Disentangling genetic structure for genetic monitoring of complex populations

Abstract

Genetic monitoring estimates temporal changes in population parameters from molecular marker information. Most populations are complex in structure and change through time by expanding or contracting their geographic range, becoming fragmented or coalescing, or increasing or decreasing density. Traditional approaches to genetic monitoring rely on quantifying temporal shifts of specific population metrics—heterozygosity, numbers of alleles, effective population size—or measures of geographic differentiation such as FST. However, the accuracy and precision of the results can be heavily influenced by the type of genetic marker used and how closely they adhere to analytical assumptions. Care must be taken to ensure that inferences reflect actual population processes rather than changing molecular techniques or incorrect assumptions of an underlying model of population structure. In many species of conservation concern, true population structure is unknown, or structure might shift over time. In these cases, metrics based on inappropriate assumptions of population structure may not provide quality information regarding the monitored population. Thus, we need an inference model that decouples the complex elements that define population structure from estimation of population parameters of interest and reveals, rather than assumes, fine details of population structure. Encompassing a broad range of possible population structures would enable comparable inferences across biological systems, even in the face of range expansion or contraction, fragmentation, or changes in density. Currently, the best candidate is the spatial Λ‐Fleming‐Viot (SLFV) model, a spatially explicit individually based coalescent model that allows independent inference of two of the most important elements of population structure: local population density and local dispersal. We support increased use of the SLFV model for genetic monitoring by highlighting its benefits over traditional approaches. We also discuss necessary future directions for model development to support large genomic datasets informing real‐world management and conservation issues.

Next‐generation conservation genetics and biodiversity monitoring

“…Milligan et al. () focused on expanding methods for independent inference of local dispersal and population density. The spatial Λ‐Fleming‐Viot (SLFV) model is well suited to decouple population parameter estimates from the processes that define population structure.…”

“…As molecular methods change and expand, model assumptions must be carefully investigated to ensure that inferences are reflective of population processes. Milligan et al (2018) focused on expanding methods for independent inference of local dispersal and population density. The spatial Λ-Fleming-Viot (SLFV) model is well suited to decouple population parameter estimates from the processes that define population structure.…”

Comparative analysis of adaptive and neutral markers ofDrosophila mediopunctatapopulations dispersed among forest fragments

“…Forest fragmentation and deforestation are believed to make environmental conditions more heterogeneous, with pronounced changes in biotic and abiotic conditions (Keyghobadi, ). Stochastic shifts in frequencies of genetic markers and high divergence among populations are expected to be the major responses of populations from a fragmented landscape (Milligan et al, ; Schippers et al, ).…”

“…Stochastic shifts in frequencies of genetic markers and high divergence among populations are expected to be the major responses of populations from a fragmented landscape (Milligan et al, 2018;Schippers et al, 2015).…”

Robin S. Waples—Recipient of the 2018 Molecular Ecology Prize

“…Robin has also published extensively on evaluating genetic risks and benefits of fish propagation (e.g., salmon hatcheries and marine fish aquaculture; Waples, ; Waples & Drake, ; Waples, Hindar, Karlsson, & Hard, ), on integrating genetic and demographic factors into analysis of age structure and spatial structure (Waples, Scribner, et al, ) and on monitoring genetic and demographic population structure in managed species (Milligan et al, ; Schwartz, Luikart, & Waples, ; Tallmon et al, ). Recently, he has begun to explore the evolutionary responses of natural populations to human‐altered environments (Waples et al, ) and the potential of genomics to assist conservation efforts (Hendricks et al, ; Waples & Lindley, ).…”