TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



## Appendix B-11: Analysis of FDEP’s Rapid Periphyton Survey DataTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-1 

### Appendix B-11. Analysis of FDEP’s Rapid Periphyton Survey Data
Rapid Periphyton Survey (RPS) Florida Department of Environmental Protec tion (FDEP) conducted ra pid periphyton surveys (RPS) as a measure of periphyton abundance in stream reaches. Since the RPS included direct measures of nuisance algae (per cent algal cover, depth of alga l slime), it was thought to be a potentially useful indicator of use impairment. Th e objectives of this analysis were to determine associations between nutrient enrichment and alga l response as measured with the RPS, and to develop potential nutrient endpoints.

RPSs used a 100 m stream reach consistent w ith the FDEP bioassessment protocols. The 100 m segment of stream was evenly divided into 11 tr ansects and each transe ct was flagged/marked. Nine sampling points were selected approximate ly equidistant along e ach transect. Periphyton abundance/thickness was visually estimated at each sampling point and was assigned a rank from 0 to 5, where 0 meant no algae and 5 meant high algal abundance (>20 mm). In the middle of each transect, canopy cover was measured usi ng a spherical densitometer and percent canopy cover was calculated for each transect.



### Conceptual Model A simple conceptual model of factors that may poten tially contribute to algal thickness in Florida streams was developed (Figure B11-1)
Six principal factors may affect algal production in Florida streams, i.e., temperature, flow (velocity), turbidity (color), canopy cover, gr azing, and nutrients. The effects of accumulation time and physical habitat/substrate on algal biomass are hard to measure in observational studies, so they were not included in our model. The fo llowing were expected: 1) higher temperatures and nutrient concentrations to stimulate the grow th of periphyton and consequently lead to high algal biomass, 2) higher turbidity and canopy cove r to lead to light limitation and reduce the abundance of benthic algal biomass, and 3) higher grazing also to reduce algal biomass. Stream velocity was more complicated to predict sin ce flow could increase nutrient uptake rates for periphyton at intermediate level but reduce al gal biomass during scour ing floods. A surrogate measure of flow velocity from FDEP’s habi tat measurement was used and the effect of temperature on stream algal abundance was examin ed. It was discovered that this surrogate measure of flow either did not reflect scouring floods or did not have a strong effect on algal biomass. The correlation between temperat ure and algal biomass was weak (Spearman r =0.08). Therefore, both flow and temperature were removed from further analysis. Information on grazers was available as scra per density from the benthic macroinvertebrate sampling and showed a slightly positive correl ation with algal thickness (Table B11-1). This was expected to be due to inaccurate measure of true grazer density so this parameter was removed as well. The most critical predictors remaining were measur es of light penetrati on (canopy cover and water color) and nutrients (total nitroge n (TN) and total phosphorus (TP)).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-2 A theoretical model of algal biomass respons e to light and nutrie nt concentration was represented with the following equation (Yi is algal biomass):

Yi = a[0] + a[1] * nitrogen + a[2] * phosphorus + a[3] * canopy + a[4] * color
Data Exploration – Correlations
Part of the rapid periphyton surv ey was measuring algal mat thic kness in six ordinal categories from 0 to 5 according to the following rules:
“1” for algae less than 0.5 mm OR no algae visi ble but surface is slimy (including muck); “2” for 0.5 mm to 1 mm; “3” indicates an algal cover of greater than 1 mm but less than 6 mm; “4” for 6-20 mm; “5” for algae over 20 mm.
In order to compare periphyton thickness among str eam reaches or among transects and to relate nutrient concentrations to algal thickness in a reach, raw categorical thickness data were transformed by FDEP into a thickness score. The algal thickness score was a ranking based on the following:
Thickness Raw Thickness Category Thickness Score 0 0 0 <6 mm <3 1 <6-20mm <5 2 >20 >=5 3
The type score was based on the following:
Algal Type Type Score None 0 Diatoms 1 Filamentous 2
Also the final algal score was the product of the thickness score and the type score (i.e., thickness score * type score). All scores were assigned to each of th e potential 99 monitoring points comprising a sampling event. The sum and average scores were calculated to summarize the data for each sampling transect and stream reach.
Objectives:
1. To examine potential causal relationships be tween nutrients, other covariates, and algal mat thickness in Florida streams
2. To detect any change points or non-linear responses from LOESS regression and change point analysesTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-3 The bivariate plots showing relationships betw een algal biomass and various environmental variables are presented (Figure B11-2). Spearman correlations between diffe rent parameters of algal biomass and environmental variables, LOE SS lines along the environmental gradients, and change point analysis results ar e also presented in these graphs . Several environmental variables were unexpectedly correlated with algal biomass, including pH, Total Kjeldahl Nitrogen (TKN), and organic nitrogen (Table B11-1). TKN, orga nic nitrogen, and pH, as expected, were strongly correlated with color (Spearma n correlations were 0.79, 0.80, and -0.52, respectively). These strong correlations may explain the unexpected correlations between pH, organic nitrogen, and periphyton thickness since water color can cause light limitation and therefore directly reduce periphyton thickness. Other noticeable correlations were observed among conductivity, TP (r = 0.39), and chl. a (r=0.45), which are common correlations in streams. High conductivity, TP, and water column chl. a are often associated w ith watershed urbanization.
Periphyton thickness was corre lated with nitrate (NO 3 ) plus nitrite (NO 2 ), canopy cover, and water color, but not with TP (Table B11-1, Figure B11-1). According to these analyses, periphyton abundance increased when the nitrate plus nitrite (NO 2+3 ) concentration was above 140 μ g/L and reached a peak when NO 2+3 concentrations reached 1100 μ g/L. A decline of algal abundance was observed when color increased from approximately 20 platinum-cobalt units (PCU) to 150 PCU, and average canopy cover increased from 45% to 80%. Color was a strong confounding factor in all of thes e analyses since it was correlated to some extent with canopy cover, NO 2+3 concentration, and TP concentration.
Summary:
Scatter plots with change point analysis, LOESS regression, and Spearman correlation coefficients were created for each pair of relationships. Noticeable relationships were:

a. Algal thickness was strongly light limited in most Florida streams, and was limited by both forest canopy cover and water color. b. Algal thickness was strongly correlated with color, a factor that potentially led to light limitation but also released algae from nutrient limitation since higher color was associated with higher nutrient concentrations.

c. The only nutrient variable that was significantly correlated with algal thickness was NO 2+3 concentration. However, this correlation was confounded by the correlation between color and algal thickness; in other wo rds, colored streams had both lower NO 2+3 concentrations and reduced algal thickness compared to clear streams.



### Nutrient Limitation (N:P Ratios) Objectives: To examine whether Florida streams were primarily limited by nitrogen or phosphorus, or whether they may be co-limited.
Summary:
Based on nitrogen to phosphorus (N:P) ratios, NO 2+3 was likely the principal limiting nutrient for algal growth in most Florida streams, but a mi nority of streams appear to be co-limited. The dissolved inorganic nitrogen (DIN ) to TP (DIN:TP) ratio and NO 2+3 :TP ratio were examined in benchmark sites and few samples had a molar N:P ratio of more than 17. The NO 2+3 :TP ratio wasTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-4 also examined in the gradient study sites and more than 90% of the samples had both DIN:TP and NO 2+3 :TP ratios less than 17. It is possibl e that these streams are either NO 3 limited or colimited by nitrogen and phosphorus. Therefore, fo llow-up analyses consider both nitrogen and phosphorus.



### Periphyton and Phytoplankton In examining the RPS data, a relationship was found between algal mat thickness, and phytoplankton (as chlorophyll). This relationshi p was only found in streams with low canopy cover, that is, in streams where sufficient lig ht reached the water surface. The relationship between periphyton and phytoplankton is further explored below.
Objectives:

1. To further explore relationships between nutrien ts and algal thickness in classified Florida streams (clear, medium, and colored streams)

2. To examine the possible inter actions between light and nutrient limitations and possible interactions between diffe rent nutrient variables



### Stream Classification The shift in periphyton abundance response to color mostly occurr ed when water color exceeded 75 PCU (Figure B11-2). For bett er confidence, streams were classified into three color categories: color <= 40 PCU as clear streams, 40-100 PCU as intermediate streams, and >100 PCU as colored streams. Transects with less th an 40% canopy cover were selected and algal thickness in the remaining transects averaged as the mean algal thickness in each stream reach (Figure B11-5). Since the shaded stream segmen ts were most likely light limited, excluding these light limited stream segments was designed to strengthen the signal of the algal abundance response along an increasing nut rient concentration gradient.
The observed patterns of algal biomass in different colored streams (Figure B11-3) indicated that clear streams had higher algal thic kness than colored streams. At the same time, intermediate colored streams had lower periphyton biomass than clear streams, but higher planktonic chl. a (measure of algal biomass). Highly colored str eams had significantly lo wer periphyton biomass than both clear and intermediate colored stream s, but had comparable phytoplankton biomass to clear streams.
The relationships between nutrients and periphyt on thickness were strong er in clear streams (Figure B11-4, a1-a3) than in colored streams (Figure B11-4, c1-c3), indicated by the LOESS regressions and higher Spearman co rrelations in clear st reams. Greater correlative relationships were observed between nitrogen (especially TN) and benthic algal thickness. The effect of phosphorus seemed to be negligible, except in cl ear streams. Nutrients were not related to benthic algal biomass accumulation in colored str eams, probably due to potentially stronger light limitation.
In contrast to attached algae, relation ships between nutrients and phytoplankton chl. a were stronger in colored streams (Figure B11-5, b1-c3) than in clear streams (Figure B11-5, a1-a3),TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-5 especially with TN. The spearman correlations between chl. a and TN were 0.32 and 0.42 respectively in intermediate and high color streams but was -0.16 in clear streams. With increased color and potentially stronger light limitation for periphyton, phytoplankton appear more responsive to nutrient c oncentrations than periphyton in more colored streams. There may be signal of chl. a response to phosphorus in colored stream s, but it was weaker than that for TN.
The observed relationships between algal thickness and nutrient con centrations were weak due to light limitation in most of these streams. Light li mitation to periphyton is driven by three factors: canopy cover, color, and particulate turbidity (including phytoplankton). In relatively clear streams, periphyton biomass is mainly regulated by nutrient limitation so it is easier to find a relationship between periphyton thickness and nutr ient concentrations. In heavily shaded / colored streams, light limitation becomes the do minant limiting factor to periphyton growth so nutrient limitation is less of a driving force for periphyton growth.
If light penetration into the water column was limited by water color rather than tree shading, then phytoplankon algae, which are floating cells, would take advantage of the available light because they can float at or near the surface, a nd are not attached to substrates on the stream bottom. In this situation (col ored streams with limited or no tree canopy), one may expect phytoplankton chlorophyll to respond to nutrient enrichment.
Due to co-linearity among nutrients and a num ber of factors, e.g., color and canopy cover, alternative approaches could be used to infer the causal effect of nutrients on algal abundance. Classification based on stream color may be able to solve most of the problems and would be simple and easy to interpret.
Clear stream (color<=40)



### Bivariate analysis The mean periphyton thickness scores in clear ( <=40 color unit) and unsh aded streams (<=40% canopy cover) responded weakly to nutrient conc entrations (Figure B 11-6, a-c). Among all variables, TN and TIN were most strongly a ssociated with algal abundance measurements (Figure B11-6, b and c). TP concentration was no t a strong predictor of algal biomass rank alone (Figure B11-6, a). However, nitrogen and phosphorus could be co-limiting algal growth in the stream system. To verify that, nutrient concentrat ions were standardized/scaled according to their range, mean, and standard deviation, and ranked both scaled nitrogen and phosphorus from 1 to 10 according to their percentile range. The nutrient rank was then summarized at a single site as the sum of nitrogen and phosphorus ranks. The co mbined rank of nutrient variables (TN + TP and NO x + TP) showed stronger effects on algal th ickness (Figure B11-6, d and e), indicating possible nitrogen and phosphorus co-limitation. Ch ange points based on th is analysis were observed at a TN + TP (TNP) rank of approximately (10, 13, 14) and a NO x + TP (NO x P) rank of (8, 11, 14). If both nitrogen and phosphorus we re controlled equall y, the corresponding concentrations are shown in Table B11-3.

To calculate the TNP and NO x P rank in Figure B11-6, d and e, nutrient concentrations were first scaled/standardized according to their mean and standard deviation; and were then were ranked 1 to 10 according to their percentile range. Nutrient rank (TNP and NO x P) at a single site as theTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-6 sum of nitrogen and phosphorus ranks. Each dot re presents a stream reach. The dashed curves are the LOESS regression lines 95% confidence li mits; and the vertical lines are change points and 95% confidence limits.



### Logistic Regression and Contour Plots Since the response variable alga l thickness rank is a cat egorical variable, logistic regression analysis may be more appropriate for examin ing the causal effect of nutrients and other environmental factors. To expand sample size and examine the effect of canopy and nutrient interactions, each transect was treated as an independent sample. When 50% or more of a transect exceeded an algal th ickness rank of 5 (>20 mm thickness) , it was considered impacted. A number of logistic regressi on models were constructed to examine the relationship between the probability of impact and TN, TP, ca nopy cover, and their interaction terms.
Akaike's information criterion (AIC) is a m easure of the goodness of fit of an estimated statistical model and describes th e tradeoff between bias and varian ce in model construction. It is often used for model selection. The best regression model for TN, TP, and canopy cover according to AIC criterion was: TN + TP + canopy. The best regression model for NO x , TP, and canopy cover according to AIC criterion was: NO x * TP + NO x * Canopy + TP * canopy (Table B11-4).
A C-index (the probability of concordance) was used to measure the predicted probability of response and actual response. A value of 0.5 indicates random pr edictions and a value of 1 indicates perfect prediction. Both models perf ormed reasonably well, though the C-index for both models was less than 0.8. The Nitrate mode l (model 2) was more complicated due to significant interactions among pred ictor variables, and was relativ ely less precise than the TN model (model 1).
The model results were plotted as contour plots where impact probabilities are shown as contour lines and the two nutrient predictors compose th e two axes (Figure B11-7). Mean values of canopy cover were used in the model prediction. Each blue dot represents a site and red lines are the 25% probability of impairment, a threshold that was considered as the unacceptable level based on consistency with the U.S. EPA nutrient criteria guidance (25% of all sites). The purple triangles are the maximum allowable nitrogen an d phosphorus concentrations that have less then or equal to 25% impact probability. According to this analysis, the TN and TP criteria would be 1.54 and 0.11 mg/L, respectively (Table B11-5).
Intermediate colored (40~100) a nd colored streams (color>100)
The negative relationships between Stream Condition Index (SC I) scores and chl. a concentrations in colored stream s (Figure B11-8) indicate declining water quality with increased phytoplankton biomass. At the same time, dissolv ed oxygen also declines with elevated chl. a levels in the water column. Th e declines in dissolved oxygen (DO) concentrations may be affected by elevated microbial respiration at the bottom of these li ght limited streams.
Summary: Stream classification.

a. Clear streamsTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-7 i. Algal thickness in clear streams (30 str eams, 220 observations) was correlated with TN concentrations only; not with other nutrient variables; multiple variable interactions were possible. ii. Multiple logistic regression analyses indicated potential interactions among predictors. Both TN and TP, and NO x and TP models were significant. iii. Contour plots may be able to help guide nutrient criteria selection; however, the impairment thresholds and the allowable im pairment probabilities would have to be determined by management.

b. Colored streams i. Periphyton thickness became increasingly limited by light and correlations with nutrients were very weak; however, water column chl. a concentrations became increasingly more highly correlated with nutrients. ii. Planktonic algal biomass (chl. a ) was correlated with TN and TKN concentrations. iii. Dissolved oxygen in the water column was negatively correlated with water column chl. a concentrations. iv. SCI was also negatively correlated with chl. a in the water.

Table B11-1. Spearman Correlations between Al gal Thickness Parameters and Environmental Variables
Mean Thick Max Thick Mean ThickSc Max ThickSc Mean TypeSc Max TypeSc Mean AlgSc Max AlgSc Chl. a 0.22 0.18 0.22 0.16 0.16 0.16 0.16 0.14 CanopyCov -0.38 -0.38 -0.36 -0.37 -0.35 -0.36 -0.35 -0.35 MinCanopy -0.41 -0.42 -0.39 -0.40 -0.37 -0.40 -0.38 -0.39 TN -0.05 -0.09 -0.06 -0.12 -0.07 -0.08 -0.10 -0.13 TIN 0.22 0.18 0.22 0.17 0.17 0.16 0.16 0.14 OrgN -0.29 -0.33 -0.30 -0.36 -0.25 -0.25 -0.29 -0.32 NH 4 -0.16 -0.21 -0.15 -0.23 -0.19 -0.20 -0.23 -0.26 TKN -0.28 -0.32 -0.29 -0.35 -0.25 -0.25 -0.29 -0.33 NO x 0.29 0.26 0.29 0.36 0.15 0.25 0.25 0.23 TP -0.04 -0.04 -0.04 -0.03 -0.04 -0.03 -0.04 -0.05 DO 0.01 0.10 0.00 0.09 0.06 0.11 0.09 0.15 COND 0.25 0.23 0.24 0.22 0.18 0.23 0.22 0.21 pH 0.24 0.24 0.24 0.24 0.20 0.24 0.24 0.24 Color -0.42 -0.42 -0.42 -0.42 -0.36 -0.38 -0.40 -0.41 TEMP 0.07 0.05 0.09 0.07 0.09 0.05 0.04 0.04 Velocity 0.00 0.04 0.00 0.04 0.02 0.03 0.04 0.04 Scrap_Dns 0.22 0.29 0.21 0.28 0.19 0.24 0.25 0.29TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-8 Table B11-2. Molar N:P Ratios in Benchm ark Sites and Gradient Stream Sites

Percentile 0 0.1 0.25 0.5 0.75 0.9 1 Percentile distribution for benchmark sites NO 2+3 :TP 0.0 0.1 0.1 0.8 3.9 10.8 36.5 DIN:TP 0.1 0.4 0.8 2.5 6.0 12.2 37.6 TN:TP 2.3 6.8 14.2 22.7 45.5 79.1 108.6 Percentile distribution for gradient stream sites NO 2+3 :TP 0.0 0.0 0.2 0.7 2.7 12.5 770.0 DIN:TP 0.0 0.3 0.6 1.6 4.0 15.5 776.0 TN:TP 0.1 3.2 6.3 12.0 25.3 47.1 4110.9

Table B11-3. Change Point Analysis from Fi gure B11-6, d and e, and Corresponding Nutrient Concentrations

Table B11-4. Logistic Regression Models of Pr obability of Algal Thickness Impairment as a Function of Nutrient and Shading Variables Estimate Std. Error z value Pr(>|z|) Significant Model 1: Prob= TN + TP + canopy (Intercept) -1. 5542 0.2119 -7.336 2.20e-13 *** TN 0.8849 0.2083 4.248 2.15e-05 *** TP 0.6050 0.1878 3.221 0.00128 ** Canopy 0.6348 0.1897 3.346 0.00082 *** AIC: 184.2, C-index=0.779

Model 2: Prob= NO x * TP + NO x * Canopy + TP * canopy Intercept -1.7133 0.2522 -6.79 0 *** NO x 0.9733 0.2518 3.87 0.0001 *** TP 0.6513 0.2149 3.03 0.0024 ** Canopy 1.0472 0.2256 4.64 0 *** NO x * TP -0.7474 0.2815 -2.66 0.0079 ** NO x * Canopy -0.1664 0.1924 -0.86 0.3872 TP * Canopy -0.504 0.2184 -2.31 0.021 * AIC=211.59, C-index=0.765 Model TNP NO x P ChangePoint Lower CL Mean Upper CL Lower CL Mean Upper CL Rank 10 13 14 8 11 14 TN/NO x 1.02 1.39 1.55 0.240 0.85 TP 0.036 0.04 0.045 0.026 0.036 0.045TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-9 Table B11-5. Model Derived Nutrient Crit eria Based on 25% Impairment Probability Mean Upper Confidence Lower Confidence Model 1 TN 1.54 1.08 4.07 TP 0.11 0.08 0.06 Model 2 NO x 0.55 0.32 3.61 TP 0.15 0.09 0.06

Figure B11-1. Conceptual model of excessive algal accumulation in a stream ecosystem.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-10 Figure B11-2. Factors contributing to algal biomass. The smooth cu rves are the locally weighted smoothing lines. The vertical lines are the chan ge points (solid lines) and their confidence intervals (dashed lines).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-11 Figure B11-3. Periphyton and phytoplankton algal biomass in different colored streams. Different letters on top of each box indi cate significant differences (P<0.05).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-12 Figure B11-4. Relationships between nutrient concentrations and algal thickness in clear (<=40, a1-a3), intermediate (40~100, b1-b3) and colored (>100, c1-c3) streams.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-13 Figure B11-5. Relationships between nutrient concentrations and chl. a concentrations in clear (<=40, a1-a3), intermediate (40~100, b1-b3) and colored (>=100, c1-c3) streams.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-14 0.01 0.02 0.05 0.10 0.2001234

TP.rawAlgal thickness Spearman r= 0.19



### a

0.2 0.5 1.0 2.0 5.001234

TN.rawAlgal thickness Spearman r= 0.43 

### b

0.002 0.020 0.200 2.00001234

NOx.rawAlgal thickness Spearman r= 0.5 

### c

4 6 8 10 12 14 16012345

TN PAlgal thickness Spearman r= 0.56



### d

4 6 8 10 12 14 16012345

NOx PAlgal thickness Spearman r= 0.58



### e

Figure B11-6. The relationships between periphyton thickness and TN, TP, and NO x concentrations as well as combined ranks of nut rients in clear stream s (transect canopy <=40% and color <=40).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-15 Figure B11-7. Contour plots of logi stic regression results where canopy is assumed to be at its mean value. The contour lines are probability of impact. The red lines are 25% probability and the triangles are the nitrogen and phosphorus co ncentrations where both nitrogen and phosphorus are equally controlled at 25% impairment probability.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

B11-16 Figure B11-8. Correlations be tween water column chl. a and DO, SCI, and periphyton in intermediate (a1-a3) and hi gh color (b1-b3) streams.