Landscape Genetics of Schistocephalus solidus Parasites in Threespine Stickleback (Gasterosteus aculeatus) from Alaska

Abstract

The nature of gene flow in parasites with complex life cycles is poorly understood, particularly when intermediate and definitive hosts have contrasting movement potential. We examined whether the fine-scale population genetic structure of the diphyllobothriidean cestode Schistocephalus solidus reflects the habits of intermediate threespine stickleback hosts or those of its definitive hosts, semi-aquatic piscivorous birds, to better understand complex host-parasite interactions. Seventeen lakes in the Cook Inlet region of south-central Alaska were sampled, including ten in the Matanuska-Susitna Valley, five on the Kenai Peninsula, and two in the Bristol Bay drainage. We analyzed sequence variation across a 759 bp region of the mitochondrial DNA (mtDNA) cytochrome oxidase I region for 1,026 S. solidus individuals sampled from 2009-2012. We also analyzed allelic variation at 8 microsatellite loci for 1,243 individuals. Analysis of mtDNA haplotype and microsatellite genotype variation recovered evidence of significant population genetic structure within S. solidus. Host, location, and year were factors in structuring observed genetic variation. Pairwise measures revealed significant differentiation among lakes, including a pattern of isolation-by-distance. Bayesian analysis identified three distinct genotypic clusters in the study region, little admixture within hosts and lakes, and a shift in genotype frequencies over time. Evidence of fine-scale population structure in S. solidus indicates that movement of its vagile, definitive avian hosts has less influence on gene flow than expected based solely on movement potential. Observed patterns of genetic variation may reflect genetic drift, behaviors of definitive hosts that constrain dispersal, life history of intermediate hosts, and adaptive specificity of S. solidus to intermediate host genotype.

Geographic and host-mediated population genetic structure in a cestode parasite of the three-spined stickleback

“…Individual parasites were genotyped by Sprehn et al . (). Fish containing one or more parasites that were successfully genotyped were sequenced and genotyped for the current study.…”

“…Similarly, comparisons of S. solidus collected from hosts in Oregon and Alaska recovered signatures of divergence (i.e., 1% sequence divergence; Nishimura et al ., ). Several genotypic clusters and significant genetic structure also have been found across south‐central Alaska, possibly due to specificity to the intermediate host or factors limiting definitive host dispersal (Sprehn et al ., ).…”

“…The goal of this study was to interrogate a posteriori inferences derived by Sprehn et al . () to determine whether population genetic structure in S. solidus across south‐central Alaska reflects common dispersal constraints or adaptive specificity to stickleback hosts. To do so, we derived new data on the genetic variation of Alaskan populations of freshwater three‐spined stickleback hosts for comparison to available data on corresponding S. solidus parasites.…”

Comparative analysis of landscape effects on spatial genetic structure of the big brown bat and one of its cimicid ectoparasites

“…While the effects of the landscape on gene flow and genetic structure of many animal species have been described (Manel & Holderegger, ; Storfer et al., ), not much is known about how species that are dependent on the movements of other species, as is the case with many parasites, interact with the landscape (Sprehn, Blum, Quinn, & Heins, ). Our study has revealed a difference in the types of land cover that correlate with genetic differentiation of a generalist ectoparasite versus one of its potential bat host species.…”

Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework

“…Pioneered by work on rabies [11] and chronic wasting disease [12], research has targeted a handful of viruses (reviewed in [13]; see also [14]) and microbes (notably Batrachochytrium dendrobatidis [15]), helminths with direct life cycles [16,17] and their hosts. Systems involving vector-borne pathogens [18–21] or several intermediate hosts [22] have been mostly spared from investigation. We believe the application of landscape genetics to vector-borne disease agents, especially including landscape genetic simulation modelling [23] (see Glossary), has significant, underappreciated potential to inform targeted disease control strategies.…”