Creating a Resource Competition Model.
35
CREATING A RESOURCE COMPETITION MODEL www.waterandwastewater.com http://cisbc.info/activities/PublishingImages/algae-2.jpg
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In 2009, I developed and tested a resource competition model for phytoplankton using literature values and randomly generated data to test whether a dramatic change in form of nitrogen (from Ammonia to Nitrate) could have a similiar affect on phytoplankton competition as changes in nitrogen to phosphorus. Note: All data is hypothetical and was used as preliminary exercise to show my intentions prior to beginning my experiment. Abstract: The model was designed to make competition predictions for four species of phytoplankton that have changed in dramatically in abundance following upgraded wastewater treatment. Model predictions were compared to hypothetical competition experiments between the four species grown on treatments varying in both N:P and NH4+:NO3- to determine the ability of the model to capture the factors determining competition in a controlled laboratory experiment. Finally, the model was used to make predictions about the relative abundance of each of the four species in the lake since 2001 and compare with the actual data. This will not only indicate the validity of applying Tilman’s resource-ratio theory to natural systems but indicate whether the dominate N form as well as the N:P should be considered when estimating how resource competition structures phytoplankton assemblages.
Transcript of Creating a Resource Competition Model.
CREATING A RESOURCE COMPETITION MODEL
www.waterandwastewater.com
http://cisbc.info/activities/PublishingImages/algae-2.jpg
OBJECTIVES
ONONDAGA LAKE
o Historically oligo-mesotrophico Flushes 4 times per year to Seneca Rivero Watershed 642 km2 , contains 450,00 peopleo Three tributaries = 70% of water input
o (Onondaga, Ninemile and Ley Creeks
o Morphometryo Maximum depth:
19.5 mo Surface area:
12.0 km2
o Volume: 131 X 106 m3
A HISTORY OF POLLUTION
o Syracuse area was settled in the 1700so Salt deposits spurred large scale production
o Resulted in rapid industrial and population growth leading to…o Industrial pollutiono Wastewater pollution
Wastewater Effects on the Lake (timeline):
1890- Cold water fishery lost1940- Swimming banned (fecal coliform,
total phosphorus and clarity)1994- Lake and sediment added to the
EPA Superfund National Priority List
EFFECTS OF NUTRIENT LOADING
Eutrophic
Eutrophic
o Large phytoplankton bloomso Anoxic bottom waterso Toxic levels of ammonium
(NH4+) and nitrite (NO2
-)o Loss of cold water fish
Not actually Onondaga
NITROGEN IN WATER
CLEANING IT UP
o METRO effluent o 80% of yearly TN load o 68% of TPo During summer Metro largest
single inflow to the lake
o 1998- Amended consent judgment against METROo Decrease loading over 15
years o 3 phases ($380 mil)
CHANGING N LOADS
o Tributary loads constanto Metro loading decreased by ~35%
Now dominated by NO3-
o 283% increase in NO3- (4x more)
o 85% reduction in NH4+ (7x less)
b) Metro
Regime
3 4 5
Yea
rly A
verg
e N
Loa
ds (
kgN
/d)
0
1000
2000
3000
4000
5000
NH4+
NOx
CHANGING LAKE CONCENTRATIONS
o [NH4+] and [NO3
-] changed rapidly in lake
o Ratio of NH4+ : NO3
- reversed between Regime 3 and 5
5:1 5:3 2:5NH4+: NO3
-
Regime
3 4 5
Yearly
Ave
rge N
Conc
(uM
)
0
50
100
150
200
250
300
350
NH4 Nox
EFFECTS ON PHYTOPLANKTON ASSEMBLAGE
N Loading from Metro has decreased and changed since 1989
NH4+ : NO3
- is lower than ever before
How will this change affect phytoplankton?Change in species? Toxicty? Food source?
N UPTAKE AND PREFERENCES
Using NO3- instead of NH4
+ requires two additional steps
N UPTAKE AND PREFERENCES
Diatoms prefer NO3- Some Chlorophyta +
Cyanobacteria prefer NH4+
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
0.8
1.0 Charo- Zygnematophyceae Chloro- Chlorophyceae Mamiellophyceae Nephroselmidophyceae Trebouxiophyceae UlvophyceaeCrypto- CryptophyceaeCyano- CyanophyceaeDino- DinophyceaeEugleno- EulgenophyceaeOchro- Bacillarophyceae Chrysophyceae Synurophyceae Xanthophyceae
Regime 3 Regime 4 Regime 5
PHYTOPLANKTON DIVISION TRENDS
INCREASE
DECREASE
Charo. r2= 0.051p= 0.4
r2= 0.11p= 0.3
Dino.
Crypto. r2= 0.40p= 0.01
Cyano.
r2= 0.85p= <0.001
Chloro.
r2= 0.62p= 0.5
r2= 0.61p= 0.001
Ochro.= No Significant Change
PREDICTED VS. ACTUAL COMMUNITY CHANGES
o Recently evolved species increaseo Diatomso Distantly evolved species decreaseo Cyanobacteria o Green Algaeo What about Green Algae?o They have evolved over long period. o Lots of diversity.
Expected change in divisions over the study period…
Many changes
since 1989…How can we
partition out the
effect of the NH4
+ : NO3-
on the community structure?
OBJECTIVES
Onondaga Lake’s polluted past and long-term phytoplankton changes
Predicting resource competition
Testing model and results
Future research
RESOURCE COMPETITION
The paradox of the plankton (Hutchinson 1961)o Why so many species?o What about competitive exclusion?
ANSWER?o Nutrients, light, predation,
turbulence etc.. vary!o Yearly, seasonally, daily and
within seconds
How to estimate the effect of these selective forces?
o Develop model to estimate each and test it Falkowski, et al. 2004
RESOURCE COMPETITION
o Measure growth rate for 4 species on TN, TP, NH4+ and NO3-𝑊= 𝑊0𝑒𝜇𝑡 W = conc. of phytoplanktonW0 = conc. of phytoplankton (cells/ml) at time 0µ = growth rate (d-1)
RESOURCE COMPETITION
o Measure growth rate for 4 species on TN, TP, NH4+ and NO3-
o At a range of 5 concentrations for each nutrient
𝑊= 𝑊0𝑒𝜇𝑡 W = conc. of phytoplanktonW0 = conc. of phytoplankton (cells/ml) at time 0µ = growth rate (d-1)
TN TP NH4+ NO3
-
1 20.00 1.00 0.50 0.502 63.33 2.35 3.00 12.713 94.86 4.34 8.81 18.674 195.03 6.30 20.89 25.725 246.79 20.59 115.13 115.00
Concentration (µM)
RESOURCE COMPETITION
o Create growth curves (growth rate vs. nutrient concentration
o Calculate µmax and Ks
S = solute concentraitonµmax= maximum value of µKs = a constant, concentration where
µ= ½ µmax
o Calculate R* for each species when D = 0.1
o Rank species for each nutriento R*1 < R*2 < R*3 < R*4
o Species 1 has the lowest nutrient requirement
𝑅∗ = 𝐷∙𝐾𝑠𝑈𝑚𝑎𝑥 ∙𝐷 ↑Vm
KS
CHOOSING STUDY SPECIES
Present in Samples Data for
every yearHave trend with N or P loading
Increased with time
Decreased with time
P driven?
N driven?
P driven?
N driven?
AN EXCEL ADVENTURE: HYPOTHETICAL RESULTS
Please Note: The following results from slide 20 to 28 were generated using my expectations and randomized data in excel as a preliminary exercise prior to beginning my experiment.
RESULTS: GROWTH RATE
RESULTS: R* VALUES
Nutrient Species Vmax Ks R* R orderMicrocystis aeruginosa 0.45 7 2.00 2Tetraedron spp. 0.45 22 6.29 4Diatoma tenuis 0.50 10 2.50 3Aphanizomenon flos-aquae 0.65 9 1.64 1*Microcystis aeruginosa 0.25 0.19 0.127 3*Tetraedron spp. 0.69 1.22 0.207 1*Diatoma tenuis 0.48 0.15 0.0395 2*Aphanizomenon flos-aquae 0.56 0.6 0.1304 4Microcystis aeruginosa 0.38 6 2.14 2Tetraedron spp. 0.51 15 3.66 4Diatoma tenuis 0.61 13 2.55 3Aphanizomenon flos-aquae 0.39 2 0.69 1Microcystis aeruginosa 0.55 12.5 2.78 3Tetraedron spp. 0.67 2 0.35 1Diatoma tenuis 0.49 8 2.05 2Aphanizomenon flos-aquae 0.41 14 4.52 4
TN
TP
NH4+
NO3-
𝑅∗ = 𝐷∙𝐾𝑠𝑈𝑚𝑎𝑥 ∙𝐷
RESULTS: ZNGI GRAPHSTranslating R* to Graphs
RES
ULT
S:
ZN
GI
GR
APH
SN:P N:P ZNGI
TP (uM)
0.0 0.5 1.0 1.5 2.0
TN
(uM
)
0
5
10
15
20
25
M.aT.sppD.tA.f
TP (uM)
0.0 0.5 1.0 1.5
TN
(uM
)
0
5
10
15
20
TP (uM)
0.0 0.5 1.0 1.5
TP (uM)
0.0 0.5 1.0 1.5
TP (uM)
0.0 0.5 1.0 1.5 2.0
Microcystis aeruginosa
Tetraedron spp.
Diatoma tenuis
Aphanizomenon flos-aquaeM
icro
cyst
is a
erug
inos
a
Tet
raed
ron
spp.
Dia
tom
a te
nuis
Aph
aniz
omen
on fl
os-a
quae
TN
(uM
)
0
5
10
15
20
TN
(uM
)
0
5
10
15
20
TN
(uM
)
0
5
10
15
20
25
NH
4+ ( M
)
0
5
10
15
20
NH4+:NO3
- ZNGI
NO3- (M)
0 5 10 15 20
NH
4+ ( M
)
0
5
10
15
20
M.aT.sppD.tA.f
NH
4+ ( M
)
0
5
10
15
NH
4+ ( M
)
0
5
10
15
NO3- (M)
0 5 10 15
NH
4+ ( M
)
0
5
10
15
NO3- (M)
0 5 10 15
NO3- (M)
0 5 10 15
NO3- (M)
0 5 10 15 20
Microcystis aeruginosa
Tetraedron spp.
Diatoma tenuis
Aphanizomenon flos-aquaeM
icro
cyst
is a
erug
inos
a
Tet
raed
ron
spp.
Dia
tom
a te
nuis
Aph
aniz
omen
on fl
os-a
quae
RES
ULT
S:
ZN
GI
GR
APH
SNH4
+:NO3-
OBJECTIVES
COMPETITION STUDY
o Spike all four species into each treatment
o A range of N:P and NH4+:NO3
- concentrations (based on Onondaga Lake data since 1988)
o Compare relative abundance between treatments
N:P 8 6.16 0.81 0.2583 1 5 9 1345 2 6 10 1427 3 7 11 1515 4 8 12 16
NH4+:NO3
-
N:P NH4+:NO3-Min 24.05 0.26Max 78.54 12.64
Species K/K Species K/KAphanizomenon flos-aquae 15.00 Aphanizomenon flos-aquae 0.14Tetraedron spp. 18.03 Microcystis aeruginosa 0.48Microcystis aeruginosa 36.84 Diatoma tenuis 1.63Diatoma tenuis 66.67 Tetraedron spp. 7.50
N:P NH4+:NO3
-
COMPETITION STUDY: PREDICTION
N:P Dominant Species83 Diat45 Micro27 Tetra15 Aphan
if N:P dominates trends
NH4+:NO3
- Dominant Species8 Tetra
6.16 Diat0.81 Micro0.25 Aphan
if NH4+:NO3
- dominates trends
a) P limited
N:P020406080100
M.aT.sppD.tA.f
b) N limited
N:P020406080100
A.fA.fM.aD.f
T.sppA.fM.aD.f
b) NO3- limited
NH4+:NO3
-0268
a) NH4+ limited
NH4+:NO3
-0268
M.aT.sppD.tA.f
A.fM.aD.tT.spp
A.f
M.aD.tT.spp
WHICH SPECIES SHOULD DOMINATE WHEN A NUTRIENT IS LIMITING?
LONG-TERM TRENDS IN LAKE
(PREDICTIONS) o How do the expected changes compare with the changes in
the lake?
if N:P dominates trends if NH4+:NO3
- dominates trendsRegime 3 65.34 ± 4.08 7.91 ± 0.54 Diatoma tenuis Aphanizomenon flos-aquaeRegime 4 55.22 ± 2.58 3.16 ± 0.35 Microcystis aeruginosa Diatoma tenuisRegime 5 35.99 ± 2.35 0.81 ± 0.11 Tetraedron spp. Tetraedron spp.
Dominate species of the 4N:P NH4
+:NO3-
PREDICTIONS
RESULTS: LONG-TERM TRENDS IN LAKE
if N:P dominates trends if NH4+:NO3
- dominates trendsRegime 4 55.22 ± 2.58 3.16 ± 0.35 Microcystis aeruginosa Diatoma tenuisRegime 5 35.99 ± 2.35 0.81 ± 0.11 Tetraedron spp. Tetraedron spp.
Dominate species of the 4N:P NH4
+:NO3-
o Actual trends in the lake don’t match up with predictionso Note: Used data generated from randomized models and
literature values to make these predictions :-D
PREDICTIONS
-0.100.100.300.500.700.901.101.301.50
83 45 27 15
Rela
tive
Abu
ndan
ce
N:P
a) NH4:NO3 = 8 M.a T.spp
D.t A.f
DISCUSSION + CONCLUSIONS
Since I made up my own data to match my expectations...
o Very useful to go through with the FAKE datao Found several bugso Understand the models more o Better expectations of what will happeno Proves that I can make the predictions I hoped to make!
What I Learned…?
DOGGIE BAG (I.E. TAKE HOME)
o Since 1989 o NH4
+ : NO3- has decreased from 5:1
to 2:5o Phytoplankton community has
changedo NH4
+ : NO3- should be affecting
phytoplankton communityo World Wideo NH4
+ loading decreasing + urea loading increasingo Stoichiometry is IMPORTANT
o % toxic species and toxin production is correlated with changing ratio of N forms
o Are we really solving the problem just decreasing TN loads?
