Introduction
Reconstructing the evolutionary history of a species is a challenging
exercise only partially eased by the growing size of genetic data
available. Indeed, larger amounts of data will indeed provide more
precision but not more accuracy if the model(s) chosen to infer
demographic parameters is distant from the true one. Species are dynamic
entities whose geographic range has often changed in time through range
expansions, contractions and shifts (Arenas, Ray, Currat, & Excoffier,
2012; Excoffier, Foll, & Petit, 2009; Mona, Ray, Arenas, & Excoffier,
2014). As a consequence, many species are most likely organized in
meta-populations (i.e. groups of demes or sub-populations exchanging
migrants to some extent), even though the more vagile ones might be
panmictic at a large scale (Corrigan et al., 2018; Karl et al., 2010).
Neglecting the meta-population structure (i.e., performing demographic
inferences under unstructured models) may lead to spurious
inference of population size change (Chikhi, Sousa, Luisi, Goossens, &
Beaumont, 2010; Maisano Delser et al., 2019, 2016; Olivier Mazet,
RodrÃguez, & Chikhi, 2015), which is particularly worrisome for species
of conservation concern. Unfortunately, the link between the inferred
temporal trajectory of Ne and the real demographic history of the
meta-population remains largely under explored. However, the role of
connectivity, particularly the number of migrants Nm exchanged
each generation and the migration matrix, has been put forward as a key
actor in shaping the gene genealogy of lineages sampled from a deme
belonging to a meta-population (Chikhi et al., 2010; Mona et al., 2014;
Ray, Currat, & Excoffier, 2003; Städler, Haubold, Merino, Stephan, &
Pfaffelhuber, 2009).
Understanding the relations between meta-population structure, the
inferred Ne variation under unstructured models, and
species dispersal abilities, is crucial to correctly interpret the
pattern of genetic variability and to establish conservation priorities.
To search for general rules describing such relations, we followed an
inductive approach investigating species: i) with large distribution
(which in principle should guarantee an organization in
meta-populations); ii) with different life history traits (LHT) related
to dispersal; iii) of conservation concerns. In this spirit, we selected
for our genomic study four shark species (Carcharhinus
amblyrhynchos , Carcharhinus limbatus , Carcharhinus
melanopterus, and Galeocerdo cuvier ) from New Caledonia. These
species have a large and overlapping distribution in the Indo-Pacific
(https://sharksrays.org/) and they differ for LHT features such as
size (which is positively correlated with the capacity for long distance
swimming and oceanic migration (Parsons, 1990)), residency pattern, and
long-distance dispersal ability as measured by tagging data (Table S1).
Moreover, the IUCN red list reported that the black-tip shark (C. limbatus) and the tiger shark (G. cuvier) are Near Threatened (with a decreasing trend in the tiger shark), the black-tip reef shark (C. melanopterus) is Vulnerable with decreasing trend, and the grey reef shark (C. amblyrhynchos) is Endangered with decreasing trend as well. We first compared
several population genetics models by means of coalescent simulations
coupled with an approximate Bayesian computation framework
(Bertorelle, Benazzo, & Mona, 2010) to detect whether panmixia or a
meta-population model best describe the genomic variation of each
species. Then, we inferred the demographic parameters under the most
likely model and applied the stairwayplot , which assumes a
panmictic unstructured population (Liu & Fu, 2015), to detect theNe variation through time in each species. We finally run
extensive coalescent simulations under the tested meta-population models
with parameters compatible to those observed in real data. The simulated
datasets were in turn analysed with the stairwayplot to: i) help
interpreting the observed data in the four shark species; ii) providing
general coalescent arguments relating the demographic history of a
meta-population and the reconstructed variation in Ne through
time by means of unstructured models.