Scaling Up Endocrine Disruption Effects from ... - ACS Publications

Subscriber access provided by Van Pelt and Opie Library

Article

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 33

Environmental Science & Technology White et al. p. 1

1

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

2

on how many males a population needs

3

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

4

Brander†

5 6

†

7

Wilmington, North Carolina 28403 USA

8

‡

9

California Davis, Davis, California 95616 USA

10

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

11 12

Corresponding Author

13

*[email protected]

14

phone: +1-910-962-3058

15

fax: +1-910-962-4066

16 17

Authors contributions

18

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

19

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

20

contributed equally to this publication.

21 22

Notes

23

The authors declare no competing financial interests.

ACS Paragon Plus Environment

Environmental Science & Technology

Page 2 of 33 White et al. p. 2

24

Abstract

25

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

26

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

27

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

28

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

29

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

30

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

31

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

32

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

33

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

34

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

35

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

36

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

37

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

38

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

39

are equally detrimental.

40 41


Page 3 of 33


42 43

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

44

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

45

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

46

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

47

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

48

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

49

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

50

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

51

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

52

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

53

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

54

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

55

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

56

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

57

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

58

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

59

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

60

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

61

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

62

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

63

functions.5,8,22,23




64

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

65

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

66

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

67

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

68

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

69

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

70

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

71

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

72

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

73

model we characterized the inland silverside mating function in laboratory spawning

74

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

75

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

76

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

77

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

78

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

79

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

80

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

81

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

82

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

83

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

84

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

85 86

Materials and Methods


Page 5 of 33


87

Basic model dynamics

88

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

89

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

90

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

91

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

92

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

93

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

94

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

95

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

96

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

97

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

98

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

99

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

100

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

101

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

102

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

103

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

104

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

105

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

106

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

107

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

108

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

109

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




110

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

111

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

112

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

113

This approach to modeling population persistence is useful when comparing the

114

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

115

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

116

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

117

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

118

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

119

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

120

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

121

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

122

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

123

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

124

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

125

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

126

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

127

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

128

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

129

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

130

persistence.

131 132

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


Page 7 of 33


133

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

134

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

138

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

139

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 −

143

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

144

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

145

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

146

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

147

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

148

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

149

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

150

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

151

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

152

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

153

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

154 155

Model species and study sites




156

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

157

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

158

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

159

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

160

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

161

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

162

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

163

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

164

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

165

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

166

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

167

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

168

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

169

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

170

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

171

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

172

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

173

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

174

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

175

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

176 177

Laboratory spawning trials


Page 9 of 33


178

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

179

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

180

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

181

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

182

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

183

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

184

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

185

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

186

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

187

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

188

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

189

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

190

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

191

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

192

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

196

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

197

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

198

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

199

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




200

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

201

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

202

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

203

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

204

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

205

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

206

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

207

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

208

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

209

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

210

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

211

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

212

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

213

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

214

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

215

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

216

range of values estimated by Myers et al.29

217

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

218

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

219

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

220

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

221

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

222

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


Page 11 of 33


223

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

224

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

225

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

226

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

227

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

228

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

229

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

230

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

231

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

232

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

233

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

234

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

235

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

236

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

237

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

238

2013; Table S3).

239

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

240

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

241

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

242

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

243

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

244

range of population status for each of the field populations.




245

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

246

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

247

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

248

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

249

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

250

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

251

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

252

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

253

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

254

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

255

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

256

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

257

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

258

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

259

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

260

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

261

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

262

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

263

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

264

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

265

than the long-term deterministic average.16,29

266 267

Results


Page 13 of 33


268

Model analysis for generic species

269

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

270

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

271

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

272

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

273

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

274

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

275

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

276

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

277

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

278

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

279

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

280

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

281

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

282

feminization or masculinization (Fig. 3).

283

When both masculinization and feminization occur simultaneously, we found some

284

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

285

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

286

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

287

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

288

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

289

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

290

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




291

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

292

(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).

298

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

307

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

309

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

312

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).


Page 15 of 33


314

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

315

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

316

(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).

318

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

319

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.

322

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

328

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

(1)

(2) (3)

(4)

Petersen, C. W.; Warner, R. R.; Cohen, S.; Hess, H. C.; Sewell, A. T. Variable pelagic fertilization success: Implications for mate choice and spatial patterns of mating. Ecology 1992, 73, 391–401. Clotfelter, E. D.; Bell, A. M.; Levering, K. R. The role of animal behaviour in the study of endocrine-disrupting chemicals. Animal Behaviour 2004, 68 (4), 665–676. Brander, S. M. Thinking outside the box: assessing endocrine disruption in aquatic life. In Monitoring water quality: Pollution assessment, analysis, and remediation; Ajuha, S., Ed.; Elsevier: Amsterdam, 2013; pp 103–147. Schug, T. T.; Johnson, A. F.; Birnbaum, L. S.; Colborn, T.; Guillette, L. J., Jr.; Crews, D. P.; Collins, T.; Soto, A. M.; Saal, vom, F. S.; McLachlan, J. A.; et al. Minireview:


Page 25 of 33


549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594

(5)

(6)

(7)

(8) (9)

(10)

(11)

(12) (13)

(14) (15)

(16)

(17)

(18) (19)

endocrine disruptors: past lessons and future directions. Molecular Endocrinology 2016, 30 (8), 833–847. Hazlerigg, C. R. E.; Tyler, C. R.; Lorenzen, K.; Wheeler, J. R.; Thorbek, P. Population relevance of toxicant-mediated change in sex ratio in fish: an assessment using an individual-based zebrafish (Danio rerio) model. Ecological Modelling 2014, 280, 76–88. Kidd, K. A.; Blanchfield, P. J.; Mills, K. H.; Palace, V. P.; Evans, R. E.; Lazorchak, J. M.; Flick, R. W. Collapse of a fish population after exposure to a synthetic estrogen. Proc. Natl. Acad. Sci. USA 2007, 104 (21), 8897–8901. Middaugh, D. P.; Hemmer, M. J. Reproductive ecology of the inland silverside, Menidia beryllina, (Pisces: Atherinidae) from Blackwater Bay, Florida. Copeia 1992, 1992, 53– 61. Gurney, W. S. C. Modeling the demographic effects of endocrine disruptors. Environ Health Perspect 2005, 114 (S-1), 122–126. Miller, D. H.; Jensen, K. M.; Villeneuve, D. L.; Kahl, M. D.; Makynen, E. A.; Durhan, E. J.; Ankley, G. T. Linkage of biochemical responses to population-level effects: a case study with vitellogenin in the fathead minnow (Pimephales promelas). Environmental Toxicology and Chemistry 2007, 26 (3), 521. Schipper, A. M.; Hendriks, H. W. M.; Kauffman, M. J.; Hendriks, A. J.; Huijbregts, M. A. J. Modelling interactions of toxicants and density dependence in wildlife populations. J Appl Ecology 2013, 50 (6), 1469–1478. Duffy, T. A.; McElroy, A. E.; Conover, D. O. Variable susceptibility and response to estrogenic chemicals in Menidia menidia. MARINE ECOLOGY-PROGRESS SERIES2009, 380, 245–254. Rankin, D. J.; Kokko, H. Do males matter? The role of males in population dynamics. Oikos 2007, 116 (2), 335–348. Brander, S. M.; Cole, B. J.; Cherr, G. N. An approach to detecting estrogenic endocrine disruption via choriogenin expression in an estuarine model fish species. Ecotoxicology 2012, 21 (4), 1272–1280. Miller, T. E. X.; Inouye, B. D. Confronting two-sex demographic models with data. Ecology 2011, 92, 2141–2151. Brander, S. M.; Connon, R. E.; He, G.; Hobbs, J. A.; Smalling, K. L.; Teh, S. J.; White, J. W.; Werner, I.; Denison, M. S.; Cherr, G. N. From ‘omics to otoliths: responses of an estuarine fish to endocrine disrupting compounds across biological scales. PLoS ONE 2013, 8 (9), e74251. Brander, S. M.; Jeffries, K. M.; Cole, B. J.; DeCourten, B. M.; White, J. W.; Hasenbein, S.; Fangue, N. A.; Connon, R. E. Transcriptomic changes underlie altered egg protein production and reduced fecundity in an estuarine model fish exposed to bifenthrin. Aquatic Toxicology 2016, 174, 247–260. Brander, S. M.; He, G.; Smalling, K. L.; Denison, M. S.; Cherr, G. N. The in vivo estrogenic and in vitro anti-estrogenic activity of permethrin and bifenthrin. Environmental Toxicology and Chemistry 2012, 31 (12), 2848–2855. Cotton, S.; Wedekind, C. Population consequences of environmental sex reversal. Conservation Biology 2009, 23 (1), 196–206. Cole, B. J.; Brander, S. M.; Jeffries, K. M.; Hasenbein, S.; He, G.; Denison, M. S.; Fangue, N. A.; Connon, R. E. Changes in Menidia beryllina gene expression and in vitro hormone-receptor activation after exposure to estuarine waters near treated wastewater




595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27) (28)

(29)

(30) (31) (32)

(33)

(34)

(35)

outfalls. Archives of Environmental Contamination and Toxicology 2016, 71, 210–223. Scholz, N. L.; Fleishman, E.; Brown, L.; Werner, I.; Johnson, M. L.; Brooks, M. L.; Mitchelmore, C. L.; Schlenk, D. A perspective on modern pesticides, pelagic fish declines, and unknown ecological resilience in highly managed ecosystems. BioScience 2012, 62 (4), 428–434. Feyrer, F.; Nobriga, M. L.; Sommer, T. R. Multidecadal trends for three declining fish species: habitat patterns and mechanisms in the San Francisco Estuary, California, USA. Canadian Journal of Fisheries and Aquatic Sciences 2007, 64 (4), 723–734. Lavado, R.; Loyo-Rosales, J. E.; Floyd, E.; Kolodziej, E. P.; Snyder, S. A.; Sedlak, D. L.; Schlenk, D. Site-specific profiles of estrogenic activity in agricultural areas of California’s inland waters. Environ. Sci. Technol. 2009, 43 (24), 9110–9116. Schlenk, D.; Lavado, R.; Loyo-Rosales, J. E.; Jones, W.; Maryoung, L.; Riar, N.; Werner, I.; Sedlak, D. Reconstitution studies of pesticides and surfactants exploring the cause of estrogenic activity observed in surface waters of the San Francisco Bay Delta. Environ. Sci. Technol. 2012, 46 (16), 9106–9111. Kuivila, K. M.; Hladik, M. L. Understanding the occurence and transport of current-use pesticides in the San Francisco Estuary watershed. San Francisco Estuary & Watershed Science 2008, 6, 2. Brown, A. R.; Owen, S. F.; Peters, J.; Zhang, Y.; Soffker, M.; Paull, G. C.; Hosken, D. J.; Wahab, M. A.; Tyler, C. R. Climate change and pollution speed declines in zebrafish populations. Proc. Natl. Acad. Sci. USA 2015, 112 (11), E1237–E1246. Jobling, S.; Coey, S.; Whitmore, J. G.; Kime, D. E.; van Look, K. J. W.; McAllister, B. G.; Beresford, N.; Henshaw, A. C.; Brighty, G.; Tyler, C. R.; et al. Wild intersex roach (Rutilus rutilus) have reduced fertility. Biology of Reproduction 2002, 67, 515–524. Froese, R.; Pauly, D. FishBase. http://fishbase.org September 9, 2012. Kuivila, K. M.; Hladik, M. L.; Ingersoll, C. G.; Kemble, N. E.; Moran, P. W.; Calhoun, D. L.; Nowell, L. H.; Gilliom, R. J. Occurrence and potential sources of pyrethroid insecticides in stream sediments from seven U.S. metropolitan areas. Environ. Sci. Technol. 2012, 46 (8), 4297–4303. Myers, R. A.; Bowen, K. G.; Barrowman, N. J. Maximum reproductive rate of fish at low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 1999, 56, 2404–2419. Weston, D. P.; Lydy, M. J. Stormwater input of pyrethroid insecticides to an urban river. Environmental Toxicology and Chemistry 2012, 31 (7), 1579–1586. DeGroot, B. C.; Brander, S. M. The role of P450 metabolism in the estrogenic activity of bifenthrin in fish. Aquatic Toxicology 2014, 156, 17–20. Shepherd, J. G. A versatile new stock-recruitment relationship for fisheries, and the construction of sustainable yield curves. ICES Journal of Marine Science: Journal du Conseil 1992, 40, 57–75. Lor, Y.; Revak, A.; Weigand, J.; Hicks, E.; Howard, D. R.; King-Heiden, T. C. Juvenile exposure to vinclozolin shifts sex ratios and impairs reproductive capacity of zebrafish. Reproductive Toxicology 2015, 58 (C), 111–118. Sissenwine, M.; Shepherd, J. G. An alternative perspective on recruitment overfishing and biological reference points. Canadian Journal of Fisheries and Aquatic Sciences 1987, 44, 913–918. White, J. W.; Botsford, L. W.; Hastings, A.; Largier, J. L. Population persistence in


Page 27 of 33


641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670

(36)

(37) (38) (39)

(40)

(41) (42)

(43) (44)

(45)

marine reserve networks: incorporating spatial heterogeneities in larval dispersal. MARINE ECOLOGY-PROGRESS SERIES- 2010, 398, 49–67. White, J. W.; Botsford, L. W.; Moffitt, E. A. Decision analysis for designing marine protected areas for multiple species with uncertain fishery status. Ecological Applications 2010, 20, 1523–1541. Conover, D. O.; Kynard, B. E. Environmental sex determination: interaction of temperature and genotype in a fish. Science 1981, 213, 577–579. Crago, J.; Schlenk, D. Aquatic Toxicology. Aquatic Toxicology 2015, 162, 66–72. Kolok, A. S.; Sellin, M. K. The environmental impact of growth-promoting compounds employed by the United States beef cattle industry: history, current knowledge,and future directions. In Reviews of environmental contamination and toxicology; Whitacre, D. M., Ed.; Springer: New York, 2008; pp 1–30. Svensson, J.; Fick, J.; Brandt, I.; Brunström, B. The synthetic progestin levonorgestrel is a potent androgen in the three-spined stickleback (Gasterosteus aculeatus). Environ. Sci. Technol. 2013, 47 (4), 2043–2051. Knapp, R.; Marsh-Matthews, E.; Vo, L.; Rosencrans, S. Stress hormone masculinizes female morphology and behaviour. Biology Letters 2011, 7 (1), 150–152. Kokko, H.; Rankin, D. J. Lonely hearts or sex in the city? Density-dependent effects in mating systems. Philosophical Transactions of the Royal Society B: Biological Sciences 2006, 361 (1466), 319–334. Forsgren, E.; Amundsen, T.; Borg, Å. A.; Bjelvenmark, J. Unusually dynamic sex roles in a fish. Nature 2004, 429 (6991), 551–554. Ankley, G. T.; Bennett, R. S.; Erickson, R. J.; Hoff, D. J.; Hornung, M. W.; Johnson, R. D.; Mount, D. R.; Nichols, J. W.; Russom, C. L.; Schmieder, P. K.; et al. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environmental Toxicology and Chemistry 2010, 29 (3), 730–741. Weston, D. P.; Chen, D.; Lydy, M. J. Stormwater-related transport of the insecticides bifenthrin, fipronil, imidacloprid, and chlorpyrifos into a tidal wetland, San Francisco Bay, California. Science of The Total Environment 2015, 527-528 (C), 18–25.


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

0.8

0.6

0.8

1

0

1

0.2

0.4

0.6

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

Recommend Documents