| Approach | Description | Perspective | Example studies |
|---|---|---|---|
| Theory and simulation | Expectations and potential unique predictions of coupling via selection, population contraction/expansion, and range expansion; conditions favoring reinforcement under the expanded view; thorough analyses of the role of recombination rate in coupling, including interactions with initial conditions (e.g., geographic history) during coupling; systematic analysis of the effects on linkage disequilibrium (LD) between different functional types of loci (premating isolation, local adaptation, postzygotic isolation, etc.) on build-up of reproductive isolation (RI); assessment of conditions that lead to positive vs. negative LD between different sets of loci during the speciation process, and how these relate to the overall build-up of LD and RI. | All | Felsenstein 1981; Ortíz-Barrientos et al. 2016; Schuldiner-Harpaz et al. 2022; Aubier et al. 2023b |
| Documenting barriers and levels of coincidence | Documenting barriers and levels of coincidence over distributions of interacting taxa (e.g., “components of RI” and hybrid zone studies, taxa experiencing recent/ongoing altered habitats or human-mediated movement), across a wide range of taxa, geographic regions and relationships among populations, population histories and levels of divergence. Ideally, combined analysis of barrier traits, barrier loci, and barrier effects and how levels of barrier coincidence (uncoupled to fully coupled) and types of barrier effects (premating, local adaptation, postzygotic isolation, etc.) influence overall RI (measured under organismal or genetic views). Future empirical and theoretical studies could also more explicitly address and compare different “levels” of coupling and their interplay (coupling among barrier loci underlying a single barrier trait; coupling among loci underlying different traits generating a single barrier effect; coupling among loci underlying different barrier effects). | Primarily 1 and 3 but hybrid zone studies also relate to 2 | Karrenberg et al. 2019; Perini et al. 2020; Kunerth et al. 2022; Ålund et al. 2023; Firneno et al. 2023; Guevara Andino et al. 2023 |
| Landscape genetics | Analysis of whole-genome data and fine-scale documentation of barrier traits or barrier effects across distributions to study the role of geographic separation and degree of barrier coincidence on genomic structure of populations. Some studies are doing this using phenotypic measures; more direct estimates of coupling by LD of identified barrier loci are needed. | Primarily 2 but adding barrier traits implicates 1 and 3 | Edmands 2002; Safran et al. 2016; Coates et al. 2019 |
| Barrier gene identification | Analysis of functional and linked genetic variation at experimentally verified genes to identify and establish the relative roles of coupling mechanisms, including population processes, genomic architecture (rearrangement, recombination rate, pleiotropy, multiple-effect, one-allele, etc.), and adaptive coupling (see Experimental Manipulation below). Of particular importance is the identification of one-allele mechanisms at the genetic level (contrasting populations where the allele is under selection vs. not under selection [e.g., sympatric vs. allopatric populations in reinforcement scenarios after secondary contact]). Identifying genes is critical for discriminating between barrier loci and nonbarrier loci in hybrid zone or genome scan contexts, as otherwise cannot distinguish cause/effect and loci under direct selection vs. indirect selection. Combined with environmental information (see Landscape Genetics above) to test possible drivers (e.g., coupling from common drivers or from cline attraction). | Primarily 1, but also 3 especially where loci underlying multiple-effect traits and one-allele barrier effects can be included | Distinct barriers (Merrill et al. 2011; Kozak et al. 2019; Kautt et al. 2020; Unbehend et al. 2021); local adaptation (Roda et al. 2017); mating signal (Byers et al. 2021; Ritchie and Butlin 2023); mate preferences (Hench et al. 2019; Smadja et al. 2022); pollinator syndrome (Hermann et al. 2013) |
| Genomic patterns | Particularly where large-effect barrier loci cannot be identified, predictions about genomic patterns of divergence and gene flow need to be made and tested. Quantitative genetics approaches can be used to document the genomic distribution of loci underlying barrier traits or effects. Statistics like ancestry heterozygosity can be used to make inferences about barriers. Relationships between recombination and divergence can be used to test predictions about genomic architecture. | Primarily 2 | Riesch et al. 2017; Schumer et al 2018; Martin et al. 2019; Kautt et al. 2020; Thompson et al. 2022, 2023; Firneno et al. 2023 |
| Experimental manipulation | Experimental manipulations to test which trait/gene combinations are favored (individual fitnesses), and how selection might strengthen combined barrier effects through coupling, as suggested by theory and indirectly by empirical patterns (e.g., reinforcement). Allows determination of whether coupled barrier effects are directly selected or whether they might be coupled by indirect selection, and of how LD with directly selected loci arose. | Primarily 1 | Rice and Hostert 1993; Villa et al. 2019; White et al. 2020; Tusso et al. 2021 |
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