Spatial Genetic Structure of a Symbiotic Beetle-Fungal System: Toward Multi-Taxa Integrated Landscape Genetics

Abstract

Spatial patterns of genetic variation in interacting species can identify shared features that are important to gene flow and can elucidate co-evolutionary relationships. We assessed concordance in spatial genetic variation between the mountain pine beetle (Dendroctonus ponderosae) and one of its fungal symbionts, Grosmanniaclavigera, in western Canada using neutral genetic markers. We examined how spatial heterogeneity affects genetic variation within beetles and fungi and developed a novel integrated landscape genetics approach to assess reciprocal genetic influences between species using constrained ordination. We also compared landscape genetic models built using Euclidean distances based on allele frequencies to traditional pair-wise Fst. Both beetles and fungi exhibited moderate levels of genetic structure over the total study area, low levels of structure in the south, and more pronounced fungal structure in the north. Beetle genetic variation was associated with geographic location while that of the fungus was not. Pinevolume and climate explained beetle genetic variation in the northern region of recent outbreak expansion. Reciprocal genetic relationships were only detectedin the south where there has been alonger history of beetle infestations. The Euclidean distance and Fst-based analyses resulted in similar models in the north and over the entire study area, but differences between methods in the south suggest that genetic distances measures should be selected based on ecological and evolutionary contexts. The integrated landscape genetics framework we present is powerful, general, and can be applied to other systems to quantify the biotic and abiotic determinants of spatial genetic variation within and among taxa.

European phylogeography of the epiphytic lichen fungusLobaria pulmonariaand its green algal symbiont

“…; James et al . ; Fedrowitz et al . ), the redistribution of photobionts under warmer climatic conditions could indeed rely on the horizontal transmission of locally adapted photobionts, or on the presence of photobiont‐mediated guilds formed by fungal hosts with different ecological preferences and adaptive potentials, but horizontally sharing a common photobiont (Rikkinen et al .…”

“…To test the degree of congruence between two data sets, for example, between two interacting species, Procrustean superimposition has proven to be as powerful (or even more so) than the Mantel test (Gower ; Peres‐Neto & Jackson ; James et al . ). Procrustes rotation scales and rotates raw data matrices or their ordination solutions in order to find an optimal superimposition that maximizes their fit, that is, minimizing their sum‐of‐squared differences (Gower ; Peres‐Neto & Jackson ).…”

Range-wide genetic structure and demographic history in the bat ectoparasite Cimex adjunctus

“…One likely reason for this dissimilarity between C. adjunctus and C. lectularius is that the former is a weak generalist, associated with closely related species [10], while the former is a strong generalist, associated with phylogenetically very different species. Overall, sample sizes and the number of microsatellite markers used were lower in our study than in several studies of C. lectularius genetic structure [34, 40, 41], but were nonetheless appropriate given the much broader spatial and temporal scale of resolution of our analyses [5, 8, 12, 43]. …”

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

“…However, to move beyond link‐based methods we must transform individual‐based pairwise genetic distances into a single Y vector. Landscape genetic neighborhood‐based approaches can use information from pairwise links (genetic distances) to create a node‐based data structure to relate GD patterns to local landscape predictors (James, Coltman, Murray, Hamelin, & Sperling, 2011; Wagner & Fortin, 2013). Here we introduce a novel analytical framework to test whether SGS changed over time.…”