Skip to main content

Advertisement

The effect of biologging systems on reproduction, growth and survival of adult sea turtles

Article metrics

Abstract

Background

Telemetry and biologging systems, ‘tracking’ hereafter, have been instrumental in meeting the challenges associated with studying the ecology and behaviour of cryptic, wide-ranging marine mega-vertebrates. Over recent decades, globally, sea turtle tracking has increased exponentially, across species and life-stages, despite a paucity of studies investigating the effects of such devices on study animals. Indeed, such studies are key to informing whether data collected are unbiased and, whether derived estimates can be considered typical of the population at large.

Methods

Here, using a 26-year individual-based monitoring dataset on sympatric green (Chelonia mydas) and loggerhead (Caretta caretta) turtles, we provide the first analysis of the effects of device attachment on reproduction, growth and survival of nesting females.

Results

We found no significant difference in growth and reproductive correlates between tracked and non-tracked females in the years following device attachment. Similarly, when comparing pre- and post-tracking data, we found no significant difference in the reproductive correlates of tracked females for either species or significant carry-over effects of device attachment on reproductive correlates in green turtles. The latter was not investigated for loggerhead turtles due to small sample size. Finally, we found no significant effects of device attachment on return rates or survival of tracked females for either species.

Conclusion

While there were no significant detrimental effects of device attachment on adult sea turtles in this region, our study highlights the need for other similar studies elsewhere and the value of long-term individual-based monitoring.

Background

Telemetry and biologging systems, ‘tracking’ hereafter, have been instrumental in meeting the challenges associated with studying the ecology and behaviour of cryptic, wide-ranging marine mega-vertebrates [1]. Such systems have evolved greatly, particularly over the last two decades, becoming smaller, with increased storage capacity. Thus, they have provided scientists with a powerful tool, with which to obtain key information not previously available [2]. Technological advancements have permitted the tracking of smaller animals [3,4,5], across multiple life stages [6] and around the world [7, 8]. Although tracking has greatly furthered our understanding of the natural world, it is key to determine whether the data collected are unbiased and, whether derived estimates can be considered typical of the population at large.

Although benign in some instances [9,10,11,12], device attachment does not always come free of cost to study animals. For example, it can lead to increased energy expenditure [13,14,15], influence reproductive success [16,17,18] as well as alter natural behaviours [19,20,21,22]. Device improvements have led to the tracking of animals over extensive periods of time [23,24,25], which may have physiological implications [26], with potential carry-over effects [27, 28]. In addition, a recent meta-analysis on birds highlights that these effects may be cumulative, such that, for example, effects on annual survival could also impact reproduction [29]. Therefore, assessing the effects of device attachment on the overall fitness of study animals, in both the short- and long-term, focussing on multiple traits [29], is crucial to mitigate against potential deleterious effects in the future.

Over the years, tracking has increased exponentially, worldwide, across species and life-stages in sea turtles [30, 31], contributing widely to priority research questions [32]. It has allowed researchers to explore migration patterns [33], diving behaviours [34,35,36] and foraging strategies of sea turtles [37, 38], as well as providing improved estimates of sea turtle abundance [39, 40]. Despite this increase in use, the number of studies that consider ethical or welfare issues associated with device attachment is low 20%; [31], and the number of studies that investigate welfare issues as their main theme is even lower 2%; [31]. Device attachment has been modelled as increasing drag and energy expenditure [15, 41], potentially influencing reproductive correlates and survival of study animals. Empirically, differences in swimming efficiency and diving capacities, as well as differences in data quality, have been reported in leatherback turtles (Dermochelys coriacea) based on attachment methods alone [20, 21]. Such studies highlight the need for an evaluation of the effects of device attachment on life-history traits and survival of study animals, as well as the need to compare data between tracked and non-tracked individuals.

Here, we provide the first long-term analysis of the effects of device attachment on reproduction, growth and annual survival of green (Chelonia mydas) and loggerhead (Caretta caretta) turtles nesting sympatrically, using a 26-year individual-based monitoring dataset, with devices first attached in 1997. We compare differences in reproductive correlates, growth and annual survival, both between tracked and non-tracked females, and pre- and post-tracking of individual females.

Methods

Further details for each corresponding section can be found in the Additional file 1 for this article.

Study site and data collection

Data were collected at Alagadi beach, Northern Cyprus (35°33 N, 33°47 E) between 1992 and 2017, where intensive night-time monitoring and tagging programmes have been carried out (see [42] for detailed methods). Female identification was based on flipper tags and passive integrated transponder tags (PIT tags; [43]). Curved carapace length (CCL) notch to notch was used as a measure of female size. Growth was calculated from CCL measurements (see [44] for further details). Due to the intensive nature of the monitoring carried out at Alagadi, very few nests per year cannot be attributed to individual females [45]. However, when a missed nest was apparent (i.e. intervals of > 18 days observed between two clutches), clutch frequency was adjusted and referred to as ECF (estimated clutch frequency) hereafter (see [42, 45] for further details). Mean clutch size and ECF were calculated for each individual, each nesting season. Remigration interval (RI) was calculated as the number of years elapsed between two nesting seasons. Date of first nest was determined as the day of the year (d.o.y) the female was first observed laying. ECF and date of first nest were not calculated in 1992 due to incomplete survey effort.

Device attachment

A variety of devices (Table 1) were attached to nesting females between 1997 and 2017, following the protocol outlined by Godley et al. [46]. Satellite transmitters (PTTs: platform terminal transmitters) were attached for studies of migration, whereas all other devices were designed to be recovered within a breeding season to investigate inter-nesting behaviours. Given that all devices were attached in a similar manner and were of similar magnitude, we consider animals with any devices attached as ‘tracked’, irrespective of device type. In some instances, multiple devices of the same type were attached within a breeding season using the same attachment base. For the analysis, however, we focussed only on the last attached device. Note that attaching multiple devices of the same type to the same base attachment platform is no different to attaching one device early in the nesting season, which is not retrieved between clutches. Although some females were fitted simultaneously with two devices, not all females returned to foraging grounds with both devices attached (Table 1). Except for PTTs, whenever possible, devices were retrieved, leaving behind the attachment base, except in 1997, when the base was also removed. Individuals for which the base was removed were included in this analysis of return rates but were excluded for the remainder of the analysis. Females that returned to foraging grounds without any devices attached were included in the analysis because, although the attachment base was shaped to reduce drag, it could not be excluded that it did not affect individuals. We distinguish between ‘tracked’ females with a device attached (hereafter referred to as ‘device attached’) and females for which only the attachment base remained (hereafter referred to as ‘attachment base only’).

Table 1 Devices attached to nesting females

Statistical analysis – Return rates

Fisher’s exact tests were used to calculate differences in return rates among groups, looking at differences between ‘tracked’ and ‘non-tracked’ as well as within ‘tracked’ groups.

Odds ratios were used as a measure of effect size. Females that were not resighted were assumed to be dead, although it is possible that individuals migrated to other nesting beaches which are not monitored during the night.

Statistical analysis – Among-female differences

To investigate differences between ‘tracked’ and ‘non-tracked’ females, ‘initial year’, i.e. year of device attachment for ‘tracked’ females, was determined as the first year of capture for ‘non-tracked’ neophyte (first time nesters) females and was randomly generated for other ‘non-tracked’ females for which three or more captures were available. This means that the analysis only included females for which two or more captures were available.

One-way analysis of variance and linear models were used to compare differences in body size and reproductive correlates among groups. The analysis was conducted in R version 3.2.3. Models were fitted by stepwise model simplification and significance of removed terms was assessed with a threshold of P = 0.05 [47]. We checked for over-dispersion, normality, homoscedasticity and homogeneity of variance. Female size and ‘year’ were included as fixed effects to control for larger females laying larger clutches [42] and to investigate whether differences were due to annual effects. Partial omega squared ωp2 was used as a measure of effect size. Tukey post-hoc tests were used to look at pairwise comparisons, using the package multcomp [48]. Furthermore, we looked at the interactions between growth covariates and device attachment to investigate whether device attachment influenced growth of ‘tracked’ females (see [44] for further details).

Statistical analysis – Within-female differences

Generalised linear mixed models (GLMM) and generalised least squares (GLS) were used to investigate within-female differences in reproduction between pre- and post-tracking years. To detect small non-significant effects of device attachment, a systematic analysis was used to look at seasonal (mean clutch size*ECF) and annual reproductive output (seasonal reproductive output/RI). Models were implemented using nlme and mgcv packages [49, 50] and included female identity as a random effect and ‘year’ as a fixed effect. CCL was also included in models of mean clutch size and seasonal/annual reproductive output. Models were fitted as explained in the previous section.

Statistical analysis – Carry-over effects

To investigate whether device attachment had any carry-over effects, GLMM and GLS were used on a subset dataset that included only females for which two pre- (including year of device attachment) and two post-tracking seasons were available. This restricted the analysis to nine green turtle females, with ‘attachment base only’ and ‘device attached’ groups pooled. Sample size was too small for loggerhead turtles (n = 4). Models were fitted as explained in the previous section.

Statistical analysis – Survivorship estimates

Encounter histories were created based on successful nesting attempts. Survival probability was estimated using the multi-state model in the programme MARK [51], assuming a breeding state (B; observable state) and a non-breeding state (NB; unobservable state). The parameters estimated were survival probability (S), encounter probability (p) and transition probabilities between states (ψB → NB and ψNB → B). Goodness of fit was assessed using the programme U-CARE [52]. In particular, test component 3G.SR was used to evaluate the effect of presumed transient individuals on survival probabilities and, test component M.ITEC was used to test for trap-dependence. Transient individuals are individuals that are caught, marked and released but never recaptured. Such individuals can be considered in transit and therefore have a zero probability of recapture although they are alive. Model selection was based on the lowest qAICc value (corrected quasi-likelihood Akaike information criterion). Parameters were estimated using the Markov chain Monte Carlo method in MARK. Parameters estimates were based on posterior distributions and 95% highest posterior density credibility intervals were reported.

Results

Return rates

A total of 170 devices (Table 1) were attached to 51 green and 50 loggerhead turtle females between 1997 and 2017. Of these females, 13 green and 9 loggerhead turtle females had devices attached in two different years and 3 green turtle females had devices attached in three different years. However, the remainder of the analysis focused on females that had devices attached in a single year.

Almost all green turtles (93%) and just under three quarters of loggerhead turtles (70%) that had devices attached in a single year were resighted within a maximum of 15 years (Table 2). Of the females that had devices attached in a single year, 17% of green and 48% of loggerhead turtles were neophyte females. For both species, there was no significant difference in return rates between groups (Table 2 and Additional file 1: Table S1). Based on the odds ratios, ‘tracked’ neophyte females were no more or less likely to be resighted than ‘non-tracked’ neophyte females or ‘tracked’ remigrant females (Additional file 1: Table S1). Similarly, ‘attachment base only’ females were no more or less likely to be resighted than ‘device attached’ females (Additional file 1: Table S1).

Table 2 Returns rates for tracked and non-tracked females

Among-female differences

The basic parameters for each group and species are summarised in Additional file 1: Table S2.

Green turtles

For green turtles, there was a large significant difference in body size in the year of device attachment among groups (F2,125 = 9.30, P < 0.001, ωp2 = 0.115, Fig. 1a), with ‘device attached’ females being on average 6.0 cm (95% CI: 2.6–9.4, Padj < 0.001, n = 20) larger than ‘non-tracked’ females (n = 94). There was, however, no significant difference in body size between ‘device attached’ and ‘attachment base only’ females (Padj = 0.416) and between ‘non-tracked’ and ‘attachment base only’ females (Padj = 0.176, n = 14).

Fig. 1
figure1

Effects on reproduction among females. Differences in female size (a) in the year of device attachment and reproductive correlated in the year following device attachment (b-e) for groups of females. For ‘non-tracked’ females, year(s) of and following device attachment represent randomly generated following recaptures. Observed differences in mean clutch size (eggs) for green turtles and in date of first nest (d.o.y) for both species were due to annual effects rather than device attachment (see main text). Mean ± SE. CCL: curved carapace length; ECF: estimated clutch frequency; RI: remigration interval; d.o.y: day of the year

In addition, there was no significant difference in mean clutch size (F2,125 = 2.92, P = 0.058, ωp2 = 0.029, Fig. 1b), RI (F2,125 = 0.65, P = 0.586, ωp2 < 0.001, Fig. 1d) and date of first nest (F2,125 = 3.00, P = 0.053, ωp2 = 0.030, Fig. 1e) among groups in the years following device attachment. Observed difference in mean clutch size (F1,126 = 5.68, P = 0.019, ωp2 = 0.037, Fig. 1b) and date of first nest (F1,126 = 12.26, P < 0.001, ωp2 = 0.065, Fig. 1e) were due to annual effects rather than device attachment.

However, there was a significant difference in ECF among groups in the years following device attachment (F2,126 = 6.528, P = 0.002, ωp2 = 0.085, Fig. 1c), which could not be explained by annual effects (F1,125 = 1.09, P = 0.297, ωp2 = 0.006). ‘Attachment base only’ females laid on average 1.10 (0.29–1.91) clutches more and ‘device attached’ females laid on average 0.63 (− 0.07–1.33) clutches more than ‘non-tracked’ females (Padj = 0.005 and Padj = 0.084 respectively). There was, however, no significant difference in ECF between ‘device attached’ and ‘attachment base only’ females (Padj = 0.492). Finally, device attachment did not have a significant effect on post-maturity growth or compound annual growth rates (Fig. 2a and b, Additional file 1: Table S3).

Fig. 2
figure2

Effects on growth. Summary of (a, b) generalised additive mixed model and (c, d) generalised linear mixed model analyses of annual growth and compound annual growth rates (CAGR) for (a, b) green and (c, d) loggerhead turtles for the complete dataset (1992–2017) mentioned in Omeyer et al. [44]. The response variables are shown on the y axis, shifted by the intercept for ease of visualisation. Grey shaded area represents 95% confidence intervals. Dashed lines represent the absence of growth. The black lines represent the model outputs for growth records of ‘non-tracked’ and ‘tracked’ females pooled and represent the model outputs presented in Omeyer et al. [44]. The blue lines represent the models outputs for ‘non-tracked’ females and for ‘tracked’ females up until year of device attachment

Loggerhead turtles

For loggerhead turtles, there was no significant difference in size (F2,61 = 1.58, P = 0.215, ωp2 = 0.018, Fig. 1a), mean clutch size (F2,61 = 0.63, P = 0.534, ωp2 = 0.012, Fig. 1b), RI (F2,61 = 0.64, P = 0.532, ωp2 = 0.012, Fig. 1d) and date of first nest (F2,61 = 1.27, P = 0.289, ωp2 = 0.008, Fig. 1e) between ‘attachment base only’ (n = 6), ‘device attached’ (n = 8) and ‘non-tracked’ (n = 50) females in the year of device attachment for female size and in the years following device attachment for reproductive correlates. Observed differences in date of first nest were due to annual effects rather than device attachment (F1,63 = 5.98, P = 0.017, ωp2 = 0.073, Fig. 1e).

However, there was a large significant difference in ECF among groups in the years following device attachment (F2,61 = 5.06, P = 0.009, ωp2 = 0.121, Fig. 1c), which could not be explained by annual effects (F2,60 = 0.76, P = 0.386, ωp2 = 0.016). ‘Attachment base only’ females laid on average 1.47 (0.09–2.85) clutches more and ‘device attached’ females laid on average 1.20 (0.03–2.37) clutches more than ‘non-tracked’ females (Padj = 0.035 and Padj = 0.044 respectively). There was no significant difference in ECF between the ‘device attached’ and ‘attachment base only’ females (Padj = 0.921). Finally, device attachment did not have a significant effect on post-maturity growth or compound annual growth rates (Fig. 2 c and d, Additional file 1: Table S3).

Within-female differences

For both species, there was no significant difference in all reproductive correlates between pre- and post-tracking years (Fig. 3, Additional file 1: Table S4). Observed differences in mean clutch size and date of first nest for both species and, in seasonal reproductive output for green turtles between pre- and post-tracking years for particular groups were due to annual effects (Fig. 3, Additional file 1: Table S4). To further explore whether RI increased between pre- and post-tracking years, we looked at pairs of randomly generated consecutive RIs for ‘non-tracked’ females. We found that RI did not significantly increase between pairs for both species (green turtles: F1,64 = 1.64, P = 0.205; loggerhead turtles: F1,30 = 0.07, P = 0.798).

Fig. 3
figure3

Effects on reproduction within females. Differences in mean clutch size (a), estimated clutch frequency (ECF, b), remigration interval (RI, c), date of first nest (d.o.y: day of the year, d), seasonal reproductive output (SRO, e) and annual reproductive output (ARO, f) between pre- and post-tracking years for the different groups and species. Pre-tracking years include all years including year of device attachment. Observed difference in mean clutch size (‘attachment base only’ group for green turtles and pooled group for loggerhead turtles), date of first nest (for all groups) and seasonal reproductive output (‘attachment base only’ for green turtles) between pre- and post-tracking years for particular groups were due to annual effects rather than device attachment (see Table 5). Mean ± SE

Carry-over effects

For green turtles, there were no significant carry-over effects of device attachment on mean clutch size (χ21 = 0.10, P = 0.756, Fig. 4a), ECF (χ21 = 0.22, P = 0.639, Fig. 4b), date of first nest (χ21 = 1.56, P = 0.212, Fig. 4d), seasonal reproductive output (χ21 = 0.002, P = 0.963, Fig. 4e) and annual reproductive output (χ21 = 2.84, P = 0.092, Fig. 4f). However, RI significantly increased by 0.67 year over the course of 4 breeding events, with device attachment occurring on the second breeding event (χ21 = 3.93, P = 0.048, Fig. 4c). This increase in RI was not due to annual effects (χ21 = 1.88, P = 0.171). To further explore this result, we compared RI of eight non-tracked females with similar histories across the same time frame and also found RI to increase in a similar manner (χ21 = 6.44, P = 0.011).

Fig. 4
figure4

Carry-over effects on reproduction. Variation in mean clutch size (a), estimated clutch frequency (ECF, b), remigration interval (RI, c), date of first nest (d.o.y: day of year, d), seasonal reproductive output (SRO, e) and annual reproductive output (ARO, f) as a function of years since device attachment for green turtles, with year 0 being year of device attachment. The vertical dashed line represents year of device attachment. Grey shaded area represents 95% confidence intervals

Survivorship

Because no estimates are available for these two populations, we had to estimate annual survival for both ‘tracked’ and ‘non-tracked’ females. Goodness of fit results and model output tables (Additional file 1: Tables S5-S8) can be found in the supplemental material for this article. All parameter estimates for both species and groups can be found in Additional file 1: Table S9.

For green turtles, annual survival was 0.91 (0.88–0.94) for ‘non-tracked’ females and 0.97 (0.95–0.99) for ‘tracked’ females. Confidence intervals were higher for ‘tracked’ females than for ‘non-tracked’ females for green turtles.

For loggerhead turtles, annual survival was 0.44 (0.30–0.61) for transient ‘non-tracked’ females, 0.83 (0.77–0.88) for remigrant ‘non-tracked’ females and 0.82 (0.73–0.90) for ‘tracked’ females. Estimates for ‘tracked’ and ‘non-tracked’ remigrant loggerhead turtles were similar, with overlapping confidence intervals.

Discussion

Here, we provide the first analysis of the effects of device attachment on life-history traits of sea turtles. We found no evidence of deleterious effects of device attachment on reproduction, growth and annual survival of green and loggerhead turtles nesting sympatrically.

The most important effect of device attachment has been suggested to be the increase in energy expenditure, as a result of increased drag [15, 41, 53]. Sea turtles are capital breeders [54,55,56], meaning that the decision to nest in a given year results from the combination of an assessment of body condition and favourable environmental conditions [57]. Thus, an increase in energy expenditure during non-breeding years could have knock-on effects on the breeding phenology of study animals. Indeed, if device attachment results in reduced locomotor capacity through reduced swim speed, individuals fitted with devices at nesting grounds could arrive later at foraging grounds, but also at breeding grounds, if devices remain attached throughout the RI, which is not uncommon. In this study, as well as in previous studies [58, 59], females have been resighted at breeding grounds with devices still attached. Despite this, we found no evidence of a delayed arrival of ‘tracked’ females when subsequently returning to nest, both compared to the population at large, as well as within ‘tracked’ females. Similarly, no evidence of a delay in return rates has been observed for male loggerhead turtles [60]. In all instances, date of first nest was influenced by annual effects for both species, with first, median and final lay date having shifted towards earlier nesting over the study period [61]. Similar shifts in the breeding phenology of sea turtles have been observed in a number of populations, as a result of climate change [62,63,64,65].

In addition, device attachment could result in females requiring more time to accumulate sufficient resources to initiate reproduction [15, 20, 41, 66]. Indeed, if swimming efficiency and foraging ability are impaired by device attachment [20, 21], later arrival at foraging grounds could result in longer RIs. However, Benson et al. [67] noted that ‘tracked’ individuals arrived presumably on time at foraging grounds and we found no evidence for longer RIs in ‘tracked’ females. Although RI appeared to increase post-tracking, the magnitude of the effect was small and not significant (Fig. 3). Likewise, RI did not increase when comparing pairs of consecutive recaptures for ‘non-tracked’ females, suggesting that device attachment is unlikely to be the cause of the increase observed in ‘tracked’ females. The small sample size in this study, however, may have prevented the detection of such a trend, as a power analysis showed that a sample size of more than 400 would be needed to detect a small significant effect of device attachment on RI with a 0.8 probability. Furthermore, while RI appeared to increase with years since device attachment in green turtles, we found a similar significant increase for eight ‘non-tracked’ females with similar capture histories across the same time frame. This suggests that device attachment is unlikely to have had carry-over effects on RI of green turtles. Due to the scaling of the effects of drag on swim speed, the impacts of device attachment might be heightened in pursuit predators, such as penguins [68], and lessened in herbivores and benthic feeders, such as adult green and loggerhead turtles [69], which could explain the absence of an effect on RI. Despite the absence of a baseline for ‘non-tracked’ individuals, it is possible that changes in behaviour and swimming efficiency could have offset the effects of device attachment. Indeed, tracked females have been observed to forage ‘en route’ back to their over-wintering sites, travelling at depths and speeds which minimise drag [46, 70, 71] and thus, minimising the cost of migration and potentially device attachment.

Increased energy expenditure associated with device attachment may negatively influence reproductive output of study animals. Indeed, as females have to balance a tight energy budget, attaching devices during the inter-nesting period could have knock-on effects on their seasonal reproductive output [53]. Similarly, to overcome the increase in drag, when returning to nest, study animals either can (1) reduce their swim speed and arrive later at breeding grounds than the rest of the population, which does not appear to be the case or, (2) increase their power output thus decreasing the proportion of energy reserves available to fuel reproduction [53]. Here, we found no evidence for impaired reproductive output for either species, suggesting that females were not energetically compromised in the year of device attachment, as well as in subsequent breeding seasons. However, we cannot entirely exclude that some effects of device attachment on reproduction may have been masked by environmental effects, by the use of estimated reproductive correlates and by the targeted sampling of remigrant females, with high nest site fidelity, which can explain the differences in ECF among groups.

The cost of device attachment during the inter-nesting period will not be uniform across study animals, potentially preventing the detection of within-season effects on reproduction. While some females remain close to the nesting beach, resting on the sea floor and not actively foraging [34, 72, 73], others forage [72,73,74] and commute between beaches, and at times, countries, to lay their eggs [24, 39, 40]. Although it appears highly unlikely that device attachment will result in females laying fewer clutches, as improved estimates of clutch frequency have been obtained using this method, [39, 40], it will be hard to determine whether device attachment results in females laying smaller clutches due to increased energy expenditure. Depletion of resources, as shown by haematological data, is more likely to trigger the need for individuals to forage and therefore to cease reproduction [56, 75,76,77].

Due to the partitioning of finite resources [78], attaching devices to animals could compromise their growth, especially in juveniles, as growth is negligible in adults [44]. Nevertheless, Seney et al. [79] found no effect of device attachment on growth of captive reared juvenile individuals. Similarly, we found no evidence for such an effect in wild adult nesting green and loggerhead turtles. The significant difference in size at device attachment among groups for green turtles is likely due to targeted sampling of remigrant females in preference for some studies. Indeed, remigrant females are known to be significantly larger than neophyte nesters at Alagadi beach [44, 45] and represented the vast majority (92%) of tracked green turtles. By contrast, sampling for loggerhead turtles aimed to target females across a range of sizes [24], which resulted in a more even ratio (almost 1:1 ratio) of neophyte to remigrant nesters and can explain the absence of a significant difference in body size at device attachment among groups.

Ultimately, determining whether device attachment influences annual survival of study animals is crucial. Although device attachment has been suggested to influence return rates in some species [19, 80, 81], potentially due to increased energetic expenditure [82], we found no significant effects of device attachment on return rates or survival of ‘tracked’ females for either species.

Annual survival estimates for green turtles are not available for the Mediterranean [69], however, estimates calculated here for ‘non-tracked’ females (0.91, CI: 0.88–0.94) fall within the predictions for green turtles populations around the world (0.88, CI: 0.80–0.93). Annual survival estimates for ‘tracked’ green turtle females were higher, with non-overlapping confidence intervals, than those for ‘non-tracked’ females, which was likely due to targeted sampling of remigrant females, with higher nest site fidelity. For loggerhead turtles, our estimates for ‘non-tracked females’ (0.83, CI: 0.77–0.88) also fall within those predicted by Pfaller et al.’s [83] for loggerhead turtles around the world (0.82, CI: 0.79–0.85), are comparable to previous estimates calculated for loggerhead turtles in the Mediterranean [84, 85] and were similar to those of ‘tracked’ females in our study.

These estimates suggest that device attachment does not result in reduced annual survival in sea turtles and highlight yet again the heterogeneity of annual survival between green and loggerhead turtles worldwide, with loggerhead turtle estimates being consistently lower than those of green turtles [83]. The relatively low estimates for loggerhead turtles are thought to be linked to anthropogenic mortality, in particular bycatch, levels of which are unsustainable in the Mediterranean [86]. Finally, although annual survival estimates are prone to problems associated with tag loss [83], in this study, female identification was based on a combination of both flipper and PIT tag readings, making estimates more robust to tag loss, as PIT tag loss is thought to be negligible [87].

Conclusion

We provide the first analysis of the effects of device attachment on life-history traits of adult sea turtles, as well as provide the first estimates of annual survival for green turtles in the Mediterranean. Although we cannot entirely exclude that small sample size, individual variation and climate change prevented the detection of an effect, device attachment was found to have no significant detrimental effects on adult sea turtles. Nevertheless, in all instances, device attachment should aim to minimise device size and drag, using low profile tags for example [88]. Finally, this study highlights the need for other similar studies elsewhere and the value of long-term individual-based monitoring.

Abbreviations

ARO:

Annual reproductive output

CCL:

Curved carapace length

CI:

Confidence interval

d.o.y:

Day of the year

ECF:

Estimated clutch frequency

GLMM:

Generalised linear mixed model

GLS tags:

Global location sensing tags

GLS:

Generalised least squares

na:

Not applicable

PIT tag:

Passive integrated transponder tag

PTT:

Platform terminal transmitters

RI:

Remigration interval

SE:

Standard error

SRO:

Seasonal reproductive output

TDR:

Time depth recorder

References

  1. 1.

    Crossin GT, Cooke SJ, Goldbogen JA, Phillips RA. Tracking fitness in marine vertebrates: current knowledge and opportunities for future research. Mar Ecol Prog Ser. 2014;496:1–17.

  2. 2.

    Hussey NE, Kessel ST, Aarestrup K, Cooke SJ, Cowley PD, Fisk AT, et al. Aquatic animal telemetry: a panoramic window into the underwater world. Science. 2015;348:1255642.

  3. 3.

    Thums M, Whiting SD, Reisser JW, Pendoley KL, Pattiaratchi CB, Harcourt RG, et al. Tracking Sea turtle hatchlings - a pilot study using acoustic telemetry. J Exp Mar Biol Ecol. 2013;440:156–63.

  4. 4.

    Mansfield KL, Wyneken J, Porter WP, Luo J. First satellite tracks of neonate sea turtles redefine the “lost years” oceanic niche. Proc R Soc B. 2014;281:20133039.

  5. 5.

    Scott R, Biastoch A, Roder C, Stiebens VA, Eizaguirre C. Nano-tags for neonates and ocean-mediated swimming behaviours linked to rapid dispersal of hatchling sea turtles. Proc R Soc B. 2014;281:20141209.

  6. 6.

    Hazen EL, Maxwell SM, Bailey H, Bograd SJ, Hamann M, Gaspar P, et al. Ontogeny in marine tagging and tracking science: technologies and data gaps. Mar Ecol Prog Ser. 2012;457:221–40.

  7. 7.

    Block BA, Jonsen ID, Jorgensen SJ, Winship AJ, Shaffer SA, Bograd SJ, et al. Tracking apex marine predator movements in a dynamic ocean. Nature. 2011;475:86–90.

  8. 8.

    Ferreira LC, Thums M, Meeuwig JJ, Vianna GMS, Stevens J, McAuley R, et al. Crossing latitudes-long-distance tracking of an apex predator. PLoS One. 2015;10:e0116916.

  9. 9.

    McMahon CR, Field IC, Bradshaw CJA, White GC, Hindell MA. Tracking and data-logging devices attached to elephant seals do not affect individual mass gain or survival. J Exp Mar Biol Ecol. 2008;360:71–7.

  10. 10.

    Carey MJ. Leg-mounted data-loggers do not affect the reproductive performance of short-tailed shearwaters (Puffinus tenuirostris). Wildl Res. 2011;38:740–6.

  11. 11.

    Agnew P, Lalas C, Wright J, Dawson S. Effects of attached data-logging devices on little penguins (Eudyptula minor). Mar Biol. 2013;160:2375–82.

  12. 12.

    Lynch SD, Marcek BJ, Marshall HM, Bushnell PG, Bernal D, Brill RW. The effects of pop-up satellite archival tags (PSATs) on the metabolic rate and swimming kinematics of juvenile sandbar shark Carcharhinus plumbeus. Fish Res. 2017;186:205–15.

  13. 13.

    Wilson RP, Kreye JM, Lucke K, Urquhart H. Antennae on transmitters on penguins: balancing energy budgets on the high wire. J Exp Biol. 2004;207:2649–62.

  14. 14.

    Vandenabeele SP, Shepard EL, Grogan A, Wilson RP. When three per cent may not be three per cent; device-equipped seabirds experience variable flight constraints. Mar Biol. 2012;159:1–14.

  15. 15.

    Jones TT, Van Houtan KS, Bostrom BL, Ostafichuk P, Mikkelsen J, Tezcan E, et al. Calculating the ecological impacts of animal-borne instruments on aquatic organisms. Methods Ecol Evol. 2013;4:1178–86.

  16. 16.

    Ackerman JT, Adams J, Takekawa JY, Carter HR, Darrell L, Newman SH, et al. Effects of radiotransmitters on the reproductive performance of Cassin’s auklets. Wildl Soc Bull. 2004;32:1229–41.

  17. 17.

    Hamel NJ, Parrish JK, Conquest LL. Effects of tagging on behaviour provisioning, and reproduction in the common murre (Uria aalge), a diving seabird. Auk. 2004;121:1161.

  18. 18.

    Whidden SE, Williams CT, Breton AR, Buck CL. Effects of transmitters on the reproductive success of tufted puffins. J F Ornithol. 2007;78:206–12.

  19. 19.

    Paredes R, Jones IL, Boness DJ. Reduced parental care, compensatory behaviour and reproductive costs of thick-billed murres equipped with data loggers. Anim Behav. 2005;69:197–208.

  20. 20.

    Fossette S, Corbel H, Gaspar P, Le Maho Y, Georges JY. An alternative technique for the long-term satellite tracking of leatherback turtles. Endanger Species Res. 2008;4:33–41.

  21. 21.

    Sherrill-Mix SA, James MC. Evaluating potential tagging effects on leatherback sea turtles. Endanger Species Res. 2008;4:187–93.

  22. 22.

    Hoolihan JP, Luo J, Abascal FJ, Campana SE, De Metrio G, Dewar H, et al. Evaluating post-release behaviour modification in large pelagic fish deployed with pop-up satellite archival tags. ICES J Mar Sci. 2011;68:880–9.

  23. 23.

    Phillips RA, Silk JRD, Croxall JP, Afanasyev V. Year-round distribution of white-chinned petrels from South Georgia: relationships with oceanography and fisheries. Biol Conserv. 2006;129:336–47.

  24. 24.

    Snape RTE, Broderick AC, Cicek BA, Fuller WJ, Glen F, Stokes KL, et al. Shelf life: neritic habitat use of a turtle population highly threatened by fisheries. Diver Distrib. 2016;22:797–807.

  25. 25.

    Vaudo J, Byrne M, Wetherbee B, Harvey G, Shivji M. Long-term satellite tracking reveals region-specific movements of a large pelagic predator, the shortfin mako shark, in the western North Atlantic Ocean. J Appl Ecol. 2017;54:1765–75.

  26. 26.

    Elliott KH, McFarlane-Tranquilla L, Burke CM, Hedd A, Montevecchi WA, Anderson WG. Year-long deployments of small geolocators increase corticosterone levels in murres. Mar Ecol Prog Ser. 2012;466:1–7.

  27. 27.

    Gauthier-Clerc M, Gendner J-P, Ribic CA, Fraser WR, Woehler EJ, Descamps S, et al. Long-term effects of flipper bands on penguins. Proc R Soc B Biol Sci [Internet]. 2004;271:S423–S426. Available from: http://rspb.royalsocietypublishing.org/cgi/doi/https://doi.org/10.1098/rsbl.2004.0201

  28. 28.

    Igual JM, Forero MG, Tavecchia G, González-Solis J, Martínez-Abraín A, Hobson KA, et al. Short-term effects of data-loggers on Cory’s shearwater (Calonectris diomedea). Mar Biol. 2005;146:619–24.

  29. 29.

    Bodey TW, Cleasby IR, Bell F, Parr N, Schultz A, Votier SC, et al. A phylogenetically controlled meta-analysis of biologging device effects on birds: deleterious effects and a call for more standardized reporting of study data. Methods Ecol Evol. 2018;9:946–55.

  30. 30.

    Godley BJ, Blumenthal JM, Broderick AC, Coyne MS, Godfrey MH, Hawkes LA, et al. Satellite tracking of sea turtles: where have we been and where do we go next? Endanger Species Res. 2007;4:3–22.

  31. 31.

    Jeffers VF, Godley BJ. Satellite tracking in sea turtles: how do we find our way to the conservation dividends? Biol Cons Elsevier Ltd. 2016;199:172–84.

  32. 32.

    Rees AF, Alfaro-Shigueto J, Barata PCR, Bjorndal KA, Bolten AB, Bourjea J, et al. Are we working towards global research priorities for management and conservation of sea turtles? Endanger Species Res. 2016;31:337–82.

  33. 33.

    Luschi P, Casale P. Movement patterns of marine turtles in the Mediterranean Sea: a review. Ita J Zool. 2014;81:478–95.

  34. 34.

    Hays GC, Adams CR, Broderick AC, Godley BJ, Lucas DJ, Metcalfe JD, et al. The diving behaviour of green turtles at Ascension Island. Anim Behav. 2000;59:577–86.

  35. 35.

    Mori Y. Optimal diving behaviour for foraging in relation to body size. J Evol Biol. 2002;15:269–76.

  36. 36.

    Arendt MD, Segars AL, Byrd JI, Boynton J, Schwenter JA, Whitaker D, et al. Migration, distribution, and diving behavior of adult male loggerhead sea turtles (Caretta caretta) following dispersal from a major breeding aggregation in the Western North Atlantic. Mar Biol. 2012;159:113–25.

  37. 37.

    Hatase H, Takai N, Matsuzawa Y, Sakamoto W, Omuta K, Goto K, et al. Size-related differences in feeding habitat use of adult female loggerhead turtles Caretta caretta around Japan determined by stable isotope analyses and satellite telemetry. Mar Ecol Prog Ser. 2002;233:273–81.

  38. 38.

    Hatase H, Sato K, Yamaguchi M, Takahashi K, Tsukamoto K. Individual variation in feeding habitat use by adult female green sea turtles (Chelonia mydas): are they obligately neritic herbivores. Oecologia. 2006;149:52–64.

  39. 39.

    Tucker AD. Nest site fidelity and clutch frequency of loggerhead turtles are better elucidated by satellite telemetry than by nocturnal tagging efforts: implications for stock estimation. J Exp Mar Biol Ecol. 2010;383:48–55.

  40. 40.

    Weber N, Weber SB, Godley BJ, Ellick J, Witt M, Broderick AC. Telemetry as a tool for improving estimates of marine turtle abundance. Biol Conserv. 2013;167:90–6.

  41. 41.

    Watson KP, Granger RA. Hydrodynamic effect of a satellite transmitter on a juvenile green turtle (Chelonia mydas). J Exp Biol. 1998;201:2497–505.

  42. 42.

    Broderick AC, Glen F, Godley BJ, Hays GC. Variation in reproductive output of marine turtles. J Exp Mar Biol Ecol. 2003;288:95–109.

  43. 43.

    Godley BJ, Broderick AC, Moraghan S. Short-term effectiveness of passive integrated transponder tags used in the study of Mediterranean marine turtles. Chelonian Conserv Biol. 1999;3:477–9.

  44. 44.

    Omeyer LCM, Fuller WJ, Godley BJ, Snape RTE, Broderick AC. Determinate or indeterminate growth ? Revisiting the growth strategy of sea turtles. Mar Ecol Prog Ser. 2018;596:199–211.

  45. 45.

    Stokes KL, Fuller WJ, Glen F, Godley BJ, Hodgson DJ, Rhodes KA, et al. Detecting green shoots of recovery: the importance of long-term individual-based monitoring of marine turtles. Anim Conserv. 2014;17:593–602.

  46. 46.

    Godley BJ, Richardson S, Broderick AC, Coyne MS, Glen F, Hays GC. Long-term satellite telemetry of the movements and habitat utilisation by green turtles in the Mediterranean. Ecography (Cop). 2002;25:352–62.

  47. 47.

    Crawley MJ. The R book. R B. Chichester. England: Wiley; 2007.

  48. 48.

    Hothorn T, Bretz F, Westfall P. Simultaneous inference in general parametric models. Biom J. 2008;50:346–63.

  49. 49.

    Pinheiro J, Bates D, DebRoy S, Sarkar D, TRC T. nlme: linear and nonlinear mixed effects models. R Packag. 2013;186.

  50. 50.

    Wood S, Scheipl F. gamm4: generalized additive mixed models using mgcv and lme4. R Packag version 02–3. 2014.

  51. 51.

    White GC, Burnham K. Program MARK: survival estimation from populations of marked animals. Bird Study. 1999;46:120–39.

  52. 52.

    Choquet R, Reboulet A-M, Lebreton JD, Gimenez O, Pradel R. U- CARE 2.2 User’s manual. Montpellier: CEFE; 2005.

  53. 53.

    Jones TT, Bostrom BL, Carey M, Imlach B, Mikkelsen J, Ostafichuk P, et al. Determining transmitter drag and best-practice attachment procedures for sea turtle biotelemetry. In: NOAA tech memo NMFS-SWFSC-480; 2011.

  54. 54.

    Jönsson KI. Capital and income breeding as alternative tactics of resource use in reproduction. Oikos. 1997;78:57–66.

  55. 55.

    Price ER. The physiology of lipid storage and use in reptiles. Biol Rev. 2017;92:1406–26.

  56. 56.

    Perrault JR, Stacy NI. Note on the unique physiologic state of loggerhead sea turtles (Caretta caretta) during nesting season as evidenced by a suite of health variables. Mar Biol. 2018;165:1–6.

  57. 57.

    Baron JP, Le Galliard JF, Ferrière R, Tully T. Intermittent breeding and the dynamics of resource allocation to reproduction, growth and survival. Funct Ecol. 2013;27:173–83.

  58. 58.

    Troëng S, Solano R, Díaz-Merry A, Ordoñez J, Taylor D, Evans D, et al. Report on long-term transmitter harness retention by a leatherback turtle. Mar Turt Newsl. 2006;111:6–7.

  59. 59.

    Marcovaldi MÂ, Lopez GG, Soares LS, Lima EHSM, Thomé JCA, Almeida AP. Satellite-tracking of female loggerhead turtles highlights fidelity behavior in northeastern Brazil. Endanger Species Res. 2010;12:263–72.

  60. 60.

    Schofield G, Scott R, Dimadi A, Fossette S, Katselidis KA, Koutsoubas D, et al. Evidence-based marine protected area planning for a highly mobile endangered marine vertebrate. Biol Conserv. 2013;161:101–9.

  61. 61.

    Stokes KL. Ecology of marine turtles under climate change; 2014.

  62. 62.

    Weishampel JF, Bagley DA, Ehrhart LM. Earlier nesting by loggerhead sea turtles following sea surface warming. Glob Chang Biol. 2004;10:1424–7.

  63. 63.

    Weishampel JF, Bagley DA, Ehrhart LM, Weishampel AC. Nesting phenologies of two sympatric sea turtle species related to sea surface temperatures. Endanger Species Res. 2010;12:41–7.

  64. 64.

    Mazaris AD, Kallimanis AS, Tzanopoulos J, Sgardelis SP, Pantis JD. Sea surface temperature variations in core foraging grounds drive nesting trends and phenology of loggerhead turtles in the Mediterranean Sea. J Exp Mar Bio Ecol. 2009;379:23–7.

  65. 65.

    Mazaris AD, Kallimanis AS, Pantis JD, Hays GC. Phenological response of sea turtles to environmental variation across a species’ northern range. Proc Biol Sci. 2013;280:20122397.

  66. 66.

    Byrne R, Fish J, Doyle TK, Houghton JDR. Tracking leatherback turtles (Dermochelys coriacea) during consecutive inter-nesting intervals: further support for direct transmitter attachment. J Exp Mar Biol Ecol. 2009;377:68–75.

  67. 67.

    Benson SR, Dutton PH, Hitipeuw C, Samber B, Bakarbessy J, Parker DM. Post-nesting migrations of leatherback turtles (dermochelys coriacea) from jamursba-medi, Bird’s head peninsula, Indonesia. Chelonian Conserv Biol. 2007;6:150–4.

  68. 68.

    Saraux C, Le Bohec C, Durant JM, Viblanc VA, Gauthier-Clerc M, Beaune D, et al. Reliability of flipper-banded penguins as indicators of climate change. Nature. 2011;467:203-206.

  69. 69.

    Casale P, Broderick AC, Caminas JA, Cardona L, Carreras C, Demetropoulos A, et al. Mediterranean Sea turtles: current knowledge and priorities for conservation and research. Endanger Species Res. 2018;36:229–67.

  70. 70.

    Godley BJ, Broderick AC, Glen F, Hays GC. Post-nesting movements and submergence patterns of loggerhead marine turtles in the Mediterranean assessed by satellite tracking. J Exp Mar Biol Ecol. 2003;287:119–34.

  71. 71.

    Dujon AM, Schofield G, Lester RE, Esteban N, Hays GC. Fastloc-GPS reveals daytime departure and arrival during long-distance migration and the use of different resting strategies in sea turtles. Mar Biol. 2017;164:1–14.

  72. 72.

    Hochscheid S, Godley BJ, Broderick AC, Wilson RP. Reptilian diving: highy varieble dive patterns in the green turtle Chelonia mydas. Mar Ecol Prog Ser. 1999;185:101–12.

  73. 73.

    Houghton JDR, Broderick AC, Godley BJ, Metcalfe JD, Hays GC. Diving behaviour during the internesting interval for loggerhead turtles Caretta caretta nesting in Cyprus. Mar Ecol Prog Ser. 2002;227:63–70.

  74. 74.

    Houghton JDR, Woolmer A, Hays GC. Sea turtle diving and foraging behaviour around the Greek Island of Kefalonia. J Mar Biol Assoc UK. 2000;80:761–2.

  75. 75.

    Goldberg DW, Leitão SAT, Godfrey MH, Lopez GG, Santos AJB, Neves FA, et al. Ghrelin and leptin modulate the feeding behaviour of the hawksbill turtle Eretmochelys imbricata during nesting season. Conserv Physiol. 2013;1.

  76. 76.

    Plot V, Jenkins T, Robin J-P, Fossette S, Georges J-Y. Leatherback turtles are capital breeders: morphometric and physiological evidence from longitudinal monitoring. Physiol Biochem Zool. 2013;86:385–97.

  77. 77.

    Perrault JR, Page-Karjian A, Miller DL. Nesting leatherback sea turtle (Dermochelys coriacea) packed cell volumes indicate decreased foraging during reproduction. Mar Biol. 2016;163:1–10.

  78. 78.

    Gadgil M, Bossert WH. Life historical consequences of natural selection. Am Nat. 1970;104:1–24.

  79. 79.

    Seney EE, Higgins BM, Landry AM. Satellite transmitter attachment techniques for small juvenile sea turtles. J Exp Mar Bio Ecol. 2010;384:61–7.

  80. 80.

    Barron DG, Brawn JD, Weatherhead PJ. Meta-analysis of transmitter effects on avian behaviour and ecology. Methods Ecol Evol. 2010;1:180–7.

  81. 81.

    Passos C, Navarro J, Giudici A, González-Solís J. Effects of extra mass on the pelagic behavior of a seabird. Auk. 2010;127:100–7.

  82. 82.

    Bowlin MS, Henningsson P, Muijres FT, Vleugels RHE, Liechti F, Hedenström A. The effects of geolocator drag and weight on the flight ranges of small migrants. Methods Ecol Evol. 2010;1:398–402.

  83. 83.

    Pfaller JB, Chaloupka M, Bolten AB, Bjorndal KA. Phylogeny , biogeography and methodology : a meta-analytic perspective on heterogeneity in adult marine turtle survival rates. Sci Rep. 2018;8:1–10.

  84. 84.

    Casale P, Mazaris AD, Freggi D, Basso R, Argano R. Survival probabilities of loggerhead sea turtles (Caretta caretta) estimated from capture-mark-recapture data in the Mediterranean Sea. Sci Mar. 2007;71:365–72.

  85. 85.

    Casale P, Freggi D, Furii G, Vallini C, Salvemini P, Deflorio M, et al. Annual survival probabilities of juvenile loggerhead sea turtles indicate high anthropogenic impact on Mediterranean populations. Aquat Conserv. 2015;25:690–700.

  86. 86.

    Casale P, Heppell SS. How much sea turtle bycatch is too much ? A stationary age distribution model for simulating population abundance and potential biological removal in the Mediterranean. Endanger Species Res. 2016;29:239–54.

  87. 87.

    McNeill JB, Schueller AM, Avens L, Goodman Hall A, Goshe LR, Epperly SP. Estimates of tag loss for Loggerhead Sea turtles (Caretta caretta) in the Western North Atlantic. Herpetol Rev. 2013;44:221–6.

  88. 88.

    Hays GC, Hawkes LA. Satellite Tracking Sea turtles: opportunities and challenges to address key questions. Front Mar Sci. 2018;5:1–12.

  89. 89.

    Broderick AC, Coyne MS, Fuller WJ, Glen F, Godley BJ. Fidelity and over-wintering of sea turtles. Proc R Soc B. 2007;274:1533–8.

  90. 90.

    Stokes KL, Broderick AC, Canbolat AF, Candan O, Fuller WJ, Glen F, et al. Migratory corridors and foraging hotspots: critical habitats identified for Mediterranean green turtles. Divers Distrib. 2015;21:665–74.

  91. 91.

    Bradshaw PJ, Broderick AC, Carreras C, Inger R, Fuller W, Snape R, et al. Satellite tracking and stable isotope analysis highlight differential recruitment among foraging areas in green turtles. Mar Ecol Prog Ser. 2017;582:201–14.

  92. 92.

    Snape RTE, Bradshaw PJ, Broderick AC, Fuller WJ, Stokes KL, Godley BJ. Off-the-shelf GPS technology to inform marine protected areas for marine turtles. Biol Conserv. 2018;227:301–9.

  93. 93.

    Fuller WJ, Broderick AC, Phillips RA, Silk JRD, Godley BJ. Utility of geolocating light loggers for indicating at-sea movements in sea turtles. Endanger Species Res. 2008;4:139–46.

  94. 94.

    Glen F, Broderick AC, Godley BJ, Metcalfe JDO, Hays GCP. Dive angles for a green turtle (Chelonia mydas). J Mar Biol Ass UK. 2001;81:683–6.

  95. 95.

    Hays GC, Broderick AC, Glen F, Godley BJ, Houghton JDR, Metcalfe JD. Water temperature and internesting intervals for loggerhead (Caretta caretta) and green (Chelonia mydas) sea turtles. J Therm Biol. 2002;27:429–32.

  96. 96.

    Hays GC, Glen F, Broderick AC, Godley BJ, Metcalfe JD. Behavioural plasticity in a large marine herbivore: contrasting patterns of depth utilisation between two green turtle (Chelonia mydas) populations. Mar Biol. 2002;141:985–90.

  97. 97.

    Fuller WJ, Broderick AC, Hooker SK, Witt MJ, Godley BJ. Insights into habitat utilization by green turtles (Chelonia mydas) during the inter-nesting period using animal-borne digital cameras. Mar Technol Soc J. 2009;43:51–9.

Download references

Acknowledgments

We thank the reviewers and the editor, whose input greatly improved the manuscript. We also thank all volunteers who assisted with the data collection of the Marine Turtle Conservation Project from 1992 to 2017, a collaboration between the Marine Turtle Research Group, the North Cyprus Department of Environmental Protection and the North Cyprus Society for the Protection of Turtles. We thank Alexander Saliveros for the illustrations. Fieldwork was supported by the British Associate of Tortoise Keepers, British Chelonia Group, British High Commission in Cyprus, British Residents Society, Carnegie Trust for the Universities of Scotland, Darwin Initiative, Erwin Warth Foundation, Friends of SPOT, Glasgow University Court, Kuzey Kıbrıs Turkcell, MEDASSET UK, and Natural Environment Research Council (NER/I/S/2001/00750, NE/C507602/1).

Availability of data and material

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Funding

No funding to declare.

Author information

ACB and BJG conceived the study, LCMO led the analyses with ACB and BJG. All authors have contributed to data collection and writing of the manuscript. All authors read and approved the final manuscript

Correspondence to Lucy C. M. Omeyer or Annette C. Broderick.

Ethics declarations

Ethics approval and consent to participate

All applicable international, national and/or institutional guidelines of the care and use of animals were followed. Fieldwork was approved by the University of Exeter Ethics Committee.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional file

Additional file 1:

Additional information relating to the methods and results are provided, as well as additional Tables. Table S1. Significance results for return rate analysis. Table S2. Significance results looking at effects of device attachment on reproductive correlates among females. Table S3. Significance results looking at growth covariates and device attachment. Table S4. Significance results of within-female differences in reproductive correlates between pre- and post-tracking years. Table S5. Summary of models analysed in MARK for ‘non-tracked’ green turtles. Table S6. Summary of models analysed in MARK for ‘tracked’ green turtles. Table S7. Summary of models analysed in MARK for ‘non-tracked’ loggerhead turtles. Table S8. Summary of models analysed in MARK for ‘tracked’ loggerhead turtles. Table S9. Summary of parameter estimates, calculated using MARK, for both species and groups. (DOCX 50.0 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Keywords

  • Animal welfare
  • Green turtles
  • Loggerhead turtles
  • Satellite tracking
  • Tagging effect
  • Tagging reflex
  • Telemetry systems