The State of the Lake and Comprehensive

Management Plan for Lake Mohegan, New York

Patrick Goodwin

Occasional Paper No. 62a

State University of New York

College at Oneonta

2018

OCCASIONAL PAPERS PUBLISHED BY THE BIOLOGICAL FIELD STATION

No. 1. The diet and feeding habits of the terrestrial stage of the common newt, Notophthalmus viridescens (Raf.). M.C. MacNamara, April 1976

No. 2. The relationship of age, growth and food habits to the relative success of the whitefish (Coregonus clupeaformis) and the cisco (C. artedi) in Otsego Lake, New York. A.J. Newell, April 1976.

No. 3. A basic limnology of Otsego Lake (Summary of research 1968-75). W. N. Harman and L. P. Sohacki, June 1976. No. 4. An ecology of the Unionidae of Otsego Lake with special references to the immature stages. G. P. Weir, November

1977. No. 5. A history and description of the Biological Field Station (1966-1977). W. N. Harman, November 1977. No. 6. The distribution and ecology of the aquatic molluscan fauna of the Black River drainage basin in northern New York.

D. E Buckley, April 1977. No. 7. The fishes of Otsego Lake. R. C. MacWatters, May 1980. No. 8. The ecology of the aquatic macrophytes of Rat Cove, Otsego Lake, N.Y. F. A Vertucci, W. N. Harman and J. H. Peverly,

December 1981. No. 9. Pictorial keys to the aquatic mollusks of the upper Susquehanna. W. N. Harman, April 1982. No. 10. The dragonflies and damselflies (Odonata: Anisoptera and Zygoptera) of Otsego County, New York with illustrated

keys to the genera and species. L.S. House III, September 1982. No. 11. Some aspects of predator recognition and anti-predator behavior in the Black-capped chickadee (Parus atricapillus). A.

Kevin Gleason, November 1982. No. 12. Mating, aggression, and cement gland development in the crayfish, Cambarus bartoni. Richard E. Thomas, Jr., February

1983. No. 13. The systematics and ecology of Najadicola ingens (Koenike 1896) (Acarina: Hydrachnida) in Otsego Lake, New York.

Thomas Simmons, April 1983. No. 14. Hibernating bat populations in eastern New York State. Donald B. Clark, June 1983. No. 15. The fishes of Otsego Lake (2nd edition). R. C MacWatters, July 1983. No. 16. The effect of the internal seiche on zooplankton distribution in Lake Otsego. J. K. Hill, October 1983. No. 17. The potential use of wood as a supplemental energy source for Otsego County, New York: A preliminary examination.

Edward M. Mathieu, February 1984. No. 18. Ecological determinants of distribution for several small mammals: A central New York perspective. Daniel Osenni,

November 1984. No. 19. A self-guided tour of Goodyear Swamp Sanctuary. W. N. Harman and B. Higgins, February 1986. No. 20. The Chironomidae of Otsego Lake with keys to the immature stages of the subfamilies Tanypodinae and Diamesinae

(Diptera). J. P. Fagnani and W. N. Harman, August 1987. No. 21. The aquatic invertebrates of Goodyear Swamp Sanctuary, Otsego Lake, Otsego County, New York. Robert J. Montione,

April 1989. No. 22. The lake book: a guide to reducing water pollution at home. Otsego Lake Watershed Planning Report #1. W. N.

Harman, March 1990. No. 23. A model land use plan for the Otsego Lake Watershed. Phase II: The chemical limnology and water quality of Otsego

Lake, New York. Otsego Lake Watershed Planning Report Nos. 2a, 2b. T. J. Iannuzzi, January 1991. No. 24. The biology, invasion and control of the Zebra Mussel (Dreissena polymorpha) in North America. Otsego Lake

Watershed Planning Report No. 3. Leann Maxwell, February 1992. No. 25. Biological Field Station safety and health manual. W. N. Harman, May 1997. No. 26. Quantitative analysis of periphyton biomass and identification of periphyton in the tributaries of Otsego Lake, NY in

relation to selected environmental parameters. S. H. Komorosky, July 1994. No. 27. A limnological and biological survey of Weaver Lake, Herkimer County, New York. C.A. McArthur, August 1995. No. 28. Nested subsets of songbirds in Upstate New York woodlots. D. Dempsey, March 1996. No. 29. Hydrological and nutrient budgets for Otsego lake, N. Y. and relationships between land form/use and export rates of

its sub -basins. M. F. Albright, L. P. Sohacki, W. N. Harman, June 1996. No. 30. The State of Otsego Lake 1936-1996. W. N. Harman, L. P. Sohacki, M. F. Albright, January 1997. No. 31. A self-guided tour of Goodyear Swamp Sanctuary. W. N. Harman and B. Higgins (Revised by J. Lopez),1998. No. 32. Alewives in Otsego Lake N. Y.: A comparison of their direct and indirect mechanisms of impact on transparency and

Chlorophyll a. D. M. Warner, December 1999. No.33. Moe Pond limnology and fish population biology: An ecosystem approach. C. Mead McCoy, C. P. Madenjian, V. J.

Adams, W. N. Harman, D. M. Warner, M. F. Albright and L. P. Sohacki, January 2000. No. 34. Trout movements on Delaware River System tail-waters in New York State. Scott D. Stanton, September 2000. No. 35. Geochemistry of surface and subsurface water flow in the Otsego lake basin, Otsego County New York. Andrew R.

Fetterman, June 2001. No. 36 A fisheries survey of Peck Lake, Fulton County, New York. Laurie A. Trotta. June 2002. No. 37 Plans for the programmatic use and management of the State University of New York College at Oneonta Biological

Field Station upland natural resources, Willard N. Harman. May 2003. Continued inside back cover Annual Reports and Technical Reports published by the Biological Field Station are available at:

http://www.oneonta.edu/academics/biofld/publications.asp

The State of the Lake and Comprehensive

Management Plan for Lake Mohegan, New York

Patrick Goodwin

Biological Field Station, Cooperstown, New York

bfs.oneonta.edu

STATE UNIVERSITY COLLEGE

AT ONEONTA

The information contained herein may not be

reproduced without permission of the author(s) or the SUNY Oneonta

Biological Field Station

1

Acknowledgements

Daniel Stich

Holly Waterfield

Ken Wagner

Kiyoko Yokota

Matt Albright

Willard Harman

2

Table of Contents

Acknowledgements ..................................................................................................................................... 1

Table of Contents ........................................................................................................................................ 2

List of Tables ............................................................................................................................................... 4

List of Figures .............................................................................................................................................. 5

Abstract ........................................................................................................................................................ 7

Chapter 1: 2016 Limnological Study ......................................................................................................... 7

Introduction............................................................................................................................................... 7

Methods ..................................................................................................................................................... 8

Results ..................................................................................................................................................... 12

Lake Monitoring Results ......................................................................................................................... 12

Discussion ............................................................................................................................................... 19

References ............................................................................................................................................... 20

Chapter 2: Using Citizen Science Monitoring and Climate Data to Make Water Quality Predictions

in Lake Mohegan....................................................................................................................................... 21

Introduction............................................................................................................................................. 21

Methods ................................................................................................................................................... 22

Results ..................................................................................................................................................... 23

Discussion ............................................................................................................................................... 28

References ............................................................................................................................................... 33

Chapter 3: Nutrient Budget ..................................................................................................................... 35

Introduction............................................................................................................................................. 35

Methods ................................................................................................................................................... 36

Discussion ............................................................................................................................................... 47

References ............................................................................................................................................... 49

Chapter 4: Plants in Lake Mohegan ....................................................................................................... 50

Introduction............................................................................................................................................. 50

Methods ................................................................................................................................................... 51

Results ..................................................................................................................................................... 52

Discussion ............................................................................................................................................... 53

Chapter 5: Phytoplankton in Lake Mohegan ......................................................................................... 55

Introduction............................................................................................................................................. 55

Methods ................................................................................................................................................... 56

Results ..................................................................................................................................................... 58

3

Discussion ............................................................................................................................................... 60

References ............................................................................................................................................... 63

Chapter 6: Zooplankton in Lake Mohegan ............................................................................................ 64

Introduction............................................................................................................................................. 64

Methods ................................................................................................................................................... 65

Results ..................................................................................................................................................... 67

Discussion ............................................................................................................................................... 68

References ............................................................................................................................................... 70

Chapter 7: Fish in Lake Mohegan ........................................................................................................... 71

Introduction............................................................................................................................................. 71

Methods ................................................................................................................................................... 71

Results ..................................................................................................................................................... 73

Discussion ............................................................................................................................................... 77

References ............................................................................................................................................... 79

Chapter 8: A Comprehensive Lake Management Plan for Lake Mohegan ........................................ 80

Introduction............................................................................................................................................. 80

Nutrients .................................................................................................................................................... 80

Nutrients from Internal Loading ......................................................................................................... 80

Nutrients from Watershed ................................................................................................................... 83

Nutrients from Waterfowl.................................................................................................................... 86

Nutrients from Septic Systems ............................................................................................................. 86

Sedimentation, Erosion, and Lake Depth ........................................................................................... 88

Aquatic Plants ....................................................................................................................................... 89

Algae ....................................................................................................................................................... 91

Fishery .................................................................................................................................................... 92

Summary of Management Objectives ................................................................................................. 93

Appendix A: ............................................................................................................................................... 96

4

Table of Contents

List of Tables

Table 1. Summary of water chemistry methods used to estimate TP, TN, calcium, and alkalinity in

samples collected in Lake Mohegan. .......................................................................................................... 10

Table 2. Hydroacoustic data compiled from CiBiobase (Navico 2017). Volume and area associated with

depth contours for Lake Mohegan .............................................................................................................. 17

Table 3. Unconsolidated sediment depth results. ........................................................................................ 18

Table 4. Average, maximum and minimum annual precipitation (PRCP), flushes in years (y) and days (d),

and WRT (y) for Lake Mohegan. Precipitation values were inputted into the Lake Loading Response

Model (Wagner 2017a) to predict lake flushing and water retention times. ............................................... 24

Table 5. August average, median, maximum, and minimum of water-quality parameters in Mohegan Lake

for all available monitoring years. Parameters include TP; TN; weight molar TN:TP ratio; true color units:

TCU, reported as Platinum-Cobalt Units (Pt-Co); pH; conductivity: cond.; chl. a; and surface water

temperature: temp. ...................................................................................................................................... 25

Table 6. August average, median, maximum and minimum trophic state index (TSI) values for all

available monitoring years. Parameters include total phosphorus: TP; chlorophyll a: chl. a and Zsd. TSI

was calculated from (Carlson 1977). .......................................................................................................... 25

Table 7. Types and sources of data used for LLRM set up. ........................................................................ 36

Table 8. Summary table for automated gravity fed stormwater samplers. Data represents the first flushing

of 6 storm events during the summer of 2016. The number of first flush storm events captured, average,

median, standard (Std.) deviation, maximum and minimum loading rates (kg / day) for TP, TN, and

nitrate. ......................................................................................................................................................... 42

Table 9. Water volumes (m3 / day) prior (February 2, 2016) and during a stormwater event (February 3,

2016). Grey highlights indicate maximum values. ..................................................................................... 43

Table 10. Chloride loading (kg / day) prior (February 2, 2016) and during a stormwater event (February 3,

2016). Grey highlights indicate maximum values. ..................................................................................... 43

Table 11. Conductivity (mS cm -1

) levels prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values. ................................................................. 43

Table 12. TP loading (kg / day) prior (February 2, 2016) and during a stormwater event (February 3,

2016). Grey highlights indicate maximum values. ..................................................................................... 44

Table 13. TN loading (kg / day) prior (February 2, 2016) and during a stormwater event (February 3,

2016). Grey highlights indicate maximum values. ..................................................................................... 44

Table 14. Nitrate loading (kg / day) prior (February 2, 2016) and during a stormwater event (February 3,

2016). Grey highlights indicate maximum values. ..................................................................................... 44

Table 15. TSS loading (kg / day) prior (February 2, 2016) and during a stormwater event (February 3,

2016). Grey highlights indicate maximum values. ..................................................................................... 45

Table 16. Summary Table for Scenario Testing using the LLRM (Wagner 2017). ................................... 46

Table 17. Carlson’s trophic state index values and classification of lakes (Carlson 1977). ....................... 48

Table 18. Guidance values for the relative probability of health effects resulting from exposure to

cyanobacteria blooms and microcystin taken from the World Health Organization (1999). ...................... 61

Table 19. Equations used to determine zooplankton dry weight (Peters and Downing 1984), filtering rates

(Knoechel and Blair Holtby 1986), and P regeneration rates (Esjmont-Karabin 1984). ............................ 66

Table 20. Length (mm) categories for a variety of fish species proposed by Gabelhouse (1984) .............. 73

Table 21. Proportional stock density (PSD) for a variety of fish species sampled in 2009 and 2017. Note:

the 2009 data set is for all fish caught. ........................................................................................................ 75

5

Table 22. Suggested target ranges for size structure indices for a variety of species. From Willis et al.

(1993). ......................................................................................................................................................... 77

Table 23. Suggested target ranges for size structure indices for largemouth bass and bluegill under three

different management options. From Willis et al. (1993). .......................................................................... 78

Table 24. Lake Mohegan management objectives ...................................................................................... 93

Table 25. Additional Management Objectives ........................................................................................... 94

List of Figures

Figure 1. Bathymetric map of Lake Mohegan and the Citizens Statewide Lake Assessment Program

(CSLAP) monitoring locations 1 & 2 (Navico 2017). .................................................................................. 9

Figure 2. Air station map for Lake Mohegan. ............................................................................................. 11

Figure 3. Secchi values (in meters) taken at site 1 in Lake Mohegan. ........................................................ 12

Figure 4. Total phosphorus (TP; µg N L -1

) data taken at the surface (dark grey bars) and bottom (black

bars) of Lake Mohegan at site 1. ................................................................................................................. 13

Figure 5. Total nitrogen (TN; µg N L -1

) data taken at the surface (dark grey bars) and bottom (black bars)

of Lake Mohegan at site 1. .......................................................................................................................... 13

Figure 6. Mean monthly TP retention (%) in Lake Mohegan. Positive values indicate internal P loading.

.................................................................................................................................................................... 14

Figure 7. Sum of relative thermal resistance to mixing (RTRM) in Lake Mohegan for each sampling

outing. Grey line represents weak stratification (> 30) and black line represents strong stratification (>

80). .............................................................................................................................................................. 15

Figure 8. Temperature isopleth (°C; R Core Team 2017). Black dots indicate sample dates. .................... 15

Figure 9. Dissolved oxygen isopleth (mg / L). Black dots indicate sample dates. ...................................... 16

Figure 10. pH isopleth. Black dots indicate sample dates. .......................................................................... 16

Figure 11. Conductivity isopleth (µS / cm). Black dots indicate sample dates. ......................................... 16

Figure 12. Bottom hardness map and unconsolidated sediment depth sampling locations (M1 – M10;

Navico 2017). Lighter colors indicate softer sediments while darker colors indicate hard sediments. ...... 18

Figure 13. Average annual temperature (°C; top) and precipitation (meters/yr.; bottom) from 1966 to

2016. The dashed line represents the five-year moving average. ............................................................... 24

Figure 14. Boxplot of Zsd over time (1936 to 2016), the bold, black line is the median Zsd for each year,

the box ends represent the 25th and 75

th percentiles, the whiskers indicate 1

st and 99

th percentiles, and open

circles indicate outliers. Black point in 2002 represents an alum treatment. .............................................. 26

Figure 15. Boxplot of TP over time (1987 to 2016). Symbols are defined as in Figure 14. ....................... 26

Figure 16. Boxplot of chl. a over time (1998 to 2016). Symbols are defined as in Figure 14. ................... 26

Figure 17. Boxplot of TN:TP ratio (by weight) over time (2002 to 2016). Symbols are defined as in

Figure 14. .................................................................................................................................................... 27

Figure 18. TP predictive models for chl. a (A) and Zsd (B). The blue line represents mean prediction and

red dashed lines are 95% prediction intervals (PI). Grey points represent all summer (July and August)

observations of Zsd and TP (1987-2016). .................................................................................................... 28

Figure 19. Land use and cover map. ........................................................................................................... 38

Figure 20. Stormwater sampling sites (#1-#6) with site #4 being the outflow (indicted by red pointer).

Sub-watershed basins were delineated using USGS topography. ............................................................... 39

Figure 21. Automated storm water sampler used at inflow 3. .................................................................... 40

Figure 22. Annual TP loading to Lake Mohegan (kg / yr., %). .................................................................. 45

6

Figure 23. Annual nitrogen loading to Lake Mohegan (kg / y, %). ............................................................ 46

Figure 24. Acoustic mapping survey conducted in Lake Mohegan on June 30, 2016, in (Navico 2017).

The red to yellow areas on the maps indicate the highest density of plants with the blue areas

corresponding to lower densities................................................................................................................. 52

Figure 25. Bathymetric map of Lake Mohegan and the Citizens Statewide Lake Assessment Program

(CSLAP) monitoring locations 1 & 2. ........................................................................................................ 57

Figure 26. Harmful algal blooms (HABs) occurring on Lake Mohegan in 2016. Picture A) shows the first

observable bloom during the 2016 growing season (6/17/2016) and B) shows the microscopic view (40 ×)

of that bloom. Note the dominant alga Woronichinia spp. Picture C) shows a late season (10/2/2016)

HAB of mixed cyanobacteria with Microcystis spp. dominating. .............................................................. 59

Figure 27. Open water algal enumeration results (cells / ml) for fall 2015 and 2016 samples. .................. 59

Figure 28. Summary of the ecological responses and impacts associated with cyanobacterial blooms.

Taken from Havens (2008). ........................................................................................................................ 62

Figure 29. Bathymetric map of Lake Mohegan and the Citizens Statewide Lake Assessment Program

(CSLAP) monitoring locations 1 & 2. ........................................................................................................ 66

Figure 30. Zooplankton biomass (µg / L) for rotifers, copepods, and Cladocerans in Lake Mohegan from

October 2015 to November 2016 at site #1. ............................................................................................... 67

Figure 31. Average length (mm) for all zooplankton and Cladocera over the 2016 study period (n = 16) 68

Figure 32. Electrofishing transect map taken from the 2008/2009 (Allied Biological) study. Transects 1,

4, and 6 were sampled in the 2017 study. ................................................................................................... 72

Figure 33. Relative fish abundance in Lake Mohegan in 2017 for transects 1, 4, and 5. ........................... 74

Figure 34. Relative fish abundance in Lake Mohegan in 2009 for all transects. ........................................ 74

Figure 35. Relative fish abundance in Lake Mohegan in 2009 for transects 1, 4, and 5. ........................... 75

Figure 36. Length-frequency histogram for bluegill sunfish collected in Lake Mohegan during 2017.

Letters indicate threshold lengths used to assign Stock (S), Quality (Q), Preferred (P), and Memorable

(M) size ranges (Gabelhouse 1984). ........................................................................................................... 76

Figure 37. Length-frequency histogram for pumpkinseed sunfish collected in Lake Mohegan during 2017.

Letters indicate threshold lengths used to assign Stock (S), Quality (Q), Preferred (P), and Memorable

(M) size ranges (Gabelhouse 1984). ........................................................................................................... 76

Figure 38. Length-frequency histogram for largemouth bass collected in Lake Mohegan during 2017.

Letters indicate ranges for Stock (S), Quality (Q), Preferred (P), and Memorable (M) size ranges

(Gabelhouse 1984). ..................................................................................................................................... 77

Figure 39. Tic tac to plot of bluegill compared largemouth bass PSD showing management strategies that

target a “balanced fishery” (center square). ................................................................................................ 78

Figure 40. Land use and cover map (ESRI 2011; Jin et al. 2013). ............................................................. 84

Figure 41. Lake Mohegan soil type for septic tank absorption field ranking from poor absorption of

pollutants (red) to good absorption of pollutants (green; ESRI 2011; Jin et al. 2013). .............................. 87

Figure 42. Acoustic mapping survey conducted in Lake Mohegan June 30, 2016 in (Navico 2017). The

red to yellow areas on the maps indicate the highest density of plants with the blue areas corresponding to

lower densities. ........................................................................................................................................... 90

7

Abstract

Lake Mohegan is a 100-acre lake located in Westchester County within New York State. Like

many lakes throughout the northeast, Lake Mohegan faces threats to its ecological and

recreational balance. Invasive aquatic plant introductions and cultural eutrophication that

stimulate harmful algal blooms (HAB’s) threaten desired uses in both the short and long-term.

To address these issues, the Mohegan Lake Improvement District (MLID) has arranged with the

State University of New York (SUNY) Oneonta to develop a comprehensive lake management

plan. This plan is intended to provide guidance to the MLID in meeting desired uses for the lake

both short and long-term. For such a plan to be developed a lakes physical, chemical, and

biological characteristic along with its drainage basin need to be studied. After a thorough

limnological study is complete desired uses can be established and management strategies

developed with input from the MLID.

Chapter 1: 2016 Limnological Study

Introduction

Sedimentation in lakes is a major environmental issue that leads to reduced water volume

and habitat loss, among other management concerns. Sedimentation to lakes and ponds is

considered an illicit discharge and is regulated by US Environmental Protection Agency (EPA)

under the National Pollutant Discharge Elimination System (NPDES). There was limited

information regarding the amount of sediment in Lake Mohegan except for a survey that was

done by K-V Associates, Inc. (1982) indicating at least 10 ft. of muck; (which they assumed to

be more but their core could not go down further than 10ft). Personal communication with the

Mohegan Lake Improvement District (MLID) board has indicated the muck depth/volume is a

concern. Thus, a more detailed assessment of the lakes muck depth/volume was undertaken

during the 2016 study and is presented.

Lake stratification (thermal layering of water due to density differences) can affect many

chemical and biological processes in a lake. Probably the most notable effect that can occur

when stratification sets in are the loss of oxygen in the hypolimnion and the subsequent release

of reduced chemical constituents (e.g., hydrogen sulfide, ammonia, manganese, ferrous iron,

phosphate; Kalff 2003). Stratification can occur in both shallow and deep lakes and has been

defined as having a temperature difference from the surface waters and the bottom waters of > 2

- 3 °C (Cooke et al. 2005). Oxygen depletion may occur in the hypolimnion once stratification

sets in if oxygen demand is high and stratification persists. The areal hypolimnetic oxygen

8

demand (AHOD) has been used to quantify anoxia among lakes once stratification sets in.

Typically, values greater than 550 mg O2 / m2

/ day, which also correlates with a trophic status of

eutrophic, is when anoxia in the hypolimnion can occur (Nürnberg 1996). Both stratification and

oxygen loss in the hypolimnion have been documented in Lake Mohegan and has to lead the

MLID to aerate the lake artificially. Aeration was first added to the lake in the ealry 1990’s with

only a small part of the lake being aerated. Over time, more aeration was added covering a

greater surface area of the lake. An evaluation of the current aeration design was undertaken as

part of the 2016 study to determine if it was adequately oxygenating and circulating the lake and

to see if any modifications could be made to improve it, possibly lowering the operational cost

while maximizing benefits.

Knowing when, the extent to which and the underlying mechanisms of phosphorus (P)

cycling and retention from a lake’s sediment occurs, provides valuable management insight.

Internal cycling of P from a lake’s sediment can lead to increased algal biomass and poor water

clarity (Kalff 2003). Cycling and retention of P in Lake Mohegan were estimated using the 2016

total phosphorous (TP) data set and the predicted values for external P loading using the lake

loading response model (LLRM; Chapter 3).

In this chapter, supplemental physical and chemical data for Lake Mohegan are presented

as part of the 2016 limnological study. Much of the data presented are not part of the Citizens

Statewide Lake Assessment Program (CSLAP) but were collected as a component of this

dissertation. Lake monitoring data for water quality (dissolved oxygen, temperature, pH,

alkalinity, Secchi (Zsd), and conductivity), nutrients (surface and bottom total phosphorus (TP)

and total nitrogen (TN)), and morphological characteristics for Lake Mohegan are presented. The

supplemental monitoring effort provided in this chapter is aimed to complement other

assessments that have been done on the lake and to assist in the development of a comprehensive

management plan (Chapter 9).

Methods

Lake Monitoring

Water sampling was conducted at one open water (pelagic) station in the lake (maximum

station depth 4.5 m). This monitoring station (site 1; Figure 1) also coincides with the Citizens

Statewide Lake Assessment Program (CSLAP). Monthly (fall/winter) and bi-weekly

(spring/summer) sampling was done starting October 10, 2015 and ending November 17, 2016.

Winter months were not sampled due to unsafe ice conditions. Water samples for nutrient

analysis were collected from 0.5 m from the surface and 0.5 m from the bottom of the lake using

a Kemmerer water sampler. These samples were analyzed for TP and TN following methods

described in Table 1. Water quality data (temperature, dissolved oxygen, pH, and conductivity)

were collected in 0.5 m depth intervals (0-4.25 m) using a YSI® 650 MDS multiparameter sonde

9

calibrated to manufacturer’s specifications (YSI Inc. 2009). Secchi disc measurements were also

taken during each sampling outing.

Figure 1. Bathymetric map of Lake Mohegan and the Citizens Statewide Lake Assessment

Program (CSLAP) monitoring locations 1 & 2 (Navico 2017).

On the 10th

of August 2016, additional water samples were taken at 0, 1, 2, 3, and 4 m

water depths at site 1 to determine water alkalinity. Samples were preserved and analyzed

following APHA (1989; see Table 1).

The aeration system was operated from June to November 2016. A detailed air station

map was created to determine where mixing was strongest and to determine if there were visual

issues with the air stations. A qualitative ranking of air stations was done based on surface visual

surface mixing. Air staions with very little airflow were classified as “issues,” medium air flow

was classified as “ok,” and high airflow was classified as “good” (Figure 2). The relative thermal

resistance to mixing (RTRM) was used to characterize the stability of the water column at the

time of sampling. It was calculated according to the formula:

10

(𝜌2 − 𝜌1) ⋅ 106

8 ,

where ρ2 and ρ1 are the densities (g cm−3

) at the bottom and the top, respectively, of the stratum

being considered (Birge, 1910).

TP retention was calculated on a monthly basis as %-retention by taking the predicted in-

lake TP concertation for the total external P load using the lake loading response model (LLRM;

includes stormwater runoff, atmospheric, waterfowl, and septic loading) and the actual monthly

average in-lake TP concentration: P retention (%) = 100 * (predicted in-lake TP – actual TP) /

predicted in-lake TP.

Table 1. Summary of water chemistry methods used to estimate TP, TN, calcium, and alkalinity

in samples collected in Lake Mohegan.

Parameter Preservation Method Reference

TP H2SO4 to pH <

2

Persulfate digestion followed by

single reagent ascorbic acid

Laio and Marten

2001

TN H2SO4 to pH <

2

Cadmium reduction method

following peroxidisulfate digestion Ebina et al. 1983

Calcium Store at 4oC EDTA trimetric method EPA et al. 1983

Alkalinity Store at 4oC Titration to pH= 4.6 APHA 1989

11

Figure 2. Air station map for Lake Mohegan.

Physical limnology methods

Lake depths and bottom hardness data were collected using a hydroacoustic device

(Lowrance Gen 2 HDS-9) uploaded to BioBase for data processing and map creation. BioBase

(www.cibiobase.com ) is a cloud-based GIS software system that analyzes hydro acoustic and

GPS signals from Lowrance™ High Definition System (HDS®) consumer echo sounders

(www.lowrance.com; Navico 2018). Unconsolidated sediment depths were estimated at ten

locations (see Figure 12) throughout Lake Mohegan by driving a metered rod into the lake

sediment until it hit consolidated sediments (where the rod could not physically be pushed down

any further) and recording the total rod depth. Unconsolidated sediment depth was calculated by

subtracting the water depth (taken with a Speedtech ® Depthmate portable sounder) at each site

from the total rod depth.

12

Results

Lake Monitoring Results

Secchi values ranged from 0.65 to 3.1 m (average = 1.3 m), TP ranged from 13 to 125 µg

P / L (average = 60 µg P / L), and TN ranged from 470 to 1970 µg N / L (average = 1040 µg N /

L) during the study period (Figures 3, 4, & 5). TP values were highest and Zsd lowest during the

warmer months (Figures 3 & 4). P was also retained in cooler months and cycled in warmer

months (Figure 6). The sum of RTRM values exceeded 30 (weak stratification) six sampling

times with three of them being above 80 (strong stratification; Figure 7). Temperature, dissolved

oxygen, pH, and conductivity varied both temporally and spatially during the study (see isopleths

Figures 8 - 10). pH ranged from 9.04 to 7.27 and averaged 7.57. The greatest temperature

difference between surface and bottom waters was observed on 27 July 2016, with a surface

temperature of 30.91 °C and a bottom temperature of 26.91 °C. The lowest dissolved oxygen

value was 3.35 mg / L and which taken 1ft off the bottom on 25 May 2016. Average alkalinity

for 0, 1, 2, 3, 4 meters taken on the 10th

of August 2016 was 114 mg / L CaCO3.

Figure 3. Secchi values (in meters) taken at site 1 in Lake Mohegan.

0

1

2

3

4

10-O

ct-1

5

7-N

ov-1

5

18-M

ar-1

6

20-A

pr-

16

11-M

ay-1

6

25-M

ay-1

6

17-J

un-1

6

28-J

un-1

6

13-J

ul-

16

27-J

ul-

16

10-A

ug-1

6

23-A

ug-1

6

11-S

ep-1

6

2-O

ct-1

6

23-O

ct-1

6

17-N

ov-1

6

Zsd

(m

)

13

Figure 4. Total phosphorus (TP; µg N L -1

) data taken at the surface (dark grey bars) and bottom

(black bars) of Lake Mohegan at site 1.

Figure 5. Total nitrogen (TN; µg N L -1

) data taken at the surface (dark grey bars) and bottom

(black bars) of Lake Mohegan at site 1.

0

20

40

60

80

100

120

140T

P (

µg P

/ L

-1)

0

1

1

2

2

3

TN

(µ

g N

/ L

-1)

14

Figure 6. Mean monthly TP retention (%) in Lake Mohegan. Positive values indicate internal P

loading.

15

Figure 7. Sum of relative thermal resistance to mixing (RTRM) in Lake Mohegan for each

sampling outing. Grey line represents weak stratification (> 30) and black line represents strong

stratification (> 80).

Figure 8. Temperature isopleth (°C; R Core Team 2017). Black dots indicate sample dates.

0 20 40 60 80 100 120 140 160

10-Oct-15

7-Nov-15

18-Mar-16

20-Apr-16

12-May-16

25-May-16

17-Jun-16

28-Jun-16

13-Jul-16

27-Jul-16

9-Aug-16

23-Aug-16

11-Sep-16

2-Oct-16

23-Oct-16

17-Nov-16

Sum RTRM

16

Figure 9. Dissolved oxygen isopleth (mg / L). Black dots indicate sample dates.

Figure 10. pH isopleth. Black dots indicate sample dates.

Figure 11. Conductivity isopleth (µS / cm). Black dots indicate sample dates.

17

Lake Morphometry Results

A total of 10,564 acoustic data points were collected to generate the lake volume per

surface area summary (Table 2) and the bottom hardness map (Figure 12.). In general, softer

sediments were observed in deeper areas while harder sediments were observed closer to shore

(Figure 12). The 4 to 5 m lake depth represented the single largest surface area and water volume

for Lake Mohegan (Table 2).

Unconsolidated muck depths averaged 6.25 m (20.5 ft.), and the average water depth

where samples were taken was 4.13 m (13.5 ft.; Table 3). By taking the average muck depth and

multiplying it by the 4 to 5 m water depth surface area a total unconsolidated lake muck volume

estimate of 1,156,000 m3 (40,820,000 ft

3) is obtained. This muck volume equates to 79.7 % of

the lakes total volume.

Table 2. Hydroacoustic data compiled from CiBiobase (Navico 2017). Volume and area

associated with depth contours for Lake Mohegan

contour contour area volume Cumulative

volume

volume Cumulative

volume

(ft) (m) (m^2) (m^3) (m^3) (gal) (gal)

0 0 417,000 - - - -

5 1.5 352,000 586,128 586,128 154,737,805 154,737,805

10 3.0 292,000 490,854 1,076,982 129,585,366 284,323,171

15 4.6 58,000 266,768 1,343,750 70,426,829 354,750,000

17 5.2 - 44,207 1,387,957 11,670,732 366,420,732

18

Figure 12. Bottom hardness map and unconsolidated sediment depth sampling locations (M1 –

M10; Navico 2017). Lighter colors indicate softer sediments while darker colors indicate hard

sediments.

Table 3. Unconsolidated sediment depth results.

Location

ID

Longitude (W) Latitude (N) Water

Depth

(Meters)

Water

Depth

(Feet)

Muck

Depth

(Meters)

Muck

Depth

(Feet)

M1 -73.848961 41.314652 4.1 13.5 5.0 16.4

M2 -73.848085 41.315186 4.6 15.1 6.5 21.3

M3 -73.848638 41.316328 4.6 15.1 6.4 21.0

M4 -73.850527 41.317692 4.4 14.4 7.0 23.0

M5 -73.851032 41.319271 4 13.1 6.5 21.3

M6 -73.851752 41.31727 3.7 12.1 7.9 25.9

M7 -73.848169 41.313286 4.5 14.8 7.1 23.3

M8 -73.848502 41.311538 4.2 13.8 5.4 17.7

M9 -73.850341 41.310382 3.8 12.5 3.6 11.8

M10 -73.852677 41.309235 3.4 11.2 7.1 23.3

19

Discussion

The supplemental physical and chemical data in this chapter provide valuable

information to the MLID that would typically not be obtained by the current CSLAP monitoring

program. It has also provided a more detailed assessment of the bottom sediments in Lake

Mohegan.

A lake is is considered destratified when the temperature difference from the surface to

the bottom is < 2 – 3 °C (Cooke et al. 2005) or when the sum of RTRM values are < 30 (Wagner

2015). Results indicate the aeration system was inadequate in preventing thermal stratification

with a temperature difference reaching > 2 – 3 °C and RTRM values > 80. However, the system

was able to provide adequate oxygen and habitat throughout most of the water column. Oxygen

readings at the sediment water-interface indicated a small anoxic layer that may allow for redox-

mediated release of chemcial constituents (e.g., P cycling). Re-arranging the air stations to the

deeper parts of the lake would optimize system performance.

Total phosphorus increased, and water clarity decreased, with increasing water

temperature. This is typical for shallow eutrophic lakes with high external and internal loading.

Søndergaard et al. (2013) evaluated P cycling and retention in 6 shallow Danish lakes (mean

depth = 1.2–2.7 m) that received high external P loading and found lake sediments were able to

retain P during winter months (up to - 50 % of the external loading) but cyclyed in high amounts

during summer months (up to 300 % of the external loading). Based on the nutrient budget

created for Lake Mohegan, external P loading is being retained in the winter by up to – 72 % and

cycled by up to + 109 % in the summer. External P loading seems to determine the overall water

quality in Lake Mohegan but the sediments play a central role in the cycling of P. A more

detailed assessment of the Lake’s sediment chemistry in terms of what P is predominantly bound

to (e.g., organic-P, metal-P, saloid-P), the P binding capacity (e.g., iron to P ratio), and the depth

at which P is interacting with the water column could provide valuable insight into managing

internal P cycling in Lake Mohegan.

Lake Mohegan has a substantial amount of unconsolidated sediments on the lake bottom,

representing ~ 80 % of the total lake’s volume. The annual sedimentation rate is still unknown,

and the sediment measurements taken in this study do not account for sediments that may have

already been there when the lake was originally formed. A more detailed study of the

sedimentation rate in the lake is warranted. It would provide a direct measurement of the lakes

aging process and allow for better estimates into the utility of restoration tools like alum which

treatments can be short lived in lakes with high sedimentation rates (Garrison and Knauer 1984).

20

SUMMARY POINTS

External P loading to Lake Mohegan is being retained by lake sediments in the winter by

up to - 72 % and cycled by up to 109 % in the summer

Unconsolidated sediment depths were estimated to average 6.25 m (20.5 ft.), for a total

volume of 1,156,000 m3 (40,820,000 ft

3).

The aeration system was inadequate in preventing thermal stratification but was able to

provide adequate oxygen and habitat throughout most of the water column.

References

APHA. 1989. Standard methods for the examination of water and wastewater, 17th ed. American

Public Health Association.

Cooke, G. D., E. B. Welch, S. Peterson, and S. A. Nichols. 2005. Restoration and management

of lakes and reservoirs, CRC press, Boca Raton, Florida.

Ebina, J., T. Tsutsui, and T. Shirai. 1983. Simultaneous determination of total nitrogen and total

phosphorus in water using peroxodisulfate oxidation. Water Res. 17: 1721–1726.

EPA, S. Laboratory (Cincinnati, Ohio). 1983. Technical additions to methods for chemical

analysis of water and wastes, US Environmental Protection Agency, Environmental

Monitoring and Support Laboratory.

Garrison, P. J., and D. R. Knauer. 1984. Long-term evaluation of three alum treated lakes. Lake

Reserv. Manag. 1: 513–517.

Kalff, J. 2003. Limnology: Inland Water Ecosystems, Prentice Hall.

K-V Associates, Inc. 1982. Water Quality Assessment of Lake Mohegan Basin.

Laio, N., and S. Marten. 2001. Determination of total phosphorus by flow injection analysis

colorimetry (acid persulfate digestion method). QuikChem® Method 10–115.

Navico, I. 2017. BioBase User Reference Guide. Minneapolis, MN, USA.

21

Nürnberg, G. K. 1996. Trophic state of clear and colored, soft-and hardwater lakes with special

consideration of nutrients, anoxia, phytoplankton and fish. Lake Reserv. Manag. 12: 432–

447.

R Core Team. 2017. R: A Language and Environment for Statistical Computing, R Foundation

for Statistical Computing.

Søndergaard, M., R. Bjerring, and E. Jeppesen. 2013. Persistent internal phosphorus loading

during summer in shallow eutrophic lakes. Hydrobiologia 710: 95–107.

Wagner, K. J. 2015. Oxygenation and circulation to aid water supply reservoir management,

Water Research Foundation.

Chapter 2: Using Citizen Science Monitoring and Climate Data to Make

Water Quality Predictions in Lake Mohegan

Introduction

The ability to predict lake water-quality is an important area of research for water

resource managers. In particular, phosphorus and nitrogen concentrations have been used to

predict algal biomass (Canfieldand Bachman 1981; Smith 1982; Canfield et al. 1985), taxonomic

composition (Watson et al. 1997), water clarity (Canfield and Bachman 1981), and more recently

cyanotoxins (Dolman et al. 2012). Other predictive models have used relationships between

nutrient loading, flushing rates and lake morphology to predict trends in algal abundance and

composition (Vollenweider 1968; Dillon 1975; Soballe and Kimmel 1987). Many of these

predictive models have been developed from global and regional datasets, allowing inferences to

be made at the individual lake level. However, it is important to validate predictive models with

site-specific data, if available.

The purpose of this chapter is to use existing water-quality and climate data to: 1) assess

temporal lake trends under varying weather patterns (precipitation and temperature) and

management strategies (i.e., aeration, copper, and aluminum sulfate treatments) and 2) develop

site-specific predictive models for lake water clarity and algal biomass by using Secchi depth

(Zsd) and chlorophyll a (chl. a) as surrogates. Such an assessment should be of value to the

Mohegan Lake Improvement District (MLID) in conducting a cost-benefit analysis associated

with nutrient reduction programs to reduce algal densities and improve water clarity.

22

Methods

Existing water-quality data from Lake Mohegan, NY were gathered for evaluation of

temporal trends and predictive modeling. They were obtained from the following sources: an

August 1937 Zsd measurement form the New York State Department of Environmental

Conservation (NYSDEC); a July 1987 Zsd and TP measurement from the Adirondack Lake

Survey Corporation (ALSC); an August 1993 Zsd and TP measurement from Kortmann (1993),

and from the Citizens Statewide Lake Assessment Program (CSLAP), a volunteer lake

monitoring program run by the NYSDEC and the New York State Federation of Lake

Associations, Inc. (NYSFOLA). The MLID participated in CSLAP from 1998 to 2016, except

for 2014 at the time of writing. Sampling has been conducted through CSLAP primarily at two

pelagic sites (see Chapter 1 Figure 1). Surface water samples for TP, chl. a, true color,

conductivity, total nitrogen (TN), pH, water temperature, TN:TP ratio, and Zsd, were collected

bi-weekly from June to September. CSLAP data were organized and evaluated for possible

outliers prior to data analysis. For example, a TP value of 17 µg P / L was entered in on August

8th, 2016. However, the remaining other values for July and August were 128 and 130 µg P / L,

thus the value of 17 µg P / L was discarded. The trophic state index (TSI) was used as a

surrogate for potential recreational use in Lake Mohegan. Values for TSI were calculated

individually for TP, Zsd, and chl. a for all available data according to Carlson (1977):

TSI (Zsd) = 60 −14.41 × ln (Zsd)

TSI (chl. a) = 9.81 × ln (chl. a) + 30.6

TSI (TP) = 14.42 × ln (TP) + 4.15

To evaluate the effects of alum in 2002 and copper treatments in 2015 and 2016 on Zsd, chl. a,

and TP, each parameter was standardized by calculating the z-score for each year:

(𝑥𝑖 − �̅�)

𝜎(𝑥)

Then, to determine the significance of treatment years against non-treatments years a z-score

critical value of ± 1.67 was calculated using an α of 0.05 and compared.

Simple linear regression analysis was performed in R (R Core Team 2017) to evaluate the

significance of trends in dependent variables (TP, TN, Zsd, chl. a, color, pH, TSI, temperature,

and conductivity) over time (year as independent variable). Due to inconsistent temporal

sampling and to reduce seasonal variability, only averaged data from July and August were used.

Raw observations (i.e., not averaged) were used for constructing linear predictive models where

TP was used to predict chl. a and Zsd.

Air temperature and precipitation data were obtained from a land-based National Oceanic

Atmospheric Administration (NOAA) station (Network ID: GHCND:US1NYWC0007) located

23

in the Town of Yorktown (approximately 6.4 km (4 miles) from Lake Mohegan) (Lawrimore

2016). They were first plotted chronologically to evaluate temporal trends and then used to

calculate varying flushing rates and water retention times (WRT) by inputting the average,

minimum, and maximum precipitation data into the lake loading response model (LLRM)

developed by Wagner (2017a). Linear regression models were then made in R (R Core Team

2017) to evaluate effects of precipitation, lake flushing, WRT, water and air temperature on chl.

a., TP, and Zsd.

Results

Climate data varied between years (Figure13.). Average annual temperatures from 1966

to 2016 showed a significant increase over time (linear regression, df = 43, R2 = 0.31, p < 0.001),

whereas average annual precipitation did not change significantly over time. Annual

precipitation varied proportionally with lake flushing and with water retention time (WRT; Table

4).

24

Figure 13. Average annual temperature (°C; top) and precipitation (meters/yr.; bottom) from

1966 to 2016. The dashed line represents the five-year moving average.

Table 4. Average, maximum and minimum annual precipitation (PRCP), flushes in years (y) and

days (d), and WRT (y) for Lake Mohegan. Precipitation values were inputted into the Lake

Loading Response Model (Wagner 2017a) to predict lake flushing and water retention times.

PRCP (m) Flushing (y) Flushing (d) WRT

Average 1.30 1.30 281 0.77

Maximum 2.04 2.11 175 0.48

Minimum 0.91 0.94 386 1.06

0

2

4

6

8

10

12

14

Av

era

ge

An

nu

al T

emp

. (⁰

C)

0

0.5

1

1.5

2

2.5

1965 1975 1985 1995 2005 2015

Per

cip

itati

on

(m

eter

s/yr.

)

25

No significant increase or decrease was observed for color, pH, and TN:TP ratio over time

(1936 to 2016). However, TP, TN, chl. a, conductivity, and TSI, all showed significant increases

over time (p < 0.05), while Zsd showed a significant decrease (p < 0.05). An alum treatment in

2002 significantly reduced chl. a (z-score = -2.17, z-critical = -1.64) and increased Zsd (z-score =

1.98, z-critical = 1.64) for 2002 only. The lowest observed TP (29 µg P L-1

) coincided with the

2002 alum treatment, but it did not fall within the z-critical range of ± 1.64 (z-score = -1.58) to

be statistically significant. Copper sulfate treatments in 2015 and 2016 had no significant effect

on TP, chl. a, and Zsd on any treatment years (Figures 14 - 17). Descriptive statistics for all

CSLAP data collected are provided in Table 5 and calculated TSI values are provided in Table 6.

Table 5. August average, median, maximum, and minimum of water-quality parameters in

Mohegan Lake for all available monitoring years. Parameters include TP; TN; weight molar

TN:TP ratio; true color units: TCU, reported as Platinum-Cobalt Units (Pt-Co); pH; conductivity:

cond.; chl. a; and surface water temperature: temp.

Zsd

(m)

TP (µg

P L-1

)

TN (µg

N L-1

)

TN:TP

(mass)

TN:TP

(molar)

TCU

(Pt-Co)

pH cond. (µS

cm -1

)

chl. a

(µg L-1

)

temp.

(° C)

n=76 n=70 n=48 n=47 n=47 n=59 n=65 n=54 n=66 n=57

Average 0.8 94.3 1102.3 12.3 27.2 18.6 7.5 603.9 54.4 26.4

Median 0.8 90.6 1064.5 10.6 23.5 18.0 7.8 580.2 50.5 26.4

Maximum 1.9 194.7 2010.0 45.2 99.9 41.0 9.5 1038.0 115.8 29.0

Minimum 0.4 17.3 256.0 5.4 12.0 2.0 5.9 261.8 6.1 22.0

Table 6. August average, median, maximum and minimum trophic state index (TSI) values for all

available monitoring years. Parameters include total phosphorus: TP; chlorophyll a: chl. a and

Zsd. TSI was calculated from (Carlson 1977).

TSI (TP) TSI (chl. a) TSI (Zsd)

Average 68 68 63

Median 69 69 66

Maximum 80 77 72

Minimum 45 48 51

26

Figure 14. Boxplot of Zsd over time (1936 to 2016), the bold, black line is the median Zsd for

each year, the box ends represent the 25th

and 75th

percentiles, the whiskers indicate 1st and 99

th

percentiles, and open circles indicate outliers. Black point in 2002 represents an alum treatment.

Figure 15. Boxplot of TP over time (1987 to 2016). Symbols are defined as in Figure 14.

Figure 16. Boxplot of chl. a over time (1998 to 2016). Symbols are defined as in Figure 14.

27

Figure 17. Boxplot of TN:TP ratio (by weight) over time (2002 to 2016). Symbols are defined as

in Figure 14.

TP was a significant predictor of both Zsd (linear regression, n = 70, df = 58, R2 = 0.32, p

< 0.001) and chl. a (linear regression, n = 66, df = 67, R2 = 0.41, p < 0.001). TP had a positive

correlation on chl. a, while Zsd was inversely related (Figure 18). Lake flushing rates, WRT,

water temperature and air temperature had no effect on chl. a, TP and Zsd, with all having p-

values > 0.05.

28

Figure 18. TP predictive models for chl. a (A) and Zsd (B). The blue line represents mean

prediction and red dashed lines are 95% prediction intervals (PI). Grey points represent all

summer (July and August) observations of Zsd and TP (1987-2016).

Discussion

Citizen science monitoring and publicly operated climate programs have provided

valuable data for interpreting water quality trends in Lake Mohegan. These programs have

provided a means to assess lake management strategies and climate effects on water quality and

have allowed MLID to begin the process of establishing lake specific water quality goals for

which management can be based on in Lake Mohegan. These programs have also been

conducted at a low financial cost to MLID allowing for more resources to be allocated to

restoration (i.e., watershed best management practices (BMP’s) or alum treatments).

29

In general, cyanobacterial blooms tend to dominate in lakes with excess P (Watson et al.

1997; Downing et al. 2001) and when N is limiting (Smith 1982). Thus, low TN:TP ratios favor

cyanobacteria while high TN:TP ratios favor chlorophytes and bacillariophytes. Canfield et al.

(1989) suggested that nitrogen became the limiting nutrient when TP concentrations exceeded

100 µg P L -1

in 223 Florida lakes. Lake Mohegan appears to be co-limited by N and P at times,

with TN:TP mass ratios ranging from 45.2 : 1 to 5.4 : 1 (by weight; by molar ratio range: 99.9 : 1

to 12.0 : 1). On average Lake Mohegan is in the range where both N and P can be limiting,

according to the NYDEC general criteria < 10 (by weight) suggest N limitation, between 10 and

17 (by weight) can be either P or N limited, and > 17 (by weight) suggest P limited; NYDEC

2017). However, the lake phytoplankton is mostly dominated by cyanobacteria for much of the

growing season (see Chapter 6), which may suggest greater N limitation rather than P during

warmer months, being that cyanobacteria tend to dominate in lakes with excess P (Watson et al.

1997; Downing et al. 2001). From a management perspective, reducing the supply of P should

reduce not only total algal biomass but also the prevalence of cyanobacteria.

The linear regression models developed in this study suggest TP needs to be reduced to <

30 µg P/ L for any improvement in water clarity or reduction in chl. a for July and August. That

means the average July and August TP concentration within the lake would need to be reduced

by approximately 86 µg P / L, from the 2016 average levels. According to Carlson (1977), TP

must be reduced to below 24 µg P / L, Zsd increased to > 2 m, and chl. a reduced below 8 µg / L

for Lake Mohegan to become classified as mesotrophic. This also equates to a TSI value of < 50

for TSITP, TSIZsd, and TSIchl. a (Carlson 1977). Beach closures due to high cyanotoxins (see

Chapter 6) will be less likely in a mesotrophic lake, which can make Lake Mohegan more

suitable for swimming. While the average July and August TSI values for Lake Mohegan were

well above the mesotrophic classification (based on Carlson 1977), values < 50 have been

observed for TP and chl. a during the 2002 alum treatment year. Zsd values during the 2002 alum

treatment year were close to the mesotrophic classification of < 50 (average TSIZsd in 2002 = 54

for July and August), but were not obtained (based off Carlson 1977). Other factors such as

dissolved inorganic substances (i.e., humic and fulvic acids) and the lake's mixing depth may

explain TSIZsd not aligning with TSIchl. a and TSITP values. Log-linear relationships developed by

Nürnberg (1996) for predicting Zsd based on TP were significantly improved when color was

added as the second variable. Also, shallow lakes (< average depth (𝑧̅) = 5 m) in general, tend to

have higher TSI values over deeper lakes and has been attributed to shallow lakes having more

frequent mixing events where sediments are brought upwards to the surface (Nürnberg 1996).

Overall, inputting 24 µg P / L P into the linear regression model developed in this study does not

provide a mesotrophic value for Zsd (model value = 1.4 m) and chl. a (model value = 16 µg / L

based on Carlson 1977). For Lake Mohegan to meet the mesotrophic classification for all

parameters according to Carlson (1977), TP needs to be ≤ 10 µg P / L

30

Lake water TP concentrations in this study positively correlated with chl. a

concentrations, suggesting that increased P loading would further increase chl. a. Based on the

current P levels, N should also be a management concern since increases in bio-available N can

also cause increases in chl. a if there is already sufficient bio-available P. Donald et al. (2011)

observed a an increase in chl. a up to 350% when bio-available N was added to a lake with

soluble reactive phosphorus (SRP) > 50 µg / L. Since N is required for many of the common

cyanotoxins (i.e., microcystin; Lee 2008), increased N loading while maintaining sufficient P ( >

50 µg P / L) concentrations may also cause increases in cyanotoxin production (Donald et al.

2011; Gobler et al. 2016). Based on this study, the increased P loading to Lake Mohegan in the

future will likely result in a greater frequency of cyanobacterial blooms. Watson et al. (1997)

observed a positive relationship between TP and cyanobacterial biomass in 91 temperate lakes

and found that increasing TP from 60 to 100 µg P / L exhibited the greatest increase in

cyanobacteria biomass (~100-fold increase). TP levels between 10 and 30 µg P / L coincided

with increased diversity among algal groups (Watson et al. 1997). Thus, to reduce cyanobacteria

biomass in Lake Mohegan TP will likely need to be reduced to < 60 µg P L -1

and to increase

overall phytoplankton diversity, TP will likely need to be reduced to < 30 µg P L -1

TP.

Reducing TP by < 30 µg P / L will likely increase water clarity, possibly causing a shift

from the current phytoplankton-dominant turbid state to a macrophyte-dominanted, clear state

(Scheffer et al. 1993), possibly altering management concerns. Shallow lakes tend to be more

susceptible to this shift. Lake Mohegan is both shallow (𝑧̅ = 3.7m) and has fast-growing aquatic

invasive species such as Eurasian watermilfoil (Myriophyllum spicatum) and curly leaf pond

weed (Potamogeton crispus). Moss (1998) suggested that lakes with < 50 µg P / L TP dominated

by macrophytes and lakes with > 50 µg P / L TP could be in either state. Similarly, Zimmer et al.

(2009) reported that lakes exceeding 62 µg P / LTP were dominated by macrophytes in 72

shallow Minnesota lakes. The current state of Lake Mohegan is characterized by frequent

cyanobacterial blooms, high concentrations of cyanotoxins (see Chapter 6), and constant beach

closures. Macrophyte production might be preferable to algal production with respect to

recreation.

The proliferation of macrophytes will also depend on substrate suitability. Observations

made while conducting a sediment depth survey indicated a thick layer of flocculent sediment

(mean depth of sediment = 6.3 m or 20.7 ft.), which appeared to be rich in organics, with a

distinct black glossy color (see Chapter 1). This type of sediment may not be suitable for certain

macrophytes; for example, Eurasian watermilfoil grows in medium density (0.8-1.0 g / mL on a

dry weight basis) inorganic sediments and does not grow as well in sediments that are low

density with high (> 20%) organic content nor in high-density sediments (i.e., sand or gravel;

Barko and Smart 1983). A more detailed assessment of substrate suitability may reduce

uncertainty in predicting macrophyte spread.

31

Based on the climate data obtained, Lake Mohegan is in a geographical area where

annual average temperatures are increasing, and annual precipitation is variable. While I failed to

document correlations between increased air or water temperature and chl. a in Lake Mohegan,

there will likely be a greater occurrence of cyanobacteria with increasing temperatures (Wagner

and Adrian 2009; Kosten et al. 2012). Cyanobacteria tend to dominate over other phytoplankton

at temperatures above 20° C (Lee 2008). Kosten et al. (2012) found that increased temperatures

did not affect overall algal biomass in 143 lakes along a latitudinal transect; however, there was a

positive correlation between increased temperature and cyanobacterial abundance. The

synergistic effect of increased nutrient loading and temperature within Lake Mohegan has the

potential to increase the frequency, duration, and intensity of cyanobacteria blooms.

Lake Mohegan has a relatively large catchment per unit volume, which is associated with

rapid lake flushing and higher nutrient and ion inputs (Kalff 2003), which can lead to elevated

phytoplankton production and planktonic respiration (del Giorgio and Peters 1994). Algal

biomass can be affected when the flushing rate exceeds the growth rate of at least some

planktonic species (Kalff 2003). However, annual and summer flushing rates had no effect on

July and August chl. a within Lake Mohegan during this study. Soballe and Kimmel (1987)

reported a flushing rate threshold for temperate lakes of < 100 days before any effects on algal

biomass were observed. Even during years of rapid flushing (175 days), Lake Mohegan is still

well above that threshold. Taken together, these results suggest that more nutrients are being

retained within the lake basin than is ideal for algal reduction, promoting algal growth rather than

being flushed.

The effectiveness of P inactivation treatments with alum is also affected by flushing rates,

where treatments typically last 3 to 5 lake flushes (Harper 2017). Wagner (2017b) reported an

average duration of benefits of 6 years for unstratified lakes, where sediment P inactivation was

targeted, and 1-3 years for water column P inactivation. The 2002 alum treatment conducted on

Lake Mohegan improved water clarity and reduced chl. a for one year or 1.3 lake flushes,

according to the CSLAP data provided. While the alum treatment may have been under-dosed

for sediment deactivation in 2002, it is unlikely a treatment would have lasted longer than 1-3

years, with more than 60% of the nutrient load in Lake Mohegan coming from external sources

(see Chapter 3). Low-dose alum treatments that target water column TP before the onset of the

growing season may offer a cost-effective way to moderate algal abundance, nuisance

cyanobacteria, and water clarity. This option may be preferred over other management

techniques such as the application of copper compounds, which has little effect on algal

composition and water clarity.

Conductivity in Lake Mohegan was higher than 93% of all New York lakes participating

in CSLAP during 2016 and is approaching the Florida threshold for brackish water lakes (>1275

32

µS cm -1

, FDEP 2010). Increased conductivity in lakes has been directly related to reduced

phytoplankton diversity (Hammer et al. 1983). Changes in species composition with increased

conductivity have also been observed in zooplankton (Soto and Rios 2006) and benthic

macroinvertebrate communities (Rossaro et al. 2007). Interestingly, Soto and Rios (2006)

reported an important transition value of 1000 µS cm -1

. Lakes with values above this threshold

had reduced zooplankton diversity and significantly lower numbers of Cladocerans.

Demonstrating the direct, negative ecological effects of conductivity in Lake Mohegan is

difficult because there are many confounding factors such as increased nutrient loading and

decreased water clarity. However, ecological stress, defined by (Rykiel 1985) as “the

physiological response of an individual, or the functional response of a system caused by

disturbance or another ecological process; relative to a specified reference condition;

characterized by direction, magnitude, and persistence, is cumulative.” The possible additive

stress of high conductivity, along with elevated nutrient concentrations and low water clarity

together should be of management concern to MLID. Conductivity in Lake Mohegan will

continue to increase annually with storm water loading unless remedial actions are taken to

reduce sediment and nutrient loading from the watershed, such as limiting the amount of salt

applied to de-ice roads (see Chapter 3 for salt loading and conductivity). High conductivity

levels within lakes will also reduce efficacy of alum and copper treatments (Cooke et al. 2005).

MLID participation in CSLAP has produced valuable data for assessing changes in

water-quality and prior management strategies and has provided the necessary data to better

predict July and August chl. a and Zsd thresholds, for which management goals can be

established. Continued participation in CSLAP will help refine predictive models for chl. a and

Zsd, and allow for continued evaluation of management strategies, possibly reducing consulting

costs.

SUMMARY POINTS

TP, TN, chl. a, conductivity, air and water temperature have increased, and Zsd depth has

decreased over the study period.

Total phosphorus < 30 µg / L is where improvement in water clarity and algal biomass

will begin.

The 2002 alum treatment provided seasonal control of algal biomass and improved water

clarity.

Copper treatments had no significant effect on chl. a, TP, or Zsd

33

References

Barko, J., and R. Smart. 1983. Effects of organic matter additions to sediment on the growth of

aquatic plants. J. Ecol. 161–175.

Canfield., D. E., and R. W. Bachmann. 1981. Prediction of total phosphorus concentrations,

chlorophyll a, and Secchi depths in natural and artificial lakes. Can. J. Fish. Aquat. Sci.

38: 414–423.

Canfield., D. E., S. B. Linda, and L. M. Hodgson. 1985. Chlorophyll‐ biomass‐ nutrient

Relationships for Natural Assemblages of Florida Phytoplankton. JAWRA J. Am. Water

Resour. Assoc. 21: 381–391.

Canfield, D. E., E. Phlips, and C. M. Duarte. 1989. Factors influencing the abundance of blue-

green algae in Florida lakes. Can. J. Fish. Aquat. Sci. 46: 1232–1237.

Carlson, R. E. 1977. A trophic state index for lakes1. Limnol. Oceanogr. 22: 361–369.

Cooke, G. D., E. B. Welch, S. Peterson, and S. A. Nichols. 2005. Restoration and management

of lakes and reservoirs, CRC press.

Dillon, P. 1975. The phosphorus budget of Cameron Lake, Ontario: The importance of flushing

rate to the degree of eutrophy of lakes. Limnol. Oceanogr. 20: 28–39.

Dolman, A. M., J. Rücker, F. R. Pick, J. Fastner, T. Rohrlack, U. Mischke, and C. Wiedner.

2012. Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus. PloS

One 7: e38757.

Donald, D. B., M. J. Bogard, K. Finlay, and P. R. Leavitt. 2011. Comparative effects of urea,

ammonium, and nitrate on phytoplankton abundance, community composition, and

toxicity in hypereutrophic freshwaters. Limnol. Oceanogr. 56: 2161–2175.

Harper, H. 2017. Compilation, Analysis and Interpretation of Environmental Data. Florida Lake

Management Society.

FDEP 2010. Surface Water Quality Standards.

del Giorgio, P. A., and R. H. Peters. 1994. Patterns in planktonic P: R ratios in lakes: influence of

lake trophy and dissolved organic carbon. Limnol. Oceanogr. 39: 772–787.

Gobler, C. J., J. M. Burkholder, T. W. Davis, M. J. Harke, T. Johengen, C. A. Stow, and D. B.

Van de Waal. 2016. The dual role of nitrogen supply in controlling the growth and

toxicity of cyanobacterial blooms. Harmful Algae 54: 87–97.

34

Hammer, U. T., J. Shamess, and R. C. Haynes. 1983. The distribution and abundance of algae in

saline lakes of Saskatchewan, Canada. Hydrobiologia 105: 1–26.

Kalff, J. 2003. Limnology: Inland Water Ecosystems, Prentice Hall.

Kosten, S., V. L. Huszar, E. Bécares, and others. 2012. Warmer climates boost cyanobacterial

dominance in shallow lakes. Glob. Change Biol. 18: 118–126.

Lawrimore, J. 2016. Global Summary of the Month, Version 1.0. [indicate subset used]. NOAA

National Centers for Environmental Information. DOI:10.7289/V5QV3JJ5.

Lee, R. E. 2008. Phycology, 4th ed. Cambridge University Press.

Moss, B. 1998. Shallow lakes, biomanipulation and eutrophication, Belgium.

Nürnberg, G. K. 1996. Trophic state of clear and colored, soft-and hardwater lakes with special

consideration of nutrients, anoxia, phytoplankton and fish. Lake Reserv. Manag. 12: 432–

447.

R Core Team. 2017. R: A Language and Environment for Statistical Computing, R Foundation

for Statistical Computing.

Kortmann, R.W. 1993. Lake Mohegan 1993 Diagnostic Report.

Rossaro, B., L. Marziali, A. C. Cardoso, A. Solimini, G. Free, and R. Giacchini. 2007. A biotic

index using benthic macroinvertebrates for Italian lakes. Ecol. Indic. 7: 412–429.

Rykiel, E. J. 1985. Towards a definition of ecological disturbance. Austral Ecol. 10: 361–365.

Smith, V. H. 1982. The nitrogen and phosphorus dependence of algal biomass in lakes: an

empirical and theoretical analysis. Limnol. Oceanogr. 27: 1101–1111.

Soballe, D., and B. Kimmel. 1987. A large‐ scale comparison of factors influencing

phytoplankton abundance in rivers, lakes, and impoundments. Ecology 68: 1943–1954.

Soto, D., and P. Rios. 2006. Influence of trophic status and conductivity on zooplankton

composition in lakes and ponds of Torres del Paine National Park (Chile). Biologia

(Bratisl.) 61: 541–546. Vollenweider, R. A. 1968. Scientific fundamentals of the eutrophication of lakes and flowing

waters, with particular reference to nitrogen and phosphorus as factors in eutrophication

OECD, Tech Report DA.

35

Wagner, C., and R. Adrian. 2009. Cyanobacteria dominance: quantifying the effects of climate

change. Limnol. Oceanogr. 54: 2460–2468.

Wagner, K. J. 2017a. How to use LLRM, Wagner affiliation.

Wagner, K. J. 2017b. Preface: Advances in phosphorus inactivation, Taylor & Francis.

Watson, S. B., E. McCauley, and J. A. Downing. 1997. Patterns in phytoplankton taxonomic

composition across temperate lakes of differing nutrient status. Limnol. Oceanogr. 42:

487–495.

Zimmer, K. D., M. A. Hanson, B. R. Herwig, and M. L. Konsti. 2009. Thresholds and stability of

alternative regimes in shallow prairie–parkland lakes of central North America.

Ecosystems 12: 843–852.

Chapter 3: Nutrient Budget

Introduction

A nutrient budget can be a valuable tool for managing a lake’s trophic status and for

setting critical and permissible nutrient loading rates. Nutrient budgets can provide a means to

identify and rank nutrient sources that contribute to a lake’s trophic status. From this, a cost-

benefit analyses can be conducted to rank management strategies designed to meet permissible

nutrient loading rates. In this chapter, a nutrient budget for P and N was constructed for Lake

Mohegan using the Lake Loading Response Model (LLRM) developed by Dr. Kenneth J.

Wagner of Water Resources Services (Wagner 2017). By inputting empirical and literature

derived values for external (i.e., watershed, atmospheric, waterfowl, groundwater, etc.) and

internal loading, estimates for the total amount of phosphorus (P) and Nitrogen (N) loading to

Lake Mohegan can be estimated. Once the total nutrient load has been calculated and verified,

the LLRM can then make water-quality predictions for total phosphorus (TP), total nitrogen

(TN), chlorophyll a (chl. a), and Secchi (Zsd), from which management goals can be established.

The LRRM also provides scenario testing for internal and external management programs and

can provide estimates regarding a lakes reference or background condition (before anthropogenic

influences).

36

Methods

Loading inputs for the LLRM are broken down into external (wet and dry surface run-off,

point source run-off, ground-water, waterfowl, and atmospheric) and internal loading. Table 7

outlines the sources and inputs for setting up the LLRM.

Table 7. Types and sources of data used for LLRM set up.

Feature Purpose of Model Sources for this Study

Lake bathymetry and

volume

Determination of volume at any depth

or water level

CiBiobase bathymetric mapping.

Watershed and sub-

watershed delineation

Define areas to which loading functions

and water quality comparisons will be

applied in the model

USGS topographic maps, GIS maps,

and the national land cover data from

Homer et al. (2015)

Subwatershed land uses and

corresponding areas

Determines range of possible loading to

be used in the model

GIS maps, and the national land cover

data from Homer et al. (2015)

Precipitation

Used to calculate flows from land use

and precipitation data

NOAA station (Network ID: GHCND:

US1NYWC0007); long-term mean of

1.27 m (50 inches/yr.)

Flow Data

Used as a check on calculations from

other data

Past study, K-V Associates, Inc.

(1982) and 2016 base flow and

stormwater monitoring data.

Areal Water Yield

Used with watershed area as a check on

flow values derived from the land use

and precipitation

Literature values for the region; used

the median value of 1.6 CFSM

(Sopper and Lull 1970; Higgins and

Colonell 1971).

Point source P and N

monitoring data

Provides load from regulated sources No permitted point source discharges

to lake or tributaries

On-site wastewater

treatment (septic) system

locations within direct

drainage to the lake

Allows estimation of septic inputs by

calculation using data for distance from

lake, population served, and frequency

of use

Past study, K-V Associates, Inc.

(1982).

Wildlife P and N inputs

Allows estimation of inputs from

wildlife, mainly waterfowl

Estimates of waterfowl population

very limited; assumed 20 bird per year

for the model.

37

Atmospheric P and N

loading

Provides estimate of loading from the

atmosphere

Literature values for the region; used

median values for urban areas

(Reckhow et al. 1980, Dillon et al.

1991).

Internal P and N loading

Provides estimate of loading from lake

sediments

Internal loading estimates very

limited; calculated from the difference

between external and actual P and N

levels.

Stream P and N

concentrations

Used to check model results 2016 base flow and stormwater

monitoring data used.

In-lake water quality (P, N,

chl. a, Zsd)

Used to check model results Ongoing monitoring provides direct

measurement

Surface run-off (tributary inflow and direct runoff) inputs were calculated using empirical

data gathered throughout this study and with the use of the LLRM (Wagner 2017), where

nutrient loading and water attenuation values were entered based on the land use and cover map

(Figure 19; Homer et al. 2015). Five of the major inflows and the major outflow were monitored

directly during wet periods (Figure 20). Wet weather monitoring included automated, gravity-fed

storm-water samplers at five of the major inflows, which captured the first flushing of 6 rain

events during the summer of 2016, although only 4 of the 6 rain events had enough water flow to

trigger all samplers. Each sampler was built specifically for each inflow (stream vs. pipe inflow)

and positioned to a specified water level to capture the first flush and then capped allowing no

further water to be retained. Samplers were constructed from 0.95 L high-density

bottles (bought from Quality Containers of New England), re-bar, plastic tubing/funnels, and

metal clamps (Figure 21).

38

Figure 19. Land use and cover map.

39

Figure 20. Stormwater sampling sites (#1-#6) with site #4 being the outflow (indicted by red

pointer). Sub-watershed basins were delineated using USGS topography.

40

Figure 21. Automated storm water sampler used at inflow 3.

An additional wet weather sampling was conducted prior to and during a storm event to

obtain water volume and loading estimates for total phosphorous (TP), total nitrogen (TN), total

suspended solids (TSS), and chloride. Conductivity was also measured. Sites were sampled one

time before a rain event on 2 February 2016 at 22:50 and 4 times during the rain event on 3

February 2016 at 0907, 11:30, 13:30, and 16:20. It’s important to note that 10 days prior to the 3

February 2016 rain event the town of Yorktown received 11 inches of snow according to the

land-based National Oceanic Atmospheric Administration (NOAA) station (Network ID:

GHCND:US1NYWC0007; Lawrimore 2016). Thus, the water volumes measured include

melting snow and direct rain fall. A total of 1.4 cm (0.56 inches) of rain fell during the February

3rd

rain event (Lawrimore 2016). Water volume estimates were determined using a portable flow

meter (Marsh McBirney model 201D) and by measuring the surface area of each inflow. Water

grab samples and water volume measurements were taken during each of the five sampling

events. The water volume measurements obtained during the storm event were used to calculate

the wet weather loading rates for each site. Dry weather nutrient loading was estimated using the

LLRM (Wagner 2017) and was based on averages from the literature (Uttormark et al. 1974;

Mitchell and Asbury 1988; Miller et al. 1997).

Point source data are normally acquired from discharge monitoring reports filed under

National Pollutant Discharge Elimination System (NPDES) regulations. There are no permitted

point sources in the Mohegan Lake watershed. Inputs for ground-water were taken from an

empirically based study done by K-V Associates, Inc. (1982). Waterfowl loading was estimated

41

with the use of the LLRM (Wagner 2017). A model input value of 20 birds per year was used.

Since many of the birds observed were Canada geese (Branta canadensis) a higher coefficient

for P (1.86 kg / bird / yr.) and N (5.80 kg / bird / yr.) was used from the literature (Brezonik

1973; Uttormark et al. 1974; Manny and Johnson 1975; Gould and Fletcher 1978; Scherer et al.

1995). Atmospheric loadings was also estimated with the use of the LLRM (Wagner 2017) and

were based off the average literature values for urban areas (0.30 kg / ha / yr. P and 8.0 kg / ha/

yr. N; Reckhow et al. 1980; Dillon and Schneider 1991). Internal loading estimates were

calculated by taking the difference between the external predicted nutrient level and the actual.

Theoretical scenario testing for internal and external management programs and to determine the

lakes background condition were done by altering the inputs for internal and external loading

into the LLRM.

Theoretical scenario testing for internal and external management programs and to

determine the lake’s background condition were done by altering the inputs for internal and

external loading into the LLRM. External management programs (e.g., complete watershed build

out) were assessed by reducing external loading by 50 %. Internal management programs were

assessed by reducing internal loading by 100 %. Background conditions were determine by using

the LLRM literature values for forested watersheds, reducing internal loading by 95 %, and

setting septic loading to zero.

Results

Storm Water Run-off Results:

Of the five major inflows monitored in this study, sites 1, 3 and 6 had the highest P and N

loading rates followed by site 2 and 5 (Table 8). Wet weather loading for P ranged from 38.45

kg / day TP at site 3 to as low as 0.27 kg / day TP at site 5. Nitrogen ranged from 75.96 at site 3

to as low as 2.93 kg / day TN at site 5.

42

Table 8. Summary table for automated gravity fed stormwater samplers. Data represents the first

flushing of 6 storm events during the summer of 2016. The number of first flush storm events

captured, average, median, standard (Std.) deviation, maximum and minimum loading rates (kg /

day) for TP, TN, and nitrate.

Site #1

(n=4)

TP (kg / day) TN (kg / day) Nitrate (kg / day)

Average 11.66 17.27 12.84

Median 11.88 30.32 18.57

Std. deviation 12.72 32.53 20.71

Maximum 24.62 54.30 36.13

Minimum 2.49 15.19 9.60

Site #2

(n=4)

Average 1.21 10.24 6.70

Median 3.05 26.12 12.16

Std. deviation 3.11 28.02 14.32

Maximum 4.40 41.88 23.93

Minimum 1.97 17.95 9.04

Site #3

(n=6)

Average 12.60 17.94 11.07

Median 20.24 69.28 35.75

Std. deviation 19.03 61.68 32.40

Maximum 38.45 75.96 41.32

Minimum 5.28 29.40 12.17

Site #5

(n=4)

Average 0.95 1.57 2.15

Median 1.10 3.83 1.56

Std. deviation 1.26 4.23 2.36

Maximum 2.56 6.41 5.53

Minimum 0.27 2.83 0.79

Site #6

(n=4)

Average 9.84 20.47 13.80

Median 13.64 40.74 21.79

Std. deviation 13.74 38.24 22.66

Maximum 25.31 57.12 40.20

Minimum 2.36 14.35 6.84

43

February Rain Event Results:

During the storm event, site 3 had the highest water volumes, chloride (2,522.3 kg / day),

conductivity (1.62 mS cm -1

), TP (5.46 kg / day), and TSS (2,200.6 kg / day) loading (Tables 9 -

15). Site #6 had the highest TN (57.1 kg / day) and Nitrate (40.17 kg / day) loading during the

storm event (Tables 9 & 15).

Table 9. Water volumes (m3

/ day) prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values.

Site # 2 Feb

22:50

3 Feb

0907

3 Feb

11:30

3 Feb

13:30

3 Feb

16:20

1 1630.5 2042.1 9107.5 23009.7 23009.7

2 85.5 194.2 1328.5 23011.3 23011.3

3 1132.4 5723.5 27500.1 31780.8 31780.8

4 49.8 3780.0 2919.9 - -

5 573.3 678.7 784.1 1422.1 1422.1

6 - 113.8 781.9 13536.0 13536.0

Table 10. Chloride loading (kg / day) prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values.

Site # 2 Feb

22:50

3 Feb

0907

3 Feb

11:30

3 Feb

13:30

3 Feb

16:20

1 53.8 140.9 36.8 90.0 94.3

2 6.2 13.2 7.4 169.4 128.2

3 104.3 2522.3 209.0 241.5 334.0

4 2.2 16.6 15.0 - -

5 67.9 8.8 13.6 15.6 14.8

6 - 1.5 14.3 110.0 156.6

Table 11. Conductivity (mS cm -1

) levels prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values.

Site # 2 Feb

22:50

3 Feb

0907

3 Feb

11:30

3 Feb

13:30

3 Feb

16:20

1 0.816 1.332 0.692 0.647 0.672

2 1.624 1.237 0.971 0.498 0.905

3 1.814 6.92 1.268 1.166 1.678

4 0.879 0.863 0.868 - -

5 2.029 2.066 2.869 1.664 1.614

6 - 2.235 2.845 1.33 1.615

44

Table 12. TP loading (kg / day) prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values.

Site # 2 Feb

22:50

3 Feb

0907

3 Feb

11:30

3 Feb

13:30

3 Feb

16:20

1 0.026 0.078 1.494 2.393 2.485

2 0.003 0.007 0.159 2.207 1.875

3 0.024 0.973 4.730 5.466 4.163

4 0.001 0.085 0.054 - -

5 0.020 0.018 0.048 0.274 0.212

6 - 0.003 0.053 2.355 1.909

Table 13. TN loading (kg / day) prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values.

Site # 2 Feb

22:50

3 Feb

0907

3 Feb

11:30

3 Feb

13:30

3 Feb

16:20

1 1.9 2.3 5.4 13.3 15.1

2 0.1 0.2 1.6 13.3 17.9

3 3.3 10.1 22.2 27.5 52.4

4 0.01 4.7 2.1 - -

5 2.5 2.7 1.8 1.4 6.4

6 - 0.3 0.9 57.1 20.0

Table 14. Nitrate loading (kg / day) prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values.

Site # 2 Feb

22:50

3 Feb

0907

3 Feb

11:30

3 Feb

13:30

3 Feb

16:20

1 1.62 1.86 2.90 6.81 9.60

2 0.10 0.13 0.69 3.82 9.04

3 2.58 4.62 11.80 16.49 39.41

4 0.01 0.41 0.31 0.00 0.00

5 1.99 2.40 1.05 0.79 5.45

6 0.00 0.21 0.48 40.17 14.21

45

Table 15. TSS loading (kg / day) prior (February 2, 2016) and during a stormwater event

(February 3, 2016). Grey highlights indicate maximum values.

Site # 2 Feb

22:50

3 Feb

0907

3 Feb

11:30

3 Feb

13:30

3 Feb

16:20

1 0.0 0.0 546.4 598.3 138.1

2 0.0 0.4 77.1 690.3 138.1

3 4.5 503.7 2200.0 1652.6 190.7

4 0.2 22.7 23.4 - -

5 2.3 1.4 227.4 5.7 5.7

6 - 1.4 64.1 324.9 108.3

Lake Loading Response Model Results:

The total P loading to Lake Mohegan was estimated to be approximately 449 kg / yr.

Internal loading represented the greatest single contribution of P to Lake Mohegan (215 kg / yr.;

48 %) followed by watershed (176 kg / yr.; 39 %), waterfowl (37 kg / yr.; 8 %), atmospheric (12

kg / yr.; 3 %) and septic system (10 kg / yr.; 2 %) loading (Figure 22). The total N loading to

Lake Mohegan was found to be approximately 2975 kg / yr. N. Watershed loading constituted

the greatest contribution of N (2044 kg / yr.; 68 %) followed by internal (350 kg / yr.; 12 %),

atmospheric (320 kg / yr.; 11 %), septic system (145, kg / yr.; 5 %), and waterfowl loading (116

kg / yr.; 4 %; Figure 23).

Figure 22. Annual TP loading to Lake Mohegan (kg / yr., %).

46

Figure 23. Annual nitrogen loading to Lake Mohegan (kg / y, %).

Table 16. Summary Table for Scenario Testing using the LLRM (Wagner 2017).

Existing

Conditions

Calibrated

Model Value

Actual

Data

Complete

Build-out

Model

Value

Complete

Reduction in

Internal

Loading

Model Value

Background

Conditions

Model Value

Mean TP (µg P L -1

) 94.1 94.3 74 50.7 20.5

Mean TN (µg N L -1

) 975 1102.3 596.2 677.4 451.6

Mean chl. a (µg / L) 54.6 54.4 40 24.6 7.7

Peak chl. a (µg / L) 172.6 115.8 127.8 80 26.5

Mean Zsd (m) 0.71 0.8 0.85 1.14 2.28

Peak Zsd (m) 2.74 1.9 2.93 3.26 4.19

Bloom Probability

Probability of chl. a > 10 µg / L 99.0 % 94.4 % 93.1% 22.1 %

Probability of chl. a > 15 µg / L 93.4 % 78.2 % 74.9% 5.7 %

Probability of chl. a > 20 µg / L 82.3 % 58.1 % 53.8% 1.6 %

Probability of chl. a > 30 µg / L 54.6 % 27.2 % 23.7% 0.2 %

Probability of chl. a > 40 µg / L 32.3 % 11.9 % 9.8% 0.0 %

47

Discussion

The nutrient budget in this chapter provides valuable information for the Mohegan Lake

Improvement District (MLID). It has identified the major sources of P and N loading to the lake,

the critical and permissible in-lake nutrient concentrations, and what the expected trophic status

was before human development and what it could be under varying management strategies

(external and internal reduction programs). Wet weather stormwater monitoring has also allowed

for a greater understanding of pollutant loading to Lake Mohegan.

Wet weather pollutant loading to Lake Mohegan was highly variable among monitoring

sites. This variability can be explained in part by land use within each of the sub-watersheds. In

general, urban areas of a watershed will have greater surface runoff and less water filtration per

unit area than from forested or wetland areas. This is due to increased impervious surface, and

from other land use practices (e.g., fertilizer use). For example, the LLRM had a mean P input

coefficient for urban areas of 1.91 kg / ha / yr. compared to 0.24 kg / ha / yr. for forested areas.

The urban areas within the watershed of Lake Mohegan represented higher pollutant loading

rates per surface area. As a result, stormwater sites 3 and 6, which have smaller sub-watersheds

than site 1, had higher nutrient and pollutant loading rates. A large portion of site 1 sub-

watershed is forested, possibly allowing for greater pollutant filtration. The slope of the sub-

watersheds can also contribute to greater or lesser pollutant loading. In general, steeper slopes

have less water filtration and higher loading rates. For example, the portion of rainfall that is

converted to overland flow can be as high as 95 % for steep slopes (Dunn 1978).

The February rain event sampling was unique in that it provided direct measurements of

pollutants during snowmelt. According to the MLID, the surrounding watershed received a

significant amount of salt based de-icing products before the February 3rd

rain event. The

majority of the de-icing products were applied during and after the 11-inch snow storm that

occurred 10 days before. While the exact amount of salt applied to the watershed is unknown, the

high run-off loading rates for chloride and conductivity are likely a result of the de-icing

products applied before the rain event.

Lake Mohegan has high rates of internal P re-cycling with 48 % of the total P budget

coming directly from internal sources. The process of internal P re-cycling from lake sediments

are complex and are influenced by many factors. These factors can include biological (e.g.,

bacterial activity, mineralization processes, and bioturbation), chemical (e.g., redox conditions,

pH, iron : P ratio, nitrate availability), and physical factors such as resuspension and sediment

mixing (Sondergaard et al. 2001). Shallow lakes tend to have greater rates of internal P re-

cycling than compared to deeper lakes (Fee 1979; Sondergaard et al. 2001; Søndergaard et al.

2003). This is in part due to more frequent sediment resuspension from wind (Kristensen et al.

48

1992) and because shallow lakes tend to have a larger sediment surface area to epilimnetic

volume ratio (Fee 1979).

The predicted model values for TP, TN, chl. a, and Zsd were in good agreement with

actual monitoring data (see Table 16). Based on Carlson’s (1977) trophic state index Lake

Mohegan falls within the eutrophic classification (borderline hypereutropic), which is associated

with heavy algal blooms throughout the summer (Table 17). Scenario testing using the LLRM,

indicates that both internal and external loading needs to be addressed if a mesotrophic status is

to be obtained in the lake.

Table 17. Carlson’s trophic state index values and classification of lakes (Carlson 1977).

TSI Values

Chl. a (ug/L) Zsd (m) TP (µg P

L -1

) Trophic Status Attributes

<30 <0.95 >8 <6 Oligotrophic Clear water, oxygen throughout the year in the hypolimnion

30-40 0.95 -

2.6 8 - 4 6 - 12 Oligotrophic

A lake will still exhibit oligotrophy, but some

shallower lakes will become anoxic during the

summer

40-50 2.6 -

7.3 4 - 2 12 - 24 Mesotrophic

Water moderately clear, but increasing probability

of anoxia during the summer

50-60 7.3 - 20 2 - 1 24 - 48 Eutrophic Lower boundary of classical eutrophy, decreased

transparency, warm-water fisheries only

60-70 20 - 56 0.5 - 1 48 - 96 Eutrophic Dominance of blue-green algae, algal scum

probable, extensive macrophyte problems

70-80 56 -

155

0.25 -

0.5 96 - 192 Eutrophic

Heavy algal blooms possible throughout the

summer, often hypereutrophic

>80 >155 <0.25 192 - 384 Eutrophic Algal scum, summer fish kills, few macrophytes

SUMMARY POINTS

The total P and N loading to Lake Mohegan were found to be approximately 449 kg / yr

P and 2975 kg / yr. N.

Lake Mohegan exhibits high chloride and conductivity loading from the surrounding

watershed.

Internal loading was found to represent the single largest contribution of P loading to the

lake (48 % of the total P load).

Watershed loading was found to represent the single largest contribution of N loading to

the lake (68 % of the total N load).

LLRM results indicate the background trophic state for Lake Mohegan was mesotrophic.

Both external and internal nutrient loading would have to be addressed for Lake

Mohegan to achieve a mesotrophic status.

49

References

Brezonik, P. L. 1973. Nutrient Sources and Cycling in Natural Waters. EPA 660/3-73-002

Washington DC.

Carlson, R. E. 1977. A trophic state index for lakes1. Limnol. Oceanogr. 22: 361–369.

Dillon, P. J., and W.A. Schneider. 1991. Phosphorus and nitrogen export from forested stream

catchments in central Ontario. J Env. Qual. 20: 857–864.

Dunn, T. and. L. B. L. 1978. Water in Environmental Planning. WH Freeman Co. ISBN: 0-7167-

0079-4

Fee, E. J. 1979. A relation between lake morphometry and primary productivity and its use in

interpreting whole‐ lake eutrophication experiments. Limnol. Oceanogr. 24: 401–416.

Gould, D., and M. Fletcher. 1978. Gull droppings and their effects on water quality. Water Res.

665–672.

Higgins, G. R., and J. M. Colonell. 1971. Hydrologic Factors in the Determination of Watershed

Yields. Publication #20. WRRC, UMASS, Amherst, MA.

Homer, C., J. Dewitz, L. Yang, and others. 2015. Completion of the 2011 National Land Cover

Database for the conterminous United States–representing a decade of land cover change

information. Photogramm. Eng. Remote Sens. 81: 345–354.

Kristensen, P., M. Søndergaard, and E. Jeppesen. 1992. Resuspension in a shallow eutrophic

lake. Hydrobiologia 228: 101–109.

K-V Associates, inc. 1982. Water Quality Assessment of Lake Mohegan Basin.

Lawrimore, J. 2016. Global Summary of the Month, Version 1.0. NOAA National Centers for

Environmental Information. DOI:10.7289/V5QV3JJ5.

Manny, B. W., and W. C. Johnson. 1975. Annual contribution of carbon, nitrogen and

phosphorus by migrant Canada geese to a hardwater Pond, p. 949–951. In Verhandlungen

des Internationalen Verein Limnologie.

50

Miller, C., J. Dennis, S. Ator, and J. Brakebill. 1997. Nutrients in streams during baseflow in

selected environmental settings of the Potomac River basin. JAWRA. DOI:

10.1111/j.1752-1688.1997.tb03543.x

Mitchell, D. F., and K. J. W. and C. Asbury. 1988. Direct measurement of ground water flow and

quality as a lake management tool. Lake Reserv. Manag. 4: 169–178.

Reckhow, K. H., M. N. Beaulac, and and J. T. Simpson. 1980. Modeling phosphorus loading

and Pond response under uncertainty: a manual and compilation of export coefficients,.

US EPA 5: 80.

Scherer, N., H. Gibbons, K. Stoops, and M. Muller. 1995. Phosphorus loading of an urban Pond

by bird droppings, p. 317–327. In Pond and Reservoir Management.

Søndergaard, M., J. P. Jensen, and E. Jeppesen. 2003. Role of sediment and internal loading of

phosphorus in shallow lakes. Hydrobiologia 506: 135–145.

Sondergaard, M., P. J. Jensen, and E. Jeppesen. 2001. Retention and internal loading of

phosphorus in shallow, eutrophic lakes. Sci. World J. 1: 427–442.

Sopper, W. E., and H. W. Lull. 1970. Stream-flow characteristics of the northeastern United

States. Bull. Pa. Agric. Exp. Stn.

Uttormark, P. D., J. D. Chapin, and K. M. Green. 1974. Estimating Nutrient Loadings of Lakes from Non-Point Sources. US EPA 660: 112.

Wagner, K. J. 2017. How to use LLRM, Wagner affiliated. kjwagner@charter.net

Chapter 4: Plants in Lake Mohegan

Introduction

Aquatic macrophytes serve a variety of ecological functions. They can provide habitat for

aquatic organisms, food for waterfowl, and nursery areas for fishes and amphibians (Kalff 2003).

Aquatic plants, particularly submerged aquatic vegetation (SAV) play a critical role in

maintaining water quality and clarity by holding sediment in place, which limits particulate and

nutrient re-suspension from winds and bottom-feeding fish (Wetzel 1990; Horppila and

Nurminen 2003). Increasing the surface area of submerged aquatic plants in like Lake Mohegan

51

can, therefore, be used as a management technique to improve water clarity and reduce internal

nutrient recycling. Though, determining the amount of SAV needed to achieve water quality

improvements while still maintaining acceptable ecological function and recreational use may be

difficult to achieve.

The relative abundance and community structure of aquatic plants in Lake Mohegan were

assessed in 2008 by Allied Biological (now Solitude Lake Management) using the point intercept

rake toss relative abundance method (PIRTRAM). The July 14, 2008 study found three invasive

plant species and a variety of native species. The invasive species were Eurasian watermilfoil

(EWM; Myriophyllum spicatum) and curly-leaf pondweed (Potamogeton crispus), and water

chestnut (Trapa natans). Of the plant species found in 2008, curly leaf pondweed, EWM, and

coontail (Ceratophyllum demersum) were present in the highest densities. No detailed

assessment has been conducted since. Aquatic plant management in Lake Mohegan is mainly

done with the use of a weed harvester that is run by the Mohegan Lake Improvement District

(MLID). Hand harvesting by stakeholders for water chestnut is also done.

For this chapter, I used hydroacoustics to map densities of aquatic plants in Lake

Mohegan, NY. Hydroacoustic aquatic plant mapping can provide estimates on a variety of plant

metrics including SAV cover and biovolume. This method has been used as a fast and easy way

to assess plant densities for plant management programs attempting to set SAV or biovolume

thresholds specific to stakeholder goals (e.g., water quality and/or fish habitat improvement).

Methods

Submerged aquatic plant biovolume was assessed in Lake Mohegan on June 30, 2016,

using hydroacoustic data processing with BioBase 5.2 cloud-based software

(http://www.cibiobase.com; Navico 2017). A Lowrance HDS (model 9) with a 20° beam

transducer (model HST-WSBL), along with a wide area augmentation system–corrected global

positioning system (GPS), was used to log a 200-kHz broadband signal on a storage media card

while traveling approximately 4.8 km h-1

(3 mph) along transects spaced 10-m apart. During the

mapping survey, anecdotal observations were also made of the dominate submerged aquatic

plants present.

Data were uploaded to BioBase, where algorithms evaluated each acoustic and GPS

signal and created spatial data layers (point features) of depth, aquatic plant height, and

percentage of the water column occupied by vegetation (biovolume). A total of 10,564 data

points was collected with an additional 3,297 data points predicted by BioBase. The imported

plant and depth points were processed by using a geostatistical kriging algorithm, which creates

a regular grid of predicted aquatic-plant biovolume that can be mapped and summarized.

52

Results

The dominant submerged aquatic plants found during the June 30, 2016 survey was the

invasive species EWM, followed by curly-leaf pondweed. The percent aerial cover (PAC), or

overall surface area in which vegetation is growing in the surveyed area, was 14.1 %. The

average biovolume (BV) which refers to the percentage of the water column taken up by plants

when plants exist (areas that have no plants are not factored into this calculation) was 27.8 %

(Figure 24).

Figure 24. Acoustic mapping survey conducted in Lake Mohegan on June 30, 2016, in (Navico

2017). The red to yellow areas on the maps indicate the highest density of plants with the blue

areas corresponding to lower densities.

53

Discussion

The preliminary PAC and BV data in this study can provide a baseline for the MLID to

set macrophyte thresholds within Lake Mohegan in the future. Since many fishing boats are

already equipped with hydroacoustic systems like the one used for this study, implementing a

stakeholder macrophyte monitoring program in Lake Mohegan should be highly feasible.

The PAC of 14.1 % seems to align with the 2009 study where many of the deep-water

sampling points had no vegetation present. Arguably the most limiting factor dictating where

macrophytes can grow is the depth at which light can penetrate into the water column. Canfield

et al. (1985) developed an empirical model to predict the maximum depth of macrophyte

colonization based on Secchi depth. Using the global model from Canfield et al. (1985) and an

average Secchi of 0.8 m for Lake Mohegan the predicted maximum depth of colonization

(MDC) is 1.59 m. This depth equates to approximately 16.4 % of the lake surface area.

Of the surface area where macrophytes were found, the biovolume in those areas were

27.8 % on average. Numerous studies have demonstrated that fish feeding success and prey

availability depends on how many visual barriers are present in the water column (Baker et al.

1993). Some plant biovolume is needed to support prey communities and water quality (~ 50 %

is a good rule of thumb), but too much (> 80 %) can promote overly abundant prey and stunted

fish communities and can create recreational nuisances (Wetzel 1990; Baker et al. 1993;

Horppila and Nurminen 2003). Continued hydroacoustic plant monitoring coupled with water

clarity and fishery monitoring may determine the ideal threshold for PAC and BV in Lake

Mohegan.

Each of the three invasive plant species currently present in Lake Mohegan poses a

unique ecological threat to the lake. Dense canopies of EWM can alter the ecology of a lake

system by reducing native plant diversity (Madsen et al. 1991) and can alter its temperature and

oxygen levels leading to potential sediment nutrient release and fish habitat loss (Unmuth et al.

2000). Curly-leaf pondweed can grow abruptly early in the spring time and form dense canopies

outcompeting native plants. Typically by late June curly-leaf pondweed will senesce and releases

nutrients into the water column that can be used to promote HABs (Catling and Dobson 1985).

Water chestnut can also form dense beds, displace submerged native plants and alter oxygen

regimes (Hummel and Kiviat 2004).

Efforts should be made to reduce the spread of invasive plant species in Lake Mohegan.

However, the current management technique of mechanical harvesting may be counter-intuitive

in promoting native plants and reducing the spread of invasive species in Lake Mohegan. This is

because mechanical harvesting of EWM can increase the rate of fragmentation (Madsen et al.

1988) and promote the spread of EWM to other parts of the lake (Smith and Barko 1990). Once

EWM has established in a new area, the formation of dense beds can lead to the localized

54

elimination of other plants through competitive exclusion, resulting in biological disturbance

favoring local monocultures of EWM and promotes further spread through fragmentation (Smith

and Barko 1990). Using the 2008 aquatic plant assessment as a baseline, a follow up study is

warranted to determine if EWM has spread to new areas in Lake Mohegan and if plant diversity

has declined.

SUMMARY POINTS

The dominant submerged aquatic plants during the June 30, 2016 survey was the invasive

species Eurasian watermilfoil followed by curly leaf pondweed

The percent aerial cover (PAC) which refers to the overall surface area that vegetation is

growing in the surveyed area was 14.1 %

The average biovolume (BV) which refers to the percentage of the water column taken up

by plants when plants exist was 27.8 %

References

Allied Biological. 2008. Aquatic Macrophyte Survey July 14, 2008 Mohegan Lake, NY. BioBase 5.2. Navico inc., 4500 South 129th East Avenue, Suite 200, Tulsa, OK 74134. Canfield, D. E., K. A. Langeland, S. B. Linda, and W. T. Haller. 1985. Relations between water

transparency and maximum depth of macrophyte colonization in lakes. J. Aquat. Plant

Manag. 23: 25–28. Catling, P. M., and I. Dobson. 1985. The Biology of Canadian Weeds.: 69. Potamogeton crispus

L. Can. J. Plant Sci. 65: 655–668. Horppila, J., and L. Nurminen. 2003. Effects of submerged macrophytes on sediment

resuspension and internal phosphorus loading in Lake Hiidenvesi (southern Finland).

Water Res. 37: 4468–4474. Hummel, M., and E. Kiviat. 2004. Review of world literature on water chestnut with

implications for management in North America. J. Aquat. Plant Manag. 42: 17–27. Kalff, J. 2003. Limnology: Inland Water Ecosystems, Prentice Hall. Lowrance HDS. Navico Inc., 4500 South 129th East Avenue, Suite 200, Tulsa, OK 74134. Madsen, J. D., L. Eichlerq, and W. Boylen. 1988. Vegetative Spread of Eurasian thermilfoil in

Lake George, New York. J Aquat Plant Manage. Madsen, J. D., J. W. Sutherland, J. A. Bloomfield, L. W. Eichler, and C. W. Boylen. 1991. The

decline of native vegetation under dense Eurasian watermilfoil canopies. J. Aquat. Plant

Manag. 29: 94–99.

55

Navico, I. 2017. BioBase User Reference Guide. Minneapolis, MN, USA.

Smith, C. S., and J. W. Barko. 1990. Ecology of Eurasian watermilfoil. J. Aquat. Plant Manag.

28: 55–64. Unmuth, J. M., R. A. Lillie, D. S. Dreikosen, and D. W. Marshall. 2000. Influence of dense

growth of Eurasian watermilfoil on lake water temperature and dissolved oxygen. J.

Freshw. Ecol. 15: 497–503. Wetzel, R. 1990. Detritus, macrophytes and nutrient cycling in lakes. Mem Ist Ital Idrobiol 47:

233–249.

Chapter 5: Phytoplankton in Lake Mohegan

Introduction

Planktonic algae tend to dominate pelagic primary productivity in shallow turbid lakes

(Liboriussen and Jeppesen 2003). Having low algal densities in a lake is an indication of low

system productivity (Carpenter et al. 2001). Similarly, high algal densities that result from overly

successful growth processes (i.e., sufficient nutrients, light, temperature, etc.) and insufficient

loss processes (i.e., grazing, mortality, hydraulic washout, etc; Kalff 2003), may lead to

insufficient processing of energy by higher trophic levels (Carpenter et al. 2001). Consequently,

high algal densities can reduce system productivity by promoting greater energy flow to benthic

and detrital pathways, which can lead to greater oxygen demands. High algal density can also

lead to a variety of water quality issues including high suspended solids, low water clarity,

fluctuating dissolved oxygen and pH levels, taste and odor, and possible toxicity (Kalff 2003;

Havens 2008). These conditions can make water bodies uninhabitable for many aquatic

organisms and restrict water use, leading to resource and economic losses. Managing algal

densities to be at an intermediate level is often the goal for water resource managers since

productivity is likely greatest at an intermediate biomass (Carpenter et al. 2001) and water

impairment would be restricted.

Since algae respond to varying environmental changes, their assemblage can provide

insight into the condition of a lake. Water resource managers have used algal assemblages as

water quality indicators, where certain taxa have been ranked based off their tolerance to

pollution or lake features (i.e., mixing regimes and trophic status; Heinonen et al. 2008 (Chapter

2.1); Bellinger and Sigee 2015). For example, lakes that contain Oscillatoria and Euglena

species of algae often have high organic pollution while lakes containing Anacystis and Melosira

are typically associated with low organic pollution (Palmer 1969). The presence of colonial

56

cyanobacteria, also called blue-green algae, are typical of high nutrient lakes (Heinonen et al.

2008).

Harmful algal blooms (HABs), which are any concentration of algae that causes impacts

to an aquatic system that can be documented as hazardous to human or ecological health, occur

regularly in Lake Mohegan, NY. Identifying the risks associated with the HABs and the factors

influencing their growth should be of great importance and value to the MLID. The objective of

this chapter is to evaluate the phytoplankton community in Lake Mohegan and to provide a

literature overview of the risks associated with the HABs occurring on Lake Mohegan.

Methods

In 1993, summer phytoplankton sampling was conducted by Kortmann (1993) and in

2004 by Martin (2012), both of which reported percent composition of algae and dominant taxa

(to genus level) with the latter also recording biovolume. No detailed methods were provided in

either assessment, only that an open water surface sample was taken. This makes results difficult

or impossible to compare. Phytoplankton monitoring became part of CSLAP in 2011-2012. It

included percent composition of alga, dominant taxa (to genus level), and testing for cyanotoxins

(microcystin-LR, anatoxin-a, and cylindrospermposin) in downwind shoreline areas and in one

surface water sampling site (site 1; Figure 25). Much of the prior CSLAP data is incomplete

regarding sampling consistency. The phytoplankton monitoring as described earlier was only

done in full in 2014 and 2016, while others years had partial sampling.

From the 10th

of October 2015 to 11 Novemebr 2016, a more detailed assessment of

phytoplankton community and abundance was undertaken as part of the state of this lake report.

Surface phytoplankton samples were collected using a Kemmerer water sampler (z = 0.5 m) at

site 1 (Figure 25).

57

Figure 25. Bathymetric map of Lake Mohegan and the Citizens Statewide Lake Assessment

Program (CSLAP) monitoring locations 1 & 2.

Phytoplankton samples were preserved with Lugol’s iodine solution, using a dilution of 5

mL of Lugol’s solution per 100 mL of lake water (5%; Bellinger and Sigee 2015). All

quantitative analyses were accomplished following Bellinger and Sigee (2015), involving the use

of settling chambers and an inverted light microscope. The preserved samples were subsampled

(10 ml) and settled for > 18 hours in a 10 mL settling chamber. Many of the samples had to be

diluted (1 ml preserved sample in 9 ml of deionized water), due to high density of phytoplankton.

Microscopic analysis was done with the use of a Carl Zeiss Axio Observer.A1 with phase

contrast. All algal cells were enumerated in each 10 mL settling chamber. In some instances, cell

counts for colonial algal forms were approximated by extrapolating either an average cell density

or cell per unit length depending on aggregation type. Identification of each of the major algal

groups was recorded, and the dominant taxa for each sample were identified to the genus level.

58

Results

The phytoplankton community in Lake Mohegan has been dominated by cyanobacteria in

six of the seven sampling years, apart from 2015. In 1993, cyanobacteria represented 34 – 71 %

of the phytoplankton community and were dominated by Dolichospermum spp. (Anabaena sp.)

early in the season and then switched to Oscillatoria spp. later during the summer (Kortmann

1993). In 2004, cyanobacteria represented 62-95% of the phytoplankton community and

consisted of mainly Coelosphaerium spp. and Aphanizomenon spp (Martin 2012). throughout the

growing season. Biovolumes for 2004 ranged from 28,245 to 153,265 cells / ml cyanobacteria.

From 2012 to 2014, cyanobacteria dominated the phytoplankton community, never comprising

less than 80% of the phytoplankton community the growing season (Martin 2004). There were

no dominant taxa recorded for the 2012 sample. However, in 2013 and 2014 Microcystis sp. was

the dominant taxon.

In 2015, the percent of cyanobacteria dropped to 7 %, and chlorophytes (Pediastrum

spp.) and bacillariophytes (Fragilaria spp.) dominated. In 2016, a switch back to cyanobacteria

was observed with Woronichinia spp. dominating early in the season and Microcystis spp.

dominating later in the growing season (Figure 27). Cyanobacteria biovolumes for 2016 ranged

from 174 cells / ml on 3/18/2016 to 490,625 cells / ml on 10/2/2016 (Figure 28). Though,

biovolumes reached > 25,000,000 cells / ml in downwind areas where phytoplankton

accumulated. No cyanobacteria were found on sample dates 5/12/2016 and 5/25/2016 (Figure

27). Both chlorophytes (Pediastrum spp.) and bacillariophytes (Fragilaria spp.) were present

during each sampling event in 2016.

59

Figure 26. Harmful algal blooms (HABs) occurring on Lake Mohegan in 2016. Picture A) shows

the first observable bloom during the 2016 growing season (6/17/2016) and B) shows the

microscopic view (40 ×) of that bloom. Note the dominant alga Woronichinia spp. Picture C)

shows a late season (10/2/2016) HAB of mixed cyanobacteria with Microcystis spp. dominating.

Figure 27. Open water algal enumeration results (cells / ml) for fall 2015 and 2016 samples.

60

Of the three major cyanotoxins tested through CSLAP (microcystin-LR, anatoxin-a, and

cylindrospermposin), microcystin-LR was the only one to exceed the New York Department of

Environmental Conservation (NYDEC) bloom criteria (NYSDEC 2017). According to the

NYDEC (2017), a “confirmed bloom” threshold of > 4 µg / L microcystin in the open water

sampling sites occurred in 2011 and 2012. The “confirmed with high toxin bloom” level of > 10

µg / L microcystin in the open water sampling site was observed in 2013 and 2014, and the

threshold of > 20 µg / L for shoreline/beach sampling was confirmed in 2014 (NYDEC 2017).

Average microcystin levels were 960 µg / L with a maximum value of 7,032 µg / L in shoreline

samples collected during 2014. No toxin data were collected in 2015 ad 2016.

Discussion

During 2016, HABs began in Lake Mohegan in the middle of June and continued until

late October. The algal assemblage during the 2016 bloom was predominantly cyanobacteria. In

general, cyanobacterial blooms tend to dominate in lakes with excess P (Watson et al. 1997;

Downing et al. 2001) and when total algal biomass becomes high (> 100 mg / L algal biomass;

Canfield et al. 1989), both of which occurred during the summer of 2016. The dominant

cyanobacteria (Dolichospermum spp., Coelosphaerium spp., Aphanizomenon spp., Woronichinia

spp., and Microcystis spp.) found in Lake Mohegan during all sampling events (2011–2016), are

all indicators of nutrient-rich lakes (Heinonen et al. 2008). Even the green algae dominating in

2015 (Pediastrum spp. and Fragilaria spp.) are indicators of high nutrient status lakes (Heinonen

et al. 2008). Cyanobacteria have dominated the phytoplankton community in Lake Mohegan for

most sampling years. Other potential reasons why cyanobacteria dominant over other

phytoplankton have been outlined by Shapiro (1990) and include: 1) superior energetics in high

water temperatures (typically > 20 °C), 2) adaptation to lower light levels better than other

phytoplankton, 3) the ability to regulate themselves in the water column to obtain nutrients from

bottom waters or more light in surface waters, 4) reduced susceptibility to zooplankton grazing,

5) ability to thrive in high pH and low CO2 environments and 6) better adapted to low N

environments. As a result of these advantages, cyanobacterial blooms will likely continue each

summer in Lake Mohegan as long as the current nutrient status persists.

The timing of HABs in Lake Mohegan coincides with the recreational use of the lake,

increasing the probability of human and animal exposure to cyanotoxins. Of the three major

toxins tested, microcystin was the only toxin to go above levels set by the NYDEC and the

World Health Organization (WHO). A variety of cyanobacteria can produce microcystin.

Species that have been documented to produce microcystin that also occur on Lake Mohegan are

Oscillatoria spp.(Meriluoto et al. 1989), Microcystis spp. (Reynolds 1984), and Woronichinia

spp. (Heinonen et al. 2008).

61

Microcystin is a hepatotoxin, which causes hemorrhaging of the liver by blocking protein

phosphatases 1 and 2A with an irreversible covalent bond (MacKintosh et al. 1990). Microcystin

has roughly 70 structural analogs, each of which varies in toxicity (Microcystin-LR being the

most toxic; Rinehart et al., 1994; Sivonen & Jones, 1999). Microcystin has also been linked to

tumor production (Fitzgeorge et al. 1994). Human exposure to microcystin on Lake Mohegan

would likely be from oral consumption and/or inhalation of water during recreational use (i.e.,

swimming and boating). Microcystin may also accumulate in certain fish (Papadimitriou et al.

2012), thus consuming fish caught from Lake Mohegan may pose another pathway for

microcystin exposure.

The acute health risks associated cyanobacterial blooms on Lake Mohegan varied over

time and space with “low” probable risks in open water samples to “very high” in shoreline

downwind areas of the lake (World Health Organization 1999; Table 18). Variation in animal

species sensitivity; amount consumed by the animal; age and sex of the animal; and the amount

of other food in the animal's gut will also contribute to toxicity (Carmichael 2001). Fitzgeorge et

al. (1994) found that microcystin toxicity is cumulative, where continual low dose exposures can

lead to chronic liver damage and/or tumor production. Thus, as cyanobacterial blooms continue

to occur on Lake Mohegan, so will the potential short and long-term health risks of its users.

Table 18. Guidance values for the relative probability of health effects resulting from exposure to

cyanobacteria blooms and microcystin taken from the World Health Organization (1999).

Relative Probability of

Acute Health Effects

Cyanobacteria

(cells / mL)

Microcystin-LR

(µg / L)

Chlorophyll-a

(µg / L)

Low < 20,000 < 10 < 10

Moderate 20,000-100,000 10-20 10-50

High 100,000-

10,000,000 20-2,000 50-5,000

Very High > 10,000,000 > 2,000 > 5,000

Determining toxicity levels has been described by Havens (2008) as less predictable than

determining cyanobacteria occurrence. Cyanotoxin production during a bloom can have distinct

toxin and non-toxin producing strains of cyanobacteria, which can grow together during blooms

and vary over time and space (World Health Organization 1999; Carmichael 2001). About 60 %

of worldwide cyanobacterial blooms contain toxic cyanobacterial strains (World Health

Organization 1999). While the risks of having a known toxin producer at high concentrations in a

lake is likely high, one cannot assume toxicity. Because of this, monitoring cyanotoxin

concentrations is preferred over surrogates like algal biovolume or chl. a.

62

The ecological impact of cyanobacteria blooms can also be devastating. For simplicity,

the ecological impacts of cyanobacterial blooms have been arranged in a model taken from

Havens (2008):

Figure 28. Summary of the ecological responses and impacts associated with cyanobacterial

blooms. Taken from Havens (2008).

SUMMARY POINTS

Cyanobacteria were the dominant taxa among all sample years except for 2015.

The relative probability of health effects resulting from exposure to cyanobacteria blooms

and microcystin ranged from low to very high. Microcystin-LR in downwind areas of the lake can reach as high as 7,032 µg / L.

Continued HABs on Lake Mohegan will likely increase the potential for health risks both

short and long-term.

63

References

Bellinger, E. G., and D. C. Sigee. 2015. Freshwater algae: identification and use as bioindicators,

John Wiley & Sons. Canfield D. E., E. Phlips, and C. M. Duarte. 1989. Factors influencing the abundance of blue-

green algae in Florida lakes. Can. J. Fish. Aquat. Sci. 46: 1232–1237. Carmichael, W. W. 2001. Health effects of toxin-producing cyanobacteria:“The CyanoHABs.”

Hum. Ecol. Risk Assess. Int. J. 7: 1393–1407. Carpenter, S. R., J. J. Cole, J. R. Hodgson, and others. 2001. Trophic cascades, nutrients, and

lake productivity: whole‐ lake experiments. Ecol. Monogr. 71: 163–186. Fitzgeorge, R., S. Clark, and C. Keevil. 1994. Routes of intoxication. Spec. Publ. R. Soc. Chem.

149: 69–74. Havens, K. E. 2008. Cyanobacteria blooms: effects on aquatic ecosystems. Cyanobacterial

Harmful Algal Blooms State Sci. Res. Needs 733–747. Heinonen, P., G. Ziglio, and A. Van der Beken. 2008. Hydrological and limnological aspects of

lake monitoring. John Wiley & Sons. Kalff, J. 2003. Limnology: Inland Water Ecosystems, Prentice Hall. Kortmann, R.W.. 1993. Lake Mohegan 1993 Diagnostic Report, Liboriussen, L., and E. Jeppesen. 2003. Temporal dynamics in epipelic, pelagic and epiphytic

algal production in a clear and a turbid shallow lake. Freshw. Biol. 48: 418–431. MacKintosh, C., K. A. Beattie, S. Klumpp, P. Cohen, and G. A. Codd. 1990. Cyanobacterial

microcystin-LR is a potent and specific inhibitor of protein phosphatases 1 and 2A from

both mammals and higher plants. FEBS Lett. 264: 187–192. Martin, R. M. 2012. Mohegan Lake Diagnostic Report. Cedar Eden Environmental. Meriluoto, J. A. O., A. Sandström, J. E. Eriksson, G. Remaud, A. G. Graig, and J.

Chattopadhyaya. 1989. Structure and toxicity of a peptide hepatotoxin from the

cyanobacterium Oscillatoria agardhii. Toxicon 27: 1021–1034. NYDEC. 2017. Harmful Algal Blooms (HABs) Program Guide. Palmer, C. M. 1969. A composite rating of algae tolerating organic pollution. J. Phycol. 5: 78–

82.

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Papadimitriou, T., I. Kagalou, C. Stalikas, G. Pilidis, and I. D. Leonardos. 2012. Assessment of

microcystin distribution and biomagnification in tissues of aquatic food web

compartments from a shallow lake and evaluation of potential risks to public health.

Ecotoxicology 21: 1155–1166. Reynolds, C. S. 1984. The ecology of freshwater phytoplankton, Cambridge University Press. Shapiro, J. 1990. Current beliefs regarding dominance by blue-greens: the case for the

importance of CO_2 and pH. Verh Intern. Ver. Limnol 24: 38–54. World Health Organization. 1999. Toxic Cyanobacteria in Water: A guide to their public health

consequences, monitoring and management. Lond. E FN Spon.

Chapter 6: Zooplankton in Lake Mohegan

Introduction

Lake managers often evaluate zooplankton community and size structure to facilitate

interpretation of food-web dynamics and to evaluate current or potential management strategies

in lakes (e.g., bio-manipulation programs). Zooplankton community and size structure can be

used to complement data obtained from fishery surveys and in assessing predator to prey balance

(Mills and Schiavone 1982; Mills et al. 1987). Also, zooplankton biomass and community

grazing can make for periods of clear water phases in lakes, which is a rapid increase in Secchi

transparency (Zsd) and a drop in chlorophyll a (chl. a) and particulate organic carbon, often seen

in the spring in temperate lakes (Lampert et al. 1986).

Some bio-manipulation programs attempt to promote longer clear phase states by

reducing abundance of planktivorous fish that suppress zooplankton grazing on algae and

decrease water clarity. This technique can be used in combination with or as an alternative to

other management strategies (i.e., nutrient reduction) to alleviate eutrophication (Schindler et al.

2008). Though, for such programs to be successful, applied lake managers must have a firm

understanding of food-web dynamics in a lake.

The purpose of this chapter is to provide the Mohegan Lake Improvement District

(MLID) a detailed assessment of zooplankton in Lake Mohegan. It will also serve as the

necessary baseline on which further management and studies can be assessed. The zooplankton

metrics outlined in this chapter include information regarding zooplankton biomass, community,

and size structure, along with zooplankton filtration rates and phosphorus (P) regeneration rates.

Significance testing to determine if zooplankton biomass, length, and filtration rates are affecting

Secchi (Zsd) and chlorophyll a (chl. a) in Lake Mohegan is also presented.

65

Methods

Two prior zooplankton assessments have been conducted on Lake Mohegan. In the

summer of 1993, four samples were taken from site #1 (Figure 30) for species composition,

length, and animals / L (Kortmann 1993). Rotifers were not included in the counts, and the

length measurements were reported as greater or less than 0.8 mm for Cladocera and greater or

less than 1 mm for cyclopoids and calanoids. Another survey was done by Martin (2012) in the

summer of 2004, but he only reported percent species composition. Neither study provided

sampling methods. In the fall 2015, a more detailed assessment of the zooplankton community

was undertaken as part of this state of the lake report. From 10 October 2015 to 17 November

2016, samples were taken monthly from October 2015 to May 2016 and biweekly from May to

November 2016 at site #1 (Figure 30) to evaluate the temporal distribution of the zooplankton

community on Lake Mohegan. No samples were collected from December to mid-March; unsafe

ice conditions during the brief period of ice-cover prevented access to the lake.

A conical 63 μm plankton net with a 0.2 m diameter opening was used for collecting

zooplankton. The end of the cup was weighted, and the net was lowered to, then hauled up from,

4 m. A G.O.™ mechanical flow meter was mounted across the net opening, allowing for

calculation of the volume of lake water filtered. The concentrated samples were preserved with

ethanol to about 50%. The preserved volume was recorded.

Samples were analyzed one ml at a time on a gridded Sedgwick Rafter cell. Zooplankton

were identified, measured and enumerated using a research grade compound microscope with

digital imaging capabilities. Typically, at least 100 organisms were viewed per sample. Plankton

densities were recorded (# recorded × ml concentrated sample / ml sample viewed / L lake water

filtered).

Mean densities and lengths for Cladocerans, copepods and rotifers were used to calculate

dry weight (Peters and Downing 1984), daily filtering rate (Knoechel and Holtby 1986) and P

regeneration (Esjmon-Karabin 1983) on each date sampled according to the equations provided

in Table 19.

Simple linear regression analysis was performed in R (R Core Team 2017) to evaluate the

significance of explanatory variables (zooplankton biomass, filtration rates and length) on Zsd

and chl. a.

66

Figure 29. Bathymetric map of Lake Mohegan and the Citizens Statewide Lake Assessment

Program (CSLAP) monitoring locations 1 & 2.

Table 19. Equations used to determine zooplankton dry weight (Peters and Downing 1984),

filtering rates (Knoechel and Blair Holtby 1986), and P regeneration rates (Esjmont-Karabin

1984).

Parameter Equations

Dry weight: D.W. = 9.86 × (length in mm)2.1

Filtering Rate: F.R. = 11.695 × (length in mm)2.48

P regeneration:

Cladocerans:

Copepods:

Rotifers:

P.R. = .519 × (dry weight in µg)-.023

×e0.039

× (temp. in °C)

P.R. = .229 × (dry weight in µg)-.645

×e0.039

× (temp. in °C)

P.R. = .0514 × (dry weight in µg) 1.27

×e0.096

× (temp. in °C)

67

Results

Cladocera represented the majority of the zooplankton biomass (average 154.7 µg / L),

followed by copepoda (92.9 µg / L) and rotifera (29 µg / L). Total zooplankton biomass varied

from 690.4 µg / L to as low as 53.1 µg / L and averaged 276.7 µg / L (Appendix A). The size of

Cladocera ranged from 1.31 to 0.10 mm with an average length of 0.65 mm. Zooplankton

biomass and average zooplankton length were highest on average from March to mid-July

(Figures 31 & 32). Filtrations rates (% epilimnion filtered per day) varied from 2.77 to 41.64 %

with an average of 17 %. Phosphorous regeneration rates varied from 0.55 to 60.5 µg / L / day,

with an average of 26.3 µg / L / day (Appendix A). Linear regression modeling failed to detect

effects of zooplankton biomass, length, or filtration rate on either Zsd or chl. a. Appendix A

summarizes the data collected from site #1 over the course of the study, including mean

epilimnetic temperature (which influences P regeneration rates), zooplankton densities, mean

lengths and dry weights, dry weights per liter, P regeneration and filtration rates.

In the 1993 summer survey, the zooplankton community consisted predominantly of

small bodied Cladocera (< 0.8 mm) representing 79–96 % of the total zooplankton community.

Zooplankton abundance was highest during the two June samples (1262.2 and 402.6 animals / l)

and tapered off later in the season during the August and September sampling (153.3 and 109.1

animals / L; Kortmann 1993). The 2004 sampling done by Martin (2012) reported Cladocera as

being the dominate group making up 96 – 87 % of the total zooplankton community.

Figure 30. Zooplankton biomass (µg / L) for rotifers, copepods, and Cladocerans in Lake

Mohegan from October 2015 to November 2016 at site #1.

0

100

200

300

400

500

600

700

800

D

ry w

t. (

µg /

L)

Rotifera

Copepoda

Cladocera

68

Figure 31. Average length (mm) for all zooplankton and Cladocera over the 2016 study period (n

= 16).

Discussion

The present study provided valuable information on zooplankton in Lake Mohegan. This

should provide interpretation into the feasibility of bio-manipulation programs that could

potentially assist in improving water clarity and reducing algal biomass in Lake Mohegan.

However, as it currently stands, zooplankton had no measurable effect on Zsd or chl. a in Lake

Mohegan during the study period. This may in part be due to poor food sources (i.e.,

cyanobacteria) for zooplankton to grow (De Bernardi and Giussani 1990), a higher than preferred

nutrient status (Elser and Goldman 1991), copper treatments that can cause zooplankton

mortality and reduced biomass (chapter 9; Cooke et al. 2005), and/or high predation on

zooplankton by panfish (e.g., Baker et al. 1993).

As zooplankton body length increases so do their filtration rates (Knoechel and Blair

Holtby 1986). Therefore, large-bodied zooplankton are essential for obtaining high filtration

rates. Multiple reviews on biomanipulation suggest that large-bodied zooplankton are indeed

required for biomanipulation programs to be successful (Baker et al. 1993; Cooke et al. 2005).

The average length of zooplankton in Lake Mohegan during the study period was 0.65 mm, and

the average filtration rate was 17 %, which apparently was insufficient to have any significant

effect on Zsd or chl. a within the lake. The size of zooplankton in Lake Mohegan may in part be

attributed to a low predator to prey ratio where predation by prey fish on large sized zooplankton

is high. Mills et al. (1987) found average zooplankton lengths < 0.8 mm also exhibited low

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9A

ver

age

Len

gth

(m

m)

Total Zooplankton

Cladocera

69

predator : prey ratios (< 0.2) among 35 New York Lakes. Fish surveys conducted on Lake

Mohegan in 2009 and 2017 had predator to prey ratios of 0.09 and 0.2, respectively, thus

appearing to agree with Mills et. al., (1987). The higher predator to prey ratio in 2017 may have

been from inadequate sampling where only 184 predator and prey fish were caught compared to

554 in 2009 (see Chapter 7). Increasing the average zooplankton size (possibly above 0.8 mm)

and subsequent filtration rate may on average produce greater Zsd and reduced chl. a in Lake

Mohegan. However, the ability to achieve such a goal via bio-manipulation may be difficult

considering the complexity of the system.

Phosphorus recycling by zooplankton excretion may be an important source of nutrients

for phytoplankton growth, especially since the form of P excreted is predominantly bio-available

(Attayde and Hansson 1999). Zooplankton grazing was not high enough to suppress chl. a in this

study. The grazing rates therefore are not high enough to keep up with the growth rates of

phytoplankton. Grazing and excretion of P by zooplankton may be increasing algal biomass by

allowing a continued supply of bio-available P to be re-generated in the epilimnion. It is

important to note that the grazing rates observed in this study may be sufficient in reducing chl. a

and improving Zsd in Lake Mohegan if the current nutrient status and subsequent growth rate of

phytoplankton are reduced (Elser and Goldman 1991). Being that cyanobacteria are not the

preferred food source for zooplankton (De Bernardi and Giussani 1990), it is also possible that

zooplankton grazing in Lake Mohegan is selecting for cyanobacteria, which was the dominate

group found during the present study, by grazing on more desirable phytoplankton first.

Continued monitoring of zooplankton in Lake Mohegan may help determine what management

strategies or factors are influencing zooplanktons effect on Zsd and chl. a.

SUMMARY POINTS

Zooplankton biomass, size, and filtration rate had no significant effect on Zsd or chl. a in

Lake Mohegan throughout the study period.

The average zooplankton length during the study was found to be 0.65 mm.

In the present study, Cladocera represented the majority of the zooplankton biomass

(average 154.7 µg / L), followed by copepoda (92.9 µg / L) and rotifera (29 µg / L)

Phosphorous regeneration rates varied from 0.55 to 60.5 µg / L / day, with an average of

26.3 µg / L / day

70

References

Attayde, J. L., and L.-A. Hansson. 1999. Effects of nutrient recycling by zooplankton and fish on

phytoplankton communities. Oecologia 121: 47–54. Baker, J. P., H. Olem, C. S. Creager, M. D. Marcus, and B. R. Parkhurst. 1993. Fish and fisheries

management in lakes and reservoirs. US Environ. Prot. Agency EPA. Cooke, G. D., E. B. Welch, S. Peterson, and S. A. Nichols. 2005. Restoration and management

of lakes and reservoirs, CRC press, Boca Raton, Florida. De Bernardi, R. de, and G. Giussani. 1990. Are blue-green algae a suitable food for

zooplankton? An overview. Hydrobiologia 200: 29–41. Elser, J. J., and C. R. Goldman. 1991. Zooplankton effects on phytoplankton in lakes of

contrasting trophic status. Limnol. Oceanogr. 36: 64–90. Esjmont-Karabin, J. 1984. Phosphorus and nitrogen excretion by lake zooplankton (rotifers and

crustaceans) in relation to the individual body weights of the animals, ambient

temperature, and presence of food. Ekol. Pol. 32: 3–42. Knoechel, R., and L. Blair Holtby. 1986. Construction and validation of a body-length-based

model for the prediction of Cladoceran community filtering rates. Limnol. Oceanogr. 31:

1–16. Lampert, W., W. Fleckner, H. Rai, and B. E. Taylor. 1986. Phytoplankton control by grazing

zooplankton: a study on the spring clear-water phase. Limnol. Oceanogr. 31: 478–490. Martin.M.R.. 2012. Mohegan Lake Diagnostic Report, Cedar Eden Environmental. Mills, E. L., D. M. Green, and A. Schiavone 1987. Use of zooplankton size to assess the

community structure of fish populations in freshwater lakes. North Am. J. Fish. Manag.

7: 369–378. Mills, E. L., and A. Schiavone 1982. Evaluation of fish communities through assessment of

zooplankton populations and measures of lake productivity. North Am. J. Fish. Manag. 2:

14–27. Peters, R. H., and J. A. Downing. 1984. Empirical analysis of zooplankton filtering and feeding

rates. Limnol. Oceanogr. 29: 763–784. R Core Team. 2017. R: A Language and Environment for Statistical Computing, R Foundation

for Statistical Computing. Robert W. Kortmann. 1993. Lake Mohegan 1993 Diagnostic Report,

71

Schindler, D. W., R. Hecky, D. Findlay, and others. 2008. Eutrophication of lakes cannot be

controlled by reducing nitrogen input: results of a 37-year whole-ecosystem experiment.

Proc. Natl. Acad. Sci. 105: 11254–11258.

Chapter 7: Fish in Lake Mohegan

Introduction

The fishery in Lake Mohegan was less of a management priority to the Mohegan Lake

Improvement District (MLID) than other management issues like lake water quality and clarity,

harmful algal blooms (HABs), and invasive plant species. However, the fish community in a lake

can directly affect water quality and clarity, HABs, and aquatic plants (Cooke et al. 2005).

Certain fishes can also be used as low-cost management strategies used to address higher priority

lake goals for the MLID. Information about fish communities in lakes, specifically the ratio of

predators to prey in a lake, may provide insight into the feasibility of bio-manipulation programs

to moderate HABs and improve water clarity (Mills and Schiavone 1982; Mills et al. 1987;

Baker et al. 1993).

The MLID had detailed fishery assessments conducted by Allied Biological (now

Solitude Lake Management) November 19th

, 2008 and May 6th

, 2009. The fall 2008 study was

inconclusive due to a lack of fish caught and thus was re-done in the spring of 2009. The main

study objectives were to determine if the fishery was in good standing compared to NYS

averages for fish length, weight, and growth rates and to see if there were strategies for

improving the fishery. It did not, however, assess the fishery specifically for its utilization in a

bio-manipulation program. In the current study, fish sampling was conducted using methods

similar to the 2009 assessment to see if major changes had occurred in the fish community and to

determine if a biomanipulation program to moderate HABs and improve water clarity would be

appropriate.

Methods

Electrofishing was used to assess near shore fish communities in Lake Mohegan on

August 14, 2017. A total of three transects (1, 4, and 5 as shown in Figure 33) were conducted.

The electrofishing boat was equipped with a generator and a Type VI-A variable pulsator and

was putting out 6000 watts during the survey. Two netters were positioned on the front of the

bow, one on either side. Fish were netted and placed into 284-liter tanks and processed at the end

of each collection period. Fish were identified to lowest possible taxonomic level and total length

was measured.

72

Figure 32. Electrofishing transect map taken from the 2008/2009 (Allied Biological) study.

Transects 1, 4, and 6 were sampled in the 2017 study.

Catch per unit effort (CPUE) time was used to examine relative abundance of fishes

sampled. For selected fishes for which sufficient data were collected (> 20 stock sized fish

captured), length frequency histograms were generated along with proportional stock density

(PSD) using the FSA package (Ogle 2017) in R (R Core Team 2017). Proportional stock density

was calculated using size categories from (Gabelhouse 1984; Table 20) and the formula from

(Anderson 1996):

PSD = (Number of fish ≥ quality length/Number of fish ≥ stock length) × 100

73

Table 20. Length (mm) categories for a variety of fish species proposed by Gabelhouse (1984)

Species Stock Quality Preferred Memorable Trophy

Largemouth Bass 200 300 380 510 630 Black Crappie 100 180 230 300 375

Yellow Perch 100 200 240 315 400

Bluegill Sunfish 80 150 200 250 300

Pumpkinseed Sunfish 80 150 200 250 300

The predator to prey ratio was calculated for 2009 and 2017 by dividing the total number

of predatory fish, largemouth bass (Micropterus salmoides) by the total number for prey fish,

bluegill sunfish (Lepomis macrochirus), pumpkinseed sunfish (Lepomis gibbosus), black crappie

(Pomoxis nigromaculatus), and yellow perch (Perca flavescens) per Mills et al. (1987). In

addition, the ratio of predator biomass (largemouth bass) to prey biomass (bluegill,

pumpkinseed, black crappie, and yellow perch) was calculated using the 2009 Allied Biological

data set.

Results

A total of 192 fish were caught during the 2017 survey with bluegill sunfish (53 %),

largemouth bass (17 %), and pumpkinseed sunfish (15 %) making up the majority of the fish

community by number (Figure 34). Black crappie represented more of the percent relative

abundance in 2009 compared to 2017 (Figures 35 & 36). Less fish were caught in 2017 with a

CPUE of 128 fish / hr. compared to 237 fish / hr. in 2009. The predator to prey ratios were 0.09

in 2009 and 0.20 in 2017. Predator and prey biomasess in 2009 were 26 % and 74 %,

respectively.

74

Figure 33. Relative fish abundance in Lake Mohegan in 2017 for transects 1, 4, and 5.

Figure 34. Relative fish abundance in Lake Mohegan in 2009 for all transects.

75

Figure 35. Relative fish abundance in Lake Mohegan in 2009 for transects 1, 4, and 5.

The PSD for largemouth bass dropped from 68 to 50, while bluegill sunfish saw an

increase from 60 to 86 when comparing the 2009 sampling to 2017 (Table 21). Less than 20

stock sized fish were caught for yellow perch and black crappie in 2017, precluding robust

estimation of PSD. Largemouth bass length frequency distribution showed three potential year

classes at 80 - 100 mm, 250 -280 mm and 400 - 440 mm (Figure 38). Length frequency

historgams are also provided for bluegill and pumpkinseed sunfishes, with each showing one

potential age class at 90 – 110 mm for bluegill and 120 – 130 mm for pumpkinseed sufishes

(Figures 38 & 39).

Table 21. Proportional stock density (PSD) for a variety of fish species sampled in 2009 and

2017. Note: the 2009 data set is for all fish caught.

Species 2009 PSD 2017 PSD

Largemouth Bass 68 50 Black Crappie 76 -

Yellow Perch 80 -

Bluegill Sunfish 60 86

Pumpkinseed Sunfish 69 11

76

Figure 36. Length-frequency histogram for bluegill sunfish collected in Lake Mohegan during

2017. Letters indicate threshold lengths used to assign Stock (S), Quality (Q), Preferred (P), and

Memorable (M) size ranges (Gabelhouse 1984).

Figure 37. Length-frequency histogram for pumpkinseed sunfish collected in Lake Mohegan

during 2017. Letters indicate threshold lengths used to assign Stock (S), Quality (Q), Preferred

(P), and Memorable (M) size ranges (Gabelhouse 1984).

77

Figure 38. Length-frequency histogram for largemouth bass collected in Lake Mohegan during

2017. Letters indicate ranges for Stock (S), Quality (Q), Preferred (P), and Memorable (M) size

ranges (Gabelhouse 1984).

Discussion

Lake Mohegan provides a number of warm-water fishing options for its stakeholders

based on CPUE for largemouth bass, bluegill sunfish and pumpkinseed sunfish. The presence of

multiple age classes for largemouth bass indicates that successful recruitment has been occurring

in recent years. Largemouth bass currently have a sufficient prey source represented by the

abundance of bluegill and pumpkin seed sunfish (< 150 mm) but a decline in pumpkinseed PSD

appears to have occurred from 2009 to 2017. This possible reduction in available prey may

explain the reduced PSD for largemouth bass. While the current supply of prey should help

maintain largemouth bass populations at their current proportional stock densities any further

decline in prey could mean declines in largemouth bass recruitment and size.

To help guide the MLID in managing their fishery, suggested target ranges for size

structure indices for a variety of species and for favoring big bass or panfish are provided in

Tables 22 and 23. A tic tac toe plot for bluegill compared to largemouth bass PSD is also

included with possible management strategies (Figure 39).

Table 22. Suggested target ranges for size structure indices for a variety of species. From Willis

et al. (1993).

Species P SD P SD − P P SD − M

Largemouth Bass 40-70 10-40 0-10

Bluegill 20-60 5-20 0-10

Crappies 30-60 >10 -

Yellow Perch 30-60 - -

78

Table 23. Suggested target ranges for size structure indices for largemouth bass and bluegill

under three different management options. From Willis et al. (1993).

Option

P SD P SD − P P SD − M P SD P SD − P

Largemouth Bass Bluegill

Panfish 20-40 0-10 - 50-80 10-30

Balance 40-70 10-40 0-10 20-60 5-20

Big Bass 50-80 30-60 10-25 10-50 0-10

100 Characterized by stunted

bass and large bluegill.

Harvest more bass less

than 12 inches to correct

overcrowding. Increase

Harvest of six inch plus

bluegills.

Increase harvest of large

bluegills. If fishing

pressure is heavy,

release 12" - 15" bass.

Optimum situation for

anglers (large bass and

large blue gill).

Normally only a

temporary situation

unless no fishing occurs.

Bluegill

PSD

40 Bass reaching over

crowded condition.

Increase harvest of bass

less than 12 inches.

Watch bluegill PSD.

Balanced Release 12" -

15" bass.

Normally a temporary

situation

20 Possible habitat problem

- growth of both species

may be limited due to

heavy weed growth,

muddy condition, etc.

Increase bluegill harvest.

Bass population may be

going toward over-

population

Small bass may be out

competed for food by a

stunted bluegill

population - results in

low bass recruitment,

poor bluegill growth

40 60

Bass PSD

Figure 39. Tic tac to plot of bluegill compared largemouth bass PSD showing management

strategies that target a “balanced fishery” (center square).

The predator to prey ratio in Lake Mohegan appears to be conducive for promoting a

balanced fish population, but not for promoting increased zooplankton grazing and improved

water clarity. The current predator to prey ratio (by number) of 0.20 did not correlate with

increased zooplankton grazing rates and improved water clarity in this study (see Zooplankton

chapter 6). The percent predatory biomass of 26 % is much lower than the recommended

predatory biomass of > 40 % for improved zooplankton grazing rates and improved water clarity

79

(Baker et al. 1993). Management strategies that would further reduce planktivorous fish in Lake

Mohegan may increase zooplankton grazing rates and improve water clarity but also may affect

recreational opportunities of game fish because game fish are reliant both on planktivorous fishes

as an adult food source, and on plankton as a larval food source. A more direct bio-manipulation

strategy that would likely have fewer negative effects on game fish would be to begin a rough

fish harvesting program, mainly for common carp that are breeding in Lake Mohegan and are

likely contributing to increased rates of internal nutrient loading and poor water clarity (Weber

and Brown 2009). A low-cost way of removing carp would be to hold fish derbies and bow-

fishing tournaments.

SUMMARY POINTS

192 fish were caught during the 2017 survey with bluegill sunfish (53 %; Lepomis

macrochirus), largemouth bass (17 %; Micropterus salmoides), and pumpkinseed sunfish

(15 %; Lepomis gibbosus) making up the majority of the fish community.

Less fish was caught in 2017 with a CPUE of 128 fish / hr. compared to 237 fish / hr. in

2009.

The PSD for largemouth bass dropped from 68 to 50, while bluegill sunfish saw an

increase from 60 to 86 when comparing the 2009 sampling to 2017.

The percent predatory biomass of 26 %, is much lower than the suggested predatory

biomass of > 40 % for improved zooplankton grazing rates and improved water clarity.

References

Allied Biological. 2008/2009. Lake Mohegan Fish Population Study.

Anderson, R. O. 1996. Length, weight, and associated structural indices. Fish. Tech.

Baker, J. P., H. Olem, C. S. Creager, M. D. Marcus, and B. R. Parkhurst. 1993. Fish and fisheries

management in lakes and reservoirs. US Environ. Prot. Agency EPA.

Cooke, G. D., E. B. Welch, S. Peterson, and S. A. Nichols. 2005. Restoration and management

of lakes and reservoirs, CRC press, Boca Raton, Florida.

Gabelhouse, D. W. 1984. A length-categorization system to assess fish stocks. North Am. J.

Fish. Manag. 4: 273–285.

80

Mills, E. L., D. M. Green, and A. Schiavone 1987. Use of zooplankton size to assess the

community structure of fish populations in freshwater lakes. North Am. J. Fish. Manag.

7: 369–378.

Mills, E. L., and A. Schiavone 1982. Evaluation of fish communities through assessment of

zooplankton populations and measures of lake productivity. North Am. J. Fish. Manag. 2:

14–27.

Ogle, D. H. 2017. FSA: Fisheries Stock Analysis. R package version 0.8. 7, 2016,.

R Core Team. 2017. R: A Language and Environment for Statistical Computing, R Foundation

for Statistical Computing.

Weber, M. J., and M. L. Brown. 2009. Effects of common carp on aquatic ecosystems 80 years

after “carp as a dominant”: Ecological insights for fisheries management. Rev. Fish. Sci.

17: 524–537.

Chapter 8: A Comprehensive Lake Management Plan for Lake Mohegan

Introduction

In this final chapter, a comprehensive lake management plan is presented based on the data

collected and analyzed throughout the previous chapters. The issues identified in this study have

been summarized to include:

• Recommended goals, objectives, thresholds, and actions

• An outline of activities and restoration measures to meet recommended goals

• Any necessary permit requirements for remedial actions

• Cost and potential funding sources for remedial actions, and a

• General timetable for implementation

Nutrients

Nutrients from Internal Loading

Internal nutrient loading represented 48 % of the total annual phosphorus (P) budget and

12 % of the total annual nitrogen (N) budget to Lake Mohegan. Most of the internal loading

occurred in July and August when temperatures increased, and dissolved oxygen dropped below

2 mg / L at the sediment-water interface. Reducing internal loading, particularly P, would reduce

algal blooms and improve water clarity. Based on lake loading response model (LLRM; Wagner

81

2016), a complete reduction in internal P loading would bring the trophic status of the lake into a

less eutrophic state (see Table 16 Chapter 3). In that scenario, chlorophyll a (chl. a) would likely

average 24 ug/L and Secchi (Zsd) average between 1 – 1.5 m. Other beneficial changes (e.g.,

phytoplankton assemblage, zooplankton size, fisheries, etc.) may also occur.

Recommendations for Reducing Internal Loading:

To achieve a complete reduction in internal P loading, approximately 214 kg (472 lbs.) of

P would need to be removed from the sediments or inactivated. The most effective restoration

approach in terms of cost (lbs. P reduced per dollar), efficacy and treatment longevity would be

to modify the current aeration system to include an alum injection system. Other internal P

loading restoration techniques (i.e., dredging, rough fish harvesting, plant nutrient removal,

Phoslock®) are either too expensive and/or would not meet the objectives outlined in this plan.

While alum is not currently permitted for use in New York State, it is in the process of review

and may be available again for use shortly. In the meantime, the current aeration design needs to

be altered to maximize benefits regardless of whether alum is permitted in the future. Thus, the

first phase in addressing internal nutrient loading would be to rearrange the ~ 47 diffuser heads

such that they are more evenly distributed throughout the lake. The diffuser locations and

subsequent mixing should focus on the 4 – 5 m water depth, which is approximately 75 % of the

lake surface area. If the alum injection system is permitted, then a second phase could include

installation of alum injection lines directly to diffuser heads. The aeration system would allow

for flash mixing of alum with water column P and better floc distribution to the lake sediments.

Liquid poly aluminum chloride (Al2Cl(OH)5) may be preferred over aluminum sulfate

(Al2(SO4)3.14H2O) when using the injection system even though it is more expensive. Poly

aluminum chloride would be less corrosive on the equipment and would not reduce as much lake

alkalinity, thereby reducing the potential need for a buffering agent. Two or three of the current

aeration housing sites can potentially be retrofitted with chemical rated plastic alum holdings

tanks, peristaltic pumps, and alum lines that can be attached directly to air station heads.

The theoretical amount of alum needed to inactivate 472 lbs. of P is between 51,808 –

259,042 lbs. of alum per year. This equates to a total lake dose of 16 to 81 mg / L alum or a 10:1

– 50:1 alum to P ratio. Assuming 5.4 lbs. of alum per gallon, then 9,594 – 47,970 gallons (16 –

81 mg / L dose) would need to be stored on site to be injected over the season. Assuming 90 days

of alum injection, the amount to put out per day would be 107 – 535 gallons.

Refining estimates of the proper amount of alum needed per year in a cost-effective

manor will require starting at low rates (16 mg / L) and adding more until objectives are met. It is

therefore recommended to add one alum injection system (20,000-gallon chemical tank,

peristaltic pump(s) controlled with a 24-hour on–off timer, and alum line(s) to start. This should

be followed by in-lake monitoring for TP, Secchi, pH and alkalinity at varying lake depths (0, 1,

2, 3, 4 m). The amount of alum delivered, and where it can be delivered, will depend on the

82

maximum pound square inch (PSI) rating for the peristaltic pump. A cost-benefit analysis should

be performed in determining which peristaltic pump and diameter line to purchase. A pump that

can handle > 25 PSI would be ideal as it would allow for longer runs to deeper parts of the lake

and the possibility of having multiple injection points all on one pump (would need an additional

manifold), which may reduce capital costs.

It is important to note, a one-time high alum dose (80 – 100 mg/L) would likely be less

effective in terms of the alum to P binding ratio and overall cost when compared to an alum

injection system in Lake Mohegan. Lakes with high flushing rates, high external P loading rates,

and possibly high sedimentation rates and high organic bound P are poor candidates for onetime

high dose treatments (Garrison and Knauer 1984; Brattebo et al. 2017; Wagner 2017; Harper

2017). An alum treatment in 2002 lasted one season and provides some indication of alum

longevity in Lake Mohegan. A higher dose may provide longer lasting results (2 - 3 yrs.), but

further diagnostics of the sediments (sediment fractioning and jar testing; ~ $8,000 – $10,000)

may be required, which would increase cost. It should be made clear to the New York

Department of Environmental Conservation (NYDEC) that an alum injection system is the most

cost-effective way to apply alum and to reduce internal P cycling in Lake Mohegan over the

long-term.

Alum cost:

At rates described previously, and assuming a retail price of $1.4 per gallon alum, the

cost for just alum would be between $13,487 and $67,158 per year. The initial capital investment

to retrofit the existing aeration system, and to add an alum injection system would likely be

$30,000 - $90,000. Once the system is set up the only reoccurring cost would be refilling the

alum tanks each season and possibly consulting/maintenance fees. In-lake monitoring for TP,

chl. a, Secchi, alkalinity, and pH are already in place through Mohegan Lake Improvement

District’s (MLID) participation in the Citizen Statewide Lake Assessment Program (CSLAP).

Alkalinity would need to be monitored more frequently and can be done by purchasing an

alkalinity test kit, Hach model AL-TA for ~ $50. Alkalinity should not be depressed more than

50 % from background conditions and should never go below 50 mg / L CaCO3 as a result of

alum application. Doing annual or semi-annual surface alum treatments would likely double the

cost of alum and be far less effective long-term in Lake Mohegan.

Internal Loading Goals:

Based on modeling done in this study, a complete reduction in internal loading should

reduce the average in-lake total phosphorus (TP) from 94.1 µg P / Lto 50.7 µg P / L. By enacting

both internal and external nutrient reduction programs, an average in-lake TP of ≤ 35 µg P/

Lshould be a reasonable goal. In-lake monitoring for TP will be an important component in

determining alum efficacy and refining the annual alum dose used each year.

83

Timeline for Implementation:

Since alum is not yet permissible in NYS, the only action items that currently can be

completed are contacting local NYSDEC officials about the status of alum use in the state, and

asking about what the permit would require so that the MLID can prepare for anticipated

legalization. In the meantime the aeration system can be retrofitted and cost/design details for

installing the alum injection system(s) can be worked out. The MLID could reduce labor costs if

they were to install the alum injection system(s) themselves with the oversight of a lake

consultant professfional (preffearbly someone who has had experienece with alum injection

systems). Depending on funding, an additional one-time annual “alum fee” may need to be

imposed on lakeside residents to offset the initial captial costs of retrofitting the aeration system

and adding the alum injection system (possibly $250-$500 per resident).

Nutrients from Watershed

Nutrient loading from the watershed represents 39 % of the total annual phosphorus (P)

budget and 68 % of the total annual nitrogen (N) budget to Lake Mohegan. The watershed is

predominantly developed with only 25 % non-developed (Figure 41). In general, best

management practices (BMPs) rarely remove more than 2/3 the load of P or N, and on average

can be expected to remove around 50% of the P and 40% of the N in a given area unless very

carefully designed, built and maintained (Wagner 2016; Osgood 2017). The luxury of space is

not often affordable, forcing creativity or greater expense to achieve higher removal rates. While

BMP’s maybe be limited in the amount of nutrient loading reduced to Lake Mohegan, any

reduction will reduce the long-term management cost and therefore should be investigated.

84

Figure 40. Land use and cover map (ESRI 2011; Jin et al. 2013).

Recommendations for Reducing Nutrients from the Watershed:

As residentially owned property comprises most of Mohgan Lake’s watershed,

substantial reductions in nutrient loading to the lake are possible by encouraging homeowners to

incorporate residential best management practices. Many of these practices are inexpensive and

have the added benefit of instilling a sense of community involvement among the residents. A

public outreach campaign should be initiated to educate residents about the importance of

85

reducing nutrient loading to the lake. The following are examples of practices that should be

encouraged:

https://www.lakecountyil.gov/DocumentCenter/View/2994

http://www.phillywatersheds.org/doc/Homeowners_Guide_Stormwater_Management

.pdf

http://www.stormwaterguide.org/static/HomeownersGuide.pdf

http://dec.vermont.gov/sites/dec/files/wsm/erp/docs/VT_Guide_to_Stormwater_for_

Homeowners_DRAFT.pdf

Topics covered in the above resources include:

Assessing storm water on your property

Lawn and garden care

Pet waste

Impacts of putting sand down around shorelines

De-icing BMP’s

Buffering streams and shorelines

Plant selection

Vehicle Maintenance

Impacts of paving

Tree planting

Caring for your backyard stream

Rain barrels and gardens

Planters

Dry Wells

Infiltration test

One economical approach to treat incoming P to Lake Mohegan would be to fill barley

sacks with dry alum and place them along the major inflows. According to McComas (2003), 15

– 25 lbs. of alum is needed to inactivate 1 lb. of P in an incoming stream. Lake Mohegan

receives 388 lbs. of P from the watershed each year. Thus, adding 5,820 – 9,700 lbs. of alum

around the inflows, should greatly reduce the amount of P coming into the lake. The alum sacks

should be put out each year in the early spring and be replenished after major rain events. Dry

alum is less expensive than liquid alum and is easily handled. Another economical solution is to

increase the amount of alum to be used with the injection system outlined in the previous section

such that it accounts for external P loading. Depending on the alum to P binding efficiency this

may be obtained with the rates previously outlined.

Watershed Goals:

86

Land use regulations requiring enforcable BMPs enacted by the town planning board

should be set in the bylaws. An example goal would be: the current watershed should not be

altered in any way that will cause any significant degradation to Lake Mohegan.

Nutrients from Waterfowl

Nutrient loading from waterfowl represented 8 % of the total annual P loading and 4 % of

the total annual N loading. Anecdotally, Canada geese (Brenta canadensis) were the dominate

birds observed on Lake Mohegan during 2016.

Recommendations for Reducing Nutrients from Waterfowl:

The NYDEC has published online a detail overview of control strategies, resources, and

permit requirements, which can be found at:

https://www.dec.ny.gov/docs/wildlife_pdf/geeseproblem.pdf.

Goose control strategies include:

Habitat modification tactics

Exclusion

Frightening devices/methods

Repellents and chemicals

Capture

Egg addling

Nutrients from Septic Systems

Septic system nutrient loading represents 2 % of the total annual P budget and 5 % of the

total annual N budget to Lake Mohegan. The geology of Lake Mohegan is very limited to

somewhat limited in nutrient and other pollutant absorption capabilities (Figure 41).

87

Figure 41. Lake Mohegan soil type for septic tank absorption field ranking from poor absorption

of pollutants (red) to good absorption of pollutants (green; ESRI 2011; Jin et al. 2013).

Recommendations for Reducing Septic Loading:

According to the MLID, many residents within the watershed have now switched to city

sewer, though the exact number of people using septic systems is still unknown. Therefore,

efforts should be made to determine the number of households still on septic. The MLID can

then provide educational information for those individuals to 1) ensure septic systems are

operating properly and that BMP’s are being utilized and 2) the overall cost and benefits of

switching to city waste disposal systems. Two on-site sanitary system shorts courses (~ 43 mins

88

total) can be taken online. These courses provide the necessary information for homeowners to

begin the process of assessing their septic systems and how to improve them. Links to short

courses:

https://www.youtube.com/watch?v=udBaGyzJyU8

https://www.youtube.com/watch?v=jmBqNABTuPE

The MLID should work with homeowners in a collaborative way that preserves anonymity to

address their on-site needs.

On-site Sanitary Waste Disposal Goals:

A narrative goal for septic systems should be set in the bylaws. Inspection, repair and

enforcment of septic systems can be enacted by town law and land use regulations. An example

goal would be: septic systems should be maintained such that they do not cause any significant

degradation to Lake Mohegan.

Sedimentation, Erosion, and Lake Depth

Lake Mohegan has a substantial amount of unconsolidated sediments (average depth =

6.25 m or 20.5 ft) on the lake bottom, representing ~ 80 % of the total lake water volume.

Recommendations for Reducing Sedimentation and Erosion:

Dredging the estimated 1,166,000 m3 (1,512,000 yd

3) of sediment out of Lake Mohegan

would remove nutrients from internal cycling and greatly expand available open-water habitat in

the lake but would come at a high cost. Assuming a $4.00 to $8.00 per cubic yard cost to design

and build the spoil, area and dredge the material, then the cost to remove all the estimated

sediments in Lake Mohegan would be $6 - 12 million. It is unlikely the MLID will ever have the

capital to implement lake dredging with annual lake fees alone. Federal and state funding would

need to be acquired. For such funding to be approved, a more detailed assessment of the lake

sediments, and the dredging procedure/cost would need to be worked out. From there a grant

writer could be hired to assist in petitioning for funds.

Until dredging funds can be obtained, efforts to reduce the amount of sediment loading to

Lake Mohegan should be enacted. Most of the erosion and sedimentation will occur along the

major inflows surrounding the lake during storm events. Ensuring these areas have a sufficient

buffering of native trees, shrubs, and plants will greatly reduce erosion and sedimentation. The

town should consider enacting a mandatory “buffer strip” of at least 25’ if possible, but no less

than 10’, of dense, preferably natural vegetation to grow along the shoreline, streambanks, and

the top of streambanks. There are a number of inexpensive or free programs in New York (e.g.

“Trees for Tribs”) that may provide capital and workforce assistance. Adding polymer blocks

89

can further aid in reducing the amount of suspended materials coming into Lake Mohegan.

Polymer blocks can be placed along the inflows and will trap sediment before reaching the lake.

The blocks can be replaced each year and be purchased from Applied Polymer Systems, Inc.

(Tel: 866.200.9868) for about $100 per block.

Aquatic Plants

Lake Mohegan had a percent area covered (PAC; which refers to the overall surface area

that has vegetation growing) of 14.1% and an average biovolume (BV; which refers to the

percentage of the water column taken up by plants where plants exist) was 27.8 % in late June

(Figure 42). Numerous research studies have demonstrated that fish feeding success and prey

availability depends on how many visual barriers are present in the water column (Baker et al.

1993). Some plant biovolume is needed to support prey communities and water quality (50% is a

good rule of thumb), but too much (>80%) can promote overly abundant and stunted fish

communities and create recreational nuisances (Wetzel 1990; Baker et al. 1993; Horppila and

Nurminen 2003).

The dominant submerged aquatic plant during the 2016 survey was the invasive species

Eurasian watermilfoil (Myriophyllum spicatum) followed by curly leaf pondweed (Potamogeton

crispus). The floating leaved, invasive species water chestnut (Trapa natans) was also observed

in sparse populations. Reducing the spread of invasive aquatic plants and promoting the

establishment of native plants should be a management priority.

90

Figure 42. Acoustic mapping survey conducted in Lake Mohegan June 30, 2016 in (Navico

2017). The red to yellow areas on the maps indicate the highest density of plants with the blue

areas corresponding to lower densities.

Aquatic Plant Management Recommendations:

The MLID has a mechanical plant harvesting program in place that is well accepted

within the community. Common carp (Cyprinus carpio) are also present in the lake and can

contribute to submerged aquatic plant (SAV) reductions via herbivory (Weber and Brown 2009).

While other management approaches (e.g., herbicides) are likely cheaper, less invasive, and

more selective toward invasive species, switching to such a program is unlikely to happen in

Lake Mohegan because it is not currently institutionally accepted within the community.

There are some improvements that can be made within the current plant harvesting

program. A map of plant distributions should be made with designated areas where plant

harvesting is allowed and not allowed. Efforts should also be made to not reduce PAC below 30

91

%. To do this, Aquatic vegetation mapping to determine PAC can be done by homeowners using

an acoustic Lowrance fish finder HDS 5 models and above (~ $300 for HDS 5 unit). The

acoustic data would need to be recorded on an SD card and then uploaded to a third-party site for

data processing. This can be done using BioBasee (for more details on how to obtain and upload

acoustic data see: https://www.cibiobase.com/Home/Index). Upload fees range from $150 - $250

per upload. Mapping should occur in early spring and late to mid-summer every year. Efforts to

reduce nutrients from the harvested plants could also be enacted but would be costly. An

additional conveyer belt/truck would need to be purchased or employed to haul the plant

materials away each harvesting day.

Plant Goals:

A narrative goal should be set for plants in Lake Mohegan. An example would be: the

plant community in Lake Mohegan should not be altered in any way that causes degradation to

the ecology of the lake or the recreational services to the community. Invasive species shall be

actively managed, and native plants shall be unharmed. A numerical goal should be set for PAC

of no less than 30 % and no greater than 65 %.

Algae

The dominant species of algae found in 2016 was a cyanobacterium in the genus

Worchina. Unfortunately, the contribution of this species to carbon flow and food webs in lakes

has not received sufficient study to permit even a brief overview. However, in general,

cyanobacteria are not preferred by zooplankton (De Bernardi and Giussani 1990), and therefore

may lead to insufficient processing to higher trophic levels (i.e., fish). Harmful algal blooms

(HABs) represent the greatest threat to Lake Mohegan. Efforts should be made to moderate

HABs and to promote green algae dominance, which may lead to better energy processing up to

higher trophic levels.

Algae Recommendations:

Moderating HABs will likely best be achieved by reducing the supply of P. Specifically,

these efforts should target the reduction of internal and external P loading and maintenance of

vascular plants (~ 50 % areal cover of submerged aquatic plants). The use of alum as described

previous in this chapter would likely be the most economical and effective approach. Reducing P

could also help promote green algae by increasing the total nitrogen to total phosphorus (TN:TP)

ratio (Watson et al. 1997). Monitoring the type of algae and the TN:TP ratio over the season will

be an important component in determining what TN:TP ratio is needed to maintain green algae.

Literature values suggest the TN:TP would need to be maintained above 30 (by weight) to

establish non-cyanobacteria (Smith 1982).

92

Fishery

Fishery Recommendations

A clear consensus of the goals of a fishery management plan what the fishery in Lake

Mohegan should be utilized for and what the ideal fishery should look like needs to be agreed on

before any management actions are taken. What needs to be discussed is:

What do people want out of the fishery in Lake Mohegan regarding angling opportunity?

For example, does the community want a balanced and diverse fish community or does

the community prefer to catch only certain species of fish like big largemouth bass or

black crappie?

What is the primary use of the fishery? Is the primary use for angling opportunities with

the secondary use to possibly improve water clarity or vice versa or just one of the

above?

Once a clear and concise use of the fishery in Lake Mohegan is established, action goals

should then be set so that management can move towards those goals. Action goals will likely

need to be set with guidance from a fishery expert using the data provided in this study. A

fishery expert will also need to be part of the on-going management process. There may be

consulting costs if a private sector fishery manager is used. Contacting the NYSDEC fisheries

division Region 4 and attending NYSFOLA may provide some free consulting but may be

limited due to the fact that this is a private waterbody.

Ways to reduce common carp populations in Lake Mohegan should also be implemented

if feasible. Some possible low-cost options are hosting angling derbies or bow-hunting

tournaments. Electrofishing and seine netting could also be effective but would be expensive and

require permits.

Timeline for Implementation:

Within one year, fisheries goals should be established, and a fish expert(s) should be

found. Every 3-5 years a detailed re-evaluation of the fishery should be conducted to ensure

management is going in the right direction.

93

Summary of Management Objectives

A summary table of management objectives specific to Lake Mohegan and standards set

by NYS under part 700 are provided in Table 24. Additional management objectives are

provided in Table 25 that are not part of the NYS part 700.

Table 24. Lake Mohegan management objectives

Parameter and

Definition

Part 700 NYS

Standard

Values Obtained

from all data

Management Objective

Secchi (water clarity)

governed by the

levels of algae and

other particles in

water.

No decrease that will

cause a substantial

visible contrast to

natural conditions.

Secchi averaged 0.8

m.

Scenario testing for internal and

external nutrient reduction using

the lake loading response model

(LLRM) provided a mean

Secchi of 1.5 m. Therefore, a

mean Secchi of > 1.5 m should

be obtainable with internal and

external nutrient reduction

programs.

Phosphorus - a major

nutrient governing

primary productivity.

None in the amounts

that will result in the

growth of algae,

weeds, and slimes

that will impair the

waters for their best

usages. NYDEC

uses a statewide

guidance value of 20

µg P L -1

Mean TP was found

to be 94 µg P L -1

Scenario testing for internal and

external nutrient reduction using

the LLRM provided a mean TP

of 33 µg P L -1

. Therefore, < 35

µg P L -1

should be obtainable

with internal and external

nutrient reduction programs.

pH – the scale of pH

values range form 0

(acidic) to 14 (basic)

with pH 7.0 being a

neutral condition,

which has the least

effect on biological

conditions

No less than 6.5 nor

more than 8.5

pH ranged from 9.5

to 7

No less than 6.5 or more than

8.5.

Dissolved oxygen

(DO). Oxygen is

required for fish and

other aquatic life to

survive and

reproduce.

For non-trout waters,

the minimum daily

average shall not be

less than 5.0 mg / L,

and at no time shall

the DO

concentration be less

than 4.0 mg / L.

DO was less than

2.0 mg / L right at

the sediment water

interface.

Greater than 5.0 mg / L

throughout the entire water

column.

94

Table 25. Additional Management Objectives

Parameter and Definition Values Obtained from Current

Study

Management Objective

Chl. a – the common pigment in

algae used as an indicator of

algal levels in the lake.

Mean chl. a was 54.4 µg L -1

and

peak chl. a was 115.8 µg L -1

.

Scenario testing for internal and

external nutrient reduction using

the LLRM provided a mean chl.

a of 14.2 µg L -1

. Therefore, a

mean chl. a of < 15 µg P L -1

should be obtainable with

internal and external nutrient

reduction programs.

Aquatic vegetation Lake Mohegan had a Percent

Area Covered (PAC; which

refers to the overall surface area

that has vegetation growing) of

14.1% in late August in 2016.

Eurasian watermilfoil was the

dominate invasive species found.

PAC should not drop below 30

% and exceed 65 %.

Management activities should

address aquatic plants that are

non-native.

The Fishery The fishery in Lake Mohegan is

relatively balanced with a variety

of warm and cool water fish

species.

To be determined.

References

Baker, J. P., H. Olem, C. S. Creager, M. D. Marcus, and B. R. Parkhurst. 1993. Fish and fisheries

management in lakes and reservoirs. US Environ. Prot. Agency EPA. Brattebo, S. K., E. B. Welch, H. L. Gibbons, M. K. Burghdoff, G. N. Williams, and J. L. Oden.

2017. Effectiveness of alum in a hypereutrophic lake with substantial external loading.

Lake Reserv. Manag. 33: 108–118. De Bernardi, R. de, and G. Giussani. 1990. Are blue-green algae a suitable food for

zooplankton? An overview. Hydrobiologia 200: 29–41. ESRI, R. 2011. ArcGIS desktop: release 10. Environ. Syst. Res. Inst. CA. Garrison, P. J., and D. R. Knauer. 1984. Long-term evaluation of three alum treated lakes. Lake

Reserv. Manag. 1: 513–517. Harper, H. 2017. Compilation, analysis and interpretation of environmental data. Florida Lake

Management Society.

95

Horppila, J., and L. Nurminen. 2003. Effects of submerged macrophytes on sediment

resuspension and internal phosphorus loading in Lake Hiidenvesi (southern Finland).

Water Res. 37: 4468–4474. Jin, S., L. Yang, P. Danielson, C. Homer, F. J., and G. Xian. 2013. A comprehensive change

detection method for updating the National Land Cover Database to circa 2011. Remote

Sens. Environ. 132. McComas, S. 2003. Lake and pond management guidebook, CRC Press. Osgood, R. A. 2017. Inadequacy of best management practices for restoring eutrophic lakes in

the United States: guidance for policy and practice. Inland Waters 7: 401–407. Smith, V. H. 1982. The nitrogen and phosphorus dependence of algal biomass in lakes: an

empirical and theoretical analysis. Limnol. Oceanogr. 27: 1101–1111. US EPA, O. 2015. How to Care for Your Septic System. US EPA. Wagner, K. J. 2016. How to use LLRM, Wagner affiliation. Wagner, K. J. 2017. Preface: Advances in phosphorus inactivation, Taylor & Francis. Watson, S. B., E. McCauley, and J. A. Downing. 1997. Patterns in phytoplankton taxonomic

composition across temperate lakes of differing nutrient status. Limnol. Oceanogr. 42:

487–495. Weber, M. J., and M. L. Brown. 2009. Effects of common carp on aquatic ecosystems 80 years

after “carp as a dominant”: Ecological insights for fisheries management. Rev. Fish. Sci.

17: 524–537. Wetzel, R. 1990. Detritus, macrophytes and nutrient cycling in lakes. Mem Ist Ital Idrobiol 47:

233–249.

Appendix A: Summary of site #1 of 2016 for mean epilimnetic temperature, zooplankton densities and mean length per taxa, as well as derived

values for mean weight per individual and per liter, P regeneration per individual and per liter, filtering rates per individual and the

percent epilimnion filtered per day.

Avg

Temp. #/L

Avg

length Mean Dry Dry Wt

Phos. Regen.

Rate

(µgP*mgdrywt-1

Phos. Regen.

Rate Filtering Rates % Epilimnion

(°C) (mm) Wt (µg) (µg / L) *ind*h-1)

(µg / L / day) (ml / ind / day) Filtered / day

10/10/15 17.24

Cladocera

38.54 0.395 1.55 59.57 2.110 3.017 1.166 4.49

Copepoda

15.41 0.359 2.30 35.48 0.601 0.512 0.921 1.42

Rotifers

15.41 0.291 1.14 17.60 0.000 0.000 0.548 0.84

Total 112.65 3.529 6.76

11/07/15 13.15

Cladocera

9.03 0.403 1.73 15.64 0.651 0.244 1.227 1.11

Copepoda

34.33 0.269 1.03 35.19 0.321 0.271 0.451 1.55

Rotifers

32.52 0.094 0.07 2.29 0.636 0.035 0.034 0.11

Total 53.12 0.550 2.77

03/18/16 9.59

Cladocera

25.54 0.651 6.01 153.63 0.930 3.430 4.035 10.31

Copepoda

48.25 0.342 1.58 76.37 1.265 2.318 0.815 3.93

Rotifers

198.67 0.095 0.07 14.16 2.580 0.877 0.034 0.69

Total

244.16

6.625

14.92

04/20/16 12.88

Cladocera

68.20 0.532 3.27 222.89 5.649 30.219 2.444 16.67

Copepoda

72.90 0.370 1.62 118.01 2.399 6.794 0.990 7.22

Rotifers

25.87 0.096 0.07 1.86 6.228 0.278 0.035 0.09

Total 342.76 37.291 23.98

05/11/16 15.25

Cladocera

68.38 0.493 3.08 210.77 5.726 28.963 2.023 13.84

96

1

Copepoda

64.10 0.232 0.65 41.69 4.320 4.322 0.312 2.00

Rotifers

4.27 0.097 0.07 0.31 6.055 0.046 0.036 0.02

Total 252.78 33.331 15.85

05/25/16 19.05

Cladocera

100.57 0.587 4.04 405.85 5.382 52.418 3.122 31.40

Copepoda

56.23 0.392 2.01 112.86 2.088 5.656 1.147 6.45

Rotifers

25.95 1/0/00 0.06 1.63 7.391 0.289 0.030 0.08

Total 520.33 58.364 37.93

06/17/16 22.42

Cladocera

39.51 0.667 4.92 194.35 5.142 23.984 4.280 16.91

Copepoda

37.86 0.399 2.58 97.86 1.774 4.166 1.200 4.54

Rotifers

80.66 0.110 0.10 8.35 3.923 0.786 0.049 0.40

Total 300.56 28.936 21.85

06/28/16 24.92

Cladocera

80.57 0.365 1.81 145.66 6.473 22.629 0.960 7.73

Copepoda

77.98 0.191 0.53 41.63 4.906 4.902 0.192 1.50

Rotifers

70.18 0.220 0.88 61.79 0.259 0.384 0.274 1.92

Total 249.07 27.914 11.15

07/13/16 26.22

Cladocera

134.26 0.365 1.47 197.38 6.789 32.158 0.962 12.91

Copepoda

4.63 0.153 0.19 0.88 9.522 0.202 0.111 0.05

Rotifers

120.37 0.219 0.67 81.16 0.363 0.707 0.269 3.24

Total 279.42 33.067 16.21

7/27/16 27.80

Cladocera

27.98 0.427 2.17 60.77 6.206 9.051 1.419 3.97

Copepoda

11.52 0.350 1.34 15.42 2.712 1.004 0.865 1.00

Rotifers

162.97 0.106 0.14 22.86 2.666 1.463 0.045 0.73

Total 99.06 11.518 5.70

8/10/16 27.80

Cladocera

102.79 0.352 1.36 140.30 6.905 23.252 0.876 9.00

Copepoda

138.03 0.330 1.63 224.65 2.391 12.889 0.745 10.29

96 97

2

Rotifers

5.87 0.087 0.06 0.34 8.111 0.067 0.027 0.02

Total 365.29 36.207 19.31

8/23/16 26.74

Cladocera

72.80 0.327 1.42 103.55 6.840 17.000 0.734 5.34

Copepoda

54.97 0.211 0.54 29.87 4.850 3.477 0.247 1.36

Rotifers

127.77 0.095 0.07 9.19 6.225 1.374 0.034 0.43

Total 142.62 21.851 7.13

9/11/16 25.81

Cladocera

67.08 0.376 1.37 91.77 6.902 15.201 1.033 6.93

Copepoda

77.81 0.320 1.38 107.06 2.664 6.845 0.695 5.40

Rotifers

61.71 0.182 0.61 37.36 0.416 0.373 0.170 1.05

Total 236.18 22.419 13.38

10/2/16 18.71

Cladocera

50.53 0.506 2.91 147.16 5.801 20.488 2.161 10.92

Copepoda

106.67 0.291 1.14 121.68 3.006 8.780 0.548 5.84

Rotifers

176.85 0.100 0.09 15.54 4.829 1.801 0.039 0.68

Total 284.38 31.069 17.45

10/23/16 15.48

Cladocera

5.82 0.845 15.15 88.13 3.970 8.396 7.705 4.48

Copepoda

37.48 0.298 1.60 59.90 2.419 3.477 0.582 2.18

Rotifers

139.57 0.252 0.77 106.88 0.309 0.792 0.385 5.37

Total 254.91 12.666 12.03

11/17/16 9.51

Cladocera

129.75 0.403 1.84 238.52 6.448 36.914 1.231 15.97

Copepoda

140.13 0.448 2.62 367.69 1.757 15.502 1.598 22.39

Rotifers

825.21 0.101 0.10 84.27 3.991 8.071 0.040 3.29

Total 690.48 60.488 41.64

98

OCCASIONAL PAPERS PUBLISHED BY THE BIOLOGICAL FIELD STATION (cont.)

No. 38. Biocontrol of Eurasian water-milfoil in central New York State: Myriophyllum spicatum L., its insect herbivores and associated fish. Paul H. Lord. August 2004.

No. 39. The benthic macroinvertebrates of Butternut Creek, Otsego County, New York. Michael F. Stensland. June 2005. No. 40. Re-introduction of walleye to Otsego Lake: re-establishing a fishery and subsequent influences of a top Predator.

Mark D. Cornwell. September 2005. No. 41. 1. The role of small lake-outlet streams in the dispersal of zebra mussel (Dreissena polymorpha) veligers in the

upper Susquehanna River basin in New York. 2. Eaton Brook Reservoir boaters: Habits, zebra mussel awareness, and adult zebra mussel dispersal via boater. Michael S. Gray. 2005.

No. 42. The behavior of lake trout, Salvelinus namaycush (Walbaum, 1972) in Otsego Lake: A documentation of the strains, movements and the natural reproduction of lake trout under present conditions. Wesley T. Tibbitts. 2008.

No. 43. The Upper Susquehanna watershed project: A fusion of science and pedagogy. Todd Paternoster. 2008. No. 44. Water chestnut (Trapa natans L.) infestation in the Susquehanna River watershed: Population assessment, control,

and effects. Willow Eyres. 2009. No. 45. The use of radium isotopes and water chemistry to determine patterns of groundwater recharge to Otsego Lake,

Otsego County, New York. Elias J. Maskal. 2009. No. 46. The state of Panther Lake, 2014 and the management of Panther Lake and its watershed. Derek K. Johnson. 2015. No. 47. The state of Hatch Lake and Bradley Brook Reservoir, 2015 & a plan for the management of Hatch Lake and

Bradley Brook Reservoir. Jason E. Luce. 2015. No. 48. Monitoring of seasonal algal succession and characterization of the phytoplankton community: Canadarago Lake,

Otsego County, NY & Canadarago Lake watershed protection plan. Carter Lee Bailey. 2015. No. 49. A scenario-based framework for lake management plans: A case study of Grass Lake & A management plan for

Grass Lake. Owen Zaengle. 2015. No. 50. Cazenovia Lake: A comprehensive management plan. Daniel Kopec. 2015. No. 51. Comprehensive lake management plan, Lake Moraine, Madison County, NY. Benjamin P. German. 2016. No. 52. Determining effective decontamination methods for watercraft exposed to zebra mussels, Dreissena polymorpha

(Pallas 1776), that do not use hot water with high pressure spray. Eric A. Davis. No. 53. The state of Brant Lake, & Brant Lake management plan. Alejandro Reyes. 2016. No. 54. The state of Truesdale Lake & Truesdale Lake management plan. Christian Jenne. 2017. No. 55. The state of Rushford Lake. Edward J. Kwietniewski. 2017. No. 56. Comprehensive lake management plan Goodyear Lake, Otsego County, NY. Caitlin Stroosnyder. 2018. No. 57. The State of Windover Lake, Warren County, New York and a management plan to address stakeholder concerns.

Jenna Leskovec (with edits by W.N. Harman). 2018. No. 58. An integrative taxonomic approach to understanding diversity In Neoechinorhynchus (Acanthocephala) species in North America. Margaret L. Doolin. 2018. No. 59. The State of DeRuyter Reservoir, Madison County, NY and a Plan for the Management of DeRuyter Reservoir. Leah Gorman. 2018. No. 60. Emerald Green Lakes Comprehensive Management Plan. Maxine Verteramo (with edits by W.N. Harman). 2018. No. 61. Millsite Lake State of the Lake & Management Plan. Luke J. Gervase. 2018.

Annual Reports and Technical Reports published by the Biological Field Station are available at:

http://www.oneonta.edu/academics/biofld/publications.asp