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- Oxford University Press
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- © The Author(s) 2023. Published by Oxford University Press and the Society for Experimental Biology.
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- 2051-1434
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- 10.1093/conphys/coad006
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Abstract
Volume 11 • 2023 10.1093/conphys/coad006 Reasearch article The consequences of sea lamprey parasitism on lake trout energy budgets 1,† 2,† 1 1, Tyler J. Firkus , Konstadia Lika , Noah Dean and Cheryl A. Murphy Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, MI 48824, USA Department of Biology, University of Crete, GR-70013,P.O.Box 2208, Heraklion, Crete, Greece *Corresponding author: Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, MI 48824, USA. Email: camurphy@msu.edu Co-first author .......................................................................................................................................................... Parasitism is an energetically costly event for host species. Dynamic energy budget (DEB) theory describes the metabolic dynamics of an individual organism through its lifetime. Models derived from DEB theory specify how an organism converts food to reserves (maintenance-free energy available for metabolism) and allocates mobilized reserves to maintenance, growth (increase of structural body mass) and maturation or reproduction. DEB models thus provide a useful approach to describe the consequences of parasitism for host species. We developed a DEB model for siscowet lake trout and modeled the impact of sea lamprey parasitism on growth and reproduction using data collected from studies documenting the long- term effects following a non-lethal sea lamprey attack. The model was parameterized to reflect the changes in allocation of energy towards growth and reproduction observed in lake trout following sea lamprey parasitism and includes an estradiol module that describes the conversion of reproductive reserves to ovarian mass based on estradiol concentration. In our DEB model, parasitism increased somatic and maturity maintenance costs, reduced estradiol and decreased the estradiol- mediated conversion efficiency of reproductive reserves to ovarian mass. Muscle lipid composition of lake trout influenced energy mobilization from the reserve (efficiency of converting reserves allocated to reproduction into eggs) and reproductive efficiency. These model changes accurately reflect observed empirical changes to ovarian mass and growth. This model provides a plausible explanation of the energetic mechanisms that lead to skipped spawning following sea lamprey parasitism and could be used in population models to explore sublethal impacts of sea lamprey parasitism and other stressors on population dynamics. Editor: Steven Cooke Received 13 July 2022; Revised 22 January 2023; Editorial Decision 1 February 2023; Accepted 7 February 2023 Cite as: Firkus TJ, Lika K, Dean N, Murphy CA (2023) The consequences of sea lamprey parasitism on lake trout energy budgets. Conserv Physiol 11(1): coad006; doi:10.1093/conphys/coad006. .......................................................................................................................................................... tissue with a rasping tongue and consuming blood and tissue Introduction (Lennon, 1954). Lake trout are the preferred host species for One of the most important stressors for lake trout (Salvelinus sea lamprey in the Laurentian Great Lakes (Harvey et al., namaycush) in the Laurentian Great Lakes is parasitism from 2008; Johnson et al., 2021). Following a sea lamprey attack, non-native sea lamprey (Petromyzon marinus). Sea lamprey hosts face a series of complications including osmotic imbal- are large ectoparasites that feed by attaching to host fish with ances from a large open wound (Ebener et al., 2006; Goetz a suction-cup-like mouth, mechanically removing scales and et al., 2016; Firkus et al., 2020), low hematocrit from loss of .......................................................................................................................................................... © The Author(s) 2023. Published by Oxford University Press and the Society for Experimental Biology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/ by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Reasearch article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... blood (Edsall and Swink, 2001) and introduced compounds Muir et al., 2014, 2016; Sitar et al., 2020). A general lake trout from sea lamprey buccal gland secretions (Goetz et al., 2016). DEB model was developed using data from an inland strain Sea lamprey parasitism is often lethal to lake trout (Swink, of lake trout (Kooijman, 2019), but because the metabolic 1990, 2003; Madenjian et al., 2008), but hosts that survive are dynamics and response to sea lamprey parasitism differ so faced with energetic deficits and alterations to reproductive dramatically for the siscowet ecomorph (Goetz et al., 2010, and growth physiology, often leading to a complete cessation 2014, 2016; Smith et al., 2016; Sitar et al., 2020; Firkus of spawning (Goetz et al., 2016; Smith et al., 2016; Firkus et al., 2022), it was necessary to develop a separate siscowet- et al., 2022). Accordingly, when sea lamprey were introduced specific model. The use of empirical data from siscowet lake to the Laurentian Great Lakes in the late 1800s following trout to inform our DEB model provides a more accurate construction of the Welland Canal, lake trout populations framework to explore the influence of sea lamprey parasitism sharply declined (Hansen, 1999; Muir et al., 2013). as siscowets have the highest rates of observed sea lamprey wounding in the Laurentian Great Lakes (Horns et al., 2003; Understanding the sublethal effects of sea lamprey para- Sitar et al., 2008). We focused on female siscowet lake trout sitism on host lake trout physiology is critical for evaluating because fecundity estimates are more important for informing the effects on lake trout populations. Empirical measurements population models in the future, and there is relatively little of sublethal effects at the molecular, cellular, or tissue level information available for siscowet milt concentrations. of biological organization provide important information but In prior studies, we empirically measured the influences are not sufficient to understand effects on individual fish of sea lamprey parasitism on siscowet lake trout growth, performance. One valuable tool for modeling the energetic reproduction, energy storage and gene expression (Goetz consequences of stressors at lower levels of biological orga- et al., 2016; Smith et al., 2016; Firkus et al., 2022) which nization and linking them to individual effects is dynamic provide critical information for accounting for the effects energy budget (DEB) theory (Kooijman, 2010; Martin et al., of sea lamprey parasitism in DEB. For female siscowet lake 2013). DEB theory provides a modeling framework based trout that survive sea lamprey parasitism, a common out- on thermodynamic principles that describe the metabolic come is skipped spawning whereby an individual forgoes dynamics and energy partitioning of an individual organism reproductive output completely and instead allocates energy through its entire life cycle (Kooijman, 2010; Sousa et al., towards surviving the stress associated with the parasitism 2010; Jusup et al., 2017). DEB models are adaptable and can event. In addition to parasitism, energy storage in the form of be developed for any species. Model parameters are estimated muscle lipids and plasma estradiol concentrations also play from observed physiological data from a given species. Once an important role in the reproductive success of siscowet lake parameterized, a DEB model can describe energy dynamics trout and the likelihood of skipping spawning (Sitar et al., and simulate growth, reproduction and life history charac- 2014; Goetz et al., 2017; Firkus et al., 2022). Sea lamprey teristics under different environmental conditions, such as parasitism dramatically increases the likelihood of skipping temperature and feeding regimes, and stressors, including spawning for an individual, but if the lake trout has high contaminants, disease and parasitism at any point in an muscle lipid and plasma estradiol concentrations, the negative organism’s life cycle (Kooijman, 2010). Because these models consequences of parasitism can be overcome. Conversely, if consider the whole organism and can simultaneously account muscle lipid and plasma estradiol concentrations are low, the for stress acting on multiple physiological functions, they lake trout is likely to skip spawning even in the absence of are well suited to assimilating empirically measured sublethal parasitism. These empirically measured effects can be used to effects of sea lamprey parasitism on lake trout to help under- inform how different DEB parameters are stressed under par- stand the consequences for the entire organism. DEB models asitism and allow alterations to reproductive output, growth can also be modified to account for and integrate multiple and energy storage to be estimated within the context of the sub-organismal processes to better explain energy dynamics. whole lake trout energy budget. Once the effects of para- This study is a novel application of a DEB model to predict sitism are modeled appropriately, they can be used to explore impact of sea lamprey parasitism on host fish; specifically the the effects of parasitism under a variety of scenarios and model focuses on alterations to reproduction and growth and ultimately inform stock-recruitment relationships, individual- accounts for variation in estradiol concentrations and muscle based models and other tools critical for the management of lipid concentrations. lake trout in the Laurentian Great Lakes in efforts to restore naturally reproducing populations. We parameterized a DEB model for female siscowet lake trout using available life history data from the literature and used the resulting model to explore the effects of sea lam- Methods prey parasitism on reproduction, growth and other life his- tory characteristics. Lake trout display tremendous variation General model description throughout their range; four currently recognized lake trout ecomorphs are present in Lake Superior alone, differing in To explore the influence of sea lamprey parasitism on sis- morphology, habitat preference, metabolism and life history cowet lake trout reproduction and growth, we first devel- characteristics (Moore and Bronte, 2001; Goetz et al., 2014; oped a base DEB model that described the energy allocation .......................................................................................................................................................... 2 Conservation Physiology • Volume 11 2023 Reasearch article .......................................................................................................................................................... Figure 1: General overview of the structure of the DEB model. Red highlights indicate the components of the model that were altered to simulate the eeff cts of parasitism and blue highlights indicate components altered due to muscle lipid concentration. The orange circle between reproductive buffer and ovarian mass represents the egg sub-model that allows differences in plasma estradiol concentration to influence ovarian mass synthesis. and dynamics of siscowet lake trout throughout their entire metabolizing and excreting toxicants, etc.) have priority and lifecycle that accounts for energetic tradeoffs constrained by are paid first before remaining energy can be allocated to life history. The general structure, equations and assump- growth or the reproduction buffer. A portion of the energy tions of DEB models have been thoroughly covered pre- allocated to reproduction matures to ripe reproductive matter viously (Sousa et al., 2008, 2010; Kooijman, 2010; Jusup (hereafter referred to as ovarian mass; Kooijman, 2010). et al., 2017). Briefly, DEB models are described by four state Table 1 summarizes the state variables and their dynamics, variables (reserve energy, structural mass, cumulative energy and a generalized overview of energy allocation processes is invested to maturation for juveniles and energy invested in shown in Figure 1. reproduction for adults), and a set of differential equations The standard (std) DEB model is the simplest model in and model parameters dictate energy flux to each compart- the family of DEB models which can be adapted to model ment (Kooijman, 2010)(Figure 1). Energy enters an organism most species (Marques et al., 2018). Because DEB models are through uptake of food (with a fraction removed as feces) adaptable to any species, they use terminology that attempts and enters a reserve pool. In DEB models, reserve represents to be species generic. We used the abj typified DEB model all tissue that does not require energy for maintenance and that accounts for metabolic acceleration, a DEB term that is readily metabolizable as a source of usable energy (Jusup refers to rapid growth during early development, following et al., 2017). Energy is then mobilized from the reserve at a initiation of exogenous feeding (generalized as birth in DEB given rate and allocated towards somatic functions and mat- terminology) (Kooijman, 2014; Lika et al., 2014). Metabolic uration/reproduction following the κ-rule. The κ-rule states acceleration occurs well before the maturity threshold for that a fixed portion (κ) of mobilized energy is allocated towards somatic maintenance (e.g. maintenance of existing puberty and might or might not coincide with metamorphosis structural mass, mean level of movement costs and production (a DEB term that refers to rapid change in morphology). of scales) and growth (increase in structural mass), while the Although lake trout do not undergo metamorphosis, they remaining fraction (1-κ) is allocated towards maturity main- do undergo rapid growth post-hatch making the abj model tenance and maturation (for juveniles) or reproduction (for appropriate. The abj DEB model differs from the std DEB adults). Maturation involves continuous energy investment model by allowing for the rapid increase in respiration and as the organism becomes more complex and prepares the change in body shape that occurs during the larval or post- body for the mature adult state. For example, the prepara- hatch stages of most fish species and includes one addi- tion of reproductive machinery and development of immune tional parameter, the maturity threshold at metamorphosis defense systems require more energy as an organism matures The abj model has been used for many actinopterygians (Kooijman, 2010). Maturity maintenance is the energy spent (Lika et al., 2022). to maintain the current state of complexity. DEB models Because we are exploring the effects of parasitism, a sub- handle maturity by tracking the cumulative investment of stantial stressor that potentially affects maintenance costs, we energy towards maturation, and once a specified threshold also implemented rules that describe energy use when avail- is reached (called puberty), mobilized energy is then allo- able energy in the κ fraction is not sufficient to meet somatic cated towards a reproductive buffer for later allocation to maintenance demands (see Table 1). If there is insufficient reproductive activities, such as egg production (Kooijman, energy available to meet somatic maintenance requirements, 2010; Jusup et al., 2017). Somatic and maturity maintenance growth ceases and maintenance costs are paid from the energy processes (e.g. protein turnover, activity, immune function, .......................................................................................................................................................... 3 Reasearch article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Table 1: State variables, mass fluxes and dynamics of the standard DEB model including the egg module. Parameters are defined in Tables 2 and 3. State variables unit Description M mol Structural mass 1/3 L cm Structural volumetric length: (M / [M ]) V V M mol Mass of reserve −1 m mol mol Reserve density: M /M E E V M mol mass investment into maturation M , M mol mass investment to reproduction (unripe, ripe) R OV Fluxes (mol/d) ˙ ˙ J Assimilation rate: J fL EA EAm v ˙ J Reserve mobilization rate: M − r with EC E ˙ ˙ ˙ ˙ j m /m −j /κ j m j EAm E Em EM EAm E EM (∗) r ˙ = if ≥ m +y /κ m κ E EV Em j m EAm E r ˙ = 0 if < j /κ and M or M > 0 EM R OV Em ˙ ˙ ˙ j m /m −j /κ j m EAm E Em EM EAm E r ˙ = if < j /κ and M and M = 0 EM R OV m +κ y /κ m E G EV Em −1/3 −2/3 ˙ ˙ j = J M M [ ] EAm EAm V J Somatic maintenance rate: j L EM EM J Maturity maintenance rate: k min M , M EJ J H ˙ ˙ ˙ J Energy flux to maturation/reproduction: (1 − κ) J − J ER EC EJ M M ˙ H R J Energy flux to ovaries formation: b M H V OV M M V V Dynamics M = ˙rM V V dt ˙ ˙ M = J − J E EA EC dt M = M < M J H H ER dt d (∗∗) ˙ ˙ M = M = M J − J R H ER OV dt H d (∗∗) M = κ J OV R OV dt (∗) Condition to meet somatic maintenance costs (∗∗) Modified to cover maintenance costs available for reproductive functions (i.e. reproductive buffer similar approach to incorporating hormone dynamics into a and/or ovarian mass in proportion to their availability). If DEB model is outlined in Murphy et al. (2018) and Muller there is insufficient energy available in the κ fraction, ovarian et al. (2019); however, we simplified this approach so that mass and the reproductive buffer, energy is then taken from estradiol concentration was the only required input. This structure and the organism loses structural mass or “shrinks” approach more explicitly describes the processes involved in (Augustine et al., 2014). egg development and allowed the model to account for dif- ferences in estradiol concentration in parasitized and unpar- asitized individuals. In the egg module, reproductive reserve Egg module (energy available for use towards reproduction) molecules are combined with estradiol to synthesize the egg yolk protein To model our observed reproductive processes from Firkus vitellogenin. Processes that take place in the blood plasma et al. (2022) in our lake trout DEB, we added an egg module volume or liver are taken proportional to the structural mass. that allows reproductive hormone dynamics to dictate the The energy flux for egg mass production is triggered by conversion of energy in the reproductive buffer into eggs. Pre- estradiol density of (i.e. the ratio of mass of estradiol in plasma vious laboratory studies suggest that estradiol concentration E2 and the structural mass, m = and follows the law of E2 modulates the effects of sea lamprey parasitism on lake trout V mass action with the reproductive reserve density (m = ). reproduction (Smith et al., 2016; Firkus et al., 2022), so it R is necessary to account for estradiol’s role in our model. A Vitellogenin production occurs in the liver and is secreted .......................................................................................................................................................... 4 Conservation Physiology • Volume 11 2023 Reasearch article .......................................................................................................................................................... into plasma and travels to the ovaries where it is absorbed reproductive maturity), life span (oldest recorded age), total by ovarian follicles; all processes involved are proportional length at puberty (total length at reproductive maturity), wet to the structural mass of the fish M . Thus, the rate of egg weight at puberty (wet weight at reproductive maturity), mass production is given by J = b m m M , where the ultimate length (longest recorded length), length–number of OV H E2 R V parameters describing the conversion of reserve, estradiol and eggs (number of eggs produced at a given length), wet weight– vitellogenin to egg mass are absorbed in the proportionality number of eggs (number of eggs produced at a given weight), constant b . The dynamics of the egg ovarian mass, M , are time–length (length at age), time–wet weight (weight at age) H OV given in Table 1. and length–wet weight were obtained from observations of wild siscowet lake trout surveyed in Lake Superior (Miller Data for estradiol were obtained from laboratory stud- and Schram, 2000; Goetz et al., 2011, 2017; Sitar et al., 2014; ies of siscowet lake trout (Firkus et al., 2022) as ng/ml of Hansen et al., 2016; Froese and Pauly, 2021). Additional plasma. These data were linked to the model variable that information, such as individual egg weights, were obtained accounts for the mass of estradiol M (in C-mol): M = E2 H from laboratory rearing studies (Smith et al., 2016). Data for −9 10 [E ] V /w , where [E ] is the estradiol concentration 2 H 2 pl estradiol concentration, egg mass wet weight, muscle lipid (ng/ml of plasma), w is the molecular weight of estradiol concentration (% of total muscle mass), length at birth (length (15.1 g/C-mol) and V is the total volume of plasma in a pl at exogenous feeding), age at birth (days from fertilization to lake trout in ml given by the following equation: V = pl exogenous feeding) and length–weight over the course of a β W /100, where W is wet weight and β is the propor- w w pl pl single year for parasitized and unparasitized individuals were tionality constant (averaged value of 2.86% from Gingerich provided from a laboratory study (Firkus et al., 2022). et al., 1987 and Gingerich and Pityer, 1989). The total wet weight W has contributions from structural mass, reserve Physical length, L , is linked to the structural volumetric w w 1/3 mass and ripe (M ) and unripe (M )r reproductive mass: M OV R V length, L = , with the shape factor, δ , which w (M +M +M ) [M ] w M V V E E R OV V W = + , where w , w and d , d w V E V E d d V E differs depending on type of measurement (standard/total): are the molecular weights and densities of structure and L = L/δ . Mass quantified as total wet weight, W , has w M w reserve, respectively (Table 3). contributions from structural mass, reserve mass and ripe w M V V (M ) and unripe (M )reproductive mass: W = + R w OV w (M +M +M ) Estimation procedure E E R OV , conversion parameters are given in Tables 2 and 3. State variables in DEB models represent an aggregation of complex physiological functions, and therefore, model parameters cannot be associated directly with empirical data Validation (Nisbet et al., 2012). Auxiliary theory links the abstract DEB state variables to quantities that can be measured directly After model parameterization, the resulting DEB model was such as weight, length, feeding, respiration, egg production, validated using data collected from wild siscowet lake trout etc. (Kooijman et al., 2008; Lika et al., 2011a). We used sampled near the Keweenaw Peninsula in Lake Superior different types of empirical data (see Table 4 and Figure 2) (Goetz et al., 2011). The validation data set included estradiol to estimate the DEB parameters using the “add my pet” concentration, total length, total weight and gonadal weight procedure (Marques et al., 2018) and the covariation method from a population sampled monthly for 6 months leading to (Lika et al., 2011a; Marques et al., 2019) implemented in spawning. The data show considerable variability in length MatLab (The Math Works Inc., 2020) with the software and plasma estradiol concentration, which are skewed to package DEBtool (available at https://www.bio.vu.nl/thb/deb/ the right, and are therefore suited for a lognormal distri- deblab/). Briefly, parameter estimates are derived through bution. Using the estimated parameter set from the base simultaneously minimizing the weighted sum of squared DEB model (Tables 2 and 3), 200 Monte Carlo simulations deviations between provided data and model estimates. were performed to introduce inter-individual variability. In Model goodness of fit was evaluated with the mean relative each simulation three parameters were allowed to randomly error (MRE) and symmetric mean squared error (SMSE) vary, namely the initial fish length at the beginning of the (Marques et al., 2019). Lower MRE and SMSE indicate better simulation, the maximum surface-area specific assimilation model predictions. rate p and the conductance rate v ˙, to account respectively Am for the different individual sizes at the start of the experi- ment, assimilation (implicitly feeding) performance and lipid Data for model parameterization content. In each simulation, p and v ˙ were assigned num- Am Data used for parameter estimation were obtained from bers randomly chosen from normal distributions with mean published literature from laboratory studies and surveys of defined from estimated values of p and v ˙ (for adults) and Am wild populations (Table 4). Because there are many lake trout a 20% coefficient of variation (i.e. standard deviation equals ecomorphs with very different life histories, only data col- 0.2 times the mean). As a result of the varied initial conditions, lected specifically from the siscowet ecomorph were included simulated individuals differed in growth and reproduction for parameter estimation. Age at puberty (average age at patterns. The initial length was drawn from a lognormal .......................................................................................................................................................... 5 Reasearch article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Figure 2: DEB model predictions (black line) compared to univariate data provided from the literature (grey dots). Fecundity as a function of wet weight (A), length (B), length at time (C), wet weight at time (time since exogenous feeding; D), wet weight–total length (E) and ovarian mass at time (time in 1 year prior to reproduction; F). distribution with parameters μ = 4.09 and σ = 0.13, obtain random sets of estradiol concentration values used in (i.e. mean 60.2 mm and standard deviation 7.81 mm). The the validation. parameters μ and σ were obtained by fitting the lognormal Implementing effects of parasitism, muscle distribution to the first length measurements from the valida- tion data set. As in the DEB estimation procedure, estradiol lipid concentration and estradiol concentration was used as forcing variable in the egg module concentration for each simulation. At each sampling time for the 6 months leading to spawning, the lognormal distribution was fitted To assess the influence of sea lamprey parasitism on siscowet to the estradiol data. These distributions were then used to lake trout reproduction and growth, we made modifications .......................................................................................................................................................... 6 Conservation Physiology • Volume 11 2023 Reasearch article .......................................................................................................................................................... Table 2: Primary abj DEB parameters estimated for siscowet lake trout at a reference temperature of T = 20 C. Any rate parameter, k,can be ref T T A A converted to its value at any given temperature T by multiplying its value with the correction factor TC = exp − T T ref Symbol Value Unit Interpretation −2 −1 p 550.41 Jcm d Maximum surface-area specific assimilation rate Am −1 {v ˙} 0.01644 cm d Energy conductance κ 0.6028 – Allocation fraction to soma κ 0.95 – Reproduction efficiency −3 −1 p 31.75 Jcm d Volume-specific somatic maintenance rate −3 [E ] 5217 Jcm Specific costs for structure ˙ −1 k 0.002 d Maturity Maintenance rate coefficient E 22.28 J Maturity threshold at birth E 45.74 J Maturity threshold at metamorphosis p 5 E 4.52 10 J Maturity threshold at puberty −8 −2 h 4.253 10 d Weibull aging acceleration T 8000 K Arrhenius temperature T 293.1 K Reference temperature Ref δ 0.1116 – Shape coefficient for total length δ 0.066 – Shape coefficient embryo Me δ 0.04235 – Shape coefficient for standard length Ms 8 −1 b 110 d Rate of reproductive reserve ripeness f 0.4734 — Scaled functional response for GSI data f 0.7114 — Scaled functional response for length–weight data LW f 0.8275 — Scaled functional response for length/weight–number of eggs data LWN f 0.7173 — Scaled functional response for length data tL f 1.157 — Scaled functional response for wet weight data tWw s 0.75 — Stress factor on maintenance from parasitism to the parameterized base DEB model that reflect the ener- assumption that we are capturing instances of severe para- getic consequences of parasitism. In the context of DEB, any sitism events that lead to reproductive disruption), and our stressor that alters physiological processes must be reflected relationships between parasitism and the target DEB param- by a change in one or more model parameters (Jager, 2019). eters are therefore also binary. To implement the influence Therefore, we must identify the physiological mode of action of individual variation in muscle lipid concentration, a non- (pMoA) and specific DEB parameter(s) through which sea linear relationship between muscle lipid and associated DEB lamprey parasitism influences life history (Ashauer and Jager, parameters was developed where the DEB parameters are 2018). Once the appropriate DEB parameter(s) is identified, altered more dramatically as the muscle lipid deviates further a relationship between the stressor and model parameter, from empirically derived average muscle lipid for siscowet termed damage, must be developed. The changes to a par- lake trout. A detailed description of the rationale and process ticular DEB parameter cannot be experimentally derived and, for implementing parasitism stress, muscle lipid variation and therefore, must be developed based on best judgement and estradiol variation is outlined below. After identifying the dif- an approximation of empirically observed changes to length, ferent pMoAs, our goal was to explore a variety of scenarios weight and ovarian mass (Firkus et al., 2022). In toxicology by varying muscle lipid concentration and parasitism status. applications, the relationship between the damage function Simulations were run for a period of 365 days leading to and the change in the DEB parameter is typically expressed spawning, and only for female lake trout as female fecundity as ‘linear-with-threshold’ model that approximates a dose– is more relevant for population assessments and there is rela- response curve (Jager, 2019). To simplify our approach, we tively little information available in the literature for siscowet treat sea lamprey parasitism as a binary stressor (under the lake trout milt concentrations. For each simulated individual .......................................................................................................................................................... 7 Reasearch article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Table 3: Compound parameters, molecular weights, chemical potentials and densities. Compound Value Units Description parameters −2 −1 J = mol cm d max specific assimilation rate EAm p ˙ /μ Am E −3 [M ] = d /w mol cm specific structural mass V V V EAm −1 m = mol mol max reserve density Em [M ]v ˙ [p ]y M EV −1 −1 j = mol mol d mass-spec somatic maintenance costs EM [E ] [E ] G −1 y = mol mol coupler of reserve invested and structure EV μ [M ] E V produced κ - Growth efficiency Molecular weights, chemical potentials and densities w 23.9 g/mol Molecular weight of dry reserve w 23.9 g/mol Molecular weight of structure μ 550 KJ/mol Chemical potential of the reserve d 0.2 g/cm Specific density of dry reserve d 0.2 g/cm Specific density of structure we started the simulation year at a length of 70 cm to In addition to increasing somatic maintenance, an increase approximate the length of reproductively mature individuals in maturity maintenance is also a likely result of parasitism. from the laboratory study that evaluated the influence of Energy allocated to reproduction must pay maturity main- sea lamprey parasitism on lake trout reproduction (Firkus tenance costs prior to investment in reproductive processes. et al., 2022). Maturity maintenance encompasses the costs associated with maintaining the cumulative amount of energy that has been allocated to reach each stage of development leading to Influence of parasitism on maintenance reproductive maturity. After reaching reproductive maturity, costs additional energy is then allocated towards reproduction. Empirical evidence suggests siscowet lake trout reduce repro- Maturity maintenance costs are proportional to the total ductive output (Firkus et al., 2022) and plasma sex steroid energy invested towards reproductive maturation. Because concentrations (Smith et al., 2016; Firkus et al., 2022) follow- parasitism results in an increased immune response and ing sea lamprey parasitism, often leading to skipped spawning greater regulatory and protection costs (Goetz et al., 2016), (Goetz et al., 2014, 2017; Firkus et al., 2022). Thus, para- we expect maturity maintenance to increase in parasitized sitism should alter DEB parameters in a way that leads to siscowets. Thus, an increase in maturity maintenance rate a marked reduction of reproductive investment. Many DEB coefficient (k ) is also a likely pMoA. parameters can influence reproduction, but not all are likely It is generally good practice to alter maturity maintenance candidates given what we know about parasitism. Although to the same degree as somatic maintenance in the presence we have observed high rates of skipped spawning in para- of stress (Jager, 2019). Therefore, for both maturity and sitized siscowets, we know that some “normal” reproductive somatic maintenance, we included a stress factor s that development occurs prior to spawning, but at some point, increases both maintenance terms as follows: (1 + s ) p and m M oocytes cease further development and are resorbed (Goetz (1 + s ) k . This stress factor is zero for unparasitized cases m J et al., 2011; Sitar et al., 2014); therefore, the pMoA selected and 0.75 for parasitized cases (Table 2). The stress factor was should allow for these observed changes. One likely pMoA based on a best judgement estimate and serves as a proof is a parasitism-driven increase in somatic maintenance costs. of concept. This value could be altered to represent different Energy invested to soma (κ fraction) must first pay somatic severities of parasitism. maintenance costs, but parasitism is likely to increase these costs considerably. Because sea lamprey parasitism creates an open wound in the lake trout, the costs for maintaining Influence of parasitism on plasma estradiol osmotic concentration gradients, repairing tissue, replacing concentration lost blood cells and turning over necrotic tissue will be considerably increased (Kooijman, 2010). Thus, an increase in In addition to muscle lipid concentrations, reproductive hor- volume-specific somatic maintenance (p ) is a likely pMoA. mone dynamics also play a critical role in reproduction. .......................................................................................................................................................... 8 Conservation Physiology • Volume 11 2023 Reasearch article .......................................................................................................................................................... Plasma estradiol concentration is an important predictor mobilization seems especially likely given the functional role of the likelihood of skipping spawning for siscowet lake of lipid for maintaining neutral buoyancy at the depths sis- trout (Firkus et al., 2022). To account for the importance of cowet lake trout inhabit (Henderson and Anderson, 2002; estradiol, we implemented an egg module in the DEB model Goetz et al., 2014). We implemented changes to the energy that allows reproductive hormone dynamics to dictate the conductance parameter so that it is relative to the average conversion of energy from the reproduction buffer into eggs. muscle lipid concentration in laboratory-raised lake trout Two estradiol profiles were provided to the egg module. For that did not skip spawning (Firkus et al., 2022). As muscle parasitized fish, we provided the model with an estradiol lipid concentration strays from the average (55.58%), energy profile that approximates plasma estradiol concentrations of conductance increases or decreases following the function v ˙ = 0.35 parasitized lake trout observed to skip spawning in laboratory lipid v ˙ where “lipid” is the muscle lipid concentration 55.58 experiments (Foster et al., 1993; Firkus et al., 2022). In of an individual lake trout (Table 2). unparasitized fish, the model was provided with an estradiol profile that approximates plasma estradiol concentrations Muscle lipid concentration also likely influences reproduc- of unparasitized spawning lake trout (Foster et al., 1993; tive efficiency, represented in the DEB model as κ . Empiri- Firkus et al., 2022)(Figure 4A). It is likely that when lake cally, low muscle lipid concentration has been associated with trout skip spawning following parasitism, plasma estradiol an increased likelihood of skipped spawning for siscowet lake plays a role other than gonadal development. We generally trout (Sitar et al., 2014; Firkus et al., 2022). Because lipid think of estradiol in terms of its role in modulating hepatic reserves are drawn upon to create reproductive mass, low production and gonadal uptake of Vtg (Tyler and Sumpter, muscle lipid concentrations could mean there are insufficient 1996), but it also plays a key role in the immune systems of resources such as critical amino and fatty acids available for fish (Cabas et al., 2018). When estradiol is used for immune- gamete production, and producing gametes in the absence of related functions, more estradiol is required to produce the adequate lipid reserves results in reduced reproductive effi- same ovarian mass as an individual not facing an immune ciency. Muscle lipid concentration influencing oocyte matura- challenge. We account for the likely reduction in estradiol tion and quality is well supported in the literature for a variety availability due to an increased immune response by assuming of species (Craig et al., 2000; Rodríguez et al., 2004; Ghaedi a reduced fraction of estradiol is available for egg devel- et al., 2016). To account for the influence of low muscle opment following parasitism, i.e. by reducing the rate of lipid concentration on reproductive efficiency, the unstressed reproductive reserve ripeness b . κ value from the base model is modified for lake trout with muscle lipid concentrations below average for siscowet Influence of muscle lipid concentration on lipid lake charr (55.58%) using the equation: κ = κ R R 55.58 energy mobilization and reproductive where lipid is the percent muscle lipid concentration of an efficiency individual and 55.58% is the average percent muscle lipid concentration of siscowet lake trout used in laboratory sea Lipid storage also plays a key role in reproduction for sis- lamprey parasitism trials (Firkus et al., 2022). When muscle cowet lake trout. Surveys of wild lake trout found that lipid concentrations are equal or greater than average, κ siscowet lake trout that skipped spawning had significantly remains unchanged. With this function, the further muscle lower energy reserves than those that did not skip (Sitar lipid concentration deviates below average, the lower κ is, et al., 2014). In laboratory settings, muscle lipid concentration but as muscle lipid increases above average, κ does not prior to parasitism was a significant predictor of ovarian increase as reproduction efficiency is rarely greater than 0.95 mass and the likelihood of skipping spawning (Firkus et al., in DEB applications (Lika et al., 2011b). 2022). Therefore, accounting for individual variation and other factors that influence muscle lipid is important for accurately modeling the influence of parasitism. Approaches have been developed to account for differences in lipid storage Results in a DEB context, but they require the use of simplified models that do not include all of the components of a full DEB model DEB model parameters (Martin et al., 2017). The parameter estimates of the base siscowet lake trout Lipid storage has no direct analogue in the DEB frame- DEB model are given in Table 2. Predictions from the work, but is most analogous to the reserve compartment, parameterized DEB model matched the provided data well which primarily consists of polymers and lipids (Kooijman, and resulted in an acceptable overall goodness of fit as 2010). The energy conductance parameter v ˙ controls the rate measured by the MRE (0.087) and the SMSE (0.106) of energy mobilization from the reserve. Increasing v ˙ increases (Table 4). Estimates for length–time, length–weight and the rate at which reserves are depleted and mobilized for use. weight–time were all reasonable with relatively low relative We would expect that siscowet lake trout with low muscle error (RE < 0.15; Figure 2C, D, E). Fecundity at length was lipid storage would mobilize energy from the reserve at a slightly overestimated (RE = 0.274) and fecundity at weight much lower rate to allow lipid to accumulate. Reduced energy was slightly underestimated (RE = 0.237), but still followed .......................................................................................................................................................... 9 Reasearch article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Table 4: Comparisons of model predictions with observed life history data provided to the model and relative errors (mean of relative differences between model predictions and data used in calibration). Data type Observed data Predicted estimates Relative error Data symbol Units Observed data source age at birth (7 C) 127 128 0.008 ab d (Firkus et al., 2022) age at puberty (5 C) 4161 3804 0.086 ap d (Sitar et al., 2014) life span 18 250 18 270 0.001 am d (Froese and Pauly, 2021) length at birth 2.775 2.771 0.001 Lb cm (Firkus et al., 2022) total length at puberty 44.3 37.8 0.147 Lp cm (Sitar et al., 2014) ultimate standard length 150 134 0.106 Li cm (Froese and Pauly, 2021) egg wet weight 0.065 0.064 0.006 Ww0 g (Smith et al., 2016) wet weight at puberty 680 732 0.077 Wwp g (Sitar et al., 2014) ultimate wet weight 32 700 32 700 <0.001 Wwi g (Froese and Pauly, 2021) end of reproduction 239 221.7 0.072 tMov G (Firkus et al., 2022) cycle ovarian mass observed trends (Figure 2A, B). The model also provided ovarian weight of 222 g at spawning (day 365). A 10% reasonable estimates of egg wet weight for siscowet lake reduction in muscle lipid resulted in a reduction of ovarian trout (Figure 2F). Validation of the model (Figure 3) was mass to 137 g, while a 10% increase in muscle lipid increased performed by comparing the model predictions (body wet ovarian mass to 229 g (Figure 4B). Adding the influence of weight, length and wet weight of ovaries as functions of parasitism strongly reduced ovarian mass regardless of muscle time) to data collected from wild siscowet lake trout sampled lipid concentration, but higher muscle lipid concentration was near the Keweenaw Peninsula in Lake Superior (Goetz et al., slightly able to mitigate this reduction. Under average muscle 2011) and simulated estradiol concentrations (Figure 3C). lipid concentrations (55.58%) and parasitism, ovarian weight The 200 Monte Carlo simulations captured the variation in was 39 g at the time spawning would normally occur. A 10% total body wet weight and length well (Figure 3A, B), but reduction in muscle lipid resulted in an ovarian mass of 18 g, slightly under-predicted ovarian mass in the last two months while a 10% increase in muscle lipid increased ovarian mass prior to spawning (Figure 3D). to 48 g (Figure 4B). Differences in ovarian mass driven by parasitism and mus- Parasitism and muscle lipid concentration cle lipid are observable in the wet weight of the reproduc- tive buffer (Figure 4C). In scenarios without parasitism, the We explored the combined influence of muscle lipid concen- reproductive buffer builds until day 100 after which it begins tration and parasitism on reproduction and growth outcomes to be converted into ovarian mass (Figure 4B). In scenarios by introducing modifications to the base DEB model. Empir- with parasitism, the reproductive buffer builds more slowly ical evidence suggests that siscowet lake trout can overcome initially due to higher maintenance costs. After 100 days the adverse effects on reproduction following sea lamprey par- reproductive buffer is rapidly converted to ovarian mass in asitism if muscle lipid concentrations are sufficiently high. unparasitized scenarios, but in parasitized scenarios, slower Additionally, unparasitized lake trout with low muscle lipid conversion rates (b ) from reproductive buffer to ovarian concentrations skip spawning more frequently than unpara- mass (Figure 4C) result in a much smaller ovarian mass at sitized lake trout (Firkus et al., 2022). Therefore, our model day 365. Differences in reproductive buffer accumulation due should adequately reflect these empirical observations. In our to muscle lipid concentration are largely driven by reduced model, we altered parasitism status and provided three differ- reproductive efficiency (κ ) and less energy being mobilized ent muscle lipid concentrations representing natural variation from the reserve (v ˙). of muscle lipid in siscowet lake trout. The average muscle lipid from siscowet lake trout in laboratory studies (55.58%) Somatic growth (increase in structural mass/physical (Firkus et al., 2022) was used, as well as 65% and 45% length) was also influenced by both parasitism and muscle representing approximate high and low bounds of natural lipid concentration in our tested scenarios, albeit subtly. variation (Sitar et al., 2020). Growth was slightly lower in parasitized scenarios than in unparasitized scenarios with the same muscle lipid concen- For unparasitized siscowet lake trout, varying lipid altered tration (Figure 4D). At a 55.58% muscle lipid concentration, ovarian mass matched our expectations from the empirical the scenario without parasitism resulted in growth of 8.2 mm evidence. The muscle lipid concentration for the average by the end of the year, while the parasitism scenario with siscowet lake trout in our data (55.58%) resulted in an .......................................................................................................................................................... 10 Conservation Physiology • Volume 11 2023 Reasearch article .......................................................................................................................................................... Figure 3: Body wet weight (A), length (B), estradiol concentration (C) and wet weight of ovaries (D) as functions of time: comparison of model predictions (solid grey lines, 200 Monte Carlo simulations) to data (blue circles) from wild siscowet lake trout (Goetz et al., 2021). Estradiol data for the 6 months leading to spawning (black points). the same muscle lipid concentration resulted in an end-of- parasitism into one coherent framework that allows the year increase of only 0.9 mm (Figure 4C). In the highest consequences for many different processes to be evaluated muscle lipid concentration scenarios (65%) lake trout grew simultaneously. In this study, we developed and parameterized 10.6 mm over the course of the year without parasitism, but a DEB model that captures the energy dynamics of siscowet only grew 1.7 mm when parasitized. For the lowest muscle lake trout. The model reproduced key life history features lipid concentration scenarios (45%), lake trout grew 5.6 mm specific to the siscowet lake trout ecomorph and produced without parasitism and 0.3 mm with parasitism (Figure 4D). model estimates that adequately matched field and laboratory collected data. We also developed modifications to key DEB parameters to capture the effects of sea lamprey parasitism on reproduction and growth and account for the influence Discussion of individual variation in muscle lipid concentration and Parasitism is a complex stressor for host species and estradiol profiles observed in laboratory studies. Using influences multiple physiological processes simultaneously. these modifications, we explored several scenarios and Capturing the full extent of these effects, and their implica- evaluated their influence on ovarian mass and growth. We found that implementing stress from sea lamprey parasitism tions for the whole organism, is challenging using empirical via increases to somatic and maturity maintenance and a measurements alone. DEB theory allows us to cumulatively reduction to estradiol concentration in our model resulted incorporate empirical measurements of the effects of .......................................................................................................................................................... 11 Reasearch article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... Figure 4: Simulation of estradiol concentrations (A), wet weight of ovaries (B), wet weight of the reproductive buffer (unripe (C) and structural length (D) under different parasitism and muscle lipid concentration scenarios. Time on the x-axis indicates days beginning 365 days prior to spawning. Muscle lipid concentrations are indicated by line color (blue, 65%; black, 55% and red, 45%) and scenarios with parasitism are indicated by dotted lines. Estradiol outcomes (A) were identical for all lipid scenarios and only differed with parasitism. The grey dashed line (B) indicates the ovarian mass threshold for skipped spawning. in a good approximation of observed empirical results for (Goetz et al., 2011; Sitar et al., 2014; Firkus et al., 2022). reproduction and growth. Altering energy conductance and Therefore, we would expect low lipid levels to result in reproductive efficiency with muscle lipid concentrations lower-than-typical ovarian weight in our modeled scenarios. also represented the natural variation observed in siscowet Under the scenarios we tested, muscle lipid concentration lake trout populations well and provided insight into the had a heavy influence on reproduction regardless of para- modulating role muscle lipid concentrations can have in the sitism status. At the lowest lipid simulation (45%) without response to sea lamprey parasitism. These findings point to parasitism, ovarian mass reached 137 g just prior to spawn- the plausible physiological mechanisms at play during sea ing (Figure 4B). The threshold for skipping spawning is a lamprey parasitism and can guide future empirical studies. gonadosomatic index below 3.0 (Goetz et al., 2011). In our Because our model can estimate reproduction and growth simulations this would mean any lake trout with ovarian outcomes with and without sea lamprey parasitism and mass lower than 96 g would be deemed a skipped spawner. account for natural variation in lipid levels, it can help Despite the reduced ovarian mass in the lowest muscle lipid inform existing models that attempt to estimate lake trout scenario without parasitism, ovarian mass remained above populations under various sea lamprey control scenarios. this threshold. For ovarian mass to be below the skipped Additionally, this work provides the foundation for future spawning threshold in an unparasitized individual, muscle DEB models that wish to assess the effects of parasitism on lipid concentration would have to be 38% or lower. other species. As expected, parasitism reduced ovarian mass at all muscle lipid concentrations in our modeled scenarios (Figure 4B). Influence of parasitism and individual Even at high muscle lipid concentrations (65%), ovarian mass variation after parasitism reached only 48 g, remaining well below the threshold for skipping spawning. This outcome suggests Studies of wild and laboratory-raised siscowet lake trout that even if an individual lake trout has exceptionally high indicate unparasitized individuals skip spawning at some muscle lipid concentration, it cannot overcome the energetic baseline rate as a part of their life history, and that skipping consequences of sea lamprey parasitism for reproduction. is at least partially dependent on muscle lipid concentration .......................................................................................................................................................... 12 Conservation Physiology • Volume 11 2023 Reasearch article .......................................................................................................................................................... Average siscowet lake trout muscle lipid concentrations range other than parasitism for understanding the full scope of from 29 to 64% in the wild depending on size and the depth parasitism-driven changes to reproduction. For example, inhabited by the individual (Sitar et al., 2020), therefore lake variation in host food consumption can drastically change trout with muscle lipid concentrations sufficiently high to parasite virulence, host survival and reproduction (Hall mitigate the effects of sea lamprey parasitism would be rare. et al., 2009). Our model similarly highlights how muscle This outcome is consistent with laboratory studies where high lipid concentration interacts with parasitism to influence muscle lipid concentrations were largely insufficient to over- reproduction. Due to the critical role muscle lipid plays come the adverse effects of sea lamprey parasitism. (Firkus in siscowet lake trout reproduction (Sitar et al., 2014), et al., 2022). including the influence of muscle lipid as a pMoA on reserve mobilization and reproduction efficiency in our DEB The alterations to DEB parameters we implemented are model allows for a more complete picture of how parasitism not necessarily an accurate representation of how sea lam- influences reproduction. prey parasitism influences the energy budget of a siscowet lake trout. Because the metabolic parameters in DEB mod- els are abstract and include many processes that cannot be Model limitations directly measured, the process for implementing stress is inherently arbitrary (Jager, 2019). Regardless, the alterations It is important to highlight the limitations of this model we implemented do a reasonable job of describing the effects and resulting simulations. First, the alterations to the DEB on growth, reproduction and energy storage observed from model implemented to represent parasitism are not directly the empirical data, and at the very least serve as plausible measured. Because each DEB model parameter represents an hypotheses for future experimental work. Applying the alter- abstracted process within the organism, changes to observed ations to DEB parameters that we developed also allows us to empirical endpoints often involve many DEB parameters. examine the consequences of parasitism on reproduction and Thus, we were required to rely on our best judgement and growth under a variety of scenarios. implement changes to DEB parameters that matched our knowledge of the physiological modes of action caused by Other DEB models have similarly captured the influence of parasitism and that resulted in changes to endpoints we were parasitism on host reproduction. Hall et al. (2007) modeled able to empirically observe. The changes we implemented to two putative parasitism strategies and the consequences for DEB parameters are therefore presumptive and other pro- host reproduction and growth in a generalized DEB model; cesses that we did not consider could be important. For one strategy where the parasite affects the host simply by example, sea lamprey parasitism could potentially influence draining energy resources indiscriminately, and one strategy host feeding behavior, but we did not alter lake trout food where the parasite actively influences host energy allocation intake in our model due to a lack of empirical evidence. If away from reproduction to provide more available energy food intake is substantially reduced, it could further influence to the parasite (influencing the κ parameter). In our model, predicted reproductive and growth outcomes. Our model sea lamprey parasitism acts similar to the former strategy by therefore only serves as a reasonable hypothesis for how reducing energy available to the host through the increase parasitism, muscle lipid and estradiol concentration influ- of maintenance costs and reducing the efficiency of various ence lake trout energy budgets. Likewise, our simulation processes such as conversion of estradiol into ovarian mass. results reflect the decisions we made when developing the Although sea lamprey do manipulate some physiological relationships between parasitism, muscle lipid, estradiol and processes in hosts, including immune function and the clotting respective DEB parameters. Despite these limitations, our response (Goetz et al., 2016; Bullingham et al., 2021), our model and simulation results provide testable hypotheses that models suggests that sea lamprey do not actively induce can drive empirical research going forward. For example, energy reallocation away from reproduction in an attempt to future work looking to identify the physiological mechanisms make more energy available for consumption. Our attempts leading to skipped spawning in lake trout should consider to modify energy allocation with the κ parameter resulted in mechanisms related to energy mobilization and the efficiency more dramatic changes to lake trout mass than were observed of processes related to egg maturation as our model hypoth- empirically. If sea lamprey actively induced reallocation of esizes these factors to be critical components of reduced energy away from reproduction, we would expect to observe ovarian mass. Our model also hypothesizes that sea lamprey increased growth in lake trout following parasitism, but parasitism influences hosts by increasing energetic costs asso- changes in growth were not observed following parasitism for ciated with healing a large wound, replacing lost blood cells siscowet lake trout in laboratory studies (Smith et al., 2016; and mounting an immune response, but not by causing the Firkus et al., 2022) suggesting that sea lamprey parasitism host to reallocate energy directly away from reproduction. does not influence the allocation fraction to soma parameter A study could evaluate this hypothesis by simulating the κ for the siscowets. tissue damage and blood loss of sea lamprey parasitism on unwounded lake trout and observing if the changes to repro- Other approaches using DEB models to represent parasite– duction and growth match observations under sea lamprey host dynamics have highlighted the importance of factors parasitism. .......................................................................................................................................................... 13 Reasearch article Conservation Physiology • Volume 11 2023 .......................................................................................................................................................... provided editorial feedback. KL developed the base model Conclusions and parasitism model, developed the egg module, wrote the Modeling the effects of sea lamprey parasitism on lake trout manuscript, prepared figures and provided editorial feedback. in the context of DEB models is a powerful approach that ND analysed and prepared data and assisted with develop- accounts for the entire energy budget of the organism. Par- ment of the base model. CM conceived the project concept, asitism is a complex stressor that influences many differ- procured funding, assisted in writing the manuscript and ent physiological functions and interacts with the life his- provided editorial feedback. All authors approved the final tory of the host, which makes the understanding of the manuscript. cumulative effects on growth and reproduction challenging. The presented DEB model for siscowet lake trout allows us to explore these cumulative effects and interactions of sea Acknowledgements lamprey parasitism and is a step towards accounting for We thank Rick Goetz for data that helped validate the lake the sublethal effects of sea lamprey parasitism in lake trout trout DEB model. We thank James Bence, Weiming Li and population models. Karen Chou for providing comments on an earlier draft The DEB model presented in this paper can be useful for of this manuscript. We thank our NIMBioS working group improving existing efforts to monitor lake trout populations ‘Modeling molecules to organisms’ for ideas that inspired and direct resources for sea lamprey control in the Laurentian this modeling work, specifically linking hormone dynamics Great Lakes. If integrated into an individual-based model, this to DEB models. We also thank the anonymous reviewers for DEB model could allow lake trout populations to be estimated their careful reading of our manuscript and the insightful while accounting for the population-level influences of sea suggestions and comments. lamprey parasitism and individual variation both among and between lake trout ecomorphs. Additionally, simulations eval- uating the effects on reproduction and growth can be devel- Supplementary material oped to adjust stock-recruitment model parameters in existing Supplementary material is available at Conservation Physiol- models such as spawning stock biomass, or spawners per ogy online. recruit. Accounting for changes in spawning stock biomass or spawners per recruit with DEB model outputs is a promising approach for incorporating the sublethal effects of parasitism References and other stressors into population models going forward. Additionally, these efforts help identify knowledge gaps in our Ashauer R, Jager T (2018) Physiological modes of action across mechanistic understanding of sea lamprey parasitism and can species and toxicants: The key to predictive ecotoxicology. provide us with testable hypotheses that can inform future In Environmental Science: Processes and Impacts 20:48–57. empirical studies. https://doi.org/10.1039/C7EM00328E Augustine S, Rosa S, Kooijman SALM, Carlotti F, Poggiale JC (2014) Funding Modeling the eco-physiology of the purple mauve stinger, Pelagia noctiluca using dynamic energy budget theory. JSea Res 94: 52–64. This work was supported by a grant awarded to CM from the https://doi.org/10.1016/j.seares.2014.06.007. Great Lakes Fishery Commission. CM was also partially sup- Bullingham OMN, Firkus TJ, Goetz FW, Murphy CA, Alderman SL (2021) ported through the Michigan State University AgBioResearch Lake charr (Salvelinus namaycush) clotting response may act as a through USDA National Institute of Food and Agriculture, plasma biomarker of sea lamprey (Petromyzon marinus) parasitism: Hatch project 1014468. TF was additionally supported by the implications for management and wound assessment. J Great Lakes Howard A. Tanner Fellowship. Res 48: 207–218. https://doi.org/10.1016/j.jglr.2021.11.005. Conflict of interest statement Cabas I., Chaves-Pozo E., Mulero V., & García-Ayala A. (2018). Role of estrogens in fish immunity with special emphasis on GPER1. 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Published: Mar 8, 2023