Transactions of the American Fisheries Society 135:943–954, 2006
Ó Copyright by the American Fisheries Society 2006
DOI: 10.1577/T05-028.1
[Article]
Regulation of an Unexploited Brown Trout Population in
Spruce Creek, Pennsylvania
ROBERT F. CARLINE*
U.S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit,
402 Forest Resources Building, Pennsylvania State University, University Park, Pennsylvania 16802, USA
Abstract.—The purpose of this paper is to describe the annual variations in the density of an unexploited
population of lotic brown trout Salmo trutta that has been censused annually for 19 years and to explore the
importance of density-independent and density-dependent processes in regulating population size. Brown
trout density and indices of stream discharge and water temperature were related to annual variations in
natural mortality, recruitment, and growth. Annual mortality of age-1 and older (age-1þ) brown trout ranged
from 0.30 to 0.75 and was best explained by discharge during spring and by brown trout density. Recruitment
to age 1 varied fivefold. Density of age-1 brown trout was inversely related to spawner density and positively
related to discharge during the fall spawning period. The median length of age-1 brown trout was positively
related to discharge during summer and fall. Relative weight was inversely related to the density of age-2þ
brown trout. The interactive effects of discharge and brown trout density accounted for most of the annual
variation in mortality, recruitment, and growth during the first year of life. Annual trends in the abundance of
age-1þ brown trout were largely dictated by natural mortality.
The mechanisms that drive salmonid population
regulation and the relative importance of densitydependent and density-independent factors are recurring themes investigated in the fisheries literature.
There are many examples of how density-independent
factors can affect salmonid populations. For example,
Jensen and Johnsen (1999), Cattanéo et al. (2002), and
Lobón-Cerviá (2004) have shown that high stream
discharge during or shortly after emergence resulted in
weak year-classes. Milner et al. (2003) reviewed
natural controls of salmonid populations in streams
and argued that in the absence of catastrophic events,
density-dependent factors were largely responsible for
population regulation. Brown trout Salmo trutta have
frequently been the subject of studies on population
control because of their widespread occurrence in
Europe and North America and because of their
importance as a sport species. Among the studies that
support the notion of density-dependent regulation,
there are two apparently competing views.
Elliott’s (1994) landmark studies of a migratory
population of brown trout in the United Kingdom
provided convincing evidence that density-dependent
mortality in the early life stage was the primary
mechanism regulating density in Black Brow Beck,
a small nursery stream. He showed that a Ricker-type
stock–recruitment curve best described the relation
between the number of eggs laid and the number of
* E-mail: [email protected]
Received January 28, 2005; accepted January 11, 2006
Published online July 3, 2006
juvenile brown trout. Newly emerged brown trout took
up territories, and fry that successfully defended
territories grew faster than fry without territories. The
latter group starved and emigrated. Elliott (1984) found
no evidence of density-dependent growth; hence, he
concluded that density-dependent mortality was the
primary mechanism for regulating population size.
Jenkins et al. (1999) studied brown trout in two
California mountain streams and stream channels
containing experimentally manipulated and unmanipulated populations. They showed that growth of age0 brown trout was density dependent, while mortality
and emigration were not related to density. Jenkins et
al. (1999) argued that density-dependent growth can
regulate population size through its effects on fecundity. Numerous other studies have demonstrated
density-dependent growth in brown trout (Crisp 1993;
Nordwall et al. 2001; Bohlin et al. 2002).
Besides the theoretical interest in population regulation, there are practical reasons for wanting to
understand how populations respond to changes in
density. Fishery simulation models require accurate
descriptions of growth and mortality. Often, one is
interested in predicting the response of a fishery to
a restrictive regulation, which is likely to result in an
increase in fish density (Clark et al. 1980). If
compensatory changes in growth or mortality occur
in response to density increases, these changes need to
be incorporated into the model to ensure useful
predictions.
Studies that have made the most notable contributions to our understanding of processes that regulate
fluvial brown trout populations are long-term inves-
943
944
CARLINE
tigations from a single stream (e.g., Elliott 1994) or
shorter, multiple-year efforts from several streams (e.g.,
Cattanéo et al. 2003). When these studies are
subdivided into those dealing with migratory and
resident populations, the number per category is rather
small. Hence, these long-term data sets are extremely
valuable, yet relatively rare.
The purpose of this paper is to describe annual
variations in density of an unexploited, lotic brown
trout population that has been censused annually for 19
years and to explore the importance of densityindependent and density-dependent processes in regulating population size. The study was conducted in
a 0.7-km section of Spruce Creek, located in central
Pennsylvania. This stream section has been managed
under a no-harvest regulation (artificial lures only,
barbless hooks) since 1985, when the first annual
population estimate was made. I assumed that (1)
illegal harvest was not important because there has
been only one documented instance of poaching during
the entire study and (2) hooking mortality from
artificial lures with barbless hooks was insignificant
(Muoneke and Childress 1994). Hence, annual losses
of brown trout are attributed to natural mortality and
emigration, less gains from immigration. There were
no man-made or natural stream barriers that were likely
to prevent immigration or emigration of brown trout.
Specifically, I evaluate the relative importance of
brown trout density and indices of stream discharge
and temperature on annual variations in natural
mortality, recruitment, and growth.
Study Area
Spruce Creek, Huntingdon County, is about 26 km
long from its source springs to its confluence with the
Little Juniata River (Bachman 1984). Limestone
springs account for most of the streamflow; alkalinity
averages about 150 mg/L as CaCO3, conductivity is
about 280 lS, and nitrate-nitrogen ranges from 3.0 to
4.0 mg/L. Land cover in the watershed is primarily
hardwood forests and agricultural fields.
The study was conducted in the George W. Harvey
Experimental Fisheries Research Area of Spruce
Creek, which is approximately 1 km upstream from
the confluence with the Little Juniata River. The study
reach was divided into two segments. Section A
extended from the research area boundary upstream
to a junction of two branches, and it included the left
branch as one looks upstream. This section was 602 m
long, averaged 10.9 m in width, and had a surface area
of 0.65 ha. Section B began at the downstream junction
with section A and extended past the upstream junction
with section A to the upper research area boundary.
Section B was 515 m long, averaged 14.6 m in width,
and had a surface area of 0.75 ha. At the downstream
junction of the two sections, about 60% of the total
discharge was conveyed in section B. Mean low flow
during summer is about 2.8 m3/s (Bachman 1984).
Stream gradient is 0.8% (McLaren 1970); the predominant substrates are cobble and gravel.
Wild brown trout dominate the fish community.
Other common native species include the white sucker
Catostomus commersonii, slimy sculpin Cottus cognatus, eastern blacknose dace Rhinichthys atratulus,
longnose dace R. cataractae, cutlip minnow Exoglossum maxillingua, and tessellated darter Etheostoma
olmstedi.
Methods
The brown trout population was sampled annually
from 1985 to 2003. In most years, sampling was
conducted between June 5 and 14. Owing to equipment
failures in 1986, the stream was not sampled until June
30. During the early years of the study, sampling was
conducted in June as part of another investigation.
Thereafter, I continued sampling in June for consistency.
Sections A and B were electrofished annually from
1985 to 1991. From 1992 to 1995, only section A was
sampled; fish in section B were not exposed to
sampling as part of another study (Carline 2001). Both
sections were sampled from 1996 to 2003. Population
estimates were expressed as the number of fish per unit
surface area of the sampled sections. I used the same
surface area of each section every year to compute fish
density (rather than remeasuring the stream annually)
because I wanted to relate fish abundance to discharge,
which would affect stream width. Annual adjustments
in width could obscure relations between fish density
and discharge.
We used electrofishing gear that was mounted in
a small boat and towed upstream. During the study,
several different boats, generators, and control units
were used. We always used equipment that produced
pulsed DC at 200–250 V. Tow boats were fitted with
a 1-m-long metal cathode and two or three anodes that
were made of stainless steel and mounted on fiberglass
poles (Carline 2001).
In 1985, a three-pass successive removal procedure
was used to estimate population size (Seber 1982). In
1995, a three-pass Schnabel procedure was used. In all
other years, a Petersen population estimate was made
with 2 or 3 d between the initial marking run and the
second run (Seber 1982). During the first electrofishing
run, all fish were measured (total length) to the nearest
millimeter and were given a temporary fin clip;
a subsample was weighed to the nearest gram. Age0 brown trout were usually less than 70 mm long, and
BROWN TROUT POPULATION REGULATION
the capture efficiency for these small fish was low.
Therefore, age-0 fish were not included in the estimates
of population size.
Length-frequency analyses were used to separate
age-1 fish from other cohorts. There was no overlap in
length distributions of age-0 and age-1 fish. The
division between age-1 brown trout and age-2 and
older (age-2þ) brown trout was identified as a narrow
length interval (2–4 mm) with few or no individuals.
Length-frequency distributions were iteratively constructed with length categories of varying, though
small, widths until a marked separation could be
identified. Assignment of brown trout ages from scale
readings in an earlier study confirmed that lengthfrequency analyses could reliably separate age-1 fish
from older ones (Carline 2001). I computed separate
population estimates for age-1 and age-2þ fish because
capture efficiency averaged 0.34 for age-1 fish and
0.56 for older brown trout. Capture efficiency was
computed as the number of marked fish captured on the
second sampling run divided by the total catch in that
run. The total estimated numbers of age-1 and age-2þ
fish were allocated to 10-mm length-groups on the
basis of the proportion of fish captured in each length
interval. The mean weight of fish in each 10-mm
interval was multiplied by the estimated number in the
interval to compute biomass.
Annual survival (S) was calculated as the estimated
number of age-2þ fish in year x divided by the number
of age-1þ fish in year x – 1. Annual mortality (A) was
calculated as 1 – S. I computed the variance for each
population estimate by use of the Bailey modification
of a binomial approximation (Seber 1982). These
variance estimates were then used to estimate the
variance of S by employing a Taylor series approximation (7–9; Seber 1982). I computed 95% confidence
limits by assuming that the estimated parameters had
a lognormal distribution (Burnham et al. 1987).
Recruitment was defined as the density of age-1
brown trout in June, recognizing that these fish had
been spawned nearly 2 years earlier. The density of
age-2þ brown trout 2 years earlier was used as an index
of spawners. This index overestimates the number of
mature fish because a large proportion of females less
than 225 mm long would not be mature (Avery 1985;
R.F.C., unpublished data); nonetheless, this index
should provide a good measure of spawner density
and total egg deposition.
A standard weight equation for brown trout (Milewski and Brown 1994) was used to compute relative
weight (Wr) for each fish that was weighed (Anderson
and Neumann 1996). Brown trout with Wr values less
than 70 or greater than 120 were considered to be
outliers and were not included in subsequent analyses
945
of Wr. I computed a median Wr for 199-mm and shorter
brown trout and for 200–300-mm fish to examine
annual variations in condition. These length categories
were chosen because the smaller fish typically had
higher Wr values than did the larger fish. The median
length of all age-1 fish was used as a measure of annual
growth, and the coefficient of variation (CV ¼ 100 3
SD/mean) of length was used to assess year-to-year
variance in growth.
I assessed the potential effects of temperature and
streamflow on population statistics by first dividing
each year from 1985 to 2003 into five periods
corresponding to important life stages of brown trout:
spawning (November 1–December 15); incubation(December 16–March 15); emergence and fry stage
(March 16–June 15); summer growth (June 16–
September 15); and autumn growth (September 16–
October 31). Temperature and flow data were summarized for each of these periods.
Air temperature was used as an index of water
temperature. Daily air temperature records were
obtained from the Pennsylvania State University
weather station at Rock Springs, which is 17 km from
the study section. I assumed that air temperature from
Rock Springs could be used as a surrogate for water
temperature in Spruce Creek because (1) the mean
monthly air temperature from Rock Springs was
strongly correlated (r2 ¼ 0.91) with the mean monthly
temperature of Spring Creek, a watershed that is
adjacent to the Spruce Creek watershed and (2) the
Rock Springs weather station is located on the
boundary of the Spring Creek and Spruce Creek
watersheds. The mean daily air temperature and the
10th and 90th percentiles were computed for each of
the five life stage periods.
Streamflow data from the U.S. Geological Survey
gauge on the Little Juniata River in the town of Spruce
Creek was used as an index of streamflow at the study
site. Spruce Creek flows into the Little Juniata River
0.6 km downstream of the gauge. Streamflow in
Spruce Creek at a site 11 km upstream of the study
reach was well correlated (r2 ¼ 0.83) with streamflow
in the Little Juniata River, although the watershed area
upstream of the Little Juniata River gauge is about
twice the surface area of the Spruce Creek watershed
(570 versus 282 km2). Daily streamflow data were used
to compute the median, minimum, maximum, 10th
percentile, and 90th percentile for each of the five life
stage periods.
The dependent variables of interest were annual
mortality, density of age-1 brown trout, median length
of age-1 fish, and median Wr for two length-classes.
Because of the large number of independent variables,
I first used a best-subsets regression procedure to
946
CARLINE
identify up to two independent variables that accounted
for the most variation in the dependent variable of
interest (MINITAB 2000). This procedure ranks
regression models on the basis of maximum R2. Simple
or multiple least-squares linear regression was then
used with the selected independent variables. I used Pvalues less than 0.05 for statistical significance of
regression models. For regression models with two
independent variables, I used P-values less than 0.10 as
the criterion for including each variable in the model.
Results
Streamflow and Temperature
Mean monthly discharge in the Little Juniata River
varied by more than 100% during the study (Table 1).
The period of June 1995 to May 2002 was the most
extreme; three consecutive years of above-normal
discharge were followed by four consecutive years of
below-normal discharge, when most of the state
underwent a severe drought. There was no correspondence among years with extreme flows and cold
winters or warm summers, as illustrated by mean daily
temperatures in January and July.
Population Statistics
Typically, the brown trout population in June
consisted primarily of fish between 125 and 300 mm
long, as illustrated by data from June 2000 (Figure 1).
Though small numbers of age-0 fish were collected,
they were usually less than 70 mm long. Brown trout
longer than 300 mm accounted for less than 10% of all
age-1þ fish, and individuals longer than 400 mm were
rare. The modal length category of age-1 fish was 171–
175 mm.
The density of age-1þ brown trout averaged about
1,200 fish/ha over the 19-year period and included
nearly equal numbers of age-1 and age-2þ fish (Table
A.1 in the Appendix). Because the capture efficiency
for age-2þ brown trout was relatively high (mean ¼
0.56) and because large numbers of fish (mean ¼ 363)
were collected during each electrofishing run, 95%
confidence intervals for the estimated population size
were rather narrow (Figure 2). Confidence limits for
estimates of age-1 fish were usually wider than those
for age-2þ fish because capture efficiency was lower
for age-1 fish than for age-2þ fish and because fewer
age-1 fish (mean ¼ 252) were captured. The number of
fish in each age category varied by about fivefold
during the study. The population was remarkably stable
from 1993 to 1999, when density of age-1þ fish ranged
from 1,121 to 1,326 fish/ha. Density increased in 2000
and then declined by more than 50% over the next 3
years. The biomass of all age-1þ brown trout ranged
from 76.4 to 214.7 kg/ha and averaged 142.9 kg/ha.
TABLE 1.—Mean monthly discharge of the Little Juniata
River at the town of Spruce Creek, Pennsylvania. Values are
for June 1 to May 31 of the following year. Mean daily air
temperatures in January and July were taken at the Rocks
Springs weather station.
Mean daily air temperature (8C)
3
Year
Mean discharge (m /s)
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Mean
9.9
11.0
8.2
9.2
11.4
14.8
7.1
13.1
13.6
8.2
12.1
15.0
14.6
7.8
7.0
7.1
7.9
11.0
10.5
Jan
1.7
4.6
5.6
0.9
2.2
1.8
1.2
0.5
7.6
1.3
4.6
3.7
2.1
2.7
3.7
0.2
0.4
7.3
2.3
Jul
20.8
22.2
22.3
5.6
21.5
20.6
21.5
20.4
22.6
22.1
22.2
20.1
20.8
20.7
22.9
19.1
19.4
21.8
21.5
Annual mortality of age-1þ brown trout ranged
widely from 0.30 to 0.75; the geometric mean was 0.49
(Figure 3). Although 95% confidence limits were rather
broad for some years, there were several years in which
confidence limits did not overlap, which suggests that
annual variations could be attributed, at least in part, to
true temporal variation. Mortality tended to fluctuate
from year to year except during the last 3 years of
study, when it was consistently high.
Annual mortality was positively related to median
discharge from mid-March to mid-June (Figure 4;
Table 2). The only other single environmental or
density variable that was significantly related to annual
mortality was the 10th percentile of air temperature
during the winter months (Table 2, model 2). This
relation seemed spurious, as I expected mortality to
decrease with warmer overwinter temperatures rather
than increase as the regression indicated.
Several other variables, when combined with
March–June discharge, produced significant regressions. One of these variables was the maximum
discharge during the November–December period,
which was negatively related to annual mortality
(Table 2, model 3). There is no apparent reason why
maximum discharge might reduce mortality; given that
none of four other discharge variables for that same
period was significantly related to mortality, this is
probably another spurious relation. Similarly, the 10th
percentile of air temperature in June–September was
BROWN TROUT POPULATION REGULATION
FIGURE 1.—Length-frequency distribution of age-1 and
older brown trout collected from Spruce Creek, Pennsylvania,
in June 2000.
negatively related to annual mortality (model 5), but
neither the mean nor the 90th percentile temperature
was related to mortality. The lack of consistency
among related independent variables may indicate that
summer temperature did not strongly influence mortality. The only other variable of importance was
density of age-1þ brown trout, which was positively
related to annual mortality and produced a significant
regression when combined with March–June discharge
(Table 2, model 4).
FIGURE 2.—Estimated number (N) of fish per hectare (with
95% confidence intervals) for age-1 and age-2 and older
brown trout in Spruce Creek, Pennsylvania, 1985–2003.
947
FIGURE 3.—Annual mortality (with 95% confidence intervals) for age-1 and older brown trout in Spruce Creek,
Pennsylvania, 1985–2003.
Annual recruitment was inversely related to the
spawner index (Figure 5; Table 2, model 6). If this
spawner index is a reliable surrogate for the number of
eggs deposited, the inverse relation suggests that
juvenile mortality increased with increasing juvenile
density. The addition of numbers of older brown trout
present during the recruits’ first year of life as a second
independent variable did not produce any significant
regressions. From this, I infer that density-dependent
effects on the number of recruits were the result of
intracohort interactions.
Two environmental variables, when combined
separately with the spawner index, produced significant
regressions. The minimum air temperature in June–
September of the cohorts’ first year was positively
related to the number of recruits (Table 2, model 7).
However, neither the mean nor the maximum temperature during the same period resulted in a significant
regression. This inconsistency casts doubt on the
importance of minimum summer temperature on the
survival of age-0 fish. In contrast, the addition of
FIGURE 4.—Annual mortality of age-1 and older brown trout
in Spruce Creek, Pennsylvania, in relation to the median
discharge of the Little Juniata River at the town of Spruce
Creek during March 16–June 15, 1985–2003.
948
CARLINE
TABLE 2.—Regression models relating annual mortality of age-1 and older brown trout and age-1 recruits to seasonal discharge
and temperature surrogates and brown trout density. Independent variables are defined as follows: MdD ¼ median discharge;
10pT ¼ 10th percentile of air temperature; and MxD ¼ maximum discharge.
Model
Dependent variable
1
2
3
Annual mortality
4
5
6
7
8
Age-1 fish/ha
Independent variables
MdD, Mar–Jun
10pT, Dec–Mar
MdD, Mar–Jun
MxD, Nov–Dec
MdD, Mar–Jun
Fish/ha
MdD, Mar–Jun
10pT, Jun–Sep
Spawners/ha
Spawners/ha
10pT, Jun–Sep
Spawners/ha
MdD, Nov–Dec
median discharge during the spawning period to the
spawner index produced a significant regression that
seemed logical (Table 2, model 8) if increased
discharge resulted in increased availability of spawning
habitat.
The median length of age-1 brown trout in June
varied considerably among years (153.5–189.0 mm;
Table 3). Median length was positively related to
several discharge variables during the June–September
and November–December periods. The strongest
relations included the 90th percentile discharge during
November–December and the 10th percentile discharge during June–September (Table 4, models 1
and 2). Discharge variables during November–December were included in 27 of 35 significant (P , 0.05)
regressions with two independent variables, and most
of these relations had a second discharge variable
(Table 4, models 3 and 4). It was only during March–
June that median length was negatively related to
discharge. The only density variable that appeared in
FIGURE 5.—Recruitment of brown trout in Spruce Creek,
Pennsylvania, expressed as the number of age-1 fish per
hectare in June, in relation to the spawner index, which is the
number of adults per hectare present during the year in which
the recruits were spawned.
t-statistic
R2
F
P
3.33
2.21
4.21
2.39
3.93
2.25
3.21
1.83
3.24
3.48
2.12
3.87
1.77
0.41
0.23
0.57
11.1
4.9
10.2
0.004
0.04
0.002
0.56
9.5
0.002
0.52
8.0
0.004
0.41
0.55
10.5
8.7
0.006
0.004
0.52
7.5
0.006
multiple regressions was the density of age-1þ brown
trout that were present during the cohort’s first year of
life, which suggests that older brown trout were
negatively affecting the growth of age-0 fish (Table
4, model 5). Only four models had a temperature
variable, and these were always combined with
November–December discharge. I correlated the CV
with density because other studies have shown that the
CV, rather than a direct measure of growth, may be
density dependent. Median length and the CV were
inversely related (r2 ¼ 0.31; P ¼ 0.015), but there was
no relation between the CV and any brown trout
density variable. Thus, growth during the brown trout’s
first 15 months of life was best predicted by discharge
variables.
Median Wr, an index of growth, ranged from 87.2 to
103.1 among years for the two length-groups of brown
trout (Table 3). In every year except 1985, the median
Wr for fish less than 200 mm long was greater than that
of 200–300-mm fish. The median Wr for fish less than
200 mm was inversely related to the density of age-2þ
brown trout at the beginning of the annual interval but
was not related to any other density or environmental
variable (Table 4, model 6). The only significant
regressions with two independent variables included
density of age-2þ fish and maximum discharge in
November–December, which was positively related to
Wr. Median Wr for 200–300-mm brown trout (Wr200)
was also inversely related to the density of age-2þ fish
but not to any other single variable (Table 4, model 8).
The density of age-2þ fish, when combined with
discharge in November–December (positive effect) or
temperature in June–September (negative effect), produced significant relations (Table 4, models 9 and 10).
Discharge in November–December and temperature in
March–June in combination were positively related to
Wr200 (model 11). Thus, for both length-groups, there
949
BROWN TROUT POPULATION REGULATION
TABLE 3.—Median length and the coefficient of variation
(CV ¼ 100 3 SD/mean) for age-1 brown trout in Spruce
Creek, Pennsylvania, 1985–2003, and median relative weight
(Wr) of 120–199-mm and 200–300-mm brown trout. Samples
sizes are in parentheses.
Median Wr
Length of age-1 fish
Year
Median (mm)
CV (%)
120–199 mm
200–300 mm
1985
180.0
(251)
6.8
94.1
(19)
95.2
(93)
1986
178.0
(845)
14.0
93.2
(93)
89.5
(95)
1987
177.0
(257)
8.5
96.8
(29)
94.5
(47)
1988
173.5
(514)
10.8
98.0
(70)
91.8
(114)
1989
153.5
(234)
11.3
94.3
(25)
87.2
(72)
1990
182.0
(657)
10.2
96.1
(55)
92.4
(116)
1991
185.0
(475)
11.3
97.3
(78)
92.2
(157)
1992
171.0
(247)
10.6
100.6
(106)
91.6
(201)
1993
156.0
(208)
11.4
93.0
(67)
88.2
(136)
1994
184.0
(129)
7.7
100.5
(102)
90.5
(132)
1995
189.0
(225)
8.4
96.0
(58)
90.7
(250)
1996
169.5
(244)
11.3
96.0
(202)
88.3
(298)
1997
184.0
(202)
6.9
95.9
(69)
92.7
(233)
1998
178.5
(450)
10.3
98.8
(134)
88.9
(218)
1999
175.0
(254)
9.5
99.1
(88)
91.5
(241)
2000
173.0
(725)
11.6
95.5
(51)
90.0
(147)
2001
162.5
(578)
12.7
94.6
(115)
90.0
(199)
2002
168.0
(386)
12.6
94.6
(76)
89.5
(139)
2003
167.5
(361)
12.7
103.1
(129)
95.3
(115)
was some evidence of density-dependent growth.
Discharge during most months seemed to positively
affect Wr except during March–June. Conversely,
temperature during March–June was positively related
to Wr and negatively related during the summer
months.
Discussion
This investigation represents one of a few such longterm studies of a resident, unexploited brown trout
population. In Europe, most long-term investigations of
unexploited populations have been directed at sea-run
(Elliott 1993) or lake-run (Crisp 1993; Lobón-Cerviá
and Mortensen 2005) brown trout in nursery streams.
The longest record for a nonmigratory, lotic population
of brown trout comes from northern Spain (LobónCerviá 2003), but this short-lived population (few fish
beyond age 3) is not typical of populations in Europe
and North America, which are commonly long lived.
One of the longest records of a brown trout population
in North America is from the Au Sable River in
Michigan; there, annual mortality of brown trout in
a catch-and-release segment of the river averaged 0.56
over a 15-year period (A. Nuhfer, Michigan Department of Natural Resources, unpublished data),
which is reasonably close to that observed in Spruce
Creek.
Perhaps the most notable result from Spruce Creek is
the large temporal variation in annual mortality of age1þ fish. A large range in annual mortality (0.42–0.75)
was also documented in the Au Sable River. Such large
variations in natural mortality have significant consequences if one is trying to measure population
responses to changes in exploitation rates or to
environmental modifications at the site or watershed
scale. Population responses would have to be large and
posttreatment assessment periods would have to be
long (e.g., .5 years) to allow one to separate out
treatment responses from natural temporal variations.
Understanding the underlying mechanisms for temporal variations may help to unravel causes for population
responses to treatments and temporal variations.
Discharge during the March–June period had the
most influence on mortality of age-1þ brown trout. On
average, the highest monthly discharges occurred in
March, followed by April and then May. High
discharge could result in mortality by injuring or
displacing fish. Extreme discharges that cause substantial bed load movement in Spruce Creek, a lowgradient stream that runs through the valley floor, are
rare. High concentrations of suspended solids can
injure fish and lead to death, but concentrations need to
exceed 2,000 mg/L for more than 10 h to result in
mortality of age-0 or older salmonids (Newcombe and
MacDonald 1991). I sampled 95 storm flow events
between November 1999 and May 2003 in Spring
Creek and never observed concentrations of suspended
sediment that were high enough to cause fish mortality.
950
CARLINE
TABLE 4.—Regression models relating median length (Med len) of age-1 brown trout in June to discharge and age-1 brown
trout density during the cohort’s first year of life. Median relative weight (Wr) of brown trout less than 200 mm (Wr199) and
200–300 mm (Wr200) were correlated with density, flow, and temperature variables. Discharge variables included the median
(MdD), maximum (MxD), 10th percentile (10pD), and 90th percentile (90pD). The 90th percentile of air temperature (90pT) was
also included.
Model
Dependent variable
Independent variable
t-statistic
R2
F
P
1
2
3
Med len
90pD, Nov–Dec
10pD, Jun–Sep
MxD, Nov–Dec
90pD, Mar–Jun
MxD, Nov–Dec
MdD, Jun–Sep
Age-1 fish/ha
MdD, Sep–Nov
Age-2þfish/ha
Age-2þfish/ha
MxD, Nov–Dec
Age-2þfish/ha
Age-2þfish/ha
90pT, Jun–Sep
Age-2þfish/ha
MdD, Nov–Dec
90pT, Mar–Jun
10pD, Nov–Dec
2.72
2.16
3.77
3.51
2.39
2.49
2.49
2.86
2.41
2.98
2.12
2.48
3.39
2.38
3.49
2.37
2.86
2.28
0.32
0.23
0.56
7.4
4.7
9.4
0.015
0.046
0.002
0.43
5.7
0.015
0.42
5.5
0.016
0.27
0.44
5.8
5.8
0.029
0.014
0.28
0.48
6.13
6.8
0.025
0.008
0.47
6.8
0.008
0.37
4.3
0.033
4
5
6
7
Wr199
8
9
Wr200
10
11
Hence, I conclude that direct mortality owing to high
flows seems unlikely in Spruce Creek.
It is conceivable that high discharge could cause
brown trout to emigrate from the study section.
Upstream sections of Spruce Creek, which are entirely
in private ownership, seem to support high brown trout
densities, so that emigration would have to occur over
long stream reaches and brown trout would have to
ultimately leave the system and enter the Little Juniata
River, which supports a brown trout population that is
sustained by stocked fingerlings. Because no data are
available to support or refute the emigration hypothesis, the mechanism for the observed mortality remains
unresolved, though this question merits further study.
Density of age-1þ brown trout was negatively
related to annual mortality, which suggests that
intraspecific interactions were involved. Densitydependent mortality of age-1þ trout has been observed
in few other studies. McFadden et al. (1967) found that
mortality of brook trout Salvelinus fontinalis from age
1 to 2 was positively related to density. Langeland and
Pedersen (2000) showed that density-dependent mortality of age-1þ brown trout in a Norwegian lake was
the primary factor regulating population size. The
mechanisms causing mortality in these two studies
were not identified.
The strongest evidence for density-dependent regulation in Spruce Creek was illustrated by the inverse
relation between recruitment and an index of spawner
density. Presumably, low spawner densities in some
years would have helped to define the ascending limb
of the relationship such that it would resemble
a Ricker-type of hump-shaped curve. Elliott’s (1985)
plots of the number of eggs laid and juvenile brown
trout in Black Brow Beck nicely mimicked classic
stock–recruit curves, and the least amount of variance
was observed when fry were about 60 d old. Elliott
(1985) argued that territorial behavior of brown trout
fry forced later-emerging fry to emigrate; hence,
density regulation occurred rather early in life.
Mortensen (1977) also showed density-dependent
mortality in brown trout fry at 3 months of age in
a nursery stream. In a nonmigratory population of
brown trout, Newman (1993) found that mortality of
juveniles from August to the following April was
positively related to their density in the first year of life.
Similar results have emerged from studies on juvenile
Atlantic salmon Salmo salar; Gee et al. (1978) and
Jonsson et al. (1998) found density-dependent mortality in nursery streams.
Discharge during the spawning period was the only
environmental variable that was plausibly related to
recruitment. Above-average flows during the spawning
period could enhance fry production by increasing the
amount of available spawning habitat or by improving
intragravel flow of water, thereby improving conditions
for incubating brown trout embryos. The latter seems
unlikely because flows during most of the incubation
period (December–March) were not related to yearclass strength. Spawning habitat is probably limited in
the study section of Spruce Creek. In fall 1988, I
counted 57 redds/ha in the study section. In June 1988,
the estimated number of 230-mm and larger brown
trout was 528 fish/ha. Assuming a sex ratio of 1:1, the
estimated number of mature females was 264 fish/ha,
which was 4.6 times higher than the number of redds.
BROWN TROUT POPULATION REGULATION
Beard and Carline (1991) found a similar situation in
nearby Spring Creek, where the estimated number of
female brown trout was 7.3 times greater than the
number of redds. They concluded that certain spawning
areas were repeatedly used by different females and
that spawning habitat in Spring Creek was limited. If
spawning habitat was indeed limited in Spruce Creek,
it seems likely that above-average flows would
enhance the amount of available spawning habitat.
The lack of any relation between discharge during
March–June (when fry emerge) and recruitment is
curious. Several studies have shown that high discharge during the period of emergence and soon
thereafter can result in weak year-classes (Nuhfer et al.
1994; Jensen and Johnsen 1999; Cattanéo et al. 2002;
Lobón-Cerviá 2004). This lack of a relation is even
more surprising given that flows during this period
were positively related to annual mortality of age-1þ
brown trout. It seems counterintuitive that high flows
would cause mortality (or emigration) of older fish but
not harm newly emerged fry. It is possible that
discharge in the Little Juniata River is not a good
surrogate for discharge in Spruce Creek. Nonetheless,
this unexpected finding lends support to the notion that
March–June discharge merits further examination.
Recruitment of brown trout in Spruce Creek was not
related to the density of age-1 or age-2þ fish present
during the juvenile cohorts’ first year of life. In some
other systems, yearling density influenced survival of
age-0 fish. Titus and Mosegaard (1992) studied
a migratory population of brown trout and found that
within years and among stream sections, the density of
age-0 fish was inversely related to the density of age-1
fish. Cattanéo et al. (2002) found that the survival of
age-0 brown trout was inversely related to the density
of age-1 brown trout. Nordwall et al. (2001) experimentally manipulated brown trout density and showed
that older brown trout influenced the density of age0 fish. In two lotic populations of brook trout in central
Wisconsin, White and Hunt (1969) found alternating
strong and weak year-classes over a 12-year period.
Strong year-classes were out of phase in the two
streams, which suggested that environmental variables
were not responsible for the observed pattern. They
examined more than 1,400 stomachs of age-1þ brook
trout and found no evidence of cannibalism. They
concluded that competition for food or space was the
mechanism underlying interactions between age-0 and
yearling fish.
The above examples support the notion that
mortality of brown trout during their first year of life
is influenced by cohort density (as in Spruce Creek and
Black Brow Beck) or by density of yearlings and that
competition is the driving force. There is little evidence
that adult trout density directly influences survival of
951
trout during their first year of life. An exception is
a study of brown trout in a small regulated stream in
Norway, in which the density of age-0 fish was
inversely related to the density of large brown trout
(Vik et al. 2001). They found age-0 brown trout in the
stomachs of 21% of 170–290-mm brown trout. The
authors suggested that the reduced flows in summer
confined brown trout to small pools that lacked refuge
for juvenile brown trout.
Growth in length of brown trout during their first
year of life in Spruce Creek was positively related to
discharge in summer and late fall and negatively
related to discharge in March–June. Density of age-1þ
brown trout seemed less influential on growth than did
discharge. Above-average streamflow could positively
influence growth by enhancing the production of
invertebrates (Schlosser 1992; Boulton 2003). High
streamflow could also increase the wetted streambed
area, which would increase the number of territories,
particularly along the streambank, where age-0 fish are
found.
Newman (1993) developed a conceptual model for
the growth of stream-dwelling trout and suggested that
the growth of individuals was a function of site
(territory) quality. Individuals occupying high-quality
sites would grow equally well, while trout forced to
occupy low-quality sites would grow more slowly. He
predicted that (1) density-dependent growth would
become more evident as more fish are relegated to lowquality sites and (2) the CV in fish size would increase
as the average growth rate declines with increasing
density. Newman (1993) provided empirical evidence
for density-dependent growth and increasing CV in
juvenile brown trout. Jenkins et al. (1999) obtained
similar results for juvenile brown trout. In Spruce
Creek, the CV for the length of age-1 fish was not
related to density.
Jenkins et al. (1999) used their data and those from
Elliott (1984) to show that the detection of densitydependent growth depends upon the range of fish
densities and that the density of juvenile brown trout
(.1 fish/m2) in Black Brow Beck was never low
enough for density-dependent growth to be evident.
Lobón-Cerviá (2005) expanded upon this analysis and
confirmed conclusions by Jenkins et al. (1999). Over
the 19-year period in Spruce Creek, density of age-1þ
brown trout ranged from 0.07 to 0.18 fish/m2. Even if I
were able to estimate the density of age-0 brown trout,
it seems unlikely that density would have exceeded 1
fish/m2. Therefore, the lack of evidence for densitydependent growth in the length of age-1 brown trout in
Spruce Creek was not due to a consistently high
density of age-0 brown trout.
In contrast to the length of age-1 fish, annual
variations in Wr showed evidence of density de-
952
CARLINE
pendence. Brown trout less than 200 mm long were
nearly all age-1 fish, and their Wr values were
negatively related to the density of age-2þ fish.
Intercohort effects on growth have been noted in
several studies (Nordwall et al. 2001; Lobón-Cerviá
2005). Relative weights of 200-mm and larger fish
were also strongly related to the density of age-2þ fish,
which implies intracohort competition. However, the
consequences of this competition on population biomass were negligible. To illustrate the effects of
extreme Wr values, I used data from 1999, when
numbers of age-1 and age-2þ fish were about equal and
when the biomass was close to the long-term average. I
recomputed biomass based on the minimum and
maximum values of Wr observed for fish less than or
greater than 200 mm. Estimated biomass was 126.4 kg/
ha based on the minimum Wr and 137.6 kg/ha based on
the maximum Wr, whereas the measured value was
132.5 kg/ha. While extreme values of Wr may be of
importance to individual fish, population-level effects
are rather small.
This examination of density-dependent effects on
brown trout has been limited to intraspecific interactions. It is conceivable that the other species could
have affected recruitment, growth, or mortality of
brown trout. For example, Vøllestad et al. (2002)
showed that brown trout exhibited reduced growth
rates in the presence of Alpine bullheads C. poecilopus.
I have not attempted to estimate the density or biomass
of other species in Spruce Creek. My impression is that
the biomass of all other species was less than 25% of
the brown trout biomass. Nonetheless, interspecific
effects on brown trout cannot be discounted.
The impetus for this study was to explore the relative
importance of density-dependent and densityindependent factors in controlling the population size
of brown trout. Annual trends in abundance of age-1þ
brown trout were largely dictated by natural mortality
(including emigration), which was a function of stream
discharge during the spring months and brown trout
density. Density-dependent influences on recruitment
were also evident, and discharge during the spawning
period was secondarily important. Though there was
some evidence for density-dependent growth, effects
on population biomass for all age-groups seem minor;
the major force influencing both density and biomass
was mortality.
Acknowledgments
I am grateful for the help provided by many graduate
and undergraduate students, technicians, and colleagues; P. Kocovsky and D. Lieb were especially
helpful. D. Diefenbach provided valuable assistance
with the statistical analyses. This manuscript was
improved owing to reviews by T. Greene, D. Lieb,
M. Millard, and J. Sweka.
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Appendix follows
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Appendix: Key Brown Trout Data
TABLE A.1.—Density, biomass, and annual mortality (A) of age-1 and older brown trout in Spruce Creek, Pennsylvania, 1985–
2003. The values in parentheses are 95% confidence limits.
Density (fish/ha)
Year
Age 1
Age 2þ
Biomass (kg/ha)
A
1985
181
(177, 185)
848
(844, 852)
145.2
0.32
(0.25, 0.37)
1986
787
(729, 868)
703
(670, 757)
174.7
0.66
(0.58, 0.72)
1987
258
(226, 314)
507
(481, 550)
109.1
0.30
(0.15, 0.42)
1988
766
(641, 949)
537
(480, 620)
166.1
0.68
(0.52, 0.79)
1989
534
(382, 793)
413
(372, 477)
90.9
0.43
(0.25, 0.57)
1990
883
(773, 1,032)
537
(496, 600)
159.8
0.58
(0.47, 0.67)
1991
944
(847, 1,073)
597
(552, 662)
179.9
0.36
(0.21, 0.48)
1992
810
(638, 1,092)
993
(874, 1,173)
214.7
0.66
(0.55, 0.74)
1993
610
(492, 812)
616
(565, 704)
129.7
0.36
(0.24, 0.45)
1994
332
(268, 459)
789
(728, 888)
159.5
0.35
(0.24, 0.45)
1995
600
(484, 813)
726
(684, 856)
191.0
0.61
(0.48, 0.70)
1996
679
(575, 829)
521
(483, 577)
126.8
0.38
(0.19, 0.53)
1997
446
(316, 677)
741
(665, 847)
157.9
0.57
(0.36, 0.72)
1998
678
(555, 865)
505
(450, 588)
137.1
0.52
(0.30, 0.66)
1999
560
(460, 820)
572
(509, 664)
132.5
0.39
(0.21, 0.53)
2000
748
(677, 851)
689
(653, 746)
159.3
0.64
(0.56, 0.70)
2001
576
(529, 644)
518
(495, 554)
114.3
0.67
(0.60, 0.72)
2002
459
(405, 542)
365
(353, 392)
91.0
0.75
(0.57, 0.85)
2003
501
(423, 620)
209
(192, 245)
76.4
Mean
597
599
142.9
a
Geometric mean.
0.49a