Scaling the Sublethal Effects of Methylmercury to Population-Relevant Endpointsଝ Cheryl A

Aquatic Toxicology 86 (2008) 470–484

Modeling larval ﬁsh behavior: Scaling the sublethal effects of methylmercury to population-relevant endpointsଝ Cheryl A. Murphy a,∗, Kenneth A. Rose a, Mar´ıa del Carmen Alvarez b,1, Lee A. Fuiman b a Louisiana State University, Department of Oceanography & Coastal Sciences, Energy, Coast and Environment Building, Baton Rouge, LA 70803, USA b The University of Texas Marine Science Institute, 750 Channel View Drive, Port Aransas, TX 78373, USA Received 8 October 2007; received in revised form 14 December 2007; accepted 20 December 2007

Abstract Expressing the sublethal effects of contaminants measured on individual fish as cohort and population responses would greatly help in their interpretation. Our approach combines laboratory studies with coupled statistical and individual-based models to simulate the effects of methylmercury (MeHg) on Atlantic croaker larval survival and growth. We used results of video-taped laboratory experiments on the effects of MeHg on larval behavioral responses to artificial predatory stimuli. Laboratory results were analyzed with a regression tree to obtain the probability of control and MeHg-exposed larvae escaping a real predatory fish attack. Measured changes in swimming speeds and regression tree-predicted escape abilities induced by MeHg exposure were then inputted into an individual-based larval fish cohort model. The individual-based model predicted larval-stage growth and survival under baseline (control) conditions, and low- and high-dose MeHg exposure under two alternative predator composition scenarios (medusa-dominated and predatory fish-dominated). Under MeHg exposure, stage survival was 7–19% of baseline (control) survival, and the roughly 33-day stage duration was extended by about 1–4 days. MeHg effects on larval growth dominated the response under the medusa- dominated predator composition, while predation played a more important role under the fish-dominated predator composition. Simulation results suggest that MeHg exposures near extreme maximum values observed in field studies can have a significant impact on larval cohort dynamics, and that the characteristics of the predator–prey interactions can greatly influence the underlying causes of the predicted responses. © 2007 Elsevier B.V. All rights reserved.

Keywords: Methylmercury; Predator–prey interactions; Individual-based model; Regression tree; Larval ﬁsh; Behavior; Population-level effects

1. Introduction ology, metabolism, homeostasis, and behavior of an organism (Damstra et al., 2002). Despite intensive study on how endocrine The past few decades of toxicological research have demon- disruptors operate mechanistically (Rotchell and Ostrander, strated that it is difficult to establish a link between sublethal 2003), it often remains unclear as to how the subtle changes effects of contaminants measured on individuals and indices of caused by endocrine disruptors affect an individual’s perfor- population health (Mills and Chichester, 2005). Yet, endocrine mance in a natural setting, or how these changes relate to disruptors, which interrupt normal signaling processes within population-level responses. Ecological death is the phenomenon and between organisms, generally manifest themselves as subtle where animals are not overtly harmed by a contaminant, but changes in the reproductive system, molecular biology, physi- exposure causes alterations in their behavior or physiology such that the organism is unable to function ecologically as expected (Mesa et al., 1994; Scott and Sloman, 2004). ଝ Although the research described in this article has been funded wholly or in Behavior is often used as an endpoint in toxicological studies part by the United States Environmental Protection Agency through cooperative agreement R 829458 to CEER-GOM, it has not been subjected to the Agency’s because an animal’s behavior represents an integrated expres- required peer and policy review and therefore does not necessarily reflect the sion of its physiological response to its environment (Clotfelter views of the Agency and no official endorsement should be inferred. et al., 2004). Usually, toxicological studies that monitor behavior ∗ Corresponding author. Current address: Department of Fisheries and include behaviors that show disruption of certain physiological Wildlife, Natural Resources Building, Michigan State University, East Lansing, mechanisms. For example, in striped mullet (Mugil cephalus), MI 48824-1222, USA. Tel.: +1 517 432 7771; fax: +1 517 432 1699. E-mail address: [email protected] (C.A. Murphy). methylmercury (MeHg) was shown to decrease levels of sero- 1 Current address: Institute of Estuarine and Coastal Studies (IECS), Univer- tonin, which were then associated with the progressive loss of sity of Hull, Hull HU6 7RX, UK. motor control (Thomas et al., 1981). Increasingly, toxicologi-

0166-445X/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.aquatox.2007.12.009 C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484 471 cal experiments have been focused on behavioral endpoints that late behavioral effects associated with contaminant exposure. reflect how a contaminant will affect ecological health or fit- Atlantic croaker spawn offshore, and the egg, yolk-sac larval, ness of the exposed individual (Scott and Sloman, 2004). The and early planktonic feeding larval stages experience high mor- behaviors selected for such ecological health experiments usu- tality rates while in the ocean on their way to their juvenile ally relate to an animal’s foraging ability (Weis et al., 2001), nursery grounds in estuaries (Diamond et al., 1999). Early life predator-evasion skills (Weis and Weis, 1995a,b; Zhou and Weis, stages that exhibit high and variable mortality rates can be impor- 1998; Faulk et al., 1999; McCarthy et al., 2003; Alvarez and tant regulators of year class strength and recruitment dynamics Fuiman, 2006), or reproduction (Bjerselius et al., 2001). In only in fish (Houde, 1989; Leggett and Deblois, 1994). The ocean a few instances, the effects on predator and prey interactions larval stage of croaker may be especially sensitive to behavioral were related to cohort-level responses (e.g., Alvarez and Fuiman, effects because early ocean larvae have to undergo sufficient 2005) or to population-level responses (e.g., Fleeger et al., 2003; physiological and behavioral development to exhibit successful Alvarez et al., 2006). foraging (exogenous feeding) and predator evasion. Laboratory Individual-based models (IBMs) are well suited to link labo- experiments have demonstrated how contaminants affect the for- ratory behavioral results to population-relevant indices (Rose et aging and predator-evasion behaviors of Atlantic croaker larvae al., 1999, 2003). Individual-based models track each individual; (Faulk et al., 1999; McCarthy et al., 2003; Alvarez et al., 2006). the sum over all individuals at any given time is the cohort or the population. Laboratory toxicological effects that are reported 2.2. Laboratory studies for the individual can be easily imposed on processes repre- sented in an individual-based model, either directly or first via We used the results of a laboratory study of the effects of some conversion from a statistical model. If the IBM simulates MeHg on larval croaker swimming speed and predator-evasion the appropriate processes, sublethal effects, such as changes in skills (see Alvarez et al., 2006). Briefly, uncontaminated and behavior, can be imposed on individuals and the IBM used to contaminated food was fed to adult female croaker, who were predict the population-level response. then spawned and their eggs were collected and analyzed for In this paper, we used an IBM to scale the laboratory- MeHg. Eggs from individual adults were grouped together based measured effects of maternally transferred MeHg on larval on the MeHg concentrations measured in their eggs: control Atlantic croaker (Micropogonias undulatus) behavior to larval- (<0.05 ␮gg−1 egg), low-dose exposure (0.01–3.5 ␮gg−1), and stage survival and duration. MeHg has been shown to reduce high-dose exposure (>3.5 ␮gg−1). Eggs from the control, low, serotonin levels in the brain of fish, inhibit normal development and high MeHg dose treatments were then hatched in the labo- of the hypothalamic serotonergic system (Tsai et al., 1995), and ratory and resulting larvae were evaluated for their growth rates to affect locomotor activity and impair prey capture abilities and survival skills. At days 1, 3, 6, 11, and 17 after hatching, (Smith and Weis, 1997). In this analysis, we use the experimental larvae were removed from their rearing tanks and their lengths results of Alvarez et al. (2006), who documented MeHg effects were measured. Day 3 larvae had just completed yolk absorp- on the locomotor activity and predator-evasion skills of larval tion and were labeled as “yolk” larvae. The oil globule had been croaker. Expressing these behavioral effects as changes in stage completely absorbed by day 6, and these larvae were labeled as duration and stage survival helps to place these sublethal effects “oil” larvae. Days 11 and 17 larvae marked 4 and 11 days after into an ecological context. Stage duration and survival are fun- oil absorption, subsequently these larvae were labeled “oil+4” damental parameters in life tables and matrix projection models and “oil+11.” of population dynamics. We simulate the effects of a low and Larval fish from each of the age classes (yolk, oil, oil+4 and high dose of MeHg on larval-stage survival and duration under oil+11) and from control, low- and high-dose MeHg treatments two alternative predator compositions, and perform simulations were evaluated for routine behavior and their responses to arti- to separate the effects of MeHg exposure on larval encounters ficial startle stimuli using video-taped recordings (Alvarez et with their zooplankton prey (growth only) versus MeHg effects al., 2006). The video-taped behavior was viewed and the rou- on larval encounters with their predators (mortality only). This tine behaviors of swimming speed (mm s−1), active swimming paper provides methodological details and additional simula- speed (mm s−1), net-to-gross displacement ratio (linearity of the tion results (two predator compositions; growth versus mortality swimming path of the larvae), and percent activity were recorded effects) to the much streamlined and subset of results presented for each larva in each of the four age groupings under control, by Alvarez et al. (2006). We conclude with discussion of the low- and high-dose treatments. Each individual larva was then usefulness of our approach for relating sublethal effects of con- subjected to a visual startle stimulus and a vibratory startle stim- taminants to ecological endpoints, the realism of the modeling ulus, and the video-taped recording was analyzed to quantify results presented, and their possible relevance to field conditions. responsiveness (percent of larvae responding to the stimulus), response distance (mm), response duration (s), average response 2. Methods speed (mm s−1), maximum response speed (mm s−1), and visual reactive distance (mm). In these experiments, MeHg had no 2.1. Atlantic croaker as the test organism effect on growth rates (mean lengths), but resulted in slower swimming speeds that were statistically significant from control The ocean larval stage of Atlantic croaker is a good model values for the yolk and oil+4 ages; swimming speeds were 23% organism on which to base the larval fish IBM and to simu- and 61% slower for low and high doses respectively for yolk 472 C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484

Fig. 1. Laboratory results from MeHg experiments that were used in the individual-based model. (A) Mean swimming speeds (±S.D.) and (B) mean visual reactive distances (±S.D.) from video-taping larval croaker in the control, low- and high-dose MeHg experiments. *Signiﬁcantly different (P < 0.05) according to an ANCOVA analysis (Alvarez et al., 2006).

larva and for the oil+4 larvae were 63% and 59% slower for regression tree model as Fuiman et al. (2006), but reduced the low and high doses, respectively. The visual reactive distances number of predictor variables to swimming speed and visual were slightly reduced, but not significant, for yolk (34% shorter reactive distance because these variables were available in the for low dose and 10% shorter for high dose) and oil+4 larvae croaker experiment. The predictor variables developed for red (17% shorter for low dose and 16% shorter for high dose). For drum were transformed (normalized) so they could be used for the MeHg-exposed oil larvae the visual reactive distances were croaker larvae (Fig. 2). 38% and 64% longer for low and high doses respectively but The red drum regression tree was converted to a croaker were also not significantly different from control. The MeHg- regression tree using z-scores (standard normal deviates). Swim- exposed oil+11 also showed subtle differences that were not ming speed of red drum was log-transformed for normality, significant; low-dose larvae had 23% longer visual reactive dis- and then the transformed swimming speed and reactive distances and high-dose larvae had visual reactive distances that tance were converted to z-scores and the reduced regression tree were 5% shorter (Fig. 1). We used swimming speed directly in was re-fit to the red drum experimental data (Fig. 2). To apply the IBM to affect encounter rates of larvae with their zooplankton the reduced regression tree to data on croaker larvae, z-scores prey and their predators; swimming speed and reactive distance were computed from the square-root transformed swimming were used with the results of another analysis to estimate how speeds and Box–Cox transformed visual reactive distances of MeHg would affect the probability of the larva escaping an attack measured on the croaker larvae (λ of 0.4; SAS Version 9, SAS by a predatory fish. Institute Inc., USA), and these were inputted into the reduced regression tree based on red drum that used z-scores. Probabil- 2.3. Swimming speeds and probabilities of escape ities of escape were predicted for each croaker larvae from the swimming speeds and reactive distances measured for each age We adapted a previously developed regression tree model, grouping (yolk, oil, oil+4, and oil+11) and for the control, low- and used the modified model to relate croaker larva swimming and high-dose MeHg exposures. speed and reactive distance to the probability of escaping an Swimming speeds and regression tree-predicted probabili- attack from a real predatory fish. The original regression tree ties of escape were then expressed as multipliers and used in was based on an experiment involving red drum larvae (same family as croaker), where routine skills and predator-evasion skills in response to an artificial and to a real predatory fish were used (Fuiman et al., 2006). Regression trees are constructed by recursively partitioning data into a hierarchical succession of nodes or branches based on the observed values of a set of predictor variables. The benefits of regression trees over traditional regression techniques are that it is not necessary to assume a linear relationship over the entire range of data (Breiman et al., 1998). Fuiman et al. (2006) fitted a regression tree model to the results from a larval red drum experiment that measured the probability of escape from a real predatory fish for each individual red drum larva [P(escape)], weighted by the number of Fig. 2. Regression tree relating the probability of escaping a real predatory fish attack to the swimming speed and visual reactive distance of the fish larva. The attacks, as the response variable, and a suite of survival skills regression tree was modified from the regression tree reported in Fuiman et (e.g., visual reactive distance) also measured on the individual al. (2006) that was developed using experimental data on red drum larvae. We red drum larva as the predictor variables. We used the same applied the regression tree to experimental results on croaker larvae. C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484 473

−1 Fig. 3. Mean values of swimming speed (mm s ) and probability of escape, Fig. 4. Schematic diagram of the individual-based model larval cohort model. and frequency histograms of their multipliers, by age-grouping for control, low- The IBM simulates the daily growth and mortality of ocean larvae as they grow and high-dose MeHg treatments used in the IBM. Age groupings were: (A) from 2.5 to 11 mm. Growth is based on bioenergetics principles, with ingestion day 3 or yolk just absorbed, (B) day 6 or oil globule just absorbed, (C) day dependent on encounter rates of larvae with four types of zooplankton. Mor- 11 or 4 days past oil globule absorption, (D) day 17 or 11 days past oil globule tality was based on the encounter and capture of larvae by individual medusae, absorption. The probability of escape multipliers were derived via the regression ctenophores, and predatory fish. MeHg influenced larval swimming speed, which tree. Multipliers were created by dividing each swimming speed and probability affected encounter rates of larvae with zooplankton and with predators, and of escape value for each age grouping (days 3, 6, 11 and 17) and treatment influenced the probability of escaping a predatory fish attack (after Rose et al., (control, low or high) by the mean of the swimming speed or probability of 2003). escape for the control for its age grouping. Arrows point to treatment group for the mean swimming speeds and probability of escapes; means are ordered as high dose to left, low dose in the middle, and control to right in each panel. 2.4. Individual-based model

The IBM tracked the daily growth and mortality of a cohort the IBM. The multipliers by age grouping were obtained by of Atlantic croaker larvae from 2.5 to 11 mm in a 20,000 m3 vol- dividing each swimming speed and probability of escape by the ume of water (Fig. 4), and was used to predict ocean larval-stage mean value for control larvae in its age grouping for control, survival and duration for the control, low- and high-dose MeHg low- and high-dose MeHg treatments (Fig. 3). Swimming speed treatments. The treatments differed by the swimming speed and multipliers were generally reduced in the low- and high-dose probability of escape multipliers assigned to larvae for each age MeHg exposures relative to control swimming speed multipli- grouping. The model began with 10,000, 2.5-mm larva, assumed ers. The effect of MeHg on the probability of escape multipliers densities of four prey groups (invertebrate eggs, nauplii, cope- was not as clear; probability of escape multipliers were roughly podites and adult copepods; see Table 1), and some number and similar for yolk age larvae for all treatments, and slightly lower assigned sizes of individual predators (medusa, ctenophores and for oil and oil+4 age larvae for the low and high MeHg treat- ﬁsh). Larval growth rate each day depended on their encounters ments and lower for oil+11 larvae for the high dose. Swimming with and captures of the zooplankton prey, and their mortality speed and probability of escaping multipliers were randomly rate depended on their encounters with and escape from individ- assigned to each larva in the IBM and used with that larva until ual predators. MeHg effects on swimming speed affected larval it entered the next age grouping, when a new set of multipli- encounters with zooplankton and predators; MeHg effects on ers was randomly assigned to the larva from the next age’s the probability of escape affected the ability of larvae to escape histogram. predatory ﬁsh attacks. 474 C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484

Table 1 Length (mm), weight (␮g), and density (number mL−1) for each of the four zooplankton prey types used in the IBM Prey type Length Weight Density Sources

Invertebrate eggs 0.11 0.250 0.3 Length and weight based on the sizes of copepod eggs reported in Kiorboe and Sabatini (1994) Nauplius 0.25 0.282 0.08 Length and weight from Letcher et al. (1996) Copepodite 0.5 1.410 0.008 Length and weight from Letcher et al. (1996) Copepod 1.0 7.700 0.0008 Weight from Letcher et al. (1996) assuming a 1-mm length copepod because larval croaker eat relatively large copepods compared to most marine larvae (Govoni et al., 1986; Rose et al., 2003)

Densities were initially estimated from data reported in plankton surveys conducted offshore in the Gulf of Mexico (Al-Yimani, 1988) and in the mid-Atlantic Bight (Kane, 2003), and then were slightly adjusted to obtain realistic larval growth.

2.4.1. Growth Ingestion was determined by simulating the foraging of a The growth component of the IBM was a foraging and bioen- larva via its encounters, captures, and diet selection with four ergetics model adapted from a generic marine larval model types of zooplankton prey. The four prey types were defined by described by Letcher et al. (1996). We adjusted the temperature- their length, weight, and density (Table 1). All four prey items dependent rates in the model of Letcher et al. (1996) to apply to were found in stomachs of larval Atlantic croaker shorter than 15 ◦C. This is typical water temperature experienced by ocean 10 mm (Govoni et al., 1983, 1986). larvae in the mid-Atlantic Bight, which is an area of high croaker Daily encounter rates of each larva with each individual prey abundances (Stegmann et al., 1999). All equations for the for- type (number of encounters per day) were a product of the lar- aging and bioenergetics model came from Letcher et al. (1996) val search volume (SV, mm3 s−1), density of the prey type (ρ, unless noted otherwise. Table 1), and the number of seconds in 13 h of presumed daylight Daily change in weight (W) of an individual larva was com- (46,800): puted as ERpreytype = SV × ρ × 0.001 × 46, 800 (5) W = Wt+ − Wt = AI − M − E (1) 1 Multiplication by 0.001 converts mL to mm3. Search volume where A is the assimilation efficiency, I is the ingestion was the product of the swimming speed of the larval fish (SS, − (␮g day−1), M is the total metabolic cost (␮g day−1), and E mm s 1) and its reactive area (RA, mm2): is the combined losses due to excretion and specific dynamic SV = SS × RA (6) action. Each day, weight was converted to length for each larva using a length–weight relationship: Swimming speed was dependent on length of the fish, and . averaged about 1 body length/s (Miller et al., 1988): W = 0.1674 × length3 837 (2) . SS = 0.776 × length1 07 (7) The length of the larva was updated if the weight gain was positive and the larva had reached at least the average weight Reactive area was a half circle with radius equal to the reactive expected for its length. distance (RD, mm) defined for each prey type and larva: The combined excretion and specific dynamic action term RA = (RD )2 × π × 0.5 (8) (E) was assumed to be 30% of ingestion, and assimilation effi- preytype ciency and metabolism depended on fish weight. Assimilation Reactive distance increased with prey length and with preda- efficiency increased from a minimum of 0.45 at a weight of tor length because longer larvae have smaller angles of acuity 5.6 ␮g to a maximum of 0.6 at a weight of 1658 ␮g: (Breck and Gitter, 1983): −0.002(W−10) A = 0.6(1 − 0.25 e ) (3) lengthpreytype RD = (9) preytype 2 tan(α/2) We lowered the maximum assimilation efficiency to 0.6 from Letcher et al.’s value of 0.8 because croaker larvae grow slower where α is the minimum angle that resolves a prey item from than the generic larva simulated in Letcher et al.’s (1996) model the background. α decreased with larval length: (Nixon and Jones, 1997). Total metabolic cost (M) included a 2 α = . 9.14−2.4 ln(length)+0.229 (ln(length)) routine component plus an active component that was 2.5 times 0 0167 e (10) the routine component for daylight hours: The realized number of encounters in 13 h (ER(realized)) was 4500W 4500W calculated as a random deviate from a Poisson distribution with M = + 2.5 × × light (4) mean and variance equal to the mean encounter rate (ER ). 45, 000 + W 45, 000 + W preytype Realized encounter rates were computed daily between each where light is the number of hours of daylight (assumed to be larva and each of the four zooplankton prey types. 13) and 4500 and 45,000 are parameters that were empirically The number of each prey type encountered that were sub- derived by Letcher et al. (1996) and were based upon laboratory- sequently captured and ingested was determined based upon measured rates of metabolism from several species of larval capture success, a diet selection algorithm, and a maximum con- fish. sumption rate. Croaker larvae consume larger prey at a given size C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484 475

2.4.2. Starvation Starvation mortality occurred when a larva’s weight went to less than 25% of its maximum weight achieved to date in the simulation. Weight loss predicted by the bioenergetics model was adjusted using the same algorithm as Letcher et al. (1996) so that when fed nothing, a larval croaker at 2.5 mm total length would starve to death after roughly 5 days.

2.4.3. Mortality by predation Mortality by predation occurred when larvae were encountered and captured by any of the ctenophore, jellyfish medusae, or predatory fish individuals (Cowan et al., 1996; Rose et al., 2003). Numbers of individuals of each predator type in the modeled volume were specified, and lengths were randomly assigned to each individual predator from normal probability distributions (Table 2; Cowan et al., 1996). On each day of the simulation, the Fig. 5. Capture success as a function of fish length for Atlantic croaker larvae feeding on invertebrate eggs, nauplii, copepodites, and copepod prey types. model used a modified Gerritsen–Strickler formulation (Bailey and Batty, 1983) to compute the encounter rate of each larva with each individual of the three predator types: than typical marine larvae (Govoni et al., 1983, 1986; Soto et al., 1998; Rose et al., 2003). Therefore, capture success functions of −9 = π R + R 2C 10 Letcher et al. (1996) were altered to have higher capture proba- ERpredatortype ( L P) V (15) bilities (Fig. 5). Capture success was used in the diet selection algorithm to determine which prey types were eaten. For each where ER is the encounter rate of a larva with an individual larva on each day, handling time (Walton et al., 1992) was first predator (number of encounters in 24 h), RL is the encounter computed: radius of larva (mm), RP is the encounter radius of the predator −1 . / (mm), C is the foraging rate (mm s ), and V is the volume = 0.264×107 0151(preylength length) HTpreytype e (11) of water modeled in the simulation (20,000 m3). The encounter Then handling time and capture success were combined to radius of larva was computed from larval length (mm) as obtain mass per unit time for each prey type: 2 × length R = (16) mass × CS L π2 preytype preytype (12) HTpreytype The encounter radius of each predator type was derived from Prey types were ordered from highest to lowest based on laboratory studies (Table 2). The foraging rate (C) was deter- their mass per unit time. The profitability (defined as the ratio of mined as ⎧ energy consumed of each prey type divided by the time required D2 + D2 ⎪ L 3 P for handling those prey) of each prey type was also computed: ⎨ , if DP >DL 3D C = P (17) ⎪ D2 + 3D2 × × ⎩ P L , D

Table 2 Lengths (mm), distance swum (mm day−1), encounter radius (mm) and capture probability of the three predator types (ctenophores, medusae and predatory ﬁsh) used in the IBM Predator type

Ctenophore Medusa Predatory ﬁsh

Length/diameter Mean ± S.D. (mm) 45.0 ± 10.0 75.0 ± 10.0 35.0 ± 5.0 Min/max (mm) 30.0/60.0 60.0/90.0 25.0/45.0 −1 a Distance swum, DP (mm day ) 0.025LC × 86,400 (1.2 + 0.04LM) × 86,400 3.0LP × 86,400 a Encounter radius, RP (mm) 0.33LC 0.5LM 0.8LP 2 3 b LL LL LL LL 2 3 Capture probability 0.813 − 4.416 0.505 + 4.653 − 66.215 + 155.377 0.029 + 0.015LL − 0.003L + 0.0001L LC LM LM LM L L

LC is the length of ctenophore predator, LM is the diameter of medusae predator, and LP is the length of predatory fish. LL is the total length of larval prey in mm, which is denoted as length in the text. 86,400 is the number of seconds in a day. a Cowan and Houde (1992). b Adamack (2003). daylight (e.g., Mills, 1983) but we do not incorporate this spa- of capture. Multipliers were only applied to larval encounters tial complexity in our model. We also simulated a generic fish with individuals of the predatory fish because the red drum lab- predator that could be either nocturnal or diurnal. oratory study that provided the regression tree, only examined The realized number of encounters between a larva and each predatory fish. Also, predatory fish are able to see larval fish and individual predator was generated as a random deviate from a attack them, whereas medusae and ctenophores encounter larval Poisson distribution with mean equal to the mean encounter rate fish passively. (ERpredatortype). The number of successful realized encounters with that predator individual was then randomly drawn from a 2.5. Simulations binomial distribution with the probability of success equal to the capture success for that predator type using the predator and Two sets of model simulations were performed to explore larval lengths (see Table 2). On each day, each larva’s encounters the effects of MeHg on ocean larval-stage duration and survival. and potential capture were evaluated for all of the individual The first set (six simulations) involved baseline (control), low- predators. If a larva was successfully encountered and captured and high-dose MeHg swimming speed and probability of escape by any of the individual predators of any of the three predator multipliers under a medusa-dominated and a fish-dominated types, that larva was considered eaten and was removed from predator mix. These simulations were designed to assess the the simulation. Individual predators were evaluated in random overall impact of MeHg exposure on stage duration and survival. order each day so that we could fairly assign each larva eaten to The second set of simulations (eight simulations) repeated the a predator type and interpret the percent of mortality caused by low- and high-dose MeHg simulations under the two predator ctenophores, medusae, and predatory fish. compositions of the first set but with MeHg effects imposed on growth only and with MeHg effects imposed on predation only. 2.4.4. MeHg effects on growth and predation mortality The objective of the second set of simulations was to determine Each larva in the simulation received a new set of multipli- the relative contribution of growth and predation mortality to ers for swimming speed and probability of escape on the day overall effect of MeHg on stage duration and survival predicted they entered each age grouping (yolk, oil, oil+4, oil+11) from in the first set of simulations. Under growth only, MeHg-derived the control, low- or high-dose histograms (Fig. 3). The multipli- swimming speed multipliers affected larval encounters with ers were randomly assigned to each larva from the histograms zooplankton; control-derived multipliers of swimming speed without regard to multipliers previously assigned to that larva and probability of escape were used to simulate encounters (i.e., no memory in the multipliers). The swimming speed of with predators. Under predation only, control-derived swimming each larva (SS in Eqs. (7) and (18)) was then adjusted each day speed multipliers affected larval encounters with zooplankton, by applying the appropriate multiplier. Swimming speed (Eq. while MeHg-derived swimming speed multipliers affected larva (7)) affected the larva’s search volume (SV, Eq. (6)), which in encounters with all predators and MeHg-derived probability of turn, affected the encounter rate of the larva with each zooplank- capture multipliers affected the capture success of predatory fish ton prey (ERpreytype, Eq. (5)). Swimming speed also affected encounters. the larva’s distance swum (DL, Eq. (18)), which affected the The two predator mixes were designed to reflect two pos- foraging rate (C, Eq. (17)) which, in turn, affected the larva’s sible predator communities an ocean larva might encounter. encounter rate with each of its predators (ERpredatortype, Eq. (15)). For the medusa-dominated mix, we assumed there were 1185 The probability of a larva being captured by a predatory fish was medusae, 355 ctenophores, and 46 individual juvenile fish in the adjusted by converting the probability of capture to the probabil- 20,000 m3 volume. These numbers of predators were roughly ity of escape (escape = 1 − capture), applying the multiplier for consistent with lowered densities tilted more towards medusae the probability of escape, and then re-computing the probability than those used by Cowan et al. (1996) for their Chesapeake Bay C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484 477 application. Predators are generally found in lower densities in By days 40 and 60 of the simulation, the length frequencies offshore environments (e.g., Gulf of Mexico, SEAMAP, 2001, were irregular, with a significant proportion of the survivors still 2002; California current, Suchman and Brodeur, 2005), and off- shorter than 4 mm. Growth rates were very slow in the first few shore trawls in the Gulf of Mexico and North Atlantic during days of feeding when larvae were about 3 mm in length, and late fall and early winter indicated that medusae were at higher they rapidly increased for those that survived this initial period concentrations than ctenophores (SEAMAP,2001, 2002; Link et of first feeding (Fig. 6C). al., 2006). The medusa-dominated predator numbers produced Daily mortality rates were 0.1–0.2 day−1, and remained rel- appropriate mortality rates estimated for Atlantic croaker ocean atively constant with larval length (Fig. 6D), with no clear larva (i.e., stage survival of 0.9–1.7%; Diamond et al., 1999, difference between mean length of surviving individuals and 2000). The fish-dominated mix increased the number of preda- those that died each day (Fig. 6E). The rapid decline in the num- tory fish and reduced the number of medusa (45 medusae, 355 ber of survivors over time was due to predation (60% of all ctenophores, and 9000 juvenile fish) to reflect larvae experienc- deaths) and starvation (39% of all deaths) with 87% of the pre- ing high densities of predatory fish; predicted stage survival was dation deaths caused by medusae, 12% by predatory fish, and similar (about 1.1%). The medusa-dominated mix was based 1% by ctenophores (Fig. 6F). Starvation mostly occurred during on field data and considered realistic, while the fish-dominated days 6–10, reflecting the lag in death by starvation created by mix had very high predatory fish densities in order to provide a the adjusted rate of weight loss in poorly feeding larvae. contrast to the medusa-dominated mix. Baseline simulations with the fish-dominated predator mix Three replicate simulations that used different random num- generated similar growth-related results and total survival as ber sequences were performed for each of the conditions. those with the medusa-dominated predator mix, but differed in Baseline results are shown in more detail than the other con- the causes of the mortality (Fig. 7). The growth processes were ditions to illustrate several auxiliary outputs of the model aside not affected by changing the predator composition; therefore, from stage duration and survival (e.g., diets, length frequency larval fish diet (Fig. 7A), length distributions with time (Fig. 7B), histograms through time, mean lengths of live and dead lar- and growth rates (Fig. 7C) showed a very similar pattern to vae) and for clarity in some tables and figures, we only show that predicted under the medusa-dominated mix. As expected, results for one of the replicate simulations. Because of the high because we adjusted the number of individuals of each predator variance in larval lengths at snapshots during the simulation, type, predicted stage survival was also about 1–2%. larvae ranging in total length from 3 to 10 mm were present The fish-dominated predator mix differed from the medusa- on some days during the simulation. We therefore summarized dominated composition in that mortality rate became more some information (diets, mean growth rates, and mean mortality strongly size-selective and more deaths resulted from fish pre- rates) based on larval length, rather than time (age), in order to dation and less from starvation (Fig. 7). Daily mortality rate reduce variability and more clearly show patterns. Model pre- generally decreased with larval length (Fig. 7D), and during dictions were very similar among the three replicate simulations days 10–30 the mean lengths of surviving larvae were gener- for each condition simulated. Predicted larval-stage survival (%) ally longer than those that died on each day (Fig. 7E). Predation and duration (days) are presented as the average (with ±S.D. still dominated mortality (85% of all deaths), but under the fish- error bars) based on the three replicate simulations. dominated composition 81% of those eaten were consumed by predatory fish, 17% by medusae, and only 14% of the total mor- 3. Results tality was due to starvation (Fig. 7F). The high density of fish predators caused higher fish predation that was size-selective, 3.1. Baseline simulations which removed shorter individuals before they succumbed to starvation. Predicted larval-stage duration and stage survival differed slightly between baseline simulations that used the medusa- 3.2. Effects of MeHg dominated predator mix versus the fish-dominated predator mix. Predicted stage survival and duration for the three replicate sim- Methylmercury reduced stage survival and increased stage ulations was 1.1, 1.0, and 1.0% and 30.7, 29.0 and 29.7 days for duration similarly for the medusa-dominated and fish-dominated the medusa-dominated predator mix, and 0.9, 1.1, and 1.3% and predator mixes (white, grey, and black solid bars in Fig. 8). Aver- 32.5, 33.9, and 34.7 days for fish-dominated predator mix. aged stage survival under the medusa-dominated predator mix Detailed examination of a single replicate baseline simula- was 0.19% (19% of the 1% survival in baseline) for the low dose tion under the medusa-dominated predator mix showed that prey and 0.07% (7% of baseline) for the high dose, and was about size and growth rates increased with larval length, and that mor- 0.15–0.16% for both doses under the fish-dominated predator tality was size-independent (Fig. 6). Very small larvae (about mix. Averaged stage durations under the medusa-dominated 3 mm) tended to eat the smaller sized zooplankton groups; cope- predator mix were 32.7 days (2.9 days more than baseline) for pods dominated the diet of 6 mm and longer larvae (Fig. 6A). the low dose and 34.1 days (4.3 days more than baseline) for Length–frequency distributions showed that individuals exhib- the high dose, and under the fish-dominated predator mix were ited highly variable growth rates (Fig. 6B). The predicted length 34.6 days (0.9 days more than baseline) for the low dose and distribution was very broad by day 20, with some individuals 37.7 days (4 days more than baseline) for the high dose. The having exhibited little growth and others having reached 11 mm. high-dose MeHg exposure caused larger reductions in survival 478 C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484

Fig. 6. Results from a single replicate simulation under baseline (control) conditions with the medusa-dominated predator mix. We show results for one baseline simulation to illustrate several auxiliary outputs of the model aside from stage duration and survival. (A) Mean biomass of the four zooplankton prey types consumed by length class of larvae expressed as a percent of total biomass consumed, (B) length frequency distributions of larval fish at days 1, 10, 20, 40 and 60, (C) mean growth rate (±S.D.; mm day−1) by length class of live larvae, (D) mean mortality rate (±S.D.) by length class of dead larvae, (E) mean length of live larvae and dead larvae each day, and (F) numbers surviving (left axis) and cumulative number eaten by predator type (right axis). and greater increases in stage duration than the low dose (i.e., starvation under the medusa-dominated predator mix, whereas hint of a dose–response relationship), except for survival under predation played a more important role with the fish-dominated the fish-dominated predator mix for which survival was similar predator mix. In the medusa-dominated predator mix, the low between the low and high doses. and high MeHg doses resulted in fewer individuals being eaten While MeHg effects on stage survival and duration were simi- (48.1% and 47.8% versus 62.8% in baseline) and more indi- lar between the medusa-dominated and fish-dominated predator viduals starving to death (51.7% and 52.0% versus 36.1% in mixes, the causes of increased mortality differed between the baseline). In the fish-dominated predator mix, low dose of MeHg predator mixes (Table 3). MeHg exposure caused increased caused increased predation (90.2% versus 85.6% in baseline),

Table 3 Percent of the initial 10,000 larvae that died from predation and starvation for the control (baseline), low, and high MeHg treatments under the medusa-dominated predator mix and the ﬁsh-dominated predator mix Predation scenario Control (%) Low MeHg (%) High MeHg (%)

Predation Starvation Predation Starvation Predation Starvation

Medusa dominated 62.8 36.1 48.1 51.7 47.8 52.0 Predatory ﬁsh dominated 85.6 13.1 90.2 9.6 83.9 15.8

The percents are the average values over the three replicate simulations. C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484 479

Fig. 7. Results from a single replicate simulation under baseline (control) conditions with the fish-dominated predator mix. We show results for one baseline simulation to illustrate several auxiliary outputs of the model aside from stage duration and survival. (A) Mean biomass of the four zooplankton prey types consumed by length class of larvae expressed as a percent of total biomass consumed, (B) length frequency distributions of larval fish at days 1, 10, 20, 40 and 60, (C) mean growth rate (±S.D.; mm day−1) by length class of live larvae, (D) mean mortality rate (±S.D.) by length class of dead larvae, (E) mean length of live larvae and dead larvae each day, and (F) numbers surviving (left axis) and cumulative number eaten by predator type (right axis). which more than offset the decrease in starvation (9.6% versus and very little changes in stage duration. The MeHg effect on 13.1% in baseline) resulting in reduced overall survival. In the the probability of escape was only applied to the predatory high-dose treatment, predation only decreased slightly (83.9% fish. With the relatively few fish in the medusa-dominated mix, versus 85.6% in baseline) and starvation increased slightly MeHg effects on the probability of capture were inconsequen- (15.8% versus 13.1% in baseline) resulting in an overall effect tial. Mortality effects alone involved slower swimming speeds of reduced stage survival. that caused fewer encounters with predators and therefore cre- MeHg effects on growth dominated the response of stage ated a small increase in stage survival. The response for the survival and duration under the medusa-dominated predator medusa-dominated predator mix was determined almost com- mix, while MeHg effects on mortality by predation played a pletely by slower swimming speeds, which resulted in fewer more important role under the fish-dominated predator mix (dot- encounters with zooplankton prey and therefore slowed growth. ted and hatched bars in Fig. 8). Under the medusa-dominated Slower growth rates, combined with size-independent mortality predator mix (top panels), predicted stage survival and stage rates under the medusa-dominated predator mix, caused longer duration with MeHg effects on growth only were very sim- stage durations and higher cumulative-stage mortality. ilar to those predicted when MeHg effects were imposed on MeHg effects on predation mortality played a more impor- both growth and mortality. Indeed, under the medusa-dominated tant role in the fish-dominated predator mix (dotted and hatched predator mix, imposing low- and high-dose MeHg effects only bars in bottom panels in Fig. 8). The larger number of predatory on mortality actually resulted in an increase stage survival, fish in the fish-dominated predator mix amplified the effects of 480 C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484

Fig. 8. Mean (±S.D.) larval-stage survival and stage duration predicted by the IBM for three replicate simulations for various combinations of baseline (control) and low- and high-dose MeHg exposures. Baseline simulation used the multipliers of swim speed and probability of escape from the control treatment; low- and high-dose exposures used the multipliers from their respective treatment results. Growth only effects (dotted bars) had the swimming speed multipliers affect larval encounters with zooplankton prey. Mortality only effects (hatched bars) had larval swimming speeds affect encounters with predators and the probability of escape multiplier affect capture success of encounters with the predatory ﬁsh. The vertical dotted lines correspond with the baseline results (open bars).

MeHg on the probability of escape of the larvae. Predicted sur- croaker larvae and imposed on simulated larval individuals in vival for the predation mortality effects alone was 0.27% for the the IBM. Because swimming speeds affect larval encounters low dose (31% of baseline) and 0.13% (58% of baseline) for the with their zooplankton prey and with their predators, both the high dose. Higher survival occurred under the high dose com- low and high doses resulted in large reductions in stage survival pared to the low dose because the very slow swimming speeds and increases in stage duration, and these responses were simi- under the high dose reduced encounters that, to some degree, lar for two alternative mixtures of predators (medusa-dominated counteracted the slightly lower probabilities of escape. MeHg and fish-dominated). Given that the effects of the probability of effects on growth only with the fish-dominated predator mix escape were only imposed on predatory fish attacks, the causes reduced survival and increased duration about the same as with of the increased mortality depended on the type of predator mix. the medusa-dominated predator mix. With the fish-dominated MeHg exposure caused slowed growth and increased starva- predator mix, MeHg effects on mortality somewhat offset the tion under the medusa-dominated predator mix, whereas both effects on growth. Expected survival if mortality and growth growth and predation mortality were affected by MeHg with the operated independently was 4% for the low dose (0.31 of base- fish-dominated predator mix. line from mortality alone times 0.13 of baseline from growth As expected, both predator mixes generated similar reduc- alone) and 3% (0.58 × 0.05) for the high dose. Actual stage sur- tions in stage survival. We adjusted the number of fish vivals when mortality and growth effects were imposed together individuals in baseline simulations with the fish-dominated (14% for low dose and 15% for high dose) were higher than mix to roughly match the 1% survival obtained with the expected under complete independence. Size-selective aspects medusa-dominated predator mix. While the densities of preda- of mortality under the fish-dominated predator mix culled slow tors in the medusa-dominated mix were grounded in field data growing larvae, reducing to some extent the effects of a slower and likely realistic, we needed many fish individuals in the growth on mortality. fish-dominated mix to get both size-dependent mortality and the desired 1% survival under baseline conditions. Sustained 4. Discussion exposure of croaker larvae to 0.9 predatory fish m−3 seems extreme, but predator densities as high have been reported pre- We used a non-traditional statistical method (regression tree) viously (0.875 blueback herring predators m−3; Chick and Van and an individual-based larval fish cohort model to scale MeHg Den Avyle, 2000). However, densities of blueback herring preda- effects on behavior to changes in the larval-stage survival and tors in freshwater reservoirs are likely to be higher than densities duration of Atlantic croaker. The results of laboratory experi- of juvenile predatory fish in the open ocean. We opted for the ments on Atlantic croaker and red drum were combined to relate high predatory fish density because MeHg effects on growth a low and high MeHg exposure to swimming speeds and prob- were dominant in the medusa-dominated mix and we wanted ability of a larva escaping a predatory fish attack. Using results an alternative mix in which mortality was more size-dependent measured on red drum for croaker was partially justified as and MeHg effects on predation mortality (via changes in prob- they both belong to the same Sciaenidae family. MeHg-derived ability of escape) were more likely to be important. Contrasting effects on swimming speeds and probability of escape for the the medusa-dominated and fish-dominated mixes showed the low and high dose were determined for four age groupings of complexity of how contaminant effects on swimming speeds C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484 481 and predator evasion get combined in the IBM in a series in September and October in the mid-Atlantic Bight experience of non-linear encounter and capture calculations that depend falling plankton abundance and have slow growth rates that make on characteristics specific to the predator types (Table 2). For them prone to starvation (Nixon and Jones, 1997). Additionally, example, medusae showed no significant selection for prey size field-caught newly settled estuarine Atlantic croaker have much (Fig. 6D), whereas juvenile predatory fish were size selective higher percentages of empty stomachs than other settling larvae (Fig. 7D). Our results explore how the effects of a contaminant (Govoni et al., 1983), suggesting that by the end of the lar- on behavior documented in the laboratory (e.g., reduced proba- val period, Atlantic croaker larvae have nearly exhausted their bility of escape) can contribute to survival in the field and that stored energy reserves. With cumulative mortality so high in the outcome is dependent on the types of predators and their our model simulations, the proper balance between starvation abundance. and predation may not greatly affect predicted stage survival The realism of the predicted effects of MeHg exposure in this (slow growing larvae would ultimately get eaten if they did analysis is conditional on the realism of the IBM. The baseline not starve). However, prediction of stage survival and dura- IBM simulation results were all reasonable (Figs. 6 and 7) and tion from application of the IBM to other life stages (e.g., similar patterns with larvae fish have been shown in the lab- juveniles) and with more dilute predator mixes can be sensi- oratory and natural settings. Highly variable growth rates that tive to the relative importance attributed to starvation versus generally increase with size have been reported in laboratory predation. settings. Cohorts of fish reared in the laboratory and in large In addition to potential errors introduced from an unrealis- enclosures that had the same birth date showed high variability tic IBM, two other sources of potential uncertainty were how in growth rates (Fuiman et al., 2005), and RNA/DNA ratios (a we dealt with the age groupings in simulations and the use of measure of protein synthesis) generally increase with larval size a red drum experiment to infer how the probability of escape or age (Buckley, 1982, 1984). The sensitivity of larvae to poor of croaker would be affected by MeHg exposure. Although growth during the first few days of exogenous feeding predicted imposing age-group specific multipliers of swimming speed and by the IBM has also been widely reported. Most species of lar- probability of escape was easy within the structure of the IBM, val fish are known to have difficulties with capturing prey during multipliers assigned to a larva in one age group did not influ- the period of time when larval fish switch from endogenous to ence the multipliers it received when it went to the next age exogenous food sources (Hjort, 1914; Ware et al., 1981; Blaxter, group. We did this to mimic the laboratory results where only the 1986), and starvation is a serious risk during this period because MeHg effects on behavior of an individual within each single age of high mass-specific metabolic rates and the low energy reserves grouping were tracked. This meant that in model simulations an typical of fish larvae (Fuiman, 2002). Robinson and Ware (1988) individual fish that had above average multipliers assigned at one used RNA/DNA ratios and calculated a window of time of about age grouping (i.e., a fast swimmer) was equally likely as another 6 days after which unfed larval herring attained irreversible star- individual to be assigned below average multipliers (i.e., slow vation. Finally, the shifts to larger prey with size and age have swimmer) when it entered the next age grouping. Additional been documented for croaker larvae. First feeding croaker lar- simulations could assume a correlation in the multipliers of an vae eat small zooplankton, and switch to mostly larger copepods individual from age group to age group; performing the analo- after 6 mm in length (Govoni et al., 1983, 1986; Soto et al., 1998; gous laboratory experiments would be a challenge. Additional Rose et al., 2003). experiments that used croaker, rather than red drum, in trials with The relatively large contribution of starvation to mortality in a real predatory fish and with medusa and ctenophore predators baseline simulations was unexpected. In the IBM, slight vari- would also help refine the regression tree and the assignment of ation in metabolic rates or in the parameters controlling death swimming speed and probability of escape multipliers in model by starvation (i.e., adjustment to weight loss; 25% of maximum simulations. weight threshold) had large effects on whether a simulated lar- Our analysis has some similarities to the analysis of growth val fish died from predation or starvation (Letcher et al., 1996). rates on larval striped bass survival reported by Chick and Our more realistic medusa-dominated predator mix resulted in Van Den Avyle (2000). They grew striped bass larvae in the almost 35% of the initial larvae dying from starvation, which laboratory under different prey densities and video-taped their seemed high; the more contrived fish-dominated mix resulted in swimming speeds and responsiveness to predatory fish for use a more realistic 13%. Starvation has been suggested to be one in a larval cohort model. Chick and Van Den Avyle (2000) simu- of the main causes of mortality in marine fish larvae (Cushing, lated two alternative predatory fish in their model and concluded 1974; Lasker, 1975; Leggett and Deblois, 1994), although many that behavioral differences among the growth rate (prey density) believe that in nature weak larvae usually get eaten before treatments had little effect on predator encounters but that slowed succumbing to starvation (Cushing, 1974; Hunter, 1981). Lab- growth lead to higher cumulative mortality. The authors found oratory studies on related species and field-based studies on some differences between their two fish predator species. We Atlantic croaker suggest that larval croaker are relatively vul- also found that swimming speed can affect survival via growth nerable to starvation. Laboratory studies using RNA/DNA ratios more than by directly affecting predation mortality, but we found for larval red drum, a related species, demonstrated that red this with a predator mix dominated by medusae. Stage survival drum have a critical period (switch to exogenous feeding) during actually increased slightly over baseline when MeHg effects on which starvation is a serious risk (Westerman and Holt, 1994). swimming speed and probability of escape were only applied to Otolith studies on Atlantic croaker suggest that larvae spawned the predators under the medusa-dominated predator mix (Fig. 8). 482 C.A. Murphy et al. / Aquatic Toxicology 86 (2008) 470–484

However, when MeHg effects were applied to growth alone and 5. Conclusions to both growth and mortality, stage survival decreased (Fig. 8). When we used the fish-dominated predator mix and imposed Combining statistical- and individual-based simulation mod- MeHg effects on mortality only, stage survival was about a 50% els provides a framework for relating behavioral effects of lower than baseline. Despite the similar approaches and models contaminants to ecologically relevant endpoints. Our analy- used by us and by Chick and Van Den Avyle (2000), these dif- sis of Atlantic croaker larvae exposed to MeHg showed that ferences illustrate how the details assumed about the predators slowed swimming speed and reduced probability of escape can greatly affect behavioral effects on survival. from a predator attack, documented using video-taped labora- The relevance of our simulation results to field conditions tory experiments, may result in reduced larval-stage survival and depends on the realism of the MeHg exposures used in the labo- prolonged stage duration. Details of the causes of the increased ratory experiments, and whether we simulated the major effects mortality depended on the mixture of predator types to which of MeHg exposure on larvae that would occur in nature. Direct the larvae were exposed. These results were highlighted by field evidence in support of MeHg effects on larval croaker the use of statistical and IBM simulations and are not obvious growth and survival, as with many species and contaminants, when examining the laboratory results alone. Such changes in is either equivocal or not available. Most laboratory studies larval-stage survival and duration allow for easy comparisons of examined growth and survival of fish that were exposed to effects among life stages, species, and contaminants, and can be relatively high MeHg levels. MeHg did not affect growth of important to long-term population dynamics. The results of this laboratory-studied Atlantic croaker (Alvarez et al., 2006), larval analysis (changes in stage survival and duration) can be used and juvenile walleye (Stizostedion vitreum)(Friedmann et al., to change parameters of matrix projection models that are com- 1996; Latif et al., 2001), larval fathead minnows (Pimephales monly used to model fish populations thereby forging a strong promelas)(Hammerschmidt et al., 2002), or zebrafish larvae link between behavioral responses to contaminant exposure and (Danio rerio)(Samson et al., 2001). However, MeHg decreased long-term population dynamics. feeding and impaired the competitive abilities of grayling (Thy- mallus thymallus)(Fjeld et al., 1998), reduced prey capture ability of zebrafish (Samson et al., 2001), reduced swimming Acknowledgements ability and prey capture ability of mummichogs (Fundulus heteroclitus; Weis and Weis, 1995a,b; Zhou and Weis, 1998), and This research was supported by a grant from the US Environ- reduced the swimming ability of minnows (Kolok et al., 1998). mental Protection Agency’s Science to Achieve Results (STAR) The low MeHg treatment in the laboratory experiments used Program through funding from US EPA Agreement (R 827399) here were considered realistic, albeit near the high end, when and from the STAR and Estuarine and Great Lakes (EaGLe) pro- compared to MeHg levels observed in field-caught fish. If we gram through funding to CEER-GOM, US EPA Agreement (R assume a 10% transfer of MeHg from the body to the eggs 829458). The laboratory experiments upon which the modeling (Alvarez et al., 2006), we would back calculate that the female was based were supported by a grant from the US Environmental Atlantic croaker treated with low-dose MeHg would have body Protection Agency project number R-827399-010. The authors burdens of MeHg ranging from 14.4 to 31.3 ␮gg−1 of fish. Such thank Peter Thomas, Jaye Cable, Kevin Carman, Barry Moser levels are generally about 10-fold higher than average levels and Alan Rutherford and two anonymous reviewers for helpful observed in the field. For example, values in perch, Perca fluvi- comments. atilis, and chub, Leuciscus cephalus, were 1.7 and 2.1 ␮gg−1 in ␮ −1 the Elbe River (Marsalek et al., 2006), and were about 1.4 gg References in splendid alfonso, Beryx splendens, and bigeye tuna, Thunnus obesus, from Japan (Yamashita et al., 2005). However, our back- Adamack, A.T., 2003. 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