Abstract

1
Preliminary Analysis of Common Loon (Gavia immer)
Genetic Structure in Northeast North America
Based on Five Microsatellite Loci
Amy M. McMillan, State University of New York
College at Buffalo Mark J. Bagley, United States
Environmental Protection Agency, Cincinnati,
Ohio and David C. Evers, BioDiversity Research
Institute, Gorham, Maine

Courtesy of BioDiversity Research Institute
Abstract This study seeks to determine fine-scale
genetic structure of Common Loon breeding
populations in order to link wintering birds with
their breeding regions. Common Loons are large
piscivorous birds that breed in lakes of northern
North America and Iceland. Loons are highly
philopatric and territorial in breeding areas and
are susceptible to mercury poisoning, lake
acidification and other threats across much of
this region. Wintering loon populations
originate from a mix of breeding regions. In
North America, wintering populations are found
primarily in nearshore coastal environments and
these birds are susceptible to oil spills. Loons
also are threatened by the current botulism
poisoning outbreak, which has killed thousands of
loons in the Great Lakes. Despite significant
demographic data, little is known about the
population genetic structure of Common Loons.
Preliminary analysis using five polymorphic
microsatellite loci demonstrated strong
differentiation between loons in eastern and
western North America (R X C exact test X2
48.14, df 10, P 0.000). Differentiation
among five putative eastern loon breeding
populations was also identified. Differences
were found in four of ten pairwise comparisons.
The information developed on loon population
structure will be crucial for understanding
year-round impacts on these birds.

• Materials and Methods
• Common Loon samples collected from western
  (California, Nevada, Washington States n70) and
  Eastern (Maine, Massachusetts, New Hampshire, New
  York, Vermont, Virginia States New Brunswick,
  Nova Scotia, Quebec Provinces n323) North
  America between 1997-2001
• DNA extracted from blood samples using Qiagen
  DNeasy tissue kit
• Five polymorphic microsatellite loci (Table 1)
  used to determine population structure (McMillan
  et al. 2004)
• 15 µl PCR reactions 3 pmol each labeled forward
  and unlabeled reverse primer, 250 µM each dNTP,
  1.5 mM MgCl2, 0.6 U Taq DNA polymerase, and 15 ng
  template DNA in buffer containing 20 mM Tris-HCl
  and 50 mM KCl (pH 8.0)
• Touch-down thermal regime 1 min at 95?C 12
  cycles of 30 sec at 95?C, 30 sec at 64?C,
  dropping 0.8?C for each subsequent cycle, 45 sec
  at 72?C 23 cycles of 30 sec at 95?C, 30 sec at
  54?C, 45 sec at 72?C 5 min at 72?C 10?C hold
• Alleles visualized with MJ Research Basestation
  and sized with Cartographer software
• Data analysis with TFPGA (Miller 1997) and
  POPGENE (Yeh et al. 1997)

• Results
• Two to seven alleles were present at each
  locus. Allele frequencies for each locus varied
  from nearly monomorphic (GimE11EPA) to highly
  polymorphic (Table 2).
• All populations were in Hardy-Weinberg
  equilibrium at all loci except for locus
  GimE11EPA (POPGENE, Chi-square test). Fewer
  birds with allele 2 were seen than expected in
  the East coast birds and in the Vermont/Northern
  New Hampshire population.
• Breeding populations from the eastern and
  western portions of North America were
  genetically distinct (TFPGA, R X C exact test X2
  48.14, df 10, P 0.000 Raymond and Rousset
  1995).
• Eastern birds showed a complicated pattern of
  relationships (Table 3). For this preliminary
  study, we anticipated potential breeding
  populations based on geographic features that did
  not support breeding loons (i.e., mountain
  ranges, extensive areas without breeding-quality
  lakes, etc.). Population differentiation was
  tested based on these putative populations
  (Figure 2). Four out of 10 pairwise exact tests
  for population differentiation were found
  statistically different (Table 2) although these
  differences did not follow any obvious spatial
  pattern.

BioDiversity Research Institute (www.briloon.org)
4
3
5
1
2
Table 3. Matrix of combined probabilities for
each pairwise comparison between areas in the
eastern region (exact test for population
differentiation, Raymond and Rousset 1995).
Heading numbers correspond to Figure 2 where
1New York, 2Massachusetts-southern New
Hampshire (NH), 3Vermont and northwestern NH,
4northeastern NH and northern Maine (ME),
5southeastern ME, Nova Scotia, and New Brunswick.
Figure 2. Putative metapopulation of Common
Loons in northeastern North America.
Figure 1. Range and migratory routes of the
Common Loon in North America.
Discussion These are preliminary findings in
a long-term study of Common Loon genetic
structure. This study will help to determine the
spatial scale in which to model loon
environmental impacts, particularly MeHg. Our
results suggest a relatively long-term split
between the eastern and western geographic
regions of North America and show that the
Pacific and Atlantic migratory routes represent
genetically distinct populations (Figure 1).
Within the eastern portion of North America it
appears there are genetic difference among
regions but that that these relationships need
further exploration. For this preliminary study
we assumed populations based on geographic
structures or areas that would seem to prevent
dispersal between breeding areas. Further
investigations will consider other possible
population boundaries and a mixed stock analysis
(e.g., Pritchard et al. 2000), which will help
determine the number of populations represented
in this region. This study suggests
that locus GimE11EPA may not be useful in
distinguishing populations since it is relatively
monomorphic and does not meet Hardy-Weinberg
expectations in some cases. Overall variability
in this study is not very high. Further
development of microsatellite markers or
additional, more variable markers (i.e., AFLP)
may be used to help define the Common Loon
metapopulation structure.
Introduction Common Loons have been studied as a
primary indicator of the impacts of methyl
mercury (MeHg) and other stressors on ecological
health for over a decade (Burgess et al. 1998,
Evers et al. 1998, Meyer et al. 1998) with
sampling primarily concentrated in northeastern
North America. This has resulted in a voluminous
database on loon density and distribution,
behavior and natural history (based primarily on
marked loons), and characterization of the
exposure and hazards of mercury and other
environmental stressors. Because loons are a
high trophic predator and environmental risks are
well-documented, the U.S. Environmental
Protection Agency (USEPA) is using them to model
wildlife risk. In order to place the
environmental risk assessment of loons into a
spatial framework, the genetic structure of
Common Loons in their breeding territories must
be understood. The summer breeding range of the
Common Loon encompasses northern North America
and wintering areas include coastal waters
(Figure 1). Adult loons return to the same
breeding area and usually the same lake (mean
dispersal distance 2km) juveniles disperse
only slightly further (mean 13km Evers 2000).
This suggests a metapopulation structure where
breeding loons are divided into relatively
discrete habitat areas separated by large areas
of unsuitable habitat (Figure 2). Previous work
with RAPD markers suggests that loons from the
midwest and northeast are genetically distinct
(Dhar et al. 1997) but no other studies have been
published using molecular methods to define loon
populations. The primary objective of this
study is to define breeding populations of Common
Loons using microsatellite markers. Genetic
subdivision between loons from different breeding
areas may manifest into differences in
stressor-response profiles for these birds.
Geographic information on reproductive success,
fitness measurements (e.g., feather weight
asymmetry), demographic processes, toxicological
exposure, and non-chemical stressors will be
placed in a spatially-explicit framework for
stressor-response models if loon subpopulations
can be defined. Loon populations can be
artificially defined by lake regions, topological
features such as mountain ranges, or by
convenient distances. However, whether birds
within our defined areas interact, more
specifically, interbreed or exchange genes, is of
utmost importance to understand stressor effects.

Courtesy of BioDiversity Research Institute
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