1 Phylogeny, biogeography and methodology: a meta-analytic perspective on heterogeneity in adult marine turtle survival ratesJoseph B. Pfaller n n wx, Milani Chaloup kay, Alan B. Bolten n n x & Karen A. Bjorndal n n xComparative syntheses of key demographic parameters are critical not only for identifying data gaps, but also for evaluating sources of heterogeneity among estimates. Because demographic studies frequently exhibit heterogeneity, evaluating sources of heterogeneity among estimates can inform biological patterns and conservation actions more broadly. To better understand adult survival in marine turtles and avoid drawing inaccurate conclusions from current estimates, we conducted a comprehensive meta-analysis to test how heterogeneity among estimates was partitioned among Â’ÂŠÂ›ÂŽÂ‘Â‰Â‡ÂÂ‡Â–Â‹Â…Â„Â‹Â‘Â‰Â‡Â‘Â‰Â”ÂƒÂ’ÂŠÂ‹Â…ÂƒÂÂ†ÂÂ‡Â–ÂŠÂ‘Â†Â‘ÂŽÂ‘Â‰Â‹Â…ÂƒÂŽÂˆÂƒÂ…Â–Â‘Â”Â•tÂ‹ÂˆÂ–Â›ÂÂ‹ÂÂ‡Â•Â–Â—Â†Â‹Â‡Â•ÂˆÂ”Â‘ÂÂ¤Â‡ÂÂƒÂ”Â‹ÂÂ‡Â–Â—Â”Â–ÂŽÂ‡ species met the minimum selection criteria for inclusion in our meta-analysis. Heterogeneity among Â•Â—Â”Â‹ÂƒÂŽÂ‡Â•Â–Â‹ÂÂƒÂ–Â‡Â•Â™ÂƒÂ•Â¤Â”Â•Â–Â’ÂƒÂ”Â–Â‹Â–Â‹Â‘ÂÂ‡Â†Â„Â‡Â–Â™Â‡Â‡ÂÂ†Â‹Â¡Â‡Â”Â‡ÂÂ…Â‡Â•Â‹ÂÂ‘Â…Â‡ÂƒÂÂ„ÂƒÂ•Â‹ÂfÂÂ†Â‘ÂƒÂ…Â‹Â¤Â… versus Â–ÂŽÂƒÂÂ–Â‹Â…fÂ–ÂŠÂ‡ÂÂ„Â›Â†Â‹Â¡Â‡Â”Â‡ÂÂ…Â‡Â•ÂƒÂÂ‘ÂÂ‰ÂˆÂƒÂÂ‹ÂŽÂ›Â–Â”Â‹Â„Â‡Â™Â‹Â–ÂŠÂ‹ÂÂ–ÂŠÂ‡fÂÂ†Â‘ÂƒÂ…Â‹Â¤Â…ÂŠÂ‡ÂŽÂ‘ÂÂ‹ÂÂ‹ versus Carettini ÂƒÂÂ†Â‡Â”ÂÂ‘Â…ÂŠÂ‡ÂŽÂ‹Â†ÂƒÂ‡fÂ‘Â™Â‡Â‡Â”ÂƒÂ’Â’ÂƒÂ”Â‡ÂÂ–Â†Â‹Â¡Â‡Â”Â‡ÂÂ…Â‡Â•ÂƒÂ–Â–Â”Â‹Â„Â—Â–Â‡Â†Â–Â‘Â„Â‹Â‘Â‰Â‡Â‘Â‰Â”ÂƒÂ’ÂŠÂ›Â‘Â…Â‡ÂƒÂÂ„ÂƒÂ•Â‹ÂÂ‡Â¡Â‡Â…Â–f ÂƒÂÂ†Â’ÂŠÂ›ÂŽÂ‘Â‰Â‡ÂÂ›ÂˆÂƒÂÂ‹ÂŽÂ›Â–Â”Â‹Â„Â‡Â‡Â¡Â‡Â…Â–fÂ™Â‡Â”Â‡ÂŠÂ‹Â‰ÂŠÂŽÂ›Â…Â‘Â”Â”Â‡ÂŽÂƒÂ–Â‡Â†Â™Â‹Â–ÂŠÂÂ‡Â–ÂŠÂ‘Â†Â‘ÂŽÂ‘Â‰Â‹Â…ÂƒÂŽÂ†Â‹Â¡Â‡Â”Â‡ÂÂ…Â‡Â•Â‹ÂÂ–ÂƒÂ‰ type, model type, habitat type and study duration, thereby confounding biological interpretations ÂƒÂÂ†Â…Â‘ÂÂ’ÂŽÂ‹Â…ÂƒÂ–Â‹ÂÂ‰Â‡Â¡Â‘Â”Â–Â•Â–Â‘Â—Â•Â‡ÂÂƒÂÂ›Â…Â—Â”Â”Â‡ÂÂ–Â•Â—Â”Â‹ÂƒÂŽÂ‡Â•Â–Â‹ÂÂƒÂ–Â‡Â•Â‹ÂÂ’Â‘Â’Â—ÂŽÂƒÂ–Â‹Â‘ÂÂƒÂ•Â•Â‡Â•Â•ÂÂ‡ÂÂ–Â•Â—Â”Â”Â‡Â•Â—ÂŽÂ–Â• highlight the importance of evaluating sources of heterogeneity when interpreting patterns among Â•Â‹ÂÂ‹ÂŽÂƒÂ”Â†Â‡ÂÂ‘Â‰Â”ÂƒÂ’ÂŠÂ‹Â…Â•Â–Â—Â†Â‹Â‡Â•ÂƒÂÂ†Â†Â‹Â”Â‡Â…Â–ÂŽÂ›Â‹ÂÂˆÂ‘Â”ÂÂ‡Â¡Â‘Â”Â–Â•Â–Â‘Â‹Â†Â‡ÂÂ–Â‹ÂˆÂ›Â”Â‡Â•Â‡ÂƒÂ”Â…ÂŠÂ’Â”Â‹Â‘Â”Â‹Â–Â‹Â‡Â•ÂˆÂ‘Â”ÂÂƒÂ”Â‹ÂÂ‡Â–Â—Â”Â–ÂŽÂ‡Â• globally. Demographic studies in ecology and conservation biology form the basis for assessing population viability and managing ecological risk1. However, estimates of key demographic parameters, such as survival and recruitment, viewed in isolation oen provide limited and/or potentially biased inferences. In this regard, comparative syntheses of similar demographic studies are critical not only for identifying data gaps, but also for evaluating sources of heterogeneity among estimates. Because demographic studies frequently exhibit heterogeneity due to system-specic nature of biological phenomena2 and study-specic dierences in methodology, identifying important sources of heterogeneity can inform biological patterns and conservation actions more broadly2 Â– 4. Conversely, comparative studies that fail to evaluate sources of heterogeneity risk drawing inaccurate conclusions and misleading management decisions. We conducted a systematic review and comprehensive meta-analysis5 6 of annual survival rates for adult marine turtles to generate precision-weighted, species-specic estimates and prediction intervals from existing data7 and to explicitly model sources of heterogeneity among estimates2. In theory, because all marine turtles exhibit conserved life history patterns, including slow growth and delayed sexual maturity8, adult survival rates should be high (0.90) and exhibit limited natural heterogeneity among estimates. Our goal was to quantify heterogeneity among survival estimates and test how heterogeneity is inuenced by (a) phylogeny, (b) biogeography and (c) methodology. If heterogeneity is inuenced by phylogeny or biogeography, then biological dierences in survival among species/populations may exist. In this case, survival estimates that are lower than expected would highlight important speciesor region-specic hotspots in adult mortality Â– areas of conservation concern. wCaretta Research Project, Savannah, GA, USA. xArchie Carr Center for Sea Turtle Research and Department of Biology, University of Florida, Gainesville, FL, USA. yEcological Modelling Services Pty Ltd, University of Queensland, St. Lucia, Queensland, Australia. Correspondence and requests for materials should be addressed to J.B.P. (email: ÂŒÂ’ÂˆÂƒÂŽÂŽÂ‡Â”;Â—Â‡Â†Â— ) Received: 18 August 2017 Accepted: 28 March 2018 Published: xx xx xxxxb
2 However, if heterogeneity is also strongly inuenced by methodological dierences, then statistical biases related to certain methodologies introduce articial variation and mask important biological dierences. In this case, the use of survival estimates that are lower than expected may mislead management decisions if methodological biases are not accounted for. We expect the ndings of this synthesis to highlight the importance of evaluating sources of heterogeneity when interpreting demographic estimates2 and directly inform eorts to identify research priorities for marine turtles globally9Â–12.MethodsLiterature review and selection criteria. We followed the PRISMA protocols for assembling a dataset suitable for meta-analytic evaluation5 6. Specically, we conducted a two-tiered literature search to compile annual survival probability estimates for adult marine turtles. A structured search was conducted in Google Scholar, Sea Turtle Document Library (seaturtle.org) and Sea Turtle Online Bibliography (Archie Carr Center for Sea Turtle Research, University of Florida) using the following Boolean search terms: survival, survivorship, mortality, and the names of the seven marine turtle genera. en, an unstructured literature search was conducted by reviewing the reference lists of all the relevant publications and reports from the structured search. References compiled from the structured and unstructured search were reviewed and only those that estimated annual survival probabilities for adult turtles were retained (i.e., multiyear or stage-based estimates and estimates for juveniles were excluded). Seventy-eight survival estimates were found in the global literature review with 59 meeting the minimum selection criteria for inclusion in the meta-analysis (Supplementary TableS1). Marine turtle taxonomy/phylogeny follows Duchene et al .13. Dermochelidae (leatherbacks) is sister to Chelonidae, which includes Chelonini (atback and green turtles) and Carettini (hawksbill, loggerhead and ridley turtles). e measure of eect size and precision of each estimate was the study-specic inverse-precision weighted annual survival probability and the associated standard error, respectively.fÂÂˆÂ‘Â”ÂÂƒÂ–Â‹Â‡Â…Â‘ÂƒÂ”Â‹ÂƒÂ–Â‡Â• e following potentially informative covariates (or moderators) were compiled for all survival estimates: specic publication ID, research group (based on common authors), type of publication (journal or report), publication year, taxonomic group (species, tribe and family), study site, geographic region with subgrouping for two oceans (Indo-Pacic and Atlantic), study duration (years), habitat type (nesting or foraging), tag type and statistical estimation type with subgrouping for modeling procedures versus enumeration calculations. Each estimate was categorized by whether tag loss, imperfect detection and temporary emigration were explicitly accounted for or not. e population of interest in each study was also characterized in terms of population size, population trend, direct harvest history and sheries bycatch impact using primarily Wallace et al .14 ,15. Details on covariate characterization can be found in the Supplementary Methods. Study duration and publication year were also used to account for various forms of publication bias16,17.Statistical modeling approach. We used a precision-weighted random-eects model approach to summarize the 59 survival estimates into species-specic subgroups before accounting for informative covariates17Â– 19. In this 2-level analysis, the random eect was the specic study. Each study-specic survival estimate was a response variable so-bound between 0Â–1. Response variables were therefore logit-transformed prior to tting the random-eects models to ensure the predicted estimates were also between 0Â–120 and then back-transformed for any predicted summaries. Species-specic random-eects models were tted using the metafor package for R21 and displayed in a subgroup forest plot that was augmented with the species-specic random eects estimates and the prediction intervals for those estimates7. Because many of the potentially informative covariates in our meta-analysis were highly correlated, we could not isolate individual eects or explicitly model interactions among covariates simultaneously. erefore, we rst used a recursive partitioning or conditional inference regression tree approach22, 23 to explore underlying patterns among the potential eects of 12 correlated covariates on the 59 survival estimates. e conditional inference tree model was tted and then displayed using the partykit package for R24 with a minimum split criterion of 0.95 (so P-value t t 0.05). Because the conditional inference tree approach does not account for the precision of survival estimates, we also tested a series of meta-regression models using metafor to explore other potentially informative covariates when estimate precision was explicitly considered. Models with dierent predictors were compared based on Akaike Information Criterion (AICc). We then used a mixed-eects meta-regression approach19, 25 to model annual survival rates conditioned on the main interaction eect Â– Â‘ocean by family/tribeÂ’ Â– identied within the conditional inference tree and four additional moderators identied by exploratory meta-regression modeling Â– tag type, model type, habitat type and study duration. We also included a 3-level hierarchical structure26 27 to test for potential non-independence between studies conducted by the same research group (based on common authors), an important assumption to account for in meta-analytic studies17, 28. In this 3-level hierarchical analysis, the random eects were study within research group. e mixed eects meta-regression models were tted using a multivariate parameterization to accommodate assessment of random eect structures29. e model-predicted study-specic inverse-precision weighted annual survival estimates and covariate effects were summarized as covariate-specific boxplots. Continuous variables, like study duration, were modeled using B-splines (splines package)30 to account for potential nonlinear functional forms29 and predicted estimates were summarized using a cubic regression spline GAM-based smoother31 implemented within the ggplots2 package for R32. e I2 statistic2 33 was used to assess the level of unexplained heterogeneity estimated in the 3-level hierarchical meta-regression model t to the 59 studies, and a simple R2 measure appropriate for the multivariate parameter ization of a meta-regression model was used as a metric of the overall model t. e Cochrane QE test was used as a formal test of residual heterogeneity34, while an omnibus F -test was used to test for signicance of the set of
3 all covariates included in the meta-regression model35. Lastly, we explored potential publication bias16, 17 using a contour-enhanced funnel plot36 of the model predicted study-specic survival estimates using metafor .Data availability. e dataset supporting this article has been uploaded as part of the electronic supplementary material.ResultsÂ–Â—Â†Â›ÂƒÂÂ†Â•Â’Â‡Â…Â‹Â‡Â•Â•Â’Â‡Â…Â‹Â¤Â…Â•Â—Â”Â‹ÂƒÂŽÂ‡Â•Â–Â‹ÂÂƒÂ–Â‡Â• e 59 species-specic survival rate estimates derived from the random-eects model are summarized in Fig. 1 Here the random-eect was the specic study. It is apparent that there was considerable species-specic heterogeneity in adult survival rate estimates. is was especially apparent for green and leatherback turtles that might be attributable to geographic and methodological factors. is apparent heterogeneity was explored further using hierarchical or multilevel meta-regression modeling to account for potentially informative phylogenetic4, geographic and methodological covariates. We have also included the species-specic prediction intervals in the subgroup forest plot, which shows for instance that a new atback adult survival rate study would be expected to provide an annual estimate ca. 0.93 (95% prediction interval: 0.77Â–0.98). On the other hand, for leatherbacks it would be 0.85 (95% prediction interval: 0.45Â–0.97).fÂÂˆÂ‘Â”ÂÂƒÂ–Â‹Â‡Â…Â‘ÂƒÂ”Â‹ÂƒÂ–Â‡Â• e conditional inference tree approach identied two signicant nodes at a minimum split criterion of 0.95: (Node 1) Atlantic versus Indo-Pacic oceans and (Node 2) Chelonini versus Carettini and Dermochelidae nested within the Indo-Pacic Ocean (Fig. 2). No other covariates remained in the nal tree model at 0.95. e three terminal nodes are boxplot summaries of the model-derived survival estimates (n t t sam ple size within a terminal node). In addition to ocean and species, the best-tting models from the exploratory meta-regression approach (estimates weighted by precision) included tag type (and tag loss), model type, habitat type and study duration, suggesting the methodology as well as phylogeny and biogeography might have impor tant eects on adult marine turtle survival rates.Â‘ÂƒÂ”Â‹ÂƒÂ–Â‡Â•Â’Â‡Â…Â‹Â¤Â…ÂÂ‘Â†Â‡ÂŽÂ’Â”Â‡Â†Â‹Â…Â–Â‡Â†Â•Â—Â”Â‹ÂƒÂŽÂ‡Â•Â–Â‹ÂÂƒÂ–Â‡Â• e 3-level hierarchical meta-regression model (random eects t t study within research group) was a better t than a 2-level model (random eect t t study) (log-likelihood ratio test t t 58.1, df t t 2, P t t 0.001). e 3-level model with a nonlinear functional form for study duration was a better t than the same model with a linear functional form (log-likelihood ratio test t t 19.7, df t t 2, P t t 0.001). e inclusion of the six moderators in the accepted 3-level, nonlinear regression model led to a signicant improvement in overall model t (QE t t 121.4, df t t 14, P t t 0.001). However, ca. 97% of unaccounted variance was due to residual heterogeneity (tau2 t t 0.005, 95% CI: 0.003Â–0.011; I2 t t 97.2), and only ca. 37% of the residual heterogeneity in the accepted model was accounted for by inclusion of the six moderators (R2 t t 0.37). is high level of residual heterogeneity was signicant (QE t t 508.6, df t t 44, P t t 0.001) and apparently typical of ecological studies2,28. Covariate-specic survival estimates were derived from the 3-level hierarchical meta-regression model t to the 59 studies conditioned on family/tribe (3 levels), ocean (2 levels), tag type (4 levels), method type (5 levels), habitat type (2 levels) and the study duration by tag type interaction term (Fig. 3 ). General patterns among model-predicted survival estimates include: (1) Chelonini Carettini ~ Dermochelidae (Fig. 3a), (2) Indo-Pacic Atlantic (Fig. 3b), (3) titanium inconel ~ PIT monel (Fig. 3c), (4) statistical modeling enumeration (Fig. 3d) and (5) foraging areas nesting beaches (Fig. 3e). While tag type was mostly ocean-specic, study duration appears to have no eect or a negative eect on survival estimates from metal tags and a positive eect on survival estimates from PIT tags, though most are of relatively short duration (Fig. 3f ).Publication bias. A contour-enhanced funnel plot of the predicted study-specic survival estimates derived from the 3-level hierarchical meta-regression model t to the 59 studies showed no evidence of any form of publication bias (Supplementary Fig.S1). ere was also no temporal eect of publication year in the conditional inference tree or the exploratory meta-regression approach. erefore, we found no evidence of publication bias in the marine turtle survival rates for which we could test for using a range of approaches.DiscussionAnnual survival estimates for adult marine turtles exhibit considerable heterogeneity. Survival estimates were generated from ve turtle species, three major oceans and ve decades of research involving numerous methodological techniques. For this reason, heterogeneity among estimates might be expected. However, because adult survival is a key demographic parameter and marine turtles exhibit conserved life-history patterns, including slow growth and delayed sexual maturity8, the extent of heterogeneity found among survival estimates com plicates accurate biological interpretations. To better understand heterogeneity in adult marine turtle survival rates, we conducted a comprehensive meta-analysis to test how heterogeneity among estimates was partitioned among phylogenetic, biogeographic and methodological factors. Results from this study represent an important step towards identifying the key factors that drive dierences among survival estimates and setting research and management priorities for marine turtles globally11,12.bÂƒÂŽÂ—ÂƒÂ–Â‹ÂÂ‰Â•Â‘Â—Â”Â…Â‡Â•Â‘ÂˆÂŠÂ‡Â–Â‡Â”Â‘Â‰Â‡ÂÂ‡Â‹Â–Â› Results from the recursive partitioning or conditional infer ence regression tree approach22, 23 indicate that heterogeneity among survival estimates was rst partitioned between differences in ocean basin (Indo-Pacific versus Atlantic), then by differences among family/tribe within the Indo-Pacic (Chelonini versus Carettini and Dermochelidae). ough oen ignored, phylogenetic non-independence can change the results of ecological meta-analyses4. However, in this case, dierences were not associated with phylogenetic similarity Â– Carettini is more closely related to Chelonini (both in Chelonidae) than to Dermochelidae13. erefore, survival estimates in Carettini and Dermochelidae are likely similar and
4 lower than Chelonini due to other biological or methodological factors, not phylogenetic history. Estimates for Indo-Pacic Chelonini might be higher because this group includes atback turtles, which are endemic to the Indo-Pacic, exhibit dierent and potentially less vulnerable habitat-use patterns (exclusively neritic versus both Figure 1. Random-eects forest plot of the inverse-variance weighted annual survival estimates for the 59 marine turtle studies (letters aer reference year indicate dierent estimates from the same reference)37, 40Â– 42, 51Â– 88. e species-specic pooled or random-eect survival rate estimates (RE diamonds) are shown in addition to the prediction intervals (horizontal bar through each RE diamond). Plot ordered by eect size within each species and solid square t t survival rate and size of symbol reects relative weighting, horizontal bars t t 95% condence interval of each study-specic survival rate. Colored icons show potentially informative study-specic covariates.
5 neritic and oceanic), and have been consistently monitored with robust methodologies (see below)37. Estimates specically from the Northwest Atlantic (as there are no estimates from the Northeast or South Atlantic) are lower than the Indo-Pacic. However, this is not consistent with any apparent dierences in natural or anthro pogenic mortality rates or any known dierences in life-history behavior among populations in dierent oceans. Predictors that might be indicative of dierences in adult mortality among turtle populations such as harvest history and sheries bycatch impact14, 15 were not found to contribute to heterogeneity among survival estimates (Fig. 2 ). erefore, our results suggest that while heterogeneity is connected to biogeography (ocean eect) and phylogeny (family/tribe eect), dierences cannot be attributed to phylogenetic similarity or apparent regional dierences in adult mortality. Instead, regionand species-specic dierences appear to be tightly linked to the use of dierent methodologies applied in dierent oceans and therefore to a dierent composition of turtle species/populations. e ocean and family/tribe predictors were highly correlated with methodological predictors, including tag type, method/model type, habitat type and study duration. Results from exploratory meta-regression modeling showed that these four factors, in addition to ocean and species, were consistently found within the best-tting models. Direct monitoring of marine turtles in the wild is inherently dicult and CMR studies remain the most reliable tool for estimating annual survival rates. However, such eorts are clearly vulnerable to statistical biases when methodologies cannot account for complexities inherent to the biology and study of marine turtles such as imperfect detection, tag loss, and temporary and permanent emigration. First, our results highlight the impor tance of tag type and its ramications on tag loss on survival estimates. We found that estimates generated from monel tags were consistently lower than estimates generated primarily from inconel, titanium and PIT tags. Estimates generated from monel tags are likely biased low and eected by study duration (Fig. 3f ) due to factors related to higher rates of tag loss and subsequent individual misidentication: monel tags have lower retention rates38, tend to corrode in seawater39 and were oen applied only singly in early studies (e.g.40, 41). Similar concerns with respect to tag loss may apply to inconel tags, although to a lesser extent than monel. Second, our results show that early attempts to estimate survival via enumeration methods that rely on time-series counts of individuals violate far more CMR assumptions than statistical modeling procedures such as CJSRE and MSORD, which are designed to estimate and account for biases including imperfect detection and temporary emigration42, 43. Although robust statistical modeling procedures are well accepted and almost universally used, the accuracy of estimates will always depend on the quality of CMR data to which the models are applied. ird, our results suggest that CMR data collected in foraging areas may be less prone to statistical biases than data collected on nesting beaches. Because marine turtles spend considerably more time in foraging areas, estimates from foraging areas may be more robust to statistical biases associated with temporary, as well as permanent, emigration. Methodological dierences clearly contribute to heterogeneity among survival estimates. Patterns among dierent methodologies and their associated biases are highly correlated with biological patterns: more low estimates come from the Atlantic Ocean, Carettini and Dermochelidae, monel tags, enumeration calculations and nesting beaches, while more high estimates come from the Indo-Pacic ocean, Chelonini, Figure 2. Conditional inference tree visualization of the eect of potentially informative covariates on the study-specic marine turtle survival rates.
6 titanium tags, statistical modeling procedures and foraging areas (Figs 1 and 4 ). We proposed that if heterogeneity was inuenced by phylogeny or biogeography, then biological dierences in adult survival may exist and management action should be directed towards species/populations associated with survival estimates that are relatively low. However, this interpretation was contingent on whether or not heterogeneity was also inuenced by methodology. Because heterogeneity among survival estimates was strongly inuenced by dierent methodologies that were highly correlated with phylogenetic and biogeographic dierences, we cannot make accurate biological interpretations to condently direct management actions. Whether estimates that are lower than expected indicate elevated rates of adult mortality or simply highlight the use less robust methodologies is currently undecipherable. For this reason, eorts to apply many current survival estimates to model population viability and interpret long-term trends risk drawing inaccurate conclusions and misleading management actions. is highlights the need to critically evaluate current survival estimates and attempt to correct or account for statistical biases when possible. Additionally, future estimates should strive to apply robust methodologies Figure 3. Multi-panel display summarizing the covariate-specic model-predicted estimates derived from the 3-level hierarchical meta-regression model t to the 59 studies conditioned on the following covariates: (a) ocean (2 levels), (b) family (3 levels), (c) tag type (4 levels), (d) model type (5 levels), (e) habitat type (2 levels) and ( f) study-duration by tag-type interaction term. Closed blue dots in the boxplots shows the predicted mean, horizontal bar t t predicted median. e underlying trend in the tag-specic study-duration subplots (bottom right panel) shown by a cubic regression spline smooth with an inverse-precision weighted 95% condence polygon.
7 that eliminate or minimize statistical biases, so that the detection of important regionor species-specic dier ences in adult mortality are not masked by heterogeneity associated with dierences in methodology. Because adult mortality from over-harvesting and sheries bycatch remains a signicant conservation concern for marine turtles14, 15, more work is needed to generate new, more comparable estimates that will improve the accuracy of assessments of the status and trends in marine turtle populations9,10,44 and help guide conservation measures and management in the future.fÂ†Â‡ÂÂ–Â‹ÂˆÂ›Â‹ÂÂ‰Â†ÂƒÂ–ÂƒÂ‰ÂƒÂ’Â• Our systematic review of annual survival estimates for adult marine turtles revealed important regionand species-specic data gaps that should be high priorities for research to support conservation11, 12. At the region-level for all applicable species, there is a noticeable lack of estimates from the eastern North Atlantic (including the Mediterranean Sea), South Atlantic, western and northern Indian Ocean (including the Red Sea and Persian Gulf), western North Pacic and eastern South Pacic (Fig. 4 ), all areas that support globally important marine turtle populations14 and some that also host long-term research and monitoring projects capable of generating estimates of adult survival rates. At the species-level, we found estimates for all seven marine turtle species. However, all estimates for KempÂ’s and olive ridley turtles (n t t 4 and 1, respectively) were excluded for not meeting the minimum selection criteria for inclusion in the meta-analysis. Because these estimates were not deemed suciently robust and all are at least two decades old, new estimates for ridleys should be a research priority, especially given their susceptibility to overexploitation from human threats such as sheries bycatch45. Globally, estimates from nesting beaches are far more frequent than estimates from foraging habitats, which are primarily from eastern Australia (Fig. 4 ). Although logistically more challenging, priority should be given to estimating survival rates in foraging habitats, especially in the well-studied northwest Atlantic where estimates from nesting beaches overwhelmingly predominate. Survival estimates from unstudied regions and species will not only improve assessments of specic populations, but will also improve our understanding of the important factors that aect survival rates and allow us to better decipher biological versus methodological sources of heterogeneity among estimates. New estimates generated from traditional CMR data should avoid methodologies that may generate articially low estimates of survival rates if possible and, if used, should acknowledge and attempt to account for statistical biases associated with those methods. Future studies may strive to implement standardized robust methodologies that eliminate or minimize potential biases and isolate the biological signal resulting from actual sources of mortality. ough not yet practically implementable in most cases, the standardized use of population-wide genetic Â“taggingÂ” applied in both nesting and foraging areas holds great potential for future studies. Estimates using this approach generated across many regions and species would be directly comparable, allowing researchers and managers to identify regional hotspots in marine turtle mortality and implement more eective management strategies at a global scale.The value of meta-analysis. Meta-analysis is a powerful tool ideally suited for combining the results of demographic studies. Because demographic studies in ecology and conservation biology oen exhibit consid erable heterogeneity2, estimates of key parameters, such as adult survival rates, viewed in isolation may provide limited and/or potentially biased inferences. Our results show the value of meta-analysis in generating robust species-specic demographic estimates and identifying data gaps, but also highlight the importance of evaluating sources of heterogeneity when interpreting patterns among similar demographic studies. Because demographic studies frequently exhibit heterogeneity due to system-specic nature of biological phenomena2 and Figure 4. Global geographic distribution of the 59 survival estimates included in the meta-analysis. Icon shapes indicate dierent marine turtle species and colors indicate dierent habitats (foraging grounds versus nesting beaches). e map was generated using the Maptool function at www.seaturtle.org89.
8 study-specic dierences in methodology, identifying important sources of heterogeneity can inform biological patterns and conservation actions more broadly2 Â– 4. Synthesized data are essential for modelling wildlife population dynamics exposed to various anthropogenic hazards46 and for testing ecological hypotheses such as the importance or not of dispersal for species exposed to habitat loss47. Because there are currently few meta-analyses of wildlife demographic rates3,48, this approach represents a productive and important area for future research. Global research on marine turtles is poised to benet from the application of meta-analysis. e accumulation of demographic and biological information from decades of research across all species and biogeographic regions has elicited global assessments and numerous review articles in recent years (e.g.14, 15, 44, 49, 50). Meta-analytic comparisons of key parameters and threats associated with the biology and conservation of marine turtles such as breeding and recruitment rates, population and individual growth rates, and sheries bycatch and plastic ingestion rates will reveal novel patterns and valuable insights, while accounting for important sources of heterogeneity that may obscure important interpretations2 Â– 4 25, 28. Such analyses serve to guide research and conservation priorities for marine turtles into the future.Referencest 1.t Burgman, M. A., Ferson, S. & Aaaya, H. is assessment in conservation biology. (Chapman & Hall, 1993).t 2.t Senior, A. M. et al Heterogeneity in ecological and evolutionary meta-analyses: its magnitude and implications. Ecology 97, 3293Â–3299, https://doi.org/10.1002/ecy.1591 (2016).t 3.t Boyce, M. S., Irwin, L. L. & Barer, Demographic meta-analysis: synthesizing vital rates for spotted owls. J. Appl. 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We thank the compliers and maintainers of the Sea Turtle Document Library (seaturtle.org) and the Sea Turtle Online Bibliography (Archie Carr Center for Sea Turtle Research, University of Florida).We thank Chevron Australia for use of the Barrow Island and Mundabullangana data.Publication of this article was funded in part by the University of Florida Open Access Publishing Fund.Author ContributionsJ.B.P. and M.C. collected and managed data. All authors conceived and designed the study, analyzed the data and wrote the paper.Â†Â†Â‹Â–Â‹Â‘ÂÂƒÂŽfÂÂˆÂ‘Â”ÂÂƒÂ–Â‹Â‘ÂSupplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-24262-w Competing Interests : e authors declare no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional aliations. 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