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Digging into human impacts at
beaches
Elizabeth Halliday, Ph.D.
Marine Science Faculty, Coastal Studies for Girls
[email protected]
Coastal Studies for Girls
Freeport, ME
Woods Hole Oceanographic Institution
Dr. Rebecca Gast’s Microbial Ecology Lab
Viruses
Norovirus
Bacteria
Salmonella
Campylobacter
Protists
Giardia
Cryptosporidia
Health consequences:
annually $300 MILLION
Ralston et al. 2011
4
Fecal Indicator Bacteria (FIB) are
monitored at beaches to protect
bathers from pathogens
•
•
•
Ken Kostel, WHOI
FIB present in human and animal feces
More FIB in water = more bather illness
When bacteria exceed the water quality
standard, warning issued and water
tested again
– Enterococcus is measured at marine
beaches, limited to 104 Colony
Forming Units (CFU)/100mL
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Limited sampling and the time lag prevents realtime protection of bather health
The concentration of fecal indicator in the water yesterday is
not necessarily representative of the concentration today.
Sunday
Monday
Tuesday
Site 1
Site 2
Site 3
Site 4
Site 5
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Evidence from fresh and marine beaches that
fecal indicator bacteria persist in the environment
– Old assumption: fecal
indicator bacteria are
representative of
recent contamination
events
– First epidemiological
studies suggest
increased interaction
with sands leads to
increased outcomes of
illness
Yamahara et al., 2007
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Halliday and Gast, 2010
Environmental Science and Technology
Stormwater: ENT in beach sands and
waters peak differently
Wells, ME
Five day consecutive time series monitoring
waters in the harbor and barrier beaches
begins July 23 2009
On second day (July 24), 63mm rainfall in
ten hours preceding sample
CFU in sand and water at Wells, ME
water
wet sand
dry sand
Provincetown Harbor, MA
11
History of weekly sampling at Provincetown
by Barnstable County Health Department
shows that:
2-3 closures every summer
58% wet weather (stormwater)
41% dry weather (unknown)
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Sampling strategy
• Day-to-day monitoring
– 12 days for preliminary data in 2009, 33 days over 11 weeks in 2010
– Sampled Sunday, Monday and Tuesday at 8am
• Collect water, wet sand, dry sand
– Water at mid-calf depth
– Wet sand at water line
– Dry sand at high tide line (spatially fixed location)
• Collected environmental data from water: salinity, temperature,
DO, turbidity
• Autonomous data loggers collecting wind speed and direction, air
temperature, relative humidity, solar insolation
• Culture and qPCR methods used to measure Enterococcus
13
2009 and 2010:
Case studies in wet and dry weather
• Temperature
– Air (July)
• 2009 avg 20°C
• 2010 avg 23.4°C
Water Temperature, 2010
– Water (beginning July)
• 2009 was <18°C
• 2010 was already 22°C
• Precipitation (July)
• 2009: 12.7 cm
• 2010: 4cm
June
August
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Distribution of Enterococcus in July at Provincetown, MA
water
wet sand
dry sand
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ENT abundance increases across
exposed intertidal sands
Sand Moisture
Enterococcus
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ENT log10(CFU/100ml or 100g)
Variation with Tidal Range
water
wet sand
Neap Spring
Neap Spring
dry sand
4
3
2
1
0
Neap
Spring
Neap Spring
Significant differences in water ENT (p=0.02) and in dry
sand ENT (p<0.001) between spring and neap
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Next step: can a combination of environmental
variables be used to describe Enterococcus
abundance at the beach?
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Variables:
• Approach: Multiple Linear Regression
– Individual regression models
developed for sand and water ENT
– Offer all environmental variables as
potential predictors, use step-wise
variable selection
– Select combination of independent
environmental variables to
maximize adjusted R2
– Control for multicollinearity
• Variance Inflationary Factors <2
Water temperature
Dissolved Oxygen
Salinity
Turbidity
Moisture content of sand
Temperature of sand
Tidal range
Water level
Ebb/flood
Precip in last hr/24h/48h
Solar insolation
Air temperature
Relative Humidity
Wind direction
Wind speed
Gust speed
24h avg, 4h avg, time of sample
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MLR: culturable ENT in dry sand
• Variation in daily average
ENT in dry sand described
by :
– Tidal Range (strongly
positively correlated to
moisture content)
– Average solar insolation
(strongly negatively
correlated to relative
humidity)
=0.66
RR22=0.66
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MLR: culturable ENT in water
• Variation of daily average
ENT in water described by:
– Water temperature
– Water level
– 24h avg wind speed
R2=0.62
– Note: alternative
regression based on
turbidity alone also
strong (R2= 0.54)
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Variability of ENT over tidal cycle
Ebb
High
Low
Ebb
High
Flood
Low
Flood
Dry Sand
Wet Sand
Ebb
High
Flood
Low
Water
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Same pattern with water turbidity
1.8
1.6
Average NTU
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
4
3
2
1
5
6
1
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Logistic regression: predict exceedances from
tidal velocity
• Predictor: tidal velocity (rate of change in water
level hour before sampling)
– Categorical variable, binned at intervals of 0.5ft/hr
• Response: Sample exceeds 104CFU/100mL? (Y/N)
• R2 = 0.88 (p<0.001)
– When velocity is <0.5ft/hr, exceedance probability = 0.08
– When velocity is >2.0 ft/hr, exceedance probability = 0.55
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Beyond Enterococcus:
total community pyrosequencing
(Halliday et al. 2014, PLOS One)
• Deep sequencing of bacteria DNA samples
Sand
sample
Extract
total DNA
Short, unique
sequences
Library-based
ID of bacteria
Community
composition
ATCGCT
ATCGCT
closed
clean
– Elucidate similarities and differences among total
community, fecal bacteria, and potential
pathogens
– Compare profiles from environmental samples
with sewage samples
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Sequencing Samples
– Three days (9 composite
samples) from Avalon, CA, a
consistent “beach bum” with
frequent water quality
violations
– Five days (15 samples) from
Provincetown
– Generated 742,747 sequence
tags taxonomically identified
to bacterial phyla or beyond
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Bacterial Community Diversity:
significantly lower at site with more
violations
Examining differences in communities
• Nonmetric Multidimensional Scaling: distance between points
represents similarity
• Similarity based on sample type: the bacterial communities in
sand are more similar to communities in sand at different
locations than to bacterial communities in the local water.
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Alternative fecal indicators
McLellan et al. (2010) identified bacteria within the
orders Bacteroidales, Clostridiales and Bifidobacteriales
that are abundant in human feces, sewage influent and
effluent, and detectable in surface waters (lake and
riverine)
Extracted sequences identified to these orders from our
data, used ANOSIM to test differences between groups
When grouped by location, groups were significantly
different from one another (Global R=0.545 (p=0.001))
When grouped by sample type, the global R was small but
significant (Global R=0.298 (p=0.001))
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Summary:
• Sand likely host “naturalized” FIB populations,
and can be a source of FIB to local waters.
• Small variations in moisture content important to
enterococci abundance in dry sand
• Probability of exceedance events related to tidal
cycle
• Widespread availability of environmental
parameters highlights potential to develop
predictive models
• Fecal Indicators are only part of the story…
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Acknowledgements
•
Maine Healthy Beaches and Keri Kaczor: invitation to speak, food for thought
•
WHOI Academic Programs Office, WHOI Coastal Ocean Institute, WHOI Ocean
Venture Fund
WHOI Center for Ocean and Human Health (NSF OCE-0430724, NIEHS
P50ES012742)
•
•
Collaborators
– Dave Ralston, WHOI AOP&E
– Linda Amaral-Zettler and Mitch Sogin (MBL), Sandra McLellan (UWM)
•
The Southern California Coastal Water Research Program
•
•
•
John Griffith
For their assistance at the Wells Estuarine Research Reserve:
Michelle Dionne, Jeremy Miller, Meri Ratzel and Cayce Dalton
Provincetown Center for Coastal Studies
Amy Costa, Cape Cod Bay Monitoring Program
Barnstable County Health Department
George Heufelder, Bethany Sadlowski
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Questions?
[email protected]

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