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Molecular Markers for
Ecological Indicators
Evon Hekkala
NERL Postdoctoral Fellow
EPA Region 5
Chicago, IL
28 April 2005
Genetic Methodologies to improve
existing Ecological Indicators for
Aquatic Ecosystems
•
development of accurate and precise
methods for biological identification of
aquatic species and subspecies
•
delineation of ecological assessment
units through analysis of genetic
structure across multiple species
•
assessment of changes in genetic
diversity as an indicator of present and
historical environmental condition
•
assessment of genetic diversity at
diagnostic loci and across the genome
as an indicator of vulnerability to
future environmental perturbations
•
integrated assessments to link
landscape-level stressors to
population-level outcomes
Why?
Biodiversity
•
•
Ecosystem
•
Species
Genes
•
Genetic diversity is a
fundamental component of
biodiversity
Stressors affect genetic
diversity in predictable
ways (ecological indicator)
Genetic diversity limits
potential responses to
future stressors
(sustainability indicator)
Understanding of genetic
diversity patterns
enhances the value and
interpretation of other
ecological assessment
data
DNA ID
IMPROVED METHODS FOR SPECIES
IDENTIFICATION AND ENUMERATION
RELEVANCE:
• Understanding of ecological condition depends on accurate
description of species assemblages
• DNA provides the most accurate and precise information on
species identity
• EPA needs efficient and transferable “DNA ID” or “DNA
barcoding” methods
DELINEATION OF ECOLOGICAL
ASSESSMENT UNITS
RELEVANCE:
• Measurement and evaluation of ecological condition
must be performed at the correct environmental scale
• Many assessments incorporate the watershed or
ecoregion as the fundamental assessment unit
• For biological resources, the fundamental unit that
responds to and adapts to the environment is the
biological population
CHANGE IN GENETIC DIVERSITY AS
AN INDICATOR OF ECOLOGICAL
CONDITION
RELEVANCE:
• Environmental stressors that alter the genetics of
populations have lasting effects
• Genetic change is brought about by environmental
alterations that affect the breeding population size,
mutation rate, population connectivity, or selective
forces
• Genetic change is an indicator of population and
species-level effects, scales at which we have few
good indicators
How?
Collect Samples
Extract DNA
Amplify Sections of DNA
AFLP/RAPD
Microsatellites
Sequencing
Analyze Data
Sample Collection and Extraction
• DNA is everywhere!
 Traditional vouchering
 Non-invasive, non-destructive
• Fin Clips, scales, swabs, feces, hair, shed
antlers, egg shells, scrimshaw……
• Museum Collections ( wet/dry)
AFLP
Amplified Fragment
Length Polymorphism
AFLP
Much more repeatable
More polymorphisms
Dominant
(presence/absence),
anonymous markers
Amenable to automation
Microsatellites
•
•
•
Highly polymorphic (high mutation rates)
Well-characterized, codominant single-locus
markers
Highly amenable to automation
TATATATATATA
TATATATATATATATA
TATATATATA
TATATATATATATATATA
Microsatellites
Locus 1
Locus 2
DNA sequences
•
Intensive analysis of one locus (COI, Cytochrome B)
•
Most explicit genetic ID available
•
More costly, but allows different types of analyses
Characterization and
Identification
of species diversity
Sp. A
Sp.B
Cytochrome Oxidase I
mitochondrial gene sequences
from GenBank provides a large
framework for assignment
of experimental data to
gross taxonomic groups
(redrawn from Hebert et al. 2003)
Sp.C
Arthropoda
Chordata
Mollusca
PCR primers for amplification of targeted species
Requires identification of primer binding sites that are:
identical among individuals within a target group
absent or ineffective among members of excluded group
Requires identification of gene regions that are:
consistent within the target group
variable among members of different target groups
Examples
Current ProjectsNERL/ORD
•
Regional profile of fish genetic diversity in Eastern Cornbelt
Plains Ecoregion (Region 5 REMAP)
•
Genetic diversity of stream fish in a coal mining-impacted
region.
•
Regional profile of fish genetic diversity in Mid-Atlantic
Integrated Assessment (EMAP) area
•
Temporal trends in genetic diversity in relation to experimental
whole-lake acidification (collaboration with DFO-Canada)
•
Temporal and spatial patterns of fish genetic diversity in a
highly modified urban stream
•
Integrated ecological assessments using genetic, landscape,
and population modeling methods (cross-NERL/ORD
collaboration)
•
Development of rapid Genetic ID methods to enhance detection
and enumeration of benthic invertebrates
Genetics of Central
Stonerollers in The Eastern
Cornbelt Plains Ecoregion
Campostoma anomalum
Photo courtesy of Ohio Dept. Natural Resources
Goals
• Define meaningful population units for
ecological assessments
• Assess relationship between genetic
diversity and ecological condition
Study Sites
•
•
•
•
91 sample sites
Part of Regional
EMAP
Mostly agricultural
First-third order
streams
Genetic Analysis
•
•
•
RAPD fingerprints
mtDNA Sequences
Assess genetic
differences within/
among sites
Genetic
Relatedness
Among sites
Genetics of Creek Chubs in a
Mining-Impacted Region
Semotilus atromaculatus
Photo courtesy of Ohio Dept. Natural Resources
Mitochondrial
DNA
Population
genetic structure
Stepwise multiple regression –
nuclear DNA diversity
PCA
Factor
partial
R2
model
R2
F value
Pr > F
PCA 3
(Latitudinal clines)
0.4328
0.4328
6.10
0.0387
PCA 2
(N/P/C)
0.3489
0.7917
12.06
0.0104
PCA 5
(pH/Ammonium)
0.1841
0.9758
45.60
0.0005
98% of the differences in genetic diversity within populations
explained by geographic and environmental factors!
Regional profile of fish genetic diversity
in Mid-Atlantic Integrated Assessment
(EMAP) area
White Sucker
Catostomus commersoni
•How
•How
•How
•How
is Diversity distributed?
accurate is Morphological ID in the field?
do we identify Hybrids?
do IDs affect IBIs?
DNA Taxonomic Identification
99
Semotilus atromaculatus Group 1
(303 sequences plus Genbank reference sequence)
97
100
99
Semotilus atromaculatus Group 2 (34 sequences)
100
Semotilus corporalis (11 sequences)
Sample 9576
100
Rhinichthys atratulus
Notropis stilbius
Notropis girardi
97
Luxilus cornutus
50
Sample 3786
100
85
0.10
0.08
0.06
Linear sequence divergence
0.04
0.02
0.00
Luxilus chrysocephalus
How accurate is field identification
of stream fishes?
96% of white suckers were morphologically identified correctly
All creek chub were morphologically identified correctly, but the
taxon is composed of two morphologically similar but genetically
distinct groups in the MAIA region
A minimum of 85% of fallfish were morphologically identified
correctly
All central stonerollers were morphologically identified correctly,
but the taxon is composed of four morphologically similar but
genetically distinct groups in the MAIA region
Field morphological identification seemed to be reasonably
accurate for these taxa but morphological identification underrepresented the actual biological diversity uncovered
Morphological analysis supplemented with genetic identification is
recommended for future ecological assessments ie. DNA QA
Multi-species assessment of
fish genetic diversity in the
MAIA region
• Microsatellite diversity of white
sucker was strongly associated
with agricultural impacts and
human population density.
• Creek chub diversity was
associated with stream substrate
condition and geochemistry.
• Central stoneroller diversity was
associated with agriculture, human
population density, runoff, pH,
stream substrate and geochemistry.
• Different species and genetic
groups within recognized species
appeared to respond to different
environmental dimensions.
100
Group1 (47sequences)
78
100
100
100
85
Group 2 (16 sequences)
Group 3 (11 sequences)
100
Group 4 (8 sequences)
Outgroup
(S. atromaculatus)
0.10
0.08
0.06 0.04 0.02 0.00
Linear sequence divergence
Targeted screening for invasive
species in ballast: genomic
approaches
Who are the Culprits?
European green crab
Where do they
come from?
Zebra mussels
Daphnia sp.
Bosmina sp.
??
European
green crab
Zebra
mussel
Polychaete
Identifying species found in ballast
Traditional:
Morphological taxonomy
•technologically simple (ie. microscopy…)
• classification dependent on adult traits
• larval and egg forms poorly characterized
• requires broad knowledge of major taxonomic groups
• or requires assistance from a range of experts
• identification typically limited to family or genus level
• limited treatment of cryptic or difficult taxa
• no standard for comparison across studies
• data have limited applicability (ie. species inventories…)
DNA extraction and purification
Resting eggs or tissue
in ballast water or sediment
www.glerl.noa.gov/res/task._rpts/nsreid10-1.html
“from sludge to sequences”
Sequencing of
cloned amplicons
Allele-specific PCR
amplification
Bacterial cloning
of amplicons
Collaboration
• Research supported by the Regional
Methods program
 ORD partnering with Regions 5, 9, 10 and
GLNPO
• Novel application of allele-specific PCR
methods and DNA sequencing technology
• Development and application of bioinformatic
databases
• Research objectives:
 Exploratory characterization of species
diversity in ballast
 Targeted screening of ballast for invasive
species
Regional Implementation
Marker Development
Laboratory
Regional
Laboratory
Design Assessment
• Field Sampling
• DNA extraction
• (PCR)
Ecological
interpretation
• (PCR)
• Marker screening
• Genetic Diversity
assessment
Genetic Analysis
Laboratory
• Develop and test
microsatellites,
other markers