Recovery of a Hypereutrophic Urban Lake (Onondaga Lake, NY): Implications for Monitoring Water Quality and Phytoplankton Ecology

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Daniele Baker Master's Capstone November 15, 2013 …

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.

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  • 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.

Transcript

  • 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
  • 5. Symptoms of Eutrophication Oligotrophic Eutrophic PN PN Figure edited from University of Maryland Center of Environmental Science
  • 6. Variability in Phytoplankton  Differ dramatically in size µm 10 Fish 100 Orca 1,000 Factory 10,000 Eiffel Tower 100,000 m Manhattan Glibert and Burkholder 2011
  • 7. dr-ralf-wagner.de eos.unh.edu dnrec.state.de.us trees.com analogicalplanet.com Tolweb.org .rook.org/ea dnrec.state.de.us cfb.unh.edu/phycokey Bio.miami.edu plantbiology.msu.edu  Are extremely phylogenetically diverse Tolweb.org serc.carleton.edu nd
  • 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
  • 21. 1. Monitoring Approaches: Seasonal Variability Results Observed Chl-a vs. Predicted  Chl-a predicted from standard Chl-a, TP relationship (Vollenweider-OECD, Vollenweider and Kerekes 1980) p= 0.03 1.5 1.0 0.5 Regime 1 R2 R3 2.0 Regime 1 R2 R3 2.0 p= 0.03 1.5 1.5 Predicted 1.0 1.0 0.5 0.5 0.0 0.0 0.0 88 94 00 06 12 Annual Phosphorus (mg m-3) Method: Two-way ANOVA Observed 88 94 00 06 12 88 94 00 06 12 Annual Annual Regime 1 R2 R3 2.0 Regime 1 R2 R3 2.0 2.0 p= 0.03 p= 0.03 p= 0.03 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 88 94 00 06 12 0.0 88 94 00 06 12 Annual 88 Annual 06 12 94 00 Annual -1 R3 Log Chl-a(mg L -1) LogLog Chl-a (gLL-1) ) Chl-a (mg 2.0 Regime 1 R2 -1 Log Chl-a (mg -1 Log Chl-a (mg LL ) ) Chlorophyll-a (mg m-3) (Dillon and Rigler 1974)
  • 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
  • 35. 2. Assemblage Changes: Limnological Parameters Results Limnological Parameters  TP decreased (mol L-1) (mol L-1) TN TP Nutrient Parameters 4 Regime 1 Regime 2 Regime 3 p= 0.001 2 0 300 p= 0.2 150 0 98 99 00 01 02 03 04 05 Year Method: Linear Regression 06 07 08 09 10 11
  • 36. 2. Assemblage Changes: Limnological Parameters Results Limnological Parameters Light Parameters TN TP SD (m) Mean light-1 (mol L-1) (mol L )  TP decreased 4 0.50 Regime 1 Regime 2 Regime 3 p=p= 0.8 0.001 2 0.25 0 6 300 4 150 2 0 p= 0.2 0.9 98 99 00 01 02 03 04 05 Year Method: Linear Regression 06 07 08 09 10 11
  • 37. 2. Assemblage Changes: Limnological Parameters Results Limnological Parameters Stoichiometry Parameters  TP decreased Regime 3 p= 0.001  N:P, Si:N and 07 08 09 10 N:P Regime 3 p= 0.002 0 200 100 p= <0.001 0 0.8 11 Si:N ear 06 Regime 2 Regime 1 250 0.4 NH4+:NO3- 05 Si:P Si:P increased  NH4+:NO3- p= 0.2 decreased 500 0.0 3 2 1 0 p= 0.005 p= 0.005 98 99 00 01 02 03 04 05 Year Method: Linear Regression 06 07 08 09 10 11
  • 38. 2. Assemblage Changes: Limnological Parameters Results 2003  Regimes differ  TP, N:N, Si:N and Si:P between Regimes  Secchi depth within Regimes 1999  TP, TN, N:P most Axis 1 important drivers Axis 2 Phytoplankton Assemblage Shift 2004 Regime 3 2005 Regime 2 2006 2007 TP 2001 Si:N TN N:N 2011 2010 Si:P N:P 2000 2009 Regime 1 Secchi 1998 Method: NMDS; BIO-ENV 2002 2008
  • 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
  • 47. 2. Assemblage Changes: Specific Changes Results Cyanophyceae  Decrease in relative biomass serc.carleton.edu 1600 1200 800 400 0 2 3 Regime Method: Linear Regression; T-test 3000 2500 2000 1500 1000 500 0 Biovolume (mm3 natural unit-1) 2000 Biovolume (mm3 natural unit-1) Biovolume (mm3 natural unit-1)  Decrease in size 2 3 Regime 700 600 500 400 300 200 100 0 2 Reg
  • 48. 1 Relative Biomass 0.2 serc.carleton.edu  Decrease in size 0.0  Shift from large00 01 02 small unicellular 07 colonial to 03 04 05 06 98 99 08 09 10 11 Year Relative Biomass 2 Cyanophyceae 0.2 Relative Biomass 3 0.3 Results 0.1  Decrease in relative biomass Relative Biomass 4 2. Assemblage Changes: Specific Changes 0.4 0.1 0.1 0.0 0.0 0.4 Unicell Colonial 0.3 0.2 0.3 0.2 Plot 1 Upper specificatio Plot 1 Upper control line 0.1 0.1 0.0 98 99 00 01 02 03 0 04 05 06 07 08 09 10 11 Year 98 Linear00 01 02 03 Method: 99 Regression; T-test 04 05 06 07 08 09 10 11 0.0
  • 49. 2. Assemblage Changes: Specific Changes Results Cyanophyceae  Decrease in relative biomass serc.carleton.edu  Decrease in size  Shift from large colonial to small unicellular # Common Speices  Decrease in commonness 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
  • 50. Plot 1 Upper control line Relative Biomass 2. Assemblage Changes: Specific Changes Results 0.010 0.008 Chrysophyceae + Synurophyceae 0.006 dr-ralf-wagner.de 0.004  Chrysophyceae Synurophyceae  Increase in relative biomass 0.002 0.000 98 99 00 01 02 03 04 05 06 07 08 09 10 Relative Biomass Year 0.20 0.15 0.10 0.05 0.00 98 99 00 01 02 03 04 05 Year Method: Linear Regression; T-test 06 07 08 09 10 11 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
  • 54. Relative Biomass 2. Assemblage Changes: Specific Changes 0.4 Results Bacillariophyceae 0.3 0.2  Increase in relative biomass 0.1 0.0 98 99 00 01 02 03 04 05 06 07 08 09 10 07 08 09 10 11 Relative Biomass Year 0.6 0.4 0.2 0.0 98 99 00 01 02 03 04 05 Year Method: Linear Regression; T-test 06 11
  • 55. 1200 Bacillariophyceae 400  Increase in relative biomass 0 2 3  No change in size 2 Regime 2000 Biovolume (mm3 natural unit-1) Biovolume (mm3 natural unit-1) Regime 1500 1000 500 0 2 3 3 Regime Method: Linear Regression; T-test 4000 Regim Biovolume (mm3 natural unit-1) 800 Biovolume (mm3 natural unit- Biovolume (mm3 natural unit- Biovolume (mm3 natural unit- 2500 2000 1500 1000 500 0 1600 2. Assemblage Changes: Specific Changes 700 600 Results 500 400 300 200 100 0 2 3000 2000 1000 0 2 3 Regime 500 400 300 200 100 0 2 Regim
  • 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
  • 58. 2. Assemblage Changes: Specific Changes 0.3 Results Dinophyceae 0.2 0.1  No change in relative biomass 0.0 98 99 00 01 02 03 04 05 06 07 08 09 10 Relative Biomass Relative Biomass 0.4 11 eos.unh.edu Relative Biomass Relative Biomass Year 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 Crypto Cyano-
  • 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.
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