Edited by: Michael J. Dreslik • William K. Hayes • …dshepard/pdfs/Dreslik et al BodySize BOR...68 Dreslik et al. 2017 analyzed individual growth in two ways. First, we exam-ined

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66 Dreslik et al. 2017

INTRODUCTION

Body size is a fundamental trait in life history studies (Stearns and Koella, 1986; Stearns, 1989, 1992) and often displays geographic (Ashton and Feldman, 2003), sexual (Shine, 1978a,b, 1993, 1994), and ontogenetic variation (Andrews, 1982). In organisms with indeterminate growth, like most ectotherms, maximum body size varies along environmental and resource gradients, leading to size differences among populations (Andrews, 1982). Assessing variation in body size within species is important because size varies with many life history and ecological traits, and thus reflects adaptive variation across a species’ range

(Blueweiss et al., 1978; Calder, 1984). Growth, a temporal component of body size, is critical in predicting life history traits such as the age of sexual maturity and can have important population-level implications (Stearns, 1992). Several studies on viperids have examined growth (Heyrend and Call, 1951; Barbour, 1956; Fitch, 1960; Gibbons, 1972; Klauber, 1972; Fitch, 1985; Martin, 1988; Macartney et al., 1990); however, few have taken advantage of nonlinear modeling approaches (e.g., Madsen and Shine, 2000; Blou-in-Demers et al., 2002). Unlike other approaches, nonlinear models can yield biologically important parameters that allow quantitative comparisons (Andrews, 1982).

Determining which sex is larger in a species provides insight into selective forces driving reproductive success (Shine, 1978, 1993, 1994). Sexual size dimorphism (SSD) can occur at any life stage and may vary ontogenetically, being present at one life stage but absent at others (Shine, 1978b, 1993, 1994). SSD in neonate snakes is presumably rare because

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 distribu-tion 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.

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.

2 Correspondence e-mail: [email protected] Present address: School of Biological Sciences, Louisiana Tech

University, Ruston, Louisiana 71272, USA4 Present address: Urban Chestnut Brewing Company, St. Louis,

Missouri 63110, USA

Body Size, Growth, and Sexual Size Dimorphism in the Eastern Massasauga 67

dimorphic body plans have not yet developed as many dimorphic traits are linked to expression of sex hormones (Shine, 1978b; Fitch, 1981; King, 1989; Shine, 1993, 1994). In adult snakes, SSD is common, but the direction and extent varies among taxa. Sexual bimaturism and fecundity selection explain cases where females are larger than males (Shine, 1978a,b; Bull, 1980; Fitch, 1981; Parker and Plummer, 1987). Alternatively, male-male competition for mates and aggressive interactions incited by dense mating aggrega-tions or a male-skewed operational sex ratio may explain larger male body sizes (Shine, 1978b; Fitch, 1981; King, 1989; Shine, 1993, 1994). In species with fecundity select - ion in females and mate competition in males, the pattern of SSD will be the net result of selection on both of these important determinants of individual reproductive success.

Over the last few decades, research on snake ecology has increased to levels rivaling that of many endotherms (Shine and Bonnet, 2000) with several species (e.g., Crotalus viridis, Vipera berus, Python molurus, and Thamnophis sirtalis) emerging as models. The Eastern Massasauga (Sistrurus catenatus) is a relatively small-bodied pitviper that occu-pies a diversity of habitats across a broad geographic range (Ernst, 1992; Ernst and Ernst, 2003). This ecological niche variation provides an opportunity to compare ecological and life history patterns to increase our understanding of plasticity and adaptability in wide-ranging ectotherms. Most research on S. catenatus has focused on spatial ecology and a range-wide view of this aspect of their biology is available (Reinert and Kodrich, 1982; Weatherhead and Prior, 1992; Johnson, 2000; Parent and Weatherhead, 2000; King et al., 2004; Harvey and Weatherhead, 2006a; Marshall et al., 2006; Dreslik et al., in press). Reproductive biology has also received attention (Keenlyne, 1978; Reinert and Kodrich, 1982; Jellen et al., 2007; Aldridge et al., 2008), but few studies have focused on other aspects of life history such as body size (Seigel, 1986; Ernst and Ernst, 2011). This lack of data limits our ability to determine how ecology and life history influence large-scale evolutionary trends. To fill this gap, more population-level studies on body size and other life history traits are needed.

Here, we examine body size of a population of S. catenatus at its southern range limit. First, we determine the size structure and number of size classes. Second, we determine individual growth patterns for S. catenatus and test if sexes grow at different rates. Last, we determine if SSD is present at any life stage and whether the pattern varies ontoge-netically. Selection for larger body size in S. catenatus can operate on both sexes; males aggressively compete for mates (Chiszar et al., 1976; Shepard et al., 2003; Jellen et al., 2007) and females gain increased fecundity (Seigel, 1986; Aldridge et al., 2008). However, it is unknown if these selective pressures drive one sex to be larger or if their net effect results in no SSD.

MATERIALS AND METHODS

Study area.—Carlyle Lake, an impoundment of the Kaskaskia River in south-central Illinois, is bordered by 4,455 ha of state and federally managed lands, consisting of upland and bottomland forest, old-field, and restored prairie within a larger agricultural matrix. For a more detailed habitat description, see Dreslik (2005).

General methods.—We captured live snakes through visual encounter surveys (Heyer et al., 1994) during the spring egress from 1999 to 2010, and also included snakes encountered opportunistically throughout the active season and captive born snakes. To measure snout-vent length (SVL), we restrained the snake’s head in a clear PVC tube, took repeated measurements from the tip of the snout to the cloaca with a flexible tape until three measurements were within 1 cm, and then averaged these measurements. We measured tail length (TL) from the cloaca to the base of the rattle with a plastic ruler to the nearest 1 mm and to determine total length (TOL) we summed SVL and TL. We determined the sex of individuals by cloacal probing (Schaefer, 1934). We classified snakes as adults if their SVL exceeded the size of the smallest individual observed exhib-iting mating behavior or gravidity, which was 49.7 cm SVL for males (Jellen et al., 2007) and 48.4 cm SVL for females (Dreslik, unpubl. data). We uniquely marked snakes by clipping ventral scales (Brown and Parker, 1976), painting rattle segments with nail polish (Brown et al., 1984), and injecting a PIT tag subcutaneously. For individuals too small to be PIT-tagged (<35 cm SVL), we photographed dorsal pattern to ensure proper identification. We released all snakes at their initial point of capture and released indi-viduals born in captivity at their mother’s gestation site.

Size structure.—We constructed size frequency histo-grams for snakes captured at our most intensively moni-tored site, South Shore State Park (SSSP), by grouping snakes into 5-cm size classes by sex. We then decomposed the size frequency distributions for each sex into one to six unimodal normal distributions following the methods outlined in Ebert (1999). We selected breakpoints in the size distributions for each component, then calculated means, standard deviations, and the proportion of individuals for each component (Ebert, 1999). To represent multimodal distributions, we reassembled the component distribu-tions (Ebert, 1999). To determine the best-fit multimodal normal distributions, we used AIC and AICc (Burnham and Anderson, 1998) with supporting χ2 Goodness-of-fit tests (Ebert, 1999).

Individual growth.—We included all mark/recapture data on snakes collected throughout the Carlyle Lake region and to avoid pseudoreplication, we only used the measurements from the first and most recent captures. We


analyzed individual growth in two ways. First, we exam-ined size-based instantaneous growth rates and second, we selected the growth model that best portrayed lifetime individual growth. In both cases, we sought to determine if males and females differed. We calculated the relative annual instantaneous growth rates using a modification of Brody’s formula (Brody, 1945):

∆GR = (logeSVL2 − logeSVL1)/((t2 − t1)/365)

where loge is the base of the natural logarithm, SVL1 is size at first capture, SVL2 is size at last capture, t1 is time of first capture and t2 is time of last capture. To determine if life stage and sex (independent variables), and initial log-trans-formed SVL (covariate) affected the instantaneous growth

rate, we used the GLM procedure in IBM® SPSS® Statistics ver. 19 (New York, New York). We used AIC and AICC to determine the best-fit model from a candidate set of models (Burnham and Anderson 1998). The global model included all main effects and all two- and three-way interactions.

The four most widely used models associated with indi-vidual growth in the herpetological literature are the von Bertalanffy, Logistic, Gompertz, and Richards models. Using the methods of Fabens (1965), reparameterization of all four growth equations can include a time interval and respective sizes, as is typical with mark/recapture data when exact ages are unknown, by using time between captures, size at initial capture and size at latest recapture (Table 1). 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, τ2 – second specified age, y1 – 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/Recapturevon Bertalanffy

= −∞−L A be(1 )t

kt

(Bertalanffy 1957)

LR = A∞ − (A∞ − LC )e−kΔt

(Fabens 1965)Logistic

=+

∞−L

Abe(1 )t kt

(Verhulst 1838)

LR =A∞LC

(LC + (A∞ − LC )e−kΔt )

(Schoener and Schoener 1978)Gompertz

= ∞− −

L A etbe kt

(Gompertz 1825)

LR = A∞LC

A∞

⎛⎝⎜

⎞⎠⎟

e− kΔt

(Dodd and Dreslik 2008)Richards

( )= −∞− −L A be1t

kt m1

(1 )

(Richards 1959)

LR = A∞ 1+ LC

A∞

⎛⎝⎜

⎞⎠⎟

1(1−m)

⎛⎝⎜

⎞⎠⎟

−1⎛

⎝

⎜⎜

⎞

⎠

⎟⎟ e

−kΔt

⎛

⎝

⎜⎜⎜

⎞

⎠

⎟⎟⎟

m

(Dodd and Dreslik 2008)Schnute

= + −−−

τ

τ τ

− −

− −L y y yee

( )11

b b ba t

a

b

1 2 1

( )

( )

11

2 1

(Schnute 1981)

LR = LCbe−aΔt + (y2

b − y1b )e−a(τ 2−τ1 ) 1− e−a(Δt )

1− e−a(τ 2−τ1 )

⎛⎝⎜

⎞⎠⎟

1b

LR = LCbe−aΔt + k(1− e−aΔt )( )

1b

LR = LCbe−aΔt +A∞

b(1− e−aΔt )( )1b

(Baker et al. 1991)Tanaka

= − + − + +

= − = ( )−

L f f t c f t c fa d

c a E E f E e

1/ ln 2 ( ) 2 ( )

where

/ / 4 and f h d

2 2

( )

(Tanaka 1982, 1988)

LR = −1/ f ln 2G + 2 G2 + fa −d + LC

where

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

(Ebert and Southon 2003)


Bertalanffy exhibiting a monomolecular decay toward the asymptote, the Logistic and Gompertz having an inflexion point, and the Richards being the most flexible by encom-passing the previous three models. Given that growth may not be asymptotic and comparison of parameters among different models is difficult, we used two additional models that also encompassed the above shapes. Schnute (1981) presented a comprehensive model that encompasses the four previous asymptotic models and has been reparame-terized by Baker et al. (1991) for use with mark/recapture data (Table 1). The Tanaka model is another model capable of estimating non-asymptotic growth and accounts for an early lag phase similar to the Gompertz and Logistic models (Tanaka, 1982, 1988) and has also been reparameterized by Ebert (1999) for mark/recapture data (Table 1).

For sex-specific curves, we first coded sex into two binary variables. For sex variable 1 (S1), we coded males “1” and females “0” whereas for sex variable 2 (S2) we coded males “0” and females “1”. We then replaced each parameter with its sex-specific component; thus, when considering only males, the component S2AF reduces to zero and the same is true when considering only females for S1Am. Reparam-eterizing the growth models provided direct sex-specific estimates using the combined data set.

We conducted all nonlinear regressions using the program Datafit® ver. 8.1 (Oakdale Engineering, Oakdale, Penn-sylvania). We used the mark/recapture analogues of the von Bertalanffy, Logistic, Gompertz, Richards, Schnute, and Tanaka models (Table 1) where ∆t (t2 – t1) replaces t. We then calculated Akaike Weights (wi) and determined model likelihood. We followed the methods outlined in Dodd and Dreslik (2008) using mark/recapture analogues of growth models coupled with AIC and AICC (Burnham and Anderson, 1998) to determine the best model for the pooled data set. We then plotted the best-fit models for graphical comparisons of growth up to age 12, the oldest documented individual in our study.

Sexual size dimorphism.—We calculated a sexual size dimorphism index (SDI) for SVL using the results from the individual growth models and express SDI values over a 12-yr age range. We calculated SDI values by dividing the predicted SVL of females by the SVL of males, thus at any age an SDI >1 would represent females being larger and the reverse when SDI <1. To examine if SSD existed in TL and determine the relative allometry of TL with SVL, we used GLM in IBM® SPSS® Statistics ver. 19 (New York, New York) with sex as the independent variable and SVL as the covariate.

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 MalesModel 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+ 0.637Trimodal 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 0.0843 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 0.2792 32.3 3.59 38.9 0.135 35.8 2.63 40.9 0.0843 45.9 2.72 50.9 0.126 49.4 4.13 55.9 0.1894 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 0.1472 24.1 0.99 25.9 0.070 26.6 2.16 30.9 0.1323 32.3 3.59 38.9 0.135 35.8 2.63 40.9 0.0844 45.9 2.72 50.9 0.126 49.4 4.13 55.9 0.1895 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 0.1472 24.1 0.99 25.9 0.070 26.6 2.16 30.9 0.1323 32.3 3.59 38.9 0.135 35.8 2.63 40.9 0.0844 45.9 2.72 50.9 0.126 49.4 4.13 55.9 0.1895 57.4 4.00 65.9 0.577 60.9 5.07 69.9 0.3956 67.2 0.68 66+ 0.028 74.2 2.18 70+ 0.053


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 histo-grams 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 having the lowest ΔAIC and ΔAICC values, and highest Akaike Weights and likelihood (Table 3). Thus, the trimodal distribution had modes equating to neonate and young-of-the-year (YOY), juvenile, and adult size classes (Table 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 trimodal, pentamodal, and hexamodal curves all had low ΔAIC and ΔAICC, and high Akaike Weights and likelihoods (Table 3). The male hexamodal distribution had modes

equating to neonate, YOY, 1st year juvenile, small adult, average adult, and larger adult size classes (Table 3; Figure 1). The male pentamodal distribution had modes repre-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 where: K = the number of parameters, χ2 = the Goodness-of-fit test statistic, wi = Akaike Weights, and ER = evidence ratio.

FemalesModel K χ2 P AIC ΔAIC wi ER AICc ΔAICc wi ERTrimodal 9 79.30 0.234 97.30 0.00 0.650 1.00 98.18 0.00 0.62 1.00Biomodal 6 86.87 0.145 98.87 1.57 0.300 2.19 99.27 1.09 0.36 1.73Pentamodal 15 73.31 0.224 103.31 6.01 0.030 20.18 105.72 7.54 0.01 43.45Tetramodal 12 80.23 0.147 104.23 6.93 0.020 31.90 105.77 7.59 0.01 44.52Hexamodal 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

MalesModel K χ2 P AIC ΔAIC wi ER AICc ΔAICc wi ERHexamodal 18 77.70 0.086 113.70 0.00 0.500 1.00 117.70 1.20 0.25 1.00Pentamodal 15 84.73 0.051 114.73 1.03 0.300 1.67 117.48 0.98 0.28 0.90Trimodal 9 97.50 0.020 115.50 1.80 0.200 2.46 116.50 0.00 0.46 0.55Tetramodal 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

Figure 1. Size frequency histograms, frequency plots, and cumulative frequency plots of snout-vent lengths (SVL) for female and male Eastern Massasaugas (Sistrurus catenatus) captured at Carlyle Lake, Clinton County, Illinois, during 1999–2010 field seasons.


the trimodal distribution had modes equating to neonate and YOY, juvenile, and adult size classes (Table 3; Figure 1).

Individual growth.—For instantaneous growth rates, we used data on 127 initial and last recapture pairs (Table 4). The best model included sex, initial SVL, and the interaction between sex and SVL (Table 4). This model explained an equal amount of the variance (r2 = 0.54) as the global model but with fewer parameters (Table 4). Overall, females grew at a faster rate than males (F1,127 = 11.79, P = 0.001) and smaller snakes grew at a faster rate than larger snakes (F1,127 = 122.75, P = 0.001). When accounting for initial SVL, we also found an interaction with sex, with females growing faster at smaller sizes and males growing faster at larger sizes (F1,127 = 10.62, P = 0.001; Figure 2).

Of the 12 candidate nonlinear growth models we evalu-ated, both the Gompertz and Logistic models accounting for sex had high support (Table 5). In addition, all models accounting for sex performed better than those in which sex was not a variable (Table 5). The overall models showed little differentiation except for the Tanaka non-asymptotic model, which performed the poorest (Table 5; Figure 3). Most models predict an asymptotic size (A∞) ranging from 67.7–71.7 cm SVL and characteristic growth rates (k) ranged from 1.04•10-3–2.23•10-3 (Table 6). When we examine the Gompertz and Logistic models by sex, females grew at a faster rate to a smaller A∞ and males grew at a slower rate to a larger A∞ (Figure 4). Estimates for A∞ ranged from 63.6–66.2 cm SVL for females and 72.3–78.0 cm SVL for males (Table 6). In addition, estimates of k

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.

Model K r2 P AIC ΔAIC wi ER AICc ΔAICc wi ERX+ISVL+X•ISVL 5 0.54 <0.001 78.15 0.00 0.86 1.00 78.65 0.00 0.87 1.00Global 9 0.54 <0.001 83.68 5.52 0.05 15.84 85.21 6.57 0.03 26.68ISVL 2 0.49 <0.001 83.77 5.61 0.05 16.57 83.86 5.22 0.06 13.57X+ISVL 4 0.49 <0.001 86.67 8.52 0.01 70.80 87.00 8.35 0.01 65.10S+ISVL 4 0.49 <0.001 87.49 9.34 0.01 106.86 87.82 9.18 0.01 98.25S+X+ISVL 5 0.50 <0.001 88.26 10.11 0.01 156.45 88.75 10.11 0.01 156.45S+ISVL+S•ISVL 5 0.50 <0.001 88.34 10.19 0.01 162.90 88.83 10.19 0.01 162.90S+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 ERGompertz-Sex 4 2709.96 <0.001 1011.90 0.00 0.47 1.00 1012.22 0.00 0.48 1.00Logistic-Sex 4 2721.03 <0.001 1012.41 0.52 0.36 1.30 1012.74 0.52 0.37 1.30Schunte (eqn 11)-Sex 6 2698.85 <0.001 1015.37 3.48 0.08 5.69 1016.07 3.85 0.07 6.86Richard's-Sex 6 2710.33 <0.001 1015.91 4.02 0.06 7.45 1016.61 4.39 0.05 8.98von Bertalanffy-Sex 4 2855.48 <0.001 1018.54 6.64 0.02 27.70 1018.87 6.64 0.02 27.70Gompertz 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


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).

DISCUSSION

Body size and structure.—Apart from Crotalus pricei, the genus Sistrurus contains the smallest species of North American rattlesnakes (Ernst, 1992; Ernst and Ernst, 2003). Pygmy Rattlesnakes (S. miliarius) seldom reach body sizes

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, y1 – 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 Estimatesvon Bertalanffy A∞ = 71.7 ± 1.45, k = 1.04•10-3 ± 1.03•10-4

Logistic A∞ = 67.7 ± 0.88, k = 2.23•10-3 ± 1.64•10-4

Gompertz A∞ = 69.2 ± 1.05, k = 1.59•10-3 ± 1.28•10-4

Richard's A∞ = 70.1 ± 1.58, k = 1.35•10-3 ± 2.96•10-4, m = 2.4 ± 3.18Schnute (eqn 9) K = 13.6 ± 30.89, a = 1.24•10-3 ± 2.81•10-4, b = 0.614 ± 0.53Schnute (eqn 11) A∞ = 70.1 ± 1.58, a = 1.35•10-3 ± 2.96•10-4, b = 0.413 ± 0.54Tanaka 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 Estimatesvon Bertalanffy A∞ = 66.2 ± 2.76, k = 1.51•10-3 ± 3.51•10-4

Logistic A∞ = 63.6 ± 1.83, k = 3.05•10-3 ± 5.17•10-4

Gompertz A∞ = 64.6 ± 2.12, k = 2.21•10-3 ± 4.20•10-4

Richard's A∞ = 64.6 ± 2.30, k = 2.20•10-3 ± 4.27•10-4, m = -8.60•108 ± 6.77•108

Schnute (eqn 11) A∞ = 64.2 ± 2.43, a = 2.52•10-3 ± 1.36•10-3, b = -0.388 ± 1.74Males Parameter Estimatesvon Bertalanffy A∞ = 78.0 ± 5.34, k = 7.57•10-4 ± 2.18•10-4

Logistic A∞ = 72.3 ± 2.71, k = 1.67•10-3 ± 3.12•10-4

Gompertz A∞ = 74.4 ± 3.47, k = 1.19•10-3 ± 2.58•10-4

Richard's A∞ = 74.3 ± 4.12, k = 1.19•10-3 ± 2.75•10-4, m = -5.20•108 ± 7.93•108

Schnute (eqn 11) A∞ = 73.4 ± 4.34, a = 1.38•10-3 ± 7.68•10-4, b = -0.400 ± 1.57

Figure 2. Scatter plot and regression lines for the relationship between instantaneous growth rate and initial snout-vent length (SVL) the Eastern Massasauga (Sistrurus catenatus) from 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.


>70.7 cm SVL whereas S. catenatus may reach upwards of 100 cm SVL (Klauber, 1972; Ernst, 1992; Ernst and Ernst, 2003). Our adults averaged 57.6 cm SVL and 62.3 cm TOL for females and 59.5 cm SVL and 66.4 cm TOL for males. Adult female and male S. catenatus from Michigan averaged 61.6 cm (range 48.3–72.7 cm) and 66.2 cm (range 49.9–82.9 cm) TOL, respectively (Hallock, 1991). Female and male S. tergeminus averaged 53.9 cm and 51.3 cm SVL in Missouri (Seigel, 1986), and 36.9 cm and 35.5 cm SVL in Colorado, respectively (Hobert et al., 2004). Snakes and lizards mainly reverse Bergmann’s rule, which is they are smaller at higher latitudes and larger at lower latitudes (Ashton and Feldman, 2003). Unfortunately, there are too few data on S. catenatus body sizes range-wide to make a determination.

Decomposing the size structure of the SSSP population of S. catenatus revealed at least two or three size classes. Although some snake populations have unimodal size distributions, such as Lapemis hardwickii (Hin et al., 1991), most have multimodal distributions (Parker and Plummer, 1987; Larsen and Gregory, 1989; Mertens, 1995; Manjarrez, 1998; Winne et al., 2005; Manjarrez et al., 2007). Popula-tion size distributions of S. tergeminus appeared bimodal and potentially trimodal in Missouri (Seigel, 1986), and distinctly bimodal and potentially tetramodal in Colorado (Hobert et al., 2004).

Distributions with fewer modes (bi- and tri-) better fit the female size data whereas tri-, penta-, or hexamodal distri-butions fit better for males. One possible reason we could not discriminate among higher multimodal distributions could be that our 5-cm size classes were too coarse. Sorting data into 2-cm or 1-cm size classes may have provided better model performance, but more size classes would require larger sample sizes (Ebert, 1999). Another possible explanation is the cut-off values we chose for size classes. Although we attempted to choose cut-off values that represented low points in the distribution, the values may have affected the fit of higher multimodal distributions. Future approaches may benefit from nonlinear regression methods that estimate parameters directly from the data. Differences in size structure between the sexes may also relate to variation in individual growth rates. More modes may be present in the male size distribution because of higher variance in individual growth rates. Broader confi-dence intervals on male growth curves compared to female curves (Fig. 4) suggest this may be the case.

Individual growth.—Many North American vipers double in length by their second or third year (Fitch, 1949; Heyrend and Call, 1951; Barbour, 1956; Gibbons, 1972; Klauber, 1972; Gannon and Secoy, 1984; Fitch, 1985; Martin, 1988). In comparison, S. catenatus at Carlyle Lake doubled in length within their first year and tripled in

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Figure 3. Composite nonlinear growth models for the Eastern Massasauga (Sistrurus catenatus) from 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.

Figure 4. Sex-specific Gompertz and Logistic growth models and associated 95% confidence intervals for the Eastern Massasauga (Sistrurus catenatus) from 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. The solid horizontal line represents the minimum size of maturity for males (49.7 cm SVL) and the dashed horizontal line represents the minimum size of maturity for females (48.4 cm SVL).


length by the end of their second year. This rapid growth affects demographics because individuals can potentially mate by the end of their first full summer (transition from age 1 to 2 yrs) and are certainly able to mate and reproduce by the end of their second full summer (transition from age 2 to 3 yrs). Younger maturation is often associated with high adult mortality in squamates (Shine and Charnov, 1992), but such rapid growth in S. catenatus may also be resource-based. Long-term studies of Liasis fuscus (water python) found faster growth rates in years with higher prey abun-dances, and this translated to larger adult sizes (Madsen and Shine, 2000). Along these lines, island populations of Natrix natrix (grass snake) with reduced prey abundances showed depressed growth rates and lower maximum body size compared to mainland populations (Madsen and Shine, 1993a). Although we lack data on annual variation in prey abundances to determine if such a “silver-spoon” effect exists in S. catenatus, the pattern could arise from either increased prey abundances or foraging opportunity. Because Carlyle Lake represents the southern range limit of S. catenatus, the active season is longer compared to more northern populations. The active season at Carlyle ranges from 206–246 days (Dreslik, unpubl. data) whereas the active season is 184 days in Ontario, Canada (Harvey and Weatherhead, 2006b). This additional time to acquire resources available to individuals at the southern range limit compared to more northern sites (16–60 days compared to

Canada) could result in faster growth and younger age of maturity. The active season length at Carlyle Lake can also vary annually by ~40 days (Dreslik, unpubl. data); thus, years with longer active seasons may also provide extended foraging opportunities and time for growth. Finally, young S. catenatus at Carlyle Lake may benefit from the ability to prey on nutrient-rich mammalian prey earlier in life compared to some other populations because southern short-tailed shrews, Blarina carolinensis, only overlap the distribution of S. catenatus in southern Illinois (Shepard et al., 2004).

Female S. catenatus grew more rapidly than males, which contrasts with growth studies on Agkistrodon contortrix and C. viridis (Heyrend and Call, 1951; Fitch, 1960; Macartney et al., 1990). This disparity was most evident at younger ages and a shift in energy allocation to oogenesis and embryogenesis explains the post-maturity decrease in female growth. In females, reproduction is more ener-getically costly due to significant resource allocation to offspring (Bonnet et al., 2002). On average, female S. cate-natus at Carlyle Lake lose 43.6% of their mass giving birth (Aldridge et al., 2008). Additionally, many female snakes alter activity during gestation, focusing on optimizing embryogenesis via thermoregulation and undergoing a facultative aphagia (Gregory et al., 1999; Lourdais et al., 2002). Given the large reproductive investment by female

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Figure 5. Fitted values for age-specific sexual size dimorphism indices for the best fit growth models and associated 95% confidence intervals for the Eastern Massasauga (Sistrurus catenatus) from Carlyle Lake, Clinton County, Illinois, captured during 1999–2010 field seasons. The solid horizontal line at 1.0 represents where both sexes are equal in size.

Figure 6. Scatter plot and regression lines for the relationship between tail length and snout-vent length for the Eastern Massasauga (Sistrurus catenatus) from Carlyle Lake, Clinton County, Illinois, captured during 1999–2010 field seasons. Data represent 672 individuals with an N = 336 for each females and males.


vipers and that energetic stores must be replenished before additional reproductive attempts (Madsen and Shine, 1999; Aubret et al., 2002; Bonnet et al., 2002), little surplus energy may be available to allocate to growth after sexual maturity. In males, slower growth rates after maturity may result from energetic costs and behavioral shifts associated with mate-searching. During the mating season, male S. catenatus alter their activity and make prolonged forays in search of females (Jellen et al., 2007). In some snake species, males will undergo facultative aphagia while searching for mates (O’Donnell et al., 2004). The causal mechanism driving the difference in pre-maturation growth rates between the sexes is unknown; however, males have significantly lower meta-bolic rates than females (Baker, 2009; Baker et al., in press).

Sexual size dimorphism.—Larger body size in males is the norm among viperids presumably because males compete for mates (Shine, 1978b; Shine, 1993, 1994); however, we found no overall SSD in SVL in S. catenatus. Populations of S. tergeminus in Missouri (Seigel, 1986) and Colorado (Hobert, 1997) also exhibited no SSD in SVL, but males were larger than females in S. miliarius (Shine, 1978b). The negligible SSD we observed may be due to a small net difference in the product of different selective pressures on the sexes. In snakes, as well as many other species, female body size relates positively to fecundity (Seigel and Ford, 1987) and thus, selection favors larger size (Shine, 1978b; Shine, 1994). Such a fecundity advantage occurs in S. cate-natus (Aldridge et al., 2008).

When males aggressively compete for females, selection 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, 1986; Schuett and Gillingham, 1989; Madsen and Shine, 1993b). Male mass in S. catenatus is positively related to the number of females acquired during the mating season (Jellen et al., 2007) and males aggressively compete for mates (Shepard et al., 2003). Although a small difference in the outcome of these selective pressures on the sexes may explain an overall lack of SSD in S. catenatus, the extent and direction of SSD clearly varies with age given the differences in growth rates and asymptotic sizes we observed. Exam-ining SSD within age classes provided a very different view of SSD compared to examining the population as a whole. At ages <5 yrs, females were larger whereas males were larger at ages >5 yrs (Fig. 5). Given the pattern of SSD was reversed in the youngest and oldest age classes and a large portion of the population was composed of individuals in the 4–6 yr age range where SSD is nearly absent (Fig. 1), it is not surprising that we failed to detect SSD when examining the population as a whole. Our results demonstrate how failing to consider age-specific SSD and the age structure of populations can obscure important biological information in a species.

Consistent with previous studies on S. catenatus (Bielema, 1973; Reinert, 1978; Hallock, 1991), we found that males have longer tails than females. Moreover, this SSD was present at birth, which is rare in snakes (Shine, 1978b; Fitch, 1981). Although housing the reproductive organs constrains minimum TL in males, and coelom space for developing embryos constrains maximum TL in females (Shine, 1993; King et al., 1999), selection on TL in snakes is complex as pressures may act in opposing directions (Arnold and Bennett, 1988; Madsen and Shine, 1993b; Luiselli, 1996; Shine and Shetty, 2001). For example, longer tails in Laticauda colubrina increased survival, but aver-age-size tails optimized swimming speed and increased mating success (Shine and Shetty, 2001). In male snakes, faster tail growth is likely related to sexual maturation and sexual selection may further drive TL because males use their tails during courtship and copulation, including S. catenatus (Chiszar et al., 1976; Jellen et al., 2007).

Although body size is one of the simplest measures to obtain in free-ranging populations, data are lacking for many snake species. The extent to which populations vary in size within widely distributed species is even less known. Such data are important for determining the large-scale evolu-tionary processes producing size variation both within and among species. Because individual growth rates and age of sexual maturity covary, and the latter is a determinant of generation time, individual growth rates are a predictor of a population’s adaptive capacity. Detailed range-wide life history studies are needed to improve our understanding of plasticity and adaptability within and between species.

ACKNOWLEDGEMENTS

We thank G. Tatham, J. Birdsell, and J. Bunnell of the Illinois Department of Natural Resources (IDNR), and J. Smothers and D. Baum of the U. S. Army Corps of Engi-neers, without whom we could not accomplish much of this work. We also thank M. Redmer of the Unites States Fish and Wildlife Service and S. Ballard, M. Kemper, and K. Boyles of the IDNR for all their assistance. We thank the many fieldworkers and volunteers who helped us capture snakes since 1999. We thank the Illinois Department of Natural Resources, U. S. Fish and Wildlife Service, U. S. Army Corps of Engineers, Illinois Tollway, and the Chicago Herpetological Society for funding this project. All animals in this study received humane treatment adhering to the “Guidelines for Use of Live Amphibians and Reptiles in Field Research” published by Society for the Study of Amphibians and Reptiles, American Society of Ichthyolo-gists and Herpetologists, and Herpetologists’ League. We conducted this study in accordance under the University of Illinois IACUC protocol #08019 and Illinois Department of Natural Resources T&E species permit #05-11S.


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Edited by: Michael J. Dreslik • William K. Hayes • …dshepard/pdfs/Dreslik et al BodySize BOR...68 Dreslik et al. 2017 analyzed individual growth in two ways. First, we exam-ined

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