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Scaling up endocrine disruption effects from individuals to populations: outcomes depend on how many males a population needs J. Wilson White, Bryan J. Cole, Gary N. Cherr, Richard Edward Connon, and Susanne M Brander Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05276 • Publication Date (Web): 09 Jan 2017 Downloaded from http://pubs.acs.org on January 9, 2017

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Environmental Science & Technology White et al. p. 1

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Scaling up endocrine disruption effects from individuals to populations: outcomes depend

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on how many males a population needs

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J. Wilson White,*,† Bryan J. Cole, ‡,¶ Gary N. Cherr,¶ Richard E. Connon,‡ and Susanne M.

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Brander†

5 6

†

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Wilmington, North Carolina 28403 USA

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‡

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California Davis, Davis, California 95616 USA

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Department of Biology and Marine Biology, University of North Carolina Wilmington,

Department of Anatomy and Cell Biology, School of Veterinary Medicine, University of

¶

Bodega Marine Laboratory, University of California Davis, Bodega Bay, California 94923 USA

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Corresponding Author

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*[email protected]

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phone: +1-910-962-3058

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fax: +1-910-962-4066

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Authors contributions

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SMB and JWW designed the study, SMB and BJC collected the data, JWW conducted model

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analysis, SMB and JWW wrote the paper with input from GNC and REC. JWW and SMB

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contributed equally to this publication.

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Notes

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The authors declare no competing financial interests.

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Environmental Science & Technology

Page 2 of 33 White et al. p. 2

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Abstract

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Assessing how endocrine disrupting compounds (EDCs) affect population dynamics requires

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tracking males and females (and sex-reversed individuals) separately. A key component in any

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sex-specific model is the ‘mating function’ (the relationship between sex ratio and reproductive

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success) but this relationship is not known for any fish species. Using a model, we found that

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EDC effects on fish populations strongly depend upon the shape of the mating function.

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Additionally, masculinization is generally more detrimental to populations than feminization. We

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then quantified the mating function for the inland silverside (Menidia beryllina), and used those

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results and the model to assess the status of wild silverside populations. Contrary to the

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expectation that a few males can spawn with many females, silversides exhibited a nearly linear

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mating function. This implies that small changes in the sex ratio will reduce reproductive success.

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Four out of five wild silverside populations exhibited sex ratios far from 50:50 and thus are

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predicted to be experiencing population declines. Our results suggest that managers should place

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more emphasis on mitigating masculinizing rather than feminizing EDC effects. However, for

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species with a nearly linear mating function, such as Menidia, feminization and masculinization

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are equally detrimental.

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Introduction Endocrine disrupting compounds (EDCs) can mimic, inhibit, or synergize the effects of

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endogenous hormones, resulting in physiological and behavioral abnormalities and altered sex

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ratio in fishes and other vertebrates. Specifically, EDC exposure has led to masculinization,

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feminization, intersex, and sex reversal in a wide range of species.1-4 The importance of assessing

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how these impacts affect fecundity and hence population persistence has been emphasized in

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empirical studies2,3,5,6 and theoretical models.5,7-10

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Modeling EDC impacts presents a unique challenge, as representing population-level

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effects requires separately tracking males and females11-17 as well as sex-reversed

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individuals.15,18,19 As such, a key component in any sex-specific model is the ‘mating

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function’,14,20 defined as the relationship between the sex ratio and reproductive success.

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Typically one assumes each male to be able to fertilize multiple females, because sperm are

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inexpensive to produce relative to eggs. This would lead to a mating function in which the sex

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ratio (proportion male) must be quite low for reproductive success to be affected (such as the

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steep, saturating mating function assumed by Gurney;8,21 Fig. 1), so that feminization of males

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would have little effect on the population. However, the mating system may prevent extreme

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promiscuity by males, so that marginal decreases in the sex ratio due to feminization negatively

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affect population-scale reproductive output. Therefore, reliable prediction of population-level

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EDC effects requires advances in both modeling of two-sex systems and in the empirical

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description of the mating function. Past theoretical studies using two-sex models have assumed

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various plausible forms for the mating function, but have lacked empirical support for those

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functions.5,8,22,23




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To address these needs, we developed a model of the dynamics of a fish population

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exposed to different types of EDCs (Fig. 2). We first used that model to examine the effects of

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three different forms of mating function (representing different potential types of male

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limitation) and the general effects of different individual-scale EDC impacts on population

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dynamics. These include masculinization and/or feminization of a population (i.e., a skewed sex

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ratio24,25), as well as reduced reproductive performance.7,16

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We then used the model to estimate population-level consequences of EDCs in a model estuarine

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fish species, the inland silverside (Menidia beryllina), which are known to be sensitive to EDCs

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and to exhibit skewed sex ratios in response to EDC exposure.7,11,13,15-17 To parameterize the

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model we characterized the inland silverside mating function in laboratory spawning

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experiments. Because EDCs can also affect reproductive output directly (not just via skewed sex

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ratios) we also utilized empirical data from experiments assessing egg protein production and

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spawning in silversides exposed to the estrogenic pesticide bifenthrin to characterize the likely

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net effects of EDCs on population dynamics.16,26 Bifenthrin is widely used in urban and

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agriculture areas across the United States,27,28 highly persistent in sediments,29,30 and has been

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studied extensively as an endocrine disrupting compound that acts at aqueous concentrations

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equivalent to those detected in the environment in M. beryllina.13,16,29,31

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We then compared model predictions to field estimates of the sex ratio in five wild inland

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silverside populations in California, USA, to assess population status. Our approach allows the

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projection of individual-level EDC effects to population-level consequences, and is unique in

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that it is the first to use an empirically-derived estimate of the mating function.

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Materials and Methods


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Basic model dynamics

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We used an age-structured population model to describe a generic fish population with age-

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dependent size, maturity, and fecundity. Adult mortality is density-independent, but the model

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has a Beverton-Holt stock-recruit curve26,32 representing within-cohort density-dependent

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competition among new recruits. All model equations and parameter definitions are given in the

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Supporting Information (SI). To represent EDC effects, we tracked males (NM) and females (NF)

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separately, and specified the proportion of each sex that are sex-reversed (pM, pF) due to EDCs,

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resulting in phenotypic males that have female genotypes or vice versa. Following Cotton and

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Wedekind,18,33 we described all different possible genotypic mating combinations (e.g.,

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genotypic females that are phenotypic males mating with genotypic females, etc.). The per capita

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fecundity is determined by the mating function defined in the next section. It is possible that sex-

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reversed males or females would have reduced egg output or mating success;26 the proportional

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decline in fecundity is given by the parameters qM and qF for males and females, respectively.

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In this type of model, one can determine whether a population will persist based on lifetime

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egg production (LEP) at equilibrium and the initial slope of the Beverton-Holt stock-recruit

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curve, α.34 LEP is the total number of eggs an age-0 recruit will produce over its lifetime, on

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average, and α gives the probability that an egg will survive to become an age-0 recruit (at very

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low population density). For the population to persist, LEP must be large enough so that α × LEP

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> 1; that is, at low densities, each fish will, on average, replace itself with at least one new recruit

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over its lifetime. It is convenient to rescale the slope α by the maximum LEP of a pristine (i.e.,

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no EDC exposure, and thus no sex reversal or fecundity reduction) population, α’ = α / LEPmax.

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Now 1/α’ conveniently specifies the proportion of the maximum LEP required for persistence,29

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and models with the same α’ but different demographic parameters will exhibit nearly the same




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responses to proportional reductions in LEP.35 The population will persist if LEP/LEPmax > 1/α’,

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which is the same criterion given above. The quantity α’ is referred to as the maximum lifetime

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growth rate,29 and 1/α’ is termed the critical replacement threshold.34,35

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This approach to modeling population persistence is useful when comparing the

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responses of different fishery species to harvest, and we extend it to EDC impacts. If α’ is known

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(or a conservative α’ value is assumed) then species with different life history parameters and

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different levels of exploitation or stressor exposure (e.g., to EDCs) can be compared in terms of

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the value of LEP relative to 1/α’. Essentially this provides a common currency for comparing the

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effects of some factor on the population persistence of species with very different life histories.

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For example, it is generally the case that fast-growing, high-fecundity fish populations can

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sustain higher harvests than slow-growing, low-fecundity fish populations. To compare how

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some management practice (such as a no-take marine reserve) will affect both populations, one

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can implicitly account for those life-history differences by expressing the effects of harvest in

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terms of proportional effect on LEP, allowing a direct comparison of reserve effects that

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essentially controls for those life history differences.36 In our analysis, we define a particular

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value of α’, and then compare the effects of EDCs on populations with different life history

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parameters or mating functions. In this way, equivalent proportional reductions to LEP (relative

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to the LEPmax for that particular life history) due to EDCs will have comparable effects on

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population persistence. This has the advantage of producing results that are not sensitive to

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particular choices of life history parameters but describe the relative effects of an impact on

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persistence.

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It is not possible to write out a simple expression for LEP in a model that includes sexreversal, so in all simulations we calculated the appropriate scaled Beverton-Holt slope


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parameter α’ based on the LEP of a population with no sex reversal and a sex ratio of X = 0.5 .

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We then used the same value of α’ for simulations with different values of pM and pF.

135 136 137

Mating function The mating function specifies the probability of fertilization of a female’s eggs given the

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population phenotypic sex ratio, X (proportion male). We used the beta cumulative density

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function (cdf), G, to describe the mating function:

140 141 142

, , =

; ,

; ,

where B is the beta function, with shape parameters b1 and b2 (both positive and nonzero):

; , = 1 −

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The beta cdf has a maximum value of 1 at X = 1. However, we assume that the mating function

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reaches 100% fertilization at a sex ratio of 0.5 (1 male for every female); to represent that we

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multiply the sex ratio by 2 in the cumulative density function, G(2X, b1, b2) so that the function

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reaches the maximum value at X = 0.5. This functional form (Fig. 1) allows spawning success as

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a function of sex ratio to be A) saturating, such that few males are necessary to fertilize most

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eggs (b1 = 1, b2 > 1; as in a highly polygynous, broadcast-spawning fish such as the bluehead

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wrasse, Thalassoma bifasciatum1), B) linear, such that a sex ratio of 0.5 is necessary for full

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fertilization (b1 = 1, b2 = 1; as in a species that forms breeding pairs with little extrapair

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copulation, or when highly male- or female-biased sex ratios limit reproduction, e.g., zebrafish,

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Danio rerio5), or C) sigmoidal, such that fertilization requires some minimum sex ratio to be

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successful (b1 > 1, b2 > 1; this could occur in lekking species).

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Model species and study sites




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The inland silverside, Menidia beryllina (Atherinidae) is widely distributed in estuarine and

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brackish habitats throughout coastal North America.7 Inland silversides are used by the United

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States Environmental Protection Agency to test whole effluent toxicity, and prior work has

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shown that they are susceptible to EDCs.11,13,15-17

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Non-native inland silversides are common in the San Francisco Bay (SFB) estuary and

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associated Sacramento/San Joaquin (SSJ) river delta in California, and have been employed as an

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indictor of potential EDC impacts there.15,19 This estuary is subject to a diverse array of

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anthropogenic inputs including EDCs20 and home to many declining fish populations.21 Recently,

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awareness of EDC prevalence has increased, with estrogenic activity documented in the

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watershed’s rivers22,23 and agricultural drain water.24 The inland silverside is useful as a sentinel

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species for EDC impacts in this region because it is distributed through the entire estuary and

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shares life history traits such as habitat use, diet and short lifespan with endangered fish that

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cannot be sampled extensively, such as the delta smelt (Hypomesus transpacificus).

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We seined inland silversides from five sites following methods described by Middaugh and

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Hemmer.7 Sampled sites include Suisun Slough,and Denverton Slough (both sampled in 2009

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and 2010) and Georgiana Slough, Napa River, and Walnut Grove (all three sampled in 2012 and

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2013; SI). These sites, with the exception of the Napa River, are considered to be part of the

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greater SFB-SSJ delta, represent a range of abiotic conditions (i.e., salinity, flow rate), and are

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surrounded by a diversity of land-use types (SI). For all years and sites, up to 50 fish were

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collected monthly from each site and dissected to determine sex and GSI.

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Laboratory spawning trials


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To estimate the mating function for inland silversides, spawning trials were conducted with

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a group of 120 reproductively mature individuals obtained as juveniles (90 days post hatch) from

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Aquatic Biosystems (Ft. Collins, CO, USA) and reared to maturity at the UNCW Center for

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Marine Science. Methods followed Middaugh and Hemmer7 and are detailed in the SI. Sex of

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inland silversides can only be determined accurately by dissection, so we conducted ten

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spawning trials with different combinations of 25 individually tagged fish randomly selected

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from the pool of 120. After all trials were complete we sacrificed fish to determine sex. External

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tags allowed us to determine what the sex ratio had been in each of the spawning trials. We

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counted the number of fertilized eggs deposited on spawning substrate during each trial. Then, to

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estimate the mating function we used two-dimensional profile likelihood to fit a two-parameter

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beta cumulative density function (cdf; see Materials and Methods: Mating function) to the

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relationship between sex ratio (proportion male) and mass-specific daily fertilized egg

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production (eggs per g female mass per day). We scaled egg production to the maximum daily

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mass-specific value used, so that the beta function described the proportion of the maximum egg

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production achieved at a given sex ratio.

193 194 195

Model analysis To understand the general dynamic effects of environmental drivers of sex change on fish

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population dynamics, we examined the effects of varying the probabilities of sex reversal (pM

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and pF ) from 0 to 1 for populations with different mating functions. We considered three

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possible mating functions (Fig. 1) to illustrate a biologically plausible range of outcomes. We

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considered scenarios in which sex-reversed fish have the same reproductive capacity (sperm or




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egg production) as non-reversed fish, as well as a scenario in which sex-reversed individuals

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experience a 50% reduction in reproductive output26 (qM = qF = 0.5;).

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As described above (Methods: Basic model dynamics), to facilitate comparisons among

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model scenarios, we specified particular values of the parameter α’. This allowed us to express

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results in terms of equilibrium population size as a proportion of the unimpacted equilibrium

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population size. The results are insensitive to the values of the life history parameters used in the

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model equations (Eq. S1-S7 in SI), but in order to actually perform the calculations we used

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parameters for M. beryllina obtained from FishBase,27 which is a database compiling life-history

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parameters and other information for fishes worldwide (Table S2).

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Simulations with different life history parameters but the same α’ produced similar results,

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but model results were highly sensitive to the value of α’ used. In general, populations can

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withstand greater reductions in LEP (due to harvest, EDC, disturbance, etc.) for larger values of

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α’ (i.e., steeper density-dependent recruitment functions). To obtain general results we used a

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value of α’ = 7.4 (1/α’ = 0.14), which is the middle of the distribution of values estimated by

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Myers et al.29 for a taxonomically diverse range of species. For comparison we also conducted

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the analysis with smaller (1/α’ = 0.05) and larger (1/α’ = 0.37) values corresponding to the outer

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range of values estimated by Myers et al.29

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We also analyzed the model specifically for inland silversides by substituting in the mating

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function estimated from our laboratory spawning trials. We assumed that sex-reversed fish had

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reduced fertility (qM = qF = 0.5) as in Rutilus rutilus.26 and Danio rerio. 33 There is not, to our

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knowledge, an estimate of α’ for any atherinid species. Myers et al.2-4,29 estimated α’ for several

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clupeiform species, which are ecologically similar to atherinids in terms of trophic level, use of

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estuarine habitats, etc. We used the estimate of annual maximum reproduction for Clupeiformes


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(the mean of log values for Clupeidae and Engraulidae) from Myers et al.2,3,6,29 as an estimate of

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maximum lifetime reproduction in inland silversides. This value is 3.22, so 1/α’ = 0.31.

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When attempting to use the model to gain insight into population status at the five

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California field sites, the primary difficulty is that it is not possible to estimate the rate of

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feminization or masculinization (pF or pM) directly from field samples. Additionally, both

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feminizing and masculinizing compounds could be acting simultaneously,5,8-10,15 complicating

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that determination. Therefore we sought inferences based solely on the sex ratio observed in the

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field samples. Sex ratios in silversides populations vary through the year due to temperature-

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dependent sex determination (more females are born earlier in the year and mature faster) but the

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sex ratio is expected to be closest to 0.5 when spawning peaks in the early summer. 12,14,37 We

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determined the peak of spawning at our field sites by examining the temporal patterns in GSI,

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and obtained the sex ratio from the survey(s) corresponding to peak GSI (i.e., peak spawning) at

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each site in each year (this occurred in March-June, depending on the site and year). Because sex

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ratios may vary from year to year depending on the water temperature at each site,18,37 we

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averaged the sex ratios across both years of data collection (either 2009 and 2010, or 2012 and

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2013; Table S3).

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To relate the observed sex ratios to model outputs, we simulated population dynamics for

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inland silversides across the full range of values of pM and pF. For each simulation, we recorded

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the sex ratio and population size (relative to the maximum) at equilibrium. We then plotted the

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joint distribution of sex ratios and population sizes observed in the model simulations. The

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marginal distribution of population sizes for a given observed sex ratio then indicates the likely

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range of population status for each of the field populations.




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In addition to causing complete feminization or masculinization, EDCs can also have more

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subtle effects on reproduction. For example, in inland silversides there is a relationship between

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increasing concentrations of the pesticide bifenthrin and a reduction in choriogenin (an egg

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envelope protein) that corresponds to a reduction in egg output.14,16 Bifenthrin is representative

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of other environmental chemicals that produce endocrine effects via non-classical

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pathways,8,13,16,38 the limitation being that it is not as well classified as known synthetic estrogens

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such as ethinylestradiol. To account for these possible effects, we also made model simulations

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in which overall egg output was reduced by 63%, 28%, or 20%, corresponding to the effects seen

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in laboratory trials with 0.5, 5, or 50 ng/L bifenthrin.5,8,16 Bifenthrin has been measured in the

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water column at concentrations ranging from 0.5 to 106 ng/L in municipal wastewater effluent

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and stormwater run-off.25,30 Low concentrations (0.5 – 5 ng/L) are more typical of longer-term

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exposure, so we only included those levels when comparing model simulations to field data.

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When interpreting model results, it is important to keep in mind some of the assumptions

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inherent in the modeling framework. We represented sex reversal such that there is a constant

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probability of one-directional sex change in each individual, and no intersex. Our model does not

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account for possible differences in the growth or mortality of sex-reversed individuals (although

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it easily could be modified to do so were data available). Finally, the model is deterministic with

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constant EDC exposure and a constant maximum reproductive rate. Interannual variation in the

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reproductive rate could lead to extinction, even if the deterministic persistence criterion given

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above is met, because there could be a string of years with reproductive rates substantially lower

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than the long-term deterministic average.16,29

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Results


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Model analysis for generic species

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Analysis of the model for generic species showed the dual influence of both the mating

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function and critical replacement threshold (1/α’) on the population response to feminization and

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masculinization (Fig. 3). In general, a population will collapse as either masculinization or

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feminization approaches 100%, and faster if sex-reversed individuals have a 50% reduction in

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fecundity. Masculinization leads to an excess of males, and that effect was not altered by the

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shape of the mating function (Fig. 3a,c,e). Conversely, the shape of the mating strongly affected

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the consequences of feminization: populations with linear or sigmoidal mating functions

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collapsed at lower feminization rates because reproduction becomes male-limited, but with a

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steep, asymptotic mating function, feminization actually increased population size (until

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feminization reaches nearly 100%) because the additional females merely increased overall

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population reproductive output (Fig. 3b,d,f). The effects of the replacement threshold on these

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patterns was predictable: populations with larger values of 1/α’ (less steep stock-recruit curves)

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were more sensitive to the loss of reproductive output and went extinct under lower rates of

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feminization or masculinization (Fig. 3).

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When both masculinization and feminization occur simultaneously, we found some

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scenarios in which populations maintained near-pristine sizes despite high probabilities of both

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feminization and masculinization (Fig. 4). As in Fig. 3, high rates of either masculinization or, to

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a lesser degree, feminization, led to exinction. However, the two processes could effectively

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cancel out so long as sex-reversed individuals had normal fecundity (Fig. 4a, c, e). If sex-

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reversed individuals had 50% of normal fecundity, then no level of feminization was sufficient to

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keep the population at > 75% of the pristine population size when masculinization exceeded

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50%. Lower rates of masculinization could still counterbalance the negative effects of high




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feminization, however, keeping the population at > 75% of pristine even with 100% feminization

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(Fig. 4b, d, f). Figure 4 shows results for the baseline replacement rate (1/a’ = 0.14); lower and

293

higher values had effects similar to those depicted in Fig. 3 (Fig. S1, S2).

294 295 296

Empirical mating function There was a monotonic, positive relationship between sex ratio and mass-specific (per unit

297

female biomass) fertilized egg production over the range of sex ratios we observed (Fig. 5a).

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This relationship was described by a nearly linear sigmoidal curve with parameters b1 = 1.002, b2

299

= 1.282 (Fig. 5a; profile likelihood confidence region for the model parameters shown in Fig. S3).

300

There was no relationship between sex ratio and the proportion of fertilized eggs (data not

301

shown), and the mean fertilization rate was 87% (± 0.03% standard error).

302 303 304

EDC effects on wild silverside populations Using the life-history parameters for inland silversides (Table S2), our best estimate of the

305

maximum lifetime reproductive rate, an expected 50% reduction in fecundity of sex-reversed

306

fish, and the empirical mating function (Fig. 5a) allowed us to predict the expected effects of

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feminization and masculinization on silverside populations (Figs. 5, S4). The baseline results

308

without any additional EDC effects (Fig. 5b) closely resemble the generic model results for a

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species with 1/α’ = 0.39 and a linear mating function (Fig. S2d), as those generic model

310

parameters match the silverside parameters very closely. Due to the combination of a nearly-

311

linear mating function and a low maximum lifetime reproductive rate, the population is expected

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to collapse if masculinization or feminization exceeds 75%, except for a narrow range of high

313

feminization and moderate masculinization in which the two processes counteract (Fig. 5b).


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Adding in an additional EDC effect, the reduced fecundity caused by bifenthrin exposure,

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led to additional reductions in overall reproduction, pushing the population closer to extinction

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(Fig. 5c). With fecundity reduction equivalent to ≥ 5 ng/L bifenthrin the population was very

317

close to extinction even without any feminization or masculinization (Fig. S4).

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To use the model results to interpret the population-level consequences of the sex ratios

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observed at the five field sites, we recorded the sex ratios observed in each of the simulations

320

shown in Fig. 5, for every possible combination of the probabilities of feminization and

321

masculinization (Fig. S5). We then plotted the range of equilibrium population sizes (from Figs.

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5 and S4) for each possible sex ratio. This produces a distribution of possible population sizes,

323

given an observed sex ratio (Fig. S6a). We then used the observed mean sex ratio at each site as

324

a point estimate along the horizontal axis of that distribution, and estimated the possible range of

325

population sizes given that sex ratio (Fig. S6a). Chemical analysis of pesticide concentrations

326

was not available from those field sites, but water at several of the sites had been shown to have

327

either estrogenic or androgenic activity in the past11,13,15-17 so for comparison we estimated the

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range of possible population sizes under baseline conditions (corresponding to Fig. 5b) and with

329

levels of fecundity reduction corresponding to 0.5 and 5 ng/L bifenthrin (Fig. S6b-f).

330

The field sites varied widely in observed sex ratio, from a minimum of 0.16 at Sacramento

331

to a maximum of 0.69 at Suisun. Georgiana had the sex ratio closest to 0.5 (0.39), and

332

consequently had the highest predicted equilibrium population size, unless bifenthrin exposure is

333

≥ 5 ng/L (Fig. S6b). The Napa and Sacramento sites both had very low sex ratios, and

334

consequently were predicted to have low or near-zero equilibrium population size, even without

335

additional effects of bifenthrin on productivity (Fig. S6d,f). The status of Denverton and Suisun

336

is more difficult to interpret. The sex ratios at those two sites were > 0.6. In the model,




337

populations with highly female-biased sex ratios always had low population sizes (as predicted

338

for Napa and Sacramento), whereas a moderately male-biased sex ratio is possible in both

339

healthy populations with intermediate masculinization and low feminization or in near-zero

340

populations with high masculinization and low-to-moderate feminization (probability of

341

feminization ≤ 0.4; Fig. S5). In the latter case, most of the females in the population are sex-

342

reversed males with 50% reduced fecundity, hence the low population size despite the only

343

moderately biased sex ratio. Consequently, the range of possible population sizes for the two

344

populations with male-biased sex ratios (Denverton and Suisun) was very broad, even if the

345

possible negative effects of bifenthrin were not considered (Fig. S6c,e).

346 347

Discussion

348

Many previous sex-specific modeling approaches have assumed that male fish are

349

promiscuous and are able to fertilize multiple females.5,8,16 In contrast to this supposition, our

350

laboratory spawning trials with inland silversides revealed an approximately linear increase in

351

fertilized egg production with increasing sex ratio (up to 60% male, the most extreme ratio we

352

observed). Our model results show that accounting for such differences in mating system is

353

necessary to properly represent population dynamics, and that the type of mating function

354

assumed in a model has a substantial impact on the estimate of population size and hence overall

355

influence of EDC exposure.

356

In the model, EDC effects on population dynamics were jointly determined by the mating

357

function (relating sex ratio to egg production) and the replacement threshold, 1/α’ (which

358

determines the level of egg production required for demographic replacement and population

359

persistence). In general, masculinization (loss of females) caused an identical, steep reduction in


Page 17 of 33


360

population size for all three mating functions, whereas the effect of feminization (loss of males)

361

differed depending on the mating function. For the population with the baseline replacement

362

threshold (1/α’= 0.14), population size began to decline at a 50% probability of masculinization

363

(regardless of mating function), and was particularly severe if we assumed that EDC exposure

364

also reduced per capita fecundity. The decline at 50% probability of masculinization coincides

365

with the point at which the sex ratio begins to noticeably increase above 0.5; for lower levels of

366

masculinization, the loss of females is counterbalanced by the possibility for XX-genotype

367

females to mate with XX (sex-reversed) males, producing an excess of XX (female) eggs. As

368

such, we generally predict that populations with highly male-biased sex ratios will experience

369

population collapse. This is not surprising as the loss of females directly reduces the overall egg

370

output of the population, decreasing reproduction below the replacement threshold. Thus the

371

precise effect of masculinization depended on the replacement threshold; model populations with

372

a lower threshold were less vulnerable to masculinization.

373

In contrast, feminization did not affect populations with the baseline replacement threshold

374

until the feminization rate exceeded 80%. The effects of feminization followed directly from the

375

mating functions: for the linear function, there was a monotonic decline in population size with

376

increasing feminization, but for the asymptotic and sigmoidal functions, the population actually

377

increased somewhat with increasing feminization before eventually collapsing when there too

378

few males to allow reproduction. The latter is consistent with past empirical and theoretical

379

studies: having a female-biased sex ratio can increase the equilibrium population size, up to a

380

point (and only with a nonlinear mating function).5,12,28

381 382

Although comparatively more focus has been placed on fish feminization caused by EDCs, 6,30

our model results suggest that masculinization is actually a greater concern in terms of




383

population-level effects. Hormones present in municipal and agricultural effluent can

384

masculinize fish at environmentally relevant concentrations,13,16,31,39,40 as can exposure to other

385

stressors (e.g., high temperatures32,41). Interestingly, our model predicts that moderate rates of

386

feminization (≥ 0.5) can counteract the negative effects of masculinization on population size.

387

This possibility presents an intriguing hypothesis for empirical testing in the lab or field, but

388

depends strongly on sex-reversed individuals having at least somewhat normal fertility, which is

389

atypical according to other studies.18,26,33

390

The biological interpretation of the replacement threshold is that it integrates the variety of

391

processes related to demographic replacement, including life expectancy, age at maturity, per-

392

capita fecundity, and survival in the egg and larval stages. In general one expects species that

393

mature faster, live longer, and have higher fecundity and survival rates to have a higher lifetime

394

egg production, and thus a smaller fraction of those eggs would be required to achieve

395

replacement. The consequent effects of EDCs on population dynamics then follow logically: for

396

species with lower lifetime fecundity and survival rates and therefore higher replacement

397

thresholds, the marginal effects of reduced egg production due to EDCs will be greater. That said,

398

one possible exception to this general rule would be if longer-lived, sexually plastic species were

399

more susceptible to long-term, chronic effects of EDC exposure, whereas long-term effects are

400

less possible for very short-lived species.

401 402

Empirical determination of the mating function in Menidia beryllina

403

To our knowledge, this work represents the first empirical, lab-based determination of the

404

mating function in a fish. The nearly linear mating function was a surprising departure from the

405

only other known example: the steep, asymptotic function described in the field for T.


Page 19 of 33


406

bifasciatum.1,26 As opposed to the haremic mating system in T. bifasciatum, M. beryllina appear

407

to spawn promiscuously in mixed-sex groups, with pairs of fish alternating excursions into the

408

spawning substrate to deposit and fertilize eggs. Interestingly, fertilized egg production increased

409

with sex ratio, but the fertilization rate did not, remaining at > 85% regardless of sex ratio. Thus

410

females simply do not release as many eggs when there are too few males to fertilize them. This

411

topic bears further behavioral investigation; perhaps females prefer to spawn with a variety of

412

males.

413

In some wild vertebrate populations, mating success and mating strategies depend not only

414

on the sex ratio but also on population density.34,42,43 Our experimental approach did not address

415

this possibility, but it would be worthwhile to pursue this hypothesis given the steep declines in

416

population size predicted by our model. Indeed, our experimental data carries the caveats that a)

417

it is difficult to relate effective population densities in the experimental tanks to those

418

experienced in the field, and b) it is possible that wild fish have somewhat different spawning

419

behaviors than the laboratory-reared animals we used.

420 421 422

EDC effects on inland silverside populations Accounting for the nearly linear mating function and the estimated replacement threshold

423

(1/α’ = 0.31), the model predicted much more severe effects on silverside populations than on

424

species with the baseline replacement threshold (1/α’ = 0.14). When we added the possibility of

425

reduced fertility due to EDCs , stark reductions in population size were predicted for only

426

moderate probabilities of feminization or masculinization (< 0.3). A novelty of this approach is

427

that these predictions are based on the reduction in expression of choriogenin following EDC

428

expsoure. In the context of adverse outcome pathways (AOPs), which are conceptual constructs




429

that demonstrate known links between biochemical events and an adverse outcome at a higher

430

level of biological organization,29,44 these model simulations directly link a key event

431

(choriogenin production, downstream of a molecular initiating event) to a population-level

432

outcome. This permits the assessment of risk for exposed populations.

433

All but one of the wild silverside populations (Georgiana) had a sex ratio considerably

434

different from 0.5, and consequently were predicted to have an equilibrium population size
0.5 in the model. Confidence in

439

these population-level predictions is limited because we were attempting to translate snapshots of

440

population sex ratios into rates of feminization and masculinization, the currency in our model.

441

More information on EDC effects in the population (e.g., evidence of sex reversal, or size-

442

structure data) could strengthen these inferences. It is possible that non-EDC factors produced

443

the extreme sex ratios we observed, but given the detection of other somatic or biochemical EDC

444

effects at each site,15,19,35 it is parsimonious to attribute the sex ratios to EDCs. Additional

445

sampling of our study sites to determine rates of sex-reversal (e.g., by examining gonad

446

histology and expression of reproductive marker genes;15,16,29) would permit better model

447

estimates of population status.

448

A second form of uncertainty in the population status estimates was the level of EDC-

449

dependent reduction in fecundity to expect. Fecundity reduction equivalent to exposure to very

450

low levels of bifenthrin (0.5 ng/L, causing 68% of normal fecundity) was sufficient to reduce all

451

populations (except Georgiana) below 20% of the unimpacted size. Given that a bifenthrin


Page 21 of 33


452

concentration of 0.5 ng/L is the analytical detection limit and is at or below that typically

453

measured from waters in the SFB estuary and elsewhere in the USA,28,34,35,45 those model

454

projections are likely conservative. Although silverside populations are relatively large in

455

comparison to those of threatened species such as H. transpacificus in this region, it is possible

456

that this is due to recruitment of silverside larvae from areas less impacted than those analyzed

457

here, or that silversides would be even more abundant in the absence of EDCs. Other ecological

458

factors have likely contributed to the decline of species such as H. transpacificus, but our results

459

suggest that EDC effects on such species may also be substantial.

460

Overall, these model projections show that fish populations with a high replacement

461

threshold and slight shifts in sex ratio may be threatened in waterways globally where

462

pyrethroids and other EDCs shown to reduce fecundity are present (e.g., estrogens, progestin 6,36).

463

Thus our findings could be used to guide precautionary management, affording additional

464

protection to species with sensitive life histories (e.g., high replacement threshold) in EDC-

465

affected area. Additionally, it is straightforward to use life history parameters for a different

466

species to repeat our analysis in other locations with other fishes.

467

Our model results indicate that population-level sensitivity to EDCs is highly dependent

468

upon the shape of the mating function and the replacement threshold, particularly for

469

feminization, and that masculinization is generally more detrimental than feminization. We have

470

also shown that it is possible to use molecular and biochemical measures of fecundity, such as

471

egg protein production, to estimate fecundity rates, thus directly linking a key AOP event to

472

population-level outcomes. Given this new framework for determining population-level effects

473

of EDCs, we can now better design experimental approaches in both laboratory and field to




474

generate data that can be used to build upon the model described here and make more informed

475

inferences about the status of wild populations.

476 477 478

Acknowledgements

479

This research was supported by funding from UNCW Center for Marine Science, NC Sea Grant

480

(R/MG-1114), National Science Foundation (OCE-1435473), California Department of Fish and

481

Game (contract #E1183010), State and Federal Contractors Water Agency (contract no. 17-08), a

482

Delta Science Post-Doctoral Fellowship, and Environmental Protection Agency (STAR

483

#835799). We thank B. DeCourten, many undergraduate researchers at UNCW, and the Bodega

484

Marine Laboratory Aquatic Resources Group for field and lab assistance, R. Deanes and R.

485

Moore for seawater system expertise, and S. Kellman for advice on spawning silversides. Three

486

anonymous reviewers provided thoughtful suggestions that improved the manuscript.

487 488 489

Supporting information

490

Full description of the population model, description of field sampling sites, methodology for

491

laboratory spawning trials, mating combinations possible in the model (Table S1), life history

492

parameters used in the model (Table S2), summary of inland silversides sex ratios and GSI at

493

field sites (Table S3), additional model results (Fig. S1, S2, S4, S5), likelihood surface for the fit

494

of beta cdf to spawning trial data (Fig. S3), comparison of model results to field data (Fig. S6).

495

This information is available free of charge via the Internet at http://pubs.acs.org.

496


Page 23 of 33


497 498

Figure legends

499

Figure 1. Alternative possible forms of the mating function relating sex ratio, X, to the

500

proportion of female eggs fertilized,G(X,b1,b2). Forms used in the model for generic fish species

501

were asymptotic (dashed curve, representing a polygynous broadcast spawner); linear (solid

502

curve, representing monogamy, pair spawning, or courtship limitation); or sigmoidal (dotted

503

curve, representing a minimum-male threshold).

504 505

Figure 2. Schematic illustrating the components of the study. The central diagram shows the life

506

cycle of a fish, and the key processes in the life cycle are labeled in the gray oval. Information

507

from various sources was used to parameterize the model (indicated by arrows pointing at each

508

process): published literature (dashed outlines), prior published work by this research group

509

(dash-dot outline), and experiments described in this paper (solid outlines). Factors specified

510

directly by the model have a dotted outline. Factors describing EDC effects are shaded gray. The

511

bi-directional arrow indicates a comparison of model output to field observations.

512 513

Figure 3. Equilibrium population size (relative to that of a pristine population) under different

514

probabilities of (a,c,e) masculinization or (b,d,f) feminization due to EDCs. Color indicates

515

different shapes of the mating function (as in Fig 1): asymptotic (blue), linear (black), or

516

sigmoidal (red). In the model, sex-reversed individuals have either full fertility in the reversed

517

sex (solid curves) or 50% reduction in fertility (dashed curves). These simulations used a critical

518

replacement threshold (1/α’) of (a,b) 0.05 (lower extreme from literature estimates), (c,d) 0.14

519

(baseline), or (e,f) 0.39 (upper extreme).

520




521

Figure 4. Equilibrium population size (relative to that of a pristine population) under different

522

combinations of probabilities of feminization and masculinization due to EDCs. Results are

523

shown for (a,b) asymptotic, (c,d) linear, or (e,f) sigmoidal mating functions and either (a,c,e)

524

natural fertility for sex-reversed fish or (b,d,f) 50% reduced fertility in sex-reversed fish. These

525

simulations used a critical replacement threshold (1/α’) of 0.14.

526 527

Figure 5. Empirical and model results for inland silversides (Menidia beryllina). (a) Empirical

528

mating function based on lab trials with inland silversides. Each point represents the mass-

529

specific per-capita egg production (per gram of female biomass) observed from a single trial with

530

the indicated sex ratio. The curve is a beta cumulative distribution function fit to the data by

531

maximum likelihood (b1 = 1.002, b2 = 1.2823); dashed lines represent 90% confidence interval

532

on the fit. (b-c) Equilibrium population size (relative to that of a pristine population) under

533

different combinations of probabilities of feminization and masculinization due to EDCs for the

534

model parameterized for inland silversides (Menidia beryllina). Panels show model results that

535

include reductions in overall fecundity equivalent to bifenthrin exposure of (b) 0 ng/L or (c) 0.5

536

ng/L (0% and 68% of normal, respectively).

537 538 539 540 541 542 543 544 545 546 547 548

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Environmental Science & Technology Page 28 of 33

Population size

Egg fertilization rate

Graphical Abstract

ACS Paragon Plus Environment Sex ratio

Feminization by EDCs

Page 29 of 33Environmental Science & Technology

Figure 1

Fertilization rate (G(2X,b1,b2) )

1 b1=1 b2=40

0.8 0.6 0.4 0.2 0

b1=6 b2=15

b1=1 b2=1

0

0.1

0.2

0.3

0.4

Sex ratio (proportion male; X) ACS Paragon Plus Environment

0.5

Environmental Science & Technology Page 30 of 33

Figure 2 Model Fish Life Cycle Literature

Re p th lac re em sh e ol nt d

wth

al

Gro

Literature

Juvenile

Eggs

x t Se ina rm te

de

Adult

n

io

g tin Ma ction fun

EDC exposure in model causes feminization or masculinization

Comparison to field sex ratios to evaluate population status

Per capita fecundity

Su

rviv

Larvae

Fecundity reduction in sex-reversed fish Bifenthrin effects on choriogenin and egg production

Spawning experiment


Page 31 of 33


Population size (proportion of Nmax)

Figure 3

1/α' = 0.05

(a)

(b)

1

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0

0.2

0.4

0.6

0. 8

1

0

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0 0.2

0.4

0.6

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0.6

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1

0

1

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0.8

1

0.2

0.4

0.6

0.8

1

1/α' = 0.39

(e)

(f)

1

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2 0

0.4

(d)

1

0

0.2

1/α' = 0.14

(c)

0

0

0.2

0.4

0.6

0.8

Probability of masculinization

1

0

0

Probability of feminization



Page 32 of 33

Figure 4 (b)

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2


0

0

0.2 0.4 0.6 0.8 1

(c)

0

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2 0

0.2 0.4 0.6 0.8 1

(e)

0

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2 0

0.2 0.4 0.6 0.8 1

0

0.2 0.4 0.6 0.8 1

0

0.2 0.4 0.6 0.8 1

(f)

1

0

0

(d)

1

0

1

1

1

0.2 0.4 0.6 0.8 1

0



0


(a)

Page 33 Environmental of 33 Science & Technology

Egg production (eggs g-1 d-1)

Figure 5

(a) 400 300 200 100 0.2

0.3

0.4

0.6

Sex ratio (proportion male) (b)

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0

0.2 0.4 0.6 0.8

0

(c) 0.8 0.6 0.4 0.2 0

0

0.2 0.4 0.6 0.8





0.5

Scaling up endocrine disruption effects from ... - ACS Publications

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