Ecology of Freshwater Fish 2012: 21: 483–493 Printed in Malaysia All rights reserved
Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
ECOLOGY OF FRESHWATER FISH
Factors influencing growth of individual brown trout in three streams of the upper Midwestern United States Douglas J. Dieterman1, R. John H. Hoxmeier1, David F. Staples2 1
Minnesota Department of Natural Resources, Lake City Fisheries Research Office, Lake City, MN USA Minnesota Department of Natural Resources, Fisheries Biometrics Unit, Carlos Avery Office, Forest Lake, MN USA
2
Accepted for publication March 21, 2012
Abstract – Growth rate variation of three age groups of brown trout, Salmo trutta L. (age-0, 1 and 2, and 3+), was quantified from recaptured, individually tagged brown trout and related to season, stream reach, relative abundance, initial length and movement to examine factors influencing growth in length in three streams in the Midwestern United States. Total variation in growth was almost five times greater for age-0 than for age-3+ trout. Individual trout accounted for about 13% of total variation in age-0 growth, season about 57%, and trout initial length and relative abundance combined another 2%. The 2006 age-0 cohort had the fastest growth rates in their second spring and summer (2007) and slowest growth in their first winter (2006–2007). About 53% of total growth variation of age-1 and age-2 trout was accounted for by individual trout, season, initial length and stream reach. Predicted growth rates indicated strong effects of season and initial length. A significant interaction between these two factors indicated that, although smaller trout grew faster than larger trout, this length effect was most pronounced in spring and summer. About 35% of total growth variation of age-3+ trout was accounted for by individual trout and season. Together, season and individual trout characteristics were identified as the most important factors influencing brown trout growth in these streams. Key words: brown trout; growth; coldwater streams; seasonal growth; movement
Introduction
Growth has important ecological and management implications for salmonid populations. Fast growth can result in a larger body size that allows adults to produce more young (Helfman et al. 1997; Harwood et al. 2002), permits individuals to feed on a wider range in prey sizes (Montori et al. 2006) and may increase survival, all of which increase organism fitness (Sogard 1997; Kaspersson & Höjesjö 2009). From a management perspective, populations with poor growth may have low recreational angling value, whereas populations with good growth may provide more fish of a larger size that anglers prefer. Occasionally, large fish size may instead be the result of slower growth, because of greater longevity, a
pattern often associated with latitudinal factors such as temperature (Braaten & Guy 2002; Munch & Salinas 2009). The plethora of potential factors influencing growth, such as nutrients, genetics and water temperature (Helfman et al. 1997), requires an identification of primary factors for a specific region or population to aid management efforts and to better understand and forecast effects of changing land use or climate (Elliott 2009; Xu et al. 2010). Brown trout growth has been associated with a wide range of factors across their range (e.g., Logez & Pont 2011), but several have been repeatedly suggested: intra-specific density, social dominance, water temperature, physical habitat and movement. Intraspecific density, as an indicator of competition, has
Correspondence: D. J. Dieterman, Minnesota Department of Natural Resources, Lake City Fisheries Research Office, 1801 South Oak Street, Lake City, Minnesota 55041, USA. E-mail:
[email protected]
doi: 10.1111/j.1600-0633.2012.00567.x
483
Douglas J. Dieterman et al. been found to be negatively associated with growth in many brown trout populations (e.g., Bohlin et al. 2002; Lobón-Cerviá 2005; Kaspersson & Höjesjö 2009). Competitive interactions are often manifested through social dominance with larger brown trout exhibiting more aggressive behaviours toward smaller individuals, which enable larger fish to gain access to more resources and grow faster (Fausch 1984; Alanärä et al. 2001). Water temperature is universally recognised as a modifier of growth through its fundamental effect on metabolism (Elliott 1994). Water temperature effects on growth have been directly assessed with bioenergetics models (Jensen et al. 2000; Elliott 2009) or indirectly via proxy variables, such as different spatial locations (e.g., stream reaches) or temporal periods (e.g., seasons or years) characterised by different thermal regimes (Parra et al. 2009). Spatial and temporal differences in other habitat conditions, such as pool depth and habitat complexity, have also been suggested to influence trout growth (Greenberg & Giller 2001; LobónCerviá 2005). Finally, individual trout that move more may demonstrate faster growth than sedentary individuals (Jonsson 1985; Forseth et al. 1999). Mobile individuals may find more energetically profitable stream locations with more abundant food, more optimal temperature regimes or locations that minimise energy expenditure. Studies to examine the importance of these factors for brown trout growth have not been conducted in the 62,000 km2 Driftless Area of the upper Midwestern United States, so named because it was bypassed by the last glacial epoch (Waters 1977). Karst geology of the Driftless Area ensures considerable groundwater flow from springs, resulting in more stable baseflow hydrology and thermal regimes than other Midwestern streams (Gebert & Krug 2007). These hydro-geologic features support many recreationally important brown trout fisheries and led local biologists to theorise that brown trout are able to continue growing across all seasons (Thorn et al. 1997). However, the lack of growth information hinders trout management and makes it difficult to predict effects of changing conditions, such as climate or land use. The goal of our study was to evaluate several factors influencing brown trout growth. To do this, we developed models between observed growth rates of brown trout and some of the primary factors identified elsewhere to assess their effects in this region. Our first objective was to quantify the amount of total variation present in the growth rate observations, and our second objective was to determine how much of this total variation could be accounted for by individual trout, stream reach, season-year, relative abundance, fish size and movement in three Driftless Area streams of the upper Midwestern United States. 484
Methods
Our study was conducted in three wadeable coldwater streams in the upper Midwestern state of Minnesota, USA: Coolridge, Hemmingway and Pine creeks (study area midpoint at approximately 43° 51′ N, 91° 52′ W; elevation, 265 m a.s.l.). Coolridge and Hemmingway creeks are tributaries to Pine Creek. The entire study area represented about 5.5 km of continuous lotic habitat (Table 1), except for one section on Hemmingway Creek that was not sampled because of landowner concerns. Other fishes present in the study area included brook trout Salvelinus fontinalis, white sucker Catostomus commersonii, brook stickleback Culaea inconstans, mottled sculpin Cottus bairdii and slimy sculpin Cottus cognatus. To determine whether distinct stream reaches were present, geomorphic, instream habitat and thermal features were measured following standard methods (Bain & Stevenson 1999; MNDNR 2007). These surveys identified six hydro-geomorphically similar reaches across the three streams. Reaches were characterised by differences in water temperature and physical habitat (Table 1). Mean wetted widths during summer baseflow were 3.1 m for Coolridge Creek, 4.5 m for Hemmingway Creek and 10.4 m for Pine Creek. Temperature loggers were placed in all six reaches at the beginning of the study, but all were lost during flooding in 2007, and some replacements were lost again during 2008 flooding. Given available temperature data, Pine Creek was the warmest and deepest reach and had the most cover (i.e., greatest per cent of surface area composed of water depths > 60 cm, instream rock, overhead bank cover and instream wood). Lower Hemmingway Creek was the second warmest and deepest reach. Reaches on Coolridge Creek were cooler in summer and shallower than either Pine or lower Hemmingway. Upper Hemmingway had cooler temperatures also but tended to have more cover and depth than Coolridge Creek reaches (Table 1). We used stream reach in our analyses as a surrogate measure of differences in habitat and water temperature, in part, because so many temperature loggers were lost. If stream reach was significantly related to growth, then we might reasonably assume that some aspect of each stream reach, such as thermal regime or pool depth, could influence brown trout growth. To examine seasonal and year effects on growth, brown trout were captured and recaptured from September 2006 to September 2008 with electrofishing gear (pulsed DC; 1-3 A) at each of four times per year. Each time approximated the transition period between fall, winter, spring and summer seasons: early September, late November, mid-March and late May. Most sampling was completed within one or
Factors influencing brown trout growth Table 1. Descriptions and mean (minimum–maximum) values across 3 years, 2006–2008, measured for selected instream habitat variables for six study reaches over three interconnected Driftless Area streams in southeast Minnesota. Stream features
Hemmingway creek
Reaches
Upper hemmingway
River kilometres from terminus (total length) Instream habitat Riffle (%) Gravel (%) Mean reach depth (cm) Mean pool depth (cm) Total cover (%) Pool-to-pool spacing (m) Water temperature (°C; 2008 only†) Daily mean (±SD) summer temperature (June 25–September 1)† Optimum growth (12–19 °C; Raleigh et al. 1986),% of hours, June 25–October 15†
Pine creek
Coolridge creek
Lower hemmingway
Pine creek
Lower coolridge
Middle coolridge
Upper coolridge
3.18–2.58* (0.60)
1.26–0.0* (1.26)
8.97–7.80 (1.17)
0.47–0.0 (0.47)
1.31–0.47 (0.88)
1.62–1.31 (0.31)
38% (32–44) 33% (31–35) 22 (17–26) 28 (23–34) 11.7% (9.8–13.6) 32 (9–71)
31% (24–41) 46% (43–48) 37 (31–42) 49 (45–52) 18.4 (13.2–26.6) 36 (6–61)
27% (22–30) 37% (26–48) 53 (50–55) 66 (65–69) 34.6% (28.8–45.4) 110 (38–217)
53% (52–54) 52% (45–60) 16 (12–21) 23 (16–32) 3.3% (1.3–6.3) 34 (19–66)
60% (50–65) 53% (48–59) 17 (16–18) 27 (22–31) 4.5% (3.9–5.0) 28 (15–44)
52% (48–54) 44% (43–45) 12 (11–13) 19 (17–20) 4.8% (3.6–6.7) 23 (7–46)
11.4 (0.3)
13.7 (0.6)
16.1 (0.8)
N/A†
11.4 (0.3)
11.2 (0.4)
15
71
88
N/A†
19
11
*A portion of middle Hemmingway Creek, rkm 2.58–1.26 was not sampled because of landowner concerns. †Many temperature loggers were lost to flooding. The most complete data set available was for 2008, beginning in late June.
2 weeks. However, March 2007 sampling was interrupted by substantial flooding. Trout were collected with a backpack electrofisher in upper Hemmingway and all three Coolridge reaches. A barge electroshocker with two anodes was used in lower Hemmingway and with three anodes in Pine Creek, because these reaches were wider and deeper. One upstream electrofishing pass was made through each pool, which was individually identified and numbered throughout each stream reach, and no block nets were used. A passive integrated transponder (PIT) tag (12.45 mm, 134.2 kHz) was inserted into trout to individually identify each fish. Upon capture, brown trout were anaesthetised (MS-222), measured (mm, total length [TL]), had their adipose fin clipped as a secondary mark and tagged. Most trout were tagged in the body cavity in September 2006 and in March– April 2007. A small number of randomly selected trout had a PIT tag inserted into their dorsal musculature in March–April 2007 to evaluate tag loss. More specific information on tags, tagging methods, tag loss and scanning equipment can be found in Dieterman & Hoxmeier (2009). Recaptured trout were checked for the presence of a PIT tag and remeasured. The individual pool and reach of recapture were also recorded. To identify more specific life stages influenced by each factor, trout were placed into groups based on lengths that approximated three age groups: age-0, age-1 and -2 and age-3+. By analysing these age groups independently, we sought to identify any ontogenetic transitions in the relative importance of factors influencing brown trout growth. Age-0 trout
first became fully susceptible to capture and were deemed large enough to be tagged (120 mm TL) in early September. These trout were distinguished from older trout by obvious breaks in length/frequency histograms. These length breaks were 175 mm TL in Pine Creek and 150 mm TL in the other study reaches in September 2006. Age-0 brown trout were too small to be captured and tagged during their first spring and summer in 2006, so growth from this cohort was evaluated in their second spring (i.e., March–May 2007) and second summer (May–August 2007), when these fish were age 1. This allowed a more complete assessment of seasonal growth for this cohort. Thus, growth estimates for age-0 trout represent growth during their first autumn, first winter, and second spring and summer seasons. We did not estimate growth between hatch and early September. Finally, age-0 growth was only estimated for the 2006 cohort because the 2007 cohort was almost nonexistent because of spring and summer flooding in that year. Growth of age-1 and age-2 trout was examined as a group because these fish support the recreational fishery, comprise the bulk of the adult population and represent sizes most often caught by anglers. Age-1 trout were distinguished from age-0 trout by length-frequency breaks as described above. Age-2 trout were distinguished from age-3+ at a length of 305 mm TL because this length approximates the average back-calculated length at age 3 for brown trout in southeast Minnesota (Dieterman et al. 2004). Also, many angling regulations in southeast Minnesota use this length break as well (e.g., 12–16 inch protected slot regulation), so inferences made for trout smaller (i.e., age-1 and age-2 trout) and 485
Douglas J. Dieterman et al. larger than 305 mm (i.e., age-3 + trout) may have direct management implications. We used linear mixed effects models fit by maximum likelihood, with a random effect for individual trout, to evaluate the effects of season-year, initial stream reach, relative abundance of all trout, initial length and distance moved on growth of individual brown trout. We characterised growth as absolute growth rate, the change in length between two time periods (each seasonal interval) per unit time (i. e., mm TL/d in this study) (Isely & Grabowski 2007). For example, growth rate over the fall 2006 seasonal interval was the difference between the length in September and the subsequent November. To ensure that we correctly attributed growth rate to only one season, we only included data for fish that were captured and recaptured before and after each season. Thus, a fish caught in September 2006, not captured in November 2006, but caught in March 2007 and May 2007 only had data for the spring 2007 season included in the model. We did not partition growth rate among multiple seasons if a fish was not caught between them. Also, because fish and season constituted our statistical replicates, individual fish may have contributed more than one data point if they were captured in more than one season. Multiple recaptures of the same fish provided the random effect in our models (i.e., the estimate of the effect of individual fish), which allowed us to infer the importance of traits of individual trout (e.g., sex, genetics) on growth rates. We first fit an intercept-only model to quantify the amount of total variation present in the growth rate data for each age group. We then fit and tested models with various combinations of independent variables to determine their importance and how much of the total growth rate variance they accounted for. Variance accounted for was estimated by comparing the amount of total variation in the initial intercept-only model to the variation explained in a subsequent model with independent variables. For example, if the intercept-only model yielded a total variance estimate of 0.12 and a subsequent model with the intercept and stream reach reduced the variance estimate to 0.02, then the initial total variance estimate was reduced by 83% (1 [0.02/ 0.12]), suggesting that stream reach accounted for 83% of the total variance in our growth rate data. We always included the random effect for each individual trout (i.e., PIT tag number) in our models to determine how much of the total variation was attributed to individuals. All models were fit and tested with AICc using R software (Crawley 2007). We considered differences in AICc values greater than two to indicate support for a difference between models. 486
Initial stream reach was the reach individual brown trout was captured in prior to their change in growth. Initial reach represented an indirect measure of abiotic factors that included temperature and physical habitat, based on differences in spatial location, as opposed to direct measures of these variables. To estimate effects of trout density, we calculated the relative abundance (number of trout/m2) of all trout as the total number of trout (all sizes of both brook trout and brown trout) captured on the first electrofishing pass divided by the surface area of each stream reach. Surface area was determined from the total length of each stream reach and the mean wetted width measured during geomorphic and instream habitat surveys. By using all trout of both species present, we attempted to estimate exploitative competition for the total food supply, as opposed to interference competition for a specific drift-feeding site that would be structured more by size. Initial trout length was tested as an index of social dominance and may represent a better measure of interference competition. Initial length was the total length of each brown trout prior to its change in growth. Distance moved was the linear distance along the stream between initial capture and subsequent recapture. Each pool was individually numbered, so that the pool of capture and recapture could be recorded. Distances between the midpoints of all pools were measured and used to determine distance moved by individual fish. Results Age-0 brown trout
There were 257 growth observations from a total of 162 individual age-0 brown trout captured at the beginning and end of a specific season. Age-0 trout tagged at the beginning of this study in September 2006 averaged 141 mm (SD = 18.11). Median values of observed growth rates were highest in spring 2007, but also most variable (Fig. 1). A few individual brown trout either did not grow or lost length (i.e., growth rates 0.0) in fall, and most trout grew slowly in winter (Fig. 1). Conversely, although median observed growth was slow in fall 2006, some individuals still grew fast or faster than the median value for growth during spring or summer (Fig. 1). Most age-0 brown trout (65% of observations) were recaptured in the same pool in which they were initially captured and released. The most parsimonious model of factors related to growth rates of age-0 brown trout included season, individual trout (i.e., PIT tag number random effect), initial length and trout relative abundance. The total
Factors influencing brown trout growth 0.8
estimate for the effect of winter 2006–2007 ( 0.022) indicated that growth rates were slower in winter than in fall 2006. Conversely, spring and summer model estimates were higher than those in fall 2006, indicating faster growth in those seasons. Although season had the strongest effect on growth rates of age-0 trout, the final model also included terms for initial length and relative abundance. Initial length and relative abundance had negative model estimates, which indicated that growth rates declined as trout initial length increased and as relative abundance increased (Table 2). Calculation of predicted growth rates based on the final model allowed an integrated assessment of season, initial length and relative abundance factors (Fig. 2). Predicted growth rates reiterated the dominant effect of season. For example, predicted growth rates in spring for the largest age-0 trout in reaches with the highest density (0.30 mm·d 1) were greater than predicted growth rates in fall 2006 for the smallest age-0 trout in reaches with the lowest density (0.19 mm·d 1).
(a)
0.6
0.4
0.2
0.0
–0.2
Growth rate (mm·d–1)
0.8
(b)
0.6
0.4
0.2
0.0
–0.2 0.8
(c)
0.6
0.4
Age-1 and age-2 Brown Trout 0.2
0.0
–0.2 Fall Winter Spring Summer 2006 2006–2007 2007 2007
Fall Winter Spring Summer 2007 2007–2008 2008 2008
Season and Year
Fig. 1. Box plots summarising observed seasonal growth rates of individually tagged brown trout of three age groups (a = age-0, b = age-1 and age-2, c = age-3+) across three interconnected streams in southeast Minnesota, United States. The horizontal dashed line indicates no growth. Boxes are drawn between quartiles, black line is the median, whiskers are 10th and 90th percentiles, and dots are data outside the 10th and 90th percentiles. Age-0 brown trout were first tagged at the end of their first summer; thus, data represent their first fall and winter, but second spring and summer (i.e., these trout were age-1 in spring and summer 2007).
variance in growth rate was 0.0208 (Table 2). Variance of the final model (AICc = 499.8) was 0.0058, a 72% reduction from the total variance. Neither addition of stream reach nor trout movement improved this final model. Individual trout accounted for about 11% of the variance, season 59%, and initial length (an attribute of individual trout) and relative abundance about 2% in this final model. Age-0 brown trout growth rates were fastest in spring and summer 2007. Fall 2006 growth rates were set as the baseline (i.e., intercept = 0.148; Table 2). Thus, interpretation of model effects (i.e., model estimates) for other seasons is relative to growth rates in fall 2006. The smaller and negative
A total of 420 individual age-1 or age-2 brown trout contributed 1041 seasonal growth rate observations. Median observed growth rates were highest in spring in both years and lowest in winter (Fig. 1). However, there was considerable variability in growth rates among individual trout within each season (Fig. 1). At least a few individuals did not grow or shrunk in length in every season except spring 2008. Similar to age-0, few age-1 and age-2 brown trout were recaptured in a different pool between sampling occasions. Seventy-two per cent of age-1 and age-2 trout were recaptured in the same pool, and 81% were recaptured within 50 m of the pool in which they were initially caught. The best model identified three factors associated with growth rates of age-1 and age-2 brown trout, in addition to the random effect for individual trout: season, initial length and stream reach. This final model (AICc = 2221.0) reduced total variance from 0.011 to 0.0052, a 53% reduction (Table 2). Variability in growth rates attributed to individual trout accounted for 17% of the variance. Predicted growth rates from our final model estimates (Table 2) again illustrated the strong effect of season and initial length (Fig. 3). Predicted growth rates were fastest in spring and summer and for the smallest trout. There was an interaction between these two factors, which showed that, although smaller trout grew faster than larger trout in all seasons, the effect of size was most pronounced in spring and summer (i.e., a steeper slope). 487
Douglas J. Dieterman et al. Table 2. Final models for factors related to growth rates of three age groups of brown trout in three interconnected streams in southeast Minnesota 2006– 2008. Total variance describes the initial variance in growth rates as fit by a random intercept-only model (no effects). Variance of the final model is the remaining unaccounted for variance after inclusion of the final model’s main effects (random and fixed). Individual trout variance is the variance attributed to individual trout that were repeatedly recaptured Random effects
Fixed effects
Effects
Total variance
Variance of final model (SD)
Individual trout variance (SD)
Age-0 brown trout 0.0208
0.00582 (0.07631)
0.00236 (0.04861)
Intercept (Fall 2006) Winter 2006–2007 Spring 2007 Summer 2007 Initial length Relative abundance
0.14837 0.02214 0.27530 0.19019 0.00125 0.75700
Age-1 and age-2 brown trout 0.0110 0.00520 (0.07211)
0.00188 (0.04346)
Intercept (Fall 2006) Winter 2006–2007 Spring 2007 Summer 2007 Fall 2007 Winter 2007–2008 Spring 2008 Summer 2008 Initial length Upper Hemmingway Lower Hemmingway Pine Creek Lower Coolridge Middle Coolridge Upper Coolridge Winter 2006–2007 9 Initial length Spring 2007 9 Initial length Summer 2007 9 Initial length Fall 2007 9 Initial length Winter 2007–2008 9 Initial length Spring 2008 9 Initial length Summer 2008 9 Initial length
0.06273 (0.00709) 0.01941 (0.00881) 0.12760 (0.00804) 0.09322 (0.00945) 0.03401 (0.01050) 0.04888 (0.01116) 0.13560 (0.01083) 0.08336 (0.01456) 0.00037 (0.00015) 0.11310 (0.03550) 0.06381 (0.08472) 0.02853 (0.00811) 0.04344 (0.01975) 0.02638 (0.00880) 0.02006 (0.01938) 0.00003 (0.00024) 0.00158 (0.00020) 0.00161 (0.00026) 0.00078 (0.00029) 0.00046 (0.00031) 0.00144 (0.00031) 0.00093 (0.00039)
Age-3+ brown trout 0.0043
0.00118 (0.03442)
Intercept (Fall 2006) Winter 2006–2007 Spring 2007 Summer 2007 Fall 2007 Winter 2007–2008 Spring 2008 Summer 2008
0.00367 0.00960 0.07082 0.10021 0.04116 0.00875 0.07227 0.03971
0.00282 (0.05318)
Smaller effects from stream reach and year were also apparent. Lower Hemmingway was predicted to have the slowest growth and upper Hemmingway the fastest growth (Fig. 3). However, there were only eight growth rate observations from upper Hemmingway, and differences in predicted growth rates among remaining reaches were less than growth rate differences among seasons and initial trout lengths. Year appeared to have less effect than season and initial size also, but observed (Fig. 1) and predicted (Fig. 3) growth rates were slightly higher in summer and fall of 2007 than in other years for these respective seasons. For example, the predicted growth rate for a 150-mm brown trout in lower Hemmingway was 0.39 mm·d 1 in summer 2007, but was 0.32 mm·d 1 488
Estimate (SE)
(0.01382) (0.01497) (0.01393) (0.02293) (0.00033) (0.32147)
(0.00927) (0.01404) (0.01157) (0.01348) (0.01430) (0.01408) (0.01362) (0.01391)
in summer 2008. In fall 2007, a 150-mm trout was predicted to grow 0.26 mm·d 1, but only 0.16 mm·d 1 in fall 2006. Age-3+ brown trout
A total of 278 seasonal growth rate observations were made from 113 individual brown trout that were 305 mm TL and larger. The highest median observed growth rate was in summer 2007 (Fig. 1). Similar to other age groups, most age-3+ trout (69%) were recaptured in the same pool they were initially captured in. The range of growth was less for age-3+ trout than it was for younger age groups (Fig. 1). For example,
Factors influencing brown trout growth The best model of factors predicting growth rates of age-3+ trout only included terms for season-year and individual trout (Table 2). Season-year and individual trout effects reduced total variance from 0.0043 to 0.0028, a 35% reduction. Individual trout accounted for 27% of this variation, and season-year, the remaining 8%. Model estimates (Table 2) and predicted growth rates (Fig. 4) indicated fastest growth rates in spring 2007, summer 2007 and spring 2008 and slowest growth rates in fall 2006 and winter 2007–2008. There appeared to be a slight year effect, as observed and predicted growth rates were faster in summer and fall of 2007 than in these seasons in the respective years they were sampled (i.e., fall 2006, summer 2008) (Figs 1 and 4). There were seven observations where the final model substantially underestimated observed growth rates. Five of these observations were for five large trout (341–359 mm TL) that grew very fast in summer 2007, and the other two observations were from two of these same fish that grew very fast in spring 2007 (see dots in Fig. 1).
Predicted growth rate (mm·d–1)
0.5 Spring
0.4
Summ
0.3 0.2
2007
er 2007
Fall 2006
0.1 Winter 2006–2007
0.0 –0.1 120
140
160
180
200
220
Initial Length (mm·TL)
Fig. 2. Predicted growth rates of age-0 brown trout as a function of season, initial length and relative abundance of all trout in three interconnected streams in southeast Minnesota, United States. Upper and lower parallel lines of similar style represent two levels of relative abundance (CPUE=No. of trout/m2) for a one-pass electrofishing run. The upper relative abundance line for each pair is predicted growth for a specific season when CPUE=0.08 trout·m 2 (the 5th percentile of CPUE data in the present data set). The lower relative abundance line is equal to 0.66 trout·m 2 (the 95th percentile CPUE value). The upper pair of short dashed lines are spring, solid lines are summer, long dashes near bottom are fall, and dotted lines are winter.
Discussion
0.312 mm·d 1 was the maximum observed growth rate for an age-3+ brown trout, but was 0.755 mm·d 1 for an age-0 trout (Fig. 1). The 0.312 value for age-3+ trout was less than the median growth rate for all age-0 trout in spring 2007 (Fig. 1). Also, total variation in growth rates for age-0 trout was 0.0208, but was only 0.0043 for age-3+ trout (Table 2). Thus, there was almost five times as much total variation in growth rates of age-0 trout as compared to age-3+ trout.
0.5
The primary goal of this study was to assess the importance of individual trout characteristics and selected biotic and environmental factors on growth rates of all sizes and ages of brown trout in three streams of the upper Midwestern United States. Seasonal effects were examined in particular, because of a prevailing belief that the primarily groundwater-fed streams in this area facilitate continuous growth throughout the year, especially in reaches with the most groundwater. We found increasing importance
Fall 2006
Winter 2006–2007
Spring 2007
Summer 2007
Fall 2007
Winter 2007–2008
Spring 2008
Summer 2008
Predicted growth rate (mm·d–1)
0.4 0.3 0.2 0.1 0.0 0.5 0.4 0.3 0.2 0.1 0.0 150
200
250
300 150
200
250
300 150
200
250
300 150
200
250
300
Initial length (mm)
Fig. 3. Predicted growth rates of age-1 and age-2 brown trout as a function of season and year, initial length and stream reach in three interconnected streams in southeast Minnesota, United States. Each line represents a stream reach. The solid line = upper Hemmingway, dotted line = lower Hemmingway, medium dashed = Pine Creek, dashed line with two dots = lower Coolridge, long dashed = middle Coolridge and the dashed line with one dot = upper Coolridge.
489
Douglas J. Dieterman et al.
Mean growth rate (mm·d–1, ±1 SE)
0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 –0.02 –0.04 Fall Winter Spring Summer Fall Winter Spring Summer 2006 2006–2007 2007 2007 2007 2007–2008 2008 2008
Season and Year
Fig. 4. Predicted growth rates (±1 SE) of age-3+ brown trout (305–514 mm total length [TL]) as a function of season and year for three interconnected streams in southeast Minnesota, United States. The horizontal solid line references no growth.
of individual trout characteristics as trout got older and larger, noted a pervasive influence of season on growth rates and found that factors commonly found to be important for brown trout growth in other regions had only a modest influence in these streams. Individual trout accounted for increasing amounts of variation as trout got older and larger (13% for age0, 17% for age-1 and 2, 27% for age-3+). We have not found many other comparative studies that specifically quantify this source of variation in a similar manner (i.e., with a random effect for individual trout, or possibly a repeated measures effect), because they either focused on population-level mean growth estimates (e.g., Jensen 1990; Nicola & Almodóvar 2004) or did not specifically account for individual fish in their analyses (e.g., Carlson et al. 2007). Nevertheless, some studies have reported large variability in growth rates among individual trout (Greenberg & Giller 2001; Acolas et al. 2007), as well as other life history traits (Cucherousset et al. 2005) indicating the importance of quantifying this source of variation. Initial length was the only characteristic of individual trout we examined in this study, and it accounted for some of the variation in our final models (although it was not significant in our model for the age-3+ trout, the ontogenetic group with the largest amount of growth variation attributed to individual trout). Other unmeasured characteristics of individual trout, such as genetics, sex or possibly age or size at maturity, may explain some of this individual-level variation. For example, Serbezov et al. (2010) found that genetic heritability accounted for 16–31% of differences in length-at-age estimates among individual brown trout. Future studies should assess individual trout characteristics including sex and age at maturity to quantify their influence on growth. 490
The fast growth in spring and summer observed in this study has been observed in other brown trout populations, and a variety of factors have been suggested to explain these patterns. Fastest growth in spring and early summer seasons has been found in brown trout populations in Norway (Jensen 1990), England (Elliott 2009), eastern North America (Carlson et al. 2007) and Spain (Lobón-Cerviá 2003). Growth in fall and winter was much slower or nonexistent in most of these populations. Seasonal changes in food availability, water temperature, photoperiod and stream flow have all been suggested as possible explanations for these patterns (Beouf & Le Bail 1999; Nicola & Almodóvar 2004; Carlson et al. 2007; Elliott 2009). Although season seems like an obvious factor, verifying the presence of seasonal patterns was still important for Driftless Area streams of the upper Midwest for multiple reasons. First, Driftless Area streams are heavily influenced by groundwater springs and seeps, which often result in more stable thermal regimes than streams governed by surface run-off or mixed water sources (Gordon et al. 2004). Stable thermal regimes may moderate growth differences among seasons, resulting in more continuous growth throughout the year (Nicola & Almodóvar 2004); thus, whether growth varied seasonally or not was unknown, as was the importance of seasonal changes to overall growth rates of brown trout. Seasonal changes accounted for the most variation in growth for the smallest and youngest trout (about 57%), indicating a strong influence on this group. Second, the presence of seasonal growth patterns allowed us to identify the time periods of fastest growth, spring and summer. This information should be used to focus future research efforts to these time periods to better elucidate causal mechanisms, such as food availability or water temperature, as primary modifiers of growth. Loss of temperature loggers in this study precluded us from a more direct assessment of water temperature, but future research should strive to obtain such data. Third, our results help target the time periods when growth may be most susceptible to changing conditions, such as climate or land use. Changing conditions may have a disproportionately stronger influence on the smallest and youngest trout because of the greater variation accounted for by season for this group. Our results also suggested some minor differences in seasonal growth among years. Year-to-year differences in brown trout growth have been found to be substantial in other populations (Newman & Waters 1989; Lobón-Cerviá 2005). For example, differences among years accounted for at least half of the variation in average fork length of brown trout in streams in California (Jenkins et al. 1999). In one instance in
Factors influencing brown trout growth that study, age-0 brown trout were about 30 mm longer in 1 year than in another. In our study, growth rates tended to be higher in spring, summer and fall in 2007 than in these same seasons in other years. Our study area was subjected to extreme flooding in March and August 2007 (National Weather Service 2007). Increased stream flows have been suggested to increase brown trout growth through greater availability of drifting foods and increased trout feeding (Lagarrigue et al. 2002; Carlson et al. 2007) and may explain the increased growth rate we observed in 2007. The minor importance of initial length, stream reach, trout relative abundance and movement on growth was somewhat surprising, but may be partially explained by the productivity of Driftless Area streams in southeast Minnesota. Trout relative abundance, as an index to density, and initial length, as a surrogate measure of social dominance, were both expected to represent competitive interactions. Where food or space is limiting, growth rates are expected to be slower where trout density is highest and the largest trout within an age group should grow fastest (Alanärä et al. 2001; Lobón-Cerviá 2005). A negative relationship between growth rates and trout relative abundance was observed in this study, but only for age-0 trout. For initial length, the relationship was the opposite of what was expected. We found that larger trout were predicted to grow slower than smaller trout, offering little support for the presence of social dominance hierarchies in our study streams. Initial length was only an important modifier of growth for the two youngest age groups in this study, age-0 and age-1 and age-2. Based on the amount of variation accounted for, and the magnitude of their predicted effects, neither trout relative abundance nor initial length appeared to be large modifiers of growth, possibly indicating that food or space was not limiting in these streams. Driftless Area streams are some of the most productive streams in the world, likely because of their largely agricultural watersheds and limestone bedrock geology (Kwak & Waters 1997; Almodóvar et al. 2006). Kwak & Waters (1997) suggested that the extreme water fertility should ensure that food resources are not limiting and that salmonid production would likely be limited more by other factors, such as hydrology or instream habitat. Our observation that smaller trout within each size group grew faster than larger trout may have simply been an artefact of the higher metabolism and greater scope for growth inherent in smaller fish relative to larger fish (Elliott 1994). Larger fish, if mature, also allocate more energy into reproductive tissues rather than somatic tissue growth (Helfman et al. 1997). Abundant food resources could also
explain why trout in this study did not move extensively and why stream reach was not a primary modifier of growth. Seasonal changes in food availability and water temperature could explain the importance of season in our study and suggest a need for more detailed diet and temperature data, perhaps coupled with a mechanistic bioenergetics assessment. Alternatively, our sampling design, focusing on a seasonal timescale, may have been inadequate to properly assess the influence of some factors, such as short-term movements. Our study was designed to assess seasonal shifts to important feeding habitats whereby brown trout actively moved to specific reaches with better growing conditions in specific seasons for a growth benefit. Such a pattern would have been supported if we would have found movement to be a significant factor in conjunction with a significant reach 9 season interaction. However, such patterns were not found for this population. Radio-tagged brown trout have been found to move extensively over shorter timescales, such as overnight, before returning to the same pool during the day (Diana et al. 2004). Our study was not designed to assess such intra-season movements and cautions against over-interpreting movement results. This study identified ontogenetic changes in growth rates of brown trout and quantified the relative importance of seasonal and annual environmental changes in conjunction with the influence of characteristics of individual trout. This information should guide future studies assessing mechanistic explanations for these patterns, especially long-term ecological studies. This study also suggests that the smallest and youngest brown trout may be most susceptible to changing conditions, such as land use or climate, and that these effects may be most pronounced in spring and summer. Acknowledgements We especially thank R. Bearbower, D. Casper, S. Erickson, S. Klotz, M. Konsti, B. Lee, D. Logsdon, J. Melander, J. Roloff, D. Schultz, J. Schulz, S. Shroyer and V. Snook for assistance with fish collection and tagging. J. Weiss helped with fish collection and tagging, and geomorphological and instream habitat measurements. Previous drafts of this manuscript were reviewed by C. Anderson, P. Jacobson, D. Pereria, J. Roloff, B. Vondracek and two anonymous reviewers. Partial funding was provided by the Federal Aid in Sport Fish Restoration Program (Project F-26-R, studies 674 and 675).
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