History Matters: Evolutionary and Demographic Reconstruction of the Southwest Atlantic Loggerheads (Testudinata: Cheloniidae)

doi:10.21203/rs.3.rs-4314272/v1

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History Matters: Evolutionary and Demographic Reconstruction of the Southwest Atlantic Loggerheads (Testudinata: Cheloniidae)

https://doi.org/10.21203/rs.3.rs-4314272/v1

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published 23 Aug, 2024

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The Southwest Atlantic (SWA) is an important region for the Caretta caretta characterized by unique genetic lineages; however, their life history is still misunderstood. In this study, we evaluated the demographic patterns of four SWA rookeries using D-loop and microsatellites data looking for expansion and bottlenecks signals. Then, we simulated several colonization scenarios for the SWA using Approximate Bayesian Computation. The best-supported scenario indicated that loggerheads might have colonized the SWA region once by the ancient lineage of ES/k4 that signals a sharing ancestry history, and from it originated the other lineages by divergence and introgression processes, explaining the high admixture levels between their rookeries and genetic clusters. The D-loop recovered population stability in the past, but microsatellites identified sharp recent bottleneck events, which may have been triggered by the Last Glacial Maximum, El Niño Southern Oscillation, and anthropogenic actions. Thus, we provide, for the first time, a complete assessment of the life history and colonization of loggerhead into the SWA, demonstrating differences between markers (matrilinear and biparental) that may bias our understanding of their genetic and demographic patterns, and which should be considered for conservation programs at a global scale.

Caretta caretta

adegenet

divMigrate

effective population size

Bayesian Skyline Plot

DIYABC

Over millions of years of evolution, selective environmental forces, and demographic processes have played a fundamental role in shaping sea turtles' community composition and biogeographic patterns in ecosystems around the world (Avise 2009; Cavender-Bares et al 2009). Besides, quaternary climatic fluctuations had a major impact on worldwide biotas and organisms, including reshaping the distribution of sea turtle species and modeling their genetic structure (e.g., Reece et al 2005; Baltazar-Soares et al 2020; Vilaça et al 2022). Sea turtles are expected to show marked phylogeographic structure due to their life history traits affecting their dispersal ability, gene flow, and tendency towards philopatry (Reid et al 2019; Alvarez-Gonzalez et al 2022; Vargas et al 2022; Vilaça et al 2022; Ludwig et al 2023). Consequently, the sea turtle distribution, demography, and genetic stocks subsidize establishing the Regional Management Units (RMU) for conservation purposes (Wallace et al 2023; Komoroske et al 2017; Reid et al 2019).

One of the most widely distributed sea turtle species in the world is the loggerhead, Caretta caretta Linnaeus, 1758, present since the Pacific (PAC), Mediterranean (MED), Indo-Pacific (IND), Northwest Atlantic (NWA), and Southwest Atlantic (SWA) (Marcovaldi and Chaloupka 2007; Reid et al 2019; Okuyama et al 2022). They have a complex life cycle that involves multiple life stages (hatchings, juveniles, adults) that display a series of ontogenetic habitat shifts and migrations (Bolten 2003; Bowen and Karl 2007; Barros 2010; Marcovaldi et al 2010; Weber et al 2013; Putman and Mansfield 2015; González-Carman et al 2016). The environmental and historical events have promoted significant changes in the evolutionary trajectories of their populations, but also anthropogenic actions like habitat loss, pollution, overfishing, and bycatch (Witt et al 2010; Bolten et al 2011; Fuentes et al 2013; Patricio et al 2021; Miguel et al 2022; 2023), which can impact their demography (like effective population size; Ne) and genetic variation patterns. Thus, the International Union for Conservation of Nature (IUCN) has been recategorizing them as vulnerable globally (Casale and Tucker 2017).

The reduction of genetic variation as a consequence of the abrupt decline of the Ne (or bottleneck) and the allele frequencies through genetic drift (Spear et al 2006; Millstein 2016) compromises the ability of a population to respond to environmental change (Amos and Balmford 2001; Nadachowska‐Brzyska et al 2022). Populations with reduced genetic variation are more prompt to fix mildly deleterious alleles and may be less able to adapt to changing environmental conditions (e.g., Bourrat 2017) and they could be susceptible to extinction (e.g., Vargas et al 2022). Therefore, genetic conservation studies focus on evaluating the population demography patterns to understand the genetic variation of vulnerable sea turtle species (e.g., Garofalo et al 2013; Vargas et al 2022; Vilaça et al 2022). Thus, it is important to detect population declines early on, before the genetic variation is greatly reduced to ensure long-term persistence (e.g., Shama et al 2011), especially in the face of novel selective pressures such as those caused by anthropogenic threats.

The control region (D-loop) haplotypes of the mitochondrial genome (mtDNA) have been used to determine the loggerhead turtle's lineages and population structure. However, due to low mutation rates and extensive sharing of haplotypes among genetically distant populations, it has become challenging to comprehend their demography using this marker (Excoffier et al 2009; Shamblin et al 2014; Reid et al 2019) Furthermore, for demographic analysis is difficult to distinguish between selection and genetic drift using only control region, making it even more complicated to understand the genetic makeup of these turtles (e.g., Reis et al 2010; Baltasar-Soares et al 2020; Loisier et al 2021). Thus, more recently, other molecular markers of the nuclear genome (nDNA) have been explored in evolutionary history reconstructions and to evaluate demographic patterns for sea turtle populations worldwide (Alvarez-Vargas et al 2021; Vargas et al 2022; Vilaça et al 2022), including loggerheads populations (Ludwig et al 2023). Those nDNA markers can provide a fine-scale evaluation due to the biparental inheritance, achieving their evolutionary history and Ne changing over time (e.g., Vargas et al 2022), which are the key parameters in population genetics studies to determine the amount of genetic variation that can be maintained in a population for generations (Ficetola et al 2009; Loisier et al 2021). However, for the SWA loggerhead populations, this information has not been explored to date and, therefore, their evolutionary history is still misunderstood and has been poorly studied in comparison to other RMUs (e.g. Carreras et al 2011; 2018; Reid et al 2019; Baltazar-Soares et al 2020).

In this study, we aimed to reconstruct the evolutionary life history and demography of the SWA loggerhead population. To do that, we used samples from a long-time survey study from the 2005/2006 to 2021/2022 nesting seasons reaching the Rio de Janeiro, Espírito Santo, Bahia, and Sergipe rookeries of the SWA and accessed their genetic data using 15 nuclear microsatellite loci and compiled the D-loop haplotypes from the literature (Shamblin et al 2014; Ludwig et al 2023). Then, we evaluated their demographic patterns looking for signals of expansion and bottlenecks using neutrality tests, BOTTLENECK, and a Bayesian Skyline Plot (BSP), which detected not only stability but also bottleneck events in different time windows in the past. Then, we simulated several scenarios (bottleneck, divergence, introgression, expansion) using Approximate Bayesian Computation (DIYABC) to recover their life history and deeply understand their colonization into the SWA. With this, we provide for the first time, a complete evolutionary and demographic evaluation of the SWA loggerheads lineage's life history, and advocate that each rookery should be categorized as a demographically independent population (DIP; Taylor et al 2010) and may be considered for management and conservation efforts in the future.

Sampling

Our study was carried out along the SWA coast, which was conducted during night monitoring of females along the Espírito Santo (ES), Bahia (BA), and Sergipe (SE) rookeries and covered the 2004/2005 until 2021/2022 nesting seasons (from October to March). Blood and tissue samples were obtained with an active collection of a piece of 6 mm of epithelial tissue with a biopsy punch from the base of the neck and the beginning of the shoulder preserved in ethanol at 95%.

Laboratory procedures

Genomic DNA (gDNA) was extracted following the saline solution (Bruford et al 1992) and CTAB 2% (Doyle and Doyle 1987). We genotyped 15 nuclear microsatellite loci (SSRs dataset) using Cc7G11, Cc1F01, Cc1G02, Cc1G03, CcP7D04, CcP2F11, CcP7C06, CcP8D06, CcP1F09, CcP5C11, CcP1F01, CcP1G03, CcP1B03, CcP5C08, and CcP5H07 primers (Schuelke 2000; Shamblin et al 2007; 2009). Alleles were scored using Geneious the following literature (Schuelke 2000; Shamblin et al 2007; 2009). The identification of null allele, large allele dropout, PCR slippage, and typographic genotyping errors was evaluated by MICRO-CHECKER v2.2.3 (van Oosterhout et al 2004) with a 95% confidence interval by Monte Carlo simulation. Departures from Hardy-Weinberg equilibrium (HWE) for each locus were examined by the exact test method using GENEPOP (Rousset 2008), and the linkage disequilibrium (LD) between each pair of loci was evaluated through the significance test using the Markov Chain method (10,000 dememorizations, 20 batches, and 5000 iterations per batch) with a Bonferroni correction with the standard procedure of an initial critical value of 0.05 using the FreeNA (Kawashima et al 2009).

Additionally, we compiled D-loop haplotype sequences from a previous study (Ludwig et al 2023) for comparison levels.

Population structure analysis

The population structure of the SWA loggerhead populations was evaluated using a Bayesian clustering analysis implemented in Geneland v4.0.9 (Guillot et al 2005) for R (R Development Core Team, 2013), which detects if there is spatial genetic population structure based on the distribution of allelic frequencies (SSRs dataset) by populations and correlates with geographic distance (UTM coordinates) through an optimal value of K estimation by examining the posterior probabilities averaged over multiple runs (10 runs, K = 1–10). The analysis was set using a correlated alleles model, 100,000 iterations, thinning of 200, and a burn‐in of 500, and default settings were used for the maximum rate of the Poisson process, and the maximum number of nuclei in the Poisson–Voronoi tessellation. Based on these results, we split the individuals and their respective D-loop and SSR datasets according to the estimated genetic clusters for all further analyses (Table 1), except for RJ where the SSR dataset was not available.

Then, to properly examine and validate that there is significative partitioning of genetic variation among the genetic clusters (obtained by Genland), we performed a pairwise F_ST and Analysis of Molecular Variance (AMOVA) implemented in GenAlex (Peakall and Smouse 2012) followed by statistical significance with 9,999 permutation steps each. Besides, we performed a principal coordinate analysis (PCoA) based on global F_ST that is implemented in GeneAlex to examine the genetic variations among the genetic clusters. At last, we performed a discriminant analysis of principal components (DAPC) that is implemented in adegenet R package (Jombart and Collins 2017), to evaluate the admixture levels among the four nesting areas using the population information a priori, which could indicate a sharing ancestry.

Demographic analysis

Based on the compiled D-loop dataset, signatures for demographic expansions were evaluated in DnaSP v6.0 (Rozas et al 2017) by statistics of neutrality tests of Tajima’s D test (Tajima 1989), Fu’s Fs test (Fu 1997). In addition, in DnaSP, Mismatch distribution analyses (MD) were carried out to estimate the frequency of pairwise nucleotide-site differences between sequences using the population growth-decline model to further examine the demographic history. From that, the MD can provide distinct patterns like unimodal which is associated with a sudden population expansion, skewed unimodal is generally associated with a recent expansion or bottleneck, or multimodal and bimodal which are usually associated with the presence of two distinct lineages (e.g. Reis et al 2010). We also estimated the sum of squared deviations (SSD) between the observed and expected mismatch for each nesting area using a parametric bootstrap approach using 1,000 replicates (Harpending et al 1993). A non-significant index suggests that the observed data have a relatively good fit for the growth-decline model. In contrast, a significant index is indicative of a stable population which is typically thought to show a ‘ragged’, multi-modal mismatch (Harpending 1994).

Based on the SSR dataset, the contemporary Ne was estimated through the linkage disequilibrium (LD) method implemented in NeESTIMATOR v2 (Do et al 2014) comparing the genetic clusters. Then, the changing of Ne over the last generations through past population declines (0.5-5 Ne generations) for the SWA loggerheads was estimated in BOTTLENECK 1.2.02 (Cornuet and Luikart 1996; Piry et al 1999) that is based on the expected distribution of alleles frequencies and the significant number of loci with excess He comparing the three mutation models available (IAM of Maruyama and Fuerst 1985, SMM of Cornuet and Luikart 1996, TPM of Fu and Chakraborty 1998) with 95% single-step mutations and 5% multi-step mutations. The significance of excess heterozygosity was assessed using a one-tailed Wilcoxon sign-rank test, as recommended for fewer than 20 loci (Luikart 1997), and probability (P) for 1,000 simulations.

The BSP was performed to evaluate in an older time window the SWA loggerhead's demographic history. It was chosen because it is based on posterior probabilities derived from an approximate likelihood Markov Chain Monte Carlo (MCMC) approach. For the D-loop dataset (BSP1) was performed in BEAST v1.8.2 (Drummond et al 2013), as follows (Clusa et al 2018; Baltazar-Soares et al 2020). The substitution model was set to HKY as the best-fit model of nucleotide substitution chosen through Akaike Information Criteria (AICc). The mutation rate was set to 3,24 x 10^–3 substitutions/site/MY (Duchene et al 2012). Plots were drawn using Tracer v1.6 (Rambaut et al 2014) and 25% of each chain as burn-in. While, for the SSR dataset (BSP2) was performed in the VarEff package (Nikolic and Chevalet 2014), which applies a coalescent approach estimating changes in ancestral Ne that rely on motif distance frequencies between alleles to estimate variation in Ne over time (Global θ = 4Neµ; µ = mutation rate) (Nikolic and Chevalet 2014). We set four independent MCMC runs using a Two-Phase Mutation model with µ =5,7 x10 ^-4 (estimated mutation rate from FitzSimmons et al 1997) and 10% of the mutations greater than a single step, simulating 10 million generations sampled at every 1,000 trees, and 25% burn-in. Inferences were made about past expansions and declines based on posterior distributions and their attributes (mean, median, and mode values) (Nikolic and Chevalet 2014) along the last 1000 generations which covered the SWA loggerheads colonization period into the SWA. We transform the generation's time using 32 years as the mean age of maturation for SWA loggerheads as follows (Petitet et al 2012; Vilaça et al 2021).

Evolutionary history reconstruction

The evolutionary reconstruction history of the SWA was performed using the SSR dataset only by a Bayesian inference model (Beaumont et al 2002) that implemented the DIYABC v2.1.0 (Cornuet et al 2014), which is based on the coalescent using a similarity criterion between simulated and observed data sets. The approach allows the posterior probability (PP) of different scenarios applied to the same data set to be estimated (Miller et al 2005; Terray et al 2007), with the assumption that the scenario with the highest PP is the most accurate based on a polychotomic logistic regression of each scenario probability on the deviations between simulated and observed summary statistics (Fagundes et al 2007; Beaumont 2008; Cornuet et al 2008).

We simulated five scenarios for the divergence and colonization of loggerhead populations into the SWA region following the DAPC results: (1) null model with a split of SWA loggerhead populations by their respective rookery diverging from an unknown lineage; (2) sequential splitting model directly following results from Geneland, where one the hand the BA/k1 and SE/k2 populations raised independently each from the ES/k3 and ES/k4; 3) the BA/k1, ES/k3 and ES/k4 sharing a common origin, while SE/k2 diverged independently; 4) where the ES/k3 and ES/k4 raised independently each from BA/k1 and SE/k2; and 5) the BA/k1 originated by introgression among ES/k3 and ES/k4, and SE/k2 raised independently from others. For that, we assumed that microsatellites evolved under a stepwise mutation model (after positive indication by BOTTLENECK, see further), and all individual loci had the same value, then, we simulated 1,000,00 datasets in three independent Monte Carlo Markov Chains (MCMC), with a burn-in of 25% for every 1000 interactions. To determine the best-supported scenario, all scenarios were compared using the posterior distribution probability with 95% confidence intervals (CI) with type I and type II errors for the best-supported scenarios, and a Relative Median Absolute Deviation (RMedAD) to estimated parameters for all scenarios (details in Table S2).

We sampled 224 individuals from ES, BA, and SE rookeries (Table 1), and we compiled 382 individual D-loop sequences (Ludwig et al 2023). From that D-loop dataset, we built an ~800 bp long D-loop alignment, which retrieved eight distinct haplotypes (A4.1, A4.2, A4.3, A4.4, A4.5, A4.6, A24.1, and A25.1) that are distributed in different frequencies in each rookery, being the A25.1 and A4.5 detected only in ES, and A4.6 only in BA nesting area (Table 1). We successfully genotyped those 224 sampled individuals (SSR dataset) which detected 262 distinct alleles ranging from 39 to 215 per population, A_R ranged from 2.6 to 14.3, Ho ranged from 0.444 to 0.918, Hi ranged from 0.716 to 0.906 (Table 1). Only four loci deviated from the HWE after Bonferroni correction, and only one locus (CcP1F01) presented > 5% null allele frequency (Table S1). Estimates of contemporary Ne ranged from 3.4 to 104.2, and for SWA overall was Ne = 229.2 (Table 1).

Table 1. Genetic characterization of the SWA loggerheads rookeries, comparing their five nesting areas and respective genetic clusters.
Genetic clusters	Nesting seasons	D-loop dataset¹	SSR dataset
Genetic clusters	Nesting seasons	H/N	N	A_R	Ho	iH	Ne (CI 95%)
BA/k1	2005/2006 - 2021/2022	4/62	50	11.8	0.808	0.826	34.1 (20.5 - 99.8)
SE/k2	2004/2005 - 2009/2010	3/35	4	2.6	0.444	0.716	3.4 (1.8 - 29)
ES/k3	2018/2019 - 2021/2022	6/132	132	14.3	0.838	0.842	63.6 (17.3 - 70.1)
ES/k4	2018/2019 - 2021/2022	5/104	38	10.6	0.918	0.906	104.2 (59.1 - 198.0)
RJ	2018/2019, 2021/2022	4/49	NA	NA	NA	NA	NA
SWA overall	2005/2006 - 2021/2022	8/382	229	17.47	0.841	0.847	229.2 (197.3 - 271.3)
¹Data compiled from Ludwig et al. (2023). H/N: number of distinct haplotypes and the total number of D-loop sequences; N: number of samples genotyped; A_R: allelic richness; Ho: heterozygosity observed; iH: individual heterozygosity means. Ne (CI 95%): effective population size based on Linkage-Disequilibrium and its Confidence Interval of 95% estimated by NeEstimator. NA: not available

Population Structure

Geneland indicated K=4 as the most likely number of genetic clusters with high posterior probability. The BA individuals clustered into k1 (N=50), SE individuals into k2 (N=4), 132 individuals of the ES into k3, and 38 individuals of the ES into k4 (Fig. 1A). To facilitate identification, we renamed them BA/k1, SE/k2, ES/k3, and ES/k4. DAPC membership probability also identified five genetic clusters with high admixture levels between them (>50%), with a high contribution of ES/k3 into other genetic clusters (Fig. 1C). The AMOVA and pairwise F_STwere congruent with Geneland evidencing a significative genetic variation among the genetic clusters (p-value ≤ 0.001) with the highest genetic variations detected within individuals (77%) than between clusters (3%) (Table 2).

Table 2. Genetic variation results comparing the Southwest Atlantic loggerhead populations based on Analysis of Molecular Variance (AMOVA) using only SSR, and Pairwise F_ST(diagonal below for SSR, and diagonal above for D-loop) . Bold numbers indicate a significant p-value ≤0.001.
AMOVA				Pairwise F_ST
	SV	%V	Value		Ba/k1	SE/k2	ES/k3	ES/k4	RJ
F_ST	0.220	3	0.084	Ba/k1		0.010	0.008	0.013	0.034
F_IS	1.280	20	0.232	SE/k2	0.186		0.013	0.009	0.009
F_IT	4.961	77	0.205	ES/k3	0.216	0.107		0.001	0.024
SV: Standard deviation				ES/k4	0.203	0.122	0.077		0.021
%V: Percentage of genetic variance.				RJ	NA	NA	NA	NA

Demographic analysis

Based on the analyses of the compiled D-loop dataset, Tajima's D and Fu´s F revealed non-significant values for all genetic clusters but positive and significant values for SWA overall, indicating that the nucleotide sites are not in conformation under a neutral evolution model as expected (Fig. 2A). Although the MD evidenced distinct patterns for each SWA population. BA/k1, ES/k4, and RJ indicated skewed unimodal distribution, while bimodal to SE/k2, and multimodal to ES/k3 and SWA overall (Fig. 2A). None of the sums of squared deviations (SSD) were significant, indicating that the curves fit the sudden expansion model tested (Fig. 2A). Following these results, BSP1 detected that the SWA loggerhead populations exhibited long-term stability and similar expansion patterns in the last 4,0 million years ago (mya) (Fig. 2B), except for the SWA overall that exhibited a bottleneck event with subsequent and more recent expansion (Fig. 2B).

On the other hand, based on the analyses of the SSR dataset, BOTTLENECK exhibited significant excess of heterozygosity (p-value ≤0.001) under all mutation models tested for BA/k1 (SMM: 0.195; TPM: 0.005; IAM: 0.057), SE/k2 (SMM: 0.102; TPM: 0.016; IAM: 0.036), ES/k3 (SMM: 0.003; TPM: 0.041; IAM: 0.005), and ES/k4 (SMM: 0.108; TPM: 0.012; IAM: 0.005). For the SWA overall, the SMM and IAM models were significant, but TPM didn’t (0.002, 0.0005, 0.005; respectively) indicating that they underwent a recent bottleneck event.

The BSP2 successfully recovered the Ne changes over time and detected population stability in the last 1,000 generations (c. 3,2 kya). Still, after that, they underwent a bottleneck event in distinct time windows (Fig. 2C). The most ancient bottleneck event was detected for the ES/k3 between 800 -700 generations ago sharply reducing its Ne (Ne= 2539.4) followed by a gentle increase and population stability until nowadays (Ne=104.2; Table 1). On the other hand, the bottleneck event detected for BA/k1 was abrupt and occurred between 30 - 15 generations ago, reducing its Ne from 2590.1 to 34.1 (Fig. 2C; Table 1). Contrasting with other patterns, the ES/k4 exhibited populational stability followed by a gentle increase (Ne= 3505.8) and subsequently sharp Ne decline reaching Ne=63.6 in the last 5 generations (Fig. 2C; Table 1). When all populations were grouped into SWA overall, the BSP2 detected a stable population (Ne=2739.87) until 30 generations ago with subsequently gradual Ne decrease until reaching contemporary values of Ne=229.2 (Fig. 2C; Table 1).

Evolutionary history reconstruction

DIYABC analyses suggested the best support for Scenario 5 (asterisks in Fig. 3), with non-overlapping 95% confidence intervals, and type I and II errors for the best-supported models were low, indicating high posterior probability and confidence interval (PP = 0,849, CI = 0,300 - 0,912) in the scenario chosen (Table S3). Based on the PCA and the numerical values of the summary statistics, we observed a good congruence between the scenarios-priors combination and the observed data set (Fig. S4). The scenario 2 was the second best supported (PP = 0,117, CI = 0,09 - 0,174) (Fig. 3; Table S3). Furthermore, the mean mutation rate estimated was found to be an order of magnitude higher (mean µ=9,25x10^-4) than those available in the literature (µ =5,7x10^-4; FitzSimmons et al 1997), so, we suggest further studies using SSR dataset follow our estimates. Scenario 5 demonstrates that the ES/k3 is the most ancient lineage in the SWA loggerhead which may have originated from an unknown founder population approximately 24,0 kya (t3). During the second split (t2) there was a divergence of ES/k4 about 11,4 kya, while the third split (t1) occurred the divergence of SE/k2 on one hand, and on the other hand, BA/k1 by introgression among ES/k3 and ES/k4 about 5,6 kya (Fig. 3).

The present study is the first to provide a full evaluation of the evolutionary life history and demography reconstruction of the Southwest Atlantic loggerheads integrating the mitochondrial and nuclear molecular markers. Our results depict a more ancient history by D-loop while more recent by SSR, which as expected will never be congruent due to the inheritance nature of the genomes and their different mutation rates (Avise 1992), but they provide complementary perspectives, which were also highlighted for other sea turtles (van der Zee et al 2022; Alvarez-Gonzales et al 2022; Vargas et al 2022), including for loggerhead populations from African and Mediterranean regions (Wright et al 2012; Stiebens et al 2013; Bradshaw et al 2018; Baltazar-Soares et al 2020). These patterns were assessed across the SWA lineage distribution range evaluating four rookeries, the RJ, ES, BA, and SE which are genetically recognized as distinct MUs within the SWA RMU (Ludwig et al 2023) and are composed of four genetic clusters. The high admixture levels among them suggest a co-ancestry history that explains their low genetic variance and gives us new insights into their lineage origin and colonization into the SWA region.

The evolutionary life history of the loggerheads recovered by D-loop reconstructed approximately 4,0 mya, which fits into the strict molecular clock for colonization of the Atlantic (2,38–6,43 mya) during the Pleistocene and supercontinent Pangea (Duchene et al 2012). Therefore, during that time, the formation of the Atlantic Ocean (Barenburg et al 2018) may have changed the geomagnetic polarity of the Earth and may have altered the maritime and migratory capabilities of loggerheads to reconnect to other populations worldwide (Cameron et al 2019), which, in turn, potentiated their expansion as demonstrated by BSP1 (based on the D-loop dataset). The founder lineage could be represented by CCA4.1 (Encalada 1998) or CCA4.2 (Ludwig, S et al. unpublished work) which may have colonized the SWA region coming from the Indo-Pacific through South Africa (Bowen et al 1994; Baltazar-Soares et al 2020) after the disconnection of the African and American continents and during the LGM by the ice refuge (Martins et al 2022). Following this line, this founder lineage may have expanded within the SWA region with the subsequent establishment of the ES nesting area once the DIYABC estimated that the ES/k3 may represent the ancient lineage. These findings may explain the bimodal pattern of the MD (based on the D-loop) detected for ES/k3 suggesting a secondary contact among lineages, but also the admixture level among the genetic clusters and the high genetic load of the ES/k3 into others (Fig. 1C).

During the establishment within the SWA, the new generations that came next may have faced a global temperature increase, promoting the melting of continental ice sheets (Clark et al 2006; 2009) and influencing other abiotic or biotic processes distribution (Broecker 1982; Edwards and Richardson 2004; Cord et al 2022), which have been associated recent demographic changes (e.g. Hoban et al 2019). Then, the bottleneck event detected by BSP1 for the “SWA overall” could indicate demographic signals triggered by that time (Seltzer et al 2002; Gowan et al 2021; Martins et al 2022). Therefore, we presume that they spread along the coast in both oceanic and neritic areas exploring new areas to forage, mate, and nest based on the distribution and quality of nesting habitat availability for them (Bolten 2003; Harley et al 2006; Hawkes et al 2007). We presume that the other lineages raised with the establishment of new rookeries along the SWA coast may be strongly influenced by a link between the geomagnetic polarity and their geographic location (e.g. Cameron et al 2019), and that has been maintained by the philopatric behavior influencing their contemporary spatial structure (Ludwig et al 2023; this study). Yet, the favorable warmer climate conditions promoted by the Atlantic Ocean thermohaline circulation propitiated new warmer water routes coming from the Indian Ocean reaching the SWA region (Knorr et al 2003; Batenburg et al 2018; Petherick et al 2022) and, we believe that some individuals could finally migrate and reconnect with other RMUs (Encalada et al 1998; Reid et al 2019), as already presumed for the colonization of the West Africa region by loggerheads coming from the SWA region due to their sharing D-loop haplotypes in a more recent period of their life history (Baltazar-Soares et al 2020).

BSP2 (based on the SSR dataset) covered a more recent period (c. 3,2 kya- 160 ya). It detected four bottleneck events followed by great Ne reduction in distinct time windows depending on the population. It is suggested that the El Niño Southern Oscillation (ENSO) can affect the sea turtles' demography by decreasing their food supply (e.g, Chaloupka et al 2008) and transforming their nesting areas landscape (e.g., Matsuzawa et al 2002; Fish et al 2005) and, consequently, reduce recruitment and their population's size (census and Ne) (e.g., Mazaris et al 2004) due to the sea surface temperature anomalies and sea level rise (e.g., Casey and Cornillon 2001; Brönnimann et al 2007; Harley et al 2006). These abiotic and biotic drivers interact with other global climate phenomena and modes of variability, such as convergent zones and flooding/dried events since the LGM (Petherick et al 2022). All these historical and contemporary environmental and climate changes have already been correlated to other influences in the loggerhead’s demography worldwide (Terray et al 2007; Chaloupka et al 2008; Arendt et al 2013; Welch et al 2019; Briscoe et al 2021) and other sea turtles (Saba et al 2008; Ariano-Sánchez et al 2020; Bruno et al 2020; Tomillo et al 2020). Thus, we presume that the combination of these factors could have triggered the bottleneck events detected for ES/k4 about 2,8 - 1,9 kya, affecting their current Ne.

We cannot rule out that the SWA loggerhead populations might also be affected by the expansion of anthropogenic-related actions due to their recent exposure to local bycatch in longline, trawling, and gillnet fisheries (Lewison et al 2004; Domingo et al 2006; Rodríguez-Zárate et al 2013; Wallace et al 2013; Huang 2015), landscape transformations of their nesting areas (Domingo et al 2006; Fernandes et al 2016; López-Mendilaharsu et al 2020; Franco et al 2024) and by native and exotic predators (López-Mendilaharsu et al 2020). Yet, the proliferation of diseases in female sea turtles like fibropapillomatosis affects offspring survival rates (e.g., Miguel et al 2023), and may even affect, in the near future, Ne values (this study). Thus, we presume that the same could have affected the SWA region, and the more recent bottleneck events detected in BA/k1 (c. 960 - 480 ya) may reflect that, as well as in ES/k4 (c. 160 ya) seem to have happened in a shorter window of time (about five generations) than the others and could be associated with these anthropogenic-related actions.

All the factors pointed out have directly impacted the SWA loggerhead's lineage life history in distinct timescales and affected them as a whole (e.g., Mazaris et al 2004; Chaloupka et al 2008; Cutrim and Araújo 2021). We are alarmed that this can increase the risk of loss of population-average fitness as well as the survivorship of the next generations if management and conservation efforts do not become a priority to their genetic rescue (e.g. Santidrián et al 2020; Patrício et al 2021). The very low contemporary Ne for the loggerhead’s populations represent worrying mean values for a continental scale as SWA and are below the conservative thresholds of minimum viable population size (500–5000; Nadachowska-Brzyska et al 2022). Once the answers are probably not one-size-fits-all which raises important conservation concerns (e.g. Rees et al 2016) and emphasizes their vulnerability to stochastic demographic and genetic processes (i.e. chance fluctuations in population size and genetic drift) and make them more susceptible to further depletion. Because, if these nesting areas are extirpated, the remaining small populations might not be recolonized over the ecological time scales due to their long generation time leading to slower growth rates meaningful to their conservation. Although Pereira et al (2024) observed signs of population recovery, due to the higher amount of recruitment for Espírito Santo rookery, the contemporary and historical Ne oscillations observed in this study did not follow the same pattern. As long-term restoration of Ne after bottleneck events is a long process that requires much more time than restoring census size (Frankham et al 2010), it can take several generations to genetic rescue of this lineage.

The findings of this study demonstrate that the contemporary demographic patterns and independence of the SWA loggerhead populations restrict the distribution of their genetic makeups (haplotypes and clusters). Although the mtDNA does not reflect the isolation of each SWA rookery due to their matrilinear evolutionary history, our SSR dataset advocates that they (SE, BA, ES, and RJ) represent distinct DIPs (Demographic Isolated Populations) each. These populations must be thoroughly investigated, as their distribution is restricted and separated from other lineages around the world (Stiebens et al 2013; Shamblin et al 2014; Reid et al 2019; Baltazar-Soares et al 2020). We also advocate that the ES is an important contact zone and the main genetic source for the other SWA populations since the colonization time in the past, and the identification of cluster ES/k3 in other rookeries sustains that. Thus, we encourage continuous monitoring and long-term survey studies using biparental molecular markers (as SSR provided here) to estimate better temporal changes in population sizes, distribution, and demography to, therefore, improve our understanding of their genetic and demographic patterns and that should be taken in account for conservation programs at a global scale.

Acknowledgment We thank the Instituto Chico Mendes de Conservação da Biodiversidade/SISBIO, Centro TAMAR-ICMBio (license number #60690-1), and the Núcleo de Genética aplicada à Conservação da Biodiversidade for technical support. This study was supported in part by the IBAMA/ICMBio, the Projeto TAMAR Foundation, CAPES, FAPES, and the Federal University of Espírito Santo.

Funding: The present study was carried out as part of the Aquatic Biodiversity Monitoring Program – Ambiental Area I - established by the Technical-Scientific Agreement (DOU number 30/2018) between FEST and Renova Foundation. FEST provided scholarships of SL and SMV.

Conflicts of interest: There is not conflict of interest.

Ethics approval: This study was conducted under collecting permits #65543-3 and #42760 issued by the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio), and #A32C980 by the Sistema Nacional de Gestão do Patrimônio Genético e do Conhecimento Tradicional Associado (SisGen).

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No competing interests reported.

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History Matters: Evolutionary and Demographic Reconstruction of the Southwest Atlantic Loggerheads (Testudinata: Cheloniidae)

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