AMER. ZOOL., 34:437-451 (1994)
Insights on Species Richness and Turnover from Long-Term
Ecological Research: Fishes in North Temperate Lakes'
JOHN J. MAGNUSON, BARBARA J. BENSON, AND A N N S. MCLAIN
Center for Limnology, University of Wisconsin-Madison, Madison, Wisconsin 53706
SYNOPSIS. We examined eleven years of annual survey data on fish
assemblages in seven lakes. Expectations based on biogeographic literature
were that, owing to the isolation of lakes, fish species structure should be
relatively stable and species turnover low. Our original objective was to
determine whether lakes house relatively stable fish assemblages or ones
with high rates of species turnover. Methodological issues became apparent that caused us to consider issues of rare species and sample sufficiency.
Our data were from samples rather than complete counts and rare species
could have been missed. In our results mean annual richness was considerably lower than cumulative richness. In addition, species turnover
was overestimated and decreased exponentially as the number of years
between observation increased. Sampling variability might explain these
results; however, given the same number of survey years, cumulative
richness increased with the number of years between observations. Apparently extinctions and invasions occurred even within eleven years, but
uncertainty remains because rare taxa can be missed and their appearances
and disappearances in the record influence estimates ofrichnessand turnover. To compensate for this problem we removed rare taxa and corrected
turnover rates by removing an estimate of sampling error (the turnover
rate between adjacent years). Even using these conservative approaches,
estimates of turnover among lakes ranged from 0.36% to 0.50% per year.
Because the threshold for species detection by most sampling regimes is
greater than zero, survey data are expected to underestimate species richness and overestimate species turnover even with standardized methods.
Conservation biologists should evaluate claims of decline in species richness against such considerations.
who wrote "For most lakes, the rate of contemporary colonization is zero or nearly so."
T nn and
° n
Magnuson (19821, Magnusonxet
al
( 1 9 8 9 ) > a n d T o n n et al ( 1 9 9 ° ) f o u n d t h a t
differences in isolation were important
m explaining the richness and assemblage
offishesin small forest lakes of Finland and
Wisconsin. Magnuson et al. (unpublished)
observed that differences m extinction vana b l e s s u c h a s l a k e area
> PH a n dw n t e r anoxia
were more important in predicting richness
and assembly structure than were differences in isolation such as land barriers, steep
1
From the Symposium The Contribution of Long- connecting streams, and roads to the shore.
INTRODUCTION
Lakes are small, relatively isolated environments in which species richness should
be determined by extinction and colonization events in a manner analogous to terrestrial islands (Barbour and Brown, 1974;
Magnuson, 1976; Browne, 1981; Eadie et
al., 1986; Magnuson, 1988). Owing to the
isolation of lakes, invasion rates were
expected to be low by Barbour and Brown
Term Research to the Conservation of Biological Diver- T .
Jt h t t h i
heraii<:e thp arrival
sity presented at the Annual Meeting of the American
' i 1 ^ argued that this was because the arrival
Society of Zoologists, 26-30 December 1992, at Van- o f new taxa was infrequent, but the extinccouver, British Columbia, Canada.
tion o f a taxa once in the lake w a s rapid.
437
438
J. J. MAGNUSON ETAL.
Regardless, owing to the isolation of lakes
in a sea of land, the expectation would be
that species structure should be relatively
stable for long periods and species turnover
should be low.
Long-term annual data on fish species
composition and richness are generally not
available to examine questions of species
richness and turnover in any complete way
but one can observe extinctions and invasions. Long-term annual data are usually
catch records of commercial or recreational
fisheries and only include abundant fishes
large enough to be of interest to humans as
consumers. Fishing effort and methods typically change through time and bias interyear comparisons. Even so, such records are
sufficient to record the extinction of some
fishes and the invasion of exotics; the Laurentian Great Lakes provide a classic example (Christie, 1974). More complete fish surveys are often made, but in our experience
with the Madison lakes in Wisconsin, the
interval between sampling is variable, sampling gear is not standardized, and the
records are less frequent in the earlier years
(Bauman et al., 1974; Magnuson and
Lathrop, 1992). However, these records also
can detect invasions and extinctions even
of small species of no direct interest to fisheries (Lyons, 1989).
Our paper presents some insights from
long-term research that consistently generates annual data with standard methods for
entire fish assemblages in seven northern
Wisconsin lakes. Our example is from a
Long-Term Ecological Research (LTER) Site
funded by the U.S. National Science Foundation. This program began in 1981, so even
here where 11 years are analyzed, the record
is relatively short.
Conservation biology is focused on issues
of species richness and widespread loss of
biodiversity (Wilson, 1992). Defensible
estimates of species richness and species loss
are important to the scientific evaluation of
changes in biodiversity at the species level.
Even when a sampling protocol is standardized and repeated annually for a long
time, it does not by definition or practice
have a threshold of zero for detecting the
presence of a rare taxon. The problem of
detecting rare species confounds estimates
of species loss, and rarity is a common feature of species assemblages. We contend that
this sampling issue has not been dealt with
adequately in evaluations of biodiversity
loss. This will be an increasingly serious
problem as biodiversity issues move more
and more into the public policy arena.
The overarching question we wish to
address is whether lakes house stable assemblages of fishes in ecological time or conversely whether there is a more or less continuous turnover of species. Our objectives
are to estimate species richness and species
turnover, and to evaluate the features of
individual species in lakes related to persistence and temporal changes in presence:
absence. However, owing to the seriousness
of the sampling problem when rare species
are common in the assemblages, we also
have explored the influence of rarity on our
estimates of species richness, the resulting
slopes of species area relations, and species
turnover.
STUDY SITE AND METHODS
The seven lakes are a diverse set (Magnuson et al, 1984; Kratz et al, 1986; Magnuson et al, 1990) in the forested landscape
of Vilas County in north central Wisconsin.
Their mean annual richness differs from 1
to 22fishspecies, and includes warm-, cool-,
and coldwater fishes such as centrarchids
(sunfishes and black basses), cyprinids
(minnows) and an umbrid (mudminnow);
percids (yellow perch, walleye and darters),
esocids (northern pike and muskellunge);
and salmonids (cisco and lake trout) and a
cottid (mottled sculpin). Fish species distributions in these lakes are presented in
Table 1. The lakes range from 1 to 1,608
ha in area and 2.5 to 35.7 m in maximum
depth. Summer thermocline depths range
from none present to 10.3 m and mean summer Secchi depths range from 1.2 to 8.3 m.
Some are seepage lakes without an inlet or
outlet, others are connected by streams. They
include acidic bog or dystrophic lakes, oligotrophic and mesotrophic lakes. Several go
anoxic in some winters. The lakes [Allequash (northern basins), Big Muskellunge,
Crystal, Crystal Bog, Sparkling, Trout
RICHNESS AND TURNOVER OF FISHES IN LAKES
439
TABLE 1. List of species found in 7 lakes over 11 years of sampling at the North Temperate Lakes Long-Term
Ecological Research Site in Wisconsin*
Lake
Species name
Amia calva
Salvelinus namaycush
Coregonus clupeaformis
Coregonus artedii
Osmerus mordax
Umbra limi
Esox masquinongy
Esox lucius
Nocomis biguttatus
Notemigonus crysoleucas
Notropis heterodon
Notropis heterolepis
Notropis cornutus
Notropis volucellus
Notropis rubellus
Phoxinus eos
Pimephales notatus
Pimephales promelas
Semotilus atromaculatus
Moxostoma macrolepidotum
Catostomus commersoni
Ictalurus melas
Ictalurus natalis
Percopsis omiscomaycus
Lota lota
Culaea inconstans
Micropterus salmoides
Micropterus dolomieui
Ambloplites rupestris
Lepomis macrochirus
Lepomis gibbosus
Pomoxis nigromaculatus
Stizostedion vitreum
Perca flavescens
Etheostoma flabellare
Etheostoma exile
Etheostoma nigrum
Percina caprodes
Cottus bairdi
Cumulative richness
(11 years)
TR
AL
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
35
BM
SP
CR
TB
CB
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
26**
23
22
18
1
* Those considered rare in our analysis are underlined. Lakes are arranged in order of descending richness
from left to right. Lake names are abbreviated as follows. TR = Trout Lake, AL = Allequash, BM = Big
Muskellunge, SP = Sparkling, CR = Crystal Lake, TB = Trout Bog, and CB = Crystal Bog.
•* Includes 1 rare unidentified Moxostoma sp.
(southern basins), and Trout Bog] are the 7 analyzed from 1981 to 1991, 11 years. Samprimary lakes of the North Temperate Lakes, pling was standardized by gear and location;
Long-Term Ecological Research Site (LTER) occasional exceptions or errors are uncomstudied by the University of Wisconsin- mon and have been corrected for when posMadison's Center for Limnology. The lakes sible. For the non-bog lakes, annual samare near the Center's Trout Lake Station in pling per lake consisted of: (1) 18 night beach
Vilas County, Wisconsin.
seine hauls (Benson and Magnuson, 1992);
Fish samples from a continuing series were (2) six 24-hr inshore fyke net sets (Lyons
440
J. J. MAGNUSON ET AL.
and Magnuson, 1987); (3) 30 crayfish traps
(unpublished LTER protocol); (4) 1 night of
electroshocking from a boat in four littoral
areas (Lyons and Magnuson, 1987); (5) two
24-hr trammel net sets on the bottom across
the thermocline (unpublished LTER protocol); and (6) two 24-hr sets with 7 vertical
gill nets ranging from 19 to 89 mm stretch
mesh (Rudstam and Magnuson, 1985). In
the two bogs we only used 30 minnow traps
and six 24-hr fyke net sets per year. The six
shoreline sites for seining, fyke netting, and
minnow/crayfish trapping were chosen randomly in 1981 as were the two sites for
trammel netting; the same sites were sampled in succeeding years. The vertical gill
nets were set at the deepest location in the
offshore zone, stretched from a float roller
at the surface to a weighted bar at the bottom. Annual sampling was conducted from
the last week in July through the 3rd week
in August; we typically sampled two lakes
per week in a random sequence from 1981
to 1984 and in the same sequence (Trout,
Allequash, Crystal, Sparkling, Big Muskellunge, Crystal Bog, and Trout Bog) from
1985 to 1991.
Annual richness and species composition
were estimated from the total fish caught by
all gear in a year. Fish identifications were
almost always done in the field, but some
fish were reviewed or identified later at the
Trout Lake Station using Becker (1983).
Except for fish caught in gill nets and trammel nets, fish were usually returned to the
lake alive.
Annual turnover was estimated for each
lake as 100-(I + E)/[(S, + S n )t] where I =
number of new species, E = number of species lost, S, = number of species in year 1,
Sn = number of species n years later, and t
= number of years between samples (Brown
and Kodric-Brown, 1977; Brown and Dinsmore, 1988). In addition, this estimate was
adjusted by subtracting from it the estimate
for turnover between adjacent years. Turnover between adjacent years was the average
turnover between all pairs of adjacent years.
For richness and species turnover we analyzed patterns both for all species and with
the rare species excluded; for this purpose,
rare was defined as an average of two or
fewer individuals caught for all years in
which that species had been caught. We
investigated the effect of changes in effort
and time elapsed between samples on richness and species turnover. For a given number of years sampled and a given number
of years between samples, richness and species turnover were computed for all subsets
of the complete data set which matched this
sampling regime.
We calculated eight species area relations,
four with a log-log model (log S = a + blog A) and four with a semi-log model (S =
a + b-log A) where S was species richness
and A was lake area. For each model, the
four estimates of richness were mean annual
richness with and without rare species and
10-year cumulative richness with and without rare species. Sample size was 7 lakes for
each. We could not use analysis of covariance to test whether the four methods of
estimating richness influence the species area
relation for each model type because the
y-values for each regression are not independent. So, we used a repeated measures,
multivariate regression approach (D. M.
Heisey, personal communication). We first
created 3 new variables, which were the differences of the three pairs of richness measures within a lake. These three within-lake
differences were then simultaneously
regressed against area using multivariate
regression. (Multivariate regression, like
multivariate analysis of variance, permits
the simultaneous analysis of several statistically dependent observations.) If the original lines were parallel, the within-lake differences would not be functions of area. The
absence of an area effect in the multivariate
regression is taken as evidence that the original lines are parallel. If the intercepts in the
mulivariate regression are not significantly
different from zero, this is taken as evidence
that the intercepts of the original lines were
the same.
To explain persistence and changes in
presence:absence of individual species, we
developed measures of persistence and
turnover for individual species and a set of
explanatory variables for each species in
each lake. Persistence was defined as the
maximum number of continuous years a
441
RICHNESS AND TURNOVER OF FISHES IN LAKES
Species Richness
40 -
• Mean = Cumulative
8
•
Richness
species was present in a lake. Changes in
presence:absence of individual species was
denned as the number of times a species
switched from present to absent and vice
versa in a lake. There were five explanatory
variables. First, an index of population density was calculated for each species in each
lake as the average number of individuals
of that taxon caught per year by all gear;
second, an index of population size equaled
the density index times the shoreline length
for littoral species or the density index times
lake area for openwater species. The log of
each index was used in correlation and
regression analyses. Third, the coefficient of
variation (standard deviation/mean) of the
untransformed population density index was
calculated based on the years when a species
was present. The fourth factor was lake size.
Fifth, fish size, used as an index of longevity,
was based on the mean adult size (from
Becker, 1983) sorted into four categories:
small = 0-99 mm, medium = 100-199 mm,
large = 200-319 mm, and very large >320
mm (after Magnuson and Lathrop, 1992).
Sufficiency of the gear was evaluated both
for the whole sampling protocol and for each
individual gear. First, we found the gear
which caught the most species for each lake
and each year. We then ranked the remaining gear types sequentially by the order of
which added the most additional species to
the cumulative species list for each lake and
each year. Winners of ties were chosen randomly. Finally, the percent of total richness
achieved after including each successive rank
was averaged across lakes and years. The
same rank did not necessarily include the
same gears for each lake or year. The relation between percent richness achieved and
gear rank describes the rate at which the
sampling protocol approaches saturation.
We also examined whether individual gear
types continued to find additional species
sequentially over the number of times that
gear was used. For each lake and year we
computed the number of species added by
each successive use of the gear using the
order in which the samples were taken in
the field. We then averaged over lake and
year to examine the relation between the
number of species added and the number
•
§ 20 1
c
!
/
'
;
_„ Mean = 2/3 Cumulative
•"
*
. - • Mean = 1/2 Cumulative
l10i
-> Maximum Richness
• Mean Richness
- Minimum Richness
ft J-''*',
0
^~
10
20
30
Cumulative Richness (11 Years)
40
FIG. 1. Annual fish species richness (mean and range)
for 7 LTER lakes plotted against cumulative richness
of each lake over 11 years from 1981 to 1991. Three
construction lines are plotted: the upper one if mean
richness equaled cumulative richness; the middle one
if mean richness equaled two thirds of cumulative richness; and the bottom one if mean richness equaled one
half of cumulative richness. From left to right the lakes
are Crystal Bog, Trout Bog, Crystal, Sparkling, Big
Muskellunge, Allequash, and Trout.
of samples. This relation provides information on the extent to which each gear
failed to find new species after a given number of replications.
RESULTS
Richness and cumulative richness
Mean annual richness was approximately
% of the 11 -year cumulative richness for the
7 lakes (Fig. 1). For the individual lakes
mean annual richness ranged from 1 to 22
species while cumulative richness ranged
from 1 to 35 species. Only for Crystal Bog
did mean richness equal cumulative richness; both were 1 species. Only for Trout
Bog did the maximum richness in any year
equal cumulative richness; both were 3 species. In the lakes with more species than the
two bog lakes, mean annual richness ranged
from 40% (Crystal Lake) to 76% (Big Muskellunge Lake) of cumulative richness. In
individual lakes cumulative richness
exceeded mean richness by as many as 11
species (Crystal Lake) and 13 species (Trout
Lake).
Cumulative richness averaged across all
lakes increased smoothly and progressively
with the number of consecutive years over
which species were aggregated. This is
apparent along the left axes of Figure 2, A
442
J. J. MAGNUSON ET AL.
For example, the annual mean of all lakes
was 12 species for all taxa and 11 species
when rare taxa were removed. When we
examined two sampling years at a time, the
Increased
richness increased over the annual mean for
Richness
all taxa by about 15% for consecutive years,
30 (% above
Annual Mean)
but by about 18% for years separated by 5
years. When we examined three sampling
years at a time for all taxa, the influence of
the interval between sampling years
Annual Mean
= 12 Species
appeared to be even greater; a 24% increase
Number of
Range among
5
t
2
3
4
5
6
Years
over mean annual richness for three conLakes = 1 - 2 2
Intervening Years between Samples
Sampled
secutive years increased to a 40% increase
when each of the three years was separated
by 5 intervening years. Similar patterns were
B. Rare Species Removed
apparent with the rare taxa removed (Fig.
2B). Thus, the longer the time span represented in the aggregation of species, the
greater the increase in observed richness
Increased
even with the same overall sampling effort.
Richness
••-|-|~)-)3o (% above
We tested whether the slopes of the speAnnual Mean)
cies-area relations were influenced by the
method we chose to estimate richness
(cumulative or mean annual richness both
Annual Mean
with or without rare species included). Both
= 10.8 Species
Number c. o
the log-log and the semi-log models fit the
5 2
1
2
3
4
Range among
Years
Intervening Years between Samples Lakes = 1 - 2 2
data well; r1 values ranged from 0.88 to 0.96.
Sampled
Using the repeated measures multivariate
regression approach described in the methFIG. 2. Increased richness as a percent of the mean ods, there was no evidence that the slopes
annual richness for all seven lakes averaged, observed differed among methods for the log-log
as a function both of the number of years sampled and
the number of years between samples. Panel A is for model; in the multivariate regression analall taxa and panel B is with rare taxa removed. Results ysis of differences between methods of estiwere obtained by resampling our data with time win- mating richness, P = 0.16 for an area effect
dows ranging from 2 to 10 years with intervals between (slopes of species area relation) and P = 0.48
sampling from 1 to 6 years. Results for the longer strings for an intercept effect using Wilk's Lambda
and intervals are not available because of the relatively
with 3 degrees of freedom in the numerator
short duration of our study (11 years).
and 3 in the denominator. For the log-log
model the four slopes only ranged from 0.46
to
0.48 so the absence of a statistical difand B, which show the increase in richness
ference
was not unexpected. For the semi(percent of annual mean for all lakes comlog
model,
however, slopes of the species
bined) observed as a function of the number
area
relation
ranged from 6.6 to 10.6 and
of years sampled. The same patterns
there
was
some
statistical evidence for difoccurred in analyses with all species (Fig.
2A) and with the rare species removed (Fig. ferences. In the multivariate regression
analysis of differences between methods of
2B).
estimating richness, P = 0.055 for an area
Cumulative richness also increased with effect (slopes of species area relation) and P
the interval between sampling years. This = 0.58 for an intercept effect using Wilk's
is apparent along the front and right hand Lambda with 3 degrees of freedom in the
axes of Figure 2, A and B, which show the numerator and 3 in the denominator.
increase in richness (percent of annual mean
for all lakes combined) observed as a funcWe conclude that for the semi-log model
tion of the interval between sampling years. there was some evidence for differences in
A. All Species Included
1
2
443
RICHNESS AND TURNOVER OF FISHES IN LAKES
slopes of species area relations among methods of estimating richness. For the semi-log
model the steepest slope was for 10-year
cumulative richness with all species
included; the lowest slope was for mean
annual richness with rare species removed.
From inspection of the graphs, removing
rare species appeared to reduce the slope
more with cumulative richness data than
with mean annual richness data. Results
from the semi-log model analysis demonstrate that slopes of species-area relations
may differ statistically with the method of
richness estimation chosen by the investigator.
20
A. All Species
^~~~
o 15
o
I 10
o
1 5
V
Percent Turnover
— 100(ltE)/(S1*S2)
— 100(ltE)/(S!*S2)t
—•—16/1
"tea
0
3
4
Years between Sampling
B. Rare Species Removed
15
Species turnover
To obtain believable estimates of species
turnover, several adjustments were required. o 10
Percent Turnover
Mean species turnover between adjacent t—•— 100(l+EWS1+S2)
years averaged across all lakes was 16% for v
—
100(l+E|/(S1+S2)t
— ° — 9 6/1
all taxa and 9.6% even when rare taxa were S 5
removed (Fig. 3A, B). This is clearly an
overestimate. Turnover increased or
decreased with the interval between sam3
4
Years between Sampling
pling years, depending on the measure used.
When turnover was calculated as a percent
of species per year [100(1 + E)/[(S, + S2)t]],
it decreased rapidly from 1 year to 2 year FIG. 3. Influence of number of years between samintervals: from 16% to 7% per year for all pling on estimates of species turnover averaged across
taxa and from 10% to 5% per year when all 7 lakes. Panel A is for all species and Panel B is
rare taxa were removed. After five or six with rare species removed. Results were obtained by
resampling our 11 year data set with a sliding window
years it was near 3% per year for all taxa of
adjacent years to those up to 6 years later. Equations
and 2% per year with rare taxa removed.
for the different estimates are in the legends. The dashed
This value for annual turnover was line in each panel indicates the turnover between adjaadjusted further by subtracting the estimate cent years unadjusted by t.
of turnover between adjacent years. We
viewed changes between adjacent years to
be primarily caused by sampling noise from estimated from four values, i.e., results for
rare to uncommon species and as a time intervals of 5 and 6 years and with and withinterval too short for major changes owing out rare species removed. These ranged from
to invasion and extinction. For all species 0% to 1.09% per year: Crystal Bog = 0% per
the adjusted estimate of turnover was 0.36% year, Allequash = 0.10% per year, Sparkling
and 0.50% per year for surveys five and six = 0.41% per year, Big Muskellunge = 0.46%
years apart for a mean of 0.43% per year. per year, Trout Bog = 0.52% per year, Trout
With the rare species removed the adjusted = 0.54% per year, and Crystal = 1.09% per
estimate of turnover was 0.41% and 0.47% year. These are our most robust estimates
per year for surveys five and six years apart of species turnover caused by extinctions
for a mean of 0.44% per year. In Figure 3 and invasions. We think they are underesthese values equal the difference between timates.
the two bottom curves for five and six year
Turnover between pairs of years, calcuintervals between sampling. A mean lated without dividing by the number of
adjusted turnover for individual lakes was years that had elapsed, [100(1 + E)/(S, +
444
J. J. MAGNUSON ET AL.
-Si
I
S2)], appeared to increase with years between
sampling (Fig. 3, upper line). A monotonic
increase is supported by Kendall Rank Correlation test for ranks of the turnover rate
and year both when all species were included
(Tau = 0.73, n = 6, P = 0.04) and with rare
species removed (Tau = 0.87, n = 6, P =
0.01). These tests may be biased because
points on the graph are not independent.
Our interpretation is that species turnover
increased with years between surveys and
that this provides further evidence that
invasions and extinctions did occur.
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Persistence and presence:
Absence changes for individual species
For individual species in individual lakes,
persistence (measured as maximum number of continuous years a species was found
in the lake) and presence:absence changes
(measured as number of times a species
changed from present to absent or vice versa)
were both best explained by the log index
of population density of the species in the
lake (Table 2). Adjusting population density
to an estimate of population size did not
improve the r2 values of the regressions; r2
values declined from 0.61 to 0.53 for persistence and from 0.46 to 0.39 for species
turnover when densities were adjusted to an
index of population size. However, in the
single variable models, both regressions on
log index of population density and on log
index of population size were statistically
significant (P = 0.01) for species persistence
and species turnover, while coefficient of
variation, lake size and fish size were not
statistically significant explanatory variables.
In the stepwise regression model for persistence, log index of population density was
the most predictive variable followed by
body size rank. The regression equation was:
persistence = 0.736 + 4.077 log density
index + 0.709 fish size rank. The multiple
correlation coefficient r = 0.80 (adjusted r2
= 0.63), was only slightly greater than for
the simple regression of persistence on log
density index, r = 0.78 (r2 = 0.61). The
partial correlation coefficient for persistence
and fish size with log density index held
constant was r = 0.30 (r2 = 0.09). Thus,
while fish size was a significant factor along
445
RICHNESS AND TURNOVER OF FISHES IN LAKES
ess
erce nt of Total
with log density index in stepwise regression, it accounted for only a small portion
of the variation in persistence compared with
log density index.
In the stepwise regression model for
changes in presencerabsence, log population
density index was the most predictive variable followed by the coefficient of variation
in density index. The regression equation
was: changes = 3.642 - 1.960 log density
index + 0.743 coefficient of variation. The
multiple correlation coefficient, r = 0.72
(adjusted r2 = 0.52) was only slightly greater
than for the simple regression of turnover .cc
on log density index, r = -0.68 (adjusted o
r2 = 0.46). The partial correlation coefficient ir
for changes and coefficient of variation with
log density index held constant was r = 0.33
(r2 = 0.11). Thus, while the coefficient of
variation was a significant factor along with
log density index in stepwise regression, it
accounted for only a small portion of the
variation in changes in presence:absence
compared with log density index.
• —
1UU -
95
/
/
90 -
/
/
85 •
/
/
80 •
/
75 • /
*
<
70 •
1
1
1
1
Rank of Sampling Gear
FIG. 4. Mean cumulative richness per lake per year
(percent of total cumulative richness per lake per year)
as a function of additional sampling gears. Gears were
ordered from 1 to 6 for each lake year by the order in
which they added to cumulative richness. The same
gear was not always the same rank in different lake
years, but in general gears can be ordered from left to
right as (1) seine, (2) electroshocker, (3) vertical gill net,
(4) fyke net, (5) trammel net, and (6) crayfish trap.
Richness related to sampling
gears and replicates
Species richness averaged over the 5 nonbog lakes and 11 years appeared to saturate
by the time four of the six sampling gears
had been included in the estimate (Fig. 4).
The sampling gear catching the most species
caught on average 73% of those for all gears
combined. The seine was the gear that usu- and 11 years did not saturate with replicaally caught the most species (35 out of 55 tion for any gear type (Fig. 5). The number
lake-years). Except for the crayfish trap, each of replicates ranged from 2 to 18 for differgear caught the most species in some lake- ent gears. Even the beach seine, with 18
years. Gears that added the most new spe- replicates, did not appear to saturate comcies after the first gear already had been pletely; after the first 8 replicates each addicounted, were electroshocker and vertical tional replicate added an average of 0.1-0.4
gill nets. The electroshocker tended to add species to the richness caught by that gear.
the larger predaceous species and the ver- Other gears continued to add species simtical gill nets the openwater species which ilarly; the last of four electroshocker repliwere not found inshore. Fyke nets tended cates added 0.45 species, the last of two gill
to be the fourth gear in respect to increasing net sets added 0.56 species, and the last of
the total richness estimate. By the time the 6 fyke net sets added 0.43 species. The last
first four gears had been aggregated, 99.9% of two trammel nets added 1.7 species to
of the species observed by all gears had been the richness caught by that gear.
noted. Trammel nets tended to be the fifth
DISCUSSION
gear and crayfish traps the last. However,
the order differed among lakes and years.
Sufficiency of sampling
The richness estimated from individual
All possible conclusions in this paper
gear types averaged over the 5 non-bog lakes about biodiversity, persistence and turn-
446
J. J. MAGNUSON ET AL.
11
Cumulative
Richness
per Gear
per Lake
per Year
1
3
5
7
9
7
3
8
11
13
15
17
Sample Order
FIG. 5. Mean number of species added per lake per
year as a function of the number of replicates of that
gear type per lake per year. The mean cumulative richness per lake per year is given for each gear to the left
of the legend. Gear types in the legend are ordered from
top to bottom by the typical order in which they contributed to cumulative richness.
over have as an alternate hypothesis that
sampling was insufficient to discover all the
rare species in all years and lakes. Regardless of whether the sampling was sufficient,
the effort used was large for fish surveys of
this sort. Each year a minimum of 80 person-days in total were devoted to sample
fish on the 7 lakes. On the non-bog lakes
six different gears were used that sampled
the littoral, pelagic and benthic regions. The
number of species in the lakes, especially
the bog lakes, was rather small; mean annual
richness was 1-22 species. Our judgment
when setting up the study was that we would
have a good sample.
The sufficiency of the sampling looks adequate when the cumulative richness is
aggregated among gear types, as was done
in this study. In aggregation, saturation is
reached after four of the six gear types are
considered (Fig. 4). There is considerable
overlap of species caught by most of the
gears, and this redundancy would add to the
overall sufficiency of the total set of gears.
Even the most pelagic species, usually caught
only by gill net, occasionally turn up in other
gears. This occurs because some fishes have
diel movements between littoral and pelagic
habitat, and because of habitat changes that
occur with age of fish.
While less important for interpretation of
our results than the adequacy of the overall
sampling protocol, no individual gear type
saturated over the range of replicates
employed, not even the night seining with
18 seine hauls per lake per year (Fig. 5). The
addition to mean cumulative richness for
the gear gained with the last 5 replicate seine
hauls averaged only about 1 species per lake
for the non-bog lakes. This means that on
average, in each lake in each year 1 species
is caught in the last 5 seine hauls that was
not caught in the previous 13. For the gear
furthest from saturation, the second trammel net set catches an average of 1.7 species
that were not caught in the first set. It is
clear that in our sampling scheme no single
gear catches all the species that are present
and susceptible to that gear; some species
can be and are missed.
The threshold of detecting a species is not
zero when estimates are based on sampling
rather than a complete census. Thus, the
absence of species from a lake in a given
year cannot with certainty be considered a
true absence/extinction. Rare species that
seem to appear and reappear could do so
by being missed in some years. It was for
this reason that we eliminated the most rare
species from one version of our analyses for
both richness and turnover. In these two
cases (Figs. 2, 3) similar results were
obtained when the rare taxa were removed.
If the threshold we used for designating rare
species was coincident with the ability of
our sampling regime to discover species,
then this would have corrected for sampling
insufficiency. However, species that were
rare in some years and abundant in others
may have defeated this attempt.
Our results are influenced by two processes, 1. extinction/colonization of species
in the lakes and 2. our inability to establish
with certainty the presence/absence of a
species with samples of fish assemblages.
Thus, our interpretations below are on the
conservative side, but still suggest to us that
RICHNESS AND TURNOVER OF FISHES IN LAKES
447
these seven lakes are characterized by a continuous and frequent turnover of species.
estimated from a single year, and that it is
essential to standardize sampling effort,
dates, and locations for comparative studies
Cumulative vs. mean richness
across years and lakes.
Our analyses of richness and cumulative
If our results are general to studies of faurichness do suggest that changes in species nal diversity, some clear warnings are evipresence and absence occur frequently in dent for those wishing to chronicle declines
these lakes. Taken at face value, the greater or increases in diversity. If we estimated the
value for cumulative richness than that change in mean richness of these seven lakes
obtained in any single year would indicate over these eleven years, we would get a very
that new species are appearing and others different answer if we used a different samdisappearing over the eleven years. This is pling base for the beginning and end of the
the case even when rare species are removed period. The extent of this problem is visible
from the analyses. We, however, cannot in Figure 2. For all species (Fig. 2A), if we
convince ourselves that we have discovered used two years cumulative estimate for the
all species present in all years; an unknown, beginning and a one year estimate for the
but probably large, proportion of the dif- end, richness would be estimated to decline
ference between mean annual richness and by 15% even if no change had occurred. If
cumulative richness is the result of not being we had used a four year cumulative estimate
able to discover all rare taxa that are present at the beginning and a single year at the end,
each year. Evidence from Figure 2 does sug- the decline would have been 30%. If the first
gest that some real changes are occurring. sample were a single year and the later samOwing to the non-independence of these ple were accumulated over four years, then
results we were unable to devise a defensible richness would be said to have increased by
test of this general observation. However, 15% or 30% for these two examples. Similar
as an example of the robustness of the trend, patterns result if the same scenarios are
in Figure 2A with two years sampled, the applied to the data with rare species removed
number of times an increase in richness was (Fig. 2B). The fallacy of using such different
observed for individual lakes for intervals sampling bases to make estimates of change
between sampling of 2 to 6 years was 26 out in richness is clear when analyses such as
of 35 cases or 74% of the cases. If the phys- those in Figure 2 are available. We would
ically isolated, winterkill lake containing argue that in many situations where estionly the central mudminnow was excluded mates of diversity are made, effort is not the
from the counts, then increases were same even for single years and that data are
observed in 26 out of 30 cases or 87% of accumulated often over an unknown numthe cases. Cumulative richness is apparently ber of years. This would be especially likely
greater, even for the same number of years if early taxonomic works for a region are
sampled, when the years between sampling being compared with recent one year surveys.
is increased.
Estimation of the number of species in
these lakes appears to have no single "true"
value. Different values are realized if mean
annual richness is used versus cumulative
richness (Fig. 1). The differences are large
for the non-bog lakes. In addition, if we
accumulate richness over different numbers
of years with various intervals between
sampling years, an array of answers are
obtained with no apparent optimal number
of years or interval between sampling. We
conclude that richness determined by sampling is only an index of richness, that the
total number of fishes in a lake cannot be
Finally, we noted that the coefficients of
the species area relationships in the semilog model were sensitive to the manner in
which we estimated species richness. Others
(Schoener, 1988; Martin, 1981) have attributed changes in slope to biological interactions among species. In our results slope
estimates differed when richness was mean
annual or cumulative, and when rare species
were included or removed. We would join
the above cited authors in urging caution in
interpreting the biological meaning of species area relationships, and in the comparison of such curves from study to study.
448
J. J. MAGNUSON ETAL.
Faunal turnover
Real turnover is evident in that the estimate of turnover between years uncorrected
by the time, tends to increase with years
between sampling over that for adjacent
years (Fig. 3). We assume here that the high
turnover between adjacent years is largely
a result of sampling insufficiency. This turnover rate between adjustment years of 16%
per year for all species and 9.6% per year
with rare species removed is seriously overestimated by the noise in the data associated
with finding or not finding rare taxa in each
year. Removal of the rare taxa helped but
did not solve the problem.
Our estimates of species turnover,
adjusted for turnover between adjacent
years, provide evidence for turnover during
the eleven years. The estimate of this turnover rate for the lakes as a group is 0.42%
of species per year. A few values from the
literature provide similar values for several
faunal groups; these are 0.50% per year for
molluscs in Oneida Lake and 0.28% per year
for zooplankton in 11 Finger Lakes both
estimated over about 50 years (Browne,
1981), and 0.18% and 0.40% per year for
birds on two islands in the New Guinea
archipelago (Diamond, 1975).
Differences in turnover of individual lakes
adjusted for changes between adjacent years
ranged from 0.0% per year to 1.1% of species per year. These differences are not
related to whether the lake was connected
or unconnected by a stream; only Allequash
and Trout Lakes have connecting streams.
Lakes with no stream connections had both
the lowest (Crystal Bog) and the highest
(Crystal Lake) turnover rate. Nor are they
related to lake area, species richness or other
obvious characteristics of the lakes. Some
of the invasions undoubtedly are aided by
human transport in minnow buckets or as
intentional acts to improve the lake for fishing. A variety of species likely to be used as
bait showed up in Crystal Lake for single
years. Two lakes, Crystal and Sparkling,
experienced an invasion of the rainbow
smelt (Osmerus mordax) which is an exotic
in the lake district (McLain, 1991). Explanations for apparent extinctions are generally more enigmatic. In one case, the Cisco
in Sparkling Lake, the event seems to have
resulted from interactions with the invading
exotic, rainbow smelt (McLain, 1991).
In our results, the annualized turnover
estimate was very much influenced by the
number of years between sampling. The difference in species structure in adjacent years
was apparently about the same as for years
two to six years apart. This presents a warning to those examining data sets where longterm annual data are not available. If we
did not have annual data and were to compare turnover between surveys separated by
different number of years, the most important determinant of the turnover rate would
be the time interval used in the denominator. For an all species example (Fig. 3A),
turnover between two years would be about
6% per year if the second sample had been
collected 3 years later, but only about 3%
per year if the second sample were 6 years
later.
Persistence and changes in presence:
Absence of individual species
The primary explanatory variable in
regression models for persistence and
changes in presence:absence of individual
species was log density. Thus, species that
were low in abundance on average had
shorter runs of presence and had more presence/absence changes. One conclusion of
this result could be that extinction is more
probable for species with small population
sizes (Diamond, 1984; Pimm et al, 1988).
However, at least 2 other interpretations
seem more likely based on our eleven years
of sampling. First, rare species would be
most easily missed in our sampling; as a
consequence, continuous runs of rare species would be interrupted to produce more
presence/absence changes. Second, if a species has just invaded or is about to go extinct,
it would tend to be low in abundance; again
as a consequence, continuous runs of these
species would be interrupted to produce
more presence/absence changes than would
be the case for more abundant species. We
do not think our analysis can distinguish
between the above three alternative mechanisms for the explanatory power of log
density in these data.
RICHNESS AND TURNOVER OF FISHES IN LAKES
449
Persistence also was explained partially result is primarily caused by missing rare
by body size; the larger the species the more species in some years to produce artificial
persistent it was when log population size presence/absence changes. The coefficient
was held constant with partial correlation. of variation could be an index of the probThis result is also consistent with Pimm et ability that the species would become that
al. (1988) who found for a variety of bird rare.
studies that at a given population size, larger
species were less prone to extinction. It has Overall conclusion
been assumed that body size may indicate
Our general question was whether there
either longevity and thus persistence, or is evidence for a continuous turnover offish
reproductive rate which results in an inverse species in lakes from annual records eleven
relationship to persistence. In our fish case, years in duration from seven lakes? Above
both large and small species have large we have critically discussed our results in
fecundities and are characterized by large terms of the evidence they do or do not
variations in year class strength; these fluc- provide for rapid and continuous turnover.
tuations are the rule for fishes. However,
Results that suggest that extinctions and
larger fish species are longer lived than small invasions in these lakes occurred over interspecies. For fishes characteristic of these vals as short as eleven years are: (1) cumulakes, small species may live 2 to 5 years, a lative richness over 11 years is about 1.5
medium-sized species 3 to 10 years and a times greater than mean annual richness
large species 5 to 15 years. We hypothesize (Fig. 1), (2) the increase in cumulative richthat the explanation for our result is that a ness with the same number of years of
strong year class of a large species promotes observation increases with the number of
longer run durations than a large year class years between observations (Fig. 2), (3)
of a small species simply because large fish turnover between two years increases with
live longer and persist in the samples longer the number of years between observation
than would a large year class from a small (Fig. 3), (4) turnover rates corrected for speshort lived species. Both large and small cies changes between adjacent years averspecies could become rare enough to be aged 0.44% per year, (5) presence/absence
missed in our samples between strong year changes are explained in part by the coefficient of variation in abundance, and (6)
classes.
Presence: absence changes for individual species richness estimates reach the maxispecies also were explained partially by the mum value after species from 4 of the 6
coefficient of variation in abundance. The gears have been tallied (Fig. 4). The strength
more variable the abundance of the species of these analyses in support of continuous
relative to its mean abundance, the more and frequent turnover were weakened by
presence/absence changes occurred when log the alternative hypothesis that our results
population size was held constant with par- could be explained to a great extent by misstial correlation. This is consistent with ing rare species in our sampling. Regardless,
results of Pimm et al. (1988) for analysis of we think we can defend a minimum estibird diversity. That more variable popula- mate of turnover rate of about 0.4 to 0.5%
tions should have a higher risk of extinction species per year over the eleven years over
at the same population density seems a rea- the lakes as a group or 0.0% to 1.1% species
sonable interpretation of the fish results. In per year for individual lakes.
our analysis the coefficient of variation was
We suggest that an analysis of the patterns
estimated using only years in which the spe- of abundance of individual species will procies was present in that lake. Thus, this result vide a better way to detect invasions and
seems a bit less dependent on the vagaries extinctions than the types of analyses we
of missing a species in years when it is rare have conducted in this paper. Long-term
that have plagued our interpretation of many annual data are necessary to make such
of the results above. We cannot, however, analyses and eleven years is on the short
eliminate the alternate hypothesis that our end of the number of years required con-
450
J. J. MAGNUSON ET AL.
sidering the longevities of the taxa and the
problem of missing rare taxa in any one
year. Even with only eleven years of annual
data we know for certain that some invasions and extinctions have occurred
(McLain, 1991). In two of the lakes rainbow
smelt {Osmerus mordax) invaded during the
eleven years and these are unmistakable
invaders because the smelt is exotic to the
region. We also know that the cisco (Coregonus artedii) has disappeared from one of
the lakes as a result of the smelt invasion
(McLain, 1991). Peter Moyle (U. of California-Davis, personal communication) has
referred to the difficulty of detecting cryptic
invasions of species native to the region but
absent in specific sites. Most analyses of
individual invasions for fishes have been of
exotics. We expect, even for fishes in islandlike environments like lakes, that movement among lakes and local extinctions
occur; these processes can be important to
regional diversity. As we found in this paper
species turnover is difficult to observe,
especially of non-exotics. We think that
analyses of the patterns of abundance of
individual species from long-term data will
allow the development of alternate methods
of analysis to detect cryptic invaders and
local extinctions. We are in the process of
such analyses (Cisneros, 1993).
ACKNOWLEDGMENTS
We thank Joyce M. Tynan for conducting
many of the sample aggregations and Dennis M. Heisey for advice on testing the slopes
and intercepts of the species area relations.
We thank J. H. Brown, D. M. Miles and an
anonymous reviewer for useful comments
on the manuscript. The research is part of
the North Temperate Lakes Long-Term
Ecological Research project funded by the
National Science Foundation, Grants
BSR8514330 and DEB9012313.
REFERENCES
Barbour, C. D. and J. H. Brown. 1974. Fish species
diversity in lakes. Amer. Nat. 108:473-489.
Bauman, P. C , J. F. Kitchell, J. J. Magnuson, and T.
B.Kayes. 1974. Lake Wingra, 1837-1973: A case
history of human impact. Trans. Wise. Acad. Sci.
Arts Lett. 62:57-94.
Becker, G. C. 1983. Fishes of Wisconsin. University
of Wisconsin Press, Madison, Wisconsin.
Benson, B. J. and J. J. Magnuson. 1992. Spatial heterogeneity of littoral fish assemblages in lakes:
Relation to species diversity and habitat structure.
Can. J. Fish. Aquat. Sci. 49:1493-1500.
Brown, J. H. and A. Kodric-Brown. 1977. Turnover
rates in insular biogeography: Effect of immigration on extinction. Ecology 58:445-449.
Brown, M. and J. J. Dinsmore. 1988. Habitat islands
and the equilibrium theory of island biogeography:
Testing some predictions. Oecologia 75:426-429.
Browne, R. A. 1981. Lakes as islands: Biogeographic
distribution, turnover rates, and species composition in the lakes of central New York. J. Biogeog.
8:75-83.
Christie, W. J. 1974. Changes in thefishspecies composition of the Great Lakes. J. Fish. Res. Bd. Canada 31:827-854.
Cisneros, R. O. 1993. Detection of cryptic invasions
and local extinctions of fishes using a long-term
data base. Master's Thesis, University of Wisconsin-Madison.
Diamond, J. M. 1975. Assembly of species communities. / n M . L Cody and J. M. Diamond (eds.),
Ecology and evolution of communities, pp. 342444. Harvard University Press, Cambridge, Massachusetts.
Diamond, J. M. 1984. Normal extinctions of isolated
populations. In M. H. Nitecke (ed.), Extinction,
pp. 191-247. University of Chicago Press, Chicago, Illinois.
Eadie, J. M., T. A. Hurly, R. D. Montgomerie, and K.
L. Teather. 1986. Lakes and rivers as islands:
species-area relationships in the fish faunas of
Ontario. Environ. Biol. Fishes 15:81-89.
Kratz, T. K., J. J. Magnuson, C. B. Bowser, and T. M.
Frost. 1986. Rationale for data collection and
interpretation in the northern lakes Long-Term
Ecological Research Program. American Society
for Testing and Materials, Special Technical Publication 894.
Lyons, J. 1989. Changes in the abundance of small
littoral-zone fishes in Lake Mendota. Can. J. Zool.
67:2910-2916.
Lyons, J. and J. J. Magnuson. 1987. Effects of walleye
predation on the population dynamics of small
littoral-zone fishes in a northern Wisconsin lake.
Trans. Amer. Fish. Soc. 116:29-39.
Magnuson, J. J. 1976. Managing with exotics—a game
of chance. Trans. Amer. Fish. Soc. 105:1-9.
Magnuson, J. J. 1988. Two worlds offish recruitment: Lakes and oceans. Amer. Fish. Soc. Symp.
5:1-6.
Magnuson, J. J., B. J. Benson, and T. K. Kratz. 1990.
Temporal coherence in the limnology of a suite of
lakes in Wisconsin, U.S.A. Freshw. Biol. 23:145159.
Magnuson, J. J., C. J. Bowser, and T. K. Kratz. 1984.
Long-term ecological research (LTER) on north
temperate lakes of the United States. Verh. Internat. Verein. Limnol. 22:533-535.
Magnuson, J. J. and R. C. Lathrop. 1992. Historical
changes in the fish community. In J. F. Kitchell
(ed.), Food web management: A case study of Lake
Mendota, pp. 193-232. Springer-Verlag, New York.
RICHNESS AND TURNOVER OF FISHES IN LAKES
Magnuson, J. J., C. A. Paszkowski, F. J. Rahel, and
W. M. Tonn. 1989. Fish ecology in severe environments of small isolated lakes in northern Wisconsin. Freshwater wetlands and wildlife, 1989,
CONF-8603101, DOE Symposium Series No. 61,
R. R. Sharitz and J. W. Gibbons (eds.), USDOE
Office of Scientific and Technical Information, Oak
Ridge, Tennessee.
Martin, T. E. 1981. Species-area slopes and coefficients: a caution on their interpretation. Amer.
Nat. 118:823-837.
McLain, A. S. 1991. Conceptual and empirical analyses of biological invasion: non-native fish invasion into north temperate lakes. Ph.D. Diss., University of Wisconsin-Madison.
Pimm, S. L., H. L. Jones and J. Diamond. 1988. On
the risk of extinction. Amer. Nat. 132:757-785.
Rudstam, L. G. and J. J. Magnuson. 1985. Predicting
the vertical distribution offish populations: Anal-
451
ysis of cisco, Coregonus artedii, and yellow perch,
Percaflavescens. Can. J. Fish. Aquat. Sci. 42:1178—
1188.
Schoener, T. W. 1988. Ecological interactions. In A.
A. Myers and P. S. Giller (eds.), Analytical biogeography, pp. 255-297. Chapman and Hall, London.
Tonn, W. M. and J. J. Magnuson. 1982. Patterns in
the species composition and richness of fish
assemblages in northern Wisconsin lakes. Ecology
63:1149-1166.
Tonn, W. M., J. J. Magnuson, M. Rask, and J. Toivonen. 1990. Intercontinental comparison of
small forest lakes: Fish assemblage patterns in Finland and Wisconsin. Amer. Nat. 136:345-375.
Wilson, E. O. 1992. The diversity of life. The Belknap
Press of Harvard University Press, Cambridge,
Massachusetts.