The responses of diverse aquatic ecosystems to

The responses of diverse aquatic ecosystems to contaminants
Karen Kidd
Canadian Rivers Institute and Biology Department University of New Brunswick
What am I going to talk about?
• Mercury in aquatic food webs
– Stable nitrogen isotopes (trophic position)
– Do ecosystem characteristics affect fate of mercury?
• Regional, national and global comparisons
– Conclusion: Characteristics of systems affect trophic transfer of mercury
• Contaminants of emerging concern in municipal wastewaters
– Birth control pill and its effects on fish and their prey
– Conclusion: Estrogen can affect species both directly and indirectly
• Brief introduction to Canadian Rivers Institute and training opportunities with Swetox
Mercury (Hg) in aquatic ecosystems
wet & dry deposition
>90% of total Hg (THg) is MeHg in top predators (Bloom 1992)
catchment inputs
transport by organic matter
Hg2+
Hg2+
Hg0
% MeHg
methylmercury, MeHg
MeHg is neurotoxic and impairs reproduction in fish and fish-eating wildlife
and humans
Ecosystem characteristics and mercury
Mercury accumulation in a fish species or invertebrate taxon varies from one system to another due to:
• Chemical characteristics:
– pH, dissolved organic carbon, nutrients
• Physical characteristics:
– Surface area, depth, % wetlands
• Biological characteristics:
– Food web length, lifespan, diet
Do ecosystem characteristics affect mercury biomagnification in food webs?
How are we assessing mercury biomagnification?
15N/14N (15N) measures relative trophic level
9
Organisms retain more 15N than 14N from its diet
~3.4‰ increase from prey to predator
6
3
1
2
Trophic level
3
Stable nitrogen isotopes (15N) quantify mercury (Hg) biomagnification in aquatic food webs
• Log [MeHg] and δ15N of organisms positively related in diverse aquatic food webs
• Slope of regression is average biomagnification of Hg through food web
• Varies among systems ‐ why????
Log MeHg concentration
Slope = Food web biomagnification
Trophic position (δ15N) Do ecosystem characteristics explain among‐system differences in mercury biomagnification?
Regional Study: Kejimkujik National Park
• Encompasses ~50 lakes and ponds across ~400 km2
• Lakes are:
Carter et al. 2001
– Oligotrophic (TP 0.006‐0.014 mg/L)
– Acidic (pH < 6)
– High in organic matter
• Conditions favour Hg bioavailability • Recognized as a hotspot of biological Hg contamination in North America (Evers et al. 2007)
Study lakes in Kejimkujik Brianna Wyn 2006‐2007
Meredith Clayden 2009‐2010
George
11 lakes in total
Hilchemakaar
Study lakes were chosen to cover a range of chemical and physical characteristics Select characteristics of lakes (mean values for water chemistry, n=6‐10/lake)
Surface Area (ha)
pH
DOC (mg/L)
TP
(mg/L)
Beaverskin
39.5
5.8
2.8
0.006
North Cranberry
34.3
5.5
4.3
0.008
Pebbleloggitch
33.4
4.6
13.7
0.014
Puzzle
33.7
5.7
3.9
0.008
Big Dam East
45.5
6.2
4.5
0.008
Cobrielle
132.0
5.6
3.1
0.005
Hilchemakaar
95.4
5.8
5.7
0.012
Upper Silver
24.3
5.9
3.6
0.008
Big Dam West
105.0
5.1
12.6
0.012
George
77.8
5.1
9.6
0.012
Peskowesk
685.0
5.0
6.9
0.007
Lake
(Kerekes & Schwinghamer 1973; T.Clair and I. Dennis, unpublished data)
Relationships between mercury and nitrogen isotope ratios
Big Dam West 0.16, R2 = 0.85, p < 0.001
1.00
North Cranberry 0.19, R2 = 0.90, p < 0.001
Beaverskin 0.23, R2 = 0.92, p < 0.001
Log-Hg (µg/g dry weight)
0.50
0.00
-0.50
-1.00
-1.50
-2.00
-2.00
0.00
2.00
4.00
6.00
δ15N (‰)
8.00
10.00
 Log MeHg ‐ δ15N regression significant for each lake
(R2 0.78 – 0.92)
 Slopes range from 0.16 –
0.23 across 11 lakes
 Significant differences among lakes
 Is there any relationship to ecosystem 12.00
characteristics?
Is Hg biomagnification related to lake characteristics?
0.23
r = ‐0.765
p = 0.010
Biomagnification slope
0.22
0.21
0.20
0.19
0.18
0.17
0.16
0.15
0.0
5.0
10.0
Mean lake DOC (mg/L)
Clayden, M. et al. 2013. Environ. Sci. Technol.
Wyn, B. et al. 2009. Can. J. Fish. Aquat. Sci.  Slopes not related to  Lake pH (p = 0.30)
 Physical characteristics of lakes (e.g. depth, area); p ≥ 0.45)
 Slopes were  Negatively related to Ca, DOC, TN, TP, Fe, Al
 DOC, TN, TP, Fe, Al all highly correlated
15.0
 Using principle components and multiple regressions to understand predictors
 Ecosystem characteristics are correlated to slopes. Why?
Contrasting diverse food webs across Canada
6 lakes near Hope Bay, Nunavut (Swanson, PhD; Arctic char
Swanson et al. 2010. ES&T)
6 lakes on Cornwallis Island Lake trout
8 lakes in Alberta,
Saskatchewan
(Kidd et al. 2012 STOTEN)
6 lakes in northern
and southern Ontario
(Kidd et al. 2012 STOTEN)
What drives among‐system differences in
mercury biomagnification?
Streams vs. lakes?
Freshwater vs. marine?
(Lescord, MSc; Lescord et al. 2014. STOTEN)
Brook trout
5 lakes and 23 streams
in New Brunswick
(Jardine, PhD; Finley, MSc;
Jardine et al. 2012. Ecol. Appl.)
Yellow perch
11 lakes in Nova Scotia
(Wyn, MSc; Clayden, MSc; Clayden et al. 2013; Wyn et al. 2009)
National comparison: Mercury in food webs with diverse top predators
• 27 lakes
• Slopes variable, median 0.18, R2 69 to 0.98
– Arctic, charr
0.12 to 0.22
– Temperate, lake trout 0.12 to 0.23
– Temperate, brook trout 0.13 to 0.21
– Temperate, yellow perch 0.13 to 0.23
• Best predicted by components with high loadings in SO4, lat/long, cations, lake size, Cl
What about international mercury pollution?
 Many human sources of mercury
 Hg pollution is a global concern due to long range transport and local contamination
 Minamata convention signed by 128 countries, currently being ratified
(UNEP, 2008)
Global review of Hg biomagnification slopes –
location of food web studies
log [Hg]
(Lavoie et al. Environ. Sci. Technol. 2013)
Food web biomagnification Trophic position (δ15N) • 205 sites around the world • Freshwater sites (110 MeHg, 101 THg)
• Marine sites (14 MeHg, 26 THg)
• MeHg ‐ Slopes from 0.08 to 0.53 mean 0.24 ± 0.08 (n=124) • THg ‐ Slopes from ‐0.19 to 0.38 mean 0.16 ± 0.11 (n=137)
Increasing Hg biomagnification at higher latitudes
But very little variability explained!!!
y = 0.0012x + 0.18
R² = 0.07 P =0.003 y = 0.0006x + 0.14
R² = 0.04 P =0.001 Biomagnification slope (TMS)
0.60
0.45
0.30
0.15
0.00
MeHg slopes for freshwater sites best predicted by combination of :
 Total phosphorous (‐ve)  DOC (+ve) and pH (+ve)  p<0.001, R2=0.25, n=44
‐0.15
‐0.30
‐90
‐60
‐30
0
30
Latitude (degrees)
MeHg
THg
60
90
But, may also be related to variable protein content or composition.
• %S, %N
• cysteine
Log [Hg]
Variable slopes among systems could be related to different trophic transfer efficiencies of mercury, bioenergetics of organisms.
2
3
4
5
Trophic Level (from 15N)
Log [Hg]
Future research directions
2
3
4
5
Total S or amino acid content
Effects of climate change on mercury cycling –
ongoing collaborations with Swetox
• Pond experiment – Umeå University (J. Klaminder, J. Karlsson, E. Bjorn)
– Effects of warming and fish harvesting on mercury fate
+4oC
+4oC, fish removal
Ambient, fish removal
ambient
Effects of climate change on mercury cycling –
ongoing collaborations with Swetox
• New study led by NIVA, Oslo (involves SLU Uppsala) on mercury in fish and food webs of lakes in Norway and Sweden
• Extensive and intensive studies, field work in 2016
• Comparison to Canadian systems
Contaminants of emerging concern in municipal wastewaters
The estrogen story
• Why put a synthetic estrogen into a whole lake?
• What happened after it was added?
– Chapter 1: Direct effects on • fathead minnow
– Chapter 2: Indirect effects on • plankton and littoral macroinvertebrates
• lake trout • Lessons learned
Why did we do this study?
• Linked to presence of
natural and synthetic estrogens
• Implications for sustainability of fishes?
Male fish exposed to sewage effluents have:
• Smaller gonads
• Vitellogenin
• Intersex
Lake 260 ‐ Estrogen Addition Lake
Max depth – 14 m
Surface area – 36 ha
inflow
Fishes
Lake trout
White sucker
Fathead minnow
Pearl dace
Lake chub
Finescale dace
Slimy sculpin
17‐ethynylestradiol (EE2)
outflow
Study Design
recovery?
effects on individuals & populations EE2 additions (5‐6 ng/L)
baseline data
1999
2000
2001
2002
2003
reference lake data
2004
2005 thru 2010
Chapter 1: What happened to the main character?
Fathead minnow
‐ Mature at age 2, live 2‐3 years
‐ Spawn once mid‐summer, then most die
‐ Important prey for many larger fishes
‐
‐
‐
Sampled spring and fall
Trap nets and minnow traps
Vitellogenin, gonad histology, catch‐per‐unit‐effort
Vitellogenin in male fathead minnow
Vitellogenin (mg/g wet weight)
100000
10000
Lake 260 Lake 442
Lake 114
1000
up to 22,000x
100
10
1
0.1
Before
During EE2
What happened to their ability to develop sperm?
before
Normal sperm cells
1 year of
EE2
Delayed sperm cells, fibrotic
3 years of
EE2
Intersex
1000
Fathead minnow - YOY
CPUE
100
99%
1
0.1
0.01
1998
¨
Lake 260
Reference
2000
+EE2
2002
2004
2006
10
Fathead minnow - adults
-1
1
kg ha
Effects of EE2 on the fathead minnow population of Lake 260 –
spring catches
10
0.1
99%
0.01
+EE2
Collapse of fathead minnow – Kidd et al. 2007 PNAS 0.001
1998
Recovery of fathead minnow – Blanchfield et al. 2015
Environ. Sci. Technol. 2000
2002
2004
2006
Chapter 2: What happened to the other taxa?
Maxine Leclerc
Any effects of EE2 on plankton and littoral macroinvertebrates?
bi‐weekly or monthly samples of water column
comparable data collected on several reference lakes
Before‐After‐Control‐
Impact ANOVA •
•
•
•
•
weekly samples of emergence
comparable data collected on several reference lakes
Biomass of rotifers in Lake 260
14
12
total Rotifera
+EE2
g/L
10
8
+EE2 +EE2
6
4
2
0
1999
2000
2001
2002
2003
2004
2005
2006
What happened to lower‐trophic‐level biota?
Rotifers
‐
‐
5 dominant taxa in Lake 260
Keratella spp., Polyarthra spp.
Crustacean zooplankton
‐ 90% of biomass is 7 species
Chaoborus punctipennis
and C. flavicans
Emergence (mainly chironomids)
** p<0.05, BACI
60
5
Rotifera
3
Rotifers
50
Lake 260
Reference lakes
g L-1 dw
g L-1 dw
4
Crustacean zooplankton
+EE2
78%**
2
1
41%**
30
20
Crustacean zooplankton
+EE2
0 ‐ 90% of biomass is 7 species
10
‐ 5 dominant taxa in Lake 260
‐ 01998
Keratella
spp., Polyarthra
spp.2006
2000
2002
2004
1998
1800
1600
40
2000
2002
2004
2006
200
Chaoborus spp.
1400
Littoral insects
89%
150
70%
-2
num m
num m
-2
1200
1000
800
100
600
400
200
0
1998
50
Chaoborus+EE2
punctipennis
and C. flavicans
2000
2002
2004
2006
0
1998
+EE2
Emergence (mainly chironomids)
2000
2002
2004
2006
Increases in several taxa … indirect effects of EE2 or changes in food supply? What about the other fishes?
Pearl dace
‐ Elevated vitellogenin (< 27‐fold in females and < 16000‐fold in males)
‐ Delayed oocytes & spermatocytes
‐ Intersex in males
‐
Slimy sculpin
Vitellogenin and gonad histology not assessed
White sucker
‐ Elevated vitellogenin (~ 2‐fold in females, <118‐fold in males
‐ No effects on oocytes or spermatocytes
Lake trout
‐ Elevated vitellogenin
(< 7‐fold in females, <18,700‐fold in males)
‐ No effects on oocytes or spermatocytes
60
0.9
0.8
50
0.6
0.5
Pearl dace58%
‐ Elevated vitellogenin (< 27‐fold in 0.3
females and < 16000‐fold in males)
0.2
Pearl dace
‐ Delayed oocytes & spermatocytes
0.1
‐ Intersex in males
1998
2000
2002
2004
2006
0.4
kg ha-1
0.7
kg ha-1
White sucker
Lake 260
Reference lake
30
White sucker
‐ Elevated vitellogenin (~ 2‐fold in females, <118‐fold in males
10
‐ No effects on oocytes or spermatocytes
20
0
1998
6
5
40
2000
2002
2004
2006
16
Sculpin
Lake trout
14
3
2
1
‐
Slimy sculpin
75%
Vitellogenin and gonad histology not assessed
12
10
8
0
1998
kg ha-1
CPUE
4
2000
2002
2004
2006
6
1998
Lake trout
‐ Elevated vitellogenin 25%
(< 7‐fold in females, <18,700‐fold in males)
‐ No effects on oocytes or spermatocytes
2000
2002
2004
Decline in lake trout biomass from loss of prey species
2006
What did we learn from this study?
4. Whole lake experiments invaluable
3. Indirect effects on fish prey
1. Direct effects on fish populations
2. Indirect effects on fish predators
Kidd et al. 2014 Phil. Trans. Royal Soc. B
What are some other research needs related to contaminants of emerging concern?
• Wastewaters complex mixtures that include several antimicrobials (antibiotics, triclosan)
• Individual compounds affect plants, algae, natural bacteria
• What are the mixture effects?
• What are the food web/ecosystem impacts (nutrient cycling, prey availability, community diversity, direct and indirect effects)?
• Development and transfer of antibiotic resistant genes
What would happen if you added antimicrobials to a whole lake?
Umeå Experimental Ecosystem Facility and Lakes Great facilities for assessing effects of pharmaceuticals on food webs
What is the Canadian
Rivers Institute (CRI)?
An open, collaborative science network hosted at UNB
– founded in 2000, multi‐campus
– interdisciplinary river science, education, outreach
– unique and strong concentration of water experts
– 13 institutions in Canada and abroad, + many partnerships
Vision – To make every river a healthy river
canadianriversinstitute.com
Where are we in Canada?
Also researchers in Mexico, Italy and the U.S.; partnerships in Chile, France and Australia
CRI Trains Students and Professionals
> 450 people trained in 2014 in many aspects of water sciences
Some of the CRI courses
• Large number of courses and workshops, diverse topics
• Also have run several ecotox field courses abroad
Acknowledgements
New Brunswick Environmental Trust Fund
New Brunswick Wildlife Trust Fund
NSERC CRC, CRD and Discovery Programs
Miramar Mining Corporation
Environment Canada
Parks Canada
Toxic Substances Research Initiative
INAC Northern Contaminants Program
Polar Continental Shelf Base Project
Fisheries and Oceans Canada
Canadian Water Network
Bayer Schering Pharma AG
American Chemistry Council
Swetox
Tack!
Photo credit: Folke Ryden