Coalescence times, life history traits and conservation concerns: an example from four coastal shark species from the Indo-Pacific

Abstract

Dispersal abilities play a crucial role in shaping the extent of population genetic structure, with more mobile species being panmictic over large geographic ranges and less mobile ones organized in meta-populations exchanging migrants to different degrees. In turn, population structure directly influences the coalescence pattern of the sampled lineages, but the consequences on the estimated variation of the effective population size (Ne) over time obtained by means of unstructured demographic models remain poorly understood. However, this knowledge is crucial for biologically interpreting the observed Ne trajectory and further devising conservation strategies in endangered species. Here we investigated the demographic history of four shark species (Carharhinus melanopterus, Carharhinus limbatus, Carharhinus amblyrhynchos, Galeocerdo cuvier) with different degrees of endangered status and life history traits related to dispersal distributed in the Indo-Pacific and sampled off New Caledonia. We compared several evolutionary scenarios representing both structured (meta-population) and unstructured models and then inferred the Ne variation through time. By performing extensive coalescent simulations, we provided a general framework relating the underlying population structure and the observed Ne dynamics. On this basis, we concluded that the recent decline observed in three out of the four considered species when assuming unstructured demographic models can be explained by the presence of population structure. Furthermore, we also demonstrated the limits of the inferences based on the sole site frequency spectrum and warn that statistics based on linkage disequilibrium will be needed to exclude recent demographic events affecting meta-populations.

Like a rolling stone: colonization and migration dynamics of the grey reef shark (Carcharhinus amblyrhynchos)

“…All three SFS displayed an excess of singletons in comparison to the one inferred by ANGSD (Figure S6b), clearly determining not only a stronger ancestral expansion but also the absence of the recent bottleneck when fed to the stairwayplot algorithm (Figure S6a). We note that by using the generation time and mutation rate of Walsh et al (2022) (a mutation rate only relevant to exon capture data (Maisano Delser et al, 2016) thus lower than the one estimated for RAD-seq data (Lesturgie et al, 2022)), the Ne variation reconstructed by the stairwayplot applied to our PyRAD assembly was highly similar to the results they obtained in their Chesterfield sampling sites (compare our Figure 6b with their Figure 3). Consequently, we highlight that the SFS reported by (Walsh et al, 2022) is biased toward an excess of low frequency variants, skewing their inferred genetic diversity and the subsequent demographic modelling.…”

“…Second, the historical demography inferences performed in each sampled deme showed that the pattern of genetic variability was most likely the outcome of a non-equilibrium meta-population structured according to a stepping stone migration matrix (Table 2). In this context, both the colonization times of the meta-population estimated by the ABC (Figure S2) and the expansion times retrieved by the stairwayplot (Figure 4) harbour the signature of the RE process (Lesturgie et al, 2022): the oldest times are expected to be close to the centre of origin of the RE, while the more recent ones are likely associated to the edge of the colonization wave(s). While the large variance in Tcol estimated by ABC does not allow for an accurate interpretation of the temporal dynamics of colonisation through the Indo-Pacific, the expansion times highlighted by the stairwayplot are consistent with the RE scenario.…”

“…The stairwayplot assumes that the sampled lineages come from an isolated (panmictic) population (i.e., unstructured), which is not true in our case (see results). However, this method allows a powerful investigation of the underlying gene genealogy which provides useful elements for interpreting the evolutionary history of a meta-population (Lesturgie et al, 2022). All demographic inferences were performed using a generation time of 10 years and a mutation rate of 1.93e-8 per generation and per site following Lesturgie et al (2022).…”

“…Unfortunately, for computational reasons, many commonly used software implement, under different algorithms, unstructured models, i.e., models that consider the population under investigation as isolated or panmictic (Heled & Drummond, 2008;Heller, Chikhi, & Siegismund, 2013;Li & Durbin, 2011;Liu & Fu, 2015). Except for highly vagile species which are panmictic at a large scale (Corrigan et al, 2018;Lesturgie, Planes, & Mona, 2022;Pirog et al, 2019), broadly distributed sharks species are more likely organized in metapopulation(s) throughout their range (Maisano Delser et al, 2019Momigliano et al, 2017;Pazmiño et al, 2018). The application of unstructured models to species organised in metapopulations yield spurious signatures of effective populations size (Ne) changes through time (Chikhi, Sousa, Luisi, Goossens, & Beaumont, 2010;Maisano Delser et al, 2019;Mazet, Rodríguez, Grusea, Boitard, & Chikhi, 2016;Mazet, Rodríguez, & Chikhi, 2015), with potentially dangerous consequences in terms of conservation policies.…”

“…The application of unstructured models to species organised in metapopulations yield spurious signatures of effective populations size (Ne) changes through time (Chikhi, Sousa, Luisi, Goossens, & Beaumont, 2010;Maisano Delser et al, 2019;Mazet, Rodríguez, Grusea, Boitard, & Chikhi, 2016;Mazet, Rodríguez, & Chikhi, 2015), with potentially dangerous consequences in terms of conservation policies. However, recent studies have highlighted the usefulness of such models to characterize the gene genealogy of the sampled lineages which in turn reveals important features of the meta-population (Arredondo et al, 2021;Lesturgie et al, 2022;Rodríguez et al, 2018). This emphasizes the necessity to couple complex meta-population models and unstructured models when uncovering the demographic history of a species.…”