Population Characteristics of Black Crappies in South Dakota

204-F
North American Journal of Fisheries Management 15:754-765, 1995
© Copyright by che American Fisheries Society 1995
Population Characteristics of Black Crappies in
South Dakota Waters: A Case for
Ecosystem-Specific Management
CHRISTOPHER S. Guv 1 AND DAVID W. WILLIS
South Dakota State University, Department of Wildlife and Fisheries Sciences
Brookings, South Dakota 57007, USA
Abstract.—We sampled 22 populations of black crappie Pomoxis nigromaculatus from three
ecosystem types (large impoundments, >40 ha; small impoundments, <40 ha; natural lakes) to
determine the factors that influence population characteristics (recruitment, growth, size structure,
and condition) in South Dakota. Recruitment variability was best correlated with the logio of the
shoreline development index (r = 0.63, df = 16) and the log to of the watershed: lake area ratio
(r - 0.89, df = 12). Mean back-calculated length at age was highly variable among ecosystems
and was inversely correlated with the log!0 of the catch per unit effort (CPUE; r = -0.35 to
-0.69). Mean back-calculated length for all ages was positively correlated with mean relative
weight (r = 0.48-0.78, df = 18-21). Proportional stock density and relative stock density of
preferred-length fish were inversely correlated with Iog10 CPUE (Spearman correlation, rs = -0.31
to -0.83, df = 21) and were positively correlated with growth of black crappies (rv = 0.55-0.73,
df = 18-21). Bivariate centroids differed significantly (P ^ 0.05) among ecosystems for canonical
analysis; canonical factor scores were derived from growth, recruitment, CPUE, and condition.
These data suggest that black crappie population characteristics differ among ecosystems. Natural
lakes typically had black crappie populations with low density, unstable recruitment, fast growth
rates, and high condition factors. Conversely, small impoundments had black crappie populations
with high density, more stable recruitment, slow growth, and low condition factors. Black crappie
population characteristics in large impoundments were typically intermediate between those of
natural lakes and small impoundments. The differences observed in recruitment variability, growth,
size structure, and condition of black crappie populations among ecosystems facilitate a better
understanding of the factors that influence these variables and illustrate the importance of ecosystem-specific management.
Literature abounds on the population characteristics of crappies Pomoxis spp. and the factors that
influence them in reservoirs throughout the midwestern and southeastern USA (Mitzner 1984;
Hooe 1991), but similar information for crappie
populations in the northern Great Plains is sparse
(Guy and Willis 1994). In addition, many of the
studies on crappie population dynamics have been
conducted for single ecosystems, such as a reservoir or small impoundment (Jenkins 1970;
Range 1973; Heidinger 1977; Beam 1983; Gabelhouse 1984a; Hill 1984; Boxrucker 1987; Mitzner
1991). We believe a greater amount of information
can be obtained about crappie population dynamics by comparing populations among ecosystems.
Therefore, the objectives of this study were twofold. First, we investigated the factors that influence recruitment, growth, size structure, and condition of black crappies P. nigromaculatus in South
Dakota waters. We hypothesized that the factors
————
1
Present address: Kansas Cooperative Fish and Wildlife Research Unit. Kansas State University, Leasure
Hall. Manhattan, Kansas 66506, USA.
influencing black crappies in South Dakota would
be similar to those in midwestern and southeastern
water bodies. However, because of differences in
geography and lake morphometry, we would expect to observe slight differences in population
dynamics. Second, we assessed similarities or differences in black crappie population characteristics among aquatic ecosystems (natural lakes;
small impoundments, ^40 ha; large impoundments, >40 ha). We hypothesized that black crappie population characteristics would differ among
ecosystems, indicating a need for ecosystem-specific management,
Study Sites
were collected from 22 water
representing three ecosystem types,
bodies%
throughout South Dakota (Table 1). Physicochemistry was high i y variabie among water bodies;
however, ecosystems (i.e., natural lakes and impoundments) showed similarities. Natural lakes
commonly had the lowest maximum depth, shoreline development index (ratio of lakeshore perimeter to perimeter of a circle of equal area), and
754
BIack crappies
755
POPULATION CHARACTERISTICS OF CRAPPIES
TABLE 1.—Characteristics of 22 water bodies in South Dakota from which black crappies were collected. Blanks
indicate missing values.
Walcr body
Type-
Year
Surface area
(ha)
Alvin
Angostura
Brant
Curlew
East Vermillion
Elm
Faulkton
Herman
Isabel
Little Moreau
Madison
Mina
Mitchell
Murdo
Murdo Railroad
Pickerel
Red Plum
Richmond
Roy
Simon
Thompson
Woodruff
Si
LI
N
SI
LI
LI
SI
N
SI
SI
N
LI
LI
SI
SI
N
SI
LI
N
SI
N
SI
1990
1993
1990
1993
1992
1991
1992
1993
1992
1992
1990
1991
1990
1990
1992
1990
1991
1991
1991
1992
1992
1991
36
1,731
417
38
489
489
38
547
33
15
1,133
326
271
27
7
387
5
336
686
17
6,573
32
a
Maximum
depth (m)
7.9
15.2
3.8
6.7
9.1
9.1
7.3
2.4
5.2
7.6
6.1
8.2
7.3
7.0
5.5
14.0
2.4
8.5
5.6
4.0
5.5
Shoreline
development index
Watershed:
lake
area ratio
2.8
3.9
1.2
2.7
6.2
6.2
6.3
1.1
2.5
273
1.206
8
1.9
6.3
2.7
2.3
1.9
2.2
10
190
343
108
5.8
2.5
3.0
2.5
124
6
4.6
481
89
501
27
82
18
Total
dissolved Morphoedaphic index
solids (mg/L)
866
819
1.340
525
825
416
300
819
495
440
1,072
572
989
2,504
1.995
584
260
260
2.410
300
995
282
255
241
394
48
223
76
107
241
65
146
357
212
267
715
765
%
130
57
730
170
398
154
N = natural lake. SI = small impoundment (<40 ha), LI = large impoundment (>40 ha).
watershed : lake area ratio. In addition, natural
lakes typically had higher total dissolved solids
(TDS) and a higher morphoedaphic index (TDS/
mean depth) than impoundments. Productivity indices indicated that all the water bodies were at
least eutrophic (Ryder 1965).
Methods
Black crappies were sampled with trap nets
(modified fyke nets) during May and June 19901993 when water temperatures were between 16
and 20°C. Guy and Willis (199la) indicated that
trap-netting during May and June was effective for
sampling black crappies in South Dakota waters.
Trap nets had 1.3 X 1.6-m frames, two throats
within the hoops, a 23-m lead that was 1.2 m deep,
and 13-mm mesh (bar measure). Twenty trap nets
were set in water bodies greater than 40 ha and 10
in water bodies 40 ha or less. No more than 10
trap nets were set per night; thus, two nights were
needed to sample large water bodies. All black
crappies of stock length (5:130 mm) were separated into centimeter total length groups. Total
length (TL) to the nearest millimeter and weight
to the nearest gram were recorded, and scale samples were taken from up to 10 black crappies per
centimeter length group, if present. We sampled
numerous water bodies once instead of a few water
bodies several times because we wanted to differ-
entiate black crappie population characteristics
among ecosystems throughout South Dakota.
Recruitment.—Variability in recruitment was indexed with the recruitment variability index (RVI).
We used the rationale of the catch curve (Ricker
1975) and the Kolmogorov-Smirnov one-sample
test (Siegel and Castellan 1988) as cornerstones
for the RVI. Ricker (1975) stated that fluctuations
in recruitment among year-classes made the catch
curve more variable. Therefore, variability among
year-classes may serve as a measure of recruitment
variability. The more stable the recruitment, the
less variable the catch curve should become. We
used the cumulative relative frequency distribution
(the same procedure as used in the KolmogorovSmirnov one-sample test) to describe the magnitude and distribution of the frequency-of-occurrence-at-age data.
The RVI was calculated as
RVI = [S»/(Nm + Np)] - N,JNp\
SN is the summation of the cumulative relative
frequency distribution based on the number of fish
in each age-class, Nm is the number of age-groups
missing from the sample that should be present
(not including ages past the last age captured), Np
is the number of age-groups present in the sample,
and Np > Nm. The RVI ranges from -1 to 1. As
RVI increases, recruitment is more stable. As with
756
GUY AND WILLIS
most indices, there were several rules and assumptions that had to be met so that the index
provided reliable results. First, we used only fish
that were fully recruited to the sampling gear (i.e.,
age 2 and older). Second, we did not include samples with less than three year-classes or samples
in which the number of missing year-classes
equaled or exceeded those present. We assumed
that catch at age was a valid representation of yearclass strength. Finally, we assumed that year-classes not represented in the sample beyond the last
age-group captured did not occur.
The RVI has strengths and weaknesses similar
to those of the catch curve method used for mortality rate assessment. The RVI is weak for assessing recruitment variability from a one-time
sample. We could better document variability in
equation logi 0 W v = -5.618 + 3.345 logi()TL,
where Wv is in grams and TL is in millimeters
(Neumann and Murphy 1991). According to recommendations by Murphy et al. (1991), we calculated mean Wr by length category (S-Q, 130199 mm; Q-P, 200-249 mm; and P-M, 250-299
mm) in addition to mean Wr for stock-length black
crappies.
Catch per unit effort.—Catch per unit effort
(CPUE) was summarized as the number of fish
caught per trap-net-night and was calculated by
length category (CPUE for stock-length fish
[SCPUEJ S-Q and Q-P). We used CPUE data as
an index of relative abundance (Hubert 1983; Hall
1986; Coble 1992).
Physicochemical variables.—All lake morphometric variables were obtained from Koth (1981)
recruitment (i.e., year-class strength) if we had a
and South Dakota Department of Game, Fish and
long-term data set; however, the same can be said Parks lake maps. Water samples for TDS were obfor catch curves. The strength of the catch curve tained in July during the year the black crappie
and RVI is that we can obtain some information population was sampled. All water samples were
from a one-time sample. Nevertheless, we caution collected 0.5 m below the water surface approxithat the RVI can vary with varying mortality mately in the center of the water body. Total disamong year-classes. As with most management solved solids were determined according to the
tools, the RVI should be used in conjunction with American Public Health Association (APHA et al.
other indices or tools.
1985). The morphoedaphic index (TDS/mean
Growth.—Scales were removed from below the depth) was calculated according to Ryder (1965).
lateral line and directly behind the pectoral fin, as
Statistical analysis.—Most of the variables used
suggested by Jearld (1983). We back-calculated in the analyses were transformed (logjo(jc + 1 ]) to
mean length at age and standard error with DISB- meet the assumptions for parametric analysis (SieCAL software (Frie 1982), assuming a direct pro- gel and Castellan 1988). Despite transformations,
portion between body length and scale radius. PSD and RSD-P did not meet the assumptions of
Mean back-calculated length at age was interpret- parametric statistics; therefore, these data were aned from scale impressions under the assumption alyzed with nonparametric procedures. We used
that the y-intercept value for the scale radius-body regression analysis to examine the effects of abilength relationship was 35 mm, as suggested by otic and biotic factors on black crappie population
Carlander (1982). We used fish less than age 6 for dynamics and structure. The residuals were plotted
all analyses because sample size became small and for all regressions to determine if curvilinear reaging precision decreased for older fish (Kruse et lationships existed. We used the influence option
al. 1993).
in the Statistical Analysis System (SAS Institute
Size structure.—Size structure was quantified
1985) to determine the influence of individual data
with proportional stock density (PSD; Anderson values on the regression analysis and assist in
1976) and relative stock density of preferred- quantitatively determining outliers. Pearson (r)
length fish (RSD-P; Gabelhouse 1984b). Propor- and Spearman rank-order (r v ) correlation coeffitional stock density is the percentage of stock- cients were used to determine the degree to which
length fish that are also quality length; RSD-P is two variables covaried (Damon and Harvey 1987;
the percentage of stock-length fish that are pre- Siegel and Castellan 1988). Stepwise multiple referred length. Minimum stock (S), quality (Q), pre- gression was attempted to determine which variferred (P), and memorable (M) lengths for black ables most highly influenced black crappie recrappies are 13, 20, 25, and 30 cm, respectively. cruitment, growth, and condition; however, either
Condition.—We used relative weight (Wr = 100 multicollinearity was apparent or only a single
x individual fish weight/standard weight; Wege variable was significant in the model. Canonical
and Anderson 1978) to index condition of black discriminant function analysis was used to illuscrappies. Standard weight (Ws) is given by the trate the differences between black crappie pop-
757
POPULATION CHARACTERISTICS OF CRAPPIES
TABLE 2.—Age-frequency distributions and recruitment
variability index (RVI) values for models adapted from
Gabelhouse (1984a). Models 4-6 assume consistent recruitment. The other models have alternating strong and
weak year-classes, with more or less than 50% of the number present in the second model.
1
2a
3b
4
5a
6h
7
8"
9h
h
PH).OOOI, r=0.89
Age
Model
a
i.o
RVI
1
2
3
4
5
0.87
0.67
-0.05
0.84
0.59
0.04
0.79
0.53
0.09
150
150
0
100
100
0
50
50
0
25
25
25
50
0
50
75
0
75
38
0
0
25
25
25
12
12
12
6
6
6
12
12
0
18
18
0
9
9
9
6
6
6
3
3
3
0.6
0.4
OJ
One randomly chosen missing year-class.
Two randomly chosen missing year-classes.
0.0
0.5
ulation dynamics and structure among ecosystem
types. Ecosystem classification was established a
priori. Concentration ellipses were used on the
graphical representation to clarify the canonical
analysis (James and McCulloch 1990). All statistical analyses were conducted with the SAS (SAS
i.o r
1.0
1.5
2.0
2.5
3.0
3.5
Log|9 watershed:lake area ratio
FIGURE 2.—Relation between the recruitment variability index and log | 0 (watershed: lake area ratio) for
13 black crappic populations in South Dakota waters.
The solid line represents least-squares regression, and
dotted lines are the 95% confidence intervals about the
regression line.
Institute 1985). An alpha level of 0.05 was established a priori for all tests.
Results and Discussion
Recruitment
0.4
0.2
P-0.03, r-0.63
OJ
0.4
0.5
0.6
0.7
OJ
0.9
Log1Q shoreline development Index
FIGURE 1.—Relation between the recruitment variability index and log|0(shoreline development index) for
17 black crappie populations in South Dakota waters.
These data were best fit with a second-order polynomial.
The solid line represents least-squares regression, and
dotted lines are the 95% confidence intervals about the
regression line.
We tested the RVI for three crappie population
models based on varying year-class strengths that
were constructed by Gabelhouse (1984a). Also, we
included models with one and two randomly chosen missing year-classes (Table 2). As recruitment
became less stable (i.e., the number of missing
year-classes increased or the number in a yearclass was usually high or low), the RVI decreased
(Table 2). From these models, it appeared that the
RVI has merit. The RVI is a conservative index
because it is not as sensitive to slight variations
in recruitment variability as it is for large fluctuations.
The RVI was best correlated with the log|0 of
the shoreline development index (P = 0.03, r =
0.63 [second-order polynomial], df = 16; Figure
1) and the log|0 of the watershed : lake area ratio
(P = 0.0001, r = 0.89, df = 12; Figure 2). (See
Guy 1993 for nonsignificant correlations between
abiotic variables and RVI.)
The relation between RVI and the logio(shore-
758
GUY AND WILLIS
TABLE 3.—Recruitment variability indices (RVI), and sample sizes (/V). mean back-calculated total lengths at age
(mm), and standard deviations of length (in parentheses) for age and growth analysis of black crappies in three ecosystem
types. South Dakota.
Mean back-calculated length at age:
Ecosystem
Mean RVI
N
1
2
3
4
5
Natural lakes
Large impoundments
Small impoundments
0.34(0.13)
0.77 (0.12)
0.62 (0.25)
406
529
727
95 (22.6)
81 (15.8)
68(10.6)
190 (32.4)
153 (25.6)
124 (22.8)
242 (33.7)
191 (18.3)
160(21.3)
271 (33.9)
217(21.4)
188(17.5)
296 (20.8)
241 (18.2)
213(16.2)
line development index) was curvilinear and appeared to be asymptotic (Figure I). Thus, recruitment was more consistent in water bodies that were
more dendritic (i.e., had more embayments). We
believe the underlying catalyst for this relation is
wind. For example, spawning and nursery areas
have a higher probability of being influenced by
wave turbulence as shoreline development decreases. Several researchers have documented that
wind and waves adversely affect recruitment of
fishes (Kramerand Smith 1962; Miller and Kramer
1971; Clady and Hutchinson 1975; Eipper 1975;
Summerfelt 1975; Clady 1976). Mitzner (1991)
found that areas of Rathbun Lake, Iowa, that had
high wind values (wind direction X fetch X wind
velocity) had lower abundances of larval crappies.
In addition, Mitzner (1979) and Mayhew (1977)
found more young crappies in embayments than
in main pool regions of Rathbun Lake, and Meals
and Miranda (1991) found more young crappies
in sheltered areas of flood control reservoirs in
Mississippi.
Black crappie populations had more stable recruitment in water bodies with higher watershed :
lake area ratios. As watershed area became larger
with respect to water body surface area, recruitment became less erratic. In the northern Great
Plains where water is more limiting than in midwestern and southeastern regions in most years, a
large watershed relative to lake area may be beneficial by producing a higher frequency of increased water levels. Crappie recruitment is closely related to water level fluctuations. Several authors have documented increased numbers of crappies younger than age 2 corresponding to water
level increase (Beam 1983; Ploskey 1986; Willis
1986; McDonough and Buchanan 1991; Mitzner
1991).
Because of the complex interactions that influence variability in black crappie recruitment, we
do not expect one or two abiotic variables to explain all of the recruitment variability. Water body
morphometry may play an important role in recruitment patterns of black crappie populations in
South Dakota waters. However, these relations are
probably not cause and effect and likely vary with
amounts of wind and precipitation.
Growth
Mean back-calculated length at age was highly
variable among ecosystems (Table 3). For example, mean back-calculated length at age 3 was 160
mm for crappies in small impoundments and 242
mm for those in natural lakes. Black crappie populations typically had slower growth in small than
in large impoundments, and growth was typically
fastest in natural lakes (Guy and Willis 1993).
Mean back-calculated length at age was inversely
correlated with CPUE by length category (Guy
1993). The best relations were between logio (SQ CPUE) and mean back-calculated length at age
4 (P = 0.0009, r = -0.67, df = 20; Figure 3) and
age 5 (P = 0.001, r = -0.69, df = 18). When Iog10
(S-Q CPUE) exceeded 1.0 (a trap-net CPUE > 10),
mean back-calculated length for age-4 black crappies never surpassed 230 mm (Figure 3).
Mean back-calculated length for all ages was
positively correlated with mean Wr by length category (/> < 0.05, r = 0.48-0.78; Guy 1993). The
best relation was between mean back-calculated
length at age 3 and mean Q-P Wr (P = 0.0001, r
= 0.78, df = 18; Figure 4). Mean back-calculated
length at age 3 exceeded 200 mm only when mean
Q-P Wr was greater than 100 (Figure 4); this pattern was similar for all ages.
Mean Wr values of 100 or greater appear to distinguish between fast- and slow-growing black
crappie populations (Figure 4). Although there has
been some controversy on appropriate Wr values
for a given fish population based on ecological and
physiological optimality (Murphy et al. 1991), our
data suggest that Wr values greater than 100 would
be optimal with respect to fast black crappie
growth rates. Several authors have recommended
that optimal Wr values be near 100 (Wege and
Anderson 1978; Anderson 1980; Gabelhouse
1987). However, Wr targets for a fish population
POPULATION CHARACTERISTICS OF CRAPPIES
325
280
P=0.0009,
759
P=0.0001, r=0.78
260
300
240
275
220
250
200
225
180
200
160
175
140
150
0.0
0.5
1.0
1.5
2.0
2.5
U(IOS-QCPUE
FIGURE 3.—Relation between mean back-calculated
length at age 4 and logjo(catch per unit effort) for stockto quality-length (130 mm-199 mm; S-Q CPUE) black
crappies collected from South Dakota waters. The solid
line represents least-squares regression, and dotted lines
are the 95% confidence intervals about the regression
line.
should be set according to management objectives
(Murphy et al. 1991).
Black crappie populations with low density, as
reflected by CPUE, and high condition factors
grew faster than populations with high density.
Correlations between growth and mean Wr have
been documented for large mouth bass Micropterus
salmoides (Wege and Anderson 1978), northern
pike Esox lucius (Willis and Scalet 1989), and yellow perch Perca flavescens (Willis et al. 1991).
Intraspecific competition is likely the primary factor influencing growth rates for black crappies in
South Dakota waters. Rutledge and Barron (1972)
suggested that slow growth of crappies was probably related to the formation of strong year-classes
that cause high intra- or interspecific competition.
Mosher (1985) documented slow growth rates in
crappie populations that had consistently high recruitment, and several authors have documented
improved growth rates of crappies when the population density is reduced (Rutledge and Barron
1972; Hanson et al. 1983; Schramm et al. 1985).
Size Structure
Mean proportional stock density varied by ecosystem type (Table 4) and was inversely correlated
120
80
90
100
110
120
Q-PWr
FIGURE 4.—Relation between mean back-calculated
length at age 3 and mean relative weight for quality- to
preferred-length (200 mm-249 mm; Q-P Wr) black crappies collected from South Dakota waters. The solid line
represents least-squares regression, and dotted lines are
the 95% confidence intervals about the regression line.
with the log,0 of SCPUE (rs = -0.53; Table 5).
Similarly, RSD-P was inversely correlated with the
log,0 of SCPUE (rs = -0.70; Table 5). When black
crappie population density increased, as reflected
by CPUE, size structure decreased. High intraspecific competition in high-density populations results in a size structure dominated by black crappies shorter than 20 cm. Several researchers have
documented similar relations for largemouth bass
in small impoundments (Reynolds and Babb 1978;
Gabelhouse 1984a, I984b; Guy and Willis 1990).
Relative stock density of preferred-length black
crappies was positively correlated with P-M Wr
(r v = 0.62; Table 5) and almost so for Q-P Wr (r v
= 0.43). Proportional stock density was not significantly correlated with Wr by size category (P
> 0.05; Table 5). Several authors have documented
relations between size structure indices and Wr
(Wege and Anderson 1978; Gabelhouse 1984b;
Willis and Scalet 1989; Guy and Willis 1990; Murphy et al. 1991; Willis et al. 1991; Johnson et al.
1992). However, the correlation coefficients for
these relations were highly variable (0.10-0.90).
Murphy et al. (1991) suggested that the variability
in correlations between size structure indices (PSD
760
GUY AND WILLIS
TABLE 4.—Numbers of water bodies and black crappies sampled, proportional stock densities (PSD), relative stock
densities of preferred length fish (RSD-P; ^250 mm), and catches per unit effort (CPUE) and relative weights by size
category (S, ^130 mm, S-Q. 130-199 mm; Q-P, 200-249 mm; P-M, 250-299 mm) for three ecosystem types sampled
in South Dakota. Standard deviations are in parentheses.
Number of:
Lakes
Fish
Natural lakes
6
1,382
Large impoundments
6
7.858
Small impoundments
10
6,973
Ecosystem
.
Mean
PSD
81
(39.1)
48
(36.2)
27
(30.7)
65
(45.4)
4
(7.6)
1
(2.2)
Relative weight
CPUE
Mean .
RSD-P
S
S-Q
Q-P
S
S-Q
Q-P
P-M
12
(9.2)
66
(64.8)
56
(44.0)
4
(7.2)
49
(64.0)
38.4
(37.2)
4
(7.6)
16.2
(18.6)
17.4
(29.0)
NO
(9.3)
101
(7.5)
99
(5.7)
119
(5.8)
107
(6.5)
102
(5.4)
116
(8.4)
%
(8.8)
93
(4.0)
105
(5.3)
92
(10.6)
90
(8.2)
and RSD-P) and Wr may be related to our incomplete understanding of the conjectural basis of PSD
and RSD structural indices.
Proportional stock density and RSD-P were positively correlated with growth of black crappies
(Table 5). The best relation was between RSD-P
and mean back-calculated length at age 5 (r v =
0.73). Willis and Scalet (1989) suggested that the
relations between size and structure variables
(PSD and Wr) and growth for northern pike were
cause and effect and contended that the relation
between PSD and Wr likely covaried with growth.
We surmise that the same cause-and-effect relations existed for black crappie populations in
South Dakota waters, as supported by the statistical analyses. The correlation coefficients for the
relations between growth and structure variables
(PSD, RSD-P, and Wr) were higher than those for
size structure indices and Wr (Table 5).
It appears that PSD and RSD-P can be good
indicators of black crappie population dynamics
in South Dakota waters, despite the variability in
ecosystems we analyzed. For example, black crappie populations with low RSD-P had more consistent recruitment and were dominated by a high
number of small, slow-growing fish with low condition factors. Conversely, black crappie populations with high RSD-P values had more erratic
recruitment and were dominated by a low number
of large, fast-growing fish with high condition factors. Proportional stock density and RSD-P are not
always direct indicators of fish population dynamics because many factors can influence these relations (Willis et al. 1993).
Condition
Mean relative weight was weakly correlated
with biotic variables (Guy 1993). The best relation
was between logio (S-Q CPUE) and P-M Wr (P
= 0.05, r = -0.57, df = 12). Although the cor-
TABLE 5.—Correlation analyses (Spearman rank correlation coefficients, rA.) for proportional stock density (PSD) and
relative stock density of preferred length fish (RSD-P) as functions of catch per unit effort (CPUE), relative weight
(Wr). and mean back-calculated length at age for black crappie populations sampled from South Dakota waters, 19901993.
PSD
RSD-P
Population variables3
r,
df
P
TV
df
P
Log,0 (SCPUE)
Logio <S-Q CPUE)
Log,0 (Q-P CPUE)
S-QW,
Q-PW,
P-M W,
S W,
Mean back-calculated
length at age
1
2
3
4
5
0.53
-0.82
0.11
-0.02
0.12
0.44
-0.12
21
21
21
18
19
12
21
0.0 1
0.0001
0.63
0.93
0.62
0.13
0.59
-0.70
-0.83
-0.31
0.19
0.43
0.62
0.14
21
21
21
18
19
12
21
0.0005
0.0001
0.17
0.44
0.06
0.02
0.55
0.28
0.27
0.49
0.48
0.61
21
21
20
20
18
0.24
0.22
0.03
0.03
0.005
0.44
0.52
0.67
0.72
0.73
21
21
20
20
18
0.04
0.01
0.0006
0.0003
0.0004
a
S = stock-length fish (>I30 mm). S-Q = stock to quality lengths (130-199 mm), Q-P = quality to preferred lengths (200-249 mm),
and P-M = preferred to memorable lengths (250-299 mm).
POPULATION CHARACTERISTICS OF CRAPPIES
761
would score high on this factor. The second canonical factor (eigenvalue = 0.25) was a weighted
average of all variables in the analysis (y2 = 0.54
[mean back-calculated length at age 3) + 0.96
[RVI]). Therefore, black crappie populations with
fast growth rates and consistent recruitment would
score high on this factor.
Bivariate centroids differed significantly among
ecosystems (Figure 5). The centroid for small impoundments was significantly different from that
for large impoundments (P = 0.03) and natural
lakes (P = 0.0001); further, the centroid for large
impoundments was significantly different from
that for natural lakes (P = 0.0001; Figure 5). Natural lakes scored higher on canonical variable 1
than impoundments because black crappie populations in natural lakes typically had fast growth
rates and erratic recruitment. However, large impoundments scored higher on canonical factor 2
because black crappie populations in large im-2
-1
0
1
2
3
4
5
poundments had moderate growth rates and more
Canonical factor 1
consistent recruitment than in small impoundFIGURE 5.—Canonical discriminant function analysis ments or natural lakes.
of black crappie populations collected from small (^40For the second canonical discriminant analysis,
ha) impoundments (solid circles), large (>40-ha) im- we used variables related to black crappie popupoundments (squares), and natural lakes (triangles). Canonical factor scores were derived from mean back-cal- lation structure (S-Q CPUE and stock-length rel[S Wr]) to discriminate among ecoculated lengths at age 4 and the recruitment variability ative weight
2
2
index for 20 black crappie populations in South Dakota systems (r = 0.45 for S-Q CPUE, r = 0.30 for
waters. Ellipses denote regions of concentration about S Wr\ Figure 6). The first canonical factor (eigenthe centroids.
value = 2.06) was a contrast between S-Q CPUE
and S Wr (y, = 1.74 [S-Q CPUE] - 0.84 [S Wr]).
relations between CPUE and Wr were weak, con- Thus, black crappie populations with high density,
dition decreased as density increased. Relations as reflected by S-Q CPUE, and low condition facbetween CPUE and Wr are not well documented. tors would score high on this factor. The second
Weak correlations between CPUE and Wr probably canonical factor (eigenvalue = 0.0006) was a
result from high variability in catch rate data and weighted average of all variables in the analysis
changes in condition over sampling date (e.g., (y 2 = 0.64 [S-Q CPUE] 4- 0.78 [S Wr]). Therefore,
prespawn to postspawn periods). However, it black crappie populations with high density and
seems plausible that condition values decrease as high condition factors would score high on this
density increases (as reflected by SCPUE), prob- factor.
Bivariate centroids differed significantly beably a result of intraspecific competition.
tween natural lakes and impoundments (Figure 6).
Multivariate Analysis
The centroid for natural lakes was significantly
We used variables related to black crappie pop- different from that for small impoundments (P =
ulation dynamics (mean back-calculated length 0.0001) and large impoundments (P = 0.0002).
and RVI) to discriminate among ecosystems (r2 = However, bivariate centroids were not significantly
0.70 for mean back-calculated length at age 3, r2 different (P = 0.54) between large and small im= 0.36 for RVI; Figure 5). The first canonical fac- poundments. Natural lakes scored lower on cator (eigenvalue = 4.40) was a contrast between nonical factor 1 than impoundments because black
mean back-calculated length at age 3 and RVI (yj crappie populations in natural lakes typically had
= 1.90 [mean back-calculated length at age 3]- low densities and high condition factors. However,
0.94 [RVI]). Thus, black crappie populations with large and small impoundments scored higher on
fast growth rates and inconsistent recruitment canonical factor 1 because black crappie populaLarge impoundments
762
GUY AND WILL1S
-1
0
Canonical factor 1
FIGURE 6.—Canonical discriminant function analysis
of black crappie populations collected from small (<40ha) impoundments (solid circles), large (>40-ha) impoundments (squares), and natural lakes (triangles). Canonical factor scores were derived from stock- to quality-length catches per unit effort (130 mm-199 mm; SQ CPUE) and mean stock-length relative weights (^ 130
mm; S Wr) values for 22 black crappie populations in
South Dakota waters. Ellipses denote regions of concentration about the ceniroids.
Black crappie populations in natural lakes have
inconsistent recruitment that resulted in lower density and higher growth, condition factors, and size
structure than populations in impoundments. Although black crappie populations in natural lakes
have high growth rates, size structure, and condition factors, densities are often too low for the
species to contribute to the sport fishery. Therefore, management strategies to improve recruitment stability in natural lakes should be researched.
Although growth, size structure, and condition
factors of black crappies in large impoundments
were not as high as in natural lakes, recruitment
was more consistent. Thus, black crappies contributed to the fishery in most of the large impoundments included in this study. In general,
large impoundments had higher-quality black
crappie populations than small impoundments and
natural lakes; density was higher than in natural
lakes and size structure was more desirable than
in small impoundments. This pattern has also been
observed in southeastern and midwestern impoundments.
Black crappies commonly overpopulated small
South Dakota impoundments. Some authors have
suggested that crappies overpopulate small impoundments because recruitment is relatively consistent (Bums 1956; Jenkins 1958). Thus, growth,
size structure, and condition are poor in small imtions in impoundments had higher densities and
poundments overpopulated with black crappies.
lower condition factors.
Gabelhouse
(1984a) and Boxrucker (1987) found
Natural lakes typically had black crappie poputhat
a
high-density
population of large mouth bass,
lations with low density, unstable recruitment, fast
growth rates, and high condition factors. Converse- composed primarily of individuals less than 30 cm
ly, small impoundments had black crappie popu- long, could reduce crappie recruitment such that
lations with high density, more stable recruitment, surviving crappies reached sizes of interest to anslow growth, and low condition factors. Black crap- glers. Guy and Willis (1990, 1991b) documented
pie population dynamics and structure character- reduced recruitment of bluegills Lepomis macroistics in large impoundments were typically inter- chirus and yellow perch in small South Dakota
mediate between those of natural lakes and small impoundments with high-density largemouth bass
impoundments. These patterns are conspicuous in populations. They suggested that fisheries manvarious correlations previously explained. For ex- agers could manage for quality bluegill and yellow
ample, most of the water bodies with high RVI perch fisheries in small South Dakota impoundvalues and black crappie populations with slow to ments by maintaining a high-density largemouth
moderate growth rates were impoundments; the bass population, with most largemouth bass less
populations in natural lakes typically had faster than 30 cm. We surmise that high-quality black
growth rates and higher condition factors than those crappie populations could be developed in small
South Dakota impoundments containing a highin impoundments (Tables 1, 3, 4).
density largemouth bass population. However, the
Management Implications
habitat must be appropriate to establish a highWe surmise that the differences observed in density largemouth bass population (Guy and Wilblack crappie population characteristics among lis 1991c). Guy and Willis (1994) stated that crapecosystems were primarily related to recruitment. pie populations in South Dakota's large reservoirs
POPULATION CHARACTERISTICS OF CRAPPIES
763
lating lengths from scale measurements for some
and natural lakes were influenced primarily by encentrarchid and percid fishes. Transactions of the
vironmental factors.
American Fisheries Society 111:332-336.
Although temporal variation exists in size strucClady. M. D. 1976. Influence of temperature and wind
ture, recruitment, growth, and CPUE, our data sugon the survival of early stages of yellow perch. Pergest that black crappie population dynamics and
ca flavescens. Journal of the Fisheries Research
structure characteristics differ among ecosystems,
Board of Canada 33:1887-1893.
particularly between natural lakes and impound- Clady, M. D., and B. Hulchinson. 1975. Effect of high
winds on eggs of yellow perch, Perca flavescens, in
ments. The difference in recruitment variability,
Oneida Lake, New York. Transactions of the Amergrowth, size structure, and condition among ecoican Fisheries Society 104:524-525.
systems facilitate a better understanding of the fac- Coble, D. W. 1992. Predicting population density of
tors that influence these variables and illustrate the
largemouth bass from electrofishing catch per effort.
importance of ecosystem-specific management.
North American Journal of Fisheries Management
Acknowledgments
We thank Carter Kruse, Jason Overby, and Brian
Van Zee for their help in the field and laboratory;
Lee Tucker and Jonathan Jenks for their advice in
statistical analysis; and Charles Scalet and Walter
Duffy for reviewing the manuscript. This research
was funded by the South Dakota Department of
Game, Fish and Parks through Federal Aid in Sport
Fish Restoration, project F-15-R-28, by the South
Dakota Agricultural Experiment Station, and
South Dakota State University. The paper was approved for publication by the South Dakota Agricultural Experiment Station as Journal Series
number 2820.
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