Environ. Sci. Technol. 2005, 39, 7329-7336
Impact of Environmental Stressors on the Dynamics of Disease Transmission F R A N K J . L O G E , * ,† M A R Y R . A R K O O S H , ‡ TIMOTHY R. GINN,† LYNDAL L. JOHNSON,§ AND TRACY K. COLLIER§ Department of Civil and Environmental Engineering, University of California at Davis, 1 Shields Avenue, Davis, California 95616, Immunology and Disease, Ecotoxicology and Environmental Health Program, Environmental Conservation Division, Northwest Fisheries Science Center, NOAA Fisheries, 2032 South East OSU Drive, Newport, Oregon 97365, and Ecotoxicology and Environmental Health Program, Environmental Conservation Division, Northwest Fisheries Science Center, NOAA Fisheries, Seattle, Washington 98112
Infectious disease within outmigrant juvenile salmon in the Columbia River Basin is modulated, in part, by abiotic stressors that influence host-susceptibility. Through the application of a dose-structured population dynamic model, we show that chemical (both in the river and in the estuary) and in-river (e.g., dams and/or predation) stressors influence host-susceptibility, increasing the mean force of infection (defined as the per capita acquisition rate of infection) by a factor of 2.2 and 1.6, respectively. Using Listonella anguillarum as a model pathogen, nonchemical inriver and chemical stressors contribute equally to a cumulative incidence of delayed disease-induced mortalities in Chinook salmon that range from 3% to 18% for estuary residence times of 30-120 days, respectively. Mitigation of environmental stressors that increase host-susceptibility could represent a significant component in future management strategies to recover listed stocks.
Introduction The incidence of infectious disease specifically attributable to altered host-susceptibility arising from environmental stressors is currently unknown in outmigrant populations of Pacific salmon as well as in other wildlife populations. Wild Pacific salmon (Oncorhynchus spp.) have disappeared from approximately 40% of their historical breeding range, and many remaining stocks have declined precipitously in recent years (1). Of the 6 species of Pacific salmonids, 26 out of 51 evolutionarily significant units (ESUs) are listed as either endangered or threatened under the Endangered Species Act (2). Population numbers are strongly influenced by factors acting on the early life-history stages. Recently, it has been demonstrated that modest reductions in mortality, on the order of 10%, during juvenile residence in either the river or the estuary ecosystem, can mitigate current population declines of Snake River spring/summer Chinook salmon in * Corresponding author phone: (530)754-2297; fax: (530)752-7872; e-mail:
[email protected]. † University of California at Davis. ‡ Northwest Fisheries Science Center, Newport. § Northwest Fisheries Science Center, Seattle. 10.1021/es0481934 CCC: $30.25 Published on Web 08/06/2005
2005 American Chemical Society
the Columbia River Basin of North America (3). Factors that are thought to be responsible for the decline in salmon populations include habitat degradation, dam passage, predation, harvest practices, and disease (4). To date, little is known about disease dynamics as they relate to riverine/ estuary ecology and outmigrant survival. Understanding the impact of disease on host population dynamics is important in the management and recovery of depressed salmon stocks in the Pacific Northwest. Pathogens have been detected in a substantial portion of outmigrant juvenile Chinook and coho salmon in a survey of 11 Pacific Northwest estuaries (5). Although the specific location of infection (whether during hatchery, river, or estuary residence) is unknown, we can infer that areas within the watershed represent pathogen reservoirs that contribute to a specified incidence of disease. Furthermore, on the basis of data collected in laboratory studies, we can also infer that the incidence of disease in outmigrant populations of salmon is modulated, in part, by exposure to both chemical contaminants (6-9) and in-river stressors (10) that increase host-susceptibility (10-12). In the study reported herein, we apply dose-structured population dynamics modeling to develop a preliminary assessment of the effects of both chemical and nonchemical stressors on mortality associated with infectious disease. Our approach, illustrated schematically in Figure 1, relies on the integration of a range of disparate field and laboratory data within the model framework to estimate the effects of different stressors on delayed disease-induced mortality in outmigrant juvenile salmon in the Columbia River Basin.
Approach We are specifically interested in the population level effects of delayed disease-induced mortality in outmigrant juvenile salmon associated with immune suppression arising from exposure to chemical and in-river stressors. The terms “direct” and “delayed” have several meanings in the fisheries community; our definitions are as follows. Delayed effects are those that occur at some time after the exposure incident: for example, retardation of growth rate for a period after dam structure passage due to bruising or de-scaling in a dam bypass structure. Direct effects occur immediately: for example, destruction of a fish during passage through a turbine. In the present study, we are interested in delayed disease-induced mortalities that occur after exposure to an environmental stressor that alters host-susceptibility to infectious disease. Thus, the mortality outcome is viewed here as a serial two-step process involving exposure(s) to environmental stressor(s) and then pathogen(s). We have subdivided stressors into chemical (e.g., dietary uptake of immunomodulating contaminants) and nonchemical (e.g., dam passage or temperature) and spatially partitioned them into the river and estuary (Figure 2a). The subdivision provides four possible combinations: chemical in-river, nonchemical in-river, chemical estuary, and nonchemical estuary. Potential stressors associated with the estuary include predation, physiologically relevant water quality parameters such as temperature, and dietary uptake of immunomodulating chemicals. Similar stressors are associated with the river environment, as well as dams, spillways, and bypass structures (hydropower network). Here, we focus on total exposure to chemical stressors, representing the sum of both river and estuary exposure, and nonchemical in-river stressors (Figure 2b) based on the field and laboratory data available to-date. Currently, chemical body burdens have only been assessed in outmigrant VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Conceptual framework, following a risk assessment paradigm, illustrating the specific type and sources of data used in the calibration and subsequent application of the dose-structured population dynamic model (adapted from ref 32).
FIGURE 2. Conceptual model of possible exposure scenarios to environmental stressors during outmigration, and our simplification. salmon collected at the mouth of the Columbia Estuary (13) and, hence, represent the net exposure in both river and estuary. In addition, only one field study has been performed to-date to characterize the impact of in-river stressors on immunomodulation (10). In this study, roughly 129 000 spring/summer Chinook salmon were PIT (passive integrated transponder)-tagged at the Rapid River Hatchery located on the Salmon River, a tributary to the Snake River, near Riggins, ID (Figure 3). Approximately 3200 PIT-tagged fish were collected at Lower Granite, the first dam encountered during outmigration, and barged around the seven consecutive dams to Bonneville Dam. Both PIT-tagged in-river and bargedfish were collected at Bonneville Dam and challenged with Listonella anguillarum (14) to provide an aggregate measure of immune status. Fish that traveled in-river had a substantially higher incidence of disease-induced mortality relative to barged-fish. However, because the chemical body burden was not assessed in either barged or in-river fish, the elevated incidence of mortality observed in in-river fish cannot be specifically attributed to nonchemical in-river stressors. Because water is continuously pumped through the barge during transit, the barged-fish do not serve as a control for exposure to water temperature and chemicals associated with food sources consumed in the water column. However, the extent of dietary exposure to contaminants in barged-fish is likely small relative to in-river fish given that the travel time of the barge is 36 h, whereas the mean outmigration time between Lower Granite and Bonneville Dams is 42 days (data obtained from PIT-tag recorders located at each dam). The barged-fish serve as a control for selected in-river stressors, including the hydropower network, predation, and chemical exposure associated with food sources consumed in proximity to the sediment (currently viewed as negligible based on the analyses of the stomach contents of outmigrant salmon in the Columbia River Basin (15), which indicates the majority of food is consumed in the water column). Given the 7330
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FIGURE 3. Schematic of the name and location of dams in the Columbia River Basin. The mouth of the river is northwest of Portland, OR. Figure provided to qualitatively illustrate potential in-river stressors associated with the hydropower network (33). uncertainty associated with the extent of chemical contamination in barged versus un-barged fish, the relative significance of nonchemical in-river stressors will be assessed herein with various scenarios reflecting potential locations of chemical exposure. Model Development. The basic “SIR” epidemic model (e.g., (16)) was extended to accommodate immunosuppression by exposure to environmental stressors through structuring the model on contaminant dose using the exposuretime approach previously developed for multicomponent reactive transport (17). Details of dose-structuring SIR-type models are provided in the Supporting Information. We
TABLE 1. Summary of Variables and Parameters Governing Delayed Disease-Induced Mortality in Outmigrant Salmon State Variables number of susceptible fish number of infected fish number of dead fish ambient concentration of immunomodulating stressor; the specific units of concentration are dependent on the route of stressor exposure and may include (mg of stressor)‚L-1 for aqueous chemicals, (mg of stressor)‚(g of food)-1 for dietary uptake of chemicals, or (turbulent energy)‚(unit volume)-1 as an intrinsic measure of stress associated with dam passage ambient aqueous concentration of pathogen (cfu‚mL-1) intrafish concentration of pathogen (cfu‚(g tissue)-1 or cfu‚(mL of serum or blood)-1) initial intrafish concentration of pathogen at the time of infection, assumed proportional to the ambient aqueous concentration (cfu‚(g tissue)-1 or cfu‚(mL of serum or blood)-1)
S I M C
b V Vo
ω β
r µ
Parameters cumulated exposure to stressor (C) integrated along the fish’s path in space during outmigration ((concentration of stressor)‚day) infection rate of susceptible fish (S) associated with contact with the ambient aqueous concentration of pathogen (b) ((cfu/mL)-1‚day-1) intrafish pathogen proliferation rate ((intrafish concentration)-1‚day-1) mortality rate of infected fish (I) associated with pathogen infection ((intrafish concentration)-1‚day-1)
calibrate a simplified version of this model, described below, with laboratory data (Figure 1: dose-response assessment), and then apply the calibrated model (Figure 1: risk characterization) to field measurements of stressor exposure (Figure 1: exposure assessment) in the Columbia River Basin to characterize the incidence of delayed disease-induced mortality associated with chemical and nonchemical stressors. Both the laboratory and the field data used here were collected in previous studies described below. The population dynamic model (variables and parameters defined in Table 1) used to characterize the incidence of delayed disease-induced mortality includes salmon states of susceptible (S), infected (I), and deceased (M). In addition, the model includes dose of stressor (ω) as a structural variable (17) accumulated with infinite memory (18) and densitydependent inhibited growth kinetics for L. anguillarum (19, 20) within individual fish. The resulting dose-structured SIR model is written for three sequential phases corresponding to stressor exposure during outmigration, pathogen exposure, and subsequent disease kinetics. In Phase I, all fish remain in the susceptible state (S) but develop varying levels of stressor dose (ω):
∂S ∂S +C )0 ∂t ∂ω
(1)
Structural dose ω is defined as the cumulated exposure to ambient stressor (C(t)) integrated along the fish’s path in space between time of entry into the river system and either collection or exit from the estuary. Two assumptions were used in the present study to simplify the estimate of dose (ω) of both chemical and in-river stressors during outmigration (Figure 1: exposure assessment). First, we presume that cumulative chemical exposure gives rise to a congruently proportional chemical body burden such that the measured body burden in outmigrant salmon is proportional to chemical dose (ω). This approach is likely accurate for bioaccumulative chemicals (e.g., polychlorinated biphenyls (PCBs) and dichloro-diphenyl-trichloroenthanes (DDTs)), but would need to be revised to reflect biomarkers of exposure (e.g., biliary fluorescent aromatic compounds) for nonbioaccumulative chemicals (e.g., polycyclic aromatic hydrocarbons (PAHs)). Second, we presume that cumulative exposure to in-river stressors is proportional to dam passage such that the integer index representing the number of dam
passages is a direct measure of dose (ω) of in-river stressors. Because ambient conditions are path-dependent, the values of ω are distributed over the population reflecting (in the case of chemical stressor) the distribution of chemical body burdens arising from motion in spatially heterogeneous chemical densities, or (in the case of nonchemical stressors) dam passages representing differential points of entry into the river system, and hence differential exposures to in-river stressors. The solution to eq 1 then gives S(t, ω) as the number density of fish arriving at a specified point at time t and dose ω. In Phase II, fish are exposed to L. anguillarum and kinetically partition from S to I at a force-of-infection β(ω)‚b:
∂S ) -β(ω)bS ∂t
(2a)
∂I ) β(ω)bS ∂t
(2b)
where b is the concentration of L. anguillarum in the bulk aqueous phase and β(ω) is the initial rate of infection that is dependent on stressor dose ω. I(t) is not structured on dose in this study but, in general, may be when supporting data is available. In Phase III, infected fish die from kinetically inhibited growth of L. anguillarum within individual fish (V) per fish density at ω:
∂V ) r(V - V2) ∂t
(3a)
∂I ) -µV(t)I ∂t
(3b)
∂M ) µV(t)I ∂t
(3c)
where r and µ represent rates of inhibited growth of L. anguillarum within infected fish and fish mortality, respectively. The nonlinear kinetic appearing in eq 3a represents logistic growth and is commonly used in the field of population biology to model growth with a defined carrying capacity (21). Here, eq 3a is used to approximate the population-average intra-fish pathogen proliferation that is responsible for the frequency and timing of mortality occurrence. This model is multi-scale as it includes effective VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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intra-fish pathogen dynamics together with population-level state-change frequencies. Through this combination, we seek to capture the mechanisms within infected individuals (implicitly represented through V and r) that underlie the macroscopic population-level state changes. Population mortality at time t is calculated by integrating the M density over ω at time t. In principle, all rates (β(ω), r, and µ) could depend on generalized dose (ω), but here a parsimonious solution was found with only β as a function of ω, and no satisfactory solution was found without this dependency (data not shown). Model Calibration. The model (eqs 1-3) was applied to data collected in laboratory disease challenge studies (12, 22) using fall Chinook salmon that were not subjected to in-river or chemical stressors prior to disease challenge to obtain estimates of the model parameters (β, µ, and r); Vo was assumed proportional to b. The population dynamic model accurately simulated the kinetics of mortality over a 4-order magnitude concentration of L. anguillarum (Figure S-1). Omission of the intra-fish concentration of pathogen (V) resulted in a model that could not accurately describe the lag and sill apparent in the kinetics of the cumulative incidence of mortality for varying aqueous concentrations of L. anguillarum (b) (Figure S-1). The inversion of the model to determine fitted (β, µ, r) is potentially nonunique: we do not have independent measures of intra-fish immune-response parameters. Nevertheless, a single set of parameter values were found that fit multiple data sets. This indicates that a feasible, if nonunique, solution to the inverse problem does exist and it is one that improves the fit over classical modeling approaches that do not account for any nonlinearity or coupling (besides the classical bilinear force of infection) in the dynamics of infection and mortality.
Results and Discussion Dose-Response Assessment. The model was used to estimate the rate of infection (β(ω)), with the values of µ and r obtained above, from data collected in laboratory disease challenge studies using L. anguillarum, for salmon (a) injected intraperitoneally (IP) with either a model PAH, 7,12-dimethylbenz[a]anthracene (DMBA), or with the commercial PCB mixture, Aroclor 1254 (11) (data not shown); (b) exposed under natural field conditions to a range of chemicals, including PAHs, PCBs, and DDTs, in a highly contaminated estuary (Duwamish Estuary located in Puget Sound Washington; (12)) (depicted in reference to chemical body burden of PCBs in Figure 4); and (c) exposed to in-river stressors associated with the Columbia River Basin (10) (depicted in reference to the number of dam passages in Figure 4). For each of the dosed and undosed populations in (a), (b), and (c) above, we have only two data points reflecting either no exposure or some fixed value of exposure. Given the lack of available data to characterize the specific functional form of β(ω), the relationship was assumed linear between the two points depicted in Figure 4. Whereas IP injection involved higher chemical doses, and thus a higher ω, than fish naturally exposed in the Duwamish Estuary, these experiments did not produce congruently higher disease-induced mortalities. Hence, the value of β for a specified value of ω was much greater for salmon chemically exposed in the Duwamish Estuary than fish exposed IP. One possible explanation is that there is substantial nonchemical in-river stressors influencing the value of β estimated with the field fish. However, a similar study (12) was performed with fish collected from the Nisqually Estuary (an estuary located in Puget Sound Washington with minimal chemical contamination), and the value of β was identical to that of (unexposed) fish collected at the hatchery of origin. Note that fish in the Duwamish and Nisqually Estuaries have 7332
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FIGURE 4. Estimate of the value of β for varying doses (ω) of chemical and in-river stressors represented as chemical body burden and number of dam passages, respectively. Values were obtained from the dose-structured population dynamic model applied in the doseresponse assessment. similar life histories, the hatchery fish originate from the same egg stock, and neither river is dammed below the hatchery release point. Hence, nonchemical in-river stressors do not appear as the principal reason for the discrepancy between the value of β estimated directly from field exposed fish and the value obtained based strictly on chemical body burden introduced in the laboratory though IP injection. Accordingly, we believe the discrepancy is due to additive or synergistic interactions among multiple chemical stressors, to include immunosuppressant chemicals that have not been identified. In either case, herein we use the value of β obtained from the disease challenge study performed with fish collected from the Duwamish Estuary scaled according to the PCB body burden (ω), recognizing that the PCB body burden is used as a surrogate of the magnitude of multiple chemical exposures. This approach is supported from chemical data obtained from stomach contents of fish collected from both the Duwamish and the Columbia River Estuaries in which the PCB concentration was found to be proportional to the concentration of both PAH and DDT, principal analytes of interest to-date (Figure S-2). Exposure Assessment. In the laboratory dose-response studies, we estimate β(ω) for only two fixed values of ω (either dosed or undosed). In the exposure assessment, we treat the dose ω of in-river and chemical stressors as generally distributed quantities (Figure 5) reflecting the number of dam passages and the chemical body burden, respectively. As mentioned previously, dose represents the time-integrated quantity of stressor concentration along a fish’s path during outmigration (eq 1). We know neither the spatial densities of stressor nor the particular paths of individual fish such that we can specifically perform the integration necessary to estimate stressor dose as per eq 1. Rather, we presume that the measured body burden in outmigrant salmon is proportional to chemical dose (ω), and cumulative exposure to in-river stressors is proportional to dam passage such that the integer index representing the number of dam passages is a direct measure of dose (ω) of in-river stressors. The distributed quantities represent variable paths in a spatially heterogeneous environment of chemical and in-river stressors. The distribution of PCB in whole bodies of outmigrant salmon collected from the mouth of the Columbia estuary is illustrated in Figure 5a. The origin of the fish, whether hatchery or wild, is unknown. Collection of salmon and chemical analyses was performed as per ref 13, with chemical analytical methods described in refs 23 and 24. Chemical
FIGURE 5. Field data of the distribution of (a) PCB in whole bodies of outmigrant salmon collected from the mouth of the Columbia estuary and (b) cumulative dam passages (used as a surrogate for exposure to in-river stressors) of outmigrant fall Chinook salmon in 2003. body burdens reflect accumulation in the hatchery, river, and/or estuary; solid line represents normal probability density function fit to log-transformed values of body burden. Incremental increases in the cumulative probability density function (not shown) corresponding to a specified range of body burden were multiplied by the total number of outmigrant fall Chinook salmon in 2003 to obtain the distribution of fish with specified values of body burden. The distribution of cumulative dam passages (used as a surrogate for exposure to in-river stressors) of outmigrant salmon in 2003 (25) is illustrated in Figure 5b. Dam passage data are based on the number and location of hatchery releases of fall Chinook salmon within the Columbia River Basin. Due to the lack of quantitative habitat-use data for native salmonids, the distribution of dam passages for native outmigrant juvenile salmon is assumed to reflect the distribution associated with these hatchery fish. Risk Characterization. The dependence of β on generalized dose (ω) of either chemical body burden or in-river stressors (Figure 4), here assumed linear, was used to map the distribution of β in outmigrant salmon from their observed distributions of chemical body burden and dam passage (Figure 5a and b, respectively). In-river and chemical stressors both have a significant effect on host-susceptibility, increasing the mean force of infection by a factor of 1.6 (2.4-fold upper 95%) and 2.2 (4.8-fold upper 95%), respectively. In general terms, the magnitude of delayed disease-induced mortality associated with increased host-susceptibility is much more difficult to quantify explicitly. Factors of importance include the type and concentration of pathogen and life-history of outmigrant salmon. In the present analyses, the model was used to infer the incidence of delayed disease-induced mortalities associated with barging all outmigrant salmon (ωin-river ) 0), barging no salmon (ωin-river distributed as per Figure 5b), no chemical body burden (ωchemical ) 0), and chemical body burden (ωchemical distributed as per Figure 5a), that result from exposure to environmentally relevant
FIGURE 6. Modeled incidence of delayed disease-induced mortality of in-river spring/summer Chinook (SSC; open columns), barged SSC (crosshatched columns), fall Chinook with chemical body burdens comparable to values within fish collected at the mouth of the Columbia estuary (vertical/horizontal-hatched columns), and fall Chinook with no chemical body burden (diagonal-hatched columns) resulting from exposure to (a) 1 and (b) 10 cfu/mL of L. anguillarum continuously over specified estuary residence. concentrations of L. anguillarum (1 and 10 cfu/mL as per refs 26-28)) for estuary residence times ranging from 30 to 120 days (Figure 6). The error bars in Figure 6 are a superposition of those estimated from the raw data collected in the disease challenge study using Number Cruncher Statistical Software (Kaysville, UT) based on a nonparametric approach outlined in ref 29 at an R value of 0.05. The difference in modeled mortalities associated with respective dosed and undosed populations (Figure 6) represents the net impact of increased host-susceptibility associated with in-river and chemical stressors on delayed disease-induced mortalities. Barging presumably induces some level of stress, so the actual incidence of diseaseinduced mortality associated with in-river stressors may be greater than the estimated value. Given that we do not know the extent of chemical exposure of the salmon in the river relative to their exposure in the estuary, the significance of nonchemical in-river and chemical stressors was bracketed by evaluating results from the barge study (10) under three different scenarios. In the first scenario, the barged fish have no chemical body burden, whereas the in-river fish accumulate a chemical body burden comparable to that observed in salmon collected at the mouth of the estuary (e.g., all of the chemical body burden is generated in-river VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 7. Modeled reduction in delayed disease-induced mortality associated with elimination of cumulative, chemical, and in-river nonchemical stressors. Mortality reflects continuous exposure to 10 cfu/mL of L. anguillarum during estuary residence times of (a) 30, (b) 60, (c) 90, and (d) 120 days. before estuary entrance). Such a scenario is most likely representative of spring Chinook, which have an extended rearing period in freshwater and a limited period of estuarine residence (30, 31). In the second scenario, both in-river and barged fish have a comparable chemical body burden (e.g., all of the chemical body burden, comparable to what was found in fish at the mouth, is generated in-river prior to barging/outmigration). In the third scenario, both the inriver and the barged fish have no chemical body burden (e.g., all of the chemical body burden is generated in the estuary). This scenario is most likely representative of fall Chinook, which have an extended period of estuarine residence (30, 31). The relative incidence of delayed disease-induced mortality associated with nonchemical in-river and chemical stressors is summarized in Figure 7, along with the cumulative incidence representing the sum of each stressor, for each of the three scenarios. The error bars in Figure 7 are a superposition of those estimated from the raw data collected in the disease challenge study using Number Cruncher Statistical Software (Kaysville, UT) based on a nonparametric approach outlined in ref 29 at an R value of 0.05. Within scenario 1, barging prevents or greatly reduces contact with sources of contaminant exposure and, hence, an accumulation of a chemical body burden in outmigrant juvenile salmon. Accordingly, the difference in delayed diseaseinduced mortality may be due to both chemical and other nonchemical in-river stressors. However, given that the 7334
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reduction in delayed disease-induced mortalities associated with barging was comparable to the reduction associated with eliminating chemical exposure, nonchemical in-river stressors were viewed as providing a negligible contribution to delayed disease-induced mortality. Within scenario 2, either a comparable chemical body burden is present in barged and in-river fish prior to outmigration (with no subsequent accumulation) or the rate of accumulation is proportional to travel time during outmigration such that the body burden is comparable at the time of entrance into the estuary, and, hence, the difference in mortalities between these two groups represents the impact of nonchemical inriver stressors. The cumulative impact of chemical and nonchemical in-river stressors occurs concurrently within the river and represents the sum of both categories. Within scenario 3, the difference in disease-induced mortalities associated with barged and nonbarged fish reflects the effects of only nonchemical in-river stressors. Nonchemical in-river and chemical stressors occur sequentially during outmigration reflecting river and estuary residence with a cumulative impact similar to that of scenario 2. Cumulative delayed disease-induced mortalities under scenario 1 range from 3% to 10%, and under scenarios 2 and 3, range from 6% to 18%, for estuary residence times of 30120 days, respectively. With the exception of scenario 1, both nonchemical in-river and chemical stressors have a comparable impact on modulating host-susceptibility. An estuary mean residence time of 30 days is representative of the
stream-type life-history of spring/summer Chinook, and the associated incidence of delayed disease-induced mortalities (3-6%) is within the deterministic range presented by Kareiva (3), yet fall short of the 9-11% projections modified to represent stochasticity, necessary to mitigate declines in Snake River spring/summer Chinook salmon. Hence, delayed disease-induced mortalities could represent a significant factor influencing the recovery and conservation of this ESU. While similar analyses have not been performed with other listed threatened or endangered Chinook ESUs, including Snake River Fall-run, Lower Columbia River, Upper Willamette River, and Upper Columbia River Spring-run, mitigation of the incidence of delayed disease-induced mortalities illustrated in Figure 7 is viewed as a significant component in future management strategies to recover these listed stocks. Central issues surrounding this management strategy include: (a) partitioning cumulative chemical body burdens in outmigrant juvenile salmon into in-river and estuary residence; (b) identification and characterization of nonchemical in-river stressors; and (c) fundamental research into the interaction of selected chemicals with the regulation of immunologically and toxicologically relevant genes, dynamics of salmon habitat-use associated with hydropower systems, and the force-of-infection associated with the dynamics of host movement within a spatially heterogeneous field of chemical contamination. In the analyses presented in Figure 7, we have combined in-river stressor data obtained with spring/summer Chinook salmon with chemical data obtained with fall Chinook salmon, which is potentially error-prone given the different life-histories of these fish, to provide a semiquantitative representation of the incidence of delayed disease-induced mortality associated with exposure to L. anguillarum. This approach represents an integration of all available data todate and provides a quantitative representation of the potential magnitude of delayed disease-induced mortalities as well as direction for future management of ESU’s within the Columbia River Basin. If we can assume that spring and fall Chinook respond similarly to in-river stressors (e.g., dam passage), scenario 3 may be a fairly realistic assessment of delayed disease-induced mortality in ocean-type fall Chinook. However, there is greater uncertainty regarding the magnitude of contaminant exposure in stream-type yearling Chinook and, hence, projections of subsequent delayed disease-induced mortality. Finally, in keeping with a conservative approach to the management of endangered species, we have based our analysis on exposure to 10 cfu/ mL of L. anguillarum, which is within the range of observed concentrations (26-28), but could be deemed high on a continuous exposure basis. Pathogen exposure is assumed to take place strictly during estuary residence, which is consistent with the view that L. anguillarum is exclusively a marine pathogen. This study provides a novel and quantitative illustration of the importance of abiotic and biotic stressors on hostsusceptibility and subsequent modulation of disease dynamics in natural populations. Dynamic host-susceptibility associated with population exposures to spatiotemporally variable stressors was captured by structuring a population dynamic model on dose, a generalization of the classical structuring on age. The present approach allows parametrization of the state change kinetic rates on dose of ambient stressor that is accumulated as individuals undergo transport in a spatially heterogeneous environment. In this application, it has been shown that within the Columbia River Basin, disease is an important component influencing population dynamics of ESUs that is controllable through mitigation of not only pathogen numbers, reservoirs, and virulence, but nonchemical in-river and chemical stressors influencing hostsusceptibility.
Acknowledgments We thank James Meador and Nathaniel Scholz of the NOAA Fisheries’ Northwest Fisheries Science Center for comments. Funding for this study was provided in part by NOAA’s West Coast Center of Excellence in Oceans and Human Health and the National Science Foundation through a Faculty Early Career Development Award to F.J.L. (BES-0092312). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the supporting agencies.
Supporting Information Available Model overview and supplemental figures. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review November 17, 2004. Revised manuscript received May 16, 2005. Accepted July 12, 2005. ES0481934
