# Relations between trophic state indicators and fish in Florida (U.S.A.) lakes 1

Roger W. Bachmann, Bradley L. Jones, Donald D. Fox, Mark Hoyer, Lawrence A. Bull, and Daniel E. Canfield, Jr.

Abstract : Total fish biomass per unit area was positively correlated with total phosphorus, total nitrogen, chlorophyll a , and inversely correlated with Secchi disk transparency in 65 Florida (U.S.A.) lakes selected to range from oligotrophic to hypereutrophic. Species numbers were positively related to lake surface area but not trophic state. There were some shifts in species composition with changes in trophic state, though only a few species showed significant changes in their standing crops. In particular the recreationally important centrarchids did not show important changes with trophic state, and there were no critical points on the trophic spectrum where there were dramatic changes in fish abundance or standing crops. The facts that Florida lakes do not have deep, cold hypolimnia, do not have salmonid species, and have no ice in the winter are among the possible reasons that the more eutrophic Florida lakes do not show the same changes in fish populations often

described for northern lakes.

Résumé : Il y avait une corrélation positive entre la biomasse totale de poissons par unité de surface et le phosphore total, l’azote total et la chlorophylle a , et une corrélation inverse entre cette biomasse et la transparence mesurée à l’aide du disque de Secchi, dans 65 lacs de Floride (É.-U.) allant d’oligotrophes à hypereutrophes. Les nombres d’espèces étaient liés
positivement à la superficie des lacs, mais non à leur état trophique. La composition des espèces variait quelque peu avec l’état trophique, mais seules quelques espèces accusaient des différences importantes dans le nombre d’individus. Plus particulièrement, les centrarchidés importants sur le plan récréatif n’accusaient pas d’importants changements avec l’état trophique, et le spectre trophique ne présentait aucun point critique correspondant à des changements dramatiques de l’abondance ou du nombre d’individus. L’absence d’un hypolimnion froid et profond, l’absence de salmonidés et l’absence de glace en hiver comptent parmi les raisons pouvant expliquer pourquoi les lacs de Floride plus eutrophes n’accusent pas les mêmes variations de population de poissons dont on a souvent fait état dans les lacs nordiques. [Traduit par la Rédaction]



### Introduction

Eutrophication is a concern in Florida where a growing human population and changes in land use are increasing nutrient inputs to many lakes. The impacts of excess algal populations on water transparency and the general appearance of Florida lakes are well known (Canfield and Hodgson 1983; Canfieldet al. 1985); however, less information is available on the effects that changes in trophic state have on fish populations. There is special interest in Lake Okeechobee, a large (1730 km 2 

### ) and shallow (mean depth = 2.7 m) lake in south Florida where total phosphorus concentrations in the lake have increased over the past 20 years (Janus et al. 1990; James et al 1995). As Lake Okeechobee has a major recreational fishery, there is interest in knowing the potential impact of trophic state changes on fish populations in this and other Florida lakes.There are many quantitative studies that support the expectation that an increase in productivity at the base of food chains
in a lake should translate into an increase in the abundance offish at higher trophic levels. Oglesby (1977) found high correlations between fish yields in lakes and summer phytoplankton standing crops as measured by chlorophyll a and also between fish yield and annual primary productivity. Melack (1976) studied tropical lakes in Africa and India and found that fishyields increased logarithmically as primary production increased arithmetically. McConnell et al. (1977) found that fish yield and gross photosynthesis were highly correlated in six experimental ecosystems involving a variety of fishes. Yurkand Ney (1989) found a strong correlation between fish standing crops and total phosphorus in 22 southern Appalachian reservoirs. Downing et al. (1990) found that fish communityproduction was correlated with annual phytoplankton production, mean total phosphorus concentrations, and annual average fish standing stock. Hanson and Leggett (1982) found thatboth fish biomass and yield were predicted by lake total phosphorus concentrations as well as by the ratio of macrobenthos biomass to lake mean depth. Lee et al (1991) reanalyzed the data of Oglesby (1977) and Hanson and Leggett (1982) and found a strong correlation between fish yield and normalized phosphorus loading to lakes. In Florida, Kautz (1980) found that total fish biomass increases with trophic state as measured Received April 21, 1995. Accepted October 20, 1995.
J12888

R.W. Bachmann, 2 M. Hoyer, and D.E. Canfield, Jr. Department of Fisheries and Aquatic Sciences, University of Florida, Gainesville, FL 32653, U.S.A.
B.L. Jones. South Florida Water Management District, P.O. Box 24680, West Palm Beach, FL 33416, U.S.A.
D.D. Fox and L.A. Bull. Florida Game and Fresh Water Fish Commission, 3991 SE 27th Court, Okeechobee, FL 34974, U.S.A.

1 Journal Series No. R–04754 of the Florida Agricultural

Experiment Station, Gainesville, Fla.

2 Author to whom all correspondence should be addressed.

e-mail: rbach@nervm.nerdc.ufl.edu

Can. J. Fish. Aquat. Sci. 53 : 842–855 (1996). 842

© 1996 NRC Canada

### by total nitrogen concentrations. Bays and Crisman (1983)found a similar relationship with algal chlorophylls. The overwhelming evidence from these and other studies is that, as lakes become more eutrophic, the standing crops, productivity, and yields of fish increase. Ryding and Rast (1989) cite this as one of the positive properties of eutrophication in relation to the production of fish for food. In addition to the studies based on standing crops or commercial fish yields, Jones and Hoyer (1982) found that the yields of sport fish to angler harvest in 26 lakes and reservoirs in Iowa and Missouri were highly correlated to mean summer phytoplankton standing crop as measured by chlorophyll a

. Conversely, there are many observations in the literature that suggest that, as eutrophication proceeds, there is a decline in the quality of a fishery in terms of species composition. Larkin and Northcote (1969) reviewed a number of studiesshowing changes in fish populations as a result of eutrophication. These included changes in the fish fauna of Lake Erie coincident with eutrophication, the loss of Coregonus from Lake Muritz in northern Germany, and the loss of whitefish inthe Bodensee. In a recent review of the literature on eutrophication and fisheries, Lee et al (1991) agree that eutrophication can significantly affect the types of fish in a water body. They cite various studies showing a decline in species richness in response to an increase in trophic state. Persson et al. (1988) showed that, in going from the least productive to the most productive in a series of Swedish lakes, there was a major decrease in the proportion of piscivorous fish. In his study of 22 lakes in Florida, Kautz (1980) concluded that sport fishesreach maximum biomass and optimum densities in mesotrophic–eutrophic lakes but suffer adverse effects with further
enrichment. Many of these previous studies have been carried out on deep, northern lakes with different fish communities fromthose in Florida. The results may not directly apply to shallower lakes in a warmer climate. The purpose of our study was to examine data on trophic indicators and fish populations from a large sample of Florida lakes to determine the relationships between lake trophic state and the total biomass, species richness, species composition, and standing crops of fish.



### Methods

Fish sampling Most of the data were collected on 60 Florida lakes (Fig. 1) that were sampled specifically to develop empirical relationships between fish and trophic state variables (Canfield and Hoyer 1992). Ten to 17 lakes were sampled each year between June 1986 and June 1990. The lakes encompassed a range of trophic states and included lakeswithin the major trophic categories (oligotrophic, mesotrophic, eutrophic, and hypereutrophic) following the guidelines of Forsberg and Ryding (1980). Rotenone sampling for fish was conducted once during the warm season (May–November) at each lake to determinestanding crop and community structure. Two to twelve 0.08-ha blocknets were set at each lake depending on lake size. The majority of lakes were sampled with six nets, but Little Fish Pond (surface area 2 ha) was sampled with only 2 nets while Lake Apopka (surfacearea >12 000 ha) was sampled with 12 nets. Equal numbers of blocknets were set in littoral (with one side being the shore) and limnetichabitats. Blocknet sampling followed procedures outlined by Shireman et al. (1983). Biomass (kilograms per hectare) estimates were calculated for each fish species in each net and weighted by habitat (littoral and open-water area) to obtain whole-lake estimates. Each lake was also sampled by gillnets and electrofishing to provide additional information on fish species composition. Three experimental gillnets were fished in the majority of lakes, but six nets were set in Lake Apopka. Nets were fished along the bottom in water depths >2 m once for 24 h during the summer sampling period.
Gillnets were 50 × 2.4 m and each gillnet had five 10-m panels of different mesh size (bar mesh sizes: 19, 25, 38, 51, and 76 mm). Electrofishing was conducted at each lake once during the warm season sampling period in nearshore areas in all habitats. Two to 10 electrofishing samples (10 min transects) were taken at each lake depending on lake size. Six samples were taken at most of the lakes, but only two samples were taken at Little Fish Pond (lake surface area 2 ha). Ten samples were taken from Lake Harris (lake surface area 5580 ha). Electrofishing transects were evenly spaced around each lake and electrofishing was done for 10 min with continuous

current. For Lake Okeechobee, data were obtained from fish population surveys made by the Florida Game and Fresh Water Fish Commission (Fox et al. 1992). Fish assemblages were sampled for biomass in five vegetation communities in the northern, southern, and western regions of the lake. Sampling was carried out in the fall seasons of 1989, 1990, and 1991. Three blocknets, 3.4 m deep with 3-mm bar mesh and encompassing 0.08 ha, were set in each vegetation community in each sample area during each year. Three Wegner rings (4.0 m 2 ) (Wegner et al. 1973) were randomly placed within each blocknet to estimate abundance of fish less than 6 cm total length (Miller et al. 1990). Emulsified rotenone (5%) was applied within the net at a concentration of 2 mg

⋅ L–1 , and fish were picked up for 3 days. Percentages of fish collected on the second and third day in similar vegetation types were used to adjust numbers of fish collected in Wegner rings removed after the first day. Collected fish were sorted by species, divided into 2-cm size-clases (i.e., 4-cm group,4.00–5.99 cm), enumerated, and weighed to the nearest gram. Second- and third-day weights were assigned from length–weight tables derived from first-day weights. Although no fish biomass samples were taken from the open-water areas of Lake Okeechobee, trawl sampling data were used to check the fish species list (Fox et al. 1992). A 4.88-m, semiballoon trawl net having 35-mm stretched mesh in the wings and 25-mm stretched mesh in the bag was fished in July and January of each year beginning Fig. 1. Location of lakes sampled for this study. Bachmann et al. 843

© 1996 NRC CanadaStanding crop

Lake No. of fish species Whole lake (kg

⋅ ha–1 ) Littoral zone (kg

⋅ ha –1 ) TP (μg

⋅ L–1 ) TN (μg

⋅ L–1 ) Chl a (μg

⋅ L–1 ) SD (m) Z (m) SA (ha)

Okeechobee 41 372 92 1480 30 0.5 2.7 173 000

Crescent 44 284 30 1100 34 0.6 6 290 George 42 379 56 1400 79 0.7 18 600 Istokpoga 31 219 210 700 10 0.9 1.8 11 207 Tohopekaliga 34 507 138 1535 44 0.8 2.0 9 194 Alligator 18 112 211 371 2367 84 0.5 1.1 137 Apopka 24 24 81 140 3789 127 0.3 1.6 12 412

Baldwin 19 65 361 21 530 18 1.6 4.5 80

Barco 9 7 11 2 82 1 5.4 4.4 13

Bell 16 44 63 17 641 20 1.5 2.7 32

Bivens Arm 12 683 326 384 3256 241 0.4 1.2 76 Bonny 16 79 420 59 1858 40 0.6 2.0 143
Brim Pond 6 103 80 9 624 8 2.2 4.0 3

Bull Pond 22 69 117 11 522 3 1.4 2.3 11

Carlton 25 308 749 92 3228 173 0.4 3.6 155

Carr 15 207 8 19 874 11 1.8 1.9 254
Catherine 17 103 91 2 303 2 3.2 3.2 41 Clay 7 136 85 7 356 4 4.0 2.3 5

Clear 12 41 227 21 761 21 1.3 5.9 64

Conine 20 179 174 1043 2056 110 0.5 3.5 96

Crooked 12 106 113 7 313 5 3.1 2.3 8

Cue 6 10 44 5 91 2 5.8 3.5 59 Deep 17 88 89 2 158 1 5.1 3.0 4 Douglas 17 81 81 11 1122 2 1.5 1.2 16

Fish 21 129 210 25 935 18 1.0 1.9 89

Gate Lake 14 69 82 28 407 20 1.1 1.8 8 Grasshopper 21 135 161 6 259 1 3.7 2.7 59

Harris 34 109 206 28 1550 37 0.6 4.0 5 580 Hartridge 21 47 44 11 485 4 2.3 3.4 176

Holden 16 84 258 44 1226 64 0.5 4.5 102 Hollingsworth 16 150 1046 113 2517 135 0.3 1.5 144

Hunter 18 414 444 98 1723 82 0.5 1.7 340 Keys Pond 10 27 41 2 208 1 5.3 2.9 5 Killarny 18 230 403 21 603 22 1.0 4.7 96

Koon 23 46 45 5 687 3 1.4 1.5 44

Lawbreaker 4 32 42 1 108 1 5.5 4.3 5 Lindsey 13 78 167 19 636 6 1.9 2.2 55

Little Fish 8 240 251 21 1161 13 1.4 1.2 2

Live Oak 23 27 81 13 389 9 2.6 3.0 152

Lochloosa 29 149 160 32 1053 22 1.0 1.8 2 309

Loften 10 56 57 5 633 2 2.5 2.6 5

Marianna 21 193 244 26 1054 21 1.3 3.8 204

Mill Dam 29 42 36 11 462 4 2.7 5.7 85

Miona 24 63 62 12 867 8 1.5 2.3 169

Moore 15 42 96 5 353 3 5.3 2.9 28

Mountain 14 135 185 37 813 10 1.7 1.6 51

Mountain 2 17 47 193 17 331 2 2.4 3.3 55 Okahumpka 25 95 68 21 1033 11 1.4 0.9 271

Orienta 17 190 232 25 448 9 2.2 3.4 52

Pasadena 16 115 111 15 702 3 2.2 3.1 151

Patrick 25 170 173 10 1808 5 2.0 1.8 159

Pearl 20 84 111 28 819 228 0.9 2.0 24

Picnic 11 48 51 8 137 1 2.6 3.3 18

Round Pond 9 135 126 3 444 3 2.6 1.3 4 Table 1. Total number of fish species, whole lake fish standing crop, littoral zone fish standing crop, total phosphorus (TP), total nitrogen (TN), chlorophyll a (Chl a) , Secchi depths (SD), mean depths (Z ), and surface areas (SA) for 65 Florida lakes. Can. J. Fish. Aquat. Sci. Vol. 53, 1996 844

© 1996 NRC Canadain July 1987 and continuing through January 1991. In July 1987, one 10-min haul was taken at each of 25 open-water sites. In all subsequent sampling periods, two 10-min hauls were made at 27 sites, although some stations were missed during low-water periods in July 1990. To add more large lakes to the data set, we also used fish biomass data (Eisenhauer et al. 1993; Moxley et al. 1993; Moyer et al. 1992,1993) collected by the Florida Game and Fresh Water Fish Commission in the littoral areas of lakes Crescent, George, Istokpoga, and Tohopekaliga (Fig. 1). In the fall of both 1990 and 1991, eight littoral, standard rotenone blocknet samples (0.1 ha) were taken from Lake Crescent and four from Lake George. Data for species composition were also obtained from two fall 0.4-ha limnetic blocknet samples taken in both years on Lake George. Fish biomass data from Lake Istokpoga and Lake Tohopekaliga were obtained from six (0.4 ha) littoral rotenone blocknet sites taken from August 1988 through August 1991 and fall 1990, 1991, and 1992, respectively. Additional information on species composition was obtained by electrofishing, gill nets, and Wegner rings. We examined the potential effect of increasing lake trophy on species richness, defined as the number of different species of fish collected in each lake. First, we examined the cumulative number of fish species encountered in each lake as fishing effort increased to assess the likelihood that more sampling would add additional species.For the 60 lakes sampled by Canfield and Hoyer (1992), we constructed curves showing the cumulative numbers of fish speciesobtained as progressively more blocknets were fished. For each species in each lake, we used our catch records to determine the number of nets in which the species was found out of the total number of nets set. We then determined the probability that the species would be encountered for the first time in the first, second, third, and other nets set following the basic laws of probability (Dietrich and Kearns 1986). The expected frequencies for each species for each net were then summed to derive a curve of species found versus sampling effort. In each case the greatest increases in numbers of species found are in the first few nets with relatively little increase in the number of species caught as the last net is fished. As a measure of the flatness of the right-hand portion of the curve, the numbers of species seen after the next to last net was fished were expressed as a percentage of the number of species found after the last net was examined. In 41 of the 60 lakes, 95–100% of the species found in all nets were found by the next-to-last net. In only three cases was the value lessthan 90%. However, in addition to the species found in these blocknets, fish were also collected by gillnets and electroshocking to search for species that may have been missed. For the five lakes sampled by the Florida Game and Freshwater Fish Commission, we compiled the cumulative numbers of species collected by all means for each year of sampling. For lakes Okeechobee, Istokpoga, and Tohopekaliga, 40 of 41, 30 of 31, and 32 of 34, respectively, of the species found were noted before the last year of sampling. Lakes Crescent and George were only sampled in 2 years with 36 of 44 and 38 of 42 of the species being found in the first year. We might expect to find a few more species in the latter two lakes with more sampling. It is noted that several marine species were found in these two lakes as a result of their close connection with the St. Johns River estuary and that they had more fish species than any of the other lakes in our study. To assess whether trophic state played a role in determining the presence or absence of a fish species in lakes of a particular trophic state, we divided our lakes into four groups based on chlorophyll a concentrations in the water following the guidelines of Forsberg and Ryding (1980). Oligotrophic lakes had less than 3 mg

⋅ m –3 of chlorophyll a ( n = 14); mesotrophic, 3–7 mg

⋅ m –3 ( n = 12); eutrophic, 7–40 mg

⋅ m–3 ( n = 26); and hypereutrophic, >40 mg

⋅ m –3 ( n = 13). Achi-square test was used to determine if trophic state played a significant role in determining fish distribution in lakes. Many of the fish were found in fewer than 20 lakes, making the sample size too small for a valid chi-square test. Thus, we further grouped the lakes Standing crop

Lake No. of fish species Whole lake (kg

⋅ ha–1 ) Littoral zone (kg

⋅ ha –1 ) TP (μg

⋅ L–1 ) TN (μg

⋅ L–1 ) Chl a (μg

⋅ L–1 ) SD (m) Z (m) SA (ha)

Rowell 30 463 615 66 910 47 0.8 1.3 147 Suggs 20 19 12 66 1249 4 0.5 2.0 73
Susannah 19 217 470 23 674 25 1.5 3.9 31

Swim Pond 15 60 61 25 1025 11 0.6 0.6 9

Thomas 13 98 147 22 759 10 1.8 3.9 55

Tomahawk 13 84 99 6 192 1 4.2 4.4 15 Turkey Pen 10 52 71 2 132 1 3.2 5.0 6

Wales 15 51 42 27 899 42 0.8 3.4 132

Watertown 18 147 290 27 777 24 1.0 3.8 19 Wauberg 22 674 464 166 1478 102 0.6 3.6 100 West Moody 18 170 220 14 584 2 2.8 3.5 39

Mean 18 131 200 60 949 32 2 3 3 733

Median 17 92 147 21 759 10 1.45 2.7 59

Maximum 41 683 1046 1043 3789 241 5.8 5.9 173 000

Minimum 4 7 8 1 82 1 0.3 0.6 1.8

Note: For lakes Okeechobee, Crescent, George, Istokpoga, and Tohopekaliga the water chemistry is based on open-water samples and for the other lakes it is an average of open-water and littoral zone samples. Sampling was done between 1986 and 1992. Table 1 ( concluded ).

Y

-variable X -variable Slope Intercept r 2

Log 10 Chl a Log 10 TP 0.887 –0.174 0.73 Log 10 Chl a Log 10 TN 1.405 –2.985 0.69 Log 10 TN Log 10 TP 0.498 2.180 0.66 Log 10 SD Log 10 Chl a –0.466 0.617 0.78 Log 10 SD Log 10 TP –0.473 0.779 0.75 Log 10 SD Log 10 TN –0.790 2.394 0.78 Table 2. Slopes and intercepts for regression equations and Pearson’s correlations ( r 2) between the trophic state variables total phosphorus (TP; mg

⋅ m –3 ), total nitrogen (TN; mg

⋅ m –3 ), chlorophyll a (Chl a ;

mg ⋅ m –3 ), and Secchi disk depth (SD; m) in 65 Florida lakes. Bachmann et al. 845

© 1996 NRC Canadainto oligotrophic–mesotrophic lakes and eutrophic–hypereutrophic lakes and tested to see if the frequencies of presence or absence were randomly distributed between the two groups. If any of the expected frequencies were less than five, we used the Fisher exact probability test (Siegel 1956) to test for significant differences in distributions. To determine the effects of lake trophic state on the standing crops of individual fish species in the 60 lakes with weighted averages for fish standing crops, we determined the average standing crop for each species in each of the four trophic state categories. If a species was missing in a lake, its standing crop was included as zero. A one-way analysis of variance was used to determine if there were significant differences in the standing crops among the four trophic categories. To test the idea that the proportion of piscivorous species declines with increasing trophic state (Persson et al. 1988), we calculated the percent of the total fish biomass that consisted of piscivorous fish for the 60 lakes that had both littoral and open-water samples. Those fish considered piscivorous as adults were classified on the basis of the works by Pflieger (1975), Lee et al. (1981), and Becker (1983) and included Florida gar ( Lepisosteus platyrhincus ), longnose gar (Lepisosteus osseus ), bowfin (Amia calva ), chain pickerel ( Esox niger), redfin pickerel (Esox americanus americanus ), Atlantic needlefish (Strongylura marina), black crappie (Pomoxis nigromaculatus ), and largemouth bass (Micropterus salmoides). Proportions should be useful in detecting relative changes in piscivorous activity; whereas absolute values would be overestimates because these species
also feed on other food items. Because of their importance to the sport fishery, we also calculated the weight of all centrarchids and largemouth bass in each lake’s sample (as kilograms per hectare and as percentages of the total fish weight)

. For five important sport fish species we used the length–frequency data from the blocknet samples from the 60 lakes with weighted means of open-water and littoral fish to calculate relativestock densities (RSD-Q) for quality or larger fish following Gabelhouse (1984). For the warmouth (Lepomis gulosus ) in each lake we divided the number of fish in the sample 15 cm or greater in length by the number greater than 8 cm long. The resulting RSD-Q value represents the proportion of the stock fish (those fish greater than 8 cm) that are of quality size (15 cm) or greater. The same lengths were used for the bluegill (Lepomis macrochirus ) calculations. For the redear sunfish (Lepomis microlophus ) we divided the number of fish larger than 18 cm by those greater than 12 cm; for the black crappie, the number greater than 20 cm by those greater than 12 cm; Fig. 2. Fish standing crops (kg

⋅ ha –1 ) in relation to total phosphorus, total nitrogen, chlorophyll a , and Secchi disk transparency for 65 Florida lakes. The regression lines and r 2 values are based on the 60 lakes with whole-lake estimates of standing crops. The points for the five large lakes represent littoral fish standing crops only and are included for comparative purposes. Can. J. Fish. Aquat. Sci. Vol. 53, 1996 846

© 1996 NRC Canadaand for the largemouth bass, the number of fish greater than 32 cm by those greater than 20 cm. These are the same or close to the minimum sizes suggested by Gabelhouse (1984).

Water quality For the 60 Florida lakes sampled by Canfield and Hoyer (1992), six water-quality sampling stations were established at each lake. Summer water samples were collected from six stations (three each in the littoral and pelagic zones) on a single date at the time the fish populations were sampled. Additional samples were collected fromthe three pelagic stations on two subsequent dates. Secchi disk measurements were made only at the open-water stations. Water was collected from just below the surface (0.5 m) in acid-cleaned Nalgene bottles. Samples were placed on ice and returned to the laboratory for analysis. The measurements that are used in this analysis included total phosphorus, determined with the methods of Murphy and Riley (1962) after persulfate oxidation (Menzel and Corwin 1965); total nitrogen, determined by a modified Kjeldahl technique (Nelson and Sommers 1975); and total chlorophyll a concentrations (uncorrected for phaeophytins), determined by filtering a measured portion of lake water through a Gelman type A-E glass fiber filter and then following the method of Yentsch and Menzel (1963) and the equations of Parsons and Strickland (1963).Surface water samples (0.5 m) were collected from eight openwater stations in Lake Okeechobee. Secchi disk readings were made at the same time and sample analyses included total phosphorus, total nitrogen, and chlorophyll a . The station locations and analytical methods are discussed in James et al. (1995). The stations were sampled at least monthly and sometimes twice a month. For this study we calculated monthly averages for each variable for each station and then averaged the station means to find a lake average for each month. The lake average was taken as the mean of the monthly averages for the years 1989 through 1991. Water-quality data from the open waters of the other four large lakes were based on records and published reports of the Florida Game and Fresh Water Fish Commission (Moyer et al. 1992, 1993; Lange et al. 1993).

Statistical analysis For tests of the effects of trophic state on fish standing crops in regression analyses, logarithms of total phosphorus, total nitrogen, algal chlorophyll a , and Secchi disk depth were used because these variables span several orders of magnitude and their expected errors increase with magnitude. Both linear and curvilinear fits were tried. The parametric statistical analyses (paired t-tests, bivariate correlation coefficients, and analysis of variance) were run according to Snedecor and Cochran (1980) using the JMP statistical package (SAS Institute 1994). Multiple regressions were run with the stepwise, forwardselection mode (SAS Institute Inc. 1985). Chi-square analyses followed the procedures given in Siegel (1956). The 5% level of confidence was used to reject null hypotheses in all cases.



### Results

Two-way analysis of variance showed no differences (p < 0.05) in concentrations of total phosphorus, total nitrogen, and chlorophyll a from open-water samples, littoral area samples,and averages of the two for the 60 original lakes. No differences (p < 0.05) were found in a comparison of our openwater samples from Lake Okeechobee and littoral samples (Phlips et al. 1993) from that lake. Thus, either littoral samples Fig. 3. Relationship between the number of species of fish per lake and the logarithm of lake surface area. Bachmann et al. 847

© 1996 NRC CanadaStanding crop

Common name Scientific name No. of

lakes TP (μg

⋅ L–1 ) CHL a (μg

⋅ L–1 ) TN (μg

⋅ L–1 ) Surface area (ha) Whole

lake (kg

⋅ ha –1 ) Littoral only (kg

⋅ ha–1 ) Lake

size

effect Trophic

state
effect
Bluegill Lepomis macrochirus 64 57 29 938 413 38.102 None None Largemouth bass Micropterus salmoides 64 57 29 938 413 15.102 None None

Warmouth Lepomis gulosus 63 57 28 934 418 8.639 None None Mosquitofish Gambusia holbrooki 51 68 33 1023 504 0.274 None None

Redear sunfish Lepomis microlophus 50 73 37 1132 537 15.067 More More

Golden shiner Notemigonus crysoleucas 47 76 38 1177 571 5.321 More More

Brook silverside Labidesthes sicculus 47 53 27 939 542 0.138 None None

Lake chubsucker Erimyzon sucetta 42 27 12 735 284 11.751 None Less Black crappie Pomoxis nigromaculatus 42 84 42 1192 634 4.840 More More Swamp darter Etheostoma fusiforme 40 31 14 724 283 0.033 None None

Brown bullhead Ictalurus nebulosus 34 95 43 1229 784 2.802 More More

Yellow bullhead Ictalurus natalis 33 32 29 961 737 1.290 None None

Seminole killifish Fundulus seminolis 32 79 42 1142 822 0.628 None More Golden topminnow Fundulus chrysotus 32 22 11 688 156 0.344 None Less Bluespotted sunfish Enneacanthus gloriosus 29 22 17 846 407 1.779 More None

Bluefin killifish Lucania goodei 29 26 20 814 414 0.729 More None

Threadfin shad Dorosoma petenense 26 82 40 1195 1 003 20.940 More More

Gizzard shad Dorosoma cepedianum 25 123 67 1599 1 104 66.074 More More Florida gar Lepisosteus platyrhincus 25 47 23 883 480 2.682 More None Spotted sunfish Lepomis punctatus 24 107 40 1169 522 2.626 More More

Dollar sunfish Lepomis marginatus 24 23 15 795 366 1.212 None None

Bowfin Amia calva 20 57 26 1015 240 4.830 More None

Least killifish Heterandria formosa 19 40 12 712 173 0.084 None None Lined topminnow Fundulus lineolatus 19 10 6 422 43 0.190 Less Less Blue tilapia Tilapia aurea 18 152 80 1818 1 380 12.731 More More Tadpole madtom Noturus gyrinus 16 43 28 1128 1 468 0.279 More None

White catfish Ictalurus catus 15 122 40 1353 1 760 1.085 More More Taillight shiner Notropis maculatus 15 67 61 1598 1 915 0.673 More More Sailfin molly Poecilia latipinna 15 44 42 1316 1 919 0.085 More More Chain pickerel Esox niger 13 17 11 728 332 4.232 None None Grass carp Ctenopharyngodon idella 12 44 39 1170 1 096 17.211 None None Redfin pickerel Esox americanus americanus 12 16 7 649 572 1.035 None Less Everglades pygmy

sunfish Elassoma evergladei 11 54 19 619 45 0.033 None Less

Atlantic needlefish Strongylura marina 8 87 112 2856 6 049 0.906 More More Flagfish Jordanella floridae 8 20 7 800 136 0.347 More None

Redbreast sunfish Lepomis auritus 7 53 66 1570 1 176 1.419 None None

Bream Lepomis sp. 6 180 96 1976 156 1.267 None None

Shiners Notropis sp. 6 75 27 1094 993 0.120 None None Longnose gar Lepisosteus osseus 5 81 102 2942 4 242 0.016 More None Pygmy killifish Leptolucania ommata 5 9 4 503 31 0.002 None None Pirate perch Aphredoderus sayanus 4 42 19 975 643 0.106 None None Striped mullet Mugil cephalus 3 161 48 2477 65 963 52.537 None None

Channel catfish Ictalurus punctatus 3 144 24 2227 63 499 5.447 None None

American eel Anguilla rostrata 3 27 24 777 19 3.211 None None

Sunshine bass Morone chrysops 3 Morone
saxatilis 3 87 112 2856 6 049 0.466 None None

Flier Centrarchus macropterus 3 22 16 853 792 0.185 None None Ladyfish Elops saurus 2 195 56 2975 12 445 0.450 None None Hogchoker Trinectes maculatus 2 195 56 2975 12 445 0.350 None None Clown goby Microgobius gulosus 2 111 32 2990 89 645 0.211 None None Bay anchovy Anchoa mitchilli 2 195 56 2975 12 445 <0.100 None None Sheepshead minnow Cyprinodon variegatus 1 92 30 1480 173 000 0.310 None None

Snook Centropomus undecimalis 1 92 30 1480 173 000 0.179 None None Table 3. Fish species collected in 65 Florida lakes ranked by frequency of occurrence. Can. J. Fish. Aquat. Sci. Vol. 53, 1996 848

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### or a combination of littoral and open-water samples can be used to characterize lakes in a broad survey of this type. Total phosphorus, total nitrogen, and chlorophyll a concentrations ranged over two or three orders of magnitude (Table 1). For example, chlorophyll a ranged from 1 to 241 mg

⋅ m–3 

### . There were no significant correlations between the trophic state indicators and lake mean depth. This probably results from the very small range of mean depths in these Florida lakes (0.6–5.8 m; Table 1). There was a tendency for the larger lakes to be more eutrophic. We found weak positive correlations (p < 0.05) between total phosphorus, total nitrogen, and chlorophyll a with the logarithms of surface area ( r2


### = 0.09, 0.26, and 0.09, respectively) and a negative correlation with Secchi disk depth ( r2


### = 0.22). Chlorophyll a concentrations in these lakes were highly correlated (p < 0.01) with both total phosphorus and total nitrogen (Table 2). Because total phosphorus and total nitrogen were both correlated (p < 0.01) with each other (Table 2), these data do not indicate which is the limiting element. The Secchi disk transparency is highly correlated with chlorophyll

a , total phosphorus, and total nitrogen (p < 0.01; Table 2). For these lakes as a group the chlorophyll a concentration largelydetermines the water transparency rather than suspended solids and humic materials, although there are exceptions in some
individual lakes. The standing crop of fish based on a weighted average of catches in both littoral and open-water blocknets averaged 131 kg

⋅ ha –1 

### (range 7–683 kg

⋅ ha–1 

### ). For the 60 lakes with bothlittoral and open-water data the standing crops of fish increased with increases in chlorophyll a , total phosphorus, and total nitrogen and decreased with increases in Secchi disk transparency (Fig. 2). The correlations were significant (p < 0.01) though the r2 

### values (0.27, 0.24, 0.28, and 0.27, respectively) indicated that trophic state explained only about one fourth of the variance in fish crops. There was no significant correlation between the standing crop of fish and lake surface area. Other factors and experimental error in sampling may be responsible for a great portion of the observed variance in thestanding crop estimates. Five lakes only had estimates of littoral fish standing crops, so they were not included in the correlation analysis. The points for littoral fish crops in those five lakes are plotted in the same graphs, however, to illustrate that they fall within the same scatter as the points for the other 60 lakes (Fig. 2). On average there were 18 species of fish in each lake (range 4–44 species; Table 1). The larger the lake the more species of fish that were found. Lake surface area (Fig. 3) explained 70% of the variance in species numbers. A number of marine species (e.g., Atlantic stingray (Dasyatis sabina ), bay anchovy ( Anchoa mitchilli ), Atlantic croaker (Micropogonias undulatus ), etc.) occurred in some of the larger lakes with oceanic connections. A reanalysis with those species removed still yielded an r 2 

### of 0.67. Species richness was weakly correlated with total phosphorus, total nitrogen, chlorophyll a , and Secchi depth ( r2


### = 0.05, 0.20, 0.03, and 0.25, respectively) with a positive relationship for the first three variables and anegative relationship for the Secchi disk transparency. However, in a stepwise, multiple regression in the forward modeusing logarithms of surface area, total phosphorus, total nitrogen, chlorophyll a , and Secchi disk transparency as independent variables, log surface area was the only significant
variable selected. There was no indication that the number of fish species declined with increasing trophic state in these
lakes. Sixty-five fish species were collected in the study lakes (Table 3). Bluegill, largemouth bass, warmouth, mosquitofish ( Gambusia holbrooki ), and redear sunfish were found in 75–98% of the lakes, whereas 15 other species were collectedin only a single lake. Significant differences in their distributions between lakes of different trophic states were found for 18 species (Fig. 4). Thirteen of the fish species were more likely to be present in lakes of higher trophic states while only Standing crop

Common name Scientific name No. of

lakes TP (μg

⋅ L–1 ) CHL a (μg

⋅ L–1 ) TN (μg

⋅ L–1 ) Surface area (ha) Whole

lake (kg

⋅ ha –1 ) Littoral only (kg

⋅ ha–1 ) Lake

size

effect Trophic

state
effect

Blackbanded

sunfish Enneacanthus chaetodon 1 11 4 462 85 0.104 None None Grey snapper Lutjanus griseus 1 130 34 4500 6 290 0.100 None None Naked goby Gobiosoma bosc 1 130 34 4500 6 290 0.100 None None

Inland silversides Menidia beryllina 1 92 30 1480 173 000 0.092 None None Pugnose minnow Opsopoeodus emiliae 1 92 30 1480 173 000 0.006 None None Opossum pipefish Microphis brachyurus 1 92 30 1480 173 000 0.002 None None Walking catfish Clarias batrachus 1 92 30 1480 173 000 0.002 None None

Atlantic croaker Micropogonias undulatus 1 130 34 4500 6 290 <0.100 None None Silver jenny Eucinostomus gula 1 130 34 4500 6 290 <0.100 None None

American shad Alaso sapidissima 1 260 79 1450 18 600 <0.100 None None Atlantic stingray Dasyatis sabina 1 260 79 1450 18 600 <0.100 None None Gulf pipefish Syngnathus scovelli 1 260 79 1450 18 600 <0.100 None None Pugnose shiner Notropis anogenus 71 260 79 1450 18 600 <0.100 None None

Note: For the lakes in which they were found, average values are given for the trophic state indicators total phosphorus (TP), chlorophyll a (Chl a ), total nitrogen (TN), lake surface areas and whole lake standing crops (based on both littoral and open-water blocknets). For six of the lakes only littoral standing crop data are available. Chi-square or Fisher exact probability tests were used to determine if fish species were more or less abundant in larger rather than smaller lakes or in eutrophic–hypertrophic lakes rather than oligotrophic–mesotrophic lakes. More, significant increase; less, significant decrease; none, no difference. Table 3 ( concluded ). Bachmann et al. 849

© 1996 NRC Canada

### five species, the lake chubsucker (Erimyzon sucetta ), golden topminnow (Fundulus chrysotus ), lined topminnow (Fundulus lineolatus ), redfin pickerel, and Everglades pygmy sunfish (Elassoma evergladei), were less likely to be found in eutrophic–hypereutrophic lakes than oligotrophic–mesotrophiclakes (Table 3). Another indication of the trophic state preferences for these species can be derived by looking at the average values for the trophic state indicators for the lakes in whichthey were found (Table 3) relative to the averages for the entire sample (Table 1). For example, the redfin pickerel wasmost commonly found in lakes with low nutrient concentrations while the blue tilapia ( Tilapia aurea ) was more likely to be in lakes with higher nutrient concentrations. A similar test was made of the effect of lake size on the distribution of individual fish species. The lakes were divided into small lakes (<100 ha) and large lakes (>100 ha), and a chi-square or Fisher exact probability test (Siegel 1956) was used to test the distribution of presence or absence. Nineteen species were more likely to be found in large lakes while only one was more likely to be found in small lakes (Table 3). Inmost cases the fish most likely to be found in eutrophic–hypereutrophic lakes were the same species that were more likely to be found in large lakes. Because our larger lakes tended to be more eutrophic, it is not possible to sort out completely the effects of lake size and trophic state. Standing crops of individual species increased with trophic Fig. 4. Fraction of lakes of different trophic state in which various species of fish were present. For each of these species, except the bluegill and largemouth bass, there was a significant difference between the fraction found in oligotrophic–mesotrophic lakes and the fraction found in eutrophic–hypereutrophic lakes. Can. J. Fish. Aquat. Sci. Vol. 53, 1996 850

© 1996 NRC Canada

### state (p < 0.05) for the gizzard shad (Dorosoma cepedianum ), threadfin shad (Dorosoma petenese ), black crappie, redear sunfish, and blue tilapia while the warmouth tended to have higher standing crops in lakes of lower productivity (Fig. 5). The increases in biomass for the gizzard shad, the threadfin shad, and the blue tilapia are particularly noteworthy. All were practically absent from oligotrophic–mesotrophic lakes (Fig. 4) but increased in both frequency of occurrence andaverage standing crop in eutrophic and especially hypereutrophic lakes (Fig. 5). The average standing crop for the gizzard shad for lakes in which it was found (66 kg

⋅ ha–1 

### ) was the highest for all species encountered while the standing crop for the threadfin shad (21 kg

⋅ ha–1 

### ) ranked third behind the bluegill (38 kg

⋅ ha –1 

### ). The blue tilapia showed a similar pattern in both frequency of occurrence (Fig. 4) and average standing crop (Fig. 5) as the two shad species. The piscivorous species as a group averaged about 22% of the total fish biomass in our lakes and ranged from 0 to 73%. The absolute weights of the piscivore group tended not to change with increasing trophic state (Table 4). However,when expressed as a percentage of the total biomass the relative importance of this group declined as the lakes became more productive, though the r2 

### values are quite small (0.03– 0.12). On average the percentages of piscivorous species for Fig. 5. Average standing crops with standard errors for fish in groups of lakes of different trophic state. For each of these species a one-way analysis of variance showed significant differences (p < 0.05) among standing crops for the four trophic categories. Bachmann et al. 851

© 1996 NRC Canada

### oligotrophic, mesotrophic, eutrophic, and hypereutrophic lakes were 25, 28, 21, and 11%, respectively, of the total biomass. One of the most important piscivores, the largemouth bass, on average, made up 15% of the fish biomass with arange of from 0 to 69%. The absolute biomass of the largemouth bass showed no changes with trophic state (Table 4),though their percentage of the total biomass became less abundant at higher trophic states. On average the percentages oflargemouth bass by weight in oligotrophic, mesotrophic, eutrophic, and hypereutrophic lakes were 20, 17, 16, and 4%,respectively, of the total biomass. The centrarchids, on average, made up 66% of the total biomass with a range from 15 to 99%. They likewise showed a pattern of increasing absolutebiomass with increases in trophic state (Table 4) but decreasing in percent biomass in lakes of higher trophic states. Because of the large increase in standing crops of gizzard shad and threadfin shad found in eutrophic and hypereutrophic lakes, we analyzed the data to see if these increases might be responsible for the drops in the relative proportions of the piscivores, centrarchids, and largemouth bass. The biomasses of these two shad species were subtracted from the total fish
biomass to derive a new biomass total that did not include theshad species. This new total also showed increases with increases in trophic state (Table 4); however, both the percentpiscivores and percent centrarchids now showed no correlation with trophic state while the percent largemouth bass continued to decrease in lakes at higher trophic levels. This suggests that the addition of large standing crops of gizzard and threadfin shad to the total fish biomass was responsible forthe decrease in the proportions of the piscivores and centrarchids as groups.The largemouth bass is one of the most important fish species in terms of occurrence because it was found in 64 of 65 of our study lakes and ranked fifth in average standing crop for the lakes in which it was found (Table 2). It showed no pattern of change in standing crop with changes in lake trophic state for this group of lakes but did show a decline in percentage of total biomass as lakes became more eutrophic. At the same time, however, the proportion by number of largemouth bass of quality size or greater as defined by Gablehouse (1984) increased with increasing trophic state (Table 4). Two otherfish, the bluegill and warmouth, were also very common, being found in 64 and 63 of our lakes, respectively. The bluegill showed no changes in average standing crops or in proportions of larger fish with increases in trophic state (Table 4) while thewarmouth showed decreases in standing crop (Fig. 5) but increases in the proportion of larger sized individuals with increases in trophic state (Table 4). The redear sunfish showed increases in frequency of occurrence (Fig. 4) and increases in average standing crop (Fig. 5) but no change in RSD-Q(Table 4) as lakes increased in productivity. The other important centrarchid, the black crappie, also became more common(Fig. 4) and had larger standing crops (Fig. 5) in more eutrophic lakes but had a smaller proportion of quality-sized fish (Table 4) in the richer lakes.



### Discussion

The fish populations in the Florida lakes in this study fit some of the patterns found for other lakes discussed in the fisheries literature. There is an increase in the total standing crops of fish as the concentrations of total phosphorus, total nitrogen, and chlorophyll a increase and as the Secchi depth decreases. Similar results were found by Bays and Crisman (1983) for 30 Log total phosphorus Log total nitrogen Log chlorophyll a Log Secchi depth

Total biomass minus shad 0.14** 0.14** 0.14** (–) 0.07* Weight of piscivores 0.01 0.06* 0.02 (–) 0.01 Weight of centrarchids 0.14** 0.14** 0.13** (–) 0.06* Weight of largemouth bass (–) 0.01 (–) 0.00 (–) 0.00 0.00 Percentages based on total biomass

Piscivores (–) 0.09* (–) 0.03 (–) 0.12** 0.07* Centrarchids (–) 0.10* (–) 0.06* (–) 0.19** 0.16** Largemouth bass (–) 0.21** (–) 0.14** (–) 0.17** 0.17** Percentages based on total biomass minus biomass of shad

Piscivores (–) 0.02 (–) 0.00 (–) 0.02 0.01

Centrarchids 0.01 0.01 0.00 0.00 Largemouth bass (–) 0.14** (–) 0.10* (–) 0.09* 0.11** RSD-Q

Black crappie (–) 0.27** (–) 0.35** (–) 0.23** 0.23** Bluegill (–) 0.01 (–) 0.03 (–) 0.01 0.02 Largemouth bass 0.12** 0.16** 0.22** (–) 0.10* Redear sunfish 0.01 (–) 0.00 0.00 (–) 0.02 Warmouth bass 0.08* 0.04 0.14** (–) 0.13**

Note: The percentages were calculated on both the total weight of fish and the total weight minus the weights of gizzard shad and threadfin shad. The minus signs in parentheses indicate negative relationships. The RSD-Q represents the proportion of the fish stock greater than a minimum size (see text). Curvilinear regressions were also tried, but they did not result in improved fits for these data.

*Significant at the 5% level. **Significant at the 1% level. Table 4. Pearson’s correlations ( r 2 ) between several indices of lake trophic state and several measures of fish population composition and quality for the 60 Florida lakes with data on both open-water and littoral fish. Can. J. Fish. Aquat. Sci. Vol. 53, 1996 852

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### Florida lakes. Canfield and Hoyer (1992) also found correlations between fish standing crops and trophic state for 60 Florida lakes when they took into account the standing cropsof aquatic macrophytes in addition to phytoplankton in determining trophic state. On average the standing crops increased about one order of magnitude from the oligotrophic to the hypereutrophic Florida lakes in this study with no sign of adecrease in the most hypereutrophic lakes. There is considerable unexplained variance (about 75%) in these relationships due in part to other factors influencing fish crops and the practical sampling problems of estimating the biomass of wild fish populations. For this reason, predictions based on these relationships are imprecise; however, holding all other things constant, the standing crop of fish in Florida lakes should increase as they become more eutrophic. Increases in trophic state did not result in a decrease in thenumber of fish species per lake. The lake surface area accounted for most of the variance in total species numbers, and once area was taken into account, there was no correlation between the number of fish species per lake and the various trophic state indices. The finding that larger lakes have agreater species richness than smaller lakes has been documented in comparisons of lakes throughout the world, including North America and Africa (e.g., Barbour and Brown 1974) and in Florida (Keller and Crisman 1990). A number of studieshave documented this species–area relationship and tested hypotheses such as equilibria related to colonization and extinction, as suggested by theories of island biogeography and habitat diversity (Browne 1981; Barbour and Brown 1974; Eadie et al. 1986; Magnuson 1976; Tonn and Magnuson1982). Studies investigating species diversity and fish community assemblages have also looked at factors such as size, pH, plant nutrients, latitude, and insularity and applied statisticaltechniques to determine the factors that influence species richness (Connor and McCoy 1979; Tonn and Magnuson 1982; Keller and Crisman 1990). Results of regression analyses for lakes in Florida and on data from other parts of North America by Keller and Crisman (1990) show that lake area explains the largest proportion of the variance in fish species richness.Only five species showed decreases in frequency of occurrence with increasing trophic state, while all other species either stayed the same or increased in frequency of occurrence as lakes became more eutrophic. A similar pattern was found for average standing crops of individual species in lakes of different trophic states. Most species showed no significant change in biomass with changes in trophic state, while one species, the warmouth, decreased. Five species, the gizzard shad, threadfin shad, black crappie, redear sunfish, and blue tilapia, showed increases in standing crops with increasingtrophic state. Bays and Crisman (1983) also noted the increased standing crops of shad in Florida lakes. For the recreationally important centrarchids the only negative changes noted with higher trophic states were lower standing crops of warmouth and lower proportions of larger sized black crappie. On the positive side the centrarchids as a group increased in biomass, the redear sunfish and black crappie increased in average standing crop, and the largemouth bass had a higher proportion of larger fish in the more eutrophic lakes. Persson et al. (1988) found that the piscivorous fish in 15Swedish lakes they had studied decreased in relative abundance as their lakes became more eutrophic. The piscivores made up about 70% of the biomass in their oligotrophic lakes and dropped to about 7% in lakes of mesotrophic or higher trophic state. They thought that in very productive lakes with decreased habitat heterogeneity their primary piscivore, the perch (Perca fluviatilis ), faced increased competition from cyprinids and that this had a negative effect on the recruitment of perch. We also found declines in the relative proportions of piscivores to other fish groups when we looked at lakes ofincreasing trophic state; however, the magnitude of the decrease was not as great as that found for the Swedish lakes. In our case there was no decrease in the absolute standing crop of the piscivores, but rather higher standing crops of gizzard shad and threadfin shad in eutrophic and hypereutrophic lakes reduced the relative importance of the piscivores. We could only study the relationships between fish and trophic state over a cross section of lakes of varying trophic state rather than in individual lakes undergoing eutrophication over time. This places a limit on predictions that can be made for Lake Okeechobee or other lakes that may show changes in trophic state due to changes in nutrient inputs resulting from land use changes in their watersheds. Because of the scatter inour data, due in part to the many unexplained factors influencing fish populations and the problems in obtaining precise standing crop information for fish, we would have a problem in making a precise prediction of fish standing crops for anunknown Florida lake given information on basic water chemistry. On the other hand there were some general patterns in the relationships between lake trophic state and the total biomass, species richness, species composition, and species standing crops of fish in Florida lakes that should be useful in a predictive manner. First, the number of fish species in a lake seemed to be determined primarily by the size of the lake and not the trophic state, so we should not expect dramatic changesin total species numbers as lakes become more or less eutrophic. Second, there might be shifts in species composition with changes in trophic state, though only a few species showed significant changes in their standing crops. In particular the recreationally important centrarchids did not show dramatic changes over the trophic spectrum. Third, we did not identifyany critical points on the trophic spectrum that caused dramatic changes in fish abundance and standing crops; there was nothing comparable with the loss of dissolved oxygen in the hypolimnion of a eutrophying northern lake. Last, the trophic indicators for our sample of lakes ranged over 2 or 3 orders of magnitude. The expected change in trophic state of a Florida lake that is already in the eutrophic to hypereutrophic category will likely be much less, so changes in the fish population due to changes in trophic status are likely to be small, and anychanges in fish populations brought upon by changes in trophic state will be overshadowed by the many other variables that influence fish populations from year to year. Since these findings do not fit the expectations of several earlier studies in the literature, it is worth looking at how theseFlorida lakes might differ from the lakes in those earlier studies. One major difference is that many of the lakes where eutrophication has been cited as the cause for changes in fishpopulations have been deep, stratified lakes in temperate regions where salmonid populations have been reduced or eliminated (Larkin and Northcote 1969). The loss of dissolved oxygen in the hypolimnion of stratified lakes is an important factor in reducing the available habitat for these species of fish Bachmann et al. 853

© 1996 NRC Canada

### and their preferred foods (Colby et al. 1972). Florida lakes, however, are shallow, do not have cold hypolimnia, and lack salmonid species. Winterkills under the ice in the winter arealso related to the impacts of eutrophication on fish community structure (Lee et al. 1991), but again this situation would not be relevant in Florida’s climate. Lastly, in the case of the 22 lakes studied by Kautz (1980), his plot of sport fish biomass versus trophic state as determined by total nitrogen content did indicate a downward trend for hypereutrophic lakes. However, when he grouped the lakes by trophic state he found that thestanding crops of sport fishes for oligotrophic, mesotrophic–eutrophic, and hypereutrophic lakes were 52, 89, and 65 kg

⋅ ha–1 

### , respectively, but these values were not statistically different from each other. Thus, those data did not support the contention that sport fish biomass declined with increasing trophic state in Florida lakes. According to our study, Florida lakes with up to 240 mg

⋅ m–3 

### of algal chlorophyll a show no significant decrease in biomass of important sport fish except
the warmouth.



### Acknowledgments

The authors are grateful to Susan Gray, Charles Hanlon, Mark Hanson, Karl Havens, R. Thomas James, Ray Oglesby, Barry Rosen, and Alan Steinman for their helpful comments on various drafts of this manuscript.



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