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Appendix B-11: Analysis of FDEP’s Rapid Periphyton Survey Data 
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Appendix B-11. Analysis of FDEP’s Rapid Periphyton Survey Data 

Rapid Periphyton Survey (RPS) 

Florida Department of Environmental Protection (FDEP) conducted rapid periphyton surveys 

(RPS) as a measure of periphyton abundance in stream reaches. Since the RPS included direct 

measures of nuisance algae (percent algal cover, depth of algal slime), it was thought to be a 

potentially useful indicator of use impairment. The objectives of this analysis were to determine 

associations between nutrient enrichment and algal response as measured with the RPS, and to 

develop potential nutrient endpoints. 

RPSs used a 100 m stream reach consistent with the FDEP bioassessment protocols. The 100 m 

segment of stream was evenly divided into 11 transects and each transect was flagged/marked. 

Nine sampling points were selected approximately equidistant along each 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 using a spherical densitometer and percent canopy 

cover was calculated for each transect. 

Conceptual Model 

A simple conceptual model of factors that may potentially 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, grazing, 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 following were expected: 1) higher temperatures 

and nutrient concentrations to stimulate the growth of periphyton and consequently lead to high 

algal biomass, 2) higher turbidity and canopy cover 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 since flow could increase nutrient uptake rates for 

periphyton at intermediate level but reduce algal biomass during scouring floods. A surrogate 

measure of flow velocity from FDEP’s habitat measurement was used and the effect of 

temperature on stream algal abundance was examined. 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 temperature 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 scraper density from the benthic macroinvertebrate sampling and 

showed a slightly positive correlation 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 measures of light penetration (canopy cover and water 

color) and nutrients (total nitrogen (TN) and total phosphorus (TP)). 
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A theoretical model of algal biomass response to light and nutrient 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 survey was measuring algal mat thickness in six ordinal categories 

from 0 to 5 according to the following rules: 

“1” for algae less than 0.5 mm OR no algae visible 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 stream 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 the 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 between 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 analyses 
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The bivariate plots showing relationships between algal biomass and various environmental 

variables are presented (Figure B11-2). Spearman correlations between different parameters of 

algal biomass and environmental variables, LOESS lines along the environmental gradients, and 

change point analysis results are 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, organic nitrogen, and pH, as expected, were strongly 

correlated with color (Spearman 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 with watershed urbanization. 

Periphyton thickness was correlated with nitrate (NO3) plus nitrite (NO2), 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 (NO2+3) concentration was above 

140 μg/L and reached a peak when NO2+3 concentrations reached 1100 μg/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 these analyses since it was correlated to some extent with canopy 

cover, NO2+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 words, colored streams had both lower NO2+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, NO2+3 was likely the principal limiting nutrient for 

algal growth in most Florida streams, but a minority of streams appear to be co-limited. The 

dissolved inorganic nitrogen (DIN) to TP (DIN:TP) ratio and NO2+3:TP ratio were examined in 

benchmark sites and few samples had a molar N:P ratio of more than 17. The NO2+3:TP ratio was 
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also examined in the gradient study sites and more than 90% of the samples had both DIN:TP 

and NO2+3:TP ratios less than 17. It is possible that these streams are either NO3 limited or colimited by nitrogen and phosphorus. Therefore, follow-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 relationship was only found in streams with low canopy 

cover, that is, in streams where sufficient light reached the water surface. The relationship 

between periphyton and phytoplankton is further explored below.

Objectives: 

1.To further explore relationships between nutrients and algal thickness in classified Florida 

streams (clear, medium, and colored streams) 

2.To examine the possible interactions between light and nutrient limitations and possible 

interactions between different nutrient variables 

Stream Classification 

The shift in periphyton abundance response to color mostly occurred when water color exceeded 

75 PCU (Figure B11-2). For better 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 than 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 segments 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 nutrient concentration gradient. 

The observed patterns of algal biomass in different colored streams (Figure B11-3) indicated that 

clear streams had higher algal thickness 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 streams had significantly lower periphyton biomass 

than both clear and intermediate colored streams, but had comparable phytoplankton biomass to 

clear streams. 

The relationships between nutrients and periphyton thickness were stronger 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 correlations in clear streams. Greater correlative relationships 

were observed between nitrogen (especially TN) and benthic algal thickness. The effect of 

phosphorus seemed to be negligible, except in clear streams. Nutrients were not related to 

benthic algal biomass accumulation in colored streams, probably due to potentially stronger light 

limitation. 

In contrast to attached algae, relationships 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), 
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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 concentrations than periphyton in more colored streams. There may 

be signal of chl. a response to phosphorus in colored streams, but it was weaker than that for TN. 

The observed relationships between algal thickness and nutrient concentrations were weak due to 

light limitation in most of these streams. Light limitation 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 nutrient concentrations. In heavily shaded / 

colored streams, light limitation becomes the dominant 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, and are not attached to substrates on the stream 

bottom. In this situation (colored 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 number 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 unshaded streams (<=40% 

canopy cover) responded weakly to nutrient concentrations (Figure B11-6, a-c). Among all 

variables, TN and TIN were most strongly associated with algal abundance measurements 

(Figure B11-6, b and c). TP concentration was not 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 concentrations 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 combined rank of nutrient variables (TN + TP 

and NOx + TP) showed stronger effects on algal thickness (Figure B11-6, d and e), indicating 

possible nitrogen and phosphorus co-limitation. Change points based on this analysis were 

observed at a TN + TP (TNP) rank of approximately (10, 13, 14) and a NOx + TP (NOxP) rank of 

(8, 11, 14). If both nitrogen and phosphorus were controlled equally, the corresponding 

concentrations are shown in Table B11-3. 

To calculate the TNP and NOxP 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 NOxP) at a single site as the 
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sum of nitrogen and phosphorus ranks. Each dot represents a stream reach. The dashed curves 

are the LOESS regression lines 95% confidence limits; and the vertical lines are change points 

and 95% confidence limits. 

Logistic Regression and Contour Plots 

Since the response variable algal thickness rank is a categorical variable, logistic regression 

analysis may be more appropriate for examining 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 thickness rank of 5 (>20 mm thickness), it was considered impacted. 

A number of logistic regression models were constructed to examine the relationship between 

the probability of impact and TN, TP, canopy cover, and their interaction terms. 

Akaike's information criterion (AIC) is a measure of the goodness of fit of an estimated 

statistical model and describes the tradeoff between bias and variance 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 NOx, TP, and 

canopy cover according to AIC criterion was: NOx * TP + NOx * 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 predictions and a value of 1 

indicates perfect prediction. Both models performed reasonably well, though the C-index for 

both models was less than 0.8. The Nitrate model (model 2) was more complicated due to 

significant interactions among predictor variables, and was relatively 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 the 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 and 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) and colored streams (color>100) 

The negative relationships between Stream Condition Index (SCI) scores and chl. a

concentrations in colored streams (Figure B11-8) indicate declining water quality with increased 

phytoplankton biomass. At the same time, dissolved oxygen also declines with elevated chl. a

levels in the water column. The declines in dissolved oxygen (DO) concentrations may be 

affected by elevated microbial respiration at the bottom of these light limited streams. 

Summary: Stream classification. 

a. Clear streams 
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i. Algal thickness in clear streams (30 streams, 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 NOx and TP models were significant. 

iii. Contour plots may be able to help guide nutrient criteria selection; however, the 

impairment thresholds and the allowable impairment 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 Algal 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 

NH4 -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 

NOx 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 0.29 
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Table B11-2. Molar N:P Ratios in Benchmark Sites and Gradient Stream Sites 

Percentile 0 0.1 0.25 0.5 0.75 0.9 1 

Percentile distribution for benchmark sites 

NO2+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 

NO2+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 Figure B11-6, d and e, and Corresponding Nutrient 

Concentrations 

Table B11-4. Logistic Regression Models of Probability 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= NOx * TP + NOx * Canopy + TP * canopy 

Intercept -1.7133 0.2522 -6.79 0 *** 

NOx 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 *** 

NOx * TP -0.7474 0.2815 -2.66 0.0079 ** 

NOx * 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 NOxP 

ChangePoint Lower CL Mean Upper CL Lower CL Mean Upper CL 

Rank 10 13 14 8 11 14 

TN/NOx 1.02 1.39 1.55 0.240 0.85 

TP 0.036 0.04 0.045 0.026 0.036 0.045 
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Table B11-5. Model Derived Nutrient Criteria 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 

NOx 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. 
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Figure B11-2. Factors contributing to algal biomass. The smooth curves are the locally weighted 

smoothing lines. The vertical lines are the change points (solid lines) and their confidence 

intervals (dashed lines). 
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Figure B11-3. Periphyton and phytoplankton algal biomass in different colored streams. 

Different letters on top of each box indicate significant differences (P<0.05). 
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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. 
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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. 
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0.01 0.02 0.05 0.10 0.20

01234

TP.raw

Algal thickness

Spearman r= 0.19

a

0.2 0.5 1.0 2.0 5.0

01234

TN.raw

Algal thickness

Spearman r= 0.43

b

0.002 0.020 0.200 2.000

01234

NOx.raw

Algal thickness

Spearman r= 0.5

c

4 6 8 10 12 14 16

012345

TNP

Algal thickness

Spearman r= 0.56

d

4 6 8 10 12 14 16

012345

NOxP

Algal thickness

Spearman r= 0.58

e

Figure B11-6. The relationships between periphyton thickness and TN, TP, and NOx 

concentrations as well as combined ranks of nutrients in clear streams (transect canopy <=40% 

and color <=40). 
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Figure B11-7. Contour plots of logistic 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 concentrations where both nitrogen and phosphorus 

are equally controlled at 25% impairment probability. 
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Figure B11-8. Correlations between water column chl. a and DO, SCI, and periphyton in 

intermediate (a1-a3) and high color (b1-b3) streams.