The Biology of Rattlesnakes II Edited By: Michael J

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Manufactured in the United States of America Dreslik, M. J., D. B. Shepard, S. J. Baker, B. C. Jellen, and C. A. Phillips. 2017. Body Size, Growth, and Sexual Size Dimorphism in the Eastern Massasauga (Sistrurus catenatus). Pp. 66–78 in Dreslik, M. J., W. K. Hayes, S. J. Beaupre, and S. P. Mackessy (eds.), The Biology of Rattlesnakes II. ECO Herpetological Publishing and Distribution, Rodeo, New Mexico.

Body Size, Growth, and Sexual Size Dimorphism in the Eastern Massasauga (Sistrurus catenatus)

Michael J. Dreslik1,2, Donald B. Shepard1,3, Sarah J. Baker1, Benjamin C. Jellen1,4, and Christopher A. Phillips1

1 Illinois Natural History Survey, Prairie Research Institute, University of Illinois Urbana-Champaign, Champaign, Illinois 61820, USA

ABSTRACT.—Body size varies with many life history and ecological traits. Assessing intraspecific variation in body size, particularly in species with broad distributions, can reveal how selective pressures vary geographically and how populations have adapted locally. We examined body size, growth, and sexual size dimorphism in a population of Eastern Massasauga (Sistrurus catenatus) at the species’ southern range limit. Females averaged 46.7 cm snout-vent length (SVL) whereas males averaged 43.8 cm. Males reached a larger maximum SVL (77.8 cm) compared to females (71.5 cm). Size structure (SVL) of both sexes was multimodal with females having a bi- or trimodal distribution and males having a tri-, penta-, or hexamodal distribution. Females had a faster instantaneous growth rate than males and that pattern held for nonlinear growth curves. Females grew faster to a smaller adult body size compared to males. Growth analyses establish a pattern of age-specific sexual size dimorphism (SSD). Females were slightly longer at birth, grew faster, and reached a maximum size disparity as the larger sex by age 2. As female growth decreased at sexual maturity, SSD became absent by age 5 and males were the larger sex after age 6. Post-maturational differences in growth rates are likely due to higher reproductive costs in females; however, larger male size also provides an advantage in agonistic encounters. Finally, we found that males had slightly longer tail lengths (TLs) at birth and dimorphism in TL increased with SVL.

INTRODUCTION (Blueweiss et al., 1978; Calder, 1984). Growth, a temporal component of body size, is critical in predicting life history Body size is a fundamental trait in life history studies traits such as the age of sexual maturity and can have (Stearns and Koella, 1986; Stearns, 1989, 1992) and often important population-level implications (Stearns, 1992). displays geographic (Ashton and Feldman, 2003), sexual Several studies on viperids have examined growth (Heyrend (Shine, 1978a,b, 1993, 1994), and ontogenetic variation and Call, 1951; Barbour, 1956; Fitch, 1960; Gibbons, 1972; (Andrews, 1982). In organisms with indeterminate growth, Klauber, 1972; Fitch, 1985; Martin, 1988; Macartney et al., like most ectotherms, maximum body size varies along 1990); however, few have taken advantage of nonlinear environmental and resource gradients, leading to size modeling approaches (e.g., Madsen and Shine, 2000; Blou- differences among populations (Andrews, 1982). Assessing in-Demers et al., 2002). Unlike other approaches, nonlinear variation in body size within species is important because models can yield biologically important parameters that size varies with many life history and ecological traits, allow quantitative comparisons (Andrews, 1982). and thus reflects adaptive variation across a species’ range Determining which sex is larger in a species provides insight 2 Correspondence e-mail: [email protected] into selective forces driving reproductive success (Shine, 3 Present address: School of Biological Sciences, Louisiana Tech 1978, 1993, 1994). Sexual size dimorphism (SSD) can occur University, Ruston, Louisiana 71272, USA at any life stage and may vary ontogenetically, being present 4 Present address: Urban Chestnut Brewing Company, St. Louis, at one life stage but absent at others (Shine, 1978b, 1993, Missouri 63110, USA 1994). SSD in neonate snakes is presumably rare because

66 Dreslik et al. 2017 dimorphic body plans have not yet developed as many MATERIALS AND METHODS dimorphic traits are linked to expression of sex hormones (Shine, 1978b; Fitch, 1981; King, 1989; Shine, 1993, 1994). Study area.—Carlyle Lake, an impoundment of the In adult snakes, SSD is common, but the direction and Kaskaskia River in south-central Illinois, is bordered by extent varies among taxa. Sexual bimaturism and fecundity 4,455 ha of state and federally managed lands, consisting selection explain cases where females are larger than males of upland and bottomland forest, old-field, and restored (Shine, 1978a,b; Bull, 1980; Fitch, 1981; Parker and Plummer, prairie within a larger agricultural matrix. For a more 1987). Alternatively, male-male competition for mates and detailed habitat description, see Dreslik (2005). aggressive interactions incited by dense mating aggrega- tions or a male-skewed operational sex ratio may explain General methods.—We captured live snakes through larger male body sizes (Shine, 1978b; Fitch, 1981; King, visual encounter surveys (Heyer et al., 1994) during the 1989; Shine, 1993, 1994). In species with fecundity select- spring egress from 1999 to 2010, and also included snakes ion in females and mate competition in males, the pattern encountered opportunistically throughout the active of SSD will be the net result of selection on both of these season and captive born snakes. To measure snout-vent important determinants of individual reproductive success. length (SVL), we restrained the snake’s head in a clear PVC tube, took repeated measurements from the tip of the snout Over the last few decades, research on snake ecology has to the cloaca with a flexible tape until three measurements increased to levels rivaling that of many endotherms (Shine were within 1 cm, and then averaged these measurements. and Bonnet, 2000) with several species (e.g., Crotalus viridis, We measured tail length (TL) from the cloaca to the base Vipera berus, Python molurus, and Thamnophis sirtalis) of the rattle with a plastic ruler to the nearest 1 mm and emerging as models. The Eastern Massasauga Sistrurus( to determine total length (TOL) we summed SVL and TL. catenatus) is a relatively small-bodied pitviper that occu- We determined the sex of individuals by cloacal probing pies a diversity of habitats across a broad geographic range (Schaefer, 1934). We classified snakes as adults if their SVL (Ernst, 1992; Ernst and Ernst, 2003). This ecological niche exceeded the size of the smallest individual observed exhib- variation provides an opportunity to compare ecological iting mating behavior or gravidity, which was 49.7 cm SVL and life history patterns to increase our understanding of for males (Jellen et al., 2007) and 48.4 cm SVL for females plasticity and adaptability in wide-ranging ectotherms. (Dreslik, unpubl. data). We uniquely marked snakes by Most research on S. catenatus has focused on spatial ecology clipping ventral scales (Brown and Parker, 1976), painting and a range-wide view of this aspect of their biology is rattle segments with nail polish (Brown et al., 1984), and available (Reinert and Kodrich, 1982; Weatherhead and injecting a PIT tag subcutaneously. For individuals too Prior, 1992; Johnson, 2000; Parent and Weatherhead, 2000; small to be PIT-tagged (<35 cm SVL), we photographed King et al., 2004; Harvey and Weatherhead, 2006a; Marshall dorsal pattern to ensure proper identification. We released et al., 2006; Dreslik et al., in press). Reproductive biology all snakes at their initial point of capture and released indi- has also received attention (Keenlyne, 1978; Reinert and viduals born in captivity at their mother’s gestation site. Kodrich, 1982; Jellen et al., 2007; Aldridge et al., 2008), but few studies have focused on other aspects of life history Size structure.—We constructed size frequency histo- such as body size (Seigel, 1986; Ernst and Ernst, 2011). This grams for snakes captured at our most intensively moni- lack of data limits our ability to determine how ecology and tored site, South Shore State Park (SSSP), by grouping life history influence large-scale evolutionary trends. To fill snakes into 5-cm size classes by sex. We then decomposed this gap, more population-level studies on body size and the size frequency distributions for each sex into one to other life history traits are needed. six unimodal normal distributions following the methods outlined in Ebert (1999). We selected breakpoints in the size Here, we examine body size of a population of S. catenatus distributions for each component, then calculated means, at its southern range limit. First, we determine the size standard deviations, and the proportion of individuals for structure and number of size classes. Second, we determine each component (Ebert, 1999). To represent multimodal individual growth patterns for S. catenatus and test if sexes distributions, we reassembled the component distribu- grow at different rates. Last, we determine if SSD is present tions (Ebert, 1999). To determine the best-fit multimodal at any life stage and whether the pattern varies ontoge- normal distributions, we used AIC and AICc (Burnham netically. Selection for larger body size in S. catenatus can and Anderson, 1998) with supporting χ2 Goodness-of-fit operate on both sexes; males aggressively compete for tests (Ebert, 1999). mates (Chiszar et al., 1976; Shepard et al., 2003; Jellen et al., 2007) and females gain increased fecundity (Seigel, 1986; Individual growth.—We included all mark/recapture Aldridge et al., 2008). However, it is unknown if these data on snakes collected throughout the Carlyle Lake selective pressures drive one sex to be larger or if their net region and to avoid pseudoreplication, we only used the effect results in no SSD. measurements from the first and most recent captures. We

Body Size, Growth, and Sexual Size Dimorphism in the Eastern Massasauga 67 analyzed individual growth in two ways. First, we exam- rate, we used the GLM procedure in IBM® SPSS® Statistics ined size-based instantaneous growth rates and second, ver. 19 (New York, New York). We used AIC and AICC to we selected the growth model that best portrayed lifetime determine the best-fit model from a candidate set of models individual growth. In both cases, we sought to determine (Burnham and Anderson 1998). The global model included if males and females differed. We calculated the relative all main effects and all two- and three-way interactions. annual instantaneous growth rates using a modification of Brody’s formula (Brody, 1945): The four most widely used models associated with individual growth in the herpetological literature are the von ∆GR = (logeSVL2 − logeSVL1)/((t2 − t1)/365) Bertalanffy, Logistic, Gompertz, and Richards models. Using the methods of Fabens (1965), reparameterization of where loge is the base of the natural logarithm, SVL1 is size all four growth equations can include a time interval and at first capture, SVL2 is size at last capture, t1 is time of first respective sizes, as is typical with mark/recapture data when capture and t2 is time of last capture. To determine if life exact ages are unknown, by using time between captures, stage and sex (independent variables), and initial log-trans- size at initial capture and size at latest recapture (Table 1). formed SVL (covariate) affected the instantaneous growth These models differ in their overall shape with the von

Table 1. Age-specific and mark/recapture analogues of individual growth models used in this study. Parameters are:t – age (in years or days), Lt – size at age t, k – characteristic growth rate, A∞ – asymptotic size, b – proportion of growth remaining toward A∞ at t0, m – shape parameter for the Richard’s function. For the Schnute models, a – the constant relative rate of growth, b – incremental relative rate,

τ1 –first specified age,τ 2 – second specified age,y 1 – size at age τ1, and y2 – size at age τ2, and K, which is a function of τ1 and τ2. For the Tanaka models, a –maximum growth rate, c – age of maximal growth, d – parameter that shifts the body size at maximum growth, and f – rate of change of the growth rate.

Model Age-Specific Mark/Recapture von Bertalanffy −kt −kΔt LAt =−∞ (1 be ) LR = A∞ −(A∞ − LC )e

(Bertalanffy 1957) (Fabens 1965) Logistic A∞ A∞LC Lt = −kt LR = −kΔt (1 +be ) (LC +(A∞ − LC )e )

(Verhulst 1838) (Schoener and Schoener 1978)

Gompertz e− kΔt − kt ⎛ L ⎞ −be = C LAt = ∞e LR A∞ ⎜ ⎟ ⎝ A∞ ⎠

(Gompertz 1825) (Dodd and Dreslik 2008)

Richards m ⎛ 1 ⎞ ⎛ ⎛ ⎜ ⎟ ⎞ ⎞ 1 ⎛ ⎞ ⎝ (1−m)⎠ −kt (1−m) ⎜ ⎜ LC ⎟ −kΔt ⎟ LAt =−∞ ()1 be LR = A∞ 1+ −1 e ⎜ ⎜ ⎝⎜ A ⎠⎟ ⎟ ⎟ ⎝⎜ ⎝ ∞ ⎠ ⎠⎟

(Richards 1959) (Dodd and Dreslik 2008) Schnute 1 1 −a(Δt ) −−τ b at()1 b ⎛ b −aΔt b b −a(τ −τ ) 1− e ⎞ bbb 1− e 2 1 LR = LC e +(y2 − y1 )e −a(τ −τ ) Ly=+12()yy− 1 ⎜ 2 1 ⎟ −−a()ττ21 ⎝ 1− e ⎠ 1− e 1 b −aΔt −aΔt b LR = (LC e + k(1− e )) 1 b −aΔt b −aΔt b LR = (LC e + A∞ (1− e ))

(Schnute 1981) (Baker et al. 1991) Tanaka 22 2 Lf=−1/ ln 2(ft cf)2+−()tc++fa d LR = −1/ f ln 2G + 2 G + fa − d + LC where where

()fh()−d ( f (LC −d)) ca=−//EE4afEnd = e G = (E / 4 − fa/ E − f Δt) and E = e

(Tanaka 1982, 1988) (Ebert and Southon 2003)

68 Dreslik et al. 2017 Table 2. Mean snout-vent lengths (in cm), standard deviations (SD), cut-off values, and contribution toward the cumulative frequency (Contr.) used in the construction of multimodal normal distributions from the size-frequency histograms for female and male Eastern Massasaugas (Sistrurus catenatus) captured at Carlyle Lake, Clinton County, Illinois, during 1999–2010 field seasons.

Females Males Model Mode Mean SD Cut-off Contr. Mean SD Cut-off Contr. Unimodal 1 48.1 14.20 ------47.3 17.91 ------Bimodal 1 27.0 6.15 38.9 0.270 25.9 6.52 40.9 0.363 2 55.8 6.15 39+ 0.730 59.5 8.35 41+ 0.637 Trimodal 1 21.8 2.64 25.9 0.135 23.0 3.86 30.9 0.279 2 32.3 3.59 38.9 0.135 35.8 2.63 40.9 0.084 3 55.8 6.15 39+ 0.730 59.5 8.35 41+ 0.637 Tetramodal 1 21.8 2.64 25.9 0.135 23.0 3.86 30.9 0.279 2 32.3 3.59 38.9 0.135 35.8 2.63 40.9 0.084 3 45.9 2.72 50.9 0.126 49.4 4.13 55.9 0.189 4 57.9 4.41 51+ 0.605 63.8 5.49 56+ 0.447 Pentamodal 1 19.3 1.21 20.9 0.065 19.8 1.45 22.9 0.147 2 24.1 0.99 25.9 0.070 26.6 2.16 30.9 0.132 3 32.3 3.59 38.9 0.135 35.8 2.63 40.9 0.084 4 45.9 2.72 50.9 0.126 49.4 4.13 55.9 0.189 5 57.9 4.41 51+ 0.605 63.8 5.49 56+ 0.447 Hexamodal 1 19.3 1.21 20.9 0.065 19.8 1.45 22.9 0.147 2 24.1 0.99 25.9 0.070 26.6 2.16 30.9 0.132 3 32.3 3.59 38.9 0.135 35.8 2.63 40.9 0.084 4 45.9 2.72 50.9 0.126 49.4 4.13 55.9 0.189 5 57.4 4.00 65.9 0.577 60.9 5.07 69.9 0.395 6 67.2 0.68 66+ 0.028 74.2 2.18 70+ 0.053

Bertalanffy exhibiting a monomolecular decay toward the We conducted all nonlinear regressions using the program asymptote, the Logistic and Gompertz having an inflexion Datafit® ver. 8.1 (Oakdale Engineering, Oakdale, Penn- point, and the Richards being the most flexible by encom- sylvania). We used the mark/recapture analogues of the passing the previous three models. Given that growth may von Bertalanffy, Logistic, Gompertz, Richards, Schnute, not be asymptotic and comparison of parameters among and Tanaka models (Table 1) where ∆t (t2 – t1) replaces t. different models is difficult, we used two additional models We then calculated Akaike Weights (wi) and determined that also encompassed the above shapes. Schnute (1981) model likelihood. We followed the methods outlined in presented a comprehensive model that encompasses the Dodd and Dreslik (2008) using mark/recapture analogues four previous asymptotic models and has been reparame- of growth models coupled with AIC and AICC (Burnham terized by Baker et al. (1991) for use with mark/recapture and Anderson, 1998) to determine the best model for the data (Table 1). The Tanaka model is another model capable pooled data set. We then plotted the best-fit models for of estimating non-asymptotic growth and accounts for an graphical comparisons of growth up to age 12, the oldest early lag phase similar to the Gompertz and Logistic models documented individual in our study. (Tanaka, 1982, 1988) and has also been reparameterized by Ebert (1999) for mark/recapture data (Table 1). Sexual size dimorphism.—We calculated a sexual size dimorphism index (SDI) for SVL using the results from For sex-specific curves, we first coded sex into two binary the individual growth models and express SDI values over variables. For sex variable 1 (S1), we coded males “1” and a 12-yr age range. We calculated SDI values by dividing the females “0” whereas for sex variable 2 (S2) we coded males predicted SVL of females by the SVL of males, thus at any “0” and females “1”. We then replaced each parameter with age an SDI >1 would represent females being larger and its sex-specific component; thus, when considering only the reverse when SDI <1. To examine if SSD existed in TL males, the component S2AF reduces to zero and the same and determine the relative allometry of TL with SVL, we is true when considering only females for S1Am. Reparam- used GLM in IBM® SPSS® Statistics ver. 19 (New York, New eterizing the growth models provided direct sex-specific York) with sex as the independent variable and SVL as the estimates using the combined data set. covariate.

Body Size, Growth, and Sexual Size Dimorphism in the Eastern Massasauga 69 RESULTS

Body size and structure.—For 334 initial captures of females, SVL averaged 46.7 cm (SD = 14.6), TL 3.97 cm (SD = 1.13), and TOL 50.7 cm (SD = 15.7). For 340 initial captures of males, SVL averaged 43.8 cm (SD = 17.1), TL 5.13 cm (SD = 1.99), and TOL 48.9 cm (SD = 19.0). For 198 initial captures of adult females, SVL averaged 57.6 cm (SD = 4.41), TL 4.77 cm (SD = 0.47), and TOL 62.3 cm (SD = 4.69). For 164 initial captures of adult males, SVL averaged 59.5 cm (SD = 5.96), TL 6.91 cm (SD = 0.80), and TOL 66.4 cm (SD = 6.46). Maximum sizes for SVL, TL, and TOL for females were 71.5 cm, 6.5 cm, and 77.7 cm, respectively, and for males were 77.8 cm, 9.6 cm, and 85.8 cm, respectively.

From SVL measurements of 215 female and 190 male S. catenatus from SSSP, we constructed six distributions ranging from unimodal normal to hexamodal normal (Tables 2 and 3; Figure 1). Both the male and female histograms appeared multimodal with at least one juvenile and one adult mode (Figure 1). Female data better fit bimodal and higher distributions with the trimodal distribution Figure 1. Size frequency histograms, frequency plots, and having the lowest ΔAIC and ΔAICC values, and highest cumulative frequency plots of snout-vent lengths (SVL) for Akaike Weights and likelihood (Table 3). Thus, the trimodal female and male Eastern Massasaugas (Sistrurus catenatus) distribution had modes equating to neonate and young- captured at Carlyle Lake, Clinton County, Illinois, during 1999– of-the-year (YOY), juvenile, and adult size classes (Table 2010 field seasons. 3; Figure 1). However, the bimodal distribution also had a good fit, ranking second, with modes equating to juvenile and adult size classes (Table 3; Figure 1). For males, the equating to neonate, YOY, 1st year juvenile, small adult, trimodal, pentamodal, and hexamodal curves all had low average adult, and larger adult size classes (Table 3; Figure ΔAIC and ΔAICC, and high Akaike Weights and likelihoods 1). The male pentamodal distribution had modes repre- (Table 3). The male hexamodal distribution had modes senting all previous size classes except for large adults and

Table 3. AIC and AICC results using multimodal normal distribution fitting of size-frequency distribution data for female and male Eastern Massasaugas (Sistrurus catenatus) captured at Carlyle Lake, Clinton County, Illinois, during 1999–2010 field seasons. Sample sizes for the AIC analysis were N = 215 for females and N = 190 for males. Results for the six candidate models are sorted by ΔAIC 2 where: K = the number of parameters, χ = the Goodness-of-fit test statistic, iw = Akaike Weights, and ER = evidence ratio.

Females 2 Model K χ P AIC ΔAIC wi ER AICc ΔAICc wi ER Trimodal 9 79.30 0.234 97.30 0.00 0.650 1.00 98.18 0.00 0.62 1.00 Biomodal 6 86.87 0.145 98.87 1.57 0.300 2.19 99.27 1.09 0.36 1.73 Pentamodal 15 73.31 0.224 103.31 6.01 0.030 20.18 105.72 7.54 0.01 43.45 Tetramodal 12 80.23 0.147 104.23 6.93 0.020 31.90 105.77 7.59 0.01 44.52 Hexamodal 18 72.72 0.166 108.72 11.42 0.000 3.03•102 112.21 14.04 0.00 1.12•103 Unimodal 3 240.38 0.000 246.38 149.08 0.000 2.36•1032 246.50 148.32 0.00 1.61•1032 Males 2 Model K χ P AIC ΔAIC wi ER AICc ΔAICc wi ER Hexamodal 18 77.70 0.086 113.70 0.00 0.500 1.00 117.70 1.20 0.25 1.00 Pentamodal 15 84.73 0.051 114.73 1.03 0.300 1.67 117.48 0.98 0.28 0.90 Trimodal 9 97.50 0.020 115.50 1.80 0.200 2.46 116.50 0.00 0.46 0.55 Tetramodal 12 105.70 0.002 129.70 16.00 0.000 2.98•103 131.46 14.96 0.00 9.72•102 Bimodal 6 118.65 0.001 130.65 16.95 0.000 4.80•103 131.11 14.61 0.00 8.18•102 Unimodal 3 223.40 0.000 229.40 115.70 0.000 1.33•1025 229.53 113.02 0.00 1.92•1024

70 Dreslik et al. 2017 Table 4. AIC and AICC results for ANCOVAs using instantaneous growth rates of capture/recapture measurements of snout-vent length (SVL) for Eastern Massasaugas (Sistrurus catenatus) at Carlyle Lake, Clinton County, Illinois, during 1999–2010 field seasons. Sample sizes for the AIC analysis were N = 41 and N = 29 for female adults and juveniles, respectively, and N = 38 and N = 19 for male adults and juveniles, respectively. Results for the 10 candidate models are sorted by ΔAIC where: K = the number of parameters, wi = Akaike Weights, and ER = evidence ratio. The Global model includes the main effects of stage (S), sex (X), and initial SVL (ISVL) and all two- and three-way interactions.

2 Model K r P AIC ΔAIC wi ER AICc ΔAICc wi ER X+ISVL+X•ISVL 5 0.54 <0.001 78.15 0.00 0.86 1.00 78.65 0.00 0.87 1.00 Global 9 0.54 <0.001 83.68 5.52 0.05 15.84 85.21 6.57 0.03 26.68 ISVL 2 0.49 <0.001 83.77 5.61 0.05 16.57 83.86 5.22 0.06 13.57 X+ISVL 4 0.49 <0.001 86.67 8.52 0.01 70.80 87.00 8.35 0.01 65.10 S+ISVL 4 0.49 <0.001 87.49 9.34 0.01 106.86 87.82 9.18 0.01 98.25 S+X+ISVL 5 0.50 <0.001 88.26 10.11 0.01 156.45 88.75 10.11 0.01 156.45 S+ISVL+S•ISVL 5 0.50 <0.001 88.34 10.19 0.01 162.90 88.83 10.19 0.01 162.90 S+X 4 0.41 <0.001 106.45 28.30 0.00 1.40•106 106.78 28.13 0.00 1.29•106 S 3 0.40 <0.001 107.28 29.13 0.00 2.12•106 107.48 28.83 0.00 1.82•106 X 3 0.03 0.060 167.70 89.55 0.00 2.79•1019 167.90 89.25 0.00 2.40•1019

Table 5. AIC and AICC results for nonlinear regression fitting for female and male Eastern Massasaugas(Sistrurus catenatus) from mark/recapture data on snakes at Carlyle Lake, Clinton County, Illinois, captured during 1999–2010 field seasons. Sample sizes with at least one capture/recapture used in the AIC analysis were N = 70 for females and N = 57 for males. Results for the 12 candidate models are sorted by ΔAIC where: RSS = residual sums of squares, K = the number of parameters, wi = Akaike Weights, and ER = evidence ratio.

Model K RSS P AIC ΔAIC wi ER AICc ΔAICc wi ER Gompertz-Sex 4 2709.96 <0.001 1011.90 0.00 0.47 1.00 1012.22 0.00 0.48 1.00 Logistic-Sex 4 2721.03 <0.001 1012.41 0.52 0.36 1.30 1012.74 0.52 0.37 1.30 Schunte (eqn 11)-Sex 6 2698.85 <0.001 1015.37 3.48 0.08 5.69 1016.07 3.85 0.07 6.86 Richard's-Sex 6 2710.33 <0.001 1015.91 4.02 0.06 7.45 1016.61 4.39 0.05 8.98 von Bertalanffy-Sex 4 2855.48 <0.001 1018.54 6.64 0.02 27.70 1018.87 6.64 0.02 27.70 Gompertz 2 3294.72 <0.001 1032.71 20.81 0.00 3.31•104 1032.81 20.58 0.00 2.95•104 von Bertalanffy 2 3314.03 <0.001 1033.45 21.56 0.00 4.80•104 1033.55 21.33 0.00 4.27•104 Richard's Base 3 3279.27 <0.001 1034.11 22.22 0.00 6.67•104 1034.31 22.08 0.00 6.25•104 Schnute Base (eqn 11) 3 3279.27 <0.001 1034.11 22.22 0.00 6.67•104 1034.31 22.08 0.00 6.25•104 Schnute Base (eqn 9) 3 3283.22 <0.001 1034.27 22.37 0.00 7.20•104 1034.46 22.24 0.00 6.74•104 Logistic 2 3433.23 <0.001 1037.94 26.04 0.00 4.52•105 1038.04 25.81 0.00 4.03•105 Tanaka 3 8105.24 <0.001 1149.03 137.14 0.00 6.01•1029 1149.23 137.01 0.00 5.63•1029 the trimodal distribution had modes equating to neonate Of the 12 candidate nonlinear growth models we evalu- and YOY, juvenile, and adult size classes (Table 3; Figure 1). ated, both the Gompertz and Logistic models accounting for sex had high support (Table 5). In addition, all models Individual growth.—For instantaneous growth rates, we accounting for sex performed better than those in which used data on 127 initial and last recapture pairs (Table 4). sex was not a variable (Table 5). The overall models showed The best model included sex, initial SVL, and the interaction little differentiation except for the Tanaka non-asymptotic between sex and SVL (Table 4). This model explained an model, which performed the poorest (Table 5; Figure 3). 2 equal amount of the variance (r = 0.54) as the global model Most models predict an asymptotic size (A∞) ranging but with fewer parameters (Table 4). Overall, females grew from 67.7–71.7 cm SVL and characteristic growth rates -3 -3 at a faster rate than males (F1,127 = 11.79, P = 0.001) and (k) ranged from 1.04•10 –2.23•10 (Table 6). When we smaller snakes grew at a faster rate than larger snakes (F1,127 examine the Gompertz and Logistic models by sex, females = 122.75, P = 0.001). When accounting for initial SVL, we grew at a faster rate to a smaller A∞ and males grew at a also found an interaction with sex, with females growing slower rate to a larger A∞ (Figure 4). Estimates for A∞ faster at smaller sizes and males growing faster at larger ranged from 63.6–66.2 cm SVL for females and 72.3–78.0 sizes (F1,127 = 10.62, P = 0.001; Figure 2). cm SVL for males (Table 6). In addition, estimates of k

Body Size, Growth, and Sexual Size Dimorphism in the Eastern Massasauga 71 Table 6. Parameter estimates ± 95% confidence intervals for all models used to describe lifetime growth in the Eastern Massasauga (Sistrurus catenatus) from mark/recapture data on snakes at Carlyle Lake, Clinton County, Illinois, captured during 1999–2010 field seasons. Data represent 127 initial and last capture measurements with an N = 70 for females and N = 57 for males. Parameters are: t – age (in years or days), Lt – size at age t, k – characteristic growth rate, A∞ – asymptotic size, b – proportion of growth remaining toward

A∞ at t0, m – shape parameter for the Richard’s function. For the Schnute models: a – the constant relative rate of growth, b – incremental relative rate, τ1 –first specified age, τ2 – second specified age,y 1 – size at age τ1, and y2 – size at age τ2, and K, which is a function of τ1 and

τ2. For the Tanaka models, a –maximum growth rate, c – age of maximal growth, d – parameter that shifts the body size at maximum growth, and f – rate of change of the growth rate.

Overall Models Model Parameter Estimates -3 -4 von Bertalanffy A∞ = 71.7 ± 1.45, k = 1.04•10 ± 1.03•10 -3 -4 Logistic A∞ = 67.7 ± 0.88, k = 2.23•10 ± 1.64•10 -3 -4 Gompertz A∞ = 69.2 ± 1.05, k = 1.59•10 ± 1.28•10 -3 -4 Richard's A∞ = 70.1 ± 1.58, k = 1.35•10 ± 2.96•10 , m = 2.4 ± 3.18 Schnute (eqn 9) K = 13.6 ± 30.89, a = 1.24•10-3 ± 2.81•10-4, b = 0.614 ± 0.53 -3 -4 Schnute (eqn 11) A∞ = 70.1 ± 1.58, a = 1.35•10 ± 2.96•10 , b = 0.413 ± 0.54 Tanaka a= 9.30•10-3 ± 2.53•10-4, c = 60.3 ± 13.05, f = 4.85•10-2 ± 1.89•10-2 Sex-Specific Models Females Parameter Estimates -3 -4 von Bertalanffy A∞ = 66.2 ± 2.76, k = 1.51•10 ± 3.51•10 -3 -4 Logistic A∞ = 63.6 ± 1.83, k = 3.05•10 ± 5.17•10 -3 -4 Gompertz A∞ = 64.6 ± 2.12, k = 2.21•10 ± 4.20•10 -3 -4 8 8 Richard's A∞ = 64.6 ± 2.30, k = 2.20•10 ± 4.27•10 , m = -8.60•10 ± 6.77•10 -3 -3 Schnute (eqn 11) A∞ = 64.2 ± 2.43, a = 2.52•10 ± 1.36•10 , b = -0.388 ± 1.74 Males Parameter Estimates -4 -4 von Bertalanffy A∞ = 78.0 ± 5.34, k = 7.57•10 ± 2.18•10 -3 -4 Logistic A∞ = 72.3 ± 2.71, k = 1.67•10 ± 3.12•10 -3 -4 Gompertz A∞ = 74.4 ± 3.47, k = 1.19•10 ± 2.58•10 -3 -4 8 8 Richard's A∞ = 74.3 ± 4.12, k = 1.19•10 ± 2.75•10 , m = -5.20•10 ± 7.93•10 -3 -4 Schnute (eqn 11) A∞ = 73.4 ± 4.34, a = 1.38•10 ± 7.68•10 , b = -0.400 ± 1.57 ranged from 1.51•10-3–3.05•10-3 for females and 7.57•10- 4–1.67•10-3 for males (Table 6). This dichotomy between the sexes corroborates the analysis of instantaneous growth rates above and establishes that the pattern and extent of sexual dimorphism varies with age.

Sexual size dimorphism.—At birth, females and males have roughly the same SVL, although females are slightly longer (Figure 5). At ages 2–3, females reach maximum disparity as the larger sex and size dimorphism is absent by age 5 (Figure 5). From ages 6+, males are the larger sex (Figure 5). Results for the allometry of TL, however, show a different pattern (Figure 6). For any given SVL, males had longer tails than females (F1,672 = 5.18, P = 0.023) and male TL increased with SVL at a faster rate compared to females (F1,672 = 270.7, P <<0.001; Figure 6).

Figure 2. Scatter plot and regression lines for the relationship DISCUSSION between instantaneous growth rate and initial snout-vent length (SVL) the Eastern Massasauga (Sistrurus catenatus) from Body size and structure.—Apart from Crotalus pricei, Carlyle Lake, Clinton County, Illinois, captured during 1999– the genus Sistrurus contains the smallest species of North 2010 field seasons. Data represent 127 initial and last capture American rattlesnakes (Ernst, 1992; Ernst and Ernst, 2003). measurements with an N = 70 for females and N = 57 for males. Pygmy Rattlesnakes (S. miliarius) seldom reach body sizes

72 Dreslik et al. 2017 >70.7 cm SVL whereas S. catenatus may reach upwards of Distributions with fewer modes (bi- and tri-) better fit the 100 cm SVL (Klauber, 1972; Ernst, 1992; Ernst and Ernst, female size data whereas tri-, penta-, or hexamodal distri- 2003). Our adults averaged 57.6 cm SVL and 62.3 cm TOL butions fit better for males. One possible reason we could for females and 59.5 cm SVL and 66.4 cm TOL for males. not discriminate among higher multimodal distributions Adult female and male S. catenatus from Michigan averaged could be that our 5-cm size classes were too coarse. Sorting 61.6 cm (range 48.3–72.7 cm) and 66.2 cm (range 49.9–82.9 data into 2-cm or 1-cm size classes may have provided cm) TOL, respectively (Hallock, 1991). Female and male S. better model performance, but more size classes would tergeminus averaged 53.9 cm and 51.3 cm SVL in Missouri require larger sample sizes (Ebert, 1999). Another possible (Seigel, 1986), and 36.9 cm and 35.5 cm SVL in Colorado, explanation is the cut-off values we chose for size classes. respectively (Hobert et al., 2004). Snakes and lizards mainly Although we attempted to choose cut-off values that reverse Bergmann’s rule, which is they are smaller at higher represented low points in the distribution, the values may latitudes and larger at lower latitudes (Ashton and Feldman, have affected the fit of higher multimodal distributions. 2003). Unfortunately, there are too few data on S. catenatus Future approaches may benefit from nonlinear regression body sizes range-wide to make a determination. methods that estimate parameters directly from the data. Differences in size structure between the sexes may also Decomposing the size structure of the SSSP population relate to variation in individual growth rates. More modes of S. catenatus revealed at least two or three size classes. may be present in the male size distribution because of Although some snake populations have unimodal size higher variance in individual growth rates. Broader confi- distributions, such as Lapemis hardwickii (Hin et al., 1991), dence intervals on male growth curves compared to female most have multimodal distributions (Parker and Plummer, curves (Fig. 4) suggest this may be the case. 1987; Larsen and Gregory, 1989; Mertens, 1995; Manjarrez, 1998; Winne et al., 2005; Manjarrez et al., 2007). Popula- Individual growth.—Many North American vipers tion size distributions of S. tergeminus appeared bimodal double in length by their second or third year (Fitch, and potentially trimodal in Missouri (Seigel, 1986), and 1949; Heyrend and Call, 1951; Barbour, 1956; Gibbons, distinctly bimodal and potentially tetramodal in Colorado 1972; Klauber, 1972; Gannon and Secoy, 1984; Fitch, 1985; (Hobert et al., 2004). Martin, 1988). In comparison, S. catenatus at Carlyle Lake doubled in length within their first year and tripled in

50 Vent Length (cm) Vent -

40 Snout

30 von Bertalannfy Gompertz Logistic Richards Schnute 9 Schnute 11 Figure 4. Sex-specific Gompertz and Logistic growth models Tanaka 20 and associated 95% confidence intervals for the Eastern 0 2 4 6 8 10 12 Massasauga (Sistrurus catenatus) from Carlyle Lake, Clinton Age (Years) County, Illinois, captured during 1999–2010 field seasons. Data Figure 3. Composite nonlinear growth models for the Eastern represent 127 initial and last capture measurements with an N Massasauga (Sistrurus catenatus) from Carlyle Lake, Clinton =70 for females and N = 57 for males. The solid horizontal line County, Illinois, captured during 1999–2010 field seasons. Data represents the minimum size of maturity for males (49.7 cm represent 127 initial and last capture measurements with an N = SVL) and the dashed horizontal line represents the minimum 70 for females and N = 57 for males. size of maturity for females (48.4 cm SVL).

Body Size, Growth, and Sexual Size Dimorphism in the Eastern Massasauga 73 10 Females Males 9

Tail Length Length Tail (cm) 4

1 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Snout-Vent Length (cm)

Figure 5. Fitted values for age-specific sexual size dimorphism Figure 6. Scatter plot and regression lines for the relationship indices for the best fit growth models and associated 95% between tail length and snout-vent length for the Eastern confidence intervals for the Eastern Massasauga Sistrurus( Massasauga (Sistrurus catenatus) from Carlyle Lake, Clinton catenatus) from Carlyle Lake, Clinton County, Illinois, captured County, Illinois, captured during 1999–2010 field seasons. Data during 1999–2010 field seasons. The solid horizontal line at 1.0 represent 672 individuals with an N = 336 for each females and represents where both sexes are equal in size. males. length by the end of their second year. This rapid growth Canada) could result in faster growth and younger age of affects demographics because individuals can potentially maturity. The active season length at Carlyle Lake can also mate by the end of their first full summer (transition from vary annually by ~40 days (Dreslik, unpubl. data); thus, age 1 to 2 yrs) and are certainly able to mate and reproduce years with longer active seasons may also provide extended by the end of their second full summer (transition from age foraging opportunities and time for growth. Finally, young 2 to 3 yrs). Younger maturation is often associated with high S. catenatus at Carlyle Lake may benefit from the ability adult mortality in squamates (Shine and Charnov, 1992), to prey on nutrient-rich mammalian prey earlier in life but such rapid growth in S. catenatus may also be resource- compared to some other populations because southern based. Long-term studies of Liasis fuscus (water python) short-tailed shrews, Blarina carolinensis, only overlap the found faster growth rates in years with higher prey abun- distribution of S. catenatus in southern Illinois (Shepard et dances, and this translated to larger adult sizes (Madsen al., 2004). and Shine, 2000). Along these lines, island populations of Natrix natrix (grass snake) with reduced prey abundances Female S. catenatus grew more rapidly than males, which showed depressed growth rates and lower maximum body contrasts with growth studies on Agkistrodon contortrix size compared to mainland populations (Madsen and and C. viridis (Heyrend and Call, 1951; Fitch, 1960; Shine, 1993a). Although we lack data on annual variation Macartney et al., 1990). This disparity was most evident at in prey abundances to determine if such a “silver-spoon” younger ages and a shift in energy allocation to oogenesis effect exists in S. catenatus, the pattern could arise from and embryogenesis explains the post-maturity decrease either increased prey abundances or foraging opportunity. in female growth. In females, reproduction is more ener- Because Carlyle Lake represents the southern range limit getically costly due to significant resource allocation to of S. catenatus, the active season is longer compared to offspring (Bonnet et al., 2002). On average, femaleS. cate- more northern populations. The active season at Carlyle natus at Carlyle Lake lose 43.6% of their mass giving birth ranges from 206–246 days (Dreslik, unpubl. data) whereas (Aldridge et al., 2008). Additionally, many female snakes the active season is 184 days in Ontario, Canada (Harvey alter activity during gestation, focusing on optimizing and Weatherhead, 2006b). This additional time to acquire embryogenesis via thermoregulation and undergoing a resources available to individuals at the southern range limit facultative aphagia (Gregory et al., 1999; Lourdais et al., compared to more northern sites (16–60 days compared to 2002). Given the large reproductive investment by female

74 Dreslik et al. 2017 vipers and that energetic stores must be replenished before Consistent with previous studies on S. catenatus (Bielema, additional reproductive attempts (Madsen and Shine, 1999; 1973; Reinert, 1978; Hallock, 1991), we found that males Aubret et al., 2002; Bonnet et al., 2002), little surplus energy have longer tails than females. Moreover, this SSD was may be available to allocate to growth after sexual maturity. present at birth, which is rare in snakes (Shine, 1978b; In males, slower growth rates after maturity may result from Fitch, 1981). Although housing the reproductive organs energetic costs and behavioral shifts associated with mate- constrains minimum TL in males, and coelom space for searching. During the mating season, male S. catenatus developing embryos constrains maximum TL in females alter their activity and make prolonged forays in search of (Shine, 1993; King et al., 1999), selection on TL in snakes females (Jellen et al., 2007). In some snake species, males is complex as pressures may act in opposing directions will undergo facultative aphagia while searching for mates (Arnold and Bennett, 1988; Madsen and Shine, 1993b; (O’Donnell et al., 2004). The causal mechanism driving the Luiselli, 1996; Shine and Shetty, 2001). For example, longer difference in pre-maturation growth rates between the sexes tails in Laticauda colubrina increased survival, but aver- is unknown; however, males have significantly lower meta- age-size tails optimized swimming speed and increased bolic rates than females (Baker, 2009; Baker et al., in press). mating success (Shine and Shetty, 2001). In male snakes, faster tail growth is likely related to sexual maturation and Sexual size dimorphism.—Larger body size in males is the sexual selection may further drive TL because males use norm among viperids presumably because males compete their tails during courtship and copulation, including S. for mates (Shine, 1978b; Shine, 1993, 1994); however, we catenatus (Chiszar et al., 1976; Jellen et al., 2007). found no overall SSD in SVL in S. catenatus. Populations of S. tergeminus in Missouri (Seigel, 1986) and Colorado Although body size is one of the simplest measures to obtain (Hobert, 1997) also exhibited no SSD in SVL, but males in free-ranging populations, data are lacking for many were larger than females in S. miliarius (Shine, 1978b). snake species. The extent to which populations vary in size The negligible SSD we observed may be due to a small net within widely distributed species is even less known. Such difference in the product of different selective pressures on data are important for determining the large-scale evolu- the sexes. In snakes, as well as many other species, female tionary processes producing size variation both within and body size relates positively to fecundity (Seigel and Ford, among species. Because individual growth rates and age of 1987) and thus, selection favors larger size (Shine, 1978b; sexual maturity covary, and the latter is a determinant of Shine, 1994). Such a fecundity advantage occurs in S. cate- generation time, individual growth rates are a predictor of natus (Aldridge et al., 2008). a population’s adaptive capacity. Detailed range-wide life history studies are needed to improve our understanding When males aggressively compete for females, selection of plasticity and adaptability within and between species. favors large male body sizes because larger males are more successful in winning male-male interactions and securing mating opportunities (Andrén and Nilson, 1981; Andrén, ACKNOWLEDGEMENTS 1986; Schuett and Gillingham, 1989; Madsen and Shine, 1993b). Male mass in S. catenatus is positively related to We thank G. Tatham, J. Birdsell, and J. Bunnell of the the number of females acquired during the mating season Illinois Department of Natural Resources (IDNR), and J. (Jellen et al., 2007) and males aggressively compete for Smothers and D. Baum of the U. S. Army Corps of Engi- mates (Shepard et al., 2003). Although a small difference in neers, without whom we could not accomplish much of the outcome of these selective pressures on the sexes may this work. We also thank M. Redmer of the Unites States explain an overall lack of SSD in S. catenatus, the extent and Fish and Wildlife Service and S. Ballard, M. Kemper, and direction of SSD clearly varies with age given the differences K. Boyles of the IDNR for all their assistance. We thank the in growth rates and asymptotic sizes we observed. Exam- many fieldworkers and volunteers who helped us capture ining SSD within age classes provided a very different view snakes since 1999. We thank the Illinois Department of of SSD compared to examining the population as a whole. Natural Resources, U. S. Fish and Wildlife Service, U. S. At ages <5 yrs, females were larger whereas males were Army Corps of Engineers, Illinois Tollway, and the Chicago larger at ages >5 yrs (Fig. 5). Given the pattern of SSD was Herpetological Society for funding this project. All animals reversed in the youngest and oldest age classes and a large in this study received humane treatment adhering to the portion of the population was composed of individuals in “Guidelines for Use of Live Amphibians and Reptiles the 4–6 yr age range where SSD is nearly absent (Fig. 1), it is in Field Research” published by Society for the Study of not surprising that we failed to detect SSD when examining Amphibians and Reptiles, American Society of Ichthyolo- the population as a whole. Our results demonstrate how gists and Herpetologists, and Herpetologists’ League. We failing to consider age-specific SSD and the age structure of conducted this study in accordance under the University of populations can obscure important biological information Illinois IACUC protocol #08019 and Illinois Department of in a species. Natural Resources T&E species permit #05-11S.

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