Landscape Genetics and gene-flow in Coastal Douglas-Fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii)
Jeremy S. Johnson1, David M. Cairns1, Keith Gaddis1, and Konstantin V. Krutovsky2
1.
Department of Geography, Texas A&M University, jsjohnson@tamu.edu 2. Department of Forest Genetics and Forest Tree Breeding Büsgen-Institute, Georg-August-University of Göttingen Germany
Conclusion:
Results:
Eleven subpopulations of P menziesii were identified across a geographically large and diverse ecosystem.
Significant positive spatial autocorrelation was found, suggesting a low level of isolation by distance and a genetic
neighborhood of approximately 100km. Barriers to gene-flow were identified as additional factors contributing to
the observed genetic variation. Pairwise Euclidean kinship values suggest that gene-flow is more restricted (Gst >
0.035) along the Coastal Mountain Range and across the high peaks of the Cascade Mountain Range. Additionally,
gene-flow is more restricted between the Siskiyou Mountains and the southern Cascades, which are geographically
close. Moderate levels of genetic differentiation (Gst = 0.025-0.035) were observed across Puget Sound and
latitudinally along the east side of the Coastal Mountain Range. Low levels of differentiation (Gst < 0.025) were
observed across the Willamette Valley and along the Cascades. We found that gene-flow occurs at sufficiently high
rates over most of the study area explaining the very low population genetic differentiation.
Objective:
The objective of our study is to use a landscape genetics framework to assess the role of geographic barriers and
corridors on population genetic structure resulting from seed and pollen dispersal and gene flow in a large
continuous population of Coastal Douglas fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) in the Pacific
Northwest
Methods:
A dataset of 1363 Coastal Douglas Fir samples, genotyped at 6 SSRs and 25 Allozyme loci, was acquired from
Krutovsky et al. (2009)
Figure 1. 1363 wind-pollinated P. menziesii seeds
genotyped at six microsatellite and 25 Allozyme loci
were obtained from Krutovsky et al (2009). See Slavov
et al. 2004, St. Clair et al. 2005, Krutovsky et al. 2009
for details on sampling and genotyping). The sampling
of seeds extensively covered a large geographically
complex area of western Oregon and Washington.
To determine historic connectivity and scale of dispersal within the study area, we calculated spatial genetic
autocorrelation in adults using GenAlEx (Peakall & Smouse, 2006). We performed spatial autocorrelation analysis
by measuring the genetic similarity between all pairs of individuals, grouping the pairs into geographic distance
classes, and reducing the average kinship to a correlation coefficient (r). We chose distance classes to represent an
even number of samples in each class.
Population genetic structure was inferred using the software STRUCTURE (Pritchard 2000) and the optimal
number of clusters was identified using the Evanno method (Evanno et al. 2005) in STRUCTURE Harvester (Earl
and vonHoldt 2012) (Figure 3). Georeferenced individuals were then assigned to the population in which they had
the highest probability of membership (Figure 4A).
A
B
Figure 4. (A)Individuals are assigned to the population in which they have the highest probability of membership based on STRUCTURE runs.
Colors represent populations 1-11 and circle size represents probability of membership. (B) Cluster analysis identified significant areas of high
population probabilities and the median individual from each cluster was identified as the population center.
Cluster analysis, using Anselin local Moran’s I (Anselin 1995), was conducted to identify statistically significant
clustering of individuals with high population membership probabilities using ArcGIS’s Spatial Statistics toolbox
(ESRI 2011). The median individual from each statistically significant cluster was identified as the population center
for downstream analysis (Figure 4B). Population differentiation (Gst) was calculated in GenAlEx (Peakall and
Smouse 2006) and output as a kinship matrix for analysis using the Landscape Genetics Toolbox (Etherington 2011)
in ArcGIS.
Pairwise kinship was visualized by assigning population centers as nodes and pairwise connections between each
population center as edges. Edges were assigned the kinship value from GenAlEx and visualized to illustrate three
levels of gene flow: High (Gst < 0.025), Moderate (Gst 0.025 – 0.035), and Low (Gst > 0.035) (Figure 5A). The
linkages were then overlaid on a map of geographic features to visually identify potential barriers and corridors
impacting gene flow (Figure 5B).
Results:
0.140
A
0.120
B
Figure 5. (A) Kinship linkages were symbolized based on three levels of genetic differentiation and the inferred level of gene flow. High gene flow (Gst <0.025), moderate gene flow (Gst
0.025-0.035), and Low gene flow (Gst > 0.035). (B) Geographic and ecological factors influencing gene flow.
0.100
Discussion:
0.080
0.060
0.040
Figure 3. Optimal population structure identified as K = 11
based on the Evano method (Evano et al. 2005)
0.020
0.000
0
100000
200000
300000
400000
500000
600000
700000
800000
-0.020
y = -0.009ln(x) + 0.1155
R² = 0.5621
-0.040
Figure 2. This figure shows a trend of significant positive spatial autocorrelation below 100km, with a likely genetic neighborhood of 200km (Clark &
Richardson, 2002; Epperson, 2005; Hazlitt, Eldridge, & Goldizen, 2004). This gives a clear indication of incredible long distance dispersal in this
species, and a likely history of exceptional connectivity over large areas.
In this study, the sampled P. menziesii population is geographically continuous with unbroken
stands ranging across the entire study area. There is a high degree of within population genetic
variation but low between population genetic variation as a result of high pollen flow, which is
common in conifers (Krutovsky et al. 2012).
Visual assessment of all pairwise kinships in relation to geographic features indicates that
barriers to dispersal and gene flow exist latitudinally along the Coastal Mountain range, between
much of the Coastal Mountain and Cascade Mountain ranges and between the Siskiyou
Mountains and most other geographic regions. Also, Puget Sound in the north tends to be a
moderate barrier to gene flow. The Willamette Valley does not act as a barrier to gene flow as
evidenced by the fact that most pairwise kinships are highly similar across this region (Gst
<0.025).