel to shore, likely to enhance encounter rates with females. In contrast, most females were generally stationary (resting on the seabed or basking), likely to conserve energy for reproduction, with orientation appearing to shift in relation to male numbers at the breeding area. Thus, by identifying the main factors regulating the movement and distribution of animals, appropriate survey intervals can be selected for appropriate home range analyses. Our study demonstrates the versatility of UASs to capture the fine-scale dynamics of wildlife aggregations and associated factors, which is important for implementing effective conservation.
... Data managers were contacted between October 2018 and January 2019 to request access to their datasets with a response deadline of the end of April 2019. Studies using ARGOS Doppler tags (insufficient spatial accuracy; Thomson et al., 2017), captive birds, laboratory-based tests of GPS devices, lacking altitude data or tracking predominantly pelagic species were not included. In total, we obtained permission to use data from 65 suitable GPS tracking studies (Figure 1), representing 27 species and 1,454 individual birds. ...
Wind turbines and power lines can cause bird mortality due to collision or electrocution. The biodiversity impacts of energy infrastructure (EI) can be minimised through effective landscape-scale planning and mitigation. The identification of high-vulnerability areas is urgently needed to assess potential cumulative impacts of EI while supporting the transition to zero carbon energy.
We collected GPS location data from 1,454 birds from 27 species susceptible to collision within Europe and North Africa and identified areas where tracked birds are most at risk of colliding with existing EI. Sensitivity to EI development was estimated for wind turbines and power lines by calculating the proportion of GPS flight locations at heights where birds were at risk of collision and accounting for species' specific susceptibility to collision. We mapped the maximum collision sensitivity value obtained across all species, in each 5 × 5 km grid cell, across Europe and North Africa. Vulnerability to collision was obtained by overlaying the sensitivity surfaces with density of wind turbines and transmission power lines.
Results: Exposure to risk varied across the 27 species, with some species flying consistently at heights where they risk collision. For areas with sufficient tracking data within Europe and North Africa, 13.6% of the area was classified as high sensitivity to wind turbines and 9.4% was classified as high sensitivity to transmission power lines. Sensitive areas were concentrated within important migratory corridors and along coastlines. Hotspots of vulnerability to collision with wind turbines and transmission power lines (2018 data) were scattered across the study region with highest concentrations occurring in central Europe, near the strait of Gibraltar and the Bosporus in Turkey.
Synthesis and applications. We identify the areas of Europe and North Africa that are most sensitive for the specific populations of birds for which sufficient GPS tracking data at high spatial resolution were available. We also map vulnerability hotspots where mitigation at existing EI should be prioritised to reduce collision risks. As tracking data availability improves our method could be applied to more species and areas to help reduce bird-EI conflicts.
