Landscape Genetics of Plants: Challenges and Opportunities

Abstract

Dispersal is one of the most important but least understood processes in plant ecology and evolutionary biology. Dispersal of seeds maintains and establishes populations, and pollen and seed dispersal are responsible for gene flow within and among populations. Traditional views of dispersal and gene flow assume models that are governed solely by geographic distance and do not account for variation in dispersal vector behavior in response to heterogenous landscapes. Landscape genetics integrates population genetics with Geographic Information Systems (GIS) to evaluate the effects of landscape features on gene flow patterns (effective dispersal). Surprisingly, relatively few landscape genetic studies have been conducted on plants. Plants present advantages because their populations are stationary, allowing more reliable estimates of the effects of landscape features on effective dispersal rates. On the other hand, plant dispersal is intrinsically complex because it depends on the habitat preferences of the plant and its pollen and seed dispersal vectors. We discuss strategies to assess the separate contributions of pollen and seed movement to effective dispersal and to delineate the effects of plant habitat quality from those of landscape features that affect vector behavior. Preliminary analyses of seed dispersal for three species indicate that isolation by landscape resistance is a better predictor of the rates and patterns of dispersal than geographic distance. Rates of effective dispersal are lower in areas of high plant habitat quality, which may be due to the effects of the shape of the dispersal kernel or to movement behaviors of biotic vectors. Landscape genetic studies in plants have the potential to provide novel insights into the process of gene flow among populations and to improve our understanding of the behavior of biotic and abiotic dispersal vectors in response to heterogeneous landscapes.

Keywords: Circuitscape; ResistanceGA; cpDNA; ecological niche modeling (ENM); effective dispersal; geographic information systems (GIS).

Figure 1

The Effects of Geographic Distance, Plant Habitat Quality, and Landscape Features on Dispersal Kernels (Brown Curves), the Behavior of Large Mammals (Dispersal Vectors; Blue Curves), and the Consequences for Patterns of Effective Dispersal (Green Curves). Bars across the bottom of each graph indicate habitat quality. Green regions of the landscape indicate high habitat quality, and yellow regions indicate low quality. (A) For dispersal within high-quality habitat, the three curves are parallel such that effective dispersal reflects the dispersal kernel. (B) The movement of the vector and the dispersal kernel are similar, but plants do not become established in areas of low habitat quality, and effective dispersal declines to zero. (C) The effective dispersal kernel recovers as vectors move seeds to a separate area of high-quality habitat. (D) The presence of a river (blue region in the habitat quality bar) reduces vector movement, and consequently, there are lower rates of effective dispersal in a habitat patch on the opposite side.

Figure 2

Patterns of Isolation by Resistance for a Network of Nature Parks (Left Map; Dark Green) Distributed across an Urban/Suburban Landscape (Light Green Matrix), and Consequences for Isolation by Resistance (Right Panel; Cool Colors = High Resistance). The circuit map was generated by Circuitscape and assumes that only natural areas have low resistance to dispersal. Circuit maps are used to test the hypothesis that they predict dispersal patterns by comparing them with genetic distance matrices among sample locations.

Figure 3

Sampling Schemes for Landscape Genetics. This example landscape is a mosaic of vegetation types: shrubs (green), prairie (yellow), and swales/vernal pools (blue). Pairs of orange sample points test for resistance within each type of vegetation, and pairs of purple sample points test for resistance across a single contrasting habitat type. The pairs of red sample points are not optimal because they integrate the effects of many vegetation types across the landscape. Vegetation map modified from Grasty et al. (2020).

Figure 4

Dispersal at Different Spatial Scales. Within contiguous high-quality habitat, we expect rates of dispersal to generally follow a strongly leptokurtic dispersal kernel that is based on the movement behavior of primary and secondary dispersal vectors. Dispersal resistance may be higher within habitat fragments because many dispersal events occur over short distances. Dispersal between pairs of habitat fragments separated by low-quality habitat may appear to have lower resistance because effective dispersal is higher at the tail of the dispersal kernel (Figure 1C) and because there are higher rates of wildlife vector movement between fragments. At regional scales, effective dispersal rates become multigenerational and follow a stepping-stone model, as dispersal is more likely to occur between pairs of neighboring habitat fragments.