Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Growth and life history variability of the grey reef shark (Carcharhinus amblyrhynchos) across its range

  • Darcy Bradley ,

    Affiliation Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, California, United States of America

  • Eric Conklin,

    Affiliation The Nature Conservancy, Honolulu, Hawaii, United States of America

  • Yannis P. Papastamatiou,

    Affiliation Department of Biological Sciences, Florida International University, North Miami, Florida, United States of America

  • Douglas J. McCauley,

    Affiliations Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, California, United States of America, Marine Science Institute, University of California Santa Barbara, Santa Barbara, California, United States of America

  • Kydd Pollock,

    Affiliation The Nature Conservancy, Honolulu, Hawaii, United States of America

  • Bruce E. Kendall,

    Affiliation Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, California, United States of America

  • Steven D. Gaines,

    Affiliation Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, California, United States of America

  • Jennifer E. Caselle

    Affiliation Marine Science Institute, University of California Santa Barbara, Santa Barbara, California, United States of America

Growth and life history variability of the grey reef shark (Carcharhinus amblyrhynchos) across its range

  • Darcy Bradley, 
  • Eric Conklin, 
  • Yannis P. Papastamatiou, 
  • Douglas J. McCauley, 
  • Kydd Pollock, 
  • Bruce E. Kendall, 
  • Steven D. Gaines, 
  • Jennifer E. Caselle


For broadly distributed, often overexploited species such as elasmobranchs (sharks and rays), conservation management would benefit from understanding how life history traits change in response to local environmental and ecological factors. However, fishing obfuscates this objective by causing complex and often mixed effects on the life histories of target species. Disentangling the many drivers of life history variability requires knowledge of elasmobranch populations in the absence of fishing, which is rarely available. Here, we describe the growth, maximum size, sex ratios, size at maturity, and offer a direct estimate of survival of an unfished population of grey reef sharks (Carcharhinus amblyrhynchos) using data from an eight year tag-recapture study. We then synthesized published information on the life history of C. amblyrhynchos from across its geographic range, and for the first time, we attempted to disentangle the contribution of fishing from geographic variation in an elasmobranch species. For Palmyra’s unfished C. amblyrhynchos population, the von Bertalanffy growth function (VBGF) growth coefficient k was 0.05 and asymptotic length L was 163.3 cm total length (TL). Maximum size was 175.5 cm TL from a female shark, length at maturity was estimated at 116.7–123.2 cm TL for male sharks, maximum lifespan estimated from VBGF parameters was 18.1 years for both sexes combined, and annual survival was 0.74 year-1. Consistent with findings from studies on other elasmobranch species, we found significant intraspecific variability in reported life history traits of C. amblyrhynchos. However, contrary to what others have reported, we did not find consistent patterns in life history variability as a function of biogeography or fishing. Ultimately, the substantial, but not yet predictable variability in life history traits observed for C. amblyrhynchos across its geographic range suggests that regional management may be necessary to set sustainable harvest targets and to recover this and other shark species globally.


Intraspecific variation has been observed in many life history traits (e.g. growth, maximum size, size at maturity, fecundity) for a number of elasmobranchs (sharks and rays) [113], but the drivers of this variability are often unclear. Many elasmobranchs have broad, even circumglobal distributions, and therefore experience regional differences in environmental conditions, ecological factors, and anthropogenic stressors, all of which directly affect life history traits [14]. However, while body size and size at maturity tend to increase with latitude for bony fishes, the relationship between life history variability and latitudinal variation is less clear in sharks [3,6,12,13]. At the same time, many shark populations have been severely depleted by fishing and are considered overfished [1520], making it difficult to disentangle changes to population parameters due to geographic variability, ecological variability, and anthropogenic impacts.

For broadly distributed species such as elasmobranchs, conservation management would benefit from understanding how life history traits change in response to local environmental and ecological contexts. Unfortunately, such information is generally lacking. In fact, nearly half of the chondrichthyan species (sharks, rays, and chimaeras) are considered ‘Data Deficient’, that is, lacking requisite information to assess their status by Red List Categories and Criteria of the International Union for the Conservation of Nature [19]. Discerning context-specific effects on life history traits is further complicated by the fact that fishing causes complex and often mixed effects on the life histories of target species. For example, fishing can both increase fecundity via density dependent effects [21] or depress fecundity via size selective harvest [22]. Fishing can also have complicated effects on mortality rates [23], maximum size [1,2], size and age at maturity [2,24,25], and growth rates [1,2,22]. Understanding how fishing has altered life history traits requires knowledge of populations in the absence of fishing, which is rarely possible.

Like most fishes, sharks grow deterministically and the von Bertalanffy growth function (VBGF; [26]) is often selected as the most appropriate growth model for sharks [2729]. VBGF parameters are also used as proxies in estimations of life history parameters, including natural mortality, in fisheries stock assessments [30,31]. A biased estimate of growth can therefore bias life history estimates, resulting in inaccurate assessments of stock status (e.g. [32]). This is problematic, because stock assessments depend on accurate life history information to set harvest targets.

Although no area of the world’s ocean is unaffected by human influence [33], Palmyra Atoll in the northern Line Islands is considered a little-disturbed ecological reference site that provides an opportunity to study a coral reef ecosystem without significant human impacts, including extractive fishing pressure. Palmyra is a remote, historically uninhabited, U.S. National Wildlife Refuge in the central Pacific Ocean (5°54’N; 162°05’W) that was established in 2001. Prior to receiving federal protection, Palmyra was privately owned for over 100 years, and only briefly housed a permanent human population when it was occupied by the U.S. Navy during World War II. Although Palmyra’s lagoons were significantly impacted during Naval occupation, its outer reefs were left nearly undisturbed [34]. Under current management, commercial fishing and extractive recreational fishing are banned within 50 nautical miles of Palmyra. The uniqueness and ecological value of Palmyra’s unfished marine ecosystem has attracted considerable research attention, and researchers have shown that Palmyra’s reefs are home to a significantly higher biomass of sharks than neighboring, inhabited islands, the nearest of which is 230 km away [35,36]. Grey reef sharks (Carcharhinus amblyrhynchos) are the most abundant shark species on Palmyra in terms of biomass [37], and as such are the flagship species of the refuge. Ongoing work is showing that Palmyra supports a large and temporally stable population of these predators (Bradley, unpublished data).

Carcharhinus amblyrhynchos is a reef-associated shark species, broadly distributed throughout the Indian, and western and central Pacific Oceans [38,39]. While adult female grey reef sharks generally reach larger sizes than males, there is a high level of variability in reported maximum total length (TL). In 13 published studies with a minimum sample size of 25 sharks, adult C. amblyrhynchos maximum TL varies by region from 152.5 cm in the Line Islands (male; [40]) to 200 cm in the Marshall Islands (sex not reported; [41]), with a maximum reported size for the species of 255 cm TL [42]. Regional variation is also evident in life history analyses conducted on C. amblyrhynchos in the Northwestern Hawaiian Islands (NWHI) [43], the northern and central Great Barrier Reef (GBR) [44], and Papua New Guinea (PNG) [45], which encompass a gradient of environmental, ecological, and anthropogenic impacts. Including Palmyra, these regions span 2–28° latitude and have different ecological characteristics. C. amblyrhynchos is the most abundant reef-associated predator in terms of biomass at Palmyra [37], the GBR [44,46], and PNG (based on commercial fisheries landings data [47]), but not in the NWHI [48]. Competition and predation are therefore expected to exert different selection pressures on C. amblyrhynchos in each location. However, fishing pressure also varies regionally, with recently active commercial shark fisheries that target C. amblyrhynchos in PNG [45] and the GBR [49,50], and no shark fisheries in NWHI [51] and Palmyra. The key challenge is disentangling the contributions of fishing from geographical variation in the biological and physical characteristics of these ecosystems. One barrier to this effort is the absence of data from multiple sites with no history of fishing.

Here, we describe the growth, maximum size, sex ratios, length at maturity, and offer a direct estimate of natural mortality and survival of an unfished population of C. amblyrhynchos using data from an eight year tag-recapture study. We then synthesize published information on the life history of C. amblyrhynchos from across its geographic range, and for the first time, we attempt to disentangle the contribution of fishing from geographic variation in an elasmobranch species.


Ethics statement

This project has been certified by the Institutional Animal Care and Use Committee (IACUC), University of California, Santa Barbara, Protocol no. 856 (date of IACUC approval: 5/31/2012). Sharks were captured at Palmyra Atoll, which has been a U.S. National Wildlife Refuge since 2001 and part of the Pacific Remote Islands Marine National Monument since 2009, under U.S. Fish and Wildlife Service special use permits (Permit numbers #12533–14011, #12533–13011, #12533–12011, #12533–11007, #12533–10011, #12533–09010, #12533–08011, and #12533–07006).

Data collection

From October 2006 to October 2014, we captured and tagged C. amblyrhynchos in the forereef, backreef, lagoon, and channel habitats on Palmyra. Sharks were caught using hand lines baited with yellowfin tuna (Thunnus albacares), wahoo (Acanthocybium solandri), and/or mackerel (Scomber scombras and Decapterus macrosoma) on barbless circle hooks. Once captured, individuals were restrained at the side of the boat using a tail rope. Up to three length measurements were recorded to the nearest 0.5 cm for each shark: precaudal length (PCL), fork length (FL), and TL (in accordance with the FAO shark measurement protocol [52]). Measurements were taken in the same way throughout the study by running a measuring tape along the dorsal side of each animal. Sex was determined by the presence/absence of claspers, which were measured, and clasper state was assessed to determine maturity for all male individuals (calcified, partially calcified, not calcified [53]). Maturity estimates for female sharks were not made from direct field observations. Uniquely numbered tags were then affixed to individual sharks. Early in the study, rototags were applied with an applicator through a hole punched in the leading edge of the first dorsal fin. Starting in 2010, minimally invasive Hallprint dart tags with stainless steel heads were applied using stainless steel tag applicators, with the tag head implanted in the epaxial muscle near the base of the first dorsal fin. Handling time was <4 minutes on average for an individual shark and we found no evidence of tag loss during the study period, (for details see [54]).

Statistical analyses

We used t-tests to compare the average size of captured female and male C. amblyrhynchos, and chi-squared tests to assess whether the observed sex ratio was significantly different from 1:1 (α = 0.05). Length at maturity TLm for male sharks was estimated by logistic regression using the glm function with a binomial error distribution in R [55], solving for the total length at which 50% of males had calcified claspers (odds of calcified clasper = 1; odds of not calcified or partially calcified clasper = 0). We obtained 95% confidence intervals for regression coefficient estimates by taking 10,000 bootstrap samples. For comparison, length at maturity was also estimated for male sharks using the published elasmobranch-wide linear relationship between TLm and maximum length TLmax of a captured individual (TLmax = 175.5 cm at Palmyra) [56] (S1 Appendix). In addition, length-to-length conversion formulae (i.e. TL-PCL, TL-FL, FL-PCL, etc.) were estimated using linear regression analyses. Including gender in the slopes and intercepts of length-to-length conversion models decreased model fit (assessed using Akaike’s information criterion [57] (AIC) with improved model fit indicated by a ΔAIC value >3 with the addition of model parameters [58]), and so we report only models with sexes combined.

Using our capture-recapture data, we fit the Francis [59] formulation of the VBGF for female sharks and with both sexes combined (due to our limited sample of recaptured male sharks). Previous studies have found nearly identical growth curves in male and female requiem sharks [60,61] including C. amblyrhynchos [44,45]. For sharks recaptured on multiple occasions, the initial length and final length measurements and their corresponding dates were included in the growth analyses. The Francis [59] model includes parameters gα and gβ, which are the mean annual growth increments of a species at the arbitrary reference lengths α and β, which can be used to estimate the conventional VBGF parameters L and k (S1 Appendix). Francis [59] model L and k values estimated from tagging data have the same biological meaning as VBGF parameters derived from age-length data [59]: L (cm) is the asymptotic mean length at age (i.e. the average length of an “old” shark), and k (year-1) is a growth coefficient that describes the rate at which growth approaches L. The Francis [59] model is also more flexible than other VBGF methods for tagging data in that it allows the inclusion of additional parameters that can affect model fit, including the coefficient of variation of growth variability (v), mean (m) and standard deviation (s) of measurement error, outlier contamination (p), and seasonal variation (u, w). Several combinations of additional parameters were considered using a stepwise fitting procedure, but we only present the best fitting parameterizations. Only recaptured individuals with minimum time at large of 150 days were included in the growth analysis. Previous studies of C. amblyrhynchos used TL [44,45,62] and PCL [43] to generate growth estimates; PCL was not measured at every sampling occasion in this study and so TL was used instead. However, FL and PCL were estimated from TL using the length-to-length conversions described above. All Francis [59] model parameters were estimated using the R package fishmethods [63]. Maximum lifespan Tmax for sexes combined was then estimated as the time required to attain >99% of TL as Tmax = 5∙Ln(2)∙k-1 [64].

Model selection was performed on our different parameterizations of the Francis [59] model using likelihood ratio tests (improved model fit indicated by a likelihood value >1.92 for one additional parameter and >3.0 for two additional parameters [59]) and by comparing AIC values (as above). We also visually assessed model fit by plotting model residuals (observed-expected growth) against length at release and predicted growth; residual deviation was expected to decrease as length at release increases (L1), because the likelihood function assumes an allometric relationship between individual growth variation and mean growth, and the latter declines with length [65]. Residual deviation was also expected to positively correlate with predicted growth based on the assumed relationship for individual growth variability (S1 Appendix). We used C. amblyrhynchos reference lengths α (100 cm TL) and β (130 cm TL) to estimate k and TL (S1 Appendix). Reference lengths should lie within range of length at capture data, and should have different associated growth rates, but their values are otherwise arbitrary [59]. To ensure that the model was performing well, ranges of α (95–105 cm) and β (125–145 cm) values were examined, but parameter estimates were insensitive to values within these ranges. These k and TL parameters were then used to generate a growth curve for unfished grey reef sharks at Palmyra Atoll (sexes combined). As the Francis [59] model for tagging data does not provide an estimate of size at birth L0 (S1 Appendix), we assumed that L0 = 60 cm for Palmyra sharks [39,62] to generate our growth curve and growth curves for existing studies using the VBGF Lt = L + (LL0)ekt [26]. Capture-recapture data used to estimate our growth model is presented in S1 Table.

We note that previous authors have suggested a multimodel approach to estimate growth for sharks [12,29], particularly for tag-recapture samples [12]. For this analysis, other growth methods were considered (e.g. Gulland and Holt [66], Fabens [64], Fabens with a fixed asymptote [67]), but none were deemed as appropriate for our data as the Francis [59] method. The Fabens [64] method is known to produce biased estimates of k and L when individual growth is variable [6871] or measurement error is high [72], indicating that the Fabens [64] model would likely overestimate length at time for our data [68]. Previous studies have found that the Gulland and Holt [66] method produces growth estimates that are sometimes similar to, but often less biologically realistic than, the Francis [59] method [61,68,73,74]. The Francis [59] method is certainly not free from bias, but it is generally the least biased of the commonly used growth models for tag-recapture data [12], and it has been found to be a better fit than other methods for other requiem shark species [61,74].

Francis [69] suggested that growth at length comparisons between length-increment and age-length data studies is more appropriate than direct parameter comparisons across studies. Therefore, we calculated growth at length TL130 for each existing C. amblyrhynchos study using the VBGF reported above [26], where t = 1. Existing C. amblyrhynchos age and growth studies were consistent in estimating individual ages by counting translucent and opaque bands in the corpus calcareum, and all assumed annual growth band deposition [4345]. In two of the three existing studies, individuals were independently aged 2–3 times and either tests for systematic bias or independent external confirmation of age estimates were undertaken to ensure precision [44,45]. Although band count variability between readers and labs can bias elasmobranch age and growth studies [75,76], particularly in terms of age underestimation [77], discrepancies in Carcharhiniformes tend to be minimal (as compared to Lamniformes, for example) [78]. Smart et al. [45] determined that the average percent error for C. amblyrhynchos growth bands between readers was 9.46%, which is expected for long-lived species [76], and not large enough to affect our comparisons. Previous authors have also shown consistency between Francis [59] growth at length from tagging data and growth at length estimated from age-length data for sharks [79].

Finally, we directly estimated survival ϕ (and therefore mortality) and derived estimates of total instantaneous mortality Z for C. amblyrhynchos at Palmyra Atoll, with the expectation that Z = M given the absence of fishing. Tag-recapture data was used to formulate a restricted dynamic occupancy version of the Jolly-Seber (JS) model [80] to estimate probability of annual survival (S1 Appendix). We used a Bayesian analysis and specified a uniform prior U(0,1) on our survival parameter. Our model was estimated using data augmentation with Markov chain Monte Carlo (MCMC) sampling in the R package rjags [81]. We ran 3 chains for 25,000 iterations and discarded the first 5,000 runs as burn-in. We examined traceplots and the Gelman-Rubin statistic (values <1.1 indicate convergence) to assess model performance. Total mortality was then estimated using the Hoenig [82] equation (Z as a function of Tmax; S1 Appendix) parameterized separately for teleost fishes and for cetaceans. The parameterization for cetaceans is often used for sharks because cetaceans have demographic characteristics more similar to sharks than teleosts [83]. All statistical analyses were performed in R [55].


We captured 1399 individual C. amblyrhynchos by research fishing on Palmyra between 2006–2014; of these, 1356 individuals were tagged with either a dart and/or roto tag. The sample was slightly female biased (male:female ratio = 0.52, χ2 = 135.8, p<0.001), and female sharks were significantly larger than male sharks (Fig 1). The average captured female was 146.0 cm TL (SD = 16.6 cm) while the average captured male was 138.7 cm TL (SD = 14.2 cm) (t1109.8 = 8.6; p<0.001). The largest captured shark was a 175.5 cm TL female, and the smallest captured individuals were four 66 cm TL females. Length-to-length relationships had conversion estimates with R2 > 0.90 (Table 1). The average captured immature male shark was 123.0 ± 1.5 cm (mean ± SE), and the average captured mature male shark was 143.5 ± 0.4 cm (Fig 2A). Male C. amblyrhynchos had a 50% chance of reaching maturity at 123.2 cm TL (logistic regression, p < 0.001, 95% CI 123.0–123.7 cm TL) (Fig 2B). Length at maturity TLm estimated as a function of TLmax [56] was 116.7 cm for male sharks.

Table 1. Length-to-length relationships for Carcharhinus amblyrhynchos (sexes combined) captured by research fishing in Palmyra 2006–2014.

Fig 1. Total length frequency of female and male C. amblyrhynchos captured by research fishing in Palmyra 2006–2014.

Fig 2. Grey reef male maturity.

(A) Clasper size (cm) as a function of TL; NC = not calcified (N = 35); PC = partially calcified (N = 33); C = calcified (N = 361). (B) Only calcified (C = 1) and not calcified (NC = 0) individuals were included in the logistic regression.

Of the 1356 captured and tagged C. amblyrhynchos, 118 individuals were recaptured and released at Palmyra a minimum of 150 days after initial capture (females = 100; males = 18). Median time at large was 2.0 years and the longest time between release and recapture was 7.2 years (S1 Table). The maximum total growth recorded for a shark was 46.5 cm from a female captured at 103 cm TL and recaptured 6.8 years later at 149.5 cm TL (S1 Table). The assumptions of the Francis [59] model were confirmed by plotting residuals against length at release and predicted growth. As expected, residual deviance increased over predicted growth and decreased over length at release, reinforcing the expectation of the VBGF that growth variability should decline at increasing length (Fig 3). The female-only and combined-sexes Francis [59] growth model produced consistent results, indicating that including male sharks did not bias model estimates or increase standard errors. The growth model parameterization with the best fit to the VBGF for our capture-recapture data included gα (g100), gβ (g130), standard deviation of measurement error, and growth variability. The inclusion of parameters for mean of measurement error, seasonal variability, and outliers did not significantly improve model fit (Table 2). Growth at length was estimated at 3.33 cm/year (g100) and 1.75 cm/year (g130) for our best model (Table 2). The growth coefficient k was 0.054 and TL was 163.3 cm (Table 2). A size at birth of 60 cm, reported in [39,62], was used to fit a von Bertalanffy [26] growth curve to the data (Fig 4). Using the growth coefficient k estimated from the Francis model with sexes combined, longevity Tmax was 18.06 years. Annual survival rate estimated from tag-recapture data was 0.739 year-1 (95% CI 0.703–0.775; = 1.04). Total mortality Z from the Hoenig [82] equation was 0.232 year-1 and 0.205 year-1 (for teleost and cetacean parameterizations, respectively).

Table 2. Parameter values from the best fit estimated Francis [59] growth models for Carcharhinus amblyrhynchos at Palmyra Atoll.

Fig 3. Residual plots to assess Francis [59] model fit.

Francis growth model residuals (observed-expected growth) plotted against (A) predicted growth (TL (cm ∙ yr-1) for C. amblyrhynchos), and (B) length at release (cm); residual deviation was expected to decrease as length at release increases (L1), because the likelihood function assumes an allometric relationship between individual growth variation and mean growth, and the latter declines with length.

Fig 4. Von Bertalanffy growth curves for Palmyra C. amblyrhynchos compared to other regions (NWHI, GBR, PNG); both sexes combined.

Size at birth based on what is reported in each corresponding reference.


Consistent with findings from studies on other elasmobranch species, we found significant variation in life history traits of C. amblyrhynchos across its geographic range. However, contrary to what others have reported [13,6,13,22,24,25], we did not find consistent patterns in life history variability as a function of geographic variation or fishing. Previous studies have demonstrated that asymptotic length and growth rate have a positive relationship with latitude in some elasmobranch species [6,12]. However, the relationship between these life history traits and latitude was not consistent for C. amblyrhynchos. Growth at length was indeed highest in the highest latitude location (NWHI, 23.1–28.2° N) [43], but was also relatively large in PNG (2–11° S) [45], a low latitude location (Table 3; Fig 4). In addition, expected geographic trends in L were not observed, with the highest TL reported from the GBR, which was not the highest latitude location (Table 3). There was also no latitudinal pattern in body size (maximum total length) for C. amblyrhynchos from studies across the species’ Indo-west and central Pacific range (Table 4). Female sharks generally reach larger maximum sizes than male C. amblyrhynchos (Table 4), a pattern that was also observed at Palmyra (Fig 1). However, C. amblyrhynchos in the largest size classes that have been reported elsewhere (>180cm [39]) were not encountered in our Palmyra sample. We offer the hypothesis that local abiotic factors may exert more selection pressure on the life history of C. amblyrhynchos than abiotic factors that follow a latitudinal cline. There are substantial differences in habitat availability, habitat complexity, and connectivity in Palmyra, the NWHI, PNG, and the GBR, and the physical structure of coral reefs plays a fundamental role in determining the strength of biological interactions and the allocation of available energy to life history traits [84].

Table 3. Parameter values estimated for growth models for Carcharhinus amblyrhynchos.

Table 4. Life history characteristics for Carcharhinus amblyrhynchos from across its geographic range (only studies with a minimum of N = 25 sharks were included).

Fishing can also affect life history traits through reductions in abundance that can lead to compensatory (density dependent) responses. For example, Cassoff et al. [25] found an increase in growth rate for an exploited northwest Atlantic porbeagle (Lamna nasus) population compared to its virgin growth rate. Similarly, growth rate significantly increased for the sandbar shark (C. plumbeus) and the Atlantic sharpnose shark (Rhizoprionodon terraenovae) following decades of exploitation [1,2]. Shark fishing is also expected to decrease maximum size [22]. However, we again did not find a consistent relationship between fishing and any life history parameters in C. amblyrhynchos. Commercial fisheries and poaching activities have reduced C. amblyrhynchos abundance in the GBR [49,50,93] and PNG C. amblyrhynchos have been harvested as a target species since the early 1980s [47]. Yet, there was no maximum length response between fished and unfished regions (Table 4), and growth at length was highest in the NWHI (Table 3; Fig 4), a region without a commercial shark fishery. Interestingly, both commercially fished GBR and PNG sharks had higher growth at length than Palmyra’s unfished shark population (Table 3; Fig 4). This suggests that a compensatory growth response is possible for this shark species, but it is not sufficient to explain observed differences in growth rates across the species’ range.

C. amblyrhynchos growth at length was dramatically different in Palmyra and the NWHI (Table 3)–the two locations that lack commercial shark fisheries—suggesting that local ecological factors may exert significant selective pressures on C. amblyrhynchos life history traits. Both the NWHI and Palmyra have high predator densities [48,51], and intra and interspecific competition are fierce. Consumptive competition across all size and age classes is expected to reduce overall resource acquisition, diminishing growth and preventing sharks from reaching the largest sizes [94,95], consistent with our findings for Palmyra, but not the NWHI (Tables 3 and 4). In Palmyra, where growth was the slowest and growth at length was lowest, C. amblyrhynchos was the most abundant shark species in forereef habitat [37], and conspecifics do not comprise a known component of their diet [39]. In contrast, in the NWHI, where growth rate was the fastest and growth at length was highest, C. amblyrhynchos were outnumbered by the larger Galapagos shark (C. galapagensis) [48], which regularly consume other elasmobranchs [96]. While extremely rare in Palmyra, tiger sharks (Galeocerdo cuvier) are also common in the NWHI where elasmobranchs make up 20% of their diet [97]. A strategy of rapid growth through the juvenile stage has been suggested for both the tiger shark (G. cuvier) [61] and smalltooth sawfish (Pristis pectinata) [98], both of which experience size-dependent mortality due to high levels of predation. Ultimately, we hypothesize that local ecological differences likely drive the variability observed in growth rates for sharks. Resource limitations due to intraspecific competition may limit growth in C. amblyrhynchos in Palmyra, while risk of predation in the NWHI may favor a different resource acquisition strategy that results in faster growth. Future life history studies should explicitly consider how the ecology of their study system exerts selection pressures on elasmobranchs.

Assuming that C. amblyrhynchos growth is affected by density dependent factors, then reduced intraspecific competition for resources caused by fishing can also lead to compensatory reductions in natural mortality [1,99]. A key feature of our study was our ability to directly estimate a survival rate for C. amblyrhynchos at an unfished location using our tag-recapture data. Given that the Palmyra shark population is likely close to carrying capacity (Bradley, unpublished data), we would expect natural mortality to be high relative to fished locations. We did indeed find some evidence of reduced natural mortality in a population of commercially fished C. amblyrhyncos, with GBR sharks reported to have lower natural mortality (M = 0.04–0.17 year-1, depending on the type of indirect estimate of M used [100]) than sharks in Palmyra and the NWHI. In Palmyra, survival estimated directly from tag-recapture data was 0.74 year-1 (95% CI 0.70–0.78 year-1) equating to a 0.26 year-1 natural mortality rate, while natural morality estimated indirectly using the Hoenig method was only slightly lower (M = 0.20–0.23 year-1; assuming that Z = M in an unfished system). There are very few estimates of natural mortality in unfished shark populations, but our estimate was similar to M estimated for an unexploited population of mature porbeagle sharks (L. nasus; M = 0.15–0.20 year-1, male-female, respectively [101]). Our M estimate for Palmyra sharks was also nearly identical to M estimated for NWHI sharks using the same indirect method (M = 0.25 year-1 [83]; using data from [43,62] and again assuming that Z = M in an unfished system).

A limitation of our study is the relatively lower number of captured (and recaptured) male sharks as compared to females. The sex ratio of captured C. amblyrhynchos at Palmyra was skewed towards females, which is consistent with observations from northeastern Australia [86], and Palau [91]. However, sex ratios were skewed towards males in the Main and NWHI [39] and in PNG [45]. This likely reflects a bias in sampling location and corresponding sampling method and not a real skewed sex ratio for the entire population. Sexual segregation is a common feature observed in marine species [102], including reef-associated sharks [95,103,104]. Coastal aggregations of C. amblyrhynchos tend to be dominated by female sharks [41,91,94,105,106], while male individuals have been observed dispersing farther and exhibiting lower site fidelity [85,107]. It therefore is not surprising that near-shore handline fishing was used in all cases where female biased sex ratios were observed, and offshore or deeper water longline fishing was the primary method of sampling when a male biased sex ratio was observed. When a variety of fishing methods were employed, sex ratios were not significantly different from 50:50 in both northern Australia [87] and Madagascar [90]. Previous studies have found nearly identical growth parameters in male and female C. amblyrhynchos for both female and male skewed samples [44,45]. Therefore, we are not concerned that sampling biases influenced the results we have reported here, but significant differences have been reported in male/female life history parameters for a variety of other elasmobranchs (e.g. blacknose shark, C. acronotus [3]; dusky shark, C. obscurus [60]; tope shark, Galeorhinus galeus [12]). Method of capture and capture location should therefore be an important consideration in future elasmobranch studies.

In this study, we revealed significant intraspecific life history variability for an elasmobranch with a large geographic range. In addition, we have shown that when we consider the multiple potential sources of this variability—latitudinal differences, ecological context, and human impacts—many of the conclusions that have emerged from previous analyses of single drivers do not hold. Most importantly, our results highlight the need for future studies to directly consider local ecology and how it may exert unique selection pressures on elasmobranch life histories through competition and predation effects, even for wide-ranging species. Large-scale effects due to fishing and latitudinal gradients may interact in ways that prevent us from understanding and predicting life history variability in elasmobranchs, ultimately misleading conservation management initiatives. As managers must accelerate their efforts to recover overexploited populations of sharks and other overfished species, it will be increasingly important to synthesize available biological information and consider how local environmental factors, ecological context, and anthropogenic impacts exert varying selection pressures on life history parameters. In the meantime, the substantial, but not yet predictable variability in life history traits observed for C. amblyrhynchos across its geographic range suggests that regional management may be necessary to set sustainable harvest targets and to recover this and other shark species globally.

Supporting information

S1 Table. Capture-recapture data for Carcharhinus amblyrhynchos caught at Palmyra Atoll used to estimate the Francis growth model.



We thank The Nature Conservancy staff for support at the Palmyra Atoll research station and our volunteer field assistants J. Calhoun, R. Carr, J. Eurich, A. Filous, J. Giddens, M. Hutchinson, S. Larned, R. Most, R. Pollock, K. Stamoulis, J. Schem, M. Shepard, R. Sylva, Y. Watanabe, and T. White; we also thank Robert Warner, Heather Patterson, and two anonymous reviewers for comments that improved this manuscript considerably. This is publication number PARC-0130 from the Palmyra Atoll Research Consortium (PARC).

Author Contributions

  1. Conceptualization: DB EC YPP DJM KP JEC.
  2. Data curation: DB.
  3. Formal analysis: DB.
  4. Funding acquisition: DB EC JEC.
  5. Investigation: DB EC YPP DJM KP JEC.
  6. Methodology: DB EC YPP DJM BEK SDG JEC.
  7. Project administration: DB EC JEC.
  8. Resources: DB EC YPP DJM BEK SDG JEC.
  9. Software: DB.
  10. Supervision: DB EC SDG JEC.
  11. Validation: DB EC YPP DJM KP BEK SDG JEC.
  12. Visualization: DB.
  13. Writing – original draft: DB.
  14. Writing – review & editing: DB EC YPP DJM KP BEK SDG JEC.


  1. 1. Sminkey TR, Musick JA. Age and growth of the sandbar shark, Carcharhinus plumbeus, before and after population depletion. Copeia. 1995; 871–883.
  2. 2. Carlson JK, Baremore IE. Changes in biological parameters of Atlantic sharpnose shark Rhizoprionodon terraenovae in the Gulf of Mexico: Evidence for density-dependent growth and maturity? Mar Freshw Res. 2003;54: 227–234.
  3. 3. Driggers WB, Carlson JK, Cullum B, Dean JM, Oakley D, Ulrich G. Age and growth of the blacknose shark, Carcharhinus acronotus in the western north Atlantic Ocean with comments on regional variation in growth rates. Environ Biol Fishes. 2004;71: 171–178.
  4. 4. Neer JA, Thompson BA. Life history of the cownose ray, Rhinoptera bonasus, in the northern Gulf of Mexico, with comments on geographic variability in life history traits. Environ Biol Fishes. 2005;73: 321–331.
  5. 5. Carlson JK, Sulikowski JR, Baremore IE. Do differences in life history exist for blacktip sharks, Carcharhinus limbatus, from the United States South Atlantic Bight and Eastern Gulf of Mexico? Environ Biol Fishes. 2006;77: 279–292.
  6. 6. Lombardi-Carlson L, Cortés E, Parsons G, Manire C. Latitudinal variation in life-history traits of bonnethead sharks, Sphyrna tiburo, (Carcharhiniformes : Sphyrnidae) from the eastern Gulf of Mexico. Mar Freshw Res. 2003;54: 875–883.
  7. 7. Walker TI. Spatial and temporal variation in the reproductive biology of gummy shark Mustelus antarcticus (Chondrichthyes:Triakidae) harvested off southern Australia. Mar Freshw Res. 2007;58: 67–97.
  8. 8. Farrell ED, Mariani S, Clarke MW. Reproductive biology of the starry smooth-hound shark Mustelus asterias: Geographic variation and implications for sustainable exploitation. J Fish Biol. 2010;77: 1505–1525. pmid:21078015
  9. 9. Mourier J, Mills SC, Planes S. Population structure, spatial distribution and life-history traits of blacktip reef sharks Carcharhinus melanopterus. J Fish Biol. 2013;82: 979–993. pmid:23464555
  10. 10. Holmes BJ, Peddemors VM, Gutteridge AN, Geraghty PT, Chan RWK, Tibbetts IR, et al. Age and growth of the tiger shark Galeocerdo cuvier off the east coast of Australia. J Fish Biol. 2015;87: 422–448. pmid:26248806
  11. 11. Smart J, Chin A, Tobin A, Simpfendorfer C, White W. Age and growth of the common blacktip shark Carcharhinus limbatus from Indonesia, incorporating an improved approach to comparing regional population growth rates. African J Mar Sci. 2015;37: 177–188.
  12. 12. Dureuil M, Worm B. Estimating growth from tagging data: An application to north-east Atlantic tope shark Galeorhinus galeus. J Fish Biol. 2015;87: 1389–1410. pmid:26709213
  13. 13. Carlson JK, Parsons GR. Age and growth of the bonnethead shark, Sphyrna tiburo, from northwest Florida, with comments on clinal variation. Environ Biol Fishes. 1997;50: 331–341.
  14. 14. Kuparinen A, Merilä J. Detecting and managing fisheries-induced evolution. Trends in Ecology and Evolution. 2007;22: 652–659. pmid:17981361
  15. 15. Jackson JB, Kirby MX, Berger WH, Bjorndal KA, Botsford LW, Bourque BJ, et al. Historical overfishing and the recent collapse of coastal ecosystems. Science. 2001;293: 629–37. pmid:11474098
  16. 16. Myers RA, Worm B. Rapid worldwide depletion of predatory fish communities. Nature. 2003;423: 280–283. pmid:12748640
  17. 17. Baum JK, Myers RA. Shifting baselines and the decline of pelagic sharks in the Gulf of Mexico. Ecol Lett. 2004;7: 135–145.
  18. 18. Davidson LNK, Krawchuk MA, Dulvy NK. Why have global shark and ray landings declined: improved management or overfishing? Fish Fish. 2015;
  19. 19. Dulvy NK, Fowler SL, Musick JA, Cavanagh RD, Kyne PM, Harrison LR, et al. Extinction risk and conservation of the world’s sharks and rays. Elife. 2014;3: e00590. pmid:24448405
  20. 20. Costello C, Ovando D, Hilborn R, Gaines SD, Deschenes O, Lester SE. Status and solutions for the world’s unassessed fisheries. Science. 2012;338: 517–20. pmid:23019613
  21. 21. Ellis JR, Keable J. Fecundity of Northeast Atlantic spurdog (Squalus acanthias). ICES J Mar Sci. 2008;65: 979–981.
  22. 22. Stevens J, Bonfil R, Dulvy NK, Walker PA. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J Mar Sci. 2000;57: 476–494.
  23. 23. Wood CC, Ketchen KS, Beamish RJ. Population Dynamics of Spiny Dogfish (Squalus acanthias) in British Columbia Waters Age-Structure. J Fish Res Board Can. 1979;36: 647–656.
  24. 24. Walker TI, Taylor BL, Hudson RJ, Cottier JP. The phenomenon of apparent change of growth rate in gummy shark (Mustelus antarcticus) harvested off southern Australia. Fisheries Research. 1998. pp. 139–163.
  25. 25. Cassoff RM, Campana SE, Myklevoll S. Changes in baseline growth and maturation parameters of Northwest Atlantic porbeagle, Lamna nasus, following heavy exploitation. Can J Fish Aquat Sci. 2007;64: 19–29.
  26. 26. von Bertalanffy L. A quantitative theory of organic growth (inquiries on growth laws. II). Hum Biol. 1938;10: 181–213.
  27. 27. Beverton RJH, Holt SJ. A review on the lifespans and mortality rates of fish in nature, and their relation to growth and other physiological characteristics. CIBA Found Colloq Ageing. 1959;5: 142–180.
  28. 28. Cailliet GM, Smith WD, Mollet HF, Goldman KJ. Age and growth studies of chondrichthyan fishes: the need for consistency in terminology, verification, validation, and growth function fitting. Environ Biol Fishes. 2006;77: 211–228.
  29. 29. Smart JJ, Chin A, Tobin AJ, Simpfendorfer CA. Multimodel approaches in shark and ray growth studies: Strengths, weaknesses and the future. Fish and Fisheries. 2016.
  30. 30. Pauly D. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. J du Cons Int pour l’Exploration laMer. 1980;39: 175–192.
  31. 31. Pardo SA, Cooper AB, Dulvy NK. Avoiding fishy growth curves. Methods Ecol Evol. 2013;4: 353–360.
  32. 32. Jiao Y, Chen Y, Wroblewski J. An application of the composite risk assessment method in assessing fisheries stock status. Fish Res. 2005;72: 183.
  33. 33. Halpern BS, Walbridge S, Selkoe KA, Kappel C V, Micheli F, D’Agrosa C, et al. A global map of human impact on marine ecosystems. Science. 2008;319: 948–52. pmid:18276889
  34. 34. Dawson EY. Changes in Palmyra atoll and its vegetation through the activites of man, 1913–1958. Pacific Nat. 1959;I: 1–51.
  35. 35. Stevenson C, Katz LS, Micheli F, Block B, Heiman KW, Perle C, et al. High apex predator biomass on remote Pacific islands. Coral Reefs. 2007;26: 47–51.
  36. 36. Sandin SA, Smith JE, Demartini EE, Dinsdale EA, Donner SD, Friedlander AM, et al. Baselines and degradation of coral reefs in the Northern Line Islands. PLoS One. 2008;3: e1548. pmid:18301734
  37. 37. McCauley DJ, McLean KA, Bauer J, Young HS, Micheli F. Evaluating the performance of methods for estimating the abundance of rapidly declining coastal shark populations. Ecol Appl. 2012;22: 385–392. pmid:22611841
  38. 38. Compagno LJ V. FAO Species catalogue. Vol. 4. Sharks of the world. An annotated and illustrated cataloge of shark species known to date. Part 2. Carcharhiniformes. FAO Fish Synopsis. 1984;4: 251–655.
  39. 39. Wetherbee BM, Crow GL, Lowe CG. Distribution, reproduction and diet of the gray reef shark Carcharhinus amblyrhynchos in Hawaii. Mar Ecol Prog Ser. 1997;151: 181–189.
  40. 40. Wass RC. A comparative study of the life history, distribution, and ecology of the sandbar shark and the grey reef shark in Hawaii. PhD thesis, University of Hawaii. 1971; 382pp.
  41. 41. McKibben JN, Nelson DR. Patterns of movement and grouping of gray reef sharks, Carcharhinus amblyrhynchos, at Enewetak, Marshall Islands. Bulletin of Marine Science. 1986; 89–110.
  42. 42. Garrick JAF. Sharks of the Genus Carcharhinus National Oceanic and Atmospheric Administration. NOAA Tech Rep. 1982; 194pp..
  43. 43. De Crosta MA, Taylor LRJ, Parrish JD. Age determination, growth, and energetics of three species of carcharhinid sharks in Hawaii. Proc 2nd Symp Resour Investig NWHI, Vol 2 UNIHI-SEA-GRANT-MR-84–01 Honolulu Univ Hawaii Sea Grant. 1984; 75–95.
  44. 44. Robbins WD. Abundance, demography and population structure of the grey reef shark (Carcharhinus amblyrhynchos) and the white tip reef shark (Triaenodon obesus) (Fam. Charcharhinidae). PhD thesis, James Cook University. 2006; 197pp.
  45. 45. Smart JJ, Chin A, Baje L, Green ME, Appleyard SA, Tobin AJ, et al. Effects of including misidentified sharks in life history analyses: A case study on the grey reef shark Carcharhinus amblyrhynchos from Papua New Guinea. PLoS One. 2016;11: 1–18.
  46. 46. Rizzari JR, Frisch AJ, Magnenat KA. Diversity, abundance, and distribution of reef sharks on outer-shelf reefs of the Great Barrier Reef, Australia. Mar Biol. 2014;161: 2847–2855.
  47. 47. Kumoru L. The shark longline fishery in Papua New Guinea. Billfish By-catch Res Group, 176th Meet Standing Comm Tuna Billfish, Mooloolaba, Aust. 2003; 5pp.
  48. 48. Nadon MO, Baum JK, Williams ID, McPherson JM, Zgliczynski BJ, Richards BL, et al. Re-creating missing population baselines for Pacific reef sharks. Conserv Biol. 2012;26: 493–503. pmid:22536842
  49. 49. Robbins WD, Hisano M, Connolly SR, Choat JH. Ongoing collapse of coral-reef shark populations. Curr Biol. 2006;16: 2314–9. pmid:17141612
  50. 50. Heupel MR, Williams AJ, Welch DJ, Ballagh A, Mapstone BD, Carlos G, et al. Effects of fishing on tropical reef associated shark populations on the Great Barrier Reef. Fish Res. 2009;95: 350–361.
  51. 51. Friedlander AM, DeMartini EE. Contrasts in density, size, and biomass of reef fishes between the northwestern and the main Hawaiian islands: the effects of fishing down apex predators. Mar Ecol Prog Ser. 2002;230: 253–264.
  52. 52. Ebert DA, Stehman MF. Sharks, batoids, and chimaeras of the North Atlantic. FAO Species Catalogue for Fishery Purposes. 2013.
  53. 53. Clark E, von Schmidt K. Sharks of the Central Gulf Coast of Florida. Bull Mar Sci. 1965;15: 13–83.
  54. 54. Bradley D, Conklin E, Papastamatiou YP, McCauley DJ, Pollock K, Pollock A, et al. Resetting predator baselines in coral reef ecosystems. Scientific Reports. In press.
  55. 55. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013; URL
  56. 56. Frisk MG, Miller TJ, Fogarty MJ. Estimation and analysis of biological parameters in elasmobranch fishes: A comparative life history study. Can J Fish Aquat Sci. 2001;58: 969–981.
  57. 57. Akaike H. Information theory and an extension of the maximum likelihood principle. International Symposium on Information Theory. 1973; 267–281.
  58. 58. Richards SA. Testing ecological theory using the information-theoretic approach: Examples and cautionary results. Ecology. 2005; 2805–2814.
  59. 59. Francis RICC. Maximum likelihood estimation of growth and growth variability from tagging data. New Zeal J Mar Freshw Res. 1988;22: 43–51.
  60. 60. Simpfendorfer CA. Growth rates of juvenile dusky sharks, Carcharhinus obscurus (Lesueur, 1818), from southwestern Australia estimated from tag-recapture data. Fish Bull. 2000;98: 811–822.
  61. 61. Meyer CG, O’Malley JM, Papastamatiou YP, Dale JJ, Hutchinson MR, Anderson JM, et al. Growth and maximum size of tiger sharks (Galeocerdo cuvier) in Hawaii. PLoS One. 2014;9: e84799. pmid:24416287
  62. 62. Radtke RL, Cailliet GM. Age estimation and growth of the gray reef shark Carcharhinus amblyrhynchos from the Northwestern Hawaiian Islands. Proc 2nd Symp Resour Investig NWHI, UNIHI-SEA-GRANT-MR-84–01 Honolulu Univ Hawaii Sea Grant. 1984;2: 121–127.
  63. 63. Nelson GA. fishmethods: Fishery science methods and models in R. R package version 1.6–0. 2014; URL
  64. 64. Fabens AJ. Properties and fitting of the Von Bertalanffy growth curve. Growth. 1965;29: 265–289. pmid:5865688
  65. 65. McGarvey R, Ferguson G, Prescott J. Spatial variation in mean growth rates at size of southern rock lobster, Jasus edwardsii, in South Australian waters. Mar Freshw Res. 1999;50: 332–342.
  66. 66. Gulland JA, Holt SJ. Estimation of growth parameters for data at unequal time intervals. J Cons Cons Int pour l’Exploration la Mer. 1959;25: 47–49.
  67. 67. James IR. Estimation of von Bertalanffy growth curve parameters from recapture data. Biometrics. 1991;47: 1519–1530.
  68. 68. Sainsbury KJ. Effect of individual variability on the von Bertalanffy growth equation. Can J Fish Aquat Sci. 1980;37: 241–247.
  69. 69. Francis RICC. Are growth parameters estimated from tagging and age-length data comparable? Can J Fish Aquat Sci. 1988;45: 936–942.
  70. 70. Maller RA, DeBoer ES. An analysis of two methods of fitting the von Bertalanffy curve to capture-recapture data. Aust J Freshw Res. 1988;39: 459.
  71. 71. Kimura DK, Shimada AM, Lowe SA. Estimating von Bertalanffy growth parameters of sablefish Anoplopoma fimbria and Pacific cod Gadus macrocephalus using tag-recapture data. Fishery Bulletin. 1993. pp. 271–280.
  72. 72. Eveson JP, Polacheck T, Laslett GM. Consequences of assuming an incorrect error structure in von Bertalanffy growth models: a simulation study. Can J Fish Aquat Sci. 2007;64: 602–617.
  73. 73. Kneebone J, Natanson LJ, Andrews AH, Howell WH. Using bomb radiocarbon analyses to validate age and growth estimates for the tiger shark, Galeocerdo cuvier, in the western North Atlantic. Mar Biol. 2008;154: 423–434.
  74. 74. Skomal G, Natanson L. Age and growth of the blue shark (Prionace glauca) in the North Atlantic Ocean. Fish Bull. 2003;101: 627–639.
  75. 75. Cailliet GM, Yudin KG, Tonaka S, Taniuchi T. Growth characteristics of two populations of Mustelus manazo from Japan based upon cross-readings of vertebral bands. In: Pratt HLJ, Gruber SH, Taniuchi T, editors. Elasmobranchs as living resources: advances in the biology, ecology, systematics, and the status of fisheries. NOAA Technical Report 90; 1990. pp. 167–176.
  76. 76. Campana SE. Accuracy, precision and quality control in age determination, including a review of the use and abuse of. J Fish Biol. 2001;59: 197–242.
  77. 77. Francis MP, Campana SE, Jones CM. Age under-estimation in New Zealand porbeagle sharks (Lamna nasus): Is there an upper limit to ages that can be determined from shark vertebrae? Mar Freshw Res. 2007;58: 10–23.
  78. 78. Cailliet GM. Perspectives on elasmobranch life-history studies: A focus on age validation and relevance to fishery management. J Fish Biol. 2015;87: 1271–1292. pmid:26709208
  79. 79. Natanson LJ, Mello JJ, Campana SE. Validated age and growth of the porbeagle shark (Lamna nasus) in the western North Atlantic Ocean. Fish Bull. 2002;100: 266–278.
  80. 80. Royle JA, Dorazio RM. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Academic Press, London. 2008.
  81. 81. Plummer MM. rjags: Bayesian graphical models using MCMC. R package version 3–14. 2014; URL
  82. 82. Hoenig J. Empirical use of longevity data to estimate mortality-rates. Fishery Bulletin. 1983; 898–903.
  83. 83. Smith SE, Au DW, Show C. Intrinsic rebound potentials of 26 species of Pacific sharks. Mar Freshw Res. 1998;49: 663.
  84. 84. Almany GR. Differential effects of habitat complexity, predators and competitors on abundance of juvenile and adult coral reef fishes. Oecologia. 2004;141: 105–113. pmid:15197644
  85. 85. Espinoza M, Heupel MR, Tobin AJ, Simpfendorfer CA. Residency patterns and movements of grey reef sharks (Carcharhinus amblyrhynchos) in semi-isolated coral reef habitats. Mar Biol. 2014;
  86. 86. Barnett A, Abrantes KG, Seymour J, Fitzpatrick R. Residency and spatial use by reef sharks of an isolated seamount and its implications for conservation. PLoS One. 2012;7: e36574. pmid:22615782
  87. 87. Stevens JD, McLoughlin KJ. Distribution, size and sex composition, reproductive biology and diet of sharks from Northern Australia. Aust J Mar Freshw Res. 1991;42: 151–199.
  88. 88. Heupel MR, Simpfendorfer CA. Importance of environmental and biological drivers in the presence and space use of a reef-associated shark. Mar Ecol Prog Ser. 2014;496: 47–57.
  89. 89. Skomal G, Lobel PS, Marshall G. The use of animal-borne imaging to assess post-release behavior as it relates to capture stress in grey reef sharks, Carcharhinus amblyrhynchos. Mar Technol Soc J. 2007;41: 44–48.
  90. 90. Robinson L, Sauer W. A first description of the artisanal shark fishery in northern Madagascar: implications for management. African J Mar Sci. 2013;35: 9–15.
  91. 91. Vianna GMS, Meekan MG, Meeuwig JJ, Speed CW. Environmental influences on patterns of vertical movement and site fidelity of grey reef sharks (Carcharhinus amblyrhynchos) at aggregation sites. PLoS One. 2013;8: e60331. pmid:23593193
  92. 92. Tester AL. Cooperative shark research and control program; final report 1967‐ 69. Dep Zool Univ Hawaii, Honolulu. 1969.
  93. 93. McCook LJ, Ayling T, Cappo M, Choat JH, Evans RD, De Freitas DM, et al. Adaptive management of the Great Barrier Reef: a globally significant demonstration of the benefits of networks of marine reserves. Proc Natl Acad Sci U S A. 2010;107: 18278–85. pmid:20176947
  94. 94. Stevens JD. Life-History and Ecology of Sharks at Aldabra Atoll, Indian Ocean. Proc R Soc London Ser B, Biol Sci. 1984;222: 79–106.
  95. 95. Papastamatiou YP, Caselle JE, Friedlander AM, Lowe CG. Distribution, size frequency, and sex ratios of blacktip reef sharks Carcharhinus melanopterus at Palmyra Atoll: a predator-dominated ecosystem. J Fish Biol. 2009;75: 647–54. pmid:20738562
  96. 96. Wetherbee BM, Crow GL, Lowe CG. Biology of the Galapagos shark, Carcharhinus galapagensis, in Hawai’i. Environ Biol Fishes. 1996;45: 299–310.
  97. 97. Lowe CG, Wetherbee BM, Crow GL, Tester AL. Ontogenetic dietary shifts and feeding behavior of the tiger shark, Galeocerdo cuvier, in Hawaiian waters. Environ Biol Fishes. 1996;47: 203–211.
  98. 98. Simpfendorfer CA, Poulakis GR, O’Donnell PM, Wiley TR. Growth rates of juvenile smalltooth sawfish Pristis pectinata Latham in the western Atlantic. J Fish Biol. 2008;72: 711–723.
  99. 99. Hoenig JM, Gruber SH. Life-history patterns in the elasmobranchs: implications for fisheries management. In: Elasmobranchs as Living Resources: Advances in the Biology, Ecology, Systematics, and the Status of the Fisheries (Pratt HL Jr., Gruber SH, Taniuchi T, eds.). NOAA Tech Rep NMFS 90, US Dept of Comm Washington, DC. 1990.
  100. 100. Hisano M, Connolly SR, Robbins WD. Population growth rates of reef sharks with and without fishing on the Great Barrier Reef: robust estimation with multiple models. PLoS One. 2011;6: e25028. pmid:21966402
  101. 101. Campana SE, Joyce W, Marks L, Hurley P, Natanson LJ, Kohler NE, et al. The rise and fall (again) of the porbeagle shark population in the Northwest Atlantic. In: Sharks of the Open Ocean: Biology, Fisheries and Conservation (eds. Camhi MD, Pikitch EK, Babcock EA) Blackwell Publishing, Oxford UK. 2009.
  102. 102. Wearmouth VJ, Sims DW. Sexual segregation in marine fish, reptiles, birds and mammals behaviour patterns, mechanisms and conservation implications. Adv Mar Biol. 2008;54: 107–70. pmid:18929064
  103. 103. Knip DM, Heupel MR, Simpfendorfer CA. Habitat use and spatial segregation of adult spottail sharks Carcharhinus sorrah in tropical nearshore waters. J Fish Biol. 2012;80: 767–84. pmid:22471798
  104. 104. Chin A, Tobin AJ, Heupel MR, Simpfendorfer CA. Population structure and residency patterns of the blacktip reef shark Carcharhinus melanopterus in turbid coastal environments. J Fish Biol. 2013;82: 1192–1210. pmid:23557299
  105. 105. Economakis AE, Lobel PS. Aggregation behavior of the grey reef shark, Carcharhinus amblyrhynchos, at Johnston Atoll, Central Pacific Ocean. Environ Biol Fishes. 1998;51: 129–139.
  106. 106. Speed CW, Meekan MG, Field IC, McMahon CR, Stevens JD, McGregor F, et al. Spatial and temporal movement patterns of a multi-species coastal reef shark aggregation. Mar Ecol Prog Ser. 2011;429: 261–275.
  107. 107. Heupel MR, Simpfendorfer CA, Fitzpatrick R. Large-scale movement and reef fidelity of grey reef sharks. PLoS One. 2010;5.