R.E. and J. Simpson. 1996. A Coordinator’s Guide to Volunteer Lake Monitoring
Methods. North American Lake Management Society. 96 pp.
Contents (Click on title to move to that section)
State of the Waterbody
Calculating the TSI
Averaging TSI Values
Relating Trophic State Indices to the
State of the Waterbody
A frequently used biomass-related trophic state indices is that of
Carlson (1977). It is relatively simple to use, requires a minimum
of data, and is generally easy to understand, both in theory and
use. It is numerical, but the traditional nutrient-related trophic
state categories fit into the scheme. It seems to be ideal for use
in volunteer programs.
We define trophic state as the total weight of living biological material (biomass) in a waterbody at a specific location and time. Time and location-specific measurements can be aggregated to produce waterbody-level estimations of trophic state. Trophic state is understood to be the biological response to forcing factors such as nutrient additions (Naumann, 1919, 1929), but the effect of nutrients can be modified by factors such as season, grazing, mixing depth, etc.
with the definition of trophic state given above, the trophic
state index (TSI) of Carlson (1977) uses algal biomass as the
basis for trophic state classification. Three variables,
chlorophyll pigments, Secchi depth, and total phosphorus,
independently estimate algal biomass. Unlike Naumann’s
typological classification of trophic state (Naumann, 1929), the
index reflects a continuum of “states.” There are no lake
“types.” The trophic continuum is divided into units based on
a base-2 logarithmic transformation of Secchi depth, each 10-unit
division of the index representing a halving or doubling of Secchi
depth. Because total phosphorus often correlates with
transparency, a doubling of the total phosphorus often corresponds
to a halving of Secchi depth. Chlorophyll pigments double every 7
units rather than every 10 units (Carlson 1980).
The range of
the index is from approximately zero to 100, although the index
theoretically has no lower or upper bounds. The index has the
advantage over the use of the raw variables in that it is easier
to memorize units of 10 rather than the decimal fractions of raw
phosphorus or chlorophyll values. An early version of the index
was based on a scale of one to ten, but it became tempting to add
1, 2, or more numbers after the decimal. For this reason, the
scale was multiplied by ten to discourage any illusory precision
obtained by using more than whole numbers.
logarithmic transformation of the data normalizes the skewed data
distribution, allowing the use of parametric statistics (mean,
standard deviation, parametric comparison tests). This facilitates
not only comparison and data reduction, but communication as well,
because the user does not need to resort to graphs with
index variables are interrelated by linear regression models, and
should produce the same index value for a given combination of
variable values. Any of the three variables can therefore
theoretically be used to classify a waterbody. This is
particularly useful in citizen lake monitoring programs, where
Secchi depth is often the only variable that can be inexpensively
measured. For the purpose of classification, priority is given to
chlorophyll, because this variable is the most accurate of the
three at predicting algal biomass. According to Carlson (1977),
total phosphorus may be better than chlorophyll at predicting
summer trophic state from winter samples, and transparency should
only be used if there are no better methods available.
The index is
relatively simple to calculate and to use. Three equations are
used: Secchi disk, TSI(SD); chlorophyll pigments, TSI(CHL); and
total phosphorus, TSI(TP). The original Secchi depth equation in
Carlson (1977), reproduced below looks forbidding, but illustrates
how the index was constructed.
Secchi disk index was constructed from doublings and halvings of
Secchi disk transparency. The base index value is a Secchi disk of
1 meter, the logarithm of which is zero.
ln 1 = 0
6 - 0 = 6
6 = 60
6 - 0 = 6
6 = 60
10 x 6 = 60
TSI of a 1 meter Secchi depth is 60. If the Secchi depth were 2
ln 2 / ln 2 = 1
6 - 1 = 5
10 x 5 = 50
6 - 1 = 5
10 x 5 = 50
10 x 5 = 50
for the chlorophyll and total phosphorus are derived in a similar
manner, but, instead of a Secchi depth value in the numerator, the
empirical relationship between chlorophyll or total phosphorus and
Secchi depth is given instead.
For example, the chlorophyll TSI is:
For example, the chlorophyll TSI is:
forms of the TSI equations may illustrate how the indices were
derived, but they can be simplified for everyday use. The
simplified equations are below:
TSI(SD) = 60 - 14.41 ln(SD)
TSI(CHL) = 9.81 ln(CHL) + 30.6
TSI(TP) = 14.42 ln(TP) + 4.15
There has been
a tendency to average the three variables rather than to
prioritize their use (Osgood 1982; Kratzer and Brezonik 1981).
Perhaps this is just a natural tendency for humans to seek the
central tendency, or it might reflect the concept that trophic
state is defined by a number of variables. Whatever the
reason, averaging makes no sense at all. The index is predicated
on the idea that it is predicting algal biomass. Chlorophyll is a
better predictor than either of the other two indices. There is no
logic in combining a good predictor with two that are not (Carlson
transparency and phosphorus may co-vary with trophic state, the
changes in transparency are caused by changes in algal biomass and
total phosphorus may or may not be strongly related to algal
biomass. Neither transparency nor phosphorus are independent
estimators of trophic state. Using transparency or phosphorus as
an estimator of chlorophyll is very different than assuming equal
and independent status of the variables.
Any trophic state index gains value when it can be correlated with specific events within a waterbody. Below is a table of attributes that could be expected in a north temperate lake at various TSI values. Some characteristics, such as hypolimnetic oxygen or fisheries may be expected to vary with latitude and altitude and the table may not place these changes in the proper TSI category. We have used the classic terms of oligotrophy, mesotrophy, and eutrophy in their original context of the amount of algae in the water, not hypolimnetic oxygen concentration, so it is quite possible for an oligotrophic lake to have no hypolimnetic oxygen.
A list of possible changes that might be expected in a north temperate lake as the amount of algae changes along the trophic state gradient.
|Attributes||Water Supply||Fisheries & Recreation|
|<30||<0.95||>8||<6||Oligotrophy: Clear water, oxygen throughout the year in the hypolimnion||Water may be suitable for an unfiltered water supply.||Salmonid fisheries dominate|
|30-40||0.95-2.6||8-4||6-12||Hypolimnia of shallower lakes may become anoxic||Salmonid fisheries in deep lakes only|
|40-50||2.6-7.3||4-2||12-24||Mesotrophy: Water moderately clear; increasing probability of hypolimnetic anoxia during summer||Iron, manganese, taste, and odor problems worsen. Raw water turbidity requires filtration.||Hypolimnetic anoxia results in loss of salmonids. Walleye may predominate|
|50-60||7.3-20||2-1||24-48||Eutrophy: Anoxic hypolimnia, macrophyte problems possible||Warm-water fisheries only. Bass may dominate.|
|60-70||20-56||0.5-1||48-96||Blue-green algae dominate, algal scums and macrophyte problems||Episodes of severe taste and odor possible.||Nuisance macrophytes, algal scums, and low transparency may discourage swimming and boating.|
|96-192||Hypereutrophy: (light limited productivity). Dense algae and macrophytes|
|>80||>155||<0.25||192-384||Algal scums, few macrophytes||Rough fish dominate; summer fish kills possible|
unfortunate misconception concerning trophic state is that the
term is synonymous with the concept of water quality. Although the
concepts are related, they should not be used interchangeably.
Trophic state is an absolute scale that describes the biological
condition of a waterbody. The trophic scale is a division of
that variable(s) used in the definition of trophic state and is
not subject to change because of the attitude or biases of the
observer. An oligotrophic or a eutrophic lake has attributes of
production that remain constant no matter what the use of the
water or where the lake is located. For the trophic state terms to
have meaning at all, they must be applicable in any situation in
quality, on the other hand, is a term used to describe the
condition of a water body in relation to human needs or values.
Quality is not an absolute; the terms “good” or “poor”
water quality only have meaning relative to the use of the water
and the attitude of the user. An oligotrophic lake might have good
water quality for swimming but be considered poor water quality
for bass fishing. Confusion can ensue when the term trophic state
is used to infer quality.
for example, that a manager were to establish fishing goals based
on trophic state. Generally fish yield increases as the production
of the lake increases, but there may be changes in the dominant
fish species as a lake eutrophies (Oglesby, et al. 1987).
In northern lakes, salmonids might dominate in clear lakes having
oxygenated hypolimnia. When production increases to the point
where the hypolimnion becomes anoxic, then salmonids may
disappear, to be replaced by percids, then centrarchids, and
finally rough fish such as carp or bullheads. If a fisheries
manager wished to manage all lakes based on fish production, then
the greener the lake the better. However, what is meant by good
water quality would be different for a person wanting to catch
lake trout than a person wanting only bass. In fisheries
management, the relationship between fish production and fish
community structure and trophic state do not change. What changes
is the perception of what is good or bad water quality. In this
case, the meaning of quality water heavily depends on the goals
and expectations of the fishery and the fishermen.
use situations can cause numerous conflicts because of differing
perceptions of water quality by different users. Fishermen may
want the optimal water quality for their particular species of
game fish, boaters will want to minimize weeds, swimmers will want
to see their feet. Other users, such as drinking water utilities,
may want the clearest water possible, but ignore weeds completely.
Vant and Davies-Colley (1988), for example, found that lakes in
New Zealand ceased to be acceptable for swimming at Secchi depths
less than one meter, but Secchi depth apparently did not affect
fishing, passive recreation (relaxation/observation/
picnics/camping), sailing, or power boating. For each use, the
trophic spectrum is being referred to, but the needs of the users,
and thus the perception of quality at any given trophic state,
about water quality is also affected by the general background of
the user. General background means the attitude of the user that
is related to his or her upbringing, geographical location, and
virtually all attitudes that the user brings to lake evaluation
other than that of a user. In a study of lay attitudes about water
quality, Smeltzer and Heiskary (1990) queried volunteers as to
whether their lakes were beautiful or if enjoyment was slightly
impaired, substantially reduced, or nearly impossible. They found
that the volunteer responses varied geographically. In Vermont and
in the northeastern portion of Minnesota, volunteers were more
sensitive to changes in trophic state. In the agricultural region
of southwest Minnesota, lakes that were considered to have minor
problems would have been considered impaired in the other regions.
The lesson here is that what is judged to be good or poor water
quality is affected by regional attitudes.
indices have been constructed to be used with the basic three.
Since nitrogen limitation still classifies a lake along
Naumann’s nutrient axis, the effect of nitrogen limitation can
be estimated by having a companion index to the Total Phosphorus
TSI. Such an index was constructed by Kratzer and Brezonik (1981)
using data from the National Eutrophication Survey on Florida
lakes. This index is calculated using the formula:
TSI(TN) = 54.45 + 14.43 ln(TN) (7.7)
(Nitrogen values must be in units of mg/L.)
index of Kratzer and Brezonik were designed to be used in
nitrogen-limiting conditions, but in reality, is relatively
insensitive to the nitrogen : phosphorus ratio, while the
phosphorus TSI of Carlson deviates at low nitrogen phosphorus
ratios. This suggests that a nitrogen index value might be a more
universally applicable nutrient index than a phosphorus index, but
it also means that a correspondence of the nitrogen index with the
chlorophyll index cannot be used to indicate nitrogen limitation.
If, however, nitrogen and phosphorus indices were plotted at the
same time, then a deviation of only the phosphorus index might
indicate nitrogen limitation, while deviations of both nitrogen
and phosphorus indices might indicate situations where nitrogen or
phosphorus are not limiting.
The TSI in its
present form is based solely on algal biomass. It is therefore
blind to macrophyte biomass and may, therefore, underestimate the
trophic state of macrophyte-dominated lakes. This is a serious
drawback that needs to be addressed. The solution could be very
simple. Canfield et al. (1983) proposed a method to measure
the total phosphorus content of lakes. The total macrophyte
biomass in the lake is estimated by the equation
TSMB = SA x C x B (7.11)
TSMB = total submersed macrophyte biomass, SA = lake surface area,
C = percent cover of submersed aquatic macrophytes, and B =
average biomass collected with a sampler.
Canfield et al. (1983) estimated the total phosphorus in plant
biomass based on the phosphorus in each species and the relative
abundance of each species. The total phosphorus content of the
lake was obtained by adding the amount of phosphorus in the
macrophytes to the amount estimated to be in the water column.
There seems to be no reason why he same approach could not be used
to measure total plant biomass or chlorophyll. If it were used,
trophic state could include both macrophytes and algae, and have
internally consistent units.
the Indices Beyond
major strength of TSI is that the interrelationships between
variables can be used to identify certain conditions in the lake
or reservoir that are related to the factors that limit algal
biomass or affect the measured variables. When more than one of
the three variables are measured, it is possible that different
index values will be obtained. Because the relationships between
the variables were originally derived from regression
relationships and the correlations were not perfect, some
variability between the index values is to be expected. However,
in some situations the variation is not random and factors
interfering with the empirical relationship can be identified.
These deviations of the total phosphorus or the Secchi depth index
from the chlorophyll index can be used to identify errors in
collection or analysis or real deviations from the “standard”
expected values (Carlson 1981). Some possible interpretations of
deviations of the index values are given in the table below
(updated from Carlson 1983).
|Relationship Between TSI Variables||Conditions|
|TSI(Chl) = TSI(TP) = TSI(SD)||Algae dominate light attenuation; TN/TP ~ 33:1|
|TSI(Chl) > TSI(SD)||Large particulates, such as Aphanizomenon flakes, dominate|
|TSI(TP) = TSI(SD) > TSI(CHL)||Non-algal particulates or color dominate light attenuation|
|TSI(SD) = TSI(CHL) > TSI(TP)||Phosphorus limits algal biomass (TN/TP >33:1)|
|TSI(TP) >TSI(CHL) = TSI(SD)||Algae dominate light attenuation but some factor such as nitrogen limitation, zooplankton grazing or toxics limit algal biomass.|
simplest way to use the index for comparison of variables is to
plot the seasonal trends of each of the individual indices. If
every TSI value for each variable is similar and tracks each
other, then you know that the lake is probably phosphorus limited
(TN/TP = 33; Carlson 1992) and that most of the attenuation of
light is by algae.
some lakes, the indices do not correspond throughout the season.
In these cases, something very basic must be affecting the
relationships between the variables. The problem may be as simple
as the data were calculated incorrectly or that a measurement was
done in a manner that produced different values. For example, if
an extractant other than acetone is used for chlorophyll analysis,
a greater amount of chlorophyll might be extracted from each cell,
affecting the chlorophyll relationship with the other variables.
If a volunteer incorrectly measures Secchi depth, a systematic
deviation might also occur.
methodological errors can be ruled out, remaining systematic
seasonal deviations may be caused by interfering factors or
non-measured limiting factors. Chlorophyll and Secchi depth
indices might rise above the phosphorus index, suggesting that the
algae are becoming increasingly phosphorus limited. In other lakes
or during the season, the chlorophyll and transparency indices may
be close together, but both will fall below the phosphorus curve.
This might suggest that the algae are nitrogen-limited or at least
limited by some other factor than phosphorus. Intense zooplankton
grazing, for example, may cause the chlorophyll and Secchi depth
indices to fall below the phosphorus index as the zooplankton
remove algal cells from the water or Secchi depth may fall below
chlorophyll if the grazers selectively eliminate the smaller
turbid lakes, it is common to see a close relationship between the
total phosphorus TSI and the Secchi depth TSI, while the
chlorophyll index falls 10 or 20 units below the others. Clay
particles contain phosphorus, and therefore lakes with heavy clay
turbidity will have the phosphorus correlated with the clay
turbidity, while the algae are neither able to utilize all the
phosphorus nor contribute significantly to the light attenuation.
This relationship of the variables does not necessarily mean that
the algae is limited by light, only that not all the measured
phosphorus is being utilized by the algae.
A Multivariate Comparison
A different way of looking at deviations is reported in Carlson (1992). If both of the deviations, TSI(CHL) - TSI(TP) and TSI(CHL) - TSI(SD), are simultaneously plotted on a single graph, it is possible to identify some of these systematic deviations. The possibilities are illustrated below.
A representation of possible explanations of deviations of the Trophic State Index equations.
TSI (CHL) - TSI (TP) is plotted on the vertical axis, then points
below the X-axis would be associated situations where chlorophyll
is under-predicted by total phosphorus, i.e. situations where
phosphorus may not be limiting chlorophyll. Carlson (1992) reported that this zero line is related to
total nitrogen to total phosphorus (TN/TP) ratios greater than 33:1. Phosphorus is usually thought to
become limiting at a TN/TP ratio of 10:1, therefore slight
deviations below the zero line would not truly indicate nitrogen
limitation. A better interpretation would be that the greater the
negative deviation, the greater the probability of something other
than phosphorus limits algal growth. A combined phosphorus and nitrogen TSI deviation could also
be used for this axis to eliminate the effects of nitrogen as well
as phosphorus limitation. As points go above the zero axis, it
would suggest increasing possibility of phosphorus limitation.
lying to the right of the Y-axis indicate situations where the
transparency is greater than expected from the chlorophyll index.
These deviations may occur if large particulates, such as
blue-green algae (Cyanobacteria), dominate, and transparency is
less affected by the particulates. Deviations to the right may
also occur if zooplankton grazing removes smaller particles and
leaves only large forms. Points to the left of the Y-axis would be
related to situations where transparency is dominated by non-algal
factors such as color or turbidity or where very small particles
Points lying on the diagonal to the left of the origin indicate situations where phosphorus and transparency are correlated, but chlorophyll is not. Points on or near this line would be found in turbid situations where phosphorus is bound to clay particles and therefore turbidity and phosphorus are related, but chlorophyll is not.
form of graph collapses the deviations of the Secchi depth TSI
onto the graph of the other deviations, allowing simultaneous
viewing of the deviations of all three indices. The spatial
location of the data for a single lake or for a number of lakes
can therefore be used to infer possible relationships between the
three variables. This use of the index is still being developed
but holds considerable promise in the interpretation of data.
state determination is an important aspect of lake surveys.
Trophic state is not the same thing as water quality, but trophic
state certainly is one aspect of water quality. Several
recommendations can be made with regard to the use of trophic
1. Use the simplest definition of trophic state: the concept does not have to be so complex that it is cannot be simply explained or easily measured.
2. The recommended definition is that of plant biomass: it is
historically correct, simple to measure, and simple to understand
and explain. It also can be predicted from nutrient models and can
be used to predict other biological characteristics.
3. Remove the mystery from the term eutrophication. Rather
than linking the process to nutrients, which can cause all sorts
of interpretational problems, simply define it as a movement of
the lake’s trophic state in the direction of more plant biomass.
The definition is simple and far more functional than any other
4. The trophic state index of Carlson (1977) is recommended as
the simplest method of calculating and explaining trophic
5. If data for chlorophyll and phosphorus are available, use
chlorophyll as the primary index for trophic state classification.
Use the deviations of the Secchi depth and total phosphorus
indices from the chlorophyll index to infer additional information
about the functioning of the lake.
Use the index as a teaching tool. You can use it to discuss all
the possible factors, not just nutrients, that could make a lake
more eutrophic. For example, you can explain that the deposition
of erosional materials will cause the lake to become shallower,
and therefore enhance macrophyte growth, thus affecting the total
amount of biomass. Discuss the ramifications of change in plant
biomass, how it affects hypolimnetic oxygen and fish species and
its possible effect on food chains and recreational potential.
Be careful about using quality terms when speaking of trophic
state. Even your own perception of quality is affected by your
background and education. Be sensitive to the fact that not all
users will want the same type of water quality that you do. Not
everyone considers the ideal lake to be clear. Always be sensitive
to the background and needs of the users.
D.E. Jr., K.A. Langeland, M.J. Maceina, W.T. Haller, J.V. Shireman,
and J.R.Jones. 1983. Trophic state classification of lakes with
aquatic macrophytes. Can. J. Fish. Aquat. Sci. 40: 1713-1718.
R.E. 1977. A trophic state index for lakes. Limnology and
R.E. 1980. More complications in the chlorophyll-Secchi disk
relationship. Limnology and Oceanography. 25:378-382.
R.E. 1981. Using trophic state indices to examine the dynamics of
eutrophication. p. 218‑221. In: Proceedings of the
International Symposium on Inland Waters and Lake Restoration.
U.S. Environmental Protection Agency. EPA 440/5-81-010.
R.E. 1983. Discussion on “Using differences among Carlson’s
trophic state index values in regional water quality
assessment”, by Richard A. Osgood. Water Resources Bulletin.
R.E. 1992. Expanding the trophic state concept to identify
non-nutrient limited lakes and reservoirs. pp. 59-71 [In]
Proceedings of a National Conference on Enhancing the States’
Lake Management Programs. Monitoring and Lake Impact Assessment.
Carlson, R.E. and J. Simpson. 1996. A Coordinator’s Guide to Volunteer Lake Monitoring Methods. North American Lake Management Society. 96 pp.
C.R. and P.L. Brezonik. 1981. A Carlson‑type trophic state
index for nitrogen in Florida lakes. Water. Res. Bull. 17:
E. 1919. Nagra synpunkter angaende limnoplanktons okologi med
sarskild hansyn till fytoplankton. Sv. Bot. Tidskr. 13: 129-163.
E. 1929. The scope and chief problems of regional limnology. Int.
Revue ges. Hydrobiol. 21: 423-.
R.T., J.H. Leach, and J. Forney. 1987. Potential Stizostedion yield as a function of chlorophyll concentration with special
reference to Lake Erie. Can. J. Fish. Aquat Sci. 44(Suppl. 2):
R. 1983. Using differences among Carlson’s trophic state index
values in regional water quality assessment. Wat. Res. Bull. 18:
E. and S.A. Heiskary. 1990. Analysis and applications of lake user
survey data. Lake and Reservoir Management.
Vant, W.N. and R.J. Davies-Colley. 1988. Water appearance and recreational use of 10 lakes of the North Island (New Zealand). Verh. Internat. Verein. Limnol. 23: 611-615.