Comparative Landscape Genetics of Three Closely Related Sympatric Hesperid Butterflies with Diverging Ecological Traits

Abstract

To understand how landscape characteristics affect gene flow in species with diverging ecological traits, it is important to analyze taxonomically related sympatric species in the same landscape using identical methods. Here, we present such a comparative landscape genetic study involving three closely related Hesperid butterflies of the genus Thymelicus that represent a gradient of diverging ecological traits. We analyzed landscape effects on their gene flow by deriving inter-population connectivity estimates based on different species distribution models (SDMs), which were calculated from multiple landscape parameters. We then used SDM output maps to calculate circuit-theoretic connectivity estimates and statistically compared these estimates to actual genetic differentiation in each species. We based our inferences on two different analytical methods and two metrics of genetic differentiation. Results indicate that land use patterns influence population connectivity in the least mobile specialist T. acteon. In contrast, populations of the highly mobile generalist T. lineola were panmictic, lacking any landscape related effect on genetic differentiation. In the species with ecological traits in between those of the congeners, T. sylvestris, climate has a strong impact on inter-population connectivity. However, the relative importance of different landscape factors for connectivity varies when using different metrics of genetic differentiation in this species. Our results show that closely related species representing a gradient of ecological traits also show genetic structures and landscape genetic relationships that gradually change from a geographical macro- to micro-scale. Thus, the type and magnitude of landscape effects on gene flow can differ strongly even among closely related species inhabiting the same landscape, and depend on their relative degree of specialization. In addition, the use of different genetic differentiation metrics makes it possible to detect recent changes in the relative importance of landscape factors affecting gene flow, which likely change as a result of contemporary habitat alterations.

Regional variation in drivers of connectivity for two frog species (Rana pretiosa and R. luteiventris) from the U.S. Pacific Northwest

“…Our findings add to evidence that show that species traits can help predict which factors will affect connectivity (Dudaniec et al., ; Engler et al., ; Frantz et al., ; Goldberg & Waits, ; Richardson, ; Selkoe et al., ; Whiteley et al., ; Wultsch et al., ). For example, differences in landscape genetic patterns were attributed to ecological (species) traits in a comparative landscape genetics study of two co‐occurring amphibians in northern Idaho, the long‐toed salamander ( Ambystoma macrodactylum ) and the Columbia spotted frog ( R. luteiventris ).…”

Syntopic frogs reveal different patterns of interaction with the landscape: A comparative landscape genetic study of Pelophylax nigromaculatus and Fejervarya limnocharis from central China

“…Such studies attempted to identify common elements, such as dispersal corridor, used by multiple species (e.g., Amos et al., 2012), and are particularly useful for land‐use management and identifying priority areas for conservation (Richardson et al., 2016). On the other hand, few studies are species‐focused, which compare closely related species occurring in the same landscape, examine interspecies intrinsic attributes, and seek how species‐specific attributes influence responses to the same landscape (Engler et al., 2014). Species and landscape interaction cannot be limited to the landscape perspective as inherent peculiarities across species influence varying response patterns to the same landscape effects.…”

Identification of landscape features influencing gene flow: How useful are habitat selection models?

“…; Richardson ; Engler et al. ). However, as this error may possibility affect confidence of assessing model significance, we conservatively evaluated results of multiple assessment methods in addition to AICc weights for final model selection (described below).…”