GENETIC INFLUENCES ON EXPERIMENTAL POPULATION DYNAMICS OF THE LEAST KILLIFISH

289

Ecological Monographs, 70(2), 2000, pp. 289–309q 2000 by the Ecological Society of America

GENETIC INFLUENCES ON EXPERIMENTAL POPULATION DYNAMICSOF THE LEAST KILLIFISH

JEFF LEIPS,1 JOSEPH TRAVIS, AND F. HELEN RODD2

Department of Biological Science, Florida State University, Tallahassee, Florida 32306 USA

Abstract. When natural populations differ in density or in the dynamic fluctuations ofpopulation size, some of those differences may result from their different ecological con-ditions, and some may originate from genetically based differences in life history expres-sion. Natural populations of the live-bearing poeciliid fish, Heterandria formosa, varyconsiderably in their population dynamics, with densities that differ between populationsby as much as sevenfold. This system offers an excellent opportunity to explore the potentialrole of genetically based differences in life history expression in creating different dynamicpatterns in a common environment. We created five different genetic stocks of H. formosaby carrying out a series of crosses using fish from two North Florida populations (theWacissa River and Trout Pond) and used them to initiate replicate experimental populationsin artificial ponds. The five stocks consisted of two ‘‘controls,’’ which were pure WacissaRiver and Trout Pond stocks, and three types of hybrid stocks. The hybrid stocks differedin a regular way in the proportion of genes from one population or the other. The crossingscheme was designed so that each hybrid stock would have the same proportion of het-erozygous (or ‘‘heterodemic’’) loci but would differ in the proportion and/or identity ofhomozygous (or ‘‘homodemic’’) loci from the Wacissa River and Trout Pond populations.These populations were chosen because a previous study had found that population densitiesin the Wacissa River greatly exceeded those of Trout Pond and exhibited a higher rangeof population fluctuation during the breeding season. We addressed four questions in thisexperiment: (1) Are there genetically based differences in life history traits of fish fromthe two populations? (2) If so, do differences in life history expression produce differencesin population dynamics in a common environment? (3) Which traits have the greatestinfluence on population dynamics? (4) How do changes in density affect the phenotypesof individual traits that govern the rates of birth and death in a population? We followedexperimental populations of the five genetic stocks from their initiation at low densitythrough 4–6 generations of population growth and decline. The mean offspring size differedamong stocks by as much as 50%. At low densities, offspring size exhibited a trade-offwith brood size: Trout Pond alleles were associated with more, smaller offspring. At higherdensities, offspring sizes were similar among stocks, and the trade-off with offspring numberwas not evident. Stocks differed in realized population growth rate by as much as 70%;the rank order differences among stocks with respect to population growth rate appearedto match the genetic relatedness among stocks based on the expected percentage of TroutPond alleles. Differences in population growth rate appeared to be due to differences inbrood size among stocks at low density. Stocks did not differ in the equilibrium populationsize, which indicated the absence of a trade-off between population growth rate and carryingcapacity in this environment. Adult survival and recruitment of juveniles into the adultpopulation both declined linearly with increasing density; and stocks did not generallydiffer in those rates after the effects of density had been taken into account.

The stocks differed in their response to the depressant effects of density on life historytrait expression. The offspring size of the pure Wacissa River stock was much more sensitiveto density than was the offspring size of the pure Trout Pond stock. However, the broodsizes of the Wacissa River stock were reduced much less than those of the Trout Pond stockwhen exposed to the same high density. These results suggest that life history distinctionsamong populations, both in the mean values and plasticity of traits, play a role in creatingdifferent dynamics. We discuss the ways in which phenotypic plasticity in reproductivetraits potentially acts as a mechanism to stabilize population dynamics in this species.

Key words: demography; genetic differentiation; Heterandria formosa; life history; offspringnumber; offspring size; trade-off; plasticity; Poeciliidae; population dynamics.

Manuscript received 19 June 1998; revised 10 April 1999; accepted 9 May 1999.1 Present address: Department of Genetics, Box 7614, North Carolina State University, Raleigh, North Carolina 27695-

7614 USA. E-mail: [email protected] Present address: Department of Zoology, University of Toronto, Toronto, Ontario, Canada M5S 3G5.

290 JEFF LEIPS ET AL. Ecological MonographsVol. 70, No. 2

INTRODUCTION

Natural populations exhibit a wide variety of dy-namic patterns, from the relatively stable populationsof Spanish Imperial Eagles (Ferrar and Donazar 1996)and pine beauty moths (Turchin and Taylor 1992), reg-ular population cycles of the larch budmoth (Turchinand Taylor 1992), and unstable population fluctuationsof the Soay sheep (Clutton-Brock et al. 1997) to thenearly chaotic dynamics of boreal voles (Turchin 1993,1995). Previous studies of population dynamics havelargely focused on how specific ecological factors per-turb and perhaps regulate population size and othercharacteristics such as age structure (see reviews bySinclair 1989, Crawley 1990, Berryman 1991, Mur-doch 1994, Turchin 1995). Current theory suggests thattwo types of forces, endogenous and exogenous, con-tribute to the diversity of population patterns com-monly observed. Endogenous forces are those that af-fect and are, in turn, affected by the demographic char-acteristics of populations (e.g., emigration rates, intra-specific life history responses to density, predatordensities); exogenous forces are those that affect de-mographic aspects of the community but are not af-fected by them (e.g., abiotic conditions, immigrationrates) (Ellner and Turchin 1995). Ellner and Turchin(1995) argue that the diversity of patterns in populationdynamics results from variation in the relative strengthof endogenous forces on the dynamics of a populationand variation in the damping or amplification of ex-ternally mediated exogenous perturbations to the pop-ulations.

We know relatively little about why some popula-tions are more strongly influenced by endogenous fac-tors than others and about what causes the variation inthe damping or amplifying of perturbations to popu-lation size. One possibility, that has rarely been ad-dressed, is that genetically based variation in life his-tory expression plays an important role in determiningthe relative influences of endogenous and exogenousfactors and so leads to divergence in population dy-namics. Variation in life history expression can taketwo forms, differences in the mean value of a traitacross populations, and genetically based variation inthe plastic response of that trait to changing environ-mental conditions (e.g., density). There are two generalreasons why this variation should be important. First,field-oriented studies have provided broad interspecificcontrasts between outbreak and non-outbreak speciesor invasive and noninvasive species (among other suchdichotomies such as rare vs. common species) and im-ply that genetically based differences in life historyexpression are responsible for some of the observablevariation in population dynamic patterns (Cappuccino1987, Hanski 1987, Nothnagle and Schultz 1987, Gas-ton and Lawton 1988, Hunter 1991, 1995, Rejmanekand Richardson 1996, Clutton-Brock et al. 1997). Sec-ond, there is considerable genetically based variation

at the individual, population, and species level in howlife history traits are altered plastically in response topopulation density or per capita resource level (Travis1994). The way such traits respond to changes in pop-ulation density can alter population stability (Hutchinson1948, Gurney and Nisbet 1985, Prout 1986), especiallyif the numerical consequences of the life history re-sponse lag significantly behind the initial induction ofthat response (Ebenman 1988, Castillo-Chavez 1989,Cushing and Li 1992, Ginzburg and Taneyhill 1994).There is also good evidence that ecological factors thatinfluence vital rates can also alter the expression of lifehistory traits. For example, predators not only directlyaffect prey survival rates, but their mere presence canindirectly affect the expression of life history traitssuch as the size at metamorphosis or the initiation ofreproduction (Crowl and Covich 1990, Skelly and Wer-ner 1990). Such alterations in the timing of life historytransitions or in traits such as body size, on which manyvital rates depend, can affect subsequent populationdynamics dramatically (Rossiter 1995, Wilbur 1996).Therefore, there is good reason to believe that geneticvariation in life history traits among populations, aswell as genetically based differences in the plasticityof life history trait expression, could contribute to var-iation in population dynamics. In fact, there has beenlittle comparative work on natural populations at theintraspecific level, at which the genetic and environ-mental sources of variation in both life history ex-pression and population dynamics could be dissected(Antonovics and Via 1987). This is not for lack ofintraspecific variation in dynamic patterns (Whittaker1971, Elliott 1987, Belovsky and Joern 1995).

The attention of field-oriented evolutionary biolo-gists has been focused in the other direction, i.e., clar-ifying the role of population density as an evolutionaryforce molding life history traits. The most prominentof these demonstrate that consistent, environmentallybased differences in density regimes will select for ge-netic divergence in life histories (reviewed in Charles-worth 1994, Mueller 1997; examples include Law etal. 1977, Primack and Antonovics 1982, Bradshaw andHolzapfel 1989). However, such changes in life his-tories should themselves produce further divergence inpopulation dynamic patterns, even in a common en-vironment (Antonovics and Via 1987). Some attentionhas also been given to the theoretical problem of wheth-er natural selection for a reduced sensitivity of vitalrates to the effects of population density will cause theevolution of enhanced population stability (reviewedin Travis and Mueller 1989; see also Ferriere and Gatto1993, Doebeli 1993, 1995).

The search for a link between life history expressionand population dynamics has precedent in laboratorystudies. Evidence from studies on a variety of taxasupports the notion that there can be a significant ge-netic component to divergent population dynamics(Drosophila spp.: Dobzhansky and Spassky 1944, Le-

May 2000 291LIFE HISTORIES AND POPULATION DYNAMICS

wontin 1955, Birch et al. 1963, Ayala 1965a,b, 1966,Mueller and Ayala 1981, Marks 1982; E. coli: Luck-inbill 1978; Lucilla cuprina: Nicholson 1960; Tribol-ium spp.: Sokal and Karten 1964, Lloyd 1968, Mc-Cauley and Wade 1980, Goodnight 1988, 1989, Wade1991). In addition, density-dependent selection on Dro-sophila melanogaster populations can produce life his-tory differentiation among populations resulting in ge-netically based differences in patterns of populationdynamics in a common environment (Mueller andHuynh 1994).

While current theories of density-dependent selec-tion predict population level responses (maximizationof the density of the critical age group; Charlesworth1994), they make no specific predictions about whichtraits should respond to density-dependent selection(Mueller 1997). A comparison of genetically based dif-ferences in life history traits between populations thatare expected to differ in the probable degree of densityregulation (and so potentially differ in the degree ofdensity-dependent selection) would provide a valuablecase study of how life histories may evolve as a con-sequence of this difference. Such studies have not beendone outside of the laboratory because most naturalpopulations are not suited for separating the environ-mental and genetic influences on both life history ex-pression and population dynamics.

The least killifish, Heterandria formosa, offers anopportunity to examine this question in both naturaland experimental contexts. This small poeciliid fishoccupies a variety of freshwater habitats in the coastalplain of the southeastern United States where it en-counters widely varying abiotic characteristics and adiverse set of food sources, potential competitors, pred-ators, and pathogens. Previous work on the dynamicsof four natural populations in North Florida uncovereda series of divergent attributes that implicated a strongcausal link between life history expression and popu-lation dynamics (Leips and Travis 1999). Between themost divergent populations, the Wacissa River andTrout Pond, densities were up to seven fold higher andfluctuated to a much greater extent in the Wacissa Riverthan in Trout Pond, female body size was 35% smaller,and, after adjustment for female body size differences,brood size was 33% smaller and offspring size 45%larger. The Trout Pond population appeared to have thegreatest capacity for rapid increase, given the relativedifference in female body size (which is positively cor-related with brood size) and greater size-specific broodsize. Despite this capacity, it consistently displayed thelowest population densities.

In this paper, we present the results of an experi-mental study designed to investigate the extent towhich the differences in the dynamics of populationsof H. formosa, such as those seen between the WacissaRiver and Trout Pond, may result from geneticallybased differences in life history traits. We also explorehow the general and population-specific sensitivity of

traits to density contribute to the stage structure andnumerical behavior of populations. We address fourbroad questions using the Wacissa River and TroutPond populations: (1) Are there genetically based dif-ferences in the life history traits of fish from the twopopulations? (2) If so, do the genetically based differ-ences in life history expression produce differences inpopulation dynamics and stage structure in a commonenvironment? (3) Which traits have the greatest influ-ence on population dynamics? (4) How do changes indensity affect the phenotypes of individual traits that,in turn, govern the birth and death rates of the popu-lation? We use the answers to these questions to elu-cidate the role of life history expression on the dynam-ics of H. formosa populations, the stabilizing effect oflife history plasticity on population dynamics, and thepresence of any trade-off between the capacity for rapidincrease at low densities and the capacity for sustainedpersistence at high densities.

MATERIALS AND METHODS

Natural history

The least killifish, Heterandria formosa, is a small,live-bearing topminnow in the Poeciliid family. Theyare native to the southeastern coastal plain of the UnitedStates and occur in freshwater bodies throughout Flor-ida and along the coastal regions from Louisiana toNorth Carolina. Small body size and visual indicatorsof sexual maturity make this an ideal species for fieldand laboratory studies. In optimal lab conditions, fe-males reach sexual maturity at 8–10 mm (standardlength) in ;30–40 d; maturity is indicated by the ap-pearance of a small black dot on the anal fin (Fraserand Renton 1940). Males mature at larger body size(10–14 mm), take longer to mature (40–50 d), andmaturity is indicated by development of the gonopo-dium, a modified anal fin used for sperm transfer (Con-stanz 1989).

Individual females are prolific breeders, producingoffspring continuously from March to October in manynatural populations in North Florida (Colson 1969,Leips 1997). Females exhibit superfetation, in whichseveral different broods of offspring are carried si-multaneously, each at different stages of development.This allows for short intervals between successivebroods (from 7–21 d; Travis et al. 1987, Leips 1997)and results in several overlapping generations perbreeding season. Females are matrotrophic; embryosare nourished by direct provisioning of nutrients fromthe female (Fraser and Renton 1940, Scrimshaw 1944),enabling changes in resource availability to be trans-lated directly to developing offspring.

Overview of experiment

We followed the numerical dynamics and life historytrait expression of H. formosa from replicated exper-imental populations composed of one of five different


TABLE 1. Breeding design.

ParentalsF1 off-spring F1 crosses

F2 off-spring F2 crosses F3 stocks

Expected F3

genotypes†

TT 3 TT TT TT 3 TT TT TT 3 TT TTTT 100% TT

TT 3 TW,TT 3 WT

TTTW 50% TT,50% TW

TT 3 WW TW TW 3 TW TTWW 25% TT,50% TW,25% WW

TW 3 WW,WT 3 WW

TWWW 50% WW,50% TW

WW 3 WW WW WW 3 WW WW WW 3 WW WWWW 100% WW

Note: TT 5 Trout Pond, WW 5 Wacissa River, TW 5 hybrid (For the F2 crosses, WT 5hybrid produced from a Wacissa dam and Trout sire, TW 5 hybrid produced from a Trout damand Wacissa sire).

† Expected genotypic composition of F3 stock individuals.

genetic stocks. We created these stocks from laboratorycrosses of individuals from Trout Pond and WacissaRiver. Each replicate population was put in an exper-imental mesocosm and followed for an estimated 4–6overlapping generations (defined as the minimum pre-dicted time between the birth of an individual and thebirth of its offspring; Begon et al. 1986). Three rep-licates of each population were run in each of twophases (the first set, phase I, was started in the summerand the second set, phase II, was started in the fall).We did a complete census of all populations every fiveweeks during the breeding season and monitored num-bers, sex and stage (juveniles and adults) of individuals.At each census we also drew a sample of females foranalysis of reproductive traits.

Creation of genetic stocks

We created stock fish in the laboratory for two rea-sons. First, by rearing stocks in a common environ-ment, we minimized the influence of nongenetic ma-ternal effects that could result from differences in theenvironment between the Wacissa River and TroutPond. Second, by producing genetic stocks that variedlinearly in the proportion of alleles drawn from eachpopulation of origin, we could compare the similarityof population trajectories against the genetic similarityof various stocks. We could also test for the additivityof genetic effects and the significance of nonadditiveeffects to evaluate whether the nature of genetic in-heritance of particular traits had an observable signa-ture on population trajectories (Falconer and Mackay1996).

The five stocks used in the experiment were initiatedwith 112 gravid females and 60 males obtained fromeach population (Trout Pond and the Wacissa River)between 16–19 September 1993. Trout Pond is a 5-hapond in the Apalachicola National Forest, ;10 km westof Tallahassee, Florida, USA. The Wacissa River is aspring-fed river in Jefferson County, ;35 km east ofTallahassee (see Leips and Travis [1999] for a detaileddescription of the differences in these sites).

Breeding individuals were maintained in five gallonaquaria; six females and two or three males per tank.Tanks were arranged on shelves in a temperature- andlight-controlled laboratory (maintained at 308C, 14 : 10light : dark cycle). The laboratory temperature and daylength reflected the typical condition experienced by nat-ural populations during the breeding season (Leips1997). Newborn offspring were removed and placed innew tanks every two weeks to minimize the chance ofinbreeding with parents. As females in the offspringtanks matured, they were moved to separate tanks toprevent sib mating. As an additional precaution to min-imize inbreeding, tank pedigrees were maintained forall offspring born in a given tank. First generation in-dividuals (F1) and subsequent generations were onlycrossed with individuals that had never shared a tank.

In the first step, F1 individuals were those born ofgravid, field-caught females or produced by crossingWacissa River (WW) males with WW females andTrout Pond (TT) males with TT females in the labo-ratory. In December 1993, F1 TT and WW individualswere either crossed with other F1 individuals from thesame stock, or crossed between stocks (Table 1). SixtyF1 females and 30 F1 males were used in the WW andhybrid (TW) crosses. Trout Pond females producedfewer offspring in the laboratory, so only 42 F1 femalesand 21 F1 males were used to produce the next gen-eration of pure TT stock. The F1 hybrid crosses wereproduced by an equal number of matings between Wa-cissa males and Trout Pond females, and Wacissa fe-males and Trout Pond males. This crossing procedureproduced four different genetic stocks of F2 offspring:TT, WW, and two types of hybrids, TW (from the TTfemale 3 WW male cross) and WT (from the WWfemale 3 TT male cross). In April 1994, pure F3 off-spring (TT and WW) were produced by crossing F2

individuals from the same stock (e.g., TT 3 TT). Threetypes of F3 hybrid stocks were also produced. The firsttype was produced by crossing F2 hybrid individualsto other hybrids (e.g., TW 3 TW), using all possible


combinations of male and female parent type. CrossingF2 offspring of the TT and WW stocks to the F2 hybridstocks (TT 3 WT and TT 3 TW, WW 3 WT and WW3 TW) produced the two other types of hybrid.

This breeding design created two pure stocks, andthree different ‘‘hybrid’’ stocks that differed in a reg-ular way in the proportion of genes from one populationor the other (TTTT, TTTW, TTWW, TWWW, WWWW).The crossing scheme was designed to produce hybridstocks with the same proportion of ‘‘heterozygous’’ (or‘‘heterodemic’’) loci (50% TW), that differed in theproportion and/or identity of ‘‘homozygous’’ (or‘‘homodemic’’) alleles from the Wacissa River andTrout Pond populations. Thus, phenotypic differencesamong the three hybrid stocks should not have resultedfrom different mean levels of heterozygosity acrossloci, but instead from specific effects of the alleles oforigin, their interaction with the rest of the genome,and stock-specific differences in gene expression in agiven environment.

Initiation of experimental populations

Experimental populations were started in Nevr-rustpolyethylene cattle watering tanks (850 L maximumvolume) with 25 juveniles, haphazardly chosen fromthe pooled F3 offspring from a given stock. Thirty cattletanks were used, six tanks per genetic stock. Tanks werelocated in a former agricultural field at the Florida StateUniversity Mission Road Greenhouse Facility, Talla-hassee, Florida, United States.

We executed the experiment in two temporal phasesto avoid confounding seasonal differences in the ex-pression of individual traits with responses to density.In phase I, we started three replicate populations ofeach stock in July 1994. We started the second set ofreplicates (phase II) forty days later (September 1994).This staggered design allowed us to measure pheno-typic traits of each stock at the same time under a rangeof densities. One replicate of WWWW stock was ex-tirpated after September and was subsequently restartedwith 25 WWWW offspring. This tank was subsequent-ly counted as a phase II tank, leaving only two repli-cates of the WWWW stock in phase I but four replicatesin phase II. All populations were followed until August1995.

Cattle tank setup

Aquatic communities were established and main-tained in each tank to mimic many aspects of the naturalhabitat of Trout Pond. Cattle tanks were filled with wellwater two months before the initiation of each phaseof the experiment. Movable standpipes were placed ineach tank to allow drainage of excess water (the open-ing of each standpipe was screened to prevent the lossof fish) and adjusted to ensure that each tank containedthe same water volume throughout the experiment. Thewater level in each tank was subsequently maintainedby copious rainfall. The pH was adjusted to 4.7 (and

maintained throughout the experiment between 4.7 and5.3 reflecting the natural pH of Trout Pond; Leips 1997)with a 10% solution of sulfuric acid. Approximately20 L of leaf litter and pine needles, 100 g of PurinaRabbit Chow, and 500 mL of suspended plankton fromTrout Pond were added to each tank. In addition, float-ing and submerged aquatic vegetation from Trout pondand potted wetland plants (Sagittaria lancifolia) wereadded to each tank to provide additional cover. Tankswere covered with Lumite screens to prevent coloni-zation by insects and amphibians. The resulting aquaticcommunities were allowed to sit undisturbed until ini-tiation of the experiment.

During the course of the experiment, ;1 L of con-centrated plankton (collected using a 64 micron plank-ton net) from Trout Pond was added to each tankmonthly. Along with plankton, invertebrate predators(e.g., fishing spiders, Dolomedes triton, and variousspecies of Libellulid dragonfly larvae) and potentialcompetitors (freshwater snails, Physella sp. and Heli-soma sp.) were inadvertently introduced into the tanks.At each census, all predators and competitors that werecaught were removed.

Sampling

Populations were completely censused every fiveweeks by repeated seining (using a 2 mm mesh seinethat completely spanned the depth and diameter of eachcattle tank) until fewer than five individuals were cap-tured in three consecutive samples. Adult males, adultfemales, and immature individuals were sorted by vi-sual inspection. Each different class was placed sep-arately in water-filled plastic bins containing a metricruler where each group was photographed. Photos wereused to obtain total census counts for each category ofeach stock. Body size distributions of males and fe-males were obtained by measuring individuals fromphotographs using an image analysis system. Individ-uals were measured from the dorsal view, from the tipof the mouth to the area where the body tapered to apoint (a close approximation to standard length) (J.Leips, unpublished data).

The minimum body size of adult males and adultfemales from each tank were used as estimates of thesizes at sexual maturity. Male poeciliids grow at re-duced rates after attaining sexual maturity (Snelson1989, Trexler et al. 1990), and so the minimum sizeshould be a fairly precise estimate of the actual size atmaturity (there is no substantial male size polymor-phism in Heterandria formosa like that seen in Xiph-ophorus spp. and Poecilia spp.). Females continue togrow after sexual maturity, albeit slowly; the differencebetween the mean size of adult females and the min-imum size of adult females offers a crude estimate ofa combination of female longevity and postmaturationgrowth (under the assumption that there is no substan-tial seasonal variation in the size at maturity).

At each census, ;10% of the total number of females


FIG. 1. The natural log of population size trajectories ofall stocks of (A) phase I populations and (B) phase II pop-ulations from July 1994 to August 1995. Shown are meanvalues 61 SE.

in each tank were sampled to measure the proportiongravid, the number of broods carried simultaneously (ameasure of superfetation), the number and mass of em-bryos in each developmental stage, and the overall pro-portion of biomass devoted to reproduction. Femaleswere euthenized by an overdose of MS 222 (3-ami-nobenzoic acid ethyl ester) and preserved for dissectionin 10% formalin. A total of 559 females were examinedin this way throughout the course of the experiment.

Reproductive traits were determined by dissectingall developing embryos and ovarian tissue from pre-served females. Embryos were counted and scored fortheir developmental stage following Reznick (1981).Female body tissue, ovarian tissue, unfertilized eggs,and similar-staged embryos were freeze dried sepa-rately in microcentrifuge tubes for 24 h. Dry mass wasmeasured to the nearest 0.01 mg on a Cahn C-31 mi-crobalance.

We used the number and mass of stage-4 embryos(the penultimate pre-parturition stage) as a surrogatefor the number and size of offspring per brood. Al-though embryos in the most advanced developmentalstage would have been a better surrogate for thesetraits, stage-5 embryos were uncommon, likely indic-ative of the short duration of time that embryos remainin this stage before birth (Travis et al. 1987).

Estimation of adult mortality and juvenilerecruitment rates into the adult population

The effects of density on adult survival rates andrecruitment rates of juveniles into the adult population(the proportion of juveniles that matured between cen-suses) were estimated over three periods (November toDecember, May–June, and June–August) by a mark–recapture technique using the fluorescent dye, calcein.Calcein binds to calcium in tissues and can be seen infin rays and scales using an epifluorescence micro-scope. This dye had no effect on survival or maturationrates of H. formosa in a preliminary study and was stillvisible for up to five weeks in the laboratory (J. Leips,C. T. Baril, F. H. Rodd, J. Travis, and D. N. Reznick,unpublished manuscript).

In each tagging event, all adults from four of the sixreplicate tanks of each stock were tagged. At the fol-lowing census, all adults from each tank were returnedto the lab. Tagged and untagged individuals were sep-arated (see Leips [1997] for details of this procedure),photographed, and returned to the tanks on the follow-ing day.

The number of unmarked adults in each sample pe-riod was used as an estimate of the recruitment rate ofjuveniles into the adult population (indicating the num-ber of juveniles that had matured during the periodbetween censuses). The adult mortality rates were cal-culated as the difference in numbers of marked adultsbetween censuses. Because checking all adults fromeach population for marked individuals took severaldays, the time interval between censuses was not thesame across all tanks. We converted the observed ratesto a 35-d period for statistical comparison among stocksusing the standardization procedure outlined in Krebs(1989:413).

Statistical analyses

All population and demographic parameters werecalculated separately for each phase of the experiment.Population growth rates were calculated during the ini-tial period of population growth (the period immedi-ately following the initiation of each population) as thedifference between initial and final numbers of indi-viduals, divided by the time elapsed between censuses.The period of initial population growth was determinedby visual inspection of the population trajectories (cal-culated from the July to September 1994 census totalsfor phase I, and from the September to November 1994census totals for phase II, Fig. 1). We analyzed differ-


ences among stocks in population growth rate with AN-OVA (Snedecor and Cochran 1980). Subsequent pop-ulation trajectories were analyzed using a repeated-measures profile analysis of variance (Simms and Bur-dick 1988). The stage structure (proportion of the entirepopulation that was immature), and sex ratio (propor-tion of adults that were female) were also analyzedusing a repeated-measures profile analysis. Exceptwhere noted, all population variables were transformedto natural logs prior to statistical analysis to meet theassumptions of ANOVA (we examined the residualsfrom the analyses to check the validity of the trans-formations).

Analysis of population trajectories after the initialgrowth phase was complicated by extirpations in manytanks in the period from April to June. The prolificgrowth of a floating aquatic fern, Salvinia rotundifolia,occurred in all tanks during this period. Complete cov-erage of the water’s surface caused the extirpation of14 fish populations, perhaps a result of oxygen deple-tion. Five of the 14 populations lost were from phaseI and the remaining nine from phase II. Extirpationswere not evenly distributed across stocks. Three TTTT,one TTTW, five TTWW, three TWWW, and twoWWWW tanks were lost due to the Salvinia bloom.As a consequence, data taken from the TTWW stocksafter May must be interpreted cautiously because onlya single replicate remained after this time. An addi-tional consequence is that all profile analyses had tobe restricted to censuses from September to April forthe phase I tanks, and from October to April for phaseII tanks.

Survival rate data were arcsine transformed and re-cruitment rate data square root transformed and ana-lyzed using ANCOVA, with stock as the categoricalvariable and the density at the previous census periodas the covariate. Total density at the previous censusperiod (indicating a lag time of ;35 d) was used asthe covariate because it consistently explained a greaterproportion of variance than the effects of concurrentdensity or that of previous censuses (determined byseparate regressions of the traits of interest against thedensity at the concurrent and two prior censuses). Datafrom both phases of the experiment (populations ini-tiated in July and September) were pooled for theseanalyses. This pooling of data ignores potential blockeffects due to time (so results should be interpretedwith caution), but it allowed us to examine survivaland recruitment rates over a range of population den-sities that spanned two orders of magnitude; this cer-tainly enhanced our power to detect the effects of stock,density, and any stock by density interaction.

Analyses of the mean and minimum body sizes ofadult males and females among stocks were carried outon pooled data from both phases at each census withANCOVA. Stock was the categorical variable and pre-vious density the covariate (except for the census im-

mediately following the initiation of the population,where the concurrent density was used).

Reproductive traits of females were analyzed withANCOVA with stock as the categorical variable andthe dry mass of females as the covariate. Total massof reproductive tissue, embryo size, and female bodysize were transformed to natural logs to meet the as-sumptions of the statistical analyses. Counts of thenumber of broods carried simultaneously and offspringnumber were transformed to the ([ ] 1Ïcount 1 1[ ]) as per Snedecor and Cochran (1980). In theÏcountfirst set of analyses (September) during the initial pe-riod of population growth, reproductive traits were onlymeasured on a single female from each tank (threereplicates per stock). This was necessary because, inmost cases, no more than 10 reproductive females oc-curred in any given tank (these were the older femalesfrom the initial cohort); all other females were newlymatured. Sample sizes from subsequent periods varied,depending on the number of mature females in the pop-ulation.

Tank extirpations resulted in insufficient replicationto analyze statistical differences among stocks in theabove traits at separate censuses from March throughAugust. Instead, we pooled the data from these fourcensuses and used the tank mean values as replicates.To incorporate the influence of different conditions inthese tanks across the months that were sampled, wefirst calculated the adjusted mean value of each traitfor a given tank, adjusting for the effects of femalebody size using ANCOVA. These adjusted values werethen used to examine the effects of stock and densityon the trait of interest in ANCOVA, using density asa covariate.

When stocks differed significantly for any of thetraits examined, patterns of inheritance among stockswere analyzed with orthogonal contrasts (Judd andMcClelland 1989). Because the crossing design pro-duced stocks that differed in a regular, ordered way inthe proportion of genes from one or the other purestocks, linear contrast codes were assigned to stockcategories in the following way to extract the additivecomponent of trait inheritance: (contrast code followsstock) TTTT, 22; TTTW, 21; TTWW, 0; TWWW, 1;WWWW, 2.

All ANOVA and ANCOVA analyses were done us-ing SYSTAT (Wilkinson 1990). Type III sums ofsquares were used to account for unequal sample sizes.When significant differences among stocks were de-tected, post hoc comparisons were made using the Tu-key-Kramer adjustment of Tukey’s hsd test for unequalsample sizes (Day and Quinn 1989).

RESULTS

Population and demographic parameters

The initial population growth rate of pure Trout Pondstock exceeded that of the pure Wacissa River stock


FIG. 2. The proportion of the total population composedof immature individuals of all stocks of (A) phase I popu-lations and (B) phase II populations from July 1994 to August1995. Shown are mean values 61 SE.

by 70% in the phase I tanks (Fig. 1A; F4, 9 5 4.05, P5 0.04, r2 5 0.64). Hybrid stocks had intermediaterates of population growth; the rank order closelymatched the genetic resemblance predicted by thebreeding design (Fig. 1A). The subsequent trajectoryof the total population size appeared to reach equilib-rium after the initial phase of population growth. Adultfemale and immature numbers of all stocks were rel-atively stable throughout this period, while male num-bers exhibited more variability (Leips 1997).

Initial population growth rates of phase II tanks werelower than those of phase I, and not significantly dif-ferent among stocks (Fig. 1B; F4, 5 5 0.61, P 5 0.67).The pattern of results was consistent with those ofphase I (the pure stocks bracketed the range of growthrates with TTTT having the highest rate and WWWWthe lowest), but low growth rates and high variance inphase II replicates probably obscured genetic differ-ences among stocks. One of the populations initiatedin September was extirpated before the October census,while three others decreased in size, producing nega-tive growth rates. Because these populations were start-ed late in the breeding season, (for normal breedingphenology see Travis et al. [1987] and Leips [1997]),most individuals probably never initiated reproductionor ceased shortly after they started.

The final population size of stocks did not differ ineither phase (phase I Wilks’ lambda 5 0.05, P 5 0.33,phase II Wilks’ lambda 5 0.06, P 5 0.11). Populationtrajectories of phase II tanks were much more variablethan those of phase I and never appeared to stabilize(Fig. 1B). This instability appeared to result from ex-treme variation within each population class (females,immatures, and males) during this period (Leips 1997).While the high variance in phase II stocks may causethe lack of significance, there is no indication in thephase I data that the carrying capacities of the stockswere substantially different.

The proportion of the population comprised of im-mature individuals in the WWWW stock was 20% low-er than for all other stocks during the initial growth ofphase I populations (Fig. 2A; F4, 95 4.45, P 5 0.03).This stock also had a 30% lower proportion immaturethan the TTTT stock in phase II (Fig. 2B), but thisdifference was not significant. Stocks did not differ inthe proportion immature after the initial period of pop-ulation growth (phase I, Wilks’ lambda 5 0.03, P 50.17, phase II Wilks’ lambda 5 0.02, P 5 0.62).

The numbers of adults were biased toward femalesthroughout the experiment (Figs. 3A, B); the mean pro-portion of adults that were female ranged from 0.55 inSeptember 1994 to as high as 0.69 in March 1995. Ingeneral, the female bias was higher in the nonbreedingseason. The number of extirpations in the last third ofthe experimental period makes it difficult to interpretthe patterns further with any confidence. During theinitial period of population growth, the TTTT andTTTW stocks appeared more female biased than the

others. However, the variance among replicates washigh and there were no significant differences in sexratio among stocks (phase I, Wilks’ lambda 5 0.06, P5 0.10, phase II, Wilks’ lambda 5 0.30, P 5 0.51).

Adult survival

Mean adult survival rates among stocks from theNovember to December census ranged from 0.79 to0.96 for males and from 0.80 to 0.90 for females (Table2). Male and female survival rates declined equallywith density for all stocks; although the slope of therelationship between density and adult survival wascomparable for males and females (Figs. 4A, B), den-sity accounted for more of the variation in female sur-vival (41%) than that of males (27%; Table 3).

Adult survival rates were highly variable amongpopulations from May to June. Male survival rangedfrom 0.21 to 1.0 and female survival from 0.32 to 1.0.


FIG. 3. The proportion female of the total number ofadults of all stocks of (A) phase I populations and (B) phaseII populations from July 1994 to August 1995. Shown aremean values 61 SE.

FIG. 4. The proportion of (A) males and (B) females thatsurvived from November to December as a function of totalpopulation density in November.

TABLE 2. Mean survival rates of males and females of each stock 6 1 SE using pooled datafrom both phases.

Stock

Nov–Dec

Survival rate N

Apr–Jun

Survival rate N

Jun–Aug

Survival rate N

A) MalesTTTTTTTWTTWWTWWWWWWW

0.82 6 0.100.83 6 0.050.84 6 0.080.79 6 0.030.96 6 0.04

44443

0.26 6 0.120.42 6 0.14

1.00.21 6 0.090.59 6 0.12

34133

0.44 6 0.220.25 6 0.12

0.390.66 6 0.130.52 6 0.40

24132

B) FemalesTTTTTTTWTTWWTWWWWWWW

0.90 6 0.040.89 6 0.050.86 6 0.090.89 6 0.060.80 6 0.15

44443

0.63 6 0.370.45 6 0.11

1.00.32 6 0.030.65 6 0.18

23133

0.53 6 0.130.50 6 0.10

0.420.84 6 0.040.40 6 0.04

24132

Note: N 5 sample size (number of replicate populations).


TABLE 3. Analysis of covariance summary statistics for sur-vival rates of males and females by season using pooleddata from both phases.

Dates Sex Source df F P r2

Nov–Dec Male DensityStock

1, 134, 13

6.820.08

0.02NS

0.27

Female DensityStock

1, 134, 13

12.851.05

,0.01NS

0.41

May–Jun Male DensityStock

1, 63, 6

0.010.07

NS

NS

Female DensityStock

1, 63, 6

15.922.72

,0.010.12

0.51

Jun–Aug Male DensityStock

1, 73, 7

4.440.05

0.08NS

Female DensityStock

1, 73, 7

3.567.0

0.110.02 0.69

Note: The statistic r2 is the coefficient of determination.

TABLE 4. Mean recruitment rates of males and females of each stock 61 SE using pooled datafrom both phases.

Stock

Nov–Dec

Recruitment rate N

Apr–Jun

Recruitment rate N

Jun–Aug

Recruitment rate N

A) MalesTTTTTTTWTTWWTWWWWWWW

0.03 6 0.020.07 6 0.030.03 6 0.010.05 6 0.010.15 6 0.06

44353

0.32 6 0.120.29 6 0.080.25 6 0.180.28 6 0.080.23 6 0.16

23342

0.26 6 0.160.06 6 0.05

0.100.19 6 0.080.21 6 0.12

24133

B) FemalesTTTTTTTWTTWWTWWWWWWW

0.27 6 0.120.29 6 0.150.09 6 0.070.13 6 0.060.30 6 0.10

44353

0.38 6 0.240.36 6 0.180.33 6 0.330.23 6 0.100.34 6 0.05

23342

0.33 6 0.150.12 6 0.04

0.330.23 6 0.060.38 6 0.12

24133

Note: N 5 number of replicate populations from which the estimate was made.

Low survival rates probably resulted from the com-bined effects of high population density and the prolificSalvinia growth that occurred in most tanks. Neitherstock nor density significantly affected male survivalin this period, but female survival was negatively as-sociated with density (Table 3). Although the range ofdensities in this period was comparable to that of theprevious period (November range: 6–577; April range:13–562), the weakening of the density effect here sug-gests that other factors (e.g., natural deaths from anaging population, or a history of high density in thetanks) minimized the more proximate effects of densityon survival.

Adult survival rates were also highly variable duringthe period from June to August. Mean survival ratesranged from 0.25 to 0.66 for males and from 0.40–0.84for females (Table 2). Neither stock nor density signif-icantly affected male survival during this period (Table3). Density did not affect female survival, but for thefirst time, the stock effect was significant. Densityamong populations during this period ranged from 153–1045 individuals. A post hoc comparison of treatmentmeans indicated that the adjusted survival rate of the

WWWW stock (0.37 6 0.11, n 5 2) was significantlylower than that of the TWWW stock (0.96 6 0.09, n 53); in this case, stock differences explained 69% of thetotal variance. Despite these results, it is uncertainwhether this difference is truly due to genetic differencesbetween stocks. Sample sizes were small, and in no othercensus had the stock effect been significant.

Juvenile recruitment

The mean proportion of juveniles recruited into theadult population between November and Decemberranged from 3–15% for males and from 9–30% forfemales (Table 4). Recruitment rates for both sexesdeclined with increased density but there were two no-table distinctions between the sexes in this relationship.First, the effect of density accounted for much less ofthe variance in male (r2 5 0.28) than female (r2 50.72) recruitment rates (Table 5); this result is an echoof the stronger density dependence in female survivalrates. Second, the slope of the relationship of recruit-ment rate to density is substantially steeper for femalesthan for males (Figs. 5A, B); this distinction is a sig-nificant one (F1, 33 5 6.89, P 5 0.01). The net effectwas that female recruitment rates were much higherthan those for males at lower densities but comparableto those for males at the higher densities. Stocks didnot differ in recruitment rates after the effects of den-sity had been taken into account.

Recruitment rates were higher in the May–June pe-riod than they were from November–December period(Table 4). In this period, unlike the previous one, therates for the genders were comparable (25% to 32%for males, 23% to 38% for females). Neither stock nordensity significantly affected male or female recruit-ment rates (Table 5). The higher recruitment levels inthis period, despite the higher mean densities, mostlikely reflect the enhanced productivity during this sea-son and the resulting enhancement of juvenile survivaland growth.

Recruitment rates from June to August ranged from


TABLE 5. Analysis of covariance summary statistics for recruitment rates of juvenile malesand females into the adult population by season using pooled data from both phases.

Dates Sex Source df MS F P r2

Nov–Dec Male DensityStock

1, 134, 13

0.0530.014

8.662.22

0.01NS

0.28

Female DensityStock

1, 134, 13

0.6700.028

64.742.89

,0.010.06

0.72

May–Jun Male DensityStock

1, 63, 6

0.0200.007

0.650.24

NS

NS

Female DensityStock

1, 63, 6

0.0440.025

0.810.46

NS

NS

Jun–Aug Male DensityStock

1, 73, 7

0.2090.008

9.830.37

0.02NS

0.87

Female DensityStock

1, 73, 7

0.1220.130

13.551.42

,0.01NS

0.85

TABLE 6. Analysis of covariance of mean female body sizeusing pooled data from both phases.

Month Source df F P r2

Sep

Nov

Dec

Mar

May

Jun

Aug

DensityStockDensityStockDensityStockDensityStockDensityStockDensityStockDensityStock

1, 64, 61, 214, 211, 244, 241, 234, 231, 214, 211, 123, 121, 103, 10

5.171.70

47.991.02

52.640.53

10.670.64

32.392.0975.402.051.860.57

0.06NS

,0.01NS

,0.01NS

,0.01NS

,0.01NS

0.04NS

0.20NS

0.66

0.66

0.81

0.52

0.23FIG. 5. The proportion of (A) male and (B) female ju-veniles in November recruited to the adult stage in Decemberas a function of total population density in November. Pleasenote that the y-axes of panels (A) and (B) are not the same.

6% to 26% for males and from 12% to 38% for females(Table 4). Just as in the November–December period,and unlike the May–June period, increasing densitysignificantly reduced the recruitment rates. However inthis period the strength of the effect was comparable

in the sexes (male r2 5 0.87, female r2 5 0.85) andno significant difference between the slopes for eachgender was detected.

It is possible that the fluorescent tag faded betweencensuses; this would underestimate the number ofmarked adults in the second census and result in anoverestimate of recruitment rate and an underestimateof survival rate. If this had occurred, the estimatedrecruitment and survival rates would not be indepen-dent of each other and would exhibit an inverse cor-relation across replicates. This was generally not thecase. A single negative correlation was observed formale survival and recruitment from May to June (r 52 0.39, P 5 0.03). On all other dates the correlationwas positive and not significant (Pearson product-mo-ment correlation coefficients ranged from 0.08 to 0.74).

Body size

The mean body size of mature females decreasedwith increasing density in five of the seven censuses(r2 ranged from 0.23 to 0.81), but did not differ amongstocks (Table 6) after the effects of density were takeninto account.

The minimum body size of mature females also de-clined with increased density in six of the seven cen-suses, and density explained from 45–81% of the var-


TABLE 7. Analysis of covariance of minimum female bodysizes using pooled data from both phases.


Sep

Nov

Dec

Mar

May

Jun

Aug


1, 64, 61, 154, 151, 194, 191, 234, 231, 214, 211, 113, 111, 103, 10

5.611.16

29.550.98

33.460.71

118.880.80

47.204.045.501.208.300.01

0.06NS

,0.01NS

,0.01NS

,0.01NS

,0.010.020.04NS

0.02NS

0.61

0.61

0.81

0.560.190.50

0.45

FIG. 6. Minimum female body size (mm) of all stocks of(A) phase I populations and (B) phase II populations fromJuly 1994 to August 1995. Shown are mean values 61 SE.

TABLE 8. Analysis of covariance of mean male body sizeusing pooled data from both phases.


Sep

Nov

Dec

Mar

May

Jun

Aug

DensityStockDensityStockDensityStockDensityStockDensityStockDensityStockD 3 SDensityStock

1, 84, 81, 184, 181, 224, 221, 234, 231, 184, 181, 83, 83, 81, 93, 9

0.1261.362

30.480.67

50.440.77

39.720.12

30.950.734

16.84.604.544.101.24

NS

NS

,0.01NS

,0.01NS

,0.01NS

,0.01NS

,0.010.040.040.07NS

0.59

0.67

0.63

0.60

0.320.260.26

iance (Table 7, Figs. 6A, B). However, density increas-es reduced the minimum size to a much lesser extentthan they reduced the mean female size (not shown).This suggests that the size at maturity is less affectedby density than are postmaturation growth and/or sur-vival rates. There was no significant difference in min-imum size among stocks except in the May censusperiod, where the adjusted minimum size of WWWWfemales was 12–14% larger than the females of TTWW,TTTW, and TTTT stocks. In fact, the minimum size ofWWWW females tended to be larger than that of TTTTfemales throughout the experiment, although that dis-tinction was significant at only one census.

The mean size of mature males was extremely var-iable among stocks, ranging from 10.4 mm to 15.7 mm,and decreased significantly with increasing density (Ta-ble 8). The strength of the density effect, as indicatedby the coefficients of determination, was comparablebetween the sexes. However, the slope of the decreasein size with increasing density was shallower in malesthan in females, which indicates that male size is lesssensitive to density effects than female size (notshown). Stock differences in mean male size were notsignificant except in June, when a significant stock-by-density interaction suggested that some stocks wereresponding differently to changes in density. Becausethe stocks were not different in any other census, it isunlikely that the June results reflect genetic differencesin body size.

The minimum size of mature males ranged from amean of 9.0 to 13.8 mm among stocks, and declinedthroughout the course of the experiment (Figs. 7A, B).Increasing density significantly reduced minimum size,and explained from 34% to 93% of the total variance(Table 9). However, as with mean male size, the effectof density on the minimum male size was much smallerthan the effects in females. There were no significantdifferences among stocks.

Female life history traits

The proportion of biomass devoted to reproductionwas highly variable, ranging from a low of 1% in the


FIG. 7. Minimum male body size (mm) of all stocks of(A) phase I populations and (B) phase II populations fromJuly 1994 to August 1995. Shown are mean values 61 SE.

TABLE 9. Analysis of covariance of minimum male bodysize using pooled data from both phases.


Sep

Nov

Dec

Mar

May

Jun

Aug


1, 84, 81, 184, 181, 224, 221, 234, 231, 184, 181, 113, 111, 93, 9

0.000.35

12.882.18

91.630.46

55.990.38

30.930.738.651.133.662.97

NS

NS

,0.01NS

,0.01NS

,0.01NS

,0.01NS

0.013NS

0.090.09

0.83

0.79

0.93

0.60

0.34

November and December samples to a high of between13% and 20% in September (Leips 1997). Reproductivemass increased significantly with female size (F1, 9 55.27, P 5 0.05, r2 5 0.37) but stocks were not differentin this relationship (F4, 9 5 1.18, P 5 0.38). In the Maythrough August period, the mean reproductive tissuemass declined with increased density (F1, 49 5 10.40,P , 0.01) and density explained 14% of the variance.

The mean number of broods carried simultaneouslyvaried with season and density, ranging from 0 to 4.3broods (Leips 1997). Neither female size (F1, 9 5 1.74,P 5 0.22), nor stock (F4, 9 5 2.19, P 5 0.15) affectedthe number of broods carried during the period of initialpopulation growth. This is not surprising because fe-male sizes did not differ among stocks and most fe-males carried the maximum number of broods in thisperiod. In each census from April to August, large fe-males carried significantly more broods than small fe-males (April: F1, 90 5 10.75, P , 0.01; June: F1, 44 5

22.31, P , 0.05; August: F1, 41 5 9.94, P , 0.01), andbody size explained 6% to 20% of the variation. In-creased density had a significant negative effect onbrood numbers, independent of its effect on female size(F1, 44 5 7.42, P , 0.01, r2 5 0.13), but there was nodifference among the stocks (F4, 44 5 1.5, P 5 0.22).These results are similar to the patterns observed infield data (Leips and Travis 1999).

The mean mass of stage-4 embryos (the penultimatestage before birth) differed dramatically among stocksduring the initial period of population growth (Septem-ber census, Fig. 8A; F4, 9 5 7.29, P , 0.01, r2 5 0.70)although the total mass devoted to stage-4 embryos didnot differ (F4, 9 5 0.82, P 5 0.54). The mean embryomass of the WWWW and TTWW stocks were as muchas 50% larger than the that of the TTTT stock. Analysisusing linear contrast codes indicated that both additiveand nonadditive genetic effects were significant (ad-ditive effects: F1, 9 5 5.92, P 5 0.04; nonadditive ef-fects: F3, 9 5 7.67, P 5 , 0.01), accounting for 14%and 56% of the variance respectively. The mean off-spring mass for the WWWW stocks was comparableto values seen in the natural population in the WacissaRiver, whereas the mean offspring mass of the TTTTstocks was about 10%–15% below values seen in thenatural population in Trout Pond (Leips and Travis1999).

During the period when densities were very high(May–August), the mean embryo mass did not differamong stocks (F4, 38 5 1.82, P 5 0.14) although a powercalculation (Zar 1984) indicated that we had little pow-er to detect stock-related differences in embryo size.Mean embryo mass was not affected by density duringthis period (F1, 38 5 1.82, P 5 0.11). The pattern ofinheritance among stocks was remarkably consistentwith the pattern observed in September (compare Figs.8A and B) but the mean embryo mass was smallerduring the May to August period and the range in off-spring mass among stocks was smaller (the range wasfrom 0.33 to 0.53 mg, a 37% reduction). The valuesobserved at this time were also ;15%–20% below val-


FIG. 8. Mean dry mass of stage-4 embryos of each stockduring (A) September (the period of growth of phase I pop-ulations) and (B) May–August. Shown are mean values and95% CI.

ues in the respective natural populations. The smallerrange in the experimental stocks in this period wascaused by a decrease in the mean offspring size of thestocks that had the largest offspring in September (theWWWW and TTWW stocks); offspring sizes ofWWWW and TTWW stocks declined by 22% and 26%(between September and May–August) respectively. Incontrast, the mean size of offspring in the TTTT stockdropped by only 8%. These results indicate that theembryo masses of the TTWW and WWWW stocks arethree times as plastic as those of TTTT in response todensity.

Density significantly reduced the total biomass de-voted to stage-4 embryos during the period of highdensity (F1, 35 5 18.41, P , 0.01) but there was nodifference among stocks in total allocation of biomassto these late stage embryos (F4, 35 5 1.20, P 5 0.33).

Brood sizes (the number of stage-4 embryos) werenot significantly different among stocks in the low den-sity phase of population growth in September (F4, 9 52.33, P 5 0.13), although a power calculation indicatedthat we lacked sufficient power to detect stock differ-ences in this period. Brood sizes were unrelated tofemale body size (F1, 9 5 1.35, P 5 0.28). As with thepattern in brood number at low density, the lack of a

relationship with female body size probably indicatesthat all females were reproducing at nearly maximalrates. The brood sizes in the TTTT and WWWW stocksduring this period were almost twice the values ob-served in the respective natural populations. Althoughbrood sizes did not differ significantly among stocks,those with the largest offspring (WWWW, TTWW) hadthe fewest embryos per brood, while those with thesmallest offspring (e.g., TTTT) had the largest broodsizes, suggesting a trade-off in offspring size and num-ber (compare Figs. 8A and 9A). We investigated thistrade-off by estimating the partial correlation betweenoffspring mass and brood size across replicates, hold-ing female size constant. We used partial correlationson female size because, although female size did notaccount for a statistically significant proportion of thevariance in either brood size or mean offspring size atlow density, previous studies have shown that femalesize does affect brood size (Leips and Travis 1999). Inaddition, there was an effect of female size on the totalmass of all stage-4 embryos that would probably havebeen significant with a larger number of replicates (F1, 9

5 4.46, P50.06). The partial correlation was negative(r 5 20.63, P , 0.01), which indicates that there wasa general compromise between offspring size and num-ber.

In the period of high density (May–August), themean brood sizes among stocks exhibited a very dif-ferent pattern than the one observed in the low densityperiod (Fig. 9B). At the high densities, the mean num-ber of embryos per brood ranged from 1 to 4.4; thus,the maximum brood size in this period was 95% smallerthan the maximum number of offspring per brood inSeptember. There was a significant negative effect ofdensity on brood size (F1, 38 5 4.47, P 5 0.04) but itaccounted for only 8% of the overall variance. Afteradjustment for female body size and density, stocksdiffered significantly in mean brood sizes (F4, 38 5 3.49,P 5 0.02, r2 5 0.25). The WWWW stock had signif-icantly more offspring per brood than the TTTT andTTTW stocks (Tukey’s hsd P , 0.05). Analysis usinglinear contrast codes indicated that only additive ge-netic effects were significant (F1, 38 5 8.10, P 5 , 0.01)and accounted for 18% of the variance. Although broodsizes of all stocks were lower at high density (May–August) compared to low density conditions (Septem-ber), the brood size of the TTTT stock was most af-fected by density. The adjusted brood size of TTTTfemales in the high density period decreased by 67%from the low density period (September); in contrast,the brood size of WWWW females decreased by only43% over the same period. The relative plasticity ofbrood size in these stocks is the reverse of the patternof plasticity for mean offspring size. This relative dif-ference in plasticity altered the relationship betweenoffspring size and number for individual females ob-served earlier in the experiment; partial correlation ofthese variables indicated that unlike the results from


FIG. 9. Mean number of stage-4 embryos per brood dur-ing (A) September (the period of growth of phase I popula-tions) and (B) May–August. Shown are mean-transformed([ ] 1 [ ]) val-Ïnumber of embryos 1 1 Ïnumber of embryosues 1 1 SE.

the September analysis, there was no trade-off betweenoffspring size and number during the May–August pe-riod (r 5 2 0.05, P 5 0.58).

DISCUSSION

To summarize the results of this study, geneticallybased differences in life history traits were apparentfor late stage embryo mass and brood size but onlyobservable in a subset of environmental conditions. Atrade-off between offspring size and number appearedto contribute to differences in initial population growthrates among stocks (stocks with the largest but fewestoffspring had slower initial population growth ratesthan those with many, small offspring). Stock-specificdifferences in population growth rates were only evi-dent in one phase of the experiment (the replicate pop-ulations initiated in July). This emphasizes the impor-tance of accounting for seasonal effects on life historyexpression when inferring a relationship between lifehistory differentiation and population characteristics.Body sizes and the mean values of reproductive traitswere negatively associated with density, although theplastic response of offspring size and number to densitydiffered among the stocks. Convergence on a commonreproductive phenotype at higher densities appeared tominimize the genetic distinctions that were apparent at

low density, and stocks did not differ in the eventualdensity that could be sustained in a given tank.

The demography of Heterandria formosa and theeffects of density

The population size of the experimental stocks grewrapidly, increasing by more than an order of magnitudeand attaining the carrying capacity of the tanks in justtwo generations. Increasing population densities re-duced adult survival and recruitment rates, the mini-mum and mean body sizes of mature males and fe-males, and all measures of female reproduction.

Changes in density induced similar effects on adultmale and female survival rates but affected male andfemale recruitment rates differently. At low densities,adult survival rates dropped slowly in a linear fashionwith increasing density; at high densities, survival ratescontinued to decline but the effects of density becameweaker. There was no substantial difference betweenthe adult survival rates of males and females as a func-tion of density. In contrast, the recruitment rates offemales declined strongly and consistently with in-creasing density while the rates of male recruitmentdeclined weakly. These different relationships withdensity imply that recruitment rates will be much high-er for females than males at lower densities and roughlyequivalent, although much lower, at higher densities.

The sex biased recruitment pattern at low densitiescould have three sources. First and most obviously,females at low densities mature 10–14 d faster thanmales (J. Parrino, J. Leips, and J. Travis, unpublishedmanuscript). The equal recruitment at higher densitiescould occur through an inhibitory effect of density ongrowth and maturation rate that affects females morethan males; greater plasticity in female poeciliids isknown for some species (see Trexler et al. 1990) andour data on the response of female body size to densityis consistent with this notion. Second, females may bemore likely to survive to maturity than males. Thisdifference might be caused by an innate viability dif-ference, but it might also be an indirect effect of theshorter development time for females: if juveniles ex-perience a constant probability of daily mortality, thenfemales, by virtue of faster maturation, experience asubstantially smaller cumulative probability of mor-tality before maturation. Third, sex ratios may be fe-male biased at parturition at low density but approacha 1 : 1 ratio at higher densities. Female-biased ratios atbirth are known in some poeciliid species (Kallman1984; J. Travis, unpublished data) but there is no in-formation on its possible density dependence.

In general, male survival and recruitment appearedto be less strongly influenced by density than femalesurvival and recruitment, and in fact, male numbersappeared to fluctuate far more over the course of theexperiment than did female numbers. Although theforces driving this dynamic behavior are unknown, thispattern of fluctuation in male numbers is also observed


in natural populations (Leips and Travis 1999). To-gether these data suggest that male dynamics are pri-marily density independent, while female dynamicsmay be more likely to offer evidence of true regulation.There is good reason to suspect that females may bemore sensitive to the effects of high density (e.g., re-duced food availability) than males for the simple factthat females must devote energy to egg production.Female poeciliids in particular may suffer from foodlimitation more than males for two reasons. First, fe-males have higher rates of post maturation growth thanmales (Snelson 1989). Second, unlike lecithotrophicspecies, the live-bearing, matrotrophic reproductivestrategy requires that female H. formosa constantlysupply food to growing and developing embryos untilbirth (Fraser and Renton 1940, Scrimshaw 1944),which greatly increases the cost of reproduction to fe-males over that of males. In contrast, the reproductiveinvestment of males ends at conception as there is noparental care of young after birth in this species.

Indeed, female reproductive traits did show a strongresponse to density. Females were smaller at higherdensities, and the dependence of reproductive param-eters on body size was more marked at higher densities,probably because females were no longer operating atmaximum reproductive capacity. The total biomass de-voted to reproduction decreased with increases in den-sity. This decrease was observed in every componentof reproduction: number of broods carried simulta-neously, number of offspring per brood, and mean off-spring size. The pattern of response of these traits toincreased density closely match the patterns exhibitedwhen females encounter a reduction in food availability(Reznick et al. 1996). These changes combined to makethe per capita reproductive output at higher densitiesa small fraction of the output at lower densities. Theeffect of density was transmitted with a significant lagpresumably for two reasons. First, density influencedfemale body size, which then secondarily affected fe-male fertility rates. Second, density affected brood siz-es independently of female size. In this case, the laggedeffect of density results from the fact that once fertil-ized, embryos within a single brood must grow anddevelop within the female prior to birth. Thus there isa lag between fertilization of available eggs and even-tual birth of offspring.

It is unclear whether the lagged response of femalebody size and reproductive traits to density acts to de-stabilize the dynamics of natural populations of thisspecies. The destabilizing effects normally associatedwith lagged responses to density may be partly alle-viated in H. formosa by the combined effects of theplasticity of female size at maturity, matrotrophy, andsuperfetation in response to density. These character-istics allow changes in resource levels to be translatedalmost immediately to changes in offspring size as wellas the interval between successive broods (Travis et al.1987, Reznick et al. 1996), allowing reproductive out-

put to be modulated quickly in response to changingenvironmental conditions. Those systems in which asimilarly lagged density effect has been shown to de-stabilize population trajectories are characterized bysome combination of fixed adult female body size,batch reproduction, and significant further lag betweenenergy accrual and reproductive response (e.g., Proutand McChesney 1985, Wilbur 1996). It is interestingto speculate whether one of the evolutionary advan-tages of matrotrophy and superfetation is the consid-erable control over reproductive allocation it providesto females, resulting in the enhancement of numericalstability that such control can generate (Travis andMueller 1989).

Genetic effects: life history traits

Genetically based differences in life histories be-tween the Trout Pond and Wacissa River populationswere most apparent in two traits, the embryo mass andthe number of embryos per brood. However, populationdifferences in these traits were only evident under asubset of the environmental conditions; changes in den-sity acted to either magnify or obscure differencesamong stocks. For example, in low density, the meanembryo mass of WWWW females exceeded that of theTTTT females by ;40% but, at high density, embryomasses were not significantly different. There was nosignificant difference in brood size between the TTTTand WWWW females at low density (although themean TTTT brood size was 20% larger than the broodsize of WWWW females). This pattern was reversedat high density; WWWW females carried 25% moreoffspring per brood than TTTT females and this dif-ference was significant.

The stock-specific responses of each trait suggeststhat the natural populations have evolved different re-sponses to density. We examined this possibility byplotting the mean values for each of these traits (ad-justed for female size variation) against the cumulativepopulation density (the summed density of each ex-perimental population until the sample date; Fig. 10).The cumulative density is important to consider in thiscase as this measure incorporates the history of pop-ulation density in a given tank; a variable that is likelyto affect available resources. Fitting a linear leastsquares regression line to these data indicates that thegeneral response to density is similar in both popula-tions, although clearly the Wacissa River stock has agreater range of offspring size, while the Trout Pondstock exhibits a greater range in offspring numbers. Ofcourse there is no a priori reason to believe that theserelationships are linear; adding a higher order term tothe regression model improves the fit of the model tothe data (as expected) and also begins to reveal diver-gent responses of these populations to changes in den-sity (not shown). Nonetheless, given the post hoc na-ture of these analyses, all we are prepared to say atthis point is that these populations may have evolved


FIG. 10. (A) Mean dry mass of stage-4 embryos of eachreplicate population of the TTTT and WWWW stocks plottedagainst the cumulative population density (the summed den-sity of each experimental population until the sample date).(B) Mean number of stage-4 embryos/brood of each replicatepopulation of the TTTT and WWWW stocks plotted againstthe cumulative population density. For both (A) and (B), thesolid regression line indicates TT stock, and the dashed re-gression line indicates WWWW stock.

different norms of reaction in response to density, butthe degree to which they differ and the nature of theresponse requires additional experiments under morecontrolled conditions.

The patterns of inheritance of embryo mass andbrood size among stocks provide evidence that the ef-fects of the Trout Pond and Wacissa River ‘‘alleles’’are trait- and environment-specific, and also depend onthe genetic composition of the stock. Considering em-bryo mass first, strong additive and nonadditive alleliceffects contributed to stock differences in low density.The relatively large nonadditive effects appeared to

result from a combination of both dominance and epis-tasis. This can be seen by first comparing the meanvalues of the pure Trout Pond and Wacissa River stockswith that of the intermediate hybrid (TTWW). The em-bryo size of two stocks containing W alleles in thiscomparison were not different, but were significantlylarger than embryos of the TTTT stock. Consideredalone, this pattern would indicate complete dominanceof the W alleles (regardless of the number of loci thatactually affect the mean value of this trait). However,extending this comparison over all stocks reveals amore complex picture of the nature of inheritance ofthis trait. Specifically, the mean size of TWWW em-bryos is smaller than those of the WWWW and TTWWstocks (even though the TWWW stock is assumed tohave a higher proportion of W alleles than the TTWWstock). Thus, the degree of dominance of the W allelesappears to depend in part on the genetic backgroundof the individual, and illustrates the potential impor-tance of epistasis in determining the mean trait values.

While stocks were not significantly different in broodsize at low density, the rank order of stock means forbrood size was reversed compared to that of embryomass. This reflects the trade-off in offspring size andnumber and also suggests that additive and nonadditivegenetic effects influence inheritance of this trait. Athigh density, significant genetic differences amongstocks resulted solely from additive genetic effects in-dicating that the allelic effects on this trait are envi-ronment dependent.

Of course our interpretation of the genetic bases oftrait variation across stocks is based on simple analysesof patterns derived from population means. A moreaccurate picture of the genetic details underlying thepatterns of inheritance of these traits requires a moredetailed quantitative genetic study, and at the minimumwould necessitate a controlled mating design (seeLynch and Walsh 1998).

Genetic effects: population level traits

Genetic differences among the stocks were clearlyreflected in the differences in the initial rates of pop-ulation growth; however, those differences were nolonger apparent once the populations reached the car-rying capacities of the tanks. The population growthrate of the TTTT stock grew as much as 70% morerapidly (22% on the natural log scale shown in Fig.1A) than the WWWW stock, and the genetic inter-mediates were generally aligned in accordance withtheir relative proportions of T and W alleles. Two fac-tors, brood size and age at maturity, probably contributeto this pattern. First, brood sizes of TTTT fish wereslightly larger than those of WWWW fish at low den-sities, although the difference was not significant. Thehigher proportion of immatures in the TTTT replicatesby the September census is consistent with this obser-vation. Second, TTTT fish may mature at smaller sizesthan WWWW fish (the trend was always in this direc-


tion and was statistically significant in one period). Inthis species (Henrich 1986, Forster-Blouin 1989), andin all poeciliids studied to date, smaller size at matu-ration is associated with a shorter time to maturation,for a given density and resource level (Kallman 1989,Travis 1989). Thus, genetically based distinctionsamong stocks that were not statistically distinguishableor were not directly measured, (e.g., time to maturity,inter-brood intervals) probably combined to contributeto the differences observed among stocks in initial pop-ulation growth rates.

Interestingly, the large difference in initial popula-tion growth rates did not lead to differences in sub-sequent population trajectories. This is an unexpectedresult. If we assume that a simple logistic model ofpopulation growth with a time lag can describe thedynamics of our populations, differences in populationgrowth rates such as those seen between the stocksshould have produced dramatically different populationtrajectories (May 1974). There are at least two (notmutually exclusive) explanations for the observed pat-terns. First, the timing of the winter shutdown of re-production coincided with the populations reaching thecarrying capacity of the tanks (in phase I); this coin-cidence may have prevented any drastic overshoot ofthe carrying capacity of the tanks by the faster growingstocks and so stabilized the population size. An alter-native explanation is that the females of the TTTT andTTTW stocks were finely tuned to density changes,adjusted their reproductive output and so minimizedtime lag effects.

There was no evidence of a trade-off across stocksbetween the capacity for rapid population increase andcarrying capacity, a result found in many other exper-imental studies on a variety of organisms, includingDrosophila spp. (Ayala 1965a, 1968), Escherichia coli(Luckinbill 1978), Paramecium spp. (Luckinbill 1979),and Tribolium castaneum (Schlager 1963). However,there are cases in which a trade-off between populationgrowth rate and carrying capacity has been found(Mueller 1997), making it difficult to generalize aboutthe conditions under which such a trade-off may occur.

We hypothesize that there is a trade-off in H. for-mosa, but that it occurs through the plastic response ofoffspring size and number to changes in density. Al-though there was no inter-stock difference in the totalamount of biomass allocated to late stage embryos, theway that this energy was packaged across density gra-dients differed among stocks. At higher densities totalreproductive allocation declined across all stocks butTTTT stocks reduced the brood size much more thanthe WWWW stocks. In contrast, WWWW stocks re-duced offspring size more than did the TTTT stocks inresponse to increased density. The proportion of thepopulations composed of immatures did not differ be-tween stocks at the higher densities, suggesting one oftwo possibilities. Either the TTTT juveniles were ex-periencing slightly higher survivorship than the

WWWW juveniles or the TTTT stocks had shorter in-tervals between successive broods than WWWWstocks. Both of these would have given the stocks anequivalent net reproductive rate at the higher densities.However, inter-brood intervals did not differ betweenthese two stocks when examined in the lab followingthe end of this experiment (Leips 1997). This suggeststhat juvenile survivorship differed between stocks athigh density. If this was the case, the reduced survi-vorship of the WWWW offspring may have been afunction of their substantially reduced offspring sizesat the higher densities: larger offspring are more viablethan smaller ones in laboratory conditions (Henrich1988). The minimal effect of density on the offspringsize of the TTTT stocks may have translated into aminimal decrease in juvenile survivorship. We proposethat the trade-off in reproductive performance amongstocks has two elements. The first trade-off is that theability to have high fecundity at low density comes atthe cost of greatly reduced fecundity at high density(exemplified by the response of offspring numbers ofTTTT stocks to density). A different kind of trade-offoccurs with the WWWW stocks. In this case, the re-duction in offspring size at higher density forces a de-crease in offspring viability. The net effect is that be-cause the responses to density differ among stocks, thetrade-off with density is not reflected in different car-rying capacities.

This explanation does not address why these repro-ductive traits in the two natural populations responddifferently to density. One hypothesis is that the pop-ulations may differ due to drift, and/or relative differ-ences in the degree of inbreeding. In one experimenton laboratory populations of Drosophila melanogaster,inbred lines had reduced population growth rates butsimilar carrying capacities compared to wild type lines(Mueller and Ayala 1981). Although this pattern is sim-ilar to the one we observed in our study, differencesin the degree of inbreeding between H. formosa pop-ulations is not a likely explanation because there wasno clear evidence of inbreeding depression in the purestocks from either population. An alternative hypoth-esis is that fish in Trout Pond and the Wacissa Riverexperience characteristically different density regimesthat have selected for different combinations of off-spring size and number in each habitat. The WacissaRiver population not only has a much higher averagedensity, but also exhibits a higher range of densitiesduring the breeding season than Trout Pond (Leips andTravis 1999). The Wacissa River environment mayplace a premium on enhanced early survival, and largermean sizes of offspring may well represent the evo-lutionary response (Brockelman 1975). The greaterplasticity in offspring size in the Wacissa River pop-ulation may also be advantageous given the large rangeof population densities that occur in this habitat acrossseasons. In the laboratory, females in high density in-crease the size of their offspring when resources are


not limited; at low density offspring sizes are reduced,but the rate of offspring production increases (F. H.Rodd and J. Travis, unpublished data). In the currentexperiment, the net response of offspring size to in-creased density probably resulted from the joint effectsof reduced food availability (Reznick et al. 1996) andthe density of conspecifics. In Trout Pond, where den-sities are consistently low, the favored strategy appearsto be the production of higher offspring numbers anda relative inflexibility of offspring size. Even at lowdensity, offspring sizes of the pure Trout Pond stocksnever approached that of the pure Wacissa River stocks.Thus, the different responses to density may exist be-cause densities never drop to the Trout Pond range inthe Wacissa River and never rise to the Wacissa rangein Trout Pond during the breeding season (Leips andTravis 1999). Therefore, selection pressures are pre-sumably quite different in the two localities and so mayselect for different norms of reaction.

In a common garden experiment similar to ours,Scribner (1993) found no trade-off in populationgrowth rate and carrying capacity in a comparison oftwo species of poeciliid fish, Gambusia affinis and G.holbrooki, and their interspecific hybrid. Also, despitefinding genetically based differences in gestation time,age and size at maturity, and individual growth rates,no differences in population growth rates among thespecies or the hybrid were found although there weredifferences in the equilibrium population sizes. Im-portantly though, females of the Gambusia species andthe hybrid had similar fecundities (Scribner 1993). Iffecundity differences as a function of density drivedifferences in population growth rate (between speciesor populations), this may explain our disparate resultswith regard to population growth rates. Additionally,the differences Scribner observed in carrying capacitieswere correlated with differences in body size distri-butions of the populations and offspring recruitment.Our stocks exhibited no differences in either of thesetraits.

The role of intrinsic characteristics inpopulation regulation

The reproductive characteristics of this species maybe an important intrinsic feature affecting populationregulation. Females produce offspring continuouslythroughout the breeding season, allowing constant ad-justment of allocation to reproduction in response toenvironmental change. Direct nutritional provisioningto developing offspring also provides females with adirect mechanism to alter offspring phenotypes in re-sponse to changes in environmental conditions. Onesuch change that occurs during the breeding season ispopulation density (Leips and Travis 1999). The currentstudy showed a strong effect of density on most re-productive traits, both directly and also indirectlythrough the effects of density on female body size thatcould lead to population regulation. While there have

been a number of studies on how maternal effects in-fluence offspring fitness (reviewed by Mousseau andDingle 1991, Bridges and Heppell 1996) there are fewstudies that have examined their influence on popula-tion regulation (but see Ohgushi 1995, Rossiter 1995).Investigation of these phenotypic responses as mech-anisms for population regulation, or as mechanisms thataccount for the variation among groups in their abilityto respond to perturbations, would appear to be a fertilearea for additional research. In any case, the results ofthis study indicate clearly that population dynamics aredriven by intrinsic characteristics as well as the moreobvious external agents that influence mortality,growth, and reproduction.

ACKNOWLEDGMENTS

We thank M. Allen, C. Baer, C. Bowling, M. Childress, A.Davis, T. D’Souza, C. Hays, C. Johnson, C. Kindell, M. Kuhl-mann, N. Martin, T. McGovern, C. McManus, M. McManus,T. Miller, C. Morrison, M. Ptacek, K. Silvestre, T. Spears, A.Weglinski, and A. Winn for help at the cattle tanks. KimRiddle provided technical help with the epiflourescent mi-croscope. Karen Graffius at the F.S.U. Mission Road Green-house helped in a variety of ways with the set up and main-tenance of the cattle tanks. C. Baer, L. Horth, K. Hughes, M.McPeek, J. Richardson, P. Wainwright, A. Winn, D. Reznick,and two anonymous reviewers provided helpful comments onthe manuscript. Support for this research was provided by agrant from Sigma Xi (to J. L.), a Florida State UniversityFellowship (to J. L.), the NSERC (to F. H. R.), and two NSFgrants (DEB 92-20849 and BSR 88-18001 to J.T.)

LITERATURE CITED

Antonovics, J., and S. Via. 1987. Genetic influences on thedistribution and abundance of plants. Pages 185–203 in A.J. Davy, M. J. Hutchings, and A. R. Watkins, editors. Plantpopulation ecology. Blackwell Scientific, Oxford, UK.

Ayala, F. J. 1965a. Relative fitness of populations of Dro-sophila serrata and Drosophila birchii. Genetics 51:527–544.

Ayala, F. J. 1965b. Evolution of fitness in experimental pop-ulations of Drosophila serrata. Science 150:903–904.

Ayala, F. J. 1966. Dynamics of populations. I. Factors con-trolling population growth and population size in Dro-sophila serrata. American Naturalist 100:333–344.

Ayala, F. J. 1968. Evolution of fitness. II. Correlated effectsof natural selection on the productivity and size of exper-imental populations of Drosophila serrata. Evolution 22:55–65.

Begon, M., J. L. Harper, and C. R. Townsend. 1986. Ecology:individuals, populations, and communities. Sinauer Asso-ciates, Sunderland, Massachusetts, USA.

Belovsky, G. E., and A. Joern. 1995. The dominance of dif-ferent regulating factors for rangeland grasshoppers. Pages359–386 in N. Cappucino and P. W. Price, editors. Popu-lation dynamics: new approaches and synthesis. AcademicPress, San Diego, California, USA.

Berryman, A. A. 1991. Stabilization or regulation: what itall means! Oecologia 86:140–143.

Birch, L. C., Th. Dobzhansky, P. O. Elliott, and R. C. Le-wontin. 1963. Relative fitness of geographic races of Dro-sophila serrata. Evolution 17:72–83.

Bradshaw, W. E., and C. M. Holzapfel. 1989. Life-historicalconsequences of density-dependent selection in the pitcher-plant mosquito, Wyeomyia smithii. American Naturalist133:869–887.

Bridges, T. S., and S. Heppell. 1996. Fitness consequences


of maternal effects in Streblospio benedicti (Annelida:Poly-chaeta). American Zoologist 36:132–146.

Brockelman, W. Y. 1975. Competition, the fitness of off-spring, and optimal clutch size. American Naturalist 109:677–699.

Cappuccino, N. 1987. Comparative population dynamics oftwo goldenrod aphids: spatial patterns and temporal con-stancy. Ecology 68:1634–1646.

Castillo-Chavez, C. 1989. Some applications of structuredmodels in population dynamics. Pages 450–470 in S. A.Levin, T. G. Hallam, and L. J. Gross, editors. Applied Math-ematical Ecology. Springer-Verlag, Berlin, Germany.

Charlesworth, B. 1994. Evolution in age structured popula-tions. Second edition. Cambridge University Press, Cam-bridge, UK.

Clutton-Brock, T. H., A. W. Illius, K. Wilson, B. T. Grenfell,A. D. C. MacColl, and S. D. Albon. 1997. Stability andinstability in ungulate populations: an empirical analysis.American Naturalist 149:195–219.

Colson, C. M. 1969. Effects of daylength and temperatureon the reproduction of Heterandria formosa. Dissertation.University of Florida, Gainesville, Florida, USA.

Constanz, G. D. 1989. Reproductive biology of poeciliid fish-es. Pages 33–51 in G. K. Meffe, and F. F. Snelson, editors.Ecology and evolution of livebearing fishes. Prentice Hall,Englewood Cliffs, New Jersey, USA.

Crawley, M. J. 1990. The population dynamics of plants.Philosophical Transactions of the Royal Society of LondonB Biological Sciences 330:125–140.

Crowl, T. A., and A. P. Covich. 1990. Predator-induced lifehistory shifts in a freshwater snail. Science 247:949–951.

Cushing, J. J., and J. Li. 1992. The dynamics of a size-structured intraspecific competition model with density de-pendent juvenile growth rates. Pages 112–125 in D. L.DeAngelis and L. J. Gross, editors. Individual-based mod-els and approaches in ecology. Chapman and Hall, NewYork, New York, USA.

Day, R. W., and G. P. Quinn. 1989. Comparisons of treatmentsafter an analysis of variance in ecology. Ecological Mono-graphs 59:433–463.

Dobzhansky, Th., and B. Spassky. 1944. Genetics of naturalpopulations. VI. Manifestation of genetic variants in Dro-sophila obscura in different environments. Genetics 29:270–290.

Doebeli, M. 1993. The evolutionary advantage of controlledchaos. Proceedings of the Royal Society of London SeriesB Biological Sciences 254:281–285.

Doebeli, M. 1995. Updating Gillespie with controlled chaos.American Naturalist 146:479–487.

Ebenman, B. 1988. Dynamics of age- and size-structuredpopulations: intraspecific competition. Pages 127–139 inB. Ebenman and L. Persson, editors. Size-structured pop-ulations: ecology and evolution. Springer-Verlag, Berlin,Germany.

Elliott, J. M. 1987. Population regulation in contrasting pop-ulations of trout Salmo trutta in two Lake District streams.Journal of Animal Ecology 56:83–98.

Ellner, S., and P. Turchin. 1995. Chaos in a noisy world: newmethods and evidence from time-series analysis. AmericanNaturalist 145:343–375.

Falconer, D. S., and T. F. C. Mackay. 1996. Introduction toquantitative genetics. Fourth edition. Longman Scientificand Technical, Essex, UK.

Ferrar, M., and J. A. Donazar. 1996. Density dependent fe-cundity by habitat heterogeneity in an increasing popula-tion of Spanish imperial eagles. Ecology 77:69–74.

Ferriere, R., and M. Gatto. 1993. Chaotic population dynam-ics can result from natural selection. Proceedings of theRoyal Society of London Series B Biological Sciences 251:33–38.

Forster-Blouin, S. 1989. Genetic and environmental com-ponents of thermal tolerance in the least killifish, Heter-andria formosa. Dissertation. Florida State University, Tal-lahassee, Florida, USA.

Fraser, E. A., and R. M. Renton. 1940. Observation on thebreeding and development of the viviparous fish, Heter-andria formosa. Quarterly Journal of Microscopical Sci-ence 81:479–520.

Gaston, K. J., and J. H. Lawton. 1988. Patterns in the dis-tribution and abundance of insect populations. Nature 331:709–712.

Ginzburg, L. R., and D. E. Taneyhill. 1994. Population cyclesof forest lepidoptera: a maternal effect hypothesis. Journalof Animal Ecology 63:79–92.

Goodnight, C. 1988. Population differentiation and the trans-mission of density effects between generations. Evolution42:399–403.

Goodnight, C. 1989. Population differentiation and the cor-relation among traits at the population level. American Nat-uralist 133:888–900.

Gurney, W. S. C., and R. M. Nisbet. 1985. Fluctuation pe-riodicity, generation separation, and the expression of lar-val competition. Theoretical Population Biology 28:150–180.

Hanski, I. 1987. Pine sawfly population dynamics: patterns,processes, problems. Oikos 50:327–335.

Henrich, S. 1986. Ecological genetics of reproductive traitsin Heterandria formosa (Pisces: Poeciliidae). Thesis. Flor-ida State University, Tallahassee, Florida, USA.

Henrich, S. 1988. Variation in offspring sizes of the poeciliidfish Heterandria formosa in relation to fitness. Oikos 51:13–18.

Hunter, A. F. 1991. Traits that distinguish outbreaking andnon-outbreaking Macrolepidoptera feeding on northernhardwood trees. Oikos 60:275–282.

Hunter, A. F. 1995. Ecology, life history, and phylogeny ofoutbreak and nonoutbreak species. Pages 41–64 in N. Cap-puccino and P. W. Price, editors. Population dynamics: newapproaches and synthesis. Academic Press, San Diego, Cal-ifornia, USA.

Hutchinson, G. E. 1948. Circular causal systems in ecology.Annals of the New York Academy of Science 50:221–246.

Judd, C. M., and G. H. McClelland. 1989. Data analysis. amodel-comparison approach. Harcourt Brace Jovanovich,Orlando, Florida, USA.

Kallman, K. D. 1984. A new look at sex determination inpoeciliid fishes. Pages 95–171 in B. J. Turner, editor. Theevolutionary genetics of fishes. Plenum Press, New York,New York, USA.

Kallman, K. D. 1989. Genetic control of size at maturity inXiphophorus. Pages 163–200 in G. K. Meffe and F. F. Snel-son, editors. Ecology and evolution of livebearing fishes.Prentice Hall, Englewood Cliffs, New Jersey, USA.

Krebs, C. J. 1989. Ecological methodology. Harper and Row,New York, New York, USA.

Law, R., A. D. Bradshaw, and P. D. Putwain. 1977. Lifehistory variation in Poa annua. Evolution 31:233–246.

Leips, J. 1997. Genetic differentiation in life history traits:influence on population dynamics in the least killifish, Het-erandria formosa. Dissertation. Florida State University,Tallahassee, Florida, USA.

Leips, J., and J. Travis. 1999. The comparative expressionof life history traits and its relationship to the numericaldynamics of four populations of the least killifish, Heter-andria formosa. Journal of Animal Ecology 68:595–616.

Lewontin, R. C. 1955. The effects of population density andcomposition on viability in Drosophila melanogaster. Evo-lution 9:27–41.

Lloyd, M. 1968. Self regulation of adult numbers by can-


nibalism in two laboratory strains of flour beetles. Ecology49:245–259.

Luckinbill, L. S. 1978. r- and K-selection in experimentalpopulations of Escherichia coli. Science 202:1201–1203.

Luckinbill, L. S. 1979. Selection and the r/K continuum inexperimental populations of protozoa. American Naturalist113:427–437.

Lynch, M., and B. Walsh. 1998. Genetics and analysis ofquantitative traits. Sinauer Associates, Sunderland, Mas-sachusetts, USA.

Marks, R. W. 1982. Genetic variability for density sensitivityof three components of fitness in Drosophila melanogaster.Genetics 101:301–316.

May, R. M. 1974. Biological populations with nonoverlap-ping generations: stable points, stable cycles, and chaos.Science 186:645–647.

McCauley, D. E., and M. J. Wade. 1980. Group selection:the genetic and demographic basis for the phenotypic dif-ferentiation of small populations of Tribolium castaneum.Evolution 34:813–821.

Mousseau, T. A., and H. Dingle. 1991. Maternal effects ininsect life histories. Annual Review of Entomology 36:511–534.

Mueller, L. D. 1997. Theoretical and empirical examinationof density-dependent selection. Annual Review of Ecologyand Systematics 28:269–288.

Mueller, L. D., and F. J. Ayala. 1981. Dynamics of single-species population growth: experimental and statisticalanalysis. Theoretical Population Biology 20:101–117.

Mueller, L. D., and P. T. Huynh. 1994. Ecological determi-nants of stability in model systems. Ecology 75:430–437.

Murdoch, W. W. 1994. Population regulation in theory andpractice. Ecology 75:271–287.

Nicholson, A. J. 1960. The role of population dynamics innatural selection. Pages 477–520 in S. Tax, editor. Evo-lution after Darwin, Volume 1, The evolution of life. Uni-versity of Chicago Press, Chicago, Illinois, USA.

Nothnagle, P. J., and J. C. Schultz. 1987. What is a forestpest? Pages 31–58 in P. Barbosa and J. C. Schultz, editors.Insect outbreaks. Academic Press, London, UK.

Ohgushi, T. 1995. Adaptive behavior produces populationstability in herbivorous lady beetle populations. Pages 303–320 in N. Cappuccino and P. W. Price, editors. Populationdynamics: new approaches and synthesis. Academic Press,San Diego, California, USA.

Primack, R. B., and J. Antonovics. 1982. Experimental eco-logical genetics in Plantago. 6. The demography of seedlingtransplants of Plantago lanceolata. Journal of Ecology 70:55–76.

Prout, T. 1986. The delayed effect on fertility of preadultcompetition: Two-species population dynamics. AmericanNaturalist 127:809–818.

Prout, T., and F. McChesney. 1985. Competition among im-matures affects their adult fertility: population dynamics.American Naturalist 126:521–558.

Rejmanek, M., and D. M. Richardson. 1996. What attributesmake some plant species more invasive? Ecology 77:1655–1661.

Reznick, D. 1981. ‘‘Grandfather effects’’: genetics of inter-population differences in offspring size in mosquito fish.Evolution 35:941–953.

Reznick, D., H. Callahan, and R. Llauredo. 1996. Maternaleffects on offspring quality in poeciliid fishes. AmericanZoologist 36:147–156.

Rossiter, M. 1995. Impact of life history evolution on pop-ulation dynamics: predicting the presence of maternal ef-fects. Pages 251–275 in N. Cappuccino and P. W. Price,editors. Population dynamics: new approaches and synthe-sis. Academic Press, San Diego, California, USA.

Schlager, G. 1963. The ecological genetics of the mutantsooty in populations of Tribolium castaneum. Evolution 17:254–273.

Scribner, K. T. 1993. Hybrid zone dynamics are influencedby genotype-specific variation in life-history traits: exper-imental evidence from hybridizing Gambusia species. Evo-lution 47:632–646.

Scrimshaw, N. S. 1944. Embryonic growth in the viviparousPoeciliid, Heterandria formosa. Biological Bulletin 87:37–51.

Simms, E. L., and D. S. Burdick. 1988. Profile analysis ofvariance as a tool for correlated responses in experimentalecology. Biometrical Journal 30:229–242.

Sinclair, A. R. E. 1989. Population regulation in animals.Pages 197–241 in J. M. Cherrett, editor. Ecological Con-cepts. Blackwell Scientific, Oxford, UK.

Skelly, D. K., and E. E. Werner. 1990. Behavioral and life-historical responses of larval American toads to an odonatepredator. Ecology 71:2313–2322.

Snedecor, G. W., and W. G. Cochran. 1980. Statistical meth-ods. Iowa State University Press, Ames, Iowa, USA.

Snelson, F. F. 1989. Social and environmental control of lifehistory traits in poeciliid fishes. Pages 149–162 in G. K.Meffe and F. F. Snelson, editors. Prentice Hall, EnglewoodCliffs, New Jersey, USA.

Sokal, R. R., and J. Karten. 1964. Competition among ge-notypes in Tribolium castaneum at varying densities andgene frequencies (the black locus). Genetics 49:195–211.

Travis, J. 1989. Ecological genetics of life history traits inpoeciliid fishes. Pages 185–200 in G. K. Meffe and F. F.Snelson, editors. Ecology and evolution of livebearing fish-es. Prentice Hall, Englewood Cliffs, New Jersey, USA.

Travis, J. 1994. The ecological genetics of life histories: var-iation and its evolutionary significance. Pages 171–204 inL. A. Real, editor. Ecological genetics. Princeton Univer-sity Press, Princeton, New Jersey, USA.

Travis, J., J. A. Farr, S. Henrich, and R. T. Cheong. 1987.Testing theories of clutch overlap with the reproductiveecology of Heterandria formosa. Ecology 68:611–623.

Travis, J., and L. D. Mueller. 1989. Blending ecology andgenetics: progress toward a unified population biology.Pages 101–124 in J. Roughgarden, R. M. May, and S. A.Levin, editors. Perspectives in ecological theory. PrincetonUniversity Press, Princeton, New Jersey, USA.

Trexler, J. C., J. Travis, and M. Trexler. 1990. Phenotypicplasticity in the sailfin molly, Poecilia latipinna (Pisces:Poeciliidae): II. Laboratory experiment. Evolution 44:157–167.

Turchin, P. 1993. Chaos and stability in rodent populationdynamics: evidence from nonlinear time-series analysis.Oikos 68:167–172.

Turchin, P. 1995. Population regulation: old arguments anda new synthesis. Pages 19–40 in N. Cappuccino and P. W.Price, editors. Population dynamics: new approaches andsynthesis. Academic Press, San Diego, California, USA.

Turchin, P., and A. D. Taylor. 1992. Complex dynamics inecological time series. Ecology 73:289–305.

Wade, M. J. 1991. Genetic variance for rate of populationincrease in natural populations of flour beetles, Triboliumspp. Evolution 45:1574–1584.

Whittaker, J. B. 1971. Population changes in Neophilaenuslineatus (L.) (Homoptera: Cercopidae) in different parts ofits range. Journal of Animal Ecology 40:425–433.

Wilbur, H. M. 1996. Multi-stage life cycles. Pages 177–219in O. E. Rhodes, Jr., R. K. Chesser, and M. H. Smith,editors. Spatial and temporal aspects of population pro-cesses. University of Chicago Press, Chicago, Illinois,USA.

Wilkinson, L. 1990. SYSTAT. Evanston, Illinois, USA.Zar, J. H. 1984. Biostatistical Analysis. Prentice Hall, Inc.,

Englewood Cliffs, New Jersey, USA.

GENETIC INFLUENCES ON EXPERIMENTAL POPULATION DYNAMICS OF THE LEAST KILLIFISH

Documents

Transcript of GENETIC INFLUENCES ON EXPERIMENTAL POPULATION DYNAMICS OF THE LEAST KILLIFISH