recovery of a hypereutrophic urban lake (onondaga lake, ny): implications for monitoring water...
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Recovery of a Hypereutrophic Urban Lake (Onondaga Lake, NY): Implications for Monitoring
Water Quality and Phytoplankton Ecology
Capstone Presentation By Daniele BakerM.S. Ecology, Dept. of EFB Advisors: Dr.’s Myron Mitchell and Kimberly Schulz
Publications relating to this presentation …
Baker, D.M. 2013. Recovery of a hypereutrophic urban lake (Onondaga Lake, NY): Implications for monitoring water quality and phytoplankton ecology. Master’s thesis. SUNY, College of Environmental Science and Forestry
Baker, D.M., K.L Schulz and M.J. Mitchell. A shift in phytoplankton assemblage composition and dynamics during recovery from eutrophication. Limnology and Oceanography. (submitted)
Baker, D.M., K.L Schulz and M.J. Mitchell. Evaluating methods used in monitoring recovery from eutrophication: the importance of examining seasonal trends and the limitations of Chlorophyll-a as a proxy for phytoplankton Biomass. Fundamental and Applied Limnology. (In prep.)
Please contact me for additional information or with questions on any of the publications or this presentation. Thank you and enjoy!
Eutrophication in the U.S.
50% of the lakes classified as impaired (Conley et al. 2009)
Economic losses ~2 billion dollars (Dodds et al. 2009)
Eutrophication in the 21st Century
dailyail.co.uk
Point and non-point loading remains a problem (Schindler and Vallentyne 2008)
toledoblade.comToledoblade.com
clf.orgLake Champlain
Lake Erie
Olympic Venue, China
Symptoms of Eutrophication
P NP N
Figure edited from University of Maryland Center of Environmental Science
Oligotrophic Eutrophic
Variability in Phytoplankton
Differ dramatically in size
Glibert and Burkholder 2011
Fish Orca Factory Eiffel Tower Manhattan
Proch
loroc
occu
sSyn
echo
cocc
usEm
iliania
Astero
lampr
a
Karen
ia
Rhizos
olenia
Cerat
ium
Tricho
dem
ium
10 100 1,000 10,000 100,000
µm
m
Evolution 2nd ed 2009
dnrec.state.de.us
dnrec.state.de.us
Tolweb.org
eos.unh.edu
Bio.miami.edu
dr-ralf-wagner.de
Tolweb.org
plantbiology.msu.edu
serc.carleton.edu
Are extremely phylogenetically diverse
cfb.unh.edu/phycokey
analogicalplanet.com
.rook.org/ea
trees.com
Phytoplankton assemblage will decrease in biomassBut also may change in…
• Taxonomic composition• Diversity• Cell Size • Morphology• Chl-a concentration• Seasonal patterns• Bloom frequency
However, phytoplankton responses are generally monitored by measuring only total Chl-a
Phytoplankton Responses to Recovery from Eutrophication
wyrdscience.wordpress.com
Thesis Goals
The study focuses on the changes in the phytoplankton assemblage during the recovery of a hypereutrophic lake
Two parts:
(1) Do two common monitoring approaches accurately capture the response of phytoplankton parameters to decreasing TP?
(2) How have the dynamics, composition and morphology of the phytoplankton assemblage changed?
Onondaga Lake as a Case Study
Eutrophic due to waste water effluent (METRO)
68% of total phosphorus (TP) 80% of total nitrogen (TN) Ammonium (NH4
+) at toxic levels
Wastewater Treatment Upgrades
TP TP
Reduce Eutrophication
Symptoms
Reduce NH4
+ Levels
TP TP
NH4+ NH4+ NH4
+ NH4+ NO3
- NO3-
High-Rate Flocculated Settling
High-Rate Flocculated Settling
Tertiary Treatment
Tertiary Treatment
Upgraded Tertiary Treatment
Upgraded Tertiary Treatment
Seasonal Nitrification
Seasonal Nitrification
Upgraded Seasonal Nitrification
Upgraded Seasonal Nitrification
Biologically Aerated Filtration
Biologically Aerated Filtration
In 1998 Court order against METRO
By 2008Met NYSDEC TP guidance value of 20 µg/LRemoved from NYSDEC list for NH4
+ toxicity
(15 years, $380 million)
NO3- NO3-
Data Collection
Data for parts one and two were collected biweekly by Onondaga County (METRO)
Phytoplankton enumerated by PhycoTech Biomass data available from
1998 to 2011Calculated annual mean
epilimnion values for all parameters
Thesis Goals
Two parts:
(1) Do two common monitoring approaches accurately capture the response of phytoplankton parameters to decreasing TP?
(2) How have the dynamics, composition and morphology of the phytoplankton assemblage changed?
Part One: Monitoring Approaches
1. Monitoring Approaches Objectives
Objective 1:
Did the response of the phytoplankton parameters (Chl-a and phytoplankton biomass) to the decreases in TP vary seasonally?
Objective 2:
Has the Chl-a content per unit biomass varied due to shifts in the…
(A) light availability? (B) composition of the phytoplankton assemblage?
and/or
Month
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Bio
mas
s (u
g/L)
0
10000
20000
30000
40000
50000 Spring Stratification Fall Turnover
Lakes often monitored with calendar date defined periods Annual (April to October) sampling period
Seasonal variability in nutrients, light and mixingDrive seasonal variability in phytoplankton dynamics
Seasonal Variability
1. Monitoring Approaches Background
Different Phytoplankton Taxa
Calendar date defined periods may miss important seasonal trends
Seasonal periods can be easily defined quantitativelyBiological periods (defined with peak detector program)Physical periods (defined with NOAA’s regime shift detector)
Seasonal Periods
1. Monitoring Approaches Background
Month
Mar Apr May Jun Jul Aug Sep Oct Nov
Ch
loro
ph
yll-
a (
ug
/L)
0
10
20
30
40
50 Spring Stratification Fall Turnover
Fall BloomClear-Water
Phase(CWP)
Spring Bloom
Summer StratifiedAnnual
Summer Blooms
Chl-a as a Biomass Proxy
Chl-a used as a proxy for phytoplankton biomassBut Chl-a per unit biomass may vary due to two
mechanisms…(A) Change in light availability
(B) Change in phytoplankton composition
1. Monitoring Approaches Background
Cyanophyceae Chlorophyceae
serc.carleton.edu
Cryptophyceae
plantbiology.msu.edu dnrec.state.de.us
More Chl-aLess Chl-a
Part One: Monitoring Approaches
Objective 1: Seasonal Variability
Did the response of the phytoplankton parameters
to TP vary seasonally?
Seasonal Period
Annual Spring CWP Summer Fall
TP
(mm
ol L
-1)
-0.8
-0.4
0.0
p= 0.3
Annual Spring CWP Summer Fall
Bio
mas
s (
g L
-1)
-3000
-2000
-1000
0
Chl
-a ( g
L-1
)
-4
-2
0 ba
b b
Seasonal PeriodBloom Stratified Bloom
p= 0.03
p= 0.1
Rat
e of
Cha
nge
wit
h Y
ear
Rat
e of
Cha
nge
wit
h Y
ear
Bloom Stratified Bloom
Seasonal Period
Annual Spring CWP Summer Fall
TP
(mm
ol L
-1)
-0.8
-0.4
0.0
p= 0.3
Annual Spring CWP Summer Fall
Bio
mas
s (
g L
-1)
-3000
-2000
-1000
0
Chl
-a ( g
L-1
)-4
-2
0 ba
b b
Seasonal PeriodBloom Stratified Bloom
p= 0.03
p= 0.1
Rat
e of
Cha
nge
wit
h Y
ear
Rat
e of
Cha
nge
wit
h Y
ear
Bloom Stratified Bloom
TP decreased in all seasonal periodsChl-a varied seasonallyTrends in Chl-a and biomass differed
Seasonality in TP, Chl-a and Biomass
1. Monitoring Approaches: Seasonal Variability Results
Significant decrease
Method: ANCOVA and post-hoc F-test
Phytoplankton Response to TPPhytoplankton response to TP weaker in CWP and Fall Bloom
1. Monitoring Approaches: Seasonal Variability Results
Method: Linear Regression
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0p= 0.03
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
PredictedObserved
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
ObservedPlot 1 Upper control line
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0p= 0.03
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
PredictedObserved
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
ObservedPlot 1 Upper control line
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0p= 0.03
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
PredictedObserved
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
ObservedPlot 1 Upper control line
R2 R3Regime 1
Observed Chl-a vs. Predicted
Method: Two-way ANOVA
Chl-a predicted from standard Chl-a, TP relationship (Vollenweider-OECD, Vollenweider and Kerekes 1980)
Annual
88 94 00 06 12L
og C
hl-a
(m
g L
-1)
0.0
0.5
1.0
1.5
2.0p= 0.03
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
PredictedObserved
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
ObservedPlot 1 Upper control line
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
Spring Bloom
88 94 00 06 12
Clear Water Phase
88 94 00 06 12
Summer Stratified
88 94 00 06 12
Fall Bloom
88 94 00 06 12
PredictedObserved
Seasonal Period
R1 R2 R3
p= 0.03 p= <0.001
p= 0.001 p= 0.006
R2 R3R1 R2 R3R1 R2 R3R1R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
Spring Bloom
88 94 00 06 12
Clear Water Phase
88 94 00 06 12
Summer Stratified
88 94 00 06 12
Fall Bloom
88 94 00 06 12
PredictedObserved
Seasonal Period
R1 R2 R3 R2 R3R1 R2 R3R1 R2 R3R1R2 R3Regime 1
1. Monitoring Approaches: Seasonal Variability Results
Phosphorus (mg m-3)
Chl
orop
hyll-
a (m
g m
-3)
(Dillon and Rigler 1974)
Annual
88 94 00 06 12
Log
Chl
-a ( g
L-1
)
0.0
0.5
1.0
1.5
2.0p= 0.03
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a ( g
L-1
)
0.0
0.5
1.0
1.5
2.0
PredictedObserved
R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a ( g
L-1
)
0.0
0.5
1.0
1.5
2.0
ObservedPlot 1 Upper control line
R2 R3Regime 1
Annual88 94 00 06 12
Log
Chl
-a ( g
L-1
)
0.0
0.5
1.0
1.5
2.0
Spring Bloom88 94 00 06 12
CWP88 94 00 06 12
Summer Stratified88 94 00 06 12
Fall Bloom88 94 00 06 12
PredictedObserved
Seasonal Period
R1 R2 R3
p= 0.03 p< 0.001
p= 0.001 p= 0.01
R2 R3R1 R2 R3R1 R2 R3R1R2 R3Regime 1
Observed Chl-a vs. Predicted
Method: Two-way ANOVA
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0p= 0.03
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
PredictedObserved
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
ObservedPlot 1 Upper control line
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
Spring Bloom
88 94 00 06 12
Clear Water Phase
88 94 00 06 12
Summer Stratified
88 94 00 06 12
Fall Bloom
88 94 00 06 12
PredictedObserved
Seasonal Period
R1 R2 R3
p= 0.03 p= <0.001
p= 0.001 p= 0.006
R2 R3R1 R2 R3R1 R2 R3R1R2 R3Regime 1
Annual
88 94 00 06 12
Log
Chl
-a (
mg
L-1
)
0.0
0.5
1.0
1.5
2.0
Spring Bloom
88 94 00 06 12
Clear Water Phase
88 94 00 06 12
Summer Stratified
88 94 00 06 12
Fall Bloom
88 94 00 06 12
PredictedObserved
Seasonal Period
R1 R2 R3 R2 R3R1 R2 R3R1 R2 R3R1R2 R3Regime 1
More Chl-a
Less Chl-a
Regime 1 Regime 1 All Years
All Years
1. Monitoring Approaches: Seasonal Variability Results
Part One: Monitoring Approaches
Objective 2: Chl-a Content
Has the Chl-a content per unit biomass varied due to shifts in
(A) light availability and/or
(B) the composition of the phytoplankton assemblage?
Change in Chl-a per unit Biomass
Annual Spring CWP Summer Fall
Chl
-a :
Bio
mas
s pe
r ye
ar
-0.0010
-0.0005
0.0000
0.0005
0.0010
Seasonal Period
p= 0.3
Annual Spring CWP Summer Fall
Mea
n li
ght:
TP
pe
r ye
ar
-0.01
0.00
0.01
0.02
Seasonal Period
p= 0.3
Bloom Stratified Bloom
Bloom Stratified Bloom
Method: Linear Regression; ANCOVA and post-hoc F-test
1. Monitoring Approaches: Chl-a Content Results
Increase in Chl-a per unit biomass (Chl-a: biomass)No seasonal variability
Mechanism A: Light Availability
Increase in light availability in annual and seasonal periods
Should yield a decrease in Chl-a per unit biomass
But Chl-a per unit biomass not correlated with light availability
Method: Linear Regression; Pearson Corrleation
1. Monitoring Approaches: Chl-a Content Results
Mechanism B: Phytoplankton
Method: Pearson
Annual Spring CWP Summer Fall
Chl
-a :
Bio
mas
s pe
r ye
ar
-0.0010
-0.0005
0.0000
0.0005
0.0010
Seasonal Period
p= 0.3
Annual Spring CWP Summer Fall
Mea
n lig
ht: T
P
per
year
-0.01
0.00
0.01
0.02
Seasonal Period
p= 0.3
Bloom Stratified Bloom
Bloom Stratified Bloom
Negatively correlated with a decrease in Cyanophyceae relative biomass in Annual and CWP
Positively correlated with a decrease in Chlorophyceae relative biomass in Spring Bloom
Positively correlated with an increase in Cryptophyceae relative biomass in the Fall Bloom
1. Monitoring Approaches: Chl-a Content Results
TP, Chl-a and biomass all decreasedResponse to decreased TP varied markedly between seasons
Month
Mar Apr May Jun Jul Aug Sep Oct Nov
Ch
loro
ph
yll-
a (
ug
/L)
0
10
20
30
40
50 Spring Stratification Fall Turnover
Fall Bloom
Clear-Water Phase
Spring Bloom
Summer Stratified
Was There Seasonal Variability?
1. Monitoring Approaches: Seasonal Variability Discussion
Rate of decrease in Chl-a greatest
Weak Chl-a response to TP
Weak Chl-a + biomass response to TP
Less Chl-a than predicted
More Chl-a than predicted
Year
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11
Mon
th
JanFebMarAprMayJunJul
AugSepOct
NovDec
Year
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11
Mon
th
JanFebMarAprMayJunJul
AugSepOct
NovDec
Regime 1 Regime 2 Regime 3
Year
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11
Mon
th
JanFebMarAprMayJunJul
AugSepOct
NovDec
Year
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11
Mon
th
JanFebMarAprMayJunJul
AugSepOct
NovDec
Year
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11
Mon
th
JanFebMarAprMayJunJul
AugSepOct
NovDec
Why Seasonally Defined Periods?S
um
mer S
tratified
An
nu
al
Timing of seasonal periods varies between yearsCalendar date defined periods may fail to capture
the same ecological periods in each year
1. Monitoring Approaches: Seasonal Variability Discussion
Fall Bloom
Spring Bloom
CWP
Chl-a and biomass were not consistently correlatedChl-a per unit biomass increased
(A) Not due to light availability
(B) Driven by change in phytoplankton composition
Indicates the importance of directly measuring phytoplankton biomass
Why Did Chl-a Content Vary?
dnrec.state.de.usNegatively Correlated Positively Correlated Positively Correlated
Cyanophyceae Chlorophyceae
serc.carleton.edu
Cryptophyceae
plantbiology.msu.edudnrec.state.de.us
More Chl-aLess Chl-a
1. Monitoring Approaches: Chl-a Content Discussion
Thesis Goals
Two parts:
(1) Calendar dates defined periods miss important seasonal trends and Chl-a is a weak proxy for biomass due to the variability in Chl-a per unit biomass
(2) How have the dynamics, composition and morphology of the phytoplankton assemblage changed?
Part Two: Assemblage Changes
Objective 1:
Did changes in limnological parameters result in a distinct shift in the phytoplankton assemblage between Regimes 2 and 3 ?
Objective 2:
What were the specific changes in phytoplankton assemblage in terms of diversity, cell size, bloom dynamics, common species, and composition?
2. Assemblage Changes Background
Mean lightMean light
Effect of Limnological Parameters
Phytoplankton assemblages can vary due to...Nutrients
Stoichiometry
Light
1:1 = 1 4:8 = 1/2
2. Assemblage Changes Background
TNTN TPTP
N:PN:P Si:PSi:P Si:NSi:N
Secchi depthSecchi depth
Parameters ExaminedParameters Examined
NH4+:NO3
-NH4+:NO3
-
Increase S:V, decrease nutrient uptake rate
Vary in uptake sites and rate for different nutrients
Vary in Chl-a concentration Some mobile or bouyant
Phytoplankton assemblage may change in…
Diversity
Cell Size
Bloom frequencies
Number of common species
Taxonomic composition
• Class Level
• Functional groups
Phytoplankton Assemblage Changes
≥ 80% of phytoplankton biomassSp. 1 Sp. 2 Others
Top 90% of phytoplankton biomass
Unicellular
or
Colonial
≤ 3 species
Large Small
2. Assemblage Changes Background
Part Two: Assemblage Changes
Objective 1: Limnological Parameters
Did changes in limnological parameters
result in a distinct shift in the phytoplankton assemblage between Regimes 2 and 3 ?
Nutrient Parameters
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
NH
4+:N
O3-
0123
Si:P
0.0
0.4
0.8
N:P
0
100
200
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
SD (
m)
0
2
4
6
Mea
n li
ght
0.25
0.50
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
TN
(m
ol L
-1)
0
150
300
TP
(m
ol L
-1)
0
2
4 Regime 1 Regime 2 Regime 3
p= 0.2
p= 0.001
p= 0.8
p= 0.9Si
:N
0
250
500
p= 0.002
p= <0.001
p= 0.005
p= 0.005
Regime 1 Regime 2 Regime 3
Regime 1 Regime 2 Regime 3
Limnological Parameters
2. Assemblage Changes: Limnological Parameters Results
TP decreased
Method: Linear Regression
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
NH
4+:N
O3-
0123
Si:P
0.0
0.4
0.8
N:P
0
100
200
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
SD (
m)
0
2
4
6
Mea
n li
ght
0.25
0.50
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
TN
(m
ol L
-1)
0
150
300
TP
(m
ol L
-1)
0
2
4 Regime 1 Regime 2 Regime 3
p= 0.2
p= 0.001
p= 0.8
p= 0.9Si
:N
0
250
500
p= 0.002
p= <0.001
p= 0.005
p= 0.005
Regime 1 Regime 2 Regime 3
Regime 1 Regime 2 Regime 3
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
NH
4+:N
O3-
0123
Si:P
0.0
0.4
0.8
N:P
0
100
200
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
SD (
m)
0
2
4
6
Mea
n li
ght
0.25
0.50
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
TN
(m
ol L
-1)
0
150
300
TP
(m
ol L
-1)
0
2
4 Regime 1 Regime 2 Regime 3
p= 0.2
p= 0.001
p= 0.8
p= 0.9
Si:N
0
250
500
p= 0.002
p= <0.001
p= 0.005
p= 0.005
Regime 1 Regime 2 Regime 3
Regime 1 Regime 2 Regime 3
Light Parameters
Limnological Parameters
2. Assemblage Changes: Limnological Parameters Results
TP decreased
Method: Linear Regression
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
NH
4+:N
O3-
0123
Si:P
0.0
0.4
0.8
N:P
0
100
200
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
SD (
m)
0
2
4
6
Mea
n li
ght
0.25
0.50
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
TN
(m
ol L
-1)
0
150
300
TP
(m
ol L
-1)
0
2
4 Regime 1 Regime 2 Regime 3
p= 0.2
p= 0.001
p= 0.8
p= 0.9
Si:N
0
250
500
p= 0.002
p= <0.001
p= 0.005
p= 0.005
Regime 1 Regime 2 Regime 3
Regime 1 Regime 2 Regime 3
Stoichiometry Parameters
Limnological Parameters
2. Assemblage Changes: Limnological Parameters Results
TP decreasedN:P, Si:N and
Si:P increasedNH4
+:NO3-
decreased
Method: Linear Regression
1998
1999
2000
2001
2002
2003 2004
2005
2006
2007
2008
2009
2010
2011
Axis 1
Axi
s 2
1998
1999
2000
2001
2002
2003 2004
2005
2006
2007
2008
2009
2010
2011TN
TP
N:P
Si:N
Si:PN:N
Secchi
Axis 1
Axi
s 2
Phytoplankton Assemblage Shift
2. Assemblage Changes: Limnological Parameters Results
Regimes differTP, N:N, Si:N and Si:P
between Regimes Secchi depth
within Regimes TP, TN, N:P most
important drivers
Method: NMDS; BIO-ENV
Regime 1Regime 1
Regime 2Regime 2
RegimeRegime 33
Part Two: Assemblage Changes
Objective 2: Specific Changes
What were the specific changes in the phytoplankton
assemblage in terms of diversity, cell size, bloom
dynamics, common species and composition?
No change in Richness, Shannon’s Diversity, or Evenness
Method: Linear Regression
Diversity Indices
2. Assemblage Changes: Specific Changes Results
Change in Cell Size
Cell size (biovolume) decreased
Method: Linear Regression
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Num
ber
of W
eeks
0
5
10
15
20
25Regime 1 Regime 2 Regime 3
p= 0.02
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Num
ber
of W
eeks
0
5
10
15
20
25Regime 1 Regime 2 Regime 3
p= 0.02
Change in Bloom PeriodsStrong dominant weeks = weeks with 3 or fewer species are
> 80% of the assemblageSteady states > two consecutive strong dominant weeks
2. Assemblage Changes: Specific Changes Results
Method: Linear Regression, T-test
Number of Common SpeciesIncrease in the number of common species
Peak from 2002-2007
Method: Non-Linear Regression
2. Assemblage Changes: Specific Changes Results
Intermediate Period
Number of Common SpeciesIncrease in the number of common species
Peak from 2002-2007
Variability in common taxa
Method: Non-Linear Regression
2. Assemblage Changes: Specific Changes Results
Intermediate Period
Class Level Composition
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
by C
lass
0.0
0.2
0.4
0.6
0.8
1.0 Charo- Zygnematophyceae Chloro- Chlorophyceae Trebouxiophyceae UlvophyceaeCrypto- CryptophyceaeCyano- CyanophyceaeDino- DinophyceaeEugle- EulgenophyceaeOchro- Bacillarophyceae Chrysophyceae Synurophyceae Xanthophyceae
Clear shift in the composition
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.000
0.002
0.004
0.006
0.008
0.010
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.2
0.4
0.6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00
0.05
0.10
0.15
0.20
Decrease in relative biomass
Cyanophyceae
serc.carleton.edu
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Decrease in relative biomassDecrease in size
Cyanophyceae
serc.carleton.edu
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
50
100
150
200
250
300
Regime 2Regime 3
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
400
800
1200
1600
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
2500
3000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
2000
4000
6000
8000
10000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
100
200
300
400
500
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0100200300400500600700
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
200
400
600
800
1000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
20
40
60
80
100
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000a
b
ab
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00
0.05
0.10
0.15
0.20
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00.10.20.30.40.50.6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4UnicellColonial
Plot 1 Upper specificationPlot 1 Upper control line
Decrease in relative biomassDecrease in sizeShift from large colonial to small unicellular
Cyanophyceae
serc.carleton.edu
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
8
10
Charo- Zygnematophyceae Chloro- Chlorophyceae Trebuxiophyceae
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
8
10
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
8
10
Decrease in relative biomassDecrease in sizeShift from large colonial to small unicellularDecrease in commonness
Cyanophyceae
serc.carleton.edu
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.000
0.002
0.004
0.006
0.008
0.010
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.2
0.4
0.6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00
0.05
0.10
0.15
0.20
Chrysophyceae + Synurophyceae
Increase in relative biomass Chrysophyceae Synurophyceae
dr-ralf-wagner.de
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Chrysophyceae + Synurophyceae
Increase in relative biomassDecrease in size
Chrysophyceae Synurophyceae
dr-ralf-wagner.de
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
50
100
150
200
250
300
Regime 2Regime 3
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
400
800
1200
1600
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
2500
3000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
2000
4000
6000
8000
10000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
100
200
300
400
500
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0100200300400500600700
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
200
400
600
800
1000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
20
40
60
80
100
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000a
b
ab
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
SmallLargePlot 1 Upper specificationPlot 1 Upper control line
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
50
100
150
200
250
300
Regime 2Regime 3
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
400
800
1200
1600
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
2500
3000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
2000
4000
6000
8000
10000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
100
200
300
400
500
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0100200300400500600700
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
200
400
600
800
1000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
20
40
60
80
100
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000a
b
ab
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00
0.05
0.10
0.15
0.20
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00.10.20.30.40.50.6
Chrysophyceae + Synurophyceae
Increase in relative biomassDecrease in size Increase in both large and small Chrysophytes
Chrysophyceae Synurophyceae
dr-ralf-wagner.de
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
SmallLargePlot 1 Upper specificationPlot 1 Upper control line
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
1
2
3
4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
8
10
Chrysophyceae + Synurophyceae
Increase in relative biomassDecrease in size Increase in both large and small Chrysophytes Increase in commonness
Chrysophyceae Synurophyceae
dr-ralf-wagner.de
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.000
0.002
0.004
0.006
0.008
0.010
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.2
0.4
0.6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00
0.05
0.10
0.15
0.20
Bacillariophyceae
Increase in relative biomass
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Bacillariophyceae
Increase in relative biomassNo change in size
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
50
100
150
200
250
300
Regime 2Regime 3
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
400
800
1200
1600
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
2500
3000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
2000
4000
6000
8000
10000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
100
200
300
400
500
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0100200300400500600700
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
200
400
600
800
1000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
20
40
60
80
100
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000a
b
ab
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.2
0.4
0.6
Bacillariophyceae
Increase in relative biomassNo change in sizeShift to large species
SmallLargePlot 1 Upper specificationPlot 1 Upper control line
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
1
2
3
4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
8
10
Bacillariophyceae
Increase in relative biomassNo change in sizeShift to large species No change in commonness
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Charo- Zygnematophyceae Chloro- Chlorophyceae Mamiellophyceae Nephroselmidophyceae Trebouxiophyceae Ulvophyceae
Crypto- CryptophyceaeCyano- CyanophyceaeDino- DinophyceaeEugleno- EulgenophyceaeOchro- Bacillarophyceae Chrysophyceae Synurophyceae Xanthophyceae
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.000
0.002
0.004
0.006
0.008
0.010
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.2
0.4
0.6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11R
elat
ive
Bio
mas
s0.00
0.05
0.10
0.15
0.20
Dinophyceae
No change in relative biomasseos.unh.edu
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Dinophyceae
No change in relative biomassNo change in size eos.unh.edu
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
50
100
150
200
250
300
Regime 2Regime 3
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
400
800
1200
1600
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
2500
3000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
2000
4000
6000
8000
10000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
500
1000
1500
2000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
100
200
300
400
500
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0100200300400500600700
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
200
400
600
800
1000
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
20
40
60
80
100
Regime
2 3
Bio
volu
me
(mm
3 nat
ural
uni
t-1)
0
1000
2000
3000
4000a
b
ab
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.00
0.05
0.10
0.15
0.20
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Rel
ativ
e B
iom
ass
0.0
0.1
0.2
0.3
0.4
Dinophyceae
No change in relative biomassNo change in sizeDecrease in large species
eos.unh.edu
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
SmallLargePlot 1 Upper specificationPlot 1 Upper control line
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
1
2
3
4
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
Year
98 99 00 01 02 03 04 05 06 07 08 09 10 11
# C
omm
on S
peic
es
0
2
4
6
8
10
Dinophyceae
No change in relative biomassNo change in sizeDecrease in large speciesNo change in commonness
eos.unh.edu
Method: Linear Regression; T-test
2. Assemblage Changes: Specific Changes Results
Effect of Limnological Parameters
Shift in phytoplankton assemblage between Regimes 2 and 3
TNTN TPTP
N:PN:P Si:PSi:P Si:NSi:N
Secchi depthSecchi depth
Mean lightMean light
Parameters ExaminedParameters Examined
NH4+:NO3
-NH4+:NO3
-
Change in TP and Stoichiometric parameters
TN, TP and N:P correlated with changes in phytoplankton
2. Assemblage Changes: Limnological Parameters Discussion
Effect of Limnological Parameters
Shift in phytoplankton assemblage between Regimes 2 and 3
TNTN TPTP
N:PN:P Si:PSi:P Si:NSi:N
Secchi depthSecchi depth
Mean lightMean light
Parameters ExaminedParameters Examined
NH4+:NO3
-NH4+:NO3
-
Change in TP and Stoichiometric parameters
TN, TP and N:P correlated with changes in phytoplankton
2. Assemblage Changes: Limnological Parameters Discussion
Change in Phytoplankton Assemblage
No Change in Diversity
Decrease in Cell Size
Decrease in Bloom Frequency
Increase in # of Common Species
Shift in Taxonomic composition
• Class Level
• Functional GroupsUnicellularColonial
Eutrophic Species
MesotrophicSpecies
15.6 weeks yr-1 8 weeks yr-1
10 sp. yr-1 17 sp. yr-1
Bacillariophyceae Chrysophyceae Synurophyceae
Cyanophyceae
Large Small
Regime 3Regime 2
2. Assemblage Changes: Specific Changes Discussion
Thesis Goals
Two parts:
(1) Calendar dates defined periods miss important seasonal trends and Chl-a is a weak proxy for biomass due to the variability in Chl-a per unit biomass
(2) Changes in the phytoplankton assemblage included decreased cell size, decreased number of bloom periods, increased number of common species and a shift in composition to less eutrophic taxa
Thesis Goals
Two parts:
(1) Calendar dates defined periods miss important seasonal trends and Chl-a is a weak proxy for biomass due to the variability in Chl-a per unit biomass
(2) Changes in the phytoplankton assemblage included decreased cell size, decreased number of bloom periods, increased number of common species and a shift in composition to less eutrophic taxa
Part One: Conclusions
Julian date periods may be easy to define but may miss important seasonal trendsMethods used here represent a simple method for
consistently defining seasonal periods between years
Chl-a is a weak proxy for phytoplankton biomass
Part Two: Conclusions
Large shift in phytoplankton assemblage driven mostly by TN, TP and N:P
Edibility increasing (cell size decreased) More phytoplankton taxa are considered common Shift from eutrophic to mesotrophic species
Decrease in nuisance species (Cyanobacteria) and increase in more edible species
Chrysophyceae and Synurophyceae were not present in the lake sample record before 1998 (found only in the paleolimnogical record)
ImplicationsImplicationsUsing seasonal periods, measuring Using seasonal periods, measuring phytoplankton biomass directly and phytoplankton biomass directly and
examining the phytoplankton examining the phytoplankton assemblage will allow managers to see assemblage will allow managers to see
more directly what is driving year to more directly what is driving year to year variation in metrics associated year variation in metrics associated with improved water quality (secchi with improved water quality (secchi
depth) resulting in higher-quality depth) resulting in higher-quality management decisions. management decisions.
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