Daniele Baker Master's Capstone November 15, 2013
Abstract: A 23-year record of limnological parameters for Onondaga Lake was used to evaluate changes during recovery from eutrophication. I (1) compared phytoplankton responses to total phosphorus (TP) in ecologically defined seasonal periods with those in a calendar date defined annual period, (2) ascertained whether chlorophyll-a (Chl-a) concentration was a good proxy for phytoplankton biomass, and (3) assessed whether the phytoplankton assemblage was altered in response to the environmental remediation. Seasonal variations in the relationships between Chl-a and biomass to TP were common. Irregular temporal patterns in Chl-a per unit biomass were due to a shift from Chl-a deficient to Chl-a rich phytoplankton, not changes in light regime. The phytoplankton assemblage varied mostly as a function of changes in total nitrogen (TN), TP, and TN:TP ratios. Phytoplankton diversity did not increase, but phytoplankton bloom frequencies and cellular biovolumes decreased. Synurophyceae and Chrysophyceae, absent since the onset of eutrophication, reappeared in 1998.
Recovery of a Hypereutrophic Urban Lake (Onondaga Lake, NY): Implications for Monitoring Water Quality and Phytoplankton Ecology
1. Recovery of a Hypereutrophic
Urban Lake (Onondaga Lake, NY):
Implications for Monitoring
Water Quality and
Phytoplankton Ecology
Capstone Presentation
By Daniele Baker
M.S. Ecology, Dept. of EFB
Advisors: Dr.’s Myron Mitchell and Kimberly Schulz
2. 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!
3. 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)
4. Eutrophication in the 21st Century
Point and non-point loading
remains a problem
(Schindler and Vallentyne 2008)
dailyail.co.uk
Lake Erie
Olympic Venue, China
Lake Champlain
Toledoblade.com
toledoblade.com
clf.org
8. Phytoplankton Responses to
Recovery from Eutrophication
Phytoplankton assemblage will decrease in biomass
But also may change in…
• Taxonomic composition
• Diversity
• Cell Size
• Morphology
• Chl-a concentration
• Seasonal patterns
• Bloom frequency
wyrdscience.wordpress.com
However, phytoplankton responses are generally
monitored by measuring only total Chl-a
9. 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?
10. 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
11. Wastewater Treatment Upgrades
In 1998 Court order against METRO (15 years, $380 million)
Tertiary
Treatment
Seasonal
Nitrification
Reduce
Eutrophication
Symptoms
Reduce
NH4+ Levels
Upgraded Tertiary
Treatment
TP
TP
Upgraded Seasonal
Nitrification
NH4+
High-Rate Flocculated
Settling
NO3-
Biologically Aerated
Filtration
NH4+
By 2008
Met NYSDEC TP guidance value of 20 µg/L
Removed from NYSDEC list for NH4+ toxicity
NO3-
12. 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 2011
Calculated annual mean
epilimnion values for all parameters
13. 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?
14. 1. Monitoring Approaches
Objectives
Part One: Monitoring Approaches
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
and/or
the phytoplankton
assemblage?
15. 1. Monitoring Approaches
Background
Seasonal Variability
Lakes often monitored with calendar date defined periods
Annual (April to October) sampling period
Seasonal variability in nutrients, light and mixing
Drive seasonal variability in phytoplankton dynamics
Spring Stratification
Biomass (ug/L)
50000
Fall Turnover
Different
Phytoplankton
Taxa
40000
30000
20000
10000
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month
Calendar date defined periods may miss important
seasonal trends
16. 1. Monitoring Approaches
Background
Seasonal Periods
Seasonal periods can be easily defined quantitatively
Biological periods (defined with peak detector program)
Physical periods (defined with NOAA’s regime shift detector)
Summer Stratified
Annual
Chlorophyll-a (ug/L)
50
40
30
20
Spring Stratification
Fall Turnover
Summer Blooms
Spring Bloom
Clear-Water
Phase
(CWP)
Fall Bloom
Mar
Oct
10
0
Apr
May
Jun
Jul
Month
Aug
Sep
Nov
17. 1. Monitoring Approaches
Background
Chl-a as a Biomass Proxy
Chl-a used as a proxy for phytoplankton biomass
But Chl-a per unit biomass may vary due to two
mechanisms…
(A) Change in light availability
(B) Change in phytoplankton composition
Less Chl-a
Cyanophyceae
More Chl-a
Cryptophyceae
Chlorophyceae
serc.carleton.edu
plantbiology.msu.edu
dnrec.state.de.us
18. Did the response of the
phytoplankton parameters
to TP vary seasonally?
Part One: Monitoring Approaches
Objective 1: Seasonal Variability
19. 1. Monitoring Approaches: Seasonal Variability
Results
Seasonality in TP, Chl-a and Biomass
TP decreased in all seasonal periods
Rate of Change with Year
Rate of Change with Year
-1
Chl-a varied seasonally
-0.4
0.0
-0.8
-0.4
-0.8
p= 0.3
Annual Spring CWP Summer Fall
Significant decrease Bloom
Bloom
Stratified
Seasonal Period p= 0.3
Annual Spring CWP Summer Fall
Bloom
Stratified Bloom
Seasonal Period
Method: ANCOVA and post-hoc F-test
iomass (g L ) Chl-a-1 L-1)
Biomass (g L (g Chl-a (g L-1)
)
0.0
Rate of Change with Year
Rate of Change with Year
-1
TP (mmol L )
TP (mmol L-1)
Trends in Chl-a and biomass differed
b
0
b
b
a
-2
0
-4
b
a
b
b
p= 0.03
-2 0
-1000
-4
p= 0.03
-2000
0
-3000
-1000
-2000
p= 0.1
Annual Spring CWP Summer Fall
Bloom
Stratified Bloom
Seasonal Period 0.1
p=
20. 1. Monitoring Approaches: Seasonal Variability
Results
Phytoplankton Response to TP
Phytoplankton response to TP weaker in CWP and Fall Bloom
Method: Linear Regression
22. 1. Monitoring Approaches: Seasonal Variability
Results
Observed Chl-a vs. Predicted
Log Chl-a (g L-1)
2.0
Regime 1 R2 R3
R1
R2
R3
R2
R1
R1 R2
R3
R3
R1
R2 R3
p< 0.001
p= 0.03
1.5
1.0
0.5
p= 0.001
0.0
88 94 00 06 12 88 94 00 06 12
Annual
Less Regime 1
Chl-a
More
Chl-a
Spring Bloom
p= 0.01
88 94 00 06 12
CWP
88 94 00 06 12
Summer Stratified Fall Bloom
Seasonal Period
Regime 1
All Years
Method: Two-way ANOVA
88 94 00 06 12
All Years
Predicted
Observed
23. 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?
Part One: Monitoring Approaches
Objective 2: Chl-a Content
24. 1. Monitoring Approaches: Chl-a Content
Results
Change in Chl-a per unit Biomass
Increase in Chl-a per unit biomass (Chl-a: biomass)
No seasonal variability
Chl-a : Biomass
per year
0.0010
0.0005
0.0000
-0.0005
p= 0.3
-0.0010
Annual Spring
Bloom
CWP Summer Fall
Stratified Bloom
Seasonal Period
Method: Linear Regression; ANCOVA and post-hoc F-test
25. 1. Monitoring Approaches: Chl-a Content
Results
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
26. 1. Monitoring Approaches: Chl-a Content
Results
Mechanism B: Phytoplankton
Chl-a : Biomass
per year
0.0010
Negatively
0.0005
0.0000
-0.0005
p= 0.3
-0.0010
Annual Spring
Bloom
CWP Summer Fall
Stratified Bloom
light: TP
r year
correlated with a
Seasonal Period
Positively
decrease in
correlated with a
Cyanophyceae
decrease in
0.02
relative biomass
Chlorophyceae
in Annual and CWP
0.01
relative biomass
in Spring Bloom
Method: Pearson
Positively
correlated with an
increase in
Cryptophyceae
relative biomass
in the Fall Bloom
27. 1. Monitoring Approaches: Seasonal Variability
Discussion
Was There Seasonal Variability?
TP, Chl-a and biomass all decreased
Response to decreased TP varied markedly between seasons
Summer Stratified
Spring Stratification
Chlorophyll-a (ug/L)
50
40
Fall Turnover
Fall Bloom
Spring Bloom
30
Clear-Water
Phase
20
10
0
Mar
Apr
Rate of decrease
in Chl-a greatest
More Chl-a than
predicted
May
Jun
Jul
Weak Chl-a
Month
response to TP
Aug
Sep
Oct
Nov
Weak Chl-a + biomass
response to TP
Less Chl-a than
predicted
28. 1. Monitoring Approaches: Seasonal Variability
Discussion
Why Seasonally Defined Periods?
Timing of seasonal periods varies between years
Calendar date defined periods may fail to capture
the same ecological periods in each year
Regime 3
Fall Bloom
CWP
Spring Bloom
10 11
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11
Year
Annual
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
Regime 2
Summer Stratified
Month
Regime 1
29. 1. Monitoring Approaches: Chl-a Content
Discussion
Why Did Chl-a Content Vary?
Chl-a and biomass were not consistently correlated
Chl-a per unit biomass increased
(A) Not due to light availability
(B) Driven by change in phytoplankton composition
Less Chl-a
Cyanophyceae
More Chl-a
Cryptophyceae
plantbiology.msu.edu
serc.carleton.edu
Negatively Correlated
Positively Correlated
Chlorophyceae
dnrec.state.de.us
Positively Correlated
dnrec.state.de.us
Indicates the importance of directly measuring
phytoplankton biomass
30. 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?
31. 2. Assemblage Changes
Background
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?
32. 2. Assemblage Changes
Background
Effect of Limnological Parameters
Phytoplankton assemblages Parameters Examined
can vary due to...
Nutrients
Increase S:V,
decrease nutrient
uptake rate
TN
1:1 = 1
TP
4:8 = 1/2
Stoichiometry
N:P
Vary in uptake
sites and rate for
different nutrients
Si:P
Si:N
NH4+:NO3-
Light
Secchi depth
Vary in Chl-a concentration
Some mobile or bouyant
Mean light
33. 2. Assemblage Changes
Background
Phytoplankton Assemblage Changes
Phytoplankton assemblage may change in…
Diversity
Cell Size
Bloom frequencies
≤ 3 species
Sp. 1
Sp. 2 Others
≥ 80% of phytoplankton biomass
Number of common species
Top 90% of phytoplankton biomass
Taxonomic composition
• Class Level
• Functional groups
or
Colonial Unicellular
Large
Small
34. Did changes in
limnological parameters
result in a distinct shift in the
phytoplankton assemblage
between Regimes 2 and 3 ?
Part Two: Assemblage Changes
Objective 1: Limnological Parameters
39. What were the specific
changes in the phytoplankton
assemblage in terms of
diversity, cell size, bloom
dynamics, common species
and composition?
Part Two: Assemblage Changes
Objective 2: Specific Changes
40. 2. Assemblage Changes: Specific Changes
Diversity Indices
No change in
Richness,
Shannon’s Diversity,
or Evenness
Method: Linear Regression
Results
41. 2. Assemblage Changes: Specific Changes
Change in Cell Size
Cell size (biovolume) decreased
Method: Linear Regression
Results
42. 2. Assemblage Changes: Specific Changes
Results
Change in Bloom Periods
Strong dominant weeks = weeks with 3 or fewer species are
> 80% of the assemblage
Steady states > two consecutive strong dominant weeks
Number of Weeks
25
Regime 1
Regime 3
Regime 2
p= 0.02
20
15
10
5
0
98 99 00 01 02 03 04 05 06 07 08 09 10 11
Year
Method: Linear Regression, T-test
43. 2. Assemblage Changes: Specific Changes
Results
Number of Common Species
Increase in the number of common species
Peak from 2002-2007
Intermediate Period
Method: Non-Linear Regression
44. 2. Assemblage Changes: Specific Changes
Results
Number of Common Species
Increase in the number of common species
Peak from 2002-2007
Variability in common taxa
Intermediate Period
Method: Non-Linear Regression
45. 2. Assemblage Changes: Specific Changes
Results
Class Level Composition
Clear shift in the composition
Relative Biomass by Class
1.0
0.8
0.6
0.4
0.2
0.0
98
99
00
01
02
03
04
05
Year
06
07
08
09
10
11
46. 2. Assemblage Changes: Specific Changes
Results
Cyanophyceae
Relative Biomass
Decrease in relative biomass
serc.carleton.edu
0.4
0.3
0.2
0.1
0.0
98
99
00
01
02
03
04
05
Year
Method: Linear Regression; T-test
06
07
08
09
10
11
51. Biovolume
3 natural un
(mmBiovolume
Plot 1 Upper
15001500 control line
2. Assemblage Changes: Specific Changes
60006000
10001000
(mm3 natural un
(mm3 natural un
Biovolume
3 natural un
(mmBiovolume
30003000
Results
Chrysophyceae500 500
+ Synurophyceae
10001000
40004000
20002000
0 0
Increase in relative biomass
2 23 3
Decrease in size
dr-ralf-wagner.de
0
2
Biovolume
3 natural unit
(mmBiovolume -1)
(mm3 natural unit-1)
Biovolume
3 natural unit
(mmBiovolume -1)
(mm3 natural unit-1)
80 80
600 600
400 400
b
200 200
Small
60 60
a
Large
40 40
Plot 1 Upper specification
Plot
b 1 Upper control line
20 20
0
0
2
23
3
Regime
Regime
Method: Linear Regression; T-test
0
2
23
Regim
Regime
40004000
100 100
a
800 800
0
Chrysophyceae 0
2 3 Synurophyceae
3
Regime
Regime
Regime
Regime
10001000 a
0
20002000
30003000
a
b
20002000
b
10001000
0
2
Biovolume
3 natural unit
(mmBiovolume -1)
(mm3 natural unit-1)
(mm3 natural uni
Regime 3
Regime 3
80008000
23
3
Regime
Regime
0
0
2
23
Regi
Regime
52. Plot 1 Upper control line
2. Assemblage Changes: Specific Changes
Results
Chrysophyceae + Synurophyceae
dr-ralf-wagner.de
Chrysophyceae
Synurophyceae
Increase in relative biomass
Decrease in size
Relative Biomass
Increase in both large and small Chrysophytes
0.20
Small
Large
Plot 1 Upper specification
Plot 1 Upper control line
0.15
0.10
0.05
0.00
98
99
00
01
02
03
04
05
Year
s
Method: Linear Regression; T-test
0.4
06
07
08
09
10
11
53. Plot 1 Upper control line
2. Assemblage Changes: Specific Changes
Results
Chrysophyceae + Synurophyceae
dr-ralf-wagner.de
Chrysophyceae
Synurophyceae
Increase in relative biomass
Decrease in size
Increase in both large and small Chrysophytes
# Common Speices
Increase in commonness
4
3
2
1
0
98
99
00
01
02
03
04
05
Year
Method: Linear Regression; T-test
06
07
08
09
10
11
56. 2. Assemblage Changes: Specific Changes
Results
Bacillariophyceae
Increase in relative biomass
No change in size
Relative Biomass
Shift to large species
0.6
Small
Large
Plot 1 Upper specification
Plot 1 Upper control line
0.4
0.2
0.0
98
99
00
01
02
03
04
05
Year
Method: Linear Regression; T-test
06
07
08
09
10
11
57. # Common Speices
2. Assemblage Changes: SpecificYear
Changes
Results
Bacillariophyceae
6
4
Increase in relative biomass
2
No change in size
Shift to large species
0
98
99
00
01
02
03
No change in commonness
04
05
06
07
08
09
10
11
# Common Speices
Year
10
8
6
4
2
0
98
99
00
01
02
03
04
05
Year
Method: Linear Regression; T-test
06
07
08
09
10
11
59. 2. Assemblage Changes: Specific Changes
Results
Dinophyceae
No change in relative biomass
3000
2500
2000
1500
1000
500
0
eos.unh.edu
Biovolume
(mm3 natural unit-1)
Biovolume
(mm3 natural unit-1)
No change in size
2
3
Regime
Method: Linear Regression; T-test
700
600
500
400
300
200
100
0
2
3
Regime
60. Relative Biomass
2. Assemblage Changes: Specific Changes
Results
Dinophyceae
0.20
0.15
No change in relative biomass
0.10
No change in size
0.05
eos.unh.edu
Decrease in large species
0.00
98
99
00
01
02
03
04
05
06
07
08
09
10
11
Relative Biomass
Year
0.4
Small
Large
Plot 1 Upper spec
Plot 1 Upper cont
0.3
0.2
0.1
0.0
98
99
00
01
02
03
04
05
Year
Method: Linear Regression; T-test
06
07
08
09
10
11
61. # Common Speices
2. Assemblage Changes: Specific Changes
Results
Dinophyceae
4
3
2
No change in relative biomass
1
No change in size
eos.unh.edu
0
Decrease in large species
98
99
00
01
02
03
No change in commonness
04
05
06
07
08
09
10
11
# Common Speices
Year
6
4
2
0
98
99
00
01
02
03
04
05
Year
Method: Linear Regression; T-test
06
07
08
09
10
11
62. 2. Assemblage Changes: Limnological Parameters
Discussion
Effect of Limnological Parameters
Shift in phytoplankton assemblage between Regimes 2 and 3
Change in TP and
Stoichiometric parameters
TN, TP and N:P correlated
Parameters Examined
TN
TP
with changes in phytoplankton
N:P
Si:P
Si:N
NH4+:NO3-
Secchi depth
Mean light
63. 2. Assemblage Changes: Limnological Parameters
Discussion
Effect of Limnological Parameters
Shift in phytoplankton assemblage between Regimes 2 and 3
Change in TP and
Stoichiometric parameters
TN, TP and N:P correlated
Parameters Examined
TN
TP
with changes in phytoplankton
N:P
Si:P
Si:N
NH4+:NO3-
Secchi depth
Mean light
64. 2. Assemblage Changes: Specific Changes
Discussion
Change in Phytoplankton Assemblage
Regime 2
Regime 3
No Change in Diversity
Decrease in Cell Size
Decrease in Bloom Frequency 15.6 weeks yr-1
8 weeks yr-1
Increase in # of Common Species 10 sp. yr-1
17 sp. yr-1
Cyanophyceae
Mesotrophic
Species
Bacillariophyceae
Chrysophyceae
Synurophyceae
Colonial
Unicellular
Shift in Taxonomic composition
• Class Level
• Functional Groups
Eutrophic
Species
Large
Small
65. 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
66. 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
67. Part One: Conclusions
Julian date periods may be easy to define but may
miss important seasonal trends
Methods used here represent a simple method for
consistently defining seasonal periods between years
Chl-a is a weak proxy for phytoplankton biomass
68. 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)
69. Implications
Using seasonal periods, measuring
phytoplankton biomass directly and
examining the phytoplankton
assemblage will allow managers to see
more directly what is driving year to
year variation in metrics associated
with improved water quality (secchi
depth) resulting in higher-quality
management decisions.
70. Please Contact Me With Any
Questions or Comments
Thank you!
OnondagaLakeinfo.com
Editor's Notes
1. Cyanobacterial blooms in Lake Champlain
2. 2013 bloom of toxic microcystis in Lake Erie (shores of Ohio)
Microcystis can be toxic and were toxic in both of those blooms. Cyanobacteria species are generally considered nuisance species in eutrophic systems due to blooms, toxicity, surface scums and taste and order issues with drinking water. 3. Removing green algae (Enteromorpha) at the Olympic sailing venue in China in 2008
Phytoplankton range in size by 9 orders of magnitude
Size is in micro meters and a Log scale. Scaled up to meters to illustrate range in size
Phytoplankton are divided among 2 of 3 kingdoms.
Note differences in species for instance bacterial species… kelp…. Diatoms and Dinoflagellates with hard cell walls
Nitrification convert Nh4+ to No3-
Calendar dates may miss impt. seasonal trends. Different taxa in the different seasons can lead to different response to changes in lake conditions which may themselves vary seasonally and may be masked by averaging over a large period. Also, the seasonal periods can vary slightly in timing between years so the Calendar date defined periods may not even capture the same seasonal periods in every year, potentially leading to inaccurate estimates of trends.
Chl-a (proxy for biomass) changes over the year due to seasonal phytoplankton dynamics
CWP due (1) decrease phytoplankton biomass as a result of increased zooplankton feeding, (2) decreased nutrients and (3) phytoplankton settling out of the water.
Shows the slope for the linear regression of each parameter with year. The Y-axis is the mean rate of change in each parameter with year, where the error bars are standard error. The negative slope indicates there was an inverse relationship with time and therefore each parameter is decreasing. Open circles indicate that the change with time was significant at an alpha of 0.05
Unimodal decrease of biomass and Chl-a with TP. Shows the relationship between log TP and both log Chl-a and log biomass (linear regression)
Chl-a and TP relationship is unimodal (Organization for Economic Co-Operation and Development)
From Dillon and Rigler 1974
Determined whether the phytoplankton response (both Chl-a and Biomass) to TP in seasonal periods differed from Annual period.
Significant annual trend. Not significant seasonal trend. Indicates importance of seasonal variability
Impt. to look at seasonal periods due to different trends
Response in Fall Bloom may be weak due to TP entrainment during Fall Mixing
Phytoplankton growth in Fall Bloom is not limited by TP
Shows the peak and start and end of each period
Say that in some years the Spring Bloom falls within annual period and some years it doesn’t
CWP sometimes peaks before Summer Stratfiied. In Some years doesn’t occur at all
Fall Bloom also varies in whether occurs during or after Annual period
Variability in duration of all periods
Phytoplankton differ in relative requirements for different nutrients and may differ in uptake rates for nutrients
Be sure to say mean light in the mixed layer
Change in diversity… such as richness shown here.
This lake has had significant changes in many parameters that could be driving changes in the phytoplankton assemblage
Good case study for examining changes in the phytoplankton assemblage.
This lake has had significant changes in many parameters that could be driving changes in the phytoplankton assemblage
Good case study for examining changes in the phytoplankton assemblage.
This lake has had significant changes in many parameters that could be driving changes in the phytoplankton assemblage
Good case study for examining changes in the phytoplankton assemblage.
Non-metric dimensional scaling to examining variability with the phytoplankton assemblage between years.
Species biomass
1st axis can be interpreted as a gradient of change between Regimes.
2nd axis in mostly within regime variability and axis is the gradient of light/nutrients etc..
Corr. With axis 1 (p-values) =TP (0.0048), N:P (0.03), Si:P (0.003), N:N (0.005. Corr. W. Axis 2 (p-values) = Secchi (p= 0.02)
This is interesting. All of these changes… lake is recovering and yet there is not substantial change in diversity indices.
Decrease from 15.6 to 8 weeks.
Higher nutrients… dominated by few things. So even while diversity, evenness richness remain unchanged however have a change in the dominant species. Phytoplankton have a lot of rare species that affect the diversity indices. Here clearly see that fewer blooms overtime.
Each color is a different phytoplankton class. Not important right now to know which is which but notice that there is a change in the classes present over time. The intermediate periods is a period were there is an overlap of things that are increasing and decreasing. See that the teal disappears and the pink and red appear.
Each color is a different phytoplankton class. Not important right now to know which is which but notice that there is a change in the classes present over time. The intermediate periods is a period were there is an overlap of things that are increasing and decreasing. See that the teal disappears and the pink and red appear.
Before 1998, Chrysophytes were not present in the lake sample record; the paleolimnological record indicates they were present before lake disturbance in the 20th century.
Before 1998, Chrysophytes were not present in the lake sample record; the paleolimnological record indicates they were present before lake disturbance in the 20th century.
Before 1998, Chrysophytes were not present in the lake sample record; the paleolimnological record indicates they were present before lake disturbance in the 20th century.
Before 1998, Chrysophytes were not present in the lake sample record; the paleolimnological record indicates they were present before lake disturbance in the 20th century.
Increase in large diatoms; most are pennate diatoms which are elongated species and are generally better competitors for light.
Increase in large diatoms; most are pennate diatoms which are elongated species and are generally better competitors for light.
Increase in large diatoms; most are pennate diatoms which are elongated species and are generally better competitors for light.
Increase in large diatoms; most are pennate diatoms which are elongated species and are generally better competitors for light.
Ceratium hirundinella pictured here, only lareg species in Onondaga Lake. Common in years it was present, completely absent in other years. Years it was absent are correlated with years with a weak CWP due to decrease Daphnia biomass in those years.
Ceratium hirundinella pictured here, only lareg species in Onondaga Lake. Common in years it was present, completely absent in other years. Years it was absent are correlated with years with a weak CWP due to decrease Daphnia biomass in those years.
Ceratium hirundinella pictured here, only lareg species in Onondaga Lake. Common in years it was present, completely absent in other years. Years it was absent are correlated with years with a weak CWP due to decrease Daphnia biomass in those years.
Ceratium hirundinella pictured here, only lareg species in Onondaga Lake. Common in years it was present, completely absent in other years. Years it was absent are correlated with years with a weak CWP due to decrease Daphnia biomass in those years.
Small Dinoflagellates increased. Small Cyanobacteria increased. Chrysophyceae and Synurophyceae and diatoms increased. Increase in diatoms also important because generally considered to be more nutritious in FW systems due to high Fatty Acidy content.