Demographic inference under a spatially continuous coalescent model

Abstract

In contrast with the classical population genetics theory that models population structure as discrete panmictic units connected by migration, many populations exhibit heterogeneous spatial gradients in population connectivity across semi-continuous habitats. The historical dynamics of such spatially structured populations can be captured by a spatially explicit coalescent model recently proposed by Etheridge (2008) and Barton et al. (2010a, b) and whereby allelic lineages are distributed in a twodimensional spatial continuum and move within this continuum based on extinction and coalescent events. Though theoretically rigorous, this model, which we here refer to as the continuum model, has not yet been implemented for demographic inference. To this end, here we introduce and demonstrate a statistical pipeline that couples the coalescent simulator of Kelleher et al. (2014) that simulates genealogies under the continuum model, with an approximate Bayesian computation (ABC) framework for parameter estimation of neighborhood size (that is, the number of locally breeding individuals) and dispersal ability (that is, the distance an offspring can travel within a generation). Using empirically informed simulations and simulation-based ABC crossvalidation, we first show that neighborhood size can be accurately estimated. We then apply our pipeline to the South African endemic shrub species Berkheya cuneata to use the resulting estimates of dispersal ability and neighborhood size to infer the average population density of the species. More generally, we show that spatially explicit coalescent models can be successfully integrated into model-based demographic inference.

Inferring Recent Demography from Isolation by Distance of Long Shared Sequence Blocks

“…Several inference methods use such classical isolation by distance patterns as signals to infer the parameters of recent demography. For example, fitting increasing pairwise genetic diversity with geographic distance is widely used (Rousset 1997(Rousset , 2000Vekemans and Hardy 2004), and approximate-Bayesiancomputation methods have been applied (Joseph et al 2016). Similarly, the extent of geographic clustering of rare frequency alleles can be used as a signal for inference (Novembre and Slatkin 2009).…”

Disentangling genetic structure for genetic monitoring of complex populations

“…Currently, the only model with immediate potential to address most of the requirements for long‐term genetic monitoring is the spatial Λ‐Fleming‐Viot (SLFV) model (Barton, Etheridge, & Véber, ; Guindon, Guo, & Welch, ; Joseph, Hickerson, & Alvarado‐Serrano, ; Kelleher, Barton, & Etheridge, ). The SLFV is a spatially explicit extension of the ‐Fleming–Viot model which is itself an extension of the Fleming–Viot model (Fleming & Viot, ).…”

“…Instead of using ABC as did Joseph et al. (), Guindon et al. () generated samples from the posterior distributions of the parameters with the Metropolis‐Hastings MCMC algorithm.…”

“…Limited applicability across studies for wide-ranging species (de Groot et al, 2016) Guindon, Guo, & Welch, 2016;Joseph, Hickerson, & Alvarado-Serrano, 2016;Kelleher, Barton, & Etheridge, 2013). The SLFV is a spatially explicit extension of the Λ-Fleming-Viot model which is itself an extension of the Fleming-Viot model (Fleming & Viot, 1979).…”

Coalescent inferences in conservation genetics: should the exception become the rule?

“…Moreover, two MMCs simulators are currently available: algorithms by Kelleher et al for continuous space evolution [28] and Hybrid-Lambda for species evolution [31], which could be used to fit evolutionary hypotheses to observations using simulation approaches (table 1). Indeed, Joseph et al [32] developed an ABC pipeline based on the simulator presented in [28] (table 1). At the same time, empirical conservation biologists will benefit from being aware of the biological relevance of MMCs and when and why they should be applied.…”