Demographic Responses of Estuarine Polychaetes to Pollutants: Life Table Response Experiments

Demographic Responses of Estuarine Polychaetes to Pollutants: Life Table ResponseExperimentsAuthor(s): Lisa Levin, Hal Caswell, Todd Bridges, Claudio DiBacco, Debra Cabrera and GaylePlaiaSource: Ecological Applications, Vol. 6, No. 4 (Nov., 1996), pp. 1295-1313Published by: Ecological Society of AmericaStable URL: http://www.jstor.org/stable/2269608 .

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Ecological Applications, 6(4), 1996, pp. 1295-1313 X) 1996 by the Ecological Society of America

DEMOGRAPHIC RESPONSES OF ESTUARINE POLYCHAETES TO POLLUTANTS: LIFE TABLE RESPONSE EXPERIMENTS1

LISA LEVIN Marine Life Research Group, Scripps Institution of Oceanography, La Jolla, California 92093-0218 USA

HAL CASWELL Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543 USA

TODD BRIDGES U. S. Army Corps of Engineers, Waterways Experiment Station, 3909 Halls Ferry Road,

Vicksburg, Mississippi 39180-6199 USA

CLAUDIO DIBACCO Marine Life Research Groups, Scripps Institution of Oceanography, La Jolla, California 92093-0218 USA

DEBRA CABRERA 36 Willow St., Glen Ridge, New Jersey 07028 USA

GAYLE PLAIA Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University,

Raleigh, North Carolina 27695-8208 USA

Abstract. Capitella sp. I and Streblospio benedicti are infaunal, deposit-feeding polychaetes that occur in estuaries and littoral wetlands throughout much of the United States. Life table response experiments (sensu Caswell 1989a) were carried out in the laboratory to compare the demographic responses of these species to three common sources of estuarine contamination or enrichment: sewage (Milorganite), blue-green algae (Spirulina sp.), and hydrocarbons (No. 2 fuel oil). Life table data were used to generate two population projection models (a fully age-classified model and a simple two-stage model) for each species in each treatment and in a salt marsh sediment control. These models were used to quantify the effects of treatments on survival, reproduction, and age at maturity, and hence on population growth rate.

For both species, survival was high in all treatments except the blue-green algae treatment, where oxygen depletion (to <1 mL/L) occurred. Treatments had dramatic effects on age at maturity, fertility, and generation time, which differed between species and among contaminants. Population growth rates (X) were higher in Capitella sp. I than in S. benedicti for all treatments, primarily due to earlier maturation and a fertility advantage exhibited by Capitella during the first few weeks of reproduction. In Capitella sp. I, explosive increases in X were seen in the sewage (X = 5.31) and algae (X = 2.81) enrichments relative to the control (X = 1.86) and the hydrocarbon treatments (X = 1.67). Reduced maturation time and increases in age-specific fertility associated with rapid growth and large body size were responsible. Hydrocarbons reduced X primarily through delayed maturation and reduced age-specific fertility. Population growth rates of S. benedicti in the hydrocarbon treatment (X = 1.11) and algae treatment (X = 1.09) were reduced relative to the control (X = 1.46) and sewage treatments (X = 1.41). The hydrocarbon reduction resulted from delayed maturity and reduced fertility, whereas the algal effects were caused by reductions in both juvenile survival and fertility. Our analyses revealed that Capitella sp. I's population growth rate was less sensitive than that of S. benedicti to these three common forms of estuarine contamination, that different sources of organic enrichment (sewage and blue-green algae) introduced at the same C and N levels could have varying demographic effects, and that when two contaminants (hydrocarbons and blue-green algae) caused similar reductions in population growth rate in a species (Streblospio), the underlying mechanisms may have differed. For both species all demographically important effects of contaminants occurred early in life, suggesting a need to focus on juveniles and young adults in field and laboratory testing. The experiments performed here demonstrated the sensitivity of polychaete demographic properties to the condition of estuarine sediments. This sensitivity may be exploited to evaluate organic enrichment and hydrocarbon contamination in field settings.

Key words: age at maturity; Capitella sp. I; eutrophication; hydrocarbons; matrix population models: organic enrichment; pollution; polychaetes; population growth rate; sensitivity; sewage; Streblospio benedicti.

I Manuscript received 10 July 1995; accepted 2 November 1995; final version received 8 January 1996.

1295



1296 LISA LEVIN ET AL. Ecological Applications Vol. 6, No. 4

INTRODUCTION

Many estuaries and intertidal wetlands of North America are enriched and contaminated by human activity. Inputs include oil leakage and spills, industrial and agricultural runoff that leads to eutrophication, and sewage (Kennish 1992). Widespread, opportunistic polychaetes, such as Capitella spp., Streblospio benedicti, and Polydora ligni (Grassle and Grassle 1974) are likely to be exposed to anthropogenic inputs in estuaries, mudflats, and salt marshes throughout the United States. Under conditions of intense enrichment, one of another of these three species often numerically dominates the benthic community (Pearson and Ro- senberg 1978, Young and Young 1978, Grassle et al. 1985). For this reason infaunal invertebrates, including polychaetes, are useful indicators of anthropogenic inputs to freshwater and marine systems (Pearson and Rosenberg 1978, Word 1979, Hellawell 1986, Maurer et al. 1991, Rosenberg and Resh 1993). They are sensitive to changes in organic enrichment, oxygen level, and hydrocarbon and heavy metal contamination, as demonstrated by effects at the individual, population, and community levels (Neff and Anderson 1981, Ro- senberg and Resh 1993).

Pollutants have effects at all levels of biological or- ganization, from the cell to the ecosystem (e.g., Mor- iarty 1988, Suter 1993, Newman and Jagoe 1996), and any ecotoxicological experiment can be analyzed at more than one such level. Current practices in ecotoxicology often focus on individual responses to contaminants, either in terms of survival or sublethal effects (Calow 1993). Many studies have demonstrated chronic pollution effects on invertebrate growth and reproduction in the laboratory (Koeman and Strik 1975, Neff and Anderson 1981, Chapman and Fink 1984, Ingersoll and Nelson 1990, Moore et al. 1991, Pesch et al. 1991, Dillon et al. 1993, McGee at al. 1993). Others have documented changes in infaunal population abundance (Grassle and Grassle 1974, Pearson and Rosenberg 1978) or community composition (Word 1979, Maurer et al. 1991) due to pollutants. One of the goals of ecotoxicology is linking the physiological effects of contaminants on individuals with their population- and community-level consequences (Moriarty 1988). Structured population models are a tool for making this link (e.g., Nisbet et al. 1989, Hallam et al. 1993, Kooij- man and Metz 1994, Caswell 1996a, b). They are particularly useful when the contaminant effects on survival and reproduction are of different magnitudes, or vary in direction at different parts of the life cycle.

Life table response experiments (LTREs, sensu Ca- swell 1989a, b) are a method for evaluating the population-level consequences of pollutant effects on individual life history parameters, combining approaches that have traditionally been assessed independently. The present study employs LTREs to compare the responses of two polychaete species, Capitella sp. I and

Streblospio benedicti, to three common sources of organic enrichment or contamination in estuaries: sewage, blue-green algae, and hydrocarbons. The objec- tives of this work were to (1) evaluate polychaete population responses to different types of contamination or organic enrichment, (2) tie polychaete life history alterations to population-level consequences, and (3) develop an approach for using species-specific polychaete responses to evaluate the condition of estuarine sediments.

LTREs can be mensurative or manipulative; the response variable is a complete set of age- or stage-specific vital rates (in short, a life table). Although the term was coined recently (Caswell 1989a), the approach dates back at least to Birch (1953). The effects of treatments on the vital rates are measured directly, and then demographic models are used to calculate demographic indices, particularly the rate of increase (A or r = ln X), as summary statistics that integrate the diverse effects on survival, reproduction, growth, and development.

Among polychaetes, LTRE approaches have been used to study the effects of different genetic strains (Akesson 1982), salinity and temperature .(Redman 1984), intraspecific density (Pesch et al. 1987), larval development mode (Levin et al. 1987, Levin and Hug- gett 1990), seasonality (Zajac 1991), dredging disturbance (Zajac and Whitlatch 1989), and contaminated sediments (Akesson 1975, Pesch et al. 1991). LTREs also have been carried out for planktonic organisms exposed to toxicants (Hummon and Hummon 1975, Winner and Farrell 1976, Allan and Danials 1982, Rao and Sarma 1986). Analytical methods recently have carried LTREs a step further by dissecting the contributions of the individual vital rates to the overall treatment effect on population growth rate (Caswell 1989a, 1996a, b, Levin et al. 1987, Levin and Huggett 1990, Silva et al. 1991, Walls et al. 1991, Brault and Caswell 1993, H. Caswell and L. V. Martin, unpublished manuscript). We use these methods here to determine the part of the life cycle responsible for the population- level responses to pollutants in Capitella sp. I and S. benedicti.

In an earlier paper (Bridges et al. 1994) we analyzed the effects of the same sewage, blue-green algae, and hydrocarbon treatments discussed here, on the somatic growth of individuals, the age at maturity of individuals, the brood size of individuals, and the allocation, by individuals, of carbon and nitrogen to reproduction. In this paper we refocus attention to the level of population dynamics. We present life table data and employ structured population models to derive population statistics including net reproductive rate, generation time, stable age distribution, reproductive value, and especially population growth rate. These parameters provide valuable information unavailable from analysis of individuals. With them we can quantify the responses of population growth rate (a fitness measure) of our



November 1996 DEMOGRAPHIC RESPONSE TO POLLUTANTS 1297

study species to different types of contamination and can pinpoint the age- or stage-specific vital rates responsible for both the treatment effects and the species differences in population growth rate.

Target species

Both Capitella sp. I and S. benedicti are deposit feeders, consuming bacteria and detrital matter associated with sediments. Capitella spp. feed on subsurface sediments while S. benedicti consumes surface deposits as well as suspended matter (Dauer et al. 1981). Through their feeding activities, both taxa are readily exposed to particulate contaminants reaching the seabed.

Capitella capitata (Capitellidae) (now recognized as a sibling species complex [Grassle and Grassle 1976]) may be the most well-known indicator of marine pollution (Reish 1979). Members of the genus respond quickly and positively to many forms of organic load- ing and disturbance, and dominate benthic biomass at sewage outfalls (Pearson and Rosenberg 1978, Boesch 1982, Lear and O'Malley 1983, Reise 1983), in hydrocarbon-contaminated sediments (Grassle and Gras- sle 1974), and beneath fish farms (Tsutsumi et al. 1990, Weston 1990). Growth and reproduction of Capitella sp. I as well as other Capitella species are highly sensitive to food quantity and quality (reviewed in Bridges et al. 1994).

Streblospio benedicti (Spionidae) is frequently the most abundant macrobenthic species encountered in salt marshes and shallow subtidal communities along much of the U.S. coastline (Levin 1984, Nichols and Thompson 1985, Feller et al. 1992). Like Capitella, S. benedicti responds positively to increases in water-col- umn nutrients and increased food in sediments (Grassle et al. 1985, Levin 1986). Its relatively rapid recovery following an oil spill suggests some degree of hydrocarbon tolerance (Grassle and Grassle 1974).

MATERIALS AND METHODS

Experimental conditions

We estimated cohort life tables for each species under each of four experimental treatments. Treatments

consisted of defaunated Spartina alterniflora salt marsh sediment that had been sieved through a 500-pLm screen, frozen, then thawed and offered as a source of food either (1) alone (marsh mud control), or with additions of (2) dry Milorganite (sewage treatment), (3) dry Spirulina (blue-green algae treatment), or (4) No. 2 fuel oil (hydrocarbon treatment) (Table 1). The sewage and algae treatments were designed to add similar levels of C and N so that effects of different enrichment sources could be examined. Milorganite, a fertilizer (composted, dried sewage sludge) produced by the Mil- waukee sewage district, contained 35.8% C, 7.2% N (C:N ratio 5.0) and negligible hydrocarbons. Concen- trations of aluminum, chromium, copper and zinc were present in Milorganite at milligram per kilogram concentrations, with chromium and zinc notably elevated relative to the other treatments. Spirulina, a freshwater blue-green algae, was added to simulate estuarine "nuisance" blooms of blue-green algae that often result from anthropogenic nutrient enrichment (Paerl 1988). Initial attempts to obtain a consistent supply of a local North Carolina blue-green species, Microcystis aeru- ginosa, were unsuccessful, and commercially prepared Spirulina sp., consisting of 39.7% C and 8.7% N (C: N ratio 4.6), was used instead. No. 2 fuel oil is a refined product containing a high concentration of 2-methyl- phenanthrene and more aromatics than most crude oils (Neff and Anderson 1981). Hydrocarbon treatment sediments were prepared by adding No. 2 fuel oil at a rate of 0.74 mL/L of wet sediment. This concentration was selected to simulate chronic exposure typically experienced in shallow sediments for several years following an oil spill (e.g., Sanders et al. 1980:20-1300 mg/kg) and in past mesocosm studies (Grassle et al. 1981: 109 mg/kg). Final hydrocarbon concentrations in treatment sediments given to worms were deter- mined by extraction of wet sediments and gas chro- matography (IEA, Incorporated, method SW 846- 3550). Although preparation methods were identical for hydrocarbon/sediment slurries fed to S. benedicti and Capitella sp. I, final measured concentrations were 110 and 270 mg/kg dry mass, respectively (Table 1).

TABLE 1. Composition of sediment treatments used in evaluating the demographic responses of Capitella sp. I and Streblospio benedicti to sewage, blue-green algal, and hydrocarbon contamination.

Organic Ct Organic Nt Total hydro- (mg/wk) (mg/wk) carbont

(mg/kg dry Treatment Addition S. b. C. sp I S. b. C. sp. I mass)

Salt-marsh mud None 13 31 1.5 3.8 <2 Sewage Milorganite 17 42 2.4 6.0 <2

(12 mg/mL wet sed.) Blue-green algae Spirulina sp. 18 42 2.5 6.0 <2

(12 mg/mL wet sed.) Hydrocarbon No. 2 fuel oil 13 31 1.5 3.8 110,270

(0.74 mL/L wet sed.)

t Values are total for prepared sediments fed to worms.




Treatment sediments were added to polychaete culture dishes weekly. Capitella sp. I, which are substantially larger than S. benedicti at maturity, were reared in larger dishes containing roughly 2.5 times the sediment volume of S. benedicti. Weekly Milorganite and algae allocations of 12 mg for S. benedicti reared in small petri dishes (60 mm x 20 mm) and 30 mg for Capitella sp. I reared in larger dishes (90 mm x 25 mm) yielded identical sediment concentrations of sewage and algae for the two species. The two species were studied in sequence rather than simultaneously due to time and labor constraints. Milorganite and blue-green algae additions represented approximately a 35.0% carbon enrichment, and a 60% nitrogen enrichment over the marsh mud control and hydrocarbon treatment for both species (Table 1). Hydrocarbon additions produced no measurable change in sediment C and N content relative to control sediments. For this reason the sewage and algae treatments are referred to hereafter as organic enrichments, while the hydrocarbon treatment is not considered as such. Further details of culture conditions and treatment preparation are given in Bridges et al. (1994).

Demographic analyses

Age-specific survivorship was estimated from cohorts of 40-50 lecithotrophic larvae for each species in each treatment. The Capitella sp. I larvae were released by four females from stock cultures supplied by J.P. Grassle in August 1990. The S. benedicti larvae were released by 12 females from stock cultures es- tablished in September 1990 from collections at Pivers Island, North Carolina. For each species, equal num- bers of larvae from different females were placed in each treatment, ensuring equal representation among females. Competent larvae were settled into one of four sediment treatments and recruits were reared individually at 20?C on a 12:12 light/dark cycle at 32-36 g/kg salinity.

Cohorts were counted weekly to monitor survivorship. At maturity, cohort sizes were reduced because monitoring reproduction was too labor intensive to follow all individuals. For each species, 12-24 females per treatment (Table 2) were mated and monitored weekly (in the case of Capitella every 5-7 d) until death. The individuals discarded at maturity were treated as censored (i.e., we know that they survived up to the time they were discarded, but not how long they would have lived after that). Survivorship (1[x]) was calculated from the actuarial formula for censored data (Cox and Oakes 1984: Eq. 4.11).

Age-specific fertility was measured by counting the number of embryos or larvae per brood weekly. In many cases early-stage embryos were counted one week, and late stages of the same brood were counted the subsequent week. Fertility was assigned to the week when larvae were released. At 20'C, brood develop-

ment from fertilization to larval release takes "'~8-10 d in S. benedicti and 6-8 d in Capitella sp. I.

Two different population models, one a complete age classification and one a greatly simplified stage classification, were used to describe population growth. The age-classified model was derived from age-specific survivorship (Ix) and fertility (mr) schedules which were generated for each species in each treatment. These life tables were used to construct age-classified population projection matrices (Leslie matrices) using a projection interval of 1 wk. The survival probabilities (P,) and fertilities (F,) in the matrix were calculated using the birth-flow formula (Caswell 1989a: Eqs. 2.12, 2.22, 2.24)

p l( ( + 1) + I(i) (2) ' ()+ I(i- 1

F, - (1(0)1(1))112 (M + P1m +) (2) = 2

where 1(i) is survivorship from birth to age i and m, is the average number of female offspring per female in age class i.

Population growth rate was calculated as the dom- inant eigenvalue A of each matrix. The stable age distribution (w) and reproductive value distribution (v) were calculated as the corresponding right and left ei- genvectors, respectively. We also calculated the net reproductive rate Ro = lil.m. and the generation time T = ln RO/ln X.

The sensitivities of . to changes in the matrix entries Pi and F, were calculated from

aX WiVi+1 (3) dP, (w, v)

ax w'v, (4) dFI (w, v)

where (w,v) denotes the scalar product. The effect of each treatment on X, measured relative

to the control, was decomposed into contributions from each of the age-specific survivorship and fertility terms using the techniques outlined by Caswell (1989a). Let A(k) and c(C) denote the values of X for treatment k and the control, respectively. Then

A(k) X.(c) + E (a(k) - a XJ) ax , (5) iv daaiJ (A(k)+A(a)12

Each term in the summation is the contribution of the difference in the matrix entry a,U to the overall effect of treatment k on X. A similar formula was used to compare the two species within each treatment, de- composing the difference between population growth rate for Capitella sp. I and S. benedicti into contributions from each age-specific vital rate. Let A(c) and A(s) denote population growth rate of Capitella sp. I and S. benedicti, respectively, within a given treatment. Then

A(c) = X + (a(C) - a)) ax (6) Iv 'j aaj (A()+A(s))12




TABLE 2. Demographic data obtained for laboratory populations of Capitella sp. I and Streblospio benedicti reared in salt marsh sediment alone and with additions of composted sewage (Milorganite), blue-green algae (Spirulina), and hydrocarbons (No. 2 fuel oil). Where relevant standard errors are given, entries with the same superscript letter are not significantly different within species (P > 0.05) by ANOVA with a posteriori multiple comparison tests (Tukey).

Salt marsh sediment

Variable (control) Sewage Algae Hydrocarbon

No. females studied S. benedicti 22 18 14 14 Capitella sp. I 19 24 18 20

Average lifespan (wk) S. benedicti 53.2 ? 2.8a 42.6 ? 3.1b 21.4 ? 35c 48.3 ? 35ab

Capitella sp. I 40.1 ? 2.3a 36.4 ? 2.la 17.9 ? 2.4b 36.8 ? 2.3a Age at first reproduction (wk)

S. benedicti 11.3 li.a 11.3 ? 1.2a 17.6 ? 1.7b 30.2 ? 1.4c Capitella sp. I 9.6 ? 0.3a 4.4 ? 0.2b 5.9 ? 0.3c 10.8 ? 0.2d

Lifetime fecundity S. benedicti 606 ? 43a 527 ? 47a 43 ? 54b 79 ? 54b Capitella sp. I 1894 ? 454a 9782 ? 405b 3704 ? 467c 1245 ? 443a

Net reproductive rate (RO) (P = randomization probability for treatment vs. control) S. benedicti 323.7 237.4 (P = 0.0520) 5.3 (P < 0.0005) 31.8 (P < 0.0005)

Upper confidence interval 374.0 298.3 9.1 49.3 Lower confidence interval 275.6 178.4 1.9 15.9

Capitella sp. I 932.2 4698.8 (P < 0.0005) 989.4 (P = 0.9190) 631.7 (P = 0.6397) Upper confidence interval 1088.6 5242.0 574.4 816.2 Lower confidence interval 781.4 4150.2 1454.5 485.5

Within-treatment comparison of Ro for S. benedicti vs. Capitella sp. I P < 0.0005 P < 0.0005 P < 0.0005 P < 0.0005

Population growth rate (X/week) (P = randomization probability for treatment vs. control) S. benedicti

Age-classified model 1.46 1.41 (P = 0.4033) 1.09 (P < 0.0005) 1.11 (P < 0.0005) Upper confidence interval 1.50 1.44 1.13 1.13 Lower confidence interval 1.42 1.38 1.03 1.08

Stage-classified model 1.43 1.39 (P = 0.4293) 1.10 (P < 0.0005) 1.11 (P < 0.0005) Upper confidence interval 1.46 1.41 1.14 1.13 Lower confidence interval 1.40 1.33 1.05 1.08

Capitella sp. I Age-classified model 1.86 5.31 (P < 0.0005) 2.81 (P 0.0920) 1.67 (P = 0.7446)


Stage-classified model 1.79 4.06 (P < 0.0005) 2.55 (P = 0.0360) 1.63 (P = 0.6637) Upper confidence interval 1.84 4.30 2.73 1.68 Lower confidence interval 1.73 3.79 2.34 1.59

Generation time (T, in weeks) (P = randomization probability for treatment vs. control) S. benedicti 15.4 15.8 (P = 0.7696) 18.2 (P = 0.0225) 31.7 (P < 0.0005)


Capitella sp. I 11.0 5.1 (P < 0.0005) 6.7 (P < 0.0005) 12.6 (P = 0.0085) Upper confidence interval 11.6 5.3 7.3 13.0 Lower confidence interval 10.4 4.9 6.1 12.2

Within-treatment comparison of T for S. benedicti vs. Capitella sp. I P < 0.0005 P < 0.0005 P < 0.0030 P < 0.0005




Again, each term in the summation is a contribution, this time of a species difference in a matrix entry to the species difference in X. Comparisons among these terms pinpoint the vital rates and age classes where treatment effects have the greatest demographic consequences.

The age-classified model makes maximum use of the available demographic information. However, one of the interesting issues arising from the data is the effect of changes in age at maturity. Age at maturity is known to be demographically important, especially in rapidly growing populations (Lewontin 1965, Caswell and Hastings 1980, Caswell 1982a, Ebert 1985). However, since age at maturity does not appear explicitly in the population projection matrix, it is difficult to calculate the sensitivity of X to changes in age at maturity in that framework. Instead, we developed a simplified, two- stage model, with individuals classified only as juveniles and adults (Fig. 1). Juveniles survive to maturity with probability P. and require oX time units to do so. Because survival to adulthood requires surviving for a time units, P. can be expressed in terms of the time to maturity (ox) and the juvenile survival probability per unit time (uf1):

P= 1. (7)

Adults survive with probability PA = 92 and have an age-independent fertility F.

This alternative parameterization reduces the complete life table information to the four parameters U1, U2, F, and a. These parameters can be calculated from the life table in a number of ways. In order to get as close an agreement as possible between the age-classified and the stage-classified results, we used the following protocol. We defined m = a + 1 as the age class at maturity; m was calculated as the first age class with a nonzero fertility term in the age-classified matrix, and thus, by definition ca = m - 1. Juvenile survival probability P. was obtained as the probability of surviving to age class m in the age-classified matrix. Adult survival probability U2 and fertility F were cal-

culated as weighted means of P1 and F1 terms for the reproductive age classes in the age-classified model. Each age class was weighted according to its contribution to the stable age distribution in the age-classified model. Thus,

> w1P1 02 o (8)

(9) Ewi

E =m

The characteristic equation for this life cycle, derived from the life cycle graph using the methods of Caswell (1982b) is

PJFX-(U.+I)

1 - (10)

which can be rewritten as

At+ - U - PJF = 0. (11)

The sensitivities of A to changes in 01, 02, F, and (x can be derived by implicit differentiation of Eq. 11:

ax Pi aF (OL + 1)MO - u2X-I' (I12)

ax _ x au2 (t + 1)X -2t (13)

dA -AtX~+lln A. + u2Xoln . + (orJ'ln or,)F ax_= IInX+9 (14) dot (Y-(T + 1)IT-

= 1(15) aul ((x + 1)A' - o2a(X-X(15

The contributions of treatment effects on a,, U2, L

and F to effects on x were calculated from

X () () +ax ax ax ax -

.(k)~ A(c) + -AM/fc, + -AC2 + -AL + -=AF acr, aU2 aot aF

(16)

where AM1, Au2, Aot, and AF are the differences between treatment and control values for each of the parameters, and the sensitivities are evaluated at the mean of the two parameter sets. We used the same approach to calculate contributions of each of the four parameters to species differences within each treatment.

Estimation of confidence intervals and significance Statistical analysis of demographic data is compli-

cated by the fact that there exist no simple results giv- ing the distributions of demographic statistics such as x or LTRE contributions in terms of the sampling distribution of the vital rates. The solution is to use computer resampling methods (Caswell 1989a, Efron and

F3PJ X P (X)

PA -1

FIG. 1. The life cycle graph for the simple two-stage model used to investigate age at maturity. PJ is the probability of surviving to maturity, PA the survival probability of adults, and F the mean fertility of adults. The exponents on X indicate the time (in weeks) required for the indicated transition.




Tibshirani 1991, 1993, McPeck and Kalisz 1993). We used this approach to test the significance of treatment and species effects on X and to estimate confidence intervals on X and on the contributions to treatment effects.

We estimated confidence intervals using bootstrap resampling. The original data consist of a set of mea- surements for individual polychaetes. Some were followed for their entire life, some were censored before death and some had their reproductive activities recorded. The bootstrap procedure duplicates the design of the experiment as much as possible. Individuals, with their histories, were sampled with replacement from the set of individuals within each species-treatment combination. A life table was generated for each such combination just as for the original data, including the treatment of censored individuals. From the re- sulting life table we generated an age-classified matrix, and from the matrix we generated the two-stage model. The demographic statistics X, w and v, the sensitivities, and the contributions to treatment effects on . were calculated for each bootstrap data set. We used the percentile method to generate 95% confidence intervals for each statistic, based on 2000 bootstrap estimates. (Because the quantities are very nearly median unbi- ased, there was no need for bias-correction in the percentile method; see Efron and Tibshirani 1993).

Non-overlapping confidence intervals are often used as a crude approximate significance test of the difference between treatment means. However, a more pow- erful and rigorous approach is to use permutation tests (Edgington 1987, Manly 1991; see Walls et al. 1991 and Brault and Caswell 1993 for demographic applications).

Consider the treatment effects for one species. We want to test whether sewage, hydrocarbons, and blue- green algae have significant effects on A, measured relative to the control. In each treatment there is a set of individuals. Some are followed for their entire lives, some are censored, and some have reproductive output recorded. The null hypothesis is that the treatment received by an individual has no effect on its fate. As a test statistic we use, for treatment k

0 = IX(k) - (c)I. (17)

Under the null hypothesis, 0 = 0. We calculate the distribution of 0 under the null hypothesis by permuting individuals-with their histories-among treatments, maintaining the sample sizes for each treatment. From each permutation of the data we calculate the test statistic 0. Because the number of possible permutations is enormous in this case, we used a random sample of 2000 permutations. For each permutation, we repeat the whole series of calculations, from life table to matrix model to A, and then calculate 0. If the observed value of the test statistic is greater than (1 - c)% of the value in the permutation distribution, then the observed value of 0 is significant at the oa% level.

Permutation tests make no parametric assumptions about the distribution of the underlying data, and do not require that the data be obtained as a random sample from some hypothetical infinite population (Edgington 1987).

We performed several tests: (1) tests of the simple main effects of each treatment, relative to the control, within each species; (2) tests of the simple main effect of species differences within each treatment; (3) tests of the difference between the sewage and algae treatments within each species; and (4) a test of the interaction of species and treatment, to see if the two species respond differently to the set of pollutant treatments.

There is some dispute about testing interaction with permutation tests (cf. Edgington 1987, Manly 1991). We used the following approach, which is especially suited to cases where one of the treatments has only two levels (species in this case). The null hypothesis of no interaction implies that the species responses to the treatment are parallel. If they are, then the species differences within each treatment are all estimates of the same quantity. We use the variance of the four species differences as our test statistic, Oint. To get the distribution of Oint under the null hypothesis we permute individuals among treatments, within species. This eliminates treatment effects, so that the four species differences are four estimates of the species effect. From this permuted data set we calculate the variance of the species differences, and repeat this for 2000 random permutations. The significance of the observed value Of Oint is given by the proportion of values greater than or equal to the observed value.

RESULTS

Life table data

Treatment effects on survivorship schedules were similar for Capitella sp. I and S. benedicti (Fig. 2, Appendix). Blue-green algal treatments caused sharp reductions in survivorship during the first 5-10 wk of life (Fig. 2), and significantly shorter life-spans than the marsh mud controls in Capitella sp. I (F3 80 = 18.99, P < 0.0001) and S. benedicti (F3,67 = 18.19, P < 0.0001) (Table 2). Depletion of overlying water 02 concentration (to < 1 mL/L) was observed in the blue-green algal treatment relative to the other treatments (2.1- 4.6 mL/L) (Fig. 3 in Bridges et al. 1994). Oxygen depletion often accompanies algal-bloom related enrichment in estuaries (Paerl 1988). Oxygen reductions did not occur in the Milorganite treatment, perhaps because the organic matter (Milwaukee sewage), was less readily degraded (i.e., less labile) than that in Spirulina. The sewage treatment caused a significant decrease compared to the control in average life-span of S. benedicti but not Capitella sp. I (Table 2). The hydrocarbon treatments had no effect on survival (Fig. 2) or average life- span (Table 2) in either species compared to the control. Larval survivorship to settlement was uniformly high




(>90%) for both species in all treatments except for S. benedicti in the blue-green algae treatment (Fig. 2A, Appendix).

Fecundity was generally an order of magnitude lower in S. benedicti than in Capitella sp. I (Fig. 3, Appen- dix). Treatments affected fecundity schedules differently in each species. In S. benedicti average first reproduction was delayed -19 wk in the hydrocarbon treatment (to 30.2 wk) and -6 wk in the blue-green algae treatment (to 17.6 wk) relative to the control and sewage treatments (both 11.3 wk) (F36, = 45.526, P < 0.0001). S. benedicti females in the blue-green algae and hydrocarbon treatments exhibited large reductions in weekly and total lifetime fecundity (Ym,) relative to the control and sewage treatments (Fig. 3A, Table 2) (F367 = 36.02, P < 0.0001).

The mean age at onset of reproduction in Capitella sp. I was accelerated dramatically in the sewage (4.4 wk) and blue-green algae (5.9 wk) treatments relative to the control (9.6 wk), while the presence of hydrocarbons caused a minor, but significant delay (to 10.8 wk) in first reproduction (F3378 = 154.6, P < 0.0001; Table 2). Total lifetime fecundities (Ym,) were elevated in the two organic enrichments compared to the control treatment (F3,80 = 86.4, P < 0.0001; Table 2). This effect was especially noteworthy for the sewage treatment, where Ymx was five times that of the marsh mud

control. In the hydrocarbon treatment lm, though roughly three-quarters that of the control, was not sta- tistically different.

Population projections

Population growth rate for S. benedicti (hereafter given in units of A per week) was reduced in the blue- green algae (A = 1.09, P < 0.0005) and hydrocarbon treatments (X = 1.1 1, P < 0.0005) relative to the marsh mud control (A = 1.46), while sewage had little effect (X = 1.41, P = 0.3958) (Fig. 4A, Table 2). Negative treatment effects on net reproductive rate (RO) were even greater than observed effects on X. Net reproductive rate was marginally affected (27% reduction) in sewage, but was reduced by 98% in blue-green algae and 90% in hydrocarbon treatments relative to control values (Table 2). Differences between the magnitude of effects in the two estimates of fitness, X (the rate of increase per week) and Ro (net reproductive rate), result from the large influence of treatments on generation time (Table 2). Generation time in S. benedicti was shortest in the marsh mud control sediments (15.4 wk) and sewage treatment (15.8 wk), 18% longer in the blue-green algae treatment (18.2 wk), and approximately double the control time in the hydrocarbon treatment (31.7 wk).

control - sewage . algae -a*-hydrocarbon

A) Streblospio benedicti 1.0

*.0.8,

0.6- 0 >>6 0.4-

0.2-

0.0 0 10 20 30 40 50 60 70 80

B) Capitella sp. I

1.0

0.6

. 06,

0

0.4

0.0 0 10 20 30 40 50 60 70 80

Age (weeks)

FIG. 2. Age-specific survivorship schedules for A) S. benedicti and B) Capitella sp. I reared individually in the laboratory in four sediment treatments.

control o sewage . algae - hydrocarbon

A) Streblospio benedicti 15

20B) Capitella sp.I1

30

2L 00

IL. 5oo

0 10 20 30 40 50 60 70 80 Age (weeks)

FIG. 3. Age-specific fecundity schedules for (A) S. benedicti and (B) Capitella sp. I reared individually in the laboratory in four sediment treatments. Data are presented as 3-wk running averages for ease of visualization.




Estimated population growth rates were uniformly higher for Capitella sp. I than S. benedicti in all four treatments (P < 0.0005) (Fig. 4B, Table 2). Values of X for Capitella sp. I were elevated over control values (1.86) in the sewage treatment (5.31, P < 0.0005) and were marginally higher in the blue-green algae treatment (2.81, P = 0.0920), but were unaffected in the hydrocarbon treatment (1.67; P = 0.7446). Ro was about five times higher in the sewage treatment than the control, but was not significantly affected in the blue-green algae treatment or hydrocarbon treatment relative to the control (Table 2). Capitella sp. I generation time in control sediments was 11.0 wk, considerably shorter than that of any S. benedicti treatment. Sewage additions more than halved Capitella sp. I generation time (5.1 wk), the algal additions shortened it (6.7 wk), while hydrocarbon additions extended it to 12.6 wk (Table 2). The interaction of species and treatments was highly significant (P < 0.0005), supporting

the conclusion that population growth rates of the species respond differently to pollutants.

Net reproductive rate (RO) is sometimes assumed to be a surrogate for fitness. Our results show the danger of this assumption when the population is not station- ary. For example, Ro of S. benedicti was 600% greater in the hydrocarbon than blue-green algae treatment (31.8 vs. 5.3), whereas A was not significantly different (1.11 vs. 1.09). A conclusion, based on Ro, that S. benedicti would increase more rapidly under conditions of the hydrocarbon than the blue-green algae treatment would be wrong. Similarly, the interpretation that Cap- itella population growth would not differ in control and blue-green algae treatments, based on comparable Ro values (932.2 vs. 989.9)'also would be incorrect. Pop- ulation growth rate in the algae treatment is twice as rapid as in the control (A = 2.81 vs. 1.86). These dis- crepancies arise because Ro measures only expected reproductive output, and ignores its timing within the life cycle. As we have shown, these pollutants have major impacts on timing.

Stable age distributions differed among treatments and species. Treatments associated with larger values of X had stable age distributions skewed toward younger individuals. For example, the percentage of the S. benedicti stable population <2 wk of age was 53% in the control, 52% in sewage, 34% in blue-green algae, and 22% in the hydrocarbon treatments, and 72, 97, 91 and 65%, respectively, for Capitella sp. I. In all treatments Capitella sp. I populations are "younger" on average than those of S. benedicti, in part a conse- quence of shorter life-span (Table 2).

While reproductive values (normalized to age at birth) differed substantially among species and treatments, only in S. benedicti did general distribution patterns vary with treatment. Maximum reproductive values in S. benedicti were achieved at 11-12 wk in the marsh mud control, at 15-20 wk in the two organic enrichments, and not until 36-40 wk in the hydrocarbon treatment. Maximum reproductive values in Cap- itella sp. I were attained earlier than in S. benedicti, at 8-9 wk in the marsh mud control, 7-11 wk in the sewage treatment, 12-13 wk in the blue-green algae treatment and 10-11 wk in the hydrocarbon treatment.

Parameter values for the simple stage-classified model (Fig. 1) are given in Table 3. The stage-classified model produces values of X that agree with those from the age-classified model to within 2% for S. benedicti (Fig. 4A). The values for Capitella sp. I agree to within 10% except for the sewage treatment, which is low by 24% (Fig. 4B). This reflects the rapid reproduction and high reproductive output, and consequent rapid population growth in this treatment. In both species, however, the age-classified patterns of treatment and species differences in X are reproduced quite successfully by the stage-classified model (Fig. 4, Table 2).

A) Streblospio benedicti C Age-classified model

1.50- o Stage-classified model

B) Capitella sp. I

T 6.00 -a

E~~~~~~~ 0.50-~~~~~~~~~~~~-

4.00

coto seag 2lglh oc lo control sewage algae hydrocarbon

Treatment

FIG. 4. The values of population growth rates for (A) S. benedicti and (B) Capitella sp. I, expressed as X per week, as a function of treatment for the age-classified and the stage- classified models. Error bars are 95% bootstrap confidence intervals. Within-species pairwise comparisons of treatment Xt's to control Xt's (within model type) were made (ap < 0.0005, = 0.0920, cp = 0.0360). Nonsignificant comparisons were S. benedicti sewage treatment, age-classified P = 0.4033, stage-classified P = 0.4293; Capitella sp. I hydrocarbon treatment, age-classified P = 0.7446, stage-classified P = 0.6637. Within-treatment pairwise comparisons of Xt between species indicated Capitella Xt was higher than Stre- blospio X (P < 0.0005) for all treatments in both model types. Species x treatment interaction: P < 0.0005.




Decomposition of treatment effects on A

At this point we have documented the significant treatment effects on the age- and stage-specific vital rates. The effects differ in many ways from treatment to treatment, from age class to age class, and from one species to the other. These values of X synthesize there effects into a net impact on population growth rate. However, we have not yet considered how much each vital rate effect contributes to the overall impact on X. The decomposition analyses (Eqs. 5, 6, and 16) measure these contributions, and will allow us to identify the ages and vital rates whose modification by contaminants are responsible for the observed differences in population growth rate.

Age-classified model.-In S. benedicti, the effects of blue-green algae on X were due to roughly equal negative contributions from survival during the first 6 wk of life and from fertility during weeks 8-17 (Fig. 5A). In contrast, the comparable reduction in X in the hydrocarbon treatment was almost completely due to neg-

TABLE 3. Parameter values for the stage-classified model of a population of estuarine polychaetes shown in Fig. 1.

(a, (2 (juvenile (adult (x survival survival F (time to proba- proba- (adult matur-

Parameter bility) bility) fertility) ity)

Streblospio benedicti Control 0.9966 0.9995 7.7158 7.0 Sewage 0.9827 0.9968 8.9309 8.0 Blue-green algae 0.8891 0.9420 1.2263 9.0 Hydrocarbon 0.9885 0.9902 1.1522 18.0

Capitella sp. I Control 0.9958 0.9998 48.0793 6.0 Sewage 0.9747 1.0000 215.0093 2.0 Blue-green algae 0.8755 0.9027 104.2857 3.0 Hydrocarbon 0.9964 0.9999 31.2374 7.0

A) 0.005 0.01

0 2 0 5 o

.01 -0.01 c -0.005

-0.50 -0.02 .0 0.01

-0.05-0 -0.03

o -0.015 1 -0.04- U ~~~~~~~ ~~sewage C)sewage -0.021 .. . . . . . . .-0.05I I I

0 5 1 0 1 5 20 25 30 0 5 I 0 1 5 20 25 30 0.01- 0.01-

0. 0-

.0.01 0~~~~~~~~~' -0.01- .0 . 0.03 .0 -0.03- C 4.

o -0.04t e o -0.04 hydrocarbon hydrocarbon u

-0.051--- . I. . .I ' -0.05i 0 5 10 15 20 25 30 0 5 10 15 20 25 30

0.01- 0.01

o -o

- 0.01 0 -0.01 0

.. -0.02-0 002 .0 . 0.03-003

C~~~~~~~~~~~~ o -0.04 0 004 algae U algae

-0.05.- - -0.05 . . . . . ' o 0 1

' ..25 300 5 10 15 20 25 30

Age (weeks) Age (weeks)

FIG. 5. Decomposition analyses of treatment effects within species for the age-classified model, using Eq. 5. Error bars are pointwise 95% bootstrap confidence intervals. (A) Contributions of age-specific survivorship (P) and fertility (F) effects to treatment effects on S. benedicti population growth rate (X), shown for sewage, algae, and hydrocarbon treatments relative to controls. Negative values indicate a disadvantage (or negative contribution to X) relative to controls.




ative contributions from fertility during weeks 8-17. None of the survival or fertility differences after 20 wk of age (Figs. 2 and 3) contribute significantly to effects on X (Fig. 5A). The survival and fertility contributions to the sewage effect have pointwise confidence intervals that overlap zero, which is compatible with, although not guaranteed by, the nonsignificant effect of sewage on X.

Demographically important treatment effects on Capitella sp. I occurred at an even earlier age (Fig. 5B). The exceedingly high X obtained in the sewage treatment resulted solely from a fertility advantage during weeks 3 through 6. In the blue-green algae treatment, negative survival contributions in the first 5 wk of life were outweighed by positive fertility contributions during weeks 4-7. In the hydrocarbon treatment a negative fertility contribution during weeks 8-9 (Fig. 5B) did not cause a significant reduction in X. All of the significant contributions occur before Capitella sp. I reaches 10 wk of age; thus the large fecundity

differences observed among treatments from 10 to 35 wk of age (Fig. 3B) have little bearing on population growth.

Though the two organic enrichment treatments in- volved nearly equal additions of C and N (Table 1), values of X for both S. benedicti and Capitella sp. I were considerably higher in the sewage than in the blue-green algae treatment for both model types when compared within species (P < 0.0005 for both species, age- and stage-classified models). Decomposition analyses indicate that most of the reduction in X in the algae treatment relative to the sewage treatment resulted from fertility advantages of both species in the sewage treatment.

Life table contributions to differences between Cap- itella sp. I and S. benedicti were evaluated separately for each treatment (Fig. 6). In each case Capitella's superior X was achieved by a tremendous fertility advantage during the initial few weeks of reproduction, when S. benedicti had not yet matured. This advantage

B) 0.05- sewage 1.5- sewage

o nI 0

S -0.05 . 0.5A IC

o 0

-0.1 0 5 1 0 1 5 20 25 30 0 5 1 0 1 5 20 25 30

0.05- hydrocarbon 1.5 hydrocarbon

o o 5 0 15 C

0 o ~~~~~~~~~~~~~~~0.5

2C -0.05 0. o 0

L~~~~~~~~~~~~ 11 - -0.1 0 5 10 15 20 25 30 0 5 10 15 20 25 30

0.05- algae 1.5a lgae

o o~~~~~~~~~~~~~u 0 0-

0

o 0

.0 .0~; 0.5- 4C -0.5 0

C o 0 0

-0.1 . . . 0 5 10 15 20 25 30 0 5 10 15 20 25 30

Age (weeks) Age (weeks)

FIG. 5. (Continued.) (B) Contribution of age-specific survivorship (P,) and fertility (F,) effects to treatment effects on Capitella sp. I population growth rate (X), shown for sewage, algae, and hydrocarbon treatments relative to controls. Positive values indicate an advantage (or positive contribution to X) relative to controls. Where no error bars appear, confidence intervals are too small to visualize.




was experienced earliest in the two organically enriched treatments (Fig. 6B,D). In the algal and hydrocarbon treatments, there is a small contribution from a survival advantage over S. benedicti in the first 5 wk of life (Fig. 6C,D), suggesting superior tolerance by Capitella sp. I juveniles of conditions in these treatments.

Stage-classified model.-The contributions of juvenile and adult survival, fertility, and age at maturity to effects on A are shown in Figs. 7 and 8. For S. benedicti, the insignificant effect of sewage on A derives from a balance between negative contributions from juvenile survival and age at maturity, and a positive contribution from increased fertility (Fig. 7A). All four parameters contributed to the negative effect of the blue-green algae treatment on S. benedicti, with the least contribution from adult survival (Fig. 7A). The effect of the hydrocarbon treatment was mostly due to delayed maturity and reduced fertility (Fig. 7A). In Capitella sp. I the sewage and blue-green algae treatments increased A mainly by accelerating maturity, and secondarily by increasing fertility (Fig. 7B). These effects outweigh

the slight negative contributions of reduced juvenile survival. The hydrocarbon treatment, in contrast, had little effect on vital rates of Capitella sp. I and no significant effect on A. The interspecies differences in A in all four treatments were due to accelerated maturity and increased fertility in Capitella sp. I, compared to S. benedicti (Fig. 8). Accelerated maturity was the more important of the two effects in all treatments except the control sediments (Fig. 8), where interspecies differences in A were smallest. In control sediments, Cap- itella's fertility advantage was of primary significance. In none of the treatments did effects on juvenile or adult survival make a noticeable contribution to effects on X.

DISCUSSION

Significance of maturation time

Our results identify for the first time probable demographic mechanisms underlying Capitella's well- known propensity to dominate organically enriched settings (see citations in Bridges et al. 1994). Previous

0.2- 2.5- A) control F.|.| B)sewage

0.15 Ti 2 T C ~~~~~~~~~1.5 o 0.1 1B[

.0

-0.05 -0.

o 0.05 50 o ~ ~ ~~I..0.5

0 - NA0

-0.05-.-0.5 0 5 10 15 20 25 30 0 5 10 15 20 25 30

0.7- C) hydrocarbons D) algae

0.1 TI0.6 T :~~ 0.05 T{1~~~~~~iJ. ~0.

C ~ ~~~~.0.4

0 ~ ~ ~ ~ ~ ~ ~ 0 0.05- ~ ~ ~ ~ ~ ~ ~ 0.

-0.05.~~~~~~~~~~~03 0 T5 10 1 0 2 00 5 1 5 2 5 3

FIG. 6. Decomposition of species effects within treatments for the age-classified model, using Eq. 6. Positive values indicate a positive contribution of Capitella sp. I matrix entries relative to S. benedicti matrix entries. Error bars are pointwise 95% bootstrap confidence intervals. Where no error bars appear, confidence intervals are too small to visualize.




workers have emphasized growth and fecundity as the source of Capitella's explosive responses to enrichment (Chesney and Tenore 1985, Gremare et al. 1988). The LTREs reveal this response is driven primarily by reduced age at maturity and only secondarily by elevated fecundity early in life (Figs. SB, 7B). In expanding populations, progeny born earlier have a greater impact on population growth (Lewontin 1965, Mertz 1971, Ca- swell and Hastings 1980), while in declining populations, earlier reproduction hastens decline (Mertz 1971). In Capitella sp. I, organic enrichment accelerates maturity, thereby magnifying the importance of increased fecundity. Population explosions result from these ef-

fects, but they often are followed by enormous population declines (Chesney and Tenore 1985).

Sediments identical in C and N content, however (as in the sewage and blue-green algae treatments), can produce different life history and population-level responses, through variable effects on survival, maturation rate, and fertility. Differences in lability of the organic matter, or in the presence of micronutrients (Marsh and Tenore 1990) may be responsible.

In contrast to the two organic enrichments, which have different demographic effects on S. benedicti, hydrocarbons and blue-green algae produce comparable reductions of K in S. benedicti relative to the control

A) Streblospio benedicti B) Capitella sp.l 0.10 2.50

sewage 2.0 sewage T

::0.0 2 1.5T

0.10- 2.50

~~~'I~~LU 1.00-

C 0.10- 0 o ~~~~~~~~~~0.501

-0.20 -0.0 ,

1 F 2 GI F 0.10- 2.50

algae algae 2.00-

c 0 1.50 T C 0.10 r10 o 1 . j 0 - L 0. 0 0- - ----.50 T

-0.20 10.00-

I -0.50 p _

1 G2 F a a1 62 F a 0.10- 2.50

hydrocarbon hydrocarbon 2.00-

2 o~~~oo~~~ j~~ 1.50-

II 1.00- T ~~0.50-

-0.20 1 00-LI.

I I I I ~~~-0.50- ai G2 F a 2 F a

FiG. 7. The contributions of differences in cr (juvenile survival probability per unit time). 02 (adult survival probability), F (adult fertility), and at (time to maturity) to treatment effects on X, in the two-stage model. Contributions are calculated relative to the control using Eq. 16. (A) Streblospio benedicti, and (B) Capitella sp. I. Error bars are pointwise 95% bootstrap confidence intervals.




treatment (Fig. 4A), but do so through different life history mechanisms. Excessively delayed maturation combined with reduced fertility are the sources of hydrocarbon reductions in A, whereas inhibitory effects of blue-green algae result largely from lowered survival and fertility and increased age at maturity (Figs. 5A, 7A).

Species-specific responses to contaminants

Unlike Capitella sp. I, S. benedicti populations did not respond positively to Milorganite additions in our experiments. Although S. benedicti is sometimes associated with sewage inputs (Levin 1986), a field experiment involving the application of Milorganite in the Indian River Estuary produced the following am- biguous results. Milorganite applied in cages increased densities of S. benedicti (and also of Capitella sp.), but Milorganite applied without cages and cages without Milorganite had no such effect. The failure of S. benedicti to respond to sewage in our study could be due to lower enrichment levels, to inhibitory effects of met- als, to the culture conditions, or to the inability of S. benedicti to utilize sewage-born carbon directly (Gear- ing et al. 1991).

Comparison of the two polychaetes indicates that S. benedicti fares poorly relative to Capitella sp. I under conditions created by blue-green algal enrichment. Our analyses show that effects of the blue-green algae treat-

ment on fertility are demographically more important than effects on survival (Figs. 5, 7). While oxygen depletion may have reduced survivorship of both S. benedicti and Capitella sp. I in the blue-green algae treatment (Fig. 2), both species tolerate intermittent periods of very low oxygen in the field (Dauer et al. 1992). Forbes et al. (1994) reported higher individual growth and tissue production efficiency of Capitella sp. I fed nitrogen-enriched diets under conditions of low (- 100 pmol) than high (-200 pmol) oxygen. Based on their results we might have expected more rapid maturation and higher population growth in our low-oxygen, blue-green algae treatment than in the bet- ter oxygenated sewage treatment. However, we observed the opposite: later maturation, lower fertility and reduced X in blue-green algae relative to sewage (Table 2, Fig. 4B).

Capitella, which is recognized as tolerant of No. 2 fuel oil (Carr and Reish 1977), can resist toxic effects of petroleum by increasing the activity of a detoxifying mixed-function oxidase (Lee and Singer 1980). Both Capitella sp. and S. benedicti colonized sediments following an intertidal spill (Grassle and Grassle 1974) and following experimental field applications (Hyland et al. 1985) of No. 2 fuel oil. However, neither species was present in Narragansett Bay mesocosms treated with No. 2 fuel oil additions (Grassle et al. 1981). From our results we infer that chronic sublethal exposure to

2.50 -2.50- control sewage

2.00 -2.00- T

0 1.50 - 1.50s

g 1.00- 1.00-

o 0.50 |0. 0H5 _n 0.00 ~~~~~~~~~~0.00

-0.50 i I I I -0.50 I I I

a1 (2 F oc al a F oc

2.50 2.50- algae hydrocarbon

2.00- 2.00-

, 1.50| 1.50

1.00 1.00-

0 0.50 r 0.50- 0.00 0.00~

-0.50 i I I I -0.50- 2 F oc a2 F oc

FIG. 8. The contributions of differences in cr0, (02, F, and cx to species effects on X in the two-stage model. Positive values indicate advantage of Capitella over Streblospio. Error bars are pointwise 95% bootstrap confidence intervals. Where no error bars appear, confidence intervals are too small to visualize.




hydrocarbons, having little effect on survivorship and growth, can have enormous consequences for population dynamics through delayed maturation and reduced fecufidity. That S. benedicti is much more sensitive to these inhibitory effects (Figs. 6 and 8), may explain why Capitella usually appears before S. benedicti, and at higher densities, in the face of hydrocarbon stress (e.g., Grassle and Grassle 1974).

Limitations and benefits of laboratory experiments

The goal of these experiments was to gain insight into the mechanisms underlying the responses of polychaetes to pollutants. Like any such experiments, they eliminate or control many factors known to be important in the field. The fact that field populations do not grow at the high rates projected from the laboratory experiments (e.g., Levin and Huggett 1990) is probably related to the absence of predation, parasites, compe- tition, and water motion in our laboratory studies, and to lack of seasonal variation experienced by different cohorts. Previous demographic comparisons of field and laboratory populations of S. benedicti found that the laboratory populations resembled only one of four seasonal cohorts in the field (Levin and Huggett 1990). Life-spans, vital rate schedules, and X values for laboratory populations were more similar to the fall cohort, which overwinters, than to the shorter lived spring, early summer, or late summer cohorts (Levin and Huggett 1990). These results emphasize the potential difficulties in using single laboratory cohorts to predict overall population responses to contaminants, and indicate the importance of incorporating seasonal environmental variation (e.g., temperature, food, day length) into LTRE designs.

Demographic analyses provide the most complete picture of the population-level consequences of individual responses to environmental stress. Because collection of demographic data is difficult, it is important to extract the maximum information from the available data. Use of LTREs and decomposition analyses to identify the age- or stage-specific vital rates that are responsible for environmental effects can help focus laboratory and field investigations on life history fea- tures and life stages of greatest demographic significance. For example, results for both S. benedicti and Capitella sp. I indicate that treatment effects on older individuals (>15 wk) have little or not impact on population dynamics. Such individuals would be poor choices for use in bioassays employing opportunistic species to provide ecologically relevant measures of sediment contamination. Our results also suggest that pollution-driven changes in age at maturity may hold the key to understanding effects of sewage and hydrocarbon contamination on estuarine polychaetes. In addition, they demonstrate the potential importance of pre-reproductive survival in evaluating eutrophication effects.

LTRE's can help identify suitable indicators of en-

vironmental stress in the field and aid selection of test species for laboratory bioassays. Polychaetes such as Capitella sp. I and S. benedicti, which are geographi- cally widespread, easily reared in standing seawater, and exhibit distinct demographic responses to common contaminants, offer unusual promise in this regard. Measures of body size, age at maturity, fecundity, and changing abundance and population size structure, when interpreted within a demographic framework, can provide insight into the condition of estuarine sediments.

The combination of analytical techniques applied in this study allow thorough quantification of the significance of pollutant impact on life cycle timing and provide an exceptionally complete picture of how pollutants affect population growth. This level of understanding would not be possible without population- based approaches. It is noteworthy that these demographic methods are readily applied to factors other than pollutants. They can, for example, be employed to examine population-level consequences of natural environmental variation, life history differences, and genetic variation over space or time.

ACKNOWLEDGMENTS

This research was supported by EPA Grant R81-72-52010 to L. Levin and by EPA Grant R81-8408-0100 and NSF Grant DEB-9211945 to H. Caswell. Some of the analyses were developed at the Summer School on Modeling Structured Pop- ulations in Marine, Terrestrial, and Freshwater Ecosystems, supported by funds from ONR and NSF We thank D. Savidge for assistance with MATLAB, and L. Addessi for helping with the final stages of Capitella life table data collection. J. Grassle kindly supplied the initial Capitella sp. I used to establish our cultures and C. Oviatt provided the No. 2 Fuel Oil. The manuscript was improved by suggestions from J. Zedler, M. Cochran and an anonymous reviewer. WHOI Con- tribution 8625.

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APPENDIX Life table entries for the age-classified model. The survivorship l, is the probability of survival from birth to age x. The

maternity function m, is the mean number of offspring produced per week by an individual in age class x. Note that age is measured from zero, but that age classes begin with 1.

Marsh mud control Sewage (Milorganite)

Age Streblospio Cap itella Streblospio Capitella (wk) . M M M m

0 1.000 1.000 1.000 1.000 1 1.000 0.00 1.000 0.00 0.971 0.00 1.000 0.00 2 1.000 0.00 0.975 0.00 0.97 1 0.00 0.950 0.00 3 1.000 0.00 0.975 0.00 0.971 0.00 0.950 0.00 4 0.977 0.00 0.975 0.00 0.886 0.00 0.950 222.13 5 0.977 0.00 0.975 0.00 0.857 0.00 0.950 170.94 6 0.977 0.00 0.975 0.00 0.857 0.00 0.950 339.69 7 0.977 0.00 0.975 0.00 0.857 0.00 0.950 234.15 8 0.977 0.00 0.975 15.03 0.857 0.00 0.950 244.13 9 0.977 1.50 0.975 95.29 0.857 0.00 0.950 264.31 10 0.977 8.75 0.975 64.63 0.857 3.31 0.950 301.58 11 0.977 8.16 0.975 62.79 0.857 9.22 0.950 308.33 12 0.977 16.34 0.975 64.05 0.857 14.36 0.950 231.23 13 0.977 15.16 0.975 48.00 0.829 10.41 0.950 318.46 14 0.977 12.09 0.975 35.08 0.829 13.15 0.950 205.63 15 0.977 7.84 0.924 41.53 0.829 8.12 0.950 227.33 16 0.954 6.40 0.924 48.36 0.829 12.91 0.950 186.92 17 0.954 9.60 0.924 23.31 0.829 13.74 0.950 176.25 18 0.954 10.64 0.924 55.89 0.829 11.38 0.950 206.06 19 0.954 7.83 0.924 47.89 0.829 15.41 0.950 182.29 20 0.954 11.50 0.942 59.67 0.800 15.06 0.950 161.46 21 0.954 10.21 0.924 34.56 0.800 9.28 0.910 131.70 22 0.954 10.67 0.924 31.39 0.800 10.00 0.910 86.91 23 0.954 11.33 0.924 20.22 0.800 12.03 0.871 66.98 24 0.954 7.26 0.924 28.69 0.800 8.91 0.792 95.58 25 0.954 7.83 0.924 12.92 0.800 8.94 0.792 48.65 26 0.930 9.85 0.872 31.41 0.800 14.22 0.792 79.10 27 0.930 3.73 0.821 6.13 0.800 6.69 0.792 68.38 28 0.930 6.33 0.770 15.60 0.800 10.28 0.673 48.21 29 0.930 5.10 0.770 10.77 0.800 3.78 0.673 61.91 30 0.930 4.00 0.770 12.40 0.800 7.75 0.673 28.26 31 0.907 3.61 0.770 9.04 0.800 5.66 0.673 46.29 32 0.861 6.35 0.770 12.63 0.800 8.81 0.633 40.00 33 0.861 4.35 0.770 11.63 0.743 5.21 0.633 35.28 34 0.837 6.13 0.770 4.20 0.714 9.96 0.633 19.38 35 0.837 7.75 0.770 5.17 0.714 4.71 0.594 17.33 36 0.814 6.47 0.770 4.07 0.657 7.62 0.554 28.54 37 0.814 7.00 0.770 25.04 0.657 4.46 0.515 19.08 38 0.814 8.59 0.718 18.57 0.657 4.19 0.515 8.04 39 0.791 4.69 0.667 11.23 0.657 7.27 0.475 27.38 40 0.791 9.53 0.667 12.38 0.629 4.58 0.396 32.55 41 0.791 6.44 0.616 8.21 0.543 2.23 0.317 11.00 42 0.791 4.31 0.564 7.45 0.543 4.14 0.237 53.17 43 0.791 6.50 0.462 3.50 0.514 1.65 0.237 40.00 44 0.791 7.69 0.411 4.19 0.486 1.50 0.237 50.17 45 0.791 9.63 0.411 7.13 0.457 3.78 0.198 44.80 46 0.791 5.13 0.308 10.50 0.400 3.13 0.158 61.00 47 0.791 6.91 0.308 9.67 0.400 1.63 0.158 54.00 48 0.767 4.83 0.257 0.00 0.371 3.69 0.158 66.00 49 0.767 5.33 0.257 0.00 0.286 0.50 0.158 60.13 50 0.767 3.90 0.205 0.13 0.286 0.00 0.158 35.38 51 0.767 5.87 0.205 14.00 0.257 4.00 0.119 24.83 52 0.767 2.40 0.205 0.00 0.229 0.00 0.119 33.33 53 0.767 4.73 0.205 0.00 0.229 0.30 0.119 28.83 54 0.767 2.37 0.205 0.00 0.171 0.00 0.079 25.00 55 0.698 3.04 0.205 16.00 0.171 0.00 0.079 0.00 56 0.674 1.64 0.205 0.00 0.143 0.00 0.079 27.25 57 0.674 2.09 0.205 0.00 0.143 0.00 0.040 1.50 58 0.674 2.55 0.154 0.00 0.143 0.00 0.040 36.00 59 0.651 1.70 0.103 0.00 0.143 0.00 0.040 112.00 60 0.651 3.55 0.103 0.00 0.114 0.00 0.040 0.00 61 0.605 1.28 0.103 0.04 0.086 0.00 0.040 78.00 62 0.605 3.67 0.103 0.00 0.086 0.00 0.040 88.50 63 0.558 0.19 0.103 0.00 0.086 0.00 0.040 101.50 64 0.535 2.50 0.051 0.00 0.086 0.00 0.040 78.00 65 0.535 3.43 0.05 1 0.00 0.086 0.00 0.040 82.50 66 0.535 2.14 0.051 0.00 0.086 0.00 0.040 91.50 67 0.488 4.60 0.000 0.00 0.086 0.00 0.040 61.50 68 0.488 3.50 0.086 0.00 0.040 67.00 69 0.488 0.80 0.057 0.00 0.040 0.00 70 0.465 4.50 0.057 0.00 0.040 0.00 71 0.419 0.00 0.057 0.00 0.040 0.00 72 0.395 1.33 0.057 0.00 0.040 0.00 73 0.395 0.00 0.057 0.00 0.040 0.00 74 0.349 0.00 0.057 0.00 0.040 0.00 75 0.302 1.83 0.057 0.00 0.040 0.00 76 0.256 0.00 0.057 0.00 0.040 0.00 77 0.000 0.00 0.000 0.00 0.000 0.00




APPENDIX. Continued.

Hydrocarbon (No. 2 fuel oil) Blue-green algae (Spirulina)

Streblospio Capitella Streblospio Capitella

Ix M., MIx mxIxIx M,

1.000 1.000 1.000 1.000 0.939 0.00 1.000 0.00 0.537 0.00 0.900 0.00 0.939 0.00 1.000 0.00 0.537 0.00 0.725 0.00 0.939 0.00 0.975 0.00 0.472 0.00 0.650 0.00 0.909 0.00 0.975 0.00 0.417 0.00 0.625 0.00 0.849 0.00 0.975 0.00 0.333 0.00 0.550 103.91 0.818 0.00 0.975 0.00 0.296 0.00 0.445 126.22 0.818 0.00 0.975 0.00 0.269 0.00 0.445 101.81 0.818 0.00 0.975 0.00 0.269 0.00 0.417 203.13 0.818 0.00 0.975 11.78 0.259 0.00 0.361 188.35 0.818 0.00 0.975 40.65 0.250 0.00 0.333 190.67 0.788 0.00 0.975 61.83 0.250 0.68 0.333 30.88 0.788 0.00 0.975 35.33 0.241 1.14 0.333 181.00 0.788 0.00 0.975 50.45 0.232 0.21 0.333 275.17 0.788 0.00 0.975 26.43 0.213 0.00 0.333 196.08 0.788 0.00 0.975 34.43 0.204 0.00 0.333 172.71 0.788 0.00 0.975 28.05 0.204 4.00 0.333 218.92 0.788 0.00 0.975 37.83 0.204 0.00 0.333 145.54 0.788 0.00 0.975 43.50 0.194 0.88 0.333 138.79 0.788 0.00 0.975 11.58 0.176 0.00 0.222 58.13 0.788 1.04 0.975 10.70 0.167 8.46 0.083 0.00 0.788 0.50 0.878 16.53 0.130 0.00 0.083 69.17 0.788 1.36 0.829 20.32 0.083 5.07 0.083 193.50 0.788 0.21 0.829 15.91 0.074 4.75 0.083 239.17 0.788 1.00 0.829 28.41 0.046 0.00 0.083 59.33 0.788 1.46 0.829 18.38 0.046 3.80 0.083 126.17 0.788 0.11 0.829 26.50 0.028 1.13 0.083 0.00 0.788 2.36 0.780 14.38 0.028 3.75 0.083 0.00 0.788 2.21 0.780 17.75 0.028 0.38 0.083 29.67 0.788 1.00 0.780 18.28 0.009 0.00 0.083 0.00 0.758 3.04 0.780 7.66 0.009 0.00 0.083 244.67 0.758 0.42 0.780 11.97 0.009 0.00 0.083 0.00 0.727 1.50 0.780 10.72 0.009 0.00 0.083 96.33 0.727 0.12 0.731 3.97 0.009 0.00 0.083 10.17 0.727 0.96 0.634 7.19 0.000 0.00 0.083 0.00 0.667 1.04 0.536 11.23 0.083 17.17 0.636 1.50 0.488 11.80 0.083 5.17 0.636 0.58 0.488 12.40 0.056 98.25 0.636 2.21 0.439 20.39 0.056 0.00 0.636 0.67 0.390 11.00 0.028 0.00 0.636 1.38 0.390 30.94 0.028 0.00 0.636 3.58 0.341 23.50 0.028 0.00 0.606 0.77 0.341 9.64 0.028 0.00 0.606 1.55 0.341 6.14 0.000 0.00 0.606 2.45 0.244 4.10 0.606 4.50 0.244 7.10 0.606 2.36 0.244 0.30 0.546 6.80 0.244 1.60 0.515 0.00 0.195 0.00 0.515 0.00 0.195 0.00 0.515 0.00 0.195 0.00 0.485 0.00 0.195 13.63 0.455 0.00 0.146 9.83 0.424 0.00 0.146 0.00 0.364 0.00 0.146 0.00 0.182 0.00 0.098 0.00 0.182 0.00 0.098 0.00 0.152 0.00 0.098 0.00 0.152 0.00 0.098 0.00 0.152 0.00 0.098 0.00 0.121 0.00 0.098 0.00 0.121 0.00 0.049 0.00 0.121 0.00 0.049 0.00 0.121 0.00 0.000 0.00 0.121 0.00 0.091 0.00 0.091 0.00 0.091 0.00 0.091 0.00 0.061 0.00 0.000 0.00



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Demographic Responses of Estuarine Polychaetes to Pollutants: Life Table Response Experiments

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