U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2018–1114

Findings from a Preliminary Investigation of the Effects of 

Aquatic Habitat (Water) Availability on Giant Gartersnake 

(Thamnophis gigas) Demography and Reproduction in the 

Sacramento Valley, California, 2014–17
Cover image: Photograph showing a female giant gartersnake (Thamnophis gigas), Sacramento Valley, California. Photograph taken 

May 14th, 2018 by Zarina Sheikh, U.S. Geological Survey, Western Ecological Research Center.
U.S. Department of the Interior

U.S. Geological Survey

Findings from a Preliminary Investigation of the 

Effects of Aquatic Habitat (Water) Availability on 

Giant Gartersnake (Thamnophis gigas) Demography 

and Reproduction in the Sacramento Valley, 

California, 2014–17

By Jonathan P. Rose, Julia S.M. Ersan, Gabriel A. Reyes, K. Benjamin Gustafson, Alexandria M. Fulton, 

Kristen J. Fouts, Raymund F. Wack, Glenn D. Wylie, Michael L. Casazza, and Brian J. Halstead

Prepared in cooperation with the California Department of Water Resources

Open-File Report 2018–1114
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2018

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Suggested citation:

Rose, J.P., Ersan, J.S.M., Reyes, G.A., Gustafson, K.B., Fulton, A.M., Fouts, K.J., Wack, R.F., Wylie, G.D., 

Casazza, M.L., and Halstead, B.J., 2018, Findings from a preliminary investigation of the effects of aquatic 

habitat (water) availability on giant gartersnake (Thamnophis gigas) demography in the Sacramento Valley, 

California, 2014–17: U.S. Geological Survey Open-File Report 2018–1114, 48 p., 

https://doi.org/10.3133/ofr20181114.

ISSN 2331-1258 (online)
iii

Acknowledgments

We thank the California Department of Water Resources for supporting this project. We also thank several 

cooperative landowners for access to the study sites. We also thank the giant gartersnake Technical 

Review Committee for providing feedback and guidance for this study, and two anonymous reviewers for 

their thorough reviews of this report. We thank Lisa Parker for administrative support. This study would not 

have been possible without the work of numerous biological technicians who collected field data on giant 

gartersnakes for the U.S. Geological Survey for many years. This work was performed under Institutional 

Animal Care and Use Committee protocol WERC-2014-01 and as stipulated in California Department of 

Fish and Wildlife Scientific Collecting Permit 10779 and U.S. Fish and Wildlife Service Recovery Permits 

TE-020548-5 and TE-157216-2.
iv

Contents

Acknowledgments .........................................................................................................................................iii

Abstract ......................................................................................................................................................... 1

Background ................................................................................................................................................... 2

Purpose and Scope ....................................................................................................................................... 3

Study Area..................................................................................................................................................... 3

Goals and Objectives..................................................................................................................................... 5

Methods......................................................................................................................................................... 6

Data Collection........................................................................................................................................... 6

Reproduction.............................................................................................................................................. 9

Health Assessments................................................................................................................................... 9

Abundance, Population Growth, Capture Probability, and Survival Estimation .......................................... 9

Growth and Size Distribution.................................................................................................................... 10

Bayesian Model Fitting............................................................................................................................. 11

Results......................................................................................................................................................... 11

Capture Summary .................................................................................................................................... 11

Reproductive Exams ................................................................................................................................ 12

Abundance and Population Growth Estimates ......................................................................................... 16

Annual Apparent Survival Estimates ........................................................................................................ 24

Rice Growing Trends................................................................................................................................ 25

Size-Distribution of Captured Snakes....................................................................................................... 27

Growth...................................................................................................................................................... 30

Health Assessments................................................................................................................................. 32

Discussion ................................................................................................................................................... 37

Summary and Conclusions.......................................................................................................................... 41

References Cited......................................................................................................................................... 42

Glossary ...................................................................................................................................................... 44

Appendix A. Details of Bayesian Models ..................................................................................................... 46
v

Figures

Figure 1. Location of eight sites sampled for giant gartersnakes in the Sacramento Valley, 

California, 2014–17........................................................................................................................................ 4

Figure 2. The probability a female giant gartersnake was gravid (a) and the observed litter size of 

gravid females (b) versus female snout-vent length (SVL) .......................................................................... 14

Figure 3. Site-specific estimates for the mean probability a female giant gartersnake was gravid (a) 

and mean litter size (b) for each site in the Sacramento Valley, California .................................................. 15

Figure 4. Estimated abundance (N) of giant gartersnakes at each site in the Sacramento Valley, 

California, from 2014–17 ............................................................................................................................. 18

Figure 5. Estimated population growth rate (λ) of giant gartersnakes for 2014–15 and 2016–17 

from eight sites in the Sacramento Valley, California................................................................................... 19

Figure 6. Estimated daily capture probability (p) of giant gartersnakes from 2014–17 for 

eight sites in the Sacramento Valley, California........................................................................................... 22

Figure 7. Estimated annual capture probability (p*) of giant gartersnakes from 2014–17 for 

eight sites in the Sacramento Valley, California........................................................................................... 23

Figure 8. Estimated annual apparent survival (φ) of giant gartersnakes from 2014–17 for 

eight sites in the Sacramento Valley, California........................................................................................... 25

Figure 9. Proportional area of rice growing from 2014–17 at eight sites in the Sacramento Valley, 

California ..................................................................................................................................................... 27

Figure 10. Distribution of snout-vent lengths (SVL) of captured giant gartersnakes by site for 

eight sites in the Sacramento Valley, California, from 2014–17................................................................... 28

Figure 11. Distribution of the mass (g) of captured giant gartersnakes from 

eight sites in the Sacramento Valley, California, from 2014–17................................................................... 29

Tables

Table 1. Sampling effort and captures of giant gartersnakes in the Sacramento Valley, California, 

by site from 2014–17 ..................................................................................................................................... 7

Table 2. Frequency of year-over-year recaptures of giant gartersnakes by site in the 

Sacramento Valley, California, from 2014–17 ............................................................................................. 12

Table 3. Summary of reproductive data of giant gartersnakes collected from the Sacramento Valley, 

California, 2014–17, by site and year .......................................................................................................... 12

Table 4. Giant gartersnake abundance estimates (N) by site and year for eight study sites in the 

Sacramento Valley, California, 2014–17...................................................................................................... 16

Table 5. Giant gartersnake daily capture probability estimates (p) by site and year for 

eight study sites in the Sacramento Valley, California, 2014–17 ................................................................. 20

Table 6. Giant gartersnake annual capture probability estimates (p*) by site and year for 

eight study sites in the Sacramento Valley, California, 2014–17 ................................................................. 21

Table 7. Giant gartersnake annual apparent survival estimates (φ) by site and year for 

eight study sites in the Sacramento Valley, California, 2014–17 ................................................................. 24
vi

Table 8. Extent of rice growing and other land cover types within a 1-km buffer of 

eight study sites in the Sacramento Valley, California, from 2014–17 ......................................................... 26

Table 9. Parameter estimates from a von Bertalanffy growth model fit to growth data from giant 

gartersnakes at eight study sites sampled in the Sacramento Valley, 2014–17 .......................................... 30

Table 10. Growth rates of giant gartersnakes from 2014–17 summarized by site, sex, and year, 

for eight sites in the Sacramento Valley, California...................................................................................... 31

Table 11. Summary of blood cell counts, hemoglobin, and hematocrit of female giant gartersnakes of 

reproductive size by site and year for eight sites in the Sacramento Valley, California, 2014–17................ 33

Table 12. Summary of blood solutes of female giant gartersnakes of reproductive size by site and year for 

eight sites in the Sacramento Valley, California, 2014–17........................................................................... 35

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

millimeter (mm) 0.0393701 inch (in.)

kilometer (km) 0.621371 mile (mi)

Mass

milligram (mg) 0.000035 ounce, avoirdupois (oz)

gram (g) 0.035274 ounce, avoirdupois (oz)

Volume

milliliter (mL) 0.0338 ounce, fluid (fl. oz)

liter (L) 1.05669 quart (qt)

Abbreviations

AST aspartate aminotransferase

CMR capture-mark-recapture

CRI credible interval

JAGS Just Another Gibbs Sampler

MCMC Markov Chain Monte Carlo

PIT passive integrated transponder

Pr probability

SD standard deviation

SVL snout-vent length

USGS U.S. Geological Survey
1

Findings from a Preliminary Investigation of the 

Effects of Aquatic Habitat (Water) Availability on 

Giant Gartersnake (Thamnophis gigas) Demography 

and Reproduction in the Sacramento Valley, 

California, 2014–17

By Jonathan P. Rose,1 Julia S.M. Ersan,

1 Gabriel A. Reyes,1 K. Benjamin Gustafson,1

Alexandria M. Fulton,

1 Kristen J. Fouts,1 Raymund F. Wack,2 Glenn D. Wylie,1 Michael L. Casazza,1 and 

Brian J. Halstead1

Abstract

The giant gartersnake (Thamnophis gigas) is a semi-aquatic species of snake precinctive

to the Central Valley of California. Because the Central Valley has experienced a substantial loss 

of wetland habitat, giant gartersnake populations are largely found in aquatic habitats associated 

with rice agriculture. In dry years, less water may be available for rice agriculture, resulting in 

less aquatic habitat, which could have cascading effects on giant gartersnake populations. We 

present 2 years of data intended to examine how the demography of giant gartersnakes is 

affected by the availability of aquatic habitat on the landscape (2016–17), along with 2 years of 

(sparse) preliminary data (2014–15) collected as part of an earlier radio-telemetry study on giant 

gartersnake movement behavior. We sampled agricultural canals near rice fields for giant 

gartersnakes at 8 sites distributed throughout the Sacramento Valley. Five sites were sampled 

from 2014–17, and 3 sites were sampled from 2015–17. In total, we made 2,995 captures of 

1,011 snakes from 2014–17. We used these capture data to fit a multi-site Jolly-Seber model to 

estimate the abundance of giant gartersnakes as well as the daily and annual probability of 

capture at each site. We used remotely sensed Landsat data to characterize the extent of flooded 

rice fields surrounding each site in each year. In addition, we collected 175 females from 2014–

17 and delivered them to the Sacramento Zoo for health assessments and reproductive exams. 

 

1

U.S. Geological Survey

2

Sacramento Zoo; University of California, Davis
2

The abundance of giant gartersnakes varied among sites, and abundance estimates were 

more precise in 2016 and 2017 when sampling effort was greatest. The probability of a giant 

gartersnake being captured at least once in a year was higher in 2016 and 2017 than 2014 and 

2015, and recaptures of snakes marked the previous year were highest in 2016 and 2017 as well. 

Mean annual apparent survival was estimated to be 0.40 but varied among sites from a low of 

0.14 to a high of 0.63. Five sites had diverse size distributions that included abundant sub-adult 

and large adult female snakes. One site had a truncated size distribution with few large adult 

female snakes, and 2 sites had mostly large adult-sized snakes and few small individuals. Both 

the probability a female was gravid and a female’s litter size were positively related to the 

female’s snout-vent length. Somatic growth rates varied more among years than among sites, 

and females grew faster (in millimeters per day) than male snakes. 

The proportion of the landscape around each site under active rice cultivation fluctuated

over time (generally between 60–90 percent of the landscape was active rice growing, although 

this proportion was lower for some sites in some years), and variation in rice growing was 

asynchronous among sites. This study demonstrates that intensive demographic sampling enables 

estimation of several key demographic variables at each study site. Continued sampling would 

allow for investigating potential relationships between the amount of rice growing at a site and 

demographic parameters such as growth, survival, and reproduction.

Background

The California Department of Water Resources (DWR) has a mission to manage and 

protect California’s water resources for both human and natural environments. Supplying 

sufficient water to meet the demands of urban, residential, agricultural, and industrial users is a 

challenge in California because much of the state has a Mediterranean or desert climate in which 

precipitation is limited through much of the year. In the Sacramento Valley (northern Central 

Valley), the availability of water is a critical component of both agricultural production and the 

preservation of wildlife species that are dependent on wetland habitats. The giant gartersnake 

(Thamnophis gigas) is a semi-aquatic species of snake precinctive to the Central Valley of 

California, where it inhabits wetlands, marshes, and canals associated with rice agriculture 

(Halstead and others, 2014). Giant gartersnakes are dependent on wetland habitat throughout 

their spring and summer active season (Halstead and others, 2015b). The Central Valley has lost 

over 90 percent of its wetland habitat since the 19th century (Frayer and others, 1989), and 

consequently, the giant gartersnake is now listed as a threatened species under the State (Fish and 

Game Commission, 1971) and Federal (U.S. Fish and Wildlife Service, 1993) Endangered 

Species Acts. Giant gartersnakes now occur primarily in habitats associated with rice agriculture 

(Halstead and others, 2010) because the process of growing rice creates flooded fields and canals 

that approximate the marsh habitat needed by giant gartersnakes. 
3

Activities that influence the availability of water for rice agriculture have the potential to 

affect threatened giant gartersnakes (Reyes and others, 2017). Rice farmers can respond to dry 

years by leaving some rice fields idle and transferring their water to other users downstream. 

This helps ensure a reliable water supply for other users, but leaving rice fields idle reduces the 

amount of aquatic habitat available for wildlife on the landscape. It remains unknown how such 

water transfers and annual changes in the extent of wetland habitat affect population growth 

rates and demographic vital rates such as reproduction, survival, and somatic growth in giant 

gartersnake populations. This study seeks to understand how the availability of aquatic habitat 

influences the health of individual giant gartersnakes and the demography of giant gartersnake 

populations. 

Purpose and Scope

This report summarizes the methods and findings of a study conducted by the U.S. 

Geological Survey, in cooperation with the California Department of Water Resources, to 

investigate the effect of the availability of aquatic habitat on the demography of giant 

gartersnakes inhabiting rice growing areas in the Sacramento Valley, California. The report 

presents estimates of the abundance, somatic growth, fecundity, and survival of giant 

gartersnakes from eight sites in the Sacramento Valley studied from 2014–17. The report also 

presents data on the area of rice growing at each of the eight sites from 2014–17. Throughout this 

report, words defined in the glossary are presented in bold font upon their first use.

Study Area

The data used in this report come from eight study sites distributed throughout the 

Sacramento Valley of California in Butte, Colusa, Glenn, Sacramento, Sutter, and Yolo Counties 

(fig. 1). 
4

Figure 1. Location of eight sites sampled for giant gartersnakes in the Sacramento Valley, California, 

2014–17. Each circle represents the average Universal Transverse Mercator (UTM) coordinates for the 

traps located at that site.
5

Goals and Objectives

The goal of this study is to measure how the availability of aquatic habitat (chiefly rice 

fields and their associated canals) affects the demography of giant gartersnake populations and 

the reproductive biology of female giant gartersnakes. To meet this goal, our objectives are to 

measure the following aspects of the biology of giant gartersnakes:

1. Reproductive traits of female giant gartersnakes.

2. The survival rate of giant gartersnakes.

3. The abundance of giant gartersnakes.

4. The growth rate of giant gartersnake populations.

5. The somatic growth rate (growth in body length) of individual giant gartersnakes.

6. The health of individual giant gartersnakes.

In addition, we measure the availability of aquatic habitat at each site, in each year, by 

observing the proportion of the landscape undergoing active rice cultivation. At our study sites, 

rice fields and their associated canals provide the only aquatic habitat for giant gartersnakes. This 

information will be vital for modeling the effect of water transfers and rice idling on the 

demography of giant gartersnake populations. The demographic information collected in this 

study could be used to project the effect that water management activities will have on the 

persistence of giant gartersnake populations in the Sacramento Valley. This report presents a 

summary of the data collected from 8 populations of giant gartersnakes inhabiting canals 

associated with rice agriculture from 2014–17. This period includes 2 years of preliminary 

sampling as part of an earlier radio-telemetry study (2014–15; Reyes and others, 2017) and the 

first 2 years (2016–17) of a planned 6-year intensive demographic study. For reasons out of our 

control, the demographic study was discontinued prior to field sampling in early 2018, and this 

report is intended to present the preliminary results from the first 2 years of data collection. We 

present estimates of demographic parameters and the amount of rice growing at each site for the 

period from 2014–17. Because of the short time series of data available, and the limited sampling 

that took place in 2014 and 2015, we refrain from using the proportion of rice grown at a site 

each year as a covariate of demographic and health parameters. Instead, we present the 

proportion of rice grown at each site as an indication of the variation among sites over time that 

we might expect to observe in a long-term study. If more data are collected in the coming years, 

we could obtain more precise estimates of the demographic parameters of interest and investigate 

how the variation in rice growing over time affects giant gartersnake health, reproduction, 

somatic growth, survival, and population growth.
6

Methods

Data Collection

We sampled 5 sites for giant gartersnakes from 2014–17, and 3 sites from 2015–17 

(fig. 1; table 1). Each site comprises canals surrounded by agricultural fields. We captured giant 

gartersnakes by setting aquatic funnel traps along the edges of canals and emergent vegetation. 

We also opportunistically captured snakes by hand while setting and checking traps. We did not 

bait traps, and traps were placed so that the surface of the water was approximately at the 

midpoint of the funnel opening (Casazza and others, 2000; Halstead and others, 2013). In 2014 

and 2015, we set 100–200 traps per site, and in 2016 and 2017, we set a total of 250 traps per site 

each year. Sites were trapped more intensively in 2016 and 2017 because the objective of the 

study was to estimate abundance and survival using capture-mark-recapture data. The traplines also covered a larger area at each site in the latter 2 years to help reduce the influence of 

emigration on capture-mark-recapture estimates of apparent survival. Sites were sampled less 

intensively in 2014 and 2015 because the primary objective was to capture snakes to implant 

radio-transmitters as part of a study of giant gartersnake spatial ecology and movement (Reyes 

and others, 2017). We measured snout-vent length (SVL) by straightening snakes on a meter 

stick. We weighed snakes using a Pesola® spring scale. We determined the sex of all captured 

snakes through cloacal probing. We marked each snake by using a medical cautery device to 

brand a unique combination of ventral scales that corresponds to a numerical identifier (Winne 

and others, 2006) and also injected Passive Integrated Transponders (PIT) subcutaneously into 

snakes >30 grams (g) mass using a syringe injector.

From 2014–17, we also collected females from each site and took them to the 

Sacramento Zoo, where trained veterinarians examined their reproductive status using Xradiography and gave each snake a full health assessment. Snakes were removed from the wild 

for up to 7 days and were returned to their site of capture after the completion of health 

assessments and X-rays. A veterinarian drew 1.0 milliliters (mL) of blood from the ventral tail 

vein for analysis of plasma biochemistry and hematological parameters. These parameters can be 

evaluated to see if snakes exhibit signs of stress or infection (Wack and others, 2012). 

Hematological parameters included red blood cell count, hemoglobin (grams per deciliter), 

packed cell volume (the percent of total blood volume made up of red blood cells), and plasma 

protein (grams per deciliter). We also counted white blood cells and further classified white 

blood cells into one of 6 types: azurophils, basophils, eosinophils, heterophils, lymphocytes, and 

monocytes. Elevated white blood cell counts can indicate the presence of infection, whereas low 

white blood cell counts could indicate a weakened immune system (Reyes and others, 2017). To 

determine pack cell volume, we centrifuged microhematocrit tubes for 10 minutes and measured 

the proportion of the sample comprising red blood cells. We employed a modified 

azidemethemoglobulin reaction, following Wack and others (2012), to measure hemoglobin. We 

used a handheld refractometer (JorVet model J351, Jorgensen Laboratories Inc., Loveland, 

Colorado) to measure plasma protein. Within 3 hours of blood collection, we used the unipette 

method and a hemocytometer to manually count red blood cells and white blood cells (Campbell 

and Ellis, 2007). To measure the proportion of white blood cell types, we stained a blood smear 

and counted at least 100 white blood cells under 1,000× magnification. 
7

Table 1. Sampling effort and captures of giant gartersnakes in the Sacramento Valley, California, by site 

from 2014–17. 

[-- indicates a site was not sampled]

Year Site Start date End date Days Trap-days Snakes Captures

2014 1 5/14/2014 6/5/2014 21 1,998 41 76

2 6/27/2014 9/2/2014 64 5,747 15 20

3 5/13/2014 7/16/2014 59 5,599 27 36

4 -- -- -- -- -- --

5 6/13/2014 7/23/2014 37 3,800 10 13

6 -- -- -- -- -- --

7 -- -- -- -- -- --

8 5/19/2014 6/11/2014 20 1,996 31 40

2015 1 4/21/2015 7/10/2015 75 7,521 25 41

2 4/22/2015 4/23/2015 1 100 7 8

3 4/28/2015 9/1/2015 121 4,020 23 37

4 5/1/2015 6/21/2015 48 4,893 20 36

5 4/26/2015 8/28/2015 119 9,438 11 15

6 5/30/2015 8/31/2015 90 6,900 31 76

7 4/24/2015 8/24/2015 117 9,474 15 25

8 4/23/2015 4/27/2015 4 400 6 6

2016 1 4/24/2016 7/18/2016 83 17,613 73 189

2 4/28/2016 7/22/2016 83 19,302 88 267

3 4/25/2016 7/17/2016 81 19,862 75 216

4 5/6/2016 7/16/2016 69 15,438 55 123

5 5/4/2016 8/6/2016 92 17,458 14 29

6 4/27/2016 7/26/2016 88 18,317 81 245

7 5/2/2016 7/27/2016 84 13,811 27 60

8 4/19/2016 7/16/2016 86 19,700 127 438

2017 1 4/25/2017 7/15/2017 77 16,307 68 203

2 4/26/2017 7/15/2017 76 17,485 59 124

3 5/2/2017 7/15/2017 70 15,605 55 106

4 6/3/2017 7/22/2017 46 10,275 43 130

5 5/10/2017 7/15/2017 62 13,092 22 43

6 5/8/2017 7/15/2017 64 13,616 41 73

7 5/31/2017 7/22/2017 50 11,359 12 26

8 4/22/2017 7/15/2017 80 19,487 123 294
8

We measured a suite of plasma biochemistry parameters, including AST (aspartate 

aminotransferase; international units per liter [IU/L]), creatinine kinase (IU/L), uric acid 

(milligrams per deciliter [mg/dL]), glucose (mg/dL), calcium (mg/dL), phosphorus (mg/dL), total 

protein (grams per deciliter [g/dL]), albumin (g/dL), globulin (g/dL), potassium (millimoles per 

deciliter [mmol/dL]), and sodium (mmol/dL). To measure plasma biochemistry parameters, we 

began by centrifuging a heparinized blood microtainer tube for 10 minutes. We then used a 

VetScan analyzer (Abaxis North America, Union City, California) with an avian/reptile specific 

rotor (Avian/Reptilian Profile Plus) to measure these parameters. All plasma biochemistry 

analysis was performed within 30 minutes of the collection of the blood sample. High 

concentrations of plasma solutes such as phosphorus, sodium, and potassium can indicate signs 

of dehydration. Elevated concentrations of AST might be a sign that organs are diseased or 

damaged, and elevated creatinine kinase in blood plasma is a potential sign of damage to muscle 

tissue (Reyes and others, 2017).

Each female’s reproductive status was determined by examining abdominal X-rays for 

the presence of enlarged eggs or developing embryos. If a female was gravid, the number of 

enlarged, ovulated eggs or developing embryos was counted, and we use these counts as an 

estimate of the litter size or number of offspring a female was carrying. From 2014–16, 

examined females were primarily individuals that had radio transmitters implanted for a previous 

study (Reyes and others, 2017). In 2017, we chose females ≥500 millimeters (mm) SVL to 

receive health assessments and reproductive exams, with the exception of one female that 

measured 435 mm SVL. We did not selectively choose to examine females that appeared gravid; 

rather, we sought to examine a representative sample of females that were within the size range 

for reproductive maturity (Rose and others, 2018c). This sample allows the estimation of the 

proportion of females that are gravid that year and how body size (SVL) affects the probability a 

female is gravid.

To characterize the landscape at each site, we used land cover data within a 1-kilometer

(km) buffer of our 2017 trap locations. We decided on a 1-km buffer based on analysis of radiotelemetry data from these sites using the methods and data reported in Reyes and others (2017). 

We calculated the mean (across all snakes) of the maximum distance between pairs of 

observations of the same radio-tracked individual in the same calendar year, which was 

approximately 1 km. In other words, on average, the largest distance between locations of a 

radio-tracked snake within one year was approximately 1 km; therefore, snakes captured in traps 

would be expected to primarily inhabit the area within 1 km of the traplines. We classified 

remotely sensed Landsat 8 imagery into 3 different agricultural land cover types: active rice 

growing, fallow fields, and non-rice crops. All other land covers were classified as “other,” and 

no other wetlands were found within the 1-km buffer of the study sites. For more details on the 

method used to identify rice and other land cover types, please see the description in Reyes and 

others (2017) and Zhong and others (2016). Reyes and others (2017) compared the 2015 land 

cover classification from Landsat 8 imagery to the United States Department of Agriculture 

(USDA) “Crop Data Layer” from 2015 and found that the Landsat based tool classified rice with 

96.3-percent accuracy. We also qualitatively compared the land cover classifications from 

Landsat 8 imagery in 2017 to ground-based surveys of fields and found good agreement at each 

site. For 2017, we were able to estimate the extent of active rice growing but could not estimate 

other land cover types.
9

Reproduction

We tested for fixed effects of female SVL and the date of examination (linear and 

quadratic effects of day of year) on the probability a female was gravid using a Bayesian 

regression model with a binomial response (1 if gravid, 0 if not) and a random effect of site on 

the intercept. We tested for a fixed effect of female SVL on litter size using a Bayesian Poisson 

regression model (similar to a generalized linear model with a log link for the linear predictor) 

with litter size as the response and a random effect of site on the intercept. The random effects of 

site on the intercept for these models allow the response (either fecundity or the probability a 

female is gravid) to vary among sites, while still sharing information among sites to better 

estimate the relationship between the response and snake size. For example, we might expect the 

proportion of females that are gravid to vary among sites, owing to differences in the 

environment, and we want our model to account for that variation. We standardized female SVL 

and examination date to have a mean equal to zero and standard deviation of one and used these 

standardized values as covariates in our regression models. Prior probability distributions used 

in our Bayesian models of reproduction and fecundity are described in tables A1 and A2.

Health Assessments

We report summary statistics for several blood and plasma biochemistry parameters from 

159 snakes that received health assessments from 2014–17. We qualitatively compare the values 

of these parameters to the baseline values from a sample of “apparently healthy” giant 

gartersnakes presented by Wack and others (2012). Because of the short time series of this study 

and the small sample sizes from each site from 2014–16, we do not test for or report trends in 

blood and plasma biochemistry parameters. 

Abundance, Population Growth, Capture Probability, and Survival Estimation

We used the capture data from 2014–17 to estimate the abundance (N), daily capture 

probability (p), and the annual apparent survival rate (φ) of snakes at each study site in each 

year. The apparent survival rate is the probability of survival multiplied by the probability of an 

individual remaining at the site (1 – the probability of emigration). We fit a Bayesian multi-site 

Jolly-Seber capture-mark-recapture (CMR) model, based on models presented by Kéry and 

Schaub (2012) and Kéry and Royle (2016), to our capture data (see table A3 for details on prior 

distributions). We included random effects of site and year (nested within site) on capture 

probability and annual survival. These random effects account for variation among sites or 

among years that is not accounted for by other means. For example, we might expect survival to 

vary over time owing to fluctuations in environmental conditions, and the temporal random 

effect allows us to estimate that variation. We calculated the probability an individual was 

captured at least once in a year (“annual capture probability,” pp̂

tt

∗) using eq. 1, where pp̂

tt is the 

estimated daily capture probability in year t, and kt is the number of days a site was sampled in 

year t.

pp̂

tt

∗ = 1 − (1 − pp̂

tt)kktt (1)
10

Based on our annual estimates of abundance for each site, we also estimate the 

population growth rate for year t, λt, based on the estimated abundances in year t (NN�tt) and year 

t+1 (NN�tt+1) (eq. 2).

λλ̂

tt = NN�tt+1

NN�tt

(2)

Because our sampling intensity (number of trap-days) and the area sampled increased in 

2016 and 2017 compared to 2014 and 2015, we only present estimates of λ from 2014–15 and 

2016–17. Estimates of λ from 2015–16 are likely to be influenced by the apparent “immigration” 

of new individuals to the sampled population owing to the larger area sampled and longer 

sampling period in 2016 compared to 2015.

Growth and Size Distribution

We fit a Bayesian von Bertalanffy growth model to the SVL of snakes captured and 

measured two or more times. We followed the methods used in a previous study of giant 

gartersnake growth (Rose and others, 2018b) and included fixed effects of sex on asymptotic 

size (SVL; L∞) and the von Bertalanffy growth coefficient (k). The model also included random 

effects of site and year on k and a random effect of site on L∞. We use this model to estimate if 

sites differ in the average asymptotic SVL of snakes, and in k, the rate at which snakes grow to 

approach their asymptotic SVL. We used the same model and priors as in a previous study that 

fit a von Bertalanffy growth model to giant gartersnake growth data (table A4; Rose and others, 

2018b). 

As another method for characterizing variation in growth between the sexes and among 

sites and years, we calculated the daily somatic growth rate (mm/day) for snakes captured and 

measured in consecutive years. We averaged growth over the entire interval between captures, 

including the inactive season, and thus our growth rate estimates are averaged over both the 

inactive and active seasons. We included fixed effects of sex and SVL (at first capture), an 

interactive effect of sex and SVL on growth rate, and random effects of individual, site, and year 

to account for repeated measurements of the same individual and potential correlations of growth 

parameters within sites and years. We standardized SVL as described above and used the 

standardized SVL value as a covariate of growth rate. For our analysis of growth, and the 

distribution of snake sizes (SVL and mass) by site, we averaged the size measurements of snakes 

captured multiple times within the same year. We included all growth measurements, including 

apparent negative growth (shrinkage) between captures. 
11

Bayesian Model Fitting

We use Bayesian analysis in this study because of the flexibility it provides in designing 

statistical models tailored to our data. We fit all Bayesian models using the software Just Another 

Gibbs Sampler (JAGS; Plummer, 2003) to sample the posterior distribution using Markov 

Chain Monte Carlo (MCMC) algorithms. We accessed JAGS through the statistical program R 

(R Core Team, 2016) using the “runjags” package (Denwood, 2016). We used uninformative 

priors for parameters on the probability scale (uniform probability of any value between 0 and 

1) and weakly informative priors for all other parameters (see tables A1–A4 for prior 

distributions). We used half-Cauchy priors for the standard deviation of all random effects 

(Gelman, 2006; Polson and Scott, 2012). We ran each model on five independent chains (except 

for the CMR model, which was run on four chains) for enough iterations that the posterior 

distributions appeared to have converged on a stationary distribution (see appendix A for 

details). We assessed the convergence of models by inspecting trace plots and calculating the 

Brooks-Gelman convergence diagnostic (Brooks and Gelman, 1998). No models showed 

evidence of poor convergence. For model-estimated parameters, we present the posterior mean 

and symmetric 95-percent credible interval (CRI), unless specified otherwise. To measure the 

strength of evidence for a parameter having a value above or below a threshold (for example, λ > 

1 for population growth, or β < 0 for a slope in a regression model), we report the posterior 

probability (Pr[x] = probability that x is true) that the parameter is beyond that threshold.

Results

Capture Summary

We made 2,995 total captures of 1,011 individual snakes from 2014–17. In 2016, we 

captured 540 individual snakes a total of 1,567 times, whereas in 2017, we captured 423 snakes a 

total of 999 times. Trapping effort was highest in 2016 and lowest in 2014, and we captured 

fewer snakes in 2014 and 2015 when trapping effort was relatively low. Trapping effort in 2017 

was lower than 2016 because delayed flooding of canals in 2017 meant trapping began later at 

most sites compared to 2016 (table 1).

The frequency of year-over-year recaptures (a snake captured in more than 1 year) varied 

among sites (table 2). Of the 1,011 snakes captured at all sites, 816 were captured in 1 year, 178 

were captured in 2 years, 15 were captured in 3 years, and 2 individuals were captured all 4 

years. The recapture rate was lowest at site 4, which was trapped for 3 years (2015–17). At site 4, 

94.6 percent of snakes were only captured in 1 year: the remaining 5.4 percent of snakes were 

captured in 2 years. Year-over-year recaptures were more frequent at site 5 (31.0 percent of 

snakes captured in more than 1 year) and site 6 (29.8 percent of snakes captured in more than 1 

year). At the remaining sites, 15.8–22.7 percent of snakes were captured in more than 1 year 

(table 2). The recapture rate of snakes in the year following their initial capture was higher in 

2016 and 2017 than 2015. Of 124 snakes first captured in 2014, 18 were recaptured in 2015 (14.5 

percent). Of 120 snakes first captured in 2015, 42 were recaptured in 2016 (35.0 percent). Of 480 

snakes first captured in 2016, 118 were recaptured in 2017 (24.6 percent). Two snakes moved 

from site 3 to site 5 between 2016 and 2017.
12

Table 2. Frequency of year-over-year recaptures of giant gartersnakes by site in the Sacramento Valley, 

California, from 2014–17. 

[Note that sites 4, 6, and 7 were only sampled in 3 years; all other sites were sampled in 4 years. -- indicates that 

captures in 4 years were not possible at these sites]

# of Individuals captured in

Site Years sampled 1 year 2 years 3 years 4 years Total

1 4 127 32 4 1 164

2 4 123 23 0 0 146

3 4 122 25 2 0 149

4 3 106 6 0 -- 112

5 4 29 9 4 0 42

6 3 80 29 5 -- 114

7 3 34 10 0 -- 44

8 4 195 44 0 1 240

All 816 178 15 2 1,011

Reproductive Exams

We examined the reproductive status of 21 females in 2014, 37 females in 2015, 37 

females in 2016, and 80 females in 2017 (table 3). Because the reproductive data for 2014–16 

have been summarized previously (Rose and others, 2018a,c), we focus here on results from 

2017, the first full year in which reproductive data were collected for the current study. In 2017, 

we examined females from May 15 to July 11, and no females appeared to have given birth prior 

to examination. In 2017, 51 of 80 females were gravid. The smallest gravid female was an 

individual of 500 mm SVL collected at site 4 in 2017; the smallest female examined was 435 

mm SVL and was not gravid. Over 70 percent of examined females were gravid at sites 1, 2, 4, 

and 8 in 2017, whereas fewer than half of examined females were gravid at site 2 (table 3). The 

size of examined females varied among sites. On average, examined females were smallest at 

site 4 and largest at site 5 (table 3).

Both the probability a female was gravid (βgSVL = 1.604, 95-percent CRI = 0.832–2.479) 

and the estimated litter size (βfSVL = 0.299, 0.203–0.394) were positively related to a female’s 

SVL (fig. 2). Based on our models, a female of average SVL (718 mm) would have a 68 percent 

chance of being gravid and would be expected to produce a litter of 16 offspring on average. 

There was no support for linear (βdate = 0.188, −.400–0.791) or quadratic (βdate2 = −0.084, 

−0.803–0.670) effects of date of examination on reproductive status because both parameters 

substantially overlapped zero. After accounting for female SVL, the differences among sites in 

the probability a female was gravid and litter size were minimal (fig. 3).
13

Table 3. Summary of reproductive data of giant gartersnakes collected from the Sacramento Valley, 

California, 2014–17, by site and year.

[Litter size is a count of eggs and embryos from X-rays. 2014 and 2015 include data from radio-telemetry snakes 

examined as part of a previous study (Reyes and others, 2017), 2016 includes females examined for the current 

study and radio-telemetry females examined by Reyes and others (2017), and 2017 includes data from the current 

study only. SD = standard deviation]

Litter size Female SVL

Year Site N Gravid Mean SD Min Max Mean SD

2014 1 5 0 -- -- -- -- 763.4 81.4

2014 2 2 1 15.0 -- -- -- 767.5 24.7

2014 3 5 3 15.3 3.2 13 19 735.0 70.1

2014 4 0 -- -- -- -- -- -- --

2014 5 4 1 17.0 -- -- -- 824.8 37.3

2014 6 0 -- -- -- -- -- -- --

2014 7 0 -- -- -- -- -- -- --

2014 8 5 5 14.0 1.6 12 16 712.8 40.6

2015 1 6 4 15.3 1.7 13 17 742.9 62.7

2015 2 2 0 -- -- -- -- 844.0 22.6

2015 3 5 1 13.0 -- -- -- 751.5 64.7

2015 4 5 3 16.3 3.8 12 19 786.8 24.2

2015 5 5 1 15.0 -- -- -- 834.0 50.4

2015 6 4 2 11.0 1.4 10 12 736.0 49.4

2015 7 5 4 23.8 6.9 18 33 872.6 129.6

2015 8 5 2 16.5 0.7 17 16 810.4 47.0

2016 1 5 3 17.0 6.9 9 21 759.9 142.6

2016 2 3 1 12.0 -- -- -- 822.7 74.3

2016 3 4 3 16.3 5.5 11 22 749.8 77.0

2016 4 4 3 14.0 3.5 10 16 738.5 159.2

2016 5 5 1 21.0 -- -- -- 845.1 20.2

2016 6 3 3 15.3 4.9 12 21 771.3 68.6

2016 7 7 2 20.0 2.8 18 22 914.7 106.0

2016 8 6 5 14.4 5.4 18 19 763.8 89.2

2017 1 7 5 18.8 2.6 16 23 708.0 144.1

2017 2 12 5 19.3 4.3 13 22 693.2 127.5

2017 3 14 10 21.5 6.5 13 34 757.9 134.5

2017 4 11 8 11.6 4.3 7 18 589.2 89.4

2017 5 8 5 18.6 3.5 15 24 789.9 85.1

2017 6 10 6 22.3 6.4 15 34 731.6 69.1

2017 7 4 2 23.0 2.8 21 25 730.3 113.7

2017 8 14 10 17.2 4.8 10 24 749.8 96.4
14

Figure 2. The probability a female giant gartersnake was gravid (a) and the observed litter size of gravid 

females (b) versus female snout-vent length (SVL). Circles represent observed data from X-rays of female 

snakes collected from the Sacramento Valley, California, in 2017. In panel a, a value of 1 on the y-axis 

indicates a female was gravid, and a value of 0 indicates a female was not gravid. The solid lines are the 

mean predicted relationship, and dashed lines represent 95-percent credible intervals around this mean 

relationship. Pr(gravid) = probability a female was gravid.
15

Figure 3. Site-specific estimates for the mean probability a female giant gartersnake was gravid (a) and 

mean litter size (b) for each site in the Sacramento Valley, California. Data are from females with snoutvent length near or >500 millimeters that received X-rays in 2017. The dashed line represents the average 

parameter estimate for all sites. Pr(gravid) = probability a female was gravid.
16

Abundance and Population Growth Estimates

The highest abundance of giant gartersnakes was found at site 8 each year (table 4;

fig. 4). In contrast, the estimated abundance of giant gartersnakes was low each year at sites 5 

and 7, which had the fewest captures each year. The mean abundance estimates were higher in 

2016 and 2017 than in 2014 and 2015 for sites 1–4 and 6 (table 4; fig. 4). This apparent increase 

in abundance in the 2 most recent years could be an artifact of the increase in sampling effort and 

sampled area in 2016 and 2017 compared to 2014 and 2015 (table 1). The abundance estimates 

for site 8 and site 2 in 2014 and 2015 have wide credible intervals because of the limited 

sampling at both sites in those years (fig. 4). 

Although the mean estimates of abundance at a site appeared to change from year to year, 

the estimates of λ illustrate the uncertainty in the population growth rate. The 95-percent credible 

interval for all estimates of λ overlapped one (fig. 5). A value of λ = 1 represents a stable 

population. There was great uncertainty in the estimates of λ for 2014–15, which reflects the 

lower sampling effort in these years and consequently the high uncertainty in estimates of 

abundance (fig. 5). The estimates of λ for 2016–17 were more precise, and there was some 

evidence that the abundance of giant gartersnakes declined during this time period at site 2 

(Pr[λ�<1] = 0.95), site 4 (Pr[λ�<1] = 0.95), and site 7 (Pr[λ�<1] = 0.94), although the credible 

intervals overlapped one slightly (fig. 5). It is worth noting that our sampling effort was lower in 

2017 than 2016 at each of these three sites (table 1). There also was some evidence that the 

population grew from 2016–17 at site 5 (Pr[λ�>1] = 0.93), although the credible interval for λ�

again overlapped one slightly (fig. 5).

The mean estimated daily capture probability (p) was higher in 2016 than 2014 and 2015 

at most sites, but in general, variation between years within a site was low (table 5; fig. 6). In 

contrast, the probability of an individual being captured at least once (that is, annual capture 

probability, p*) in a year increased from 2014–16 at most sites, reflecting the increased sampling 

effort at each site in 2016 (tables 1 and 6; fig. 7). The higher sampling effort in 2016 resulted in 

more precise estimates of abundance in that year compared to previous years (fig. 4). Although 

sampling effort declined slightly in 2017 compared to 2016, the daily and annual capture 

probabilities in 2017 were similar to those in 2016 (tables 5 and 6; figs. 6 and 7).
17

Table 4. Giant gartersnake abundance estimates (NN�) by site and year for eight study sites in the 

Sacramento Valley, California, 2014–17.

NN�

Site Year Mean SD 2.50% 97.50%

1 2014 78 12 58 106

1 2015 53 8 40 72

1 2016 100 8 87 117

1 2017 101 9 85 121

2 2014 37 13 21 69

2 2015 29 13 13 61

2 2016 123 10 106 145

2 2017 99 12 80 125

3 2014 72 19 45 117

3 2015 61 14 41 93

3 2016 109 10 92 132

3 2017 106 14 83 137

4 2015 47 12 29 76

4 2016 86 10 70 109

4 2017 66 8 52 85

5 2014 32 12 15 61

5 2015 30 8 17 48

5 2016 29 5 21 41

5 2017 39 7 28 55

6 2015 48 6 37 62

6 2016 106 7 93 122

6 2017 90 14 66 121

7 2015 26 6 17 42

7 2016 43 7 32 58

7 2017 28 8 16 47

8 2014 115 31 68 189

8 2015 101 44 48 217

8 2016 171 11 152 194

8 2017 188 14 164 218
18

Figure 4. Estimated abundance (NN�) of giant gartersnakes at each site in the Sacramento Valley, California, 

from 2014–17.
19

Figure 5. Estimated population growth rate (λλ̂) of giant gartersnakes for 2014–15 and 2016–17 from eight 

sites in the Sacramento Valley, California.
20

Table 5. Giant gartersnake daily capture probability estimates (pp̂) by site and year for eight study sites in 

the Sacramento Valley, California, 2014–17.

pp�

Site Year Mean SD 2.50% 97.50%

1 2014 0.032 0.008 0.020 0.049

1 2015 0.009 0.002 0.005 0.014

1 2016 0.018 0.003 0.013 0.024

1 2017 0.017 0.003 0.012 0.023

2 2014 0.005 0.002 0.002 0.009

2 2015 0.007 0.003 0.002 0.014

2 2016 0.017 0.003 0.013 0.023

2 2017 0.012 0.002 0.008 0.017

3 2014 0.007 0.002 0.004 0.013

3 2015 0.004 0.001 0.002 0.007

3 2016 0.015 0.003 0.011 0.021

3 2017 0.010 0.002 0.006 0.014

4 2015 0.012 0.004 0.006 0.020

4 2016 0.015 0.003 0.010 0.022

4 2017 0.027 0.006 0.017 0.040

5 2014 0.010 0.004 0.004 0.021

5 2015 0.005 0.002 0.002 0.009

5 2016 0.011 0.003 0.006 0.018

5 2017 0.012 0.003 0.007 0.020

6 2015 0.013 0.003 0.008 0.019

6 2016 0.019 0.003 0.014 0.024

6 2017 0.009 0.002 0.006 0.014

7 2015 0.008 0.003 0.004 0.015

7 2016 0.012 0.003 0.007 0.020

7 2017 0.013 0.005 0.006 0.025

8 2014 0.013 0.004 0.006 0.022

8 2015 0.012 0.006 0.004 0.026

8 2016 0.019 0.002 0.015 0.024

8 2017 0.014 0.002 0.011 0.018
21

Table 6. Giant gartersnake annual capture probability estimates (pp̂

∗) by site and year for eight study sites 

in the Sacramento Valley, California, 2014–17.

pp�∗

Site Year Mean SD 2.50% 97.50%

1 2014 0.52 0.08 0.37 0.69

1 2015 0.49 0.08 0.33 0.65

1 2016 0.77 0.05 0.67 0.86

1 2017 0.72 0.06 0.60 0.83

2 2014 0.40 0.12 0.18 0.65

2 2015 0.23 0.09 0.09 0.42

2 2016 0.76 0.05 0.65 0.85

2 2017 0.60 0.07 0.47 0.73

3 2014 0.35 0.09 0.19 0.53

3 2015 0.39 0.09 0.23 0.57

3 2016 0.71 0.06 0.59 0.82

3 2017 0.49 0.07 0.36 0.63

4 2015 0.42 0.10 0.24 0.62

4 2016 0.65 0.07 0.51 0.79

4 2017 0.71 0.08 0.55 0.85

5 2014 0.30 0.11 0.13 0.54

5 2015 0.41 0.12 0.21 0.66

5 2016 0.62 0.10 0.42 0.81

5 2017 0.53 0.09 0.35 0.71

6 2015 0.69 0.07 0.53 0.83

6 2016 0.81 0.05 0.71 0.89

6 2017 0.44 0.07 0.31 0.58

7 2015 0.59 0.12 0.35 0.82

7 2016 0.64 0.09 0.45 0.81

7 2017 0.47 0.12 0.26 0.71

8 2014 0.23 0.06 0.13 0.37

8 2015 0.05 0.02 0.02 0.10

8 2016 0.81 0.04 0.72 0.88

8 2017 0.68 0.05 0.58 0.77
22

Figure 6. Estimated daily capture probability (pp̂) of giant gartersnakes from 2014–17 for eight sites in the 

Sacramento Valley, California.
23

Figure 7. Estimated annual capture probability (pp̂

∗) of giant gartersnakes from 2014–17 for eight sites in 

the Sacramento Valley, California.
24

Annual Apparent Survival Estimates

The mean estimate of annual apparent survival probability (for snakes of all sizes) 

averaged over all 8 study sites was 0.40 (95-percent CRI = 0.26–0.54). Annual apparent survival 

varied among sites, but estimates did not vary much over time within a site. Survival was lowest 

at site 4, where the mean estimate of annual apparent survival probability was less than 0.2 for 

2015–16 and 2016–17. Survival was highest at site 5, where the mean estimate of annual 

apparent survival probability was greater than 0.5 each year (table 7; fig. 8). There were no clear 

trends in survival over time during this short time series. Within a site, the credible intervals for 

survival overlapped substantially between years (fig. 8). 

Table 7. Giant gartersnake annual apparent survival estimates (φ�) by site and year for eight study sites in 

the Sacramento Valley, California, 2014–17.

φφ�

Site Year Mean SD 2.50% 97.50%

1 2014 0.46 0.08 0.33 0.63

1 2015 0.42 0.07 0.28 0.55

1 2016 0.40 0.06 0.28 0.51

2 2014 0.32 0.09 0.17 0.52

2 2015 0.34 0.09 0.19 0.56

2 2016 0.32 0.06 0.21 0.43

3 2014 0.37 0.08 0.22 0.54

3 2015 0.35 0.07 0.21 0.49

3 2016 0.39 0.07 0.27 0.54

4 2015 0.19 0.08 0.07 0.38

4 2016 0.14 0.05 0.06 0.26

5 2014 0.52 0.13 0.25 0.75

5 2015 0.59 0.10 0.38 0.79

5 2016 0.63 0.10 0.43 0.83

6 2015 0.56 0.08 0.41 0.72

6 2016 0.51 0.08 0.35 0.68

7 2015 0.39 0.11 0.21 0.62

7 2016 0.35 0.10 0.18 0.57

8 2014 0.46 0.08 0.31 0.65

8 2015 0.47 0.09 0.33 0.67

8 2016 0.42 0.05 0.32 0.52
25

Figure 8. Estimated annual apparent survival (φ�) of giant gartersnakes from 2014–17 for eight sites in the 

Sacramento Valley, California.

Rice Growing Trends

For most sites in most years, the proportional area of rice growing within a 1-km buffer 

varied between 60 and 90 percent (range 29–88 percent; interquartile range = 62–80 percent), 

although this proportion was below 50 percent for some sites in some years. The amount of rice 

grown varied over time at each site. In general, the area of rice growing increased from 2014 to 

2016 and then declined in 2017, but some sites did not follow the same trends, and the 

magnitude of the changes between years varied among sites (table 8; fig. 9). For example, the 

area of rice growing more than doubled from 2014–15, then declined sharply from 2015 to 2017

at site 8, while the amount of rice growing remained fairly consistent over the same period at 

sites 2–4. The amount of rice growing fluctuated the most at site 6, with the proportion of rice 

alternating from greater than 80 percent in 2014 and 2016 to less than 55 percent in 2015 and 

2017.
26

Table 8. Extent of rice growing and other land cover types within a 1-km buffer of eight study sites in the 

Sacramento Valley, California, from 2014–17.

[Proportions are based on classified Landsat 8 data. For 2017, no data are available yet on fallow fields, non-rice 

crops, and other land covers]

Site Year Rice Fallow Non-rice crop Other

1 2014 0.62 0.32 0.01 0.05

1 2015 0.62 0.33 0.01 0.04

1 2016 0.86 0.09 0.00 0.05

1 2017 0.76 -- -- --

2 2014 0.71 0.15 0.02 0.12

2 2015 0.7 0.19 0.01 0.1

2 2016 0.76 0.13 0.02 0.10

2 2017 0.65 -- -- --

3 2014 0.66 0.28 0.01 0.04

3 2015 0.74 0.2 0.01 0.05

3 2016 0.84 0.10 0.01 0.05

3 2017 0.75 -- -- --

4 2014 0.76 0.22 0.01 0.01

4 2015 0.83 0.14 0.02 0.01

4 2016 0.87 0.12 0.00 0.01

4 2017 0.69 -- -- --

5 2014 0.61 0.29 0.06 0.03

5 2015 0.77 0.13 0.07 0.03

5 2016 0.75 0.12 0.09 0.04

5 2017 0.62 -- -- --

6 2014 0.85 0.11 0.01 0.03

6 2015 0.54 0.41 0.01 0.04

6 2016 0.88 0.09 0.00 0.04

6 2017 0.35 -- -- --

7 2014 0.79 0.11 0.09 0.01

7 2015 0.82 0.16 0.01 0.01

7 2016 0.87 0.08 0.04 0.01

7 2017 0.63 -- -- --

8 2014 0.30 0.38 0.27 0.05

8 2015 0.63 0.13 0.19 0.05

8 2016 0.50 0.13 0.32 0.05

8 2017 0.29 -- -- --
27

Figure 9. Proportional area of rice growing from 2014–17 at eight sites in the Sacramento Valley, 

California. Values are based on proportional area where rice was grown within a 1-km buffer of our trap 

locations in 2017.

Size-Distribution of Captured Snakes

The size distribution of captured snakes was similar for most sites. The most abundant 

size class of male snakes was between approximately 550–650 mm SVL and >50 g mass at most 

sites. At site 4, smaller males with an SVL near 400 mm and a mass of approximately 50 g were 

more common, and at site 1, males near 50 g mass were the most common (figs. 10 and 11). At 

sites 1–3, 6, and 8, there were two peaks in the distribution of SVLs from captured females; one 

peak near 400–500 mm SVL and another at approximately 700–800 mm SVL (fig. 10). Sites 5 

and 7 were notable for the lack of small giant gartersnakes, particularly small females (figs. 10 

and 11). At both site 5 and site 7, the most abundant size class of female snakes was > 800 mm 

SVL and > 200 g mass. In contrast, we captured few large female giant gartersnakes at site 4: the 

distributions of SVL and mass appear truncated at this site (figs. 10 and 11). Females near 500 

mm SVL and between 50–150 g mass were the most abundant size class at site 4, and we 

captured few females >800 mm SVL and >200 g at this site. Captures of large females were 

particularly rare at site 4 in 2017.
28

Figure 10. Distribution of snout-vent lengths (SVL) of captured giant gartersnakes by site for eight sites in 

the Sacramento Valley, California, from 2014–17. Lines are kernel density estimates from observed SVLs. 

Solid lines represent females, whereas dashed lines represent males.
29

Figure 11. Distribution of the mass (g) of captured giant gartersnakes from eight sites in the Sacramento 

Valley, California, from 2014–17. Lines are kernel density estimates from observed masses. Solid lines 

represent females, whereas dashed lines represent males.
30

Growth

We obtained growth data from 192 snakes that were measured in multiple years, 

including 99 females and 93 males. We measured the SVL of 175 snakes twice, 16 snakes three 

times, and 1 snake four times, resulting in a total of 210 growth increments. As expected, 

females reached greater asymptotic SVLs (L∞) than males, but there was only weak evidence that 

the sexes differ in k, which describes the rate at which individuals grow towards their asymptotic 

SVL (table 9). 

Table 9. Parameter estimates from a von Bertalanffy growth model fit to growth data from giant 

gartersnakes at eight study sites sampled in the Sacramento Valley, 2014–17.

[SD = standard deviation; 2.5 = 2.5th percentile; 97.5 = 97.5th percentile; Pr = probability]

Percentile

Parameter Symbol Mean SD 2.5 97.5 Pr(m<0)

Asymptotic size (female) L∞ 863.5 18.4 829.6 902.0 --

Growth coefficient k 1.8 0.4 0.9 2.7 --

SD of year on k σky 0.6 0.5 0.1 1.4 --

SD of site on k σks 0.4 0.3 0.0 0.8 --

SD of site on L∞ σL 0.2 0.2 0.0 0.5 --

SD(measurement error) σe 33.7 2.3 29.1 38.3 --

Sex effect on L∞ (male) mL −186.5 23.6 −221.6 −128.5 1.0

Sex effect on k mk −0.2 0.3 −0.7 0.4 0.8

We measured 191 annual growth increments from 178 snakes captured and measured in 

consecutive years, including 93 females and 85 males. Most growth measurements (131/191) 

came from the 2016–17 interval when trapping effort was highest (table 10). Averaged over a 

365 day year, growth rates averaged 0.28 mm/day (standard deviation [SD] = 0.22) for females 

and 0.19 mm/day (SD = 0.20) for males. Our regression model showed support for a faster 

growth rate in female snakes than males (βmale = −0.27, 95-percent CRI = −0.31 to −0.23) and for 

a negative relationship between snake SVL and growth rate (βSVL = −0.19, −0.21 to −0.16). 

There was some support for an interactive effect between sex and SVL on growth rate (βmale x SVL 

= −0.03, −0.07–0.01; Pr[βmale × SVL <0 = 0.90]), although the 95-percent credible interval 

overlapped zero. This interactive effect of sex and SVL on growth rate means that male snakes’ 

growth rate decreased faster with increasing SVL. More of the variation in growth rate was 

attributable to differences among years (σyear = 0.17, 0.01–0.55) than among sites (σsite = 0.04, 

7.7e−5–0.08) or individuals (σind = 0.02, 5.1e−6–0.05).
31

Table 10. Growth rates of giant gartersnakes from 2014–17 summarized by site, sex, and year, for eight 

sites in the Sacramento Valley, California.

Growth rate (mm/day)

Site Year Sex N Mean SD

1 2014 F 6 0.38 0.34

1 2014 M 5 0.00 0.26

1 2015 F 2 0.20 0.04

1 2015 M 5 0.13 0.18

1 2016 F 6 0.33 0.31

1 2016 M 14 0.20 0.12

2 2014 F 2 0.11 0.15

2 2015 F 2 0.11 0.05

2 2015 M 1 −0.06 --

2 2016 F 10 0.31 0.25

2 2016 M 8 0.32 0.35

3 2014 F 2 0.07 0.08

3 2015 F 4 0.17 0.32

3 2016 F 12 0.36 0.28

3 2016 M 7 0.09 0.06

4 2015 F 2 0.18 0.02

4 2015 M 1 0.04 --

4 2016 F 2 0.32 0.04

4 2016 M 3 0.19 0.12

5 2014 F 1 0.19 --

5 2015 F 2 0.29 0.15

5 2015 M 2 0.41 0.56

5 2016 F 5 0.13 0.16

5 2016 M 2 0.11 0.03

6 2015 F 9 0.33 0.19

6 2015 M 8 0.06 0.21

6 2016 F 11 0.35 0.18

6 2016 M 10 0.25 0.19

7 2015 F 2 0.01 0.05

7 2015 M 2 0.24 0.22

7 2016 F 2 0.19 0.01

7 2016 M 3 0.05 0.06

8 2015 F 2 0.15 0.07

8 2016 F 18 0.31 0.19

8 2016 M 18 0.26 0.13
32

Health Assessments

Health assessments were conducted for 22 snakes in 2014, 37 snakes in 2015, 20 snakes 

in 2016, and 80 snakes in 2017. Health assessments in 2017 were only conducted on female 

snakes that were large enough that they could be reproductively mature (greater than 500 mm 

SVL except one snake with an SVL of 435 mm), whereas health assessments from 2014–16 were 

primarily conducted on snakes that were part of a radio-telemetry study (Reyes and others, 

2017), and thus snakes had to have a mass greater than or equal to 200 g to be examined. Means 

and standard deviations for health parameters by year at each site are presented in tables 11 and 

12. The data did not show any clear signs of poor health in our sample of giant gartersnakes. The 

values for the blood and plasma biochemistry parameters measured in this study are comparable 

to the baseline values from “apparently healthy” giant gartersnakes reported by Wack and others 

(2012). We did not evaluate trends in health parameters because of the small sample sizes from 

2014–16. Continued examination of larger numbers of snakes would allow us to evaluate if the 

health of snakes varies among sites and over time and if snake health is related to landscape level 

variables such as the amount of active rice growing.
33

Table 11. Summary of blood cell counts, hemoglobin, and hematocrit of female giant gartersnakes of reproductive size by site and year for eight 

sites in the Sacramento Valley, California, 2014–17.

[For blood parameters, values are means followed by standard deviations in parentheses. WBC, white blood cells (× 103 / μL); RBC, red blood cells (× 106 / μL); 

HgB, hemoglobin (g/dL); PCV, packed cell volume (%). White blood cell types (for example, heterophils – azurophils) are % of counted cells]

Site Year N WBC RBC HgB PCV Heterophils Lymphocytes Monocytes Eosinophils Basophils Azurophils

1 2014 2 6.8 (6.8) 0.8 (0) 8.4 (0.6) 25.5 (2.1) 8.5 (4.9) 36.9 (51.1) 0 (0) 0 (0) 3.5 (0.7) 13.5 (2.1)

1 2015 6 4.5 (3.3) 0.9 (0.3) 8.7 (2.3) 26.7 (6.7) 11.8 (5) 77.7 (9.8) 1.5 (1.6) 0 (0) 2.2 (2.4) 6.8 (8.4)

1 2016 4 11.5 (6.5) 0.6 (0.2) 9.1 (0.1) 27.3 (1) 8.8 (8.5) 71 (10.9) 1.3 (1.5) 0 (0) 2 (0.8) 17 (6.3)

1 2017 7 5.9 (2) 0.6 (0.2) 8 (2.3) 25.9 (7.6) 12.7 (8.3) 64.7 (11.1) 0.7 (1) 0.9 (1.2) 5.3 (3.7) 15.7 (10.5)

2 2014 4 8.3 (3.7) 0.9 (0.1) 10 (0.5) 30 (3.2) 5.3 (3.4) 74.3 (11.1) 1.3 (1.3) 0 (0) 5.8 (2.1) 13.5 (7.9)

2 2015 4 4.9 (4.2) 0.9 (0.2) 10.5 (2.4) 31 (8.2) 15.3 (7.3) 64.3 (19.6) 2.5 (2.4) 0 (0) 1.3 (1.5) 4.5 (7)

2 2016 2 2.9 (1.2) 0.6 (0.2) 9.8 (2.2) 29 (5.7) 30.5 (16.3) 69.5 (16.3) 0 (0) 0 (0) 0 (0) 0 (0)

2 2017 12 5.9 (4.7) 0.7 (0.2) 8.1 (1.5) 24.3 (3.4) 8.7 (4) 71.8 (8.2) 0.2 (0.6) 1.7 (5.8) 3.3 (4) 16.9 (8.7)

3 2014 6 4.5 (1.7) 1.2 (1.5) 8.4 (0.9) 24.8 (1.6) 15.3 (12.2) 74.7 (8.7) 0.5 (0.5) 0.2 (0.4) 3 (3.8) 6.3 (6.9)

3 2015 4 7.9 (5.4) 0.8 (0.2) 7.6 (1.9) 23.8 (5.6) 15.5 (6.2) 73.5 (5.7) 3.3 (4.5) 0 (0) 3.5 (2.5) 4.3 (5.1)

3 2016 1 12.9 (NA) 1 (NA) 10.1 (NA) 34 (NA) 18 (NA) 82 (NA) 0 (NA) 0 (NA) 0 (NA) 0 (NA)

3 2017 14 6.7 (4.7) 0.6 (0.2) 7.8 (2.5) 23.6 (7.7) 9.9 (9.4) 67.6 (11.5) 0.8 (2.9) 0 (0) 1 (1.4) 20.7 (11.5)

4 2015 5 4.3 (1.9) 0.8 (0.2) 9.5 (1.4) 29.6 (5.1) 14 (8.7) 77.8 (5.5) 0 (0) 0 (0) 5.4 (5.5) 2.4 (2.9)

4 2016 2 1.3 (1.6) 0.6 (0.1) 7.6 (0.1) 22.5 (0.7) 9.5 (0.7) 73.5 (2.1) 0.5 (0.7) 0 (0) 6.5 (2.1) 10 (0)

4 2017 11 3.8 (2.2) 0.8 (0.2) 8.2 (1.1) 25.5 (4.1) 6.5 (3.8) 77.2 (6.6) 0 (0) 0 (0) 2.5 (3.4) 13.8 (7.6)

5 2014 5 6.7 (5.3) 0.7 (0.1) 9.1 (2.5) 27.4 (7.5) 8.8 (4.1) 70.6 (10.4) 1.4 (0.9) 0.2 (0.4) 4 (2.4) 15.8 (11)

5 2015 5 7.3 (7.8) 0.7 (0.2) 7.4 (2.9) 22.8 (7.9) 15 (4.7) 75.6 (11.1) 0.4 (0.9) 0 (0) 1.8 (2) 7.2 (10.5)
34

Site Year N WBC RBC HgB PCV Heterophils Lymphocytes Monocytes Eosinophils Basophils Azurophils

5 2016 4 12 (5.7) 0.7 (0.3) 8.2 (2.9) 25.5 (9.1) 9.5 (10.6) 70.3 (16.3) 0.5 (1) 0 (0) 2 (0.8) 17.8 (5.7)

5 2017 8 5 (3.4) 1.4 (2.3) 7.6 (2.2) 22.1 (6.7) 3.8 (2.7) 70 (14.6) 1 (2.4) 0.8 (2.1) 2.8 (3.5) 21.8 (14.9)

6 2015 4 4.6 (2.1) 0.8 (0.4) 7.8 (3) 24 (9.7) 21.8 (15.1) 77.3 (13.6) 0 (0) 0 (0) 0.8 (1.5) 0.3 (0.5)

6 2016 1 3.4 (NA) 0.7 (NA) 10.4 (NA) 32 (NA) 12 (NA) 87 (NA) 0 (NA) 0 (NA) 0 (NA) 1 (NA)

6 2017 10 4.7 (3.7) 0.8 (0.2) 9.1 (2.2) 27.6 (6.1) 12.2 (5.8) 59 (16) 0 (0) 0 (0) 0.6 (0.8) 28.1 (15.8)

7 2015 4 9.6 (5.1) 0.8 (0.3) 8.7 (0.7) 27.5 (1.7) 16 (12.1) 72.3 (6.7) 1.3 (1.9) 0 (0) 1.8 (2.1) 8.8 (10.3)

7 2016 2 6.3 (3) 0.9 (0.2) 8.9 (1.8) 30 (8.5) 8 (2.8) 85.5 (0.7) 0.5 (0.7) 0 (0) 1.5 (2.1) 4.5 (0.7)

7 2017 4 5.4 (5.8) 0.6 (0.2) 7.8 (2.3) 23 (6.7) 5 (2.9) 77.5 (3.1) 0 (0) 0.3 (0.5) 0.5 (0.6) 16 (4.2)

8 2014 5 8.6 (4.3) 0.9 (0.5) 9.1 (2) 27.8 (5.4) 13 (11.6) 76.8 (10.5) 0.4 (0.5) 0.2 (0.4) 3 (3.6) 7.2 (7.9)

8 2015 5 13.1 (4.9) 0.9 (0.2) 10 (2.3) 32.2 (6.5) 10.2 (6.4) 75.6 (6.7) 2.6 (0.9) 0 (0) 0.2 (0.4) 8.4 (9.6)

8 2016 4 6.5 (4.8) 0.5 (0.3) 9 (4.5)

26.5 

(12.5) 6.8 (3) 84.8 (11.9) 0 (0) 0 (0) 2.3 (3.3) 6.3 (9.5)

8 2017 14 5.1 (3.2) 0.7 (0.2) 8 (1.7) 23.6 (6.6) 7 (4.2) 69.2 (12.2) 0.3 (0.8) 0 (0) 2.2 (2) 21.1 (11.8)
35

Table 12. Summary of blood solutes of female giant gartersnakes of reproductive size by site and year for eight sites in the Sacramento Valley, 

California, 2014–17. 

[For blood parameters, values are means followed by standard deviations in parentheses. P. protein, plasma protein (g/dL); AST, aspartate aminotransferase 

(IU/L); CK, creatinine kinase (IU/L); UA, uric acid (mg/dL); glucose (mg/dL); Ca, calcium (mg/dL); P, phosphorus (mg/dL); T. protein, total protein (g/dL); 

albumin (g/dL); globulin (g/dL), K, potassium (mmol/dL); Na, sodium (mmol/dL)]

Site Year P. protein AST CK UA Glucose Ca P T. protein Albumin Globulin K Na

1 2014 8.1 (2.1) 15.5 (0.7) 449.5 (289.2) 6.1 (0.3) 85.5 (20.5) 16.1 (0.1) 8.1 (3.3) 6.1 (0.5) 1.6 (0.3) 4.4 (0.3) 3.6 (0.4) 154.5 (0.7)

1 2015 3.7 (1.2) 19 (13.2) 311 (215.1) 6.1 (2) 76 (10.7) 13.4 (2.2) 4.1 (1.1) 3.6 (0.9) 1.1 (0.2) 1.4 (1.5) 4.8 (1) 155 (2.8)

1 2016 5.7 (0.6) 18.5 (1.7) 309.5 (213.9) 11.4 (6.8) 97.3 (7.9) 14.4 (1.2) 5 (1.3) 4.9 (0.6) 1.2 (0.1) 3.6 (0.6) 5.9 (1.3) 154.8 (6.1)

1 2017 4.9 (1.1) 11.3 (0.8) 429.5 (255.5) 7.2 (3.3) 95.3 (61.1) 13.2 (0.3) 4.9 (1.6) 4.1 (0.3) 1.2 (0.2) 2.9 (0.3) 5 (0.5) 152.8 (5.9)

2 2014 5.6 (0.4) 20.5 (7.9) 467.8 (72.9) 3.5 (1.6) 111 (31.4) 14.9 (0.9) 5 (0.7) 5 (0.3) 1.4 (0.2) 3.5 (0.2) 5.7 (0.9) 159.5 (4)

2 2015 5.9 (0.6) 13.3 (4.8) 239.8 (105.8) 5.7 (4.9) 59.3 (18.6) 15.3 (0.6) 5 (1.8) 5.1 (0.4) 1.4 (0.1) 3.7 (0.5) 5 (1.1) 162.5 (4.5)

2 2016 6.7 (2.4) 13.5 (2.1) 422 (314) 14.1 (3) 79.5 (9.2) 6.3 (8.9) 7 (4.2) 5.1 (0.9) 1.6 (0.6) 3.5 (0.4) 5.3 (0.1) 161.5 (2.1)

2 2017 5 (0.9) 12.7 (2.6) 468 (321.5) 6.5 (7.5) 82.5 (37.1) 13.9 (1.2) 5.5 (1.1) 4.3 (0.4) 1.2 (0.1) 2.8 (1.3) 5.3 (0.3) 154.3 (3.3)

3 2014 4.9 (0.4) 27.5 (17.9) 1043.2 (1021) 3.8 (1.1) 77.7 (12.8) 14.6 (1.2) 5.7 (1.3) 4.6 (0.4) 1.2 (0.1) 3.4 (0.4) 4.4 (0.5) 158.7 (7.1)

3 2015 4.7 (1) 14 (7.3) 432.8 (230.8) 4 (2.1) 63.3 (17.8) 15.8 (0.6) 6.8 (1.6) 4.2 (0.8) 1.1 (0.3) 1.8 (2.1) 5.7 (1.6) 157.8 (0.5)

3 2016 5.6 (NA) 14 (NA) 117 (NA) 8.2 (NA) 119 (NA) 13.8 (NA) 2.8 (NA) 5.2 (NA) 1.1 (NA) 4.1 (NA) 4.7 (NA) 160 (NA)

3 2017 4.9 (1.6) 13.7 (4.3) 850.7 (1047.5) 5 (3) 85.9 (29.4) 13.1 (1) 5.5 (1.8) 4.3 (0.7) 1 (0.3) 1.5 (1.8) 5.5 (1) 155.9 (3.7)

4 2015 6.2 (1) 16.6 (5.4) 571.6 (485.7) 4.1 (2.1) 64.6 (23.8) 15.8 (0.6) 6.4 (1) 4.7 (0.6) 1.3 (0.1) 3.4 (0.5) 5.3 (0.7) 160 (3.7)

4 2016 6.5 (0.1) 18.5 (3.5) 1587 (1548.6) 8.8 (3.5) 62.5 (17.7) 15.7 (0.6) 9.2 (5.8) 5.1 (0.3) 1.8 (0.4) 3.4 (0.6) 6.8 (0.3) 160.5 (0.7)

4 2017 5.3 (1.1) 16.7 (13) 457.9 (250.1) 3.9 (1.5) 79.6 (22.7) 13.8 (0.7) 4.6 (1) 4.7 (0.4) 1.3 (0.1) 3.4 (0.3) 5.2 (0.5) 155.4 (5.1)

5 2014 5.8 (0.7) 24.4 (25.8) 940 (1073.1) 4 (2.5) 77 (18.5) 15 (1.1) 5.2 (0.8) 4.9 (1.2) 1.2 (0.2) 3.3 (1.9) 4.5 (0.8) 159.6 (7.5)

5 2015 5.4 (0.8) 15.8 (3.6) 470 (357) 3.7 (2) 72.8 (13.1) 14.4 (1.2) 5.6 (3.1) 4.6 (0.4) 1.3 (0.1) 3.3 (0.4) 5.2 (0.6) 162.4 (3.8)

5 2016 4.9 (1.3) 21.5 (5.8) 698.5 (930.8) 7.9 (7) 80.3 (7.1) 13.9 (1.2) 5.5 (0.8) 4.7 (0.8) 1.2 (0.1) 2.9 (1.9) 5.2 (0.8) 158 (2.9)

5 2017 5.2 (1.3) 12.6 (5.1) 269.3 (207.8) 8.3 (8.2) 73.9 (22.3) 13.8 (1.3) 4.8 (0.7) 4.8 (1.2) 1.1 (0.3) 2.5 (2.2) 5.3 (0.6) 155.4 (4.1)
36

Site Year P. protein AST CK UA Glucose Ca P T. protein Albumin Globulin K Na

6 2015 6.1 (1.7) 17.8 (6.7) 1452.8 (975.8) 6.4 (3) 49.8 (21.7) 15.4 (1.5) 6.2 (2.4) 4.7 (1.2) 1.4 (0.3) 3.3 (0.9) 4.6 (0.1) 156 (2.9)

6 2016 4.6 (NA) 22 (NA) 415 (NA) 10.2 (NA) 75 (NA) 13.6 (NA) 5.4 (NA) 5.1 (NA) 1.4 (NA) 3.7 (NA) 5 (NA) 167 (NA)

6 2017 4.5 (1.1) 12.6 (6.2) 464.5 (564.9) 5.1 (2.6) 80.8 (37.8) 13 (1) 4 (0.5) 4.1 (0.9) 1.2 (0.3) 1.7 (1.8) 4.7 (0.4) 152.3 (2.5)

7 2015 5.4 (0.5) 12.8 (3.4) 440.5 (359.2) 5.6 (2.3) 83.3 (14.6) 14.9 (0.8) 4.6 (0.8) 5 (0.4) 1.3 (0.1) 3.7 (0.3) 4.7 (0.7) 156.5 (6)

7 2016 6.7 (1.8) 35.5 (19.1) 651 (343.7) 9 (3.4) 80.5 (14.8) 15.4 (1.1) 7.3 (3.5) 4.6 (0.3) 1.4 (0.2) 3.3 (0) 4.2 (0.6) 162 (2.8)

7 2017 4.2 (0.3) 11.3 (1.5) 388 (266) 11.2 (8.6) 106.8 (59) 13.7 (0.7) 5.4 (0.8) 3.9 (0.4) 1.2 (0.2) 2.1 (1.4) 4.6 (0.2) 153.8 (8.1)

8 2014 5.1 (0.8) 35.8 (25.9) 581.6 (442.4) 3.1 (0.8) 96.6 (17.8) 16 (0.2) 6.1 (1) 4.8 (0.7) 1.2 (0.2) 3.8 (0.5) 3.8 (0.5) 159.8 (4.6)

8 2015 5.5 (0.9) 25 (29.7) 928.4 (1124) 6 (1.8) 74.8 (21.3) 16 (0.3) 5.8 (1.6) 4.9 (0.9) 1.3 (0.2) 3.7 (0.7) 4.2 (0.5) 157.2 (4.8)

8 2016 6.6 (1.6) 17.8 (3.8) 415.8 (197.3) 6.9 (2) 86.3 (19.7) 15.6 (1.1) 7.7 (2.4) 4.7 (0.8) 1.4 (0.2) 3.4 (0.6) 5 (0.4) 158.3 (3.3)

8 2017 5 (1) 15.2 (6.4) 729.5 (938.6) 8.7 (6.3) 74.1 (25.9) 15.7 (3.8) 6 (2) 4.3 (0.5) 1.2 (0.3) 2.3 (1.5) 5.4 (0.9) 152.9 (4.5)
37

Discussion

The results from 4 years of sampling at these eight sites (2 years of preliminary sampling, 

2 years of full demographic sampling) demonstrate that we can obtain a wealth of data on the 

reproduction, survival, somatic growth, and abundance of giant gartersnakes inhabiting canals 

adjacent to rice agriculture by intensively trapping the same areas in consecutive years. It is 

especially promising that the increased sampling effort in 2016 and 2017 (the first 2 years of 

sampling designed explicitly for this study) led to more captures and recaptures of giant 

gartersnakes and more precise estimates of abundance, population growth, and survival. 

Although some sites inhabited by giant gartersnakes have been the subject of long-term studies 

(for example, Hansen and others, 2015; Rose and others, 2018a,b), this study represented, to the 

best of our knowledge, the first project to collect simultaneous, long-term data on all aspects of 

giant gartersnake demography from multiple populations in privately owned, rice-growing areas. 

The opportunity to collect more data on variation in rice growing over time and among sites 

would allow us to evaluate whether and how the amount of rice growing at a site relates to snake 

health and demographic rates of interest, such as somatic growth, survival, fecundity, the 

proportion of gravid females, and population growth. Because the study has been discontinued, 

we are unable to further investigate these questions at this time.

We observed changes in the amount of rice grown at a site over time. Although most sites 

exhibited a peak in rice production in 2016, the fluctuations in rice growing were not 

synchronous among sites. Some sites exhibited small changes between years, whereas sites 6 and 

8 had more dramatic shifts in the amount of rice grown. The observed range of the area under 

active rice cultivation was 30–90 percent of the area within a 1-km buffer of our traplines. We 

believe this range is likely biologically relevant to giant gartersnakes for two reasons. First, given 

the relationship between giant gartersnake presence and rice growing, it is unlikely that large 

populations of giant gartersnakes (that is, those amenable to demographic sampling) would 

persist in agricultural areas with no or very little rice (Halstead and others, 2010; Hansen and 

others, 2017). Second, previous research with radio-tracked adult (mostly female) giant 

gartersnakes at these sites has linked the area of rice within an individual’s home range to its 

survival rate (Reyes and others, 2017). Therefore, because the variation in the proportion of rice 

cultivation observed at these sites in the recent past appeared to influence individual survival, we 

expect the variation in rice cultivation observed in this study to potentially affect population level 

parameters such as mean survival, abundance, and population growth. Because of their 

proximity, the 1-km buffers overlap for sites 3 and 5 and for sites 7 and 8. Future analyses should 

account for the non-independence of the rice-growing conditions at these sites and for the 

dispersal of individuals between sites (see discussion of size distributions, below).
38

One of the most encouraging signs from this study was the rate of annual recaptures. 

Even though full demographic sampling only began in 2016, on average, 19 percent of snakes 

were captured in 2 or more years. Given that we estimate the annual apparent survival 

probability to be between 0.2 and 0.6 depending on the site, this means we are recapturing a 

large proportion of the marked snakes that survive (and remain on site) each year. Also, we 

emphasize that the majority of snakes (75.9 percent) were captured for the first time in 2016 

(47.5 percent) or 2017 (28.4 percent)—when sampling effort increased at the beginning of this 

study—and therefore could only have been recaptured once (if first caught in 2016) or zero times 

(if first caught in 2017). It is also notable that the rate of immediate annual recaptures (that is, a 

snake is recaptured the next year after its initial capture) was higher in the 2 most recent years, 

when sampling effort was greater than the first 2 years. Based on these trends, it is likely that a 

large proportion of the marked snakes that are available for capture would be recaptured in future 

years, as they are exposed to additional years of sampling. For example, of the 120 snakes first 

captured in 2015, 43 (35.8 percent) were recaptured in one (34 snakes) or both (9 snakes) of the 

subsequent years. The precision of annual survival estimates improves with the number of 

marked individuals, the proportion of marked individuals that are subsequently recaptured 

(recapture probability), and, for longer-lived organisms with low recapture probabilities, the 

duration of the study (Halstead and others, 2015a). Thus, had the study continued, the large 

number of marked individuals combined with the high recapture rates would have allowed for 

more precise estimates of survival, which is a key vital rate for assessing population viability. 

Also, by estimating how survival varies among sites and years, we would have been able to test 

for a relationship between the amount of aquatic habitat available (that is, the area under active 

rice growing) and annual survival. Estimating site by year differences in survival will be 

important for assessing the importance of aquatic habitat availability for giant gartersnake 

survival. Comparing how survival varies in relation to changes in the amount of rice grown at a 

site would allow us to assess if increasing or decreasing the area under rice cultivation has an 

effect on the survival of giant gartersnakes inhabiting canals near agricultural fields. 

Given the change in sampling intensity from 2014–15 (before the current study began) to 

2016–17, we caution against interpreting population trends from these data. For example, 

although the mean estimated abundance of giant gartersnakes at site 1 appeared to increase from 

55 snakes in 2015 to 101 snakes in 2016, we more than doubled our sampling effort in 2016 

compared to 2015, and our traps covered a larger area in 2016 as well. Given this change in the 

sampling effort, only the estimates of population growth for 2014–15 and 2016–17 should be 

interpreted, and we cannot draw conclusions about population growth from 2015–16. Our 

estimates of population growth were more precise for 2016–17 than 2014–15, owing to the 

greater sampling effort in the later years, which improved estimates of abundance. Our results 

suggest future studies sample the same area at each site with similar trapping effort (as was done 

in 2016–17). This would allow robust estimates of a time series of giant gartersnake abundance, 

survival, somatic growth rate, and population growth at each site. These population level 

parameters could then be compared to the rice growing conditions at each site to see whether and 

how they are influenced by the availability of aquatic habitat. 
39

Based on size distributions of captured snakes, five of the sites appeared to have 

demographically similar populations (sites 1–3, 6, and 8). The size distribution was truncated at 

site 4, which had few large snakes, especially in 2017. The truncated size distribution at site 4 in 

2017 could indicate a lack of older adult snakes (>3 years old). The reduced number of large 

female snakes at site 4 in 2017 could have been caused by delayed sampling. Due to low water 

levels in site 4’s canals in spring 2017, we did not begin sampling until June. Large gravid 

females might have lower capture probability at this time if they reduce foraging activity prior to 

parturition. Alternatively, it is possible that growth is much slower at site 4, and the small size of 

snakes is not indicative of a population dominated by young snakes. We do not think this is 

likely because variation in growth rate among sites was low, and site 4 had similar growth rates 

to other sites. It would be interesting to track the size distribution at site 4 in the coming years to 

see if snakes present in 2016 and 2017 survive and grow to become large adults or if the size 

distribution remains truncated and dominated by small individuals. In contrast, sites 5 and 7 had 

few small snakes, and large females were proportionally more common. The lack of small snakes 

raises concerns that recruitment is low at these sites, and the population is sustained by the 

continued survival of large, old adult snakes. The lack of small snakes at sites 5 and 7 is 

especially interesting considering the proximity of these sites to sites 3 and 8, respectively, which 

exhibit a more even size distribution with both small and large females. Indeed, sites 3 and 5 are 

close enough that two snakes moved from site 3 to site 5, and yet the two sites differ 

dramatically in the size distribution of snakes living there. Given the relatively low survival rate 

of giant gartersnakes (Rose and others, 2018a), low recruitment, even for a short period, would 

be expected to lead to a population-level decline in the near term. If recruitment does not 

increase at sites 5 and 7 in the next few years, and small giant gartersnakes remain scarce, it 

could signify that the populations of giant gartersnakes at these sites are declining and are at risk 

of extirpation.
40

Although we cannot capture neonate snakes, sampling from mid-April to mid-July is 

advantageous for studying giant gartersnake demography for a few reasons. Sampling during the 

“post-reproductive” period after females have begun giving birth is unlikely to result in 

substantial data on neonate survival because neonate captures and re-captures of snakes marked 

as neonates are rare (Rose and others, 2018a). The low recapture rate of neonate snakes has been 

documented for other snake species as well (Pike and others, 2008; Bauwens and Claus, 2018). 

Additionally, while captures of neonates are rare, sampling intensively while neonates are 

present exposes neonates to trap-related mortality. These small snakes can become stuck in the 

mesh of funnel traps while trying to escape, and it is imperative to minimize the risk that our 

sampling negatively affects populations of this threatened species. Also, by sampling only before 

parturition takes place, rather than both before and after parturition, we avoid one potential 

violation of the population closure assumption necessary to fit CMR models. If we sampled both 

before and after females give birth, we would potentially capture neonate snakes later in the 

summer that were not available for capture earlier in the sampling period. This influx of neonates 

could distort our estimates of abundance. By sampling during the same, pre-reproductive period 

each year, we remove this potentially confounding factor and obtain more consistent estimates of 

abundance, survival, and population growth each year. Finally, sampling during the “prereproductive” period, before parturition (birth), allows us to examine females and record their 

reproductive status and, if gravid, litter size. The proportion of females that are gravid and the 

litter size of gravid females are important demographic variables, and with additional data could 

allow us to evaluate whether and how variation in these reproductive parameters is related to the 

amount of rice grown at a site. Although we cannot directly estimate the pulse of neonates that 

enter the population in the late summer, we can measure the cohort of yearling snakes that 

appear the following spring. We also can calculate the expected reproductive output of the 

females in a population, based on observed relationships between female size and fecundity, and 

use the expected reproductive output in one year, combined with the abundance of yearling 

snakes the following year, to estimate neonate survival (Rose and others, 2018a).

The probability a female was gravid was not related to the date of examination in 2017. 

This indicates that mid-May to mid-July is a good time period during which to examine females’ 

reproductive status. It is late enough that ovulated eggs or developing embryos are visible on Xrays, but early enough that females have not begun to give birth. The first full year in which

females were explicitly sampled to examine their reproductive status was 2017. Future 

examination of a large sample of potentially reproductive females each year would allow us to 

estimate annual variation in the proportion of females that are gravid and in the fecundity (litter 

size) of those females. We could also examine if spatial and temporal variation in these 

reproductive variables is related to environmental conditions such as the amount of rice grown at 

a site each year.

The timing of reproduction can vary among populations in natricine snakes (Seigel and 

Ford, 2001). An interesting comparison for giant gartersnakes is the viperine snake (Natrix 

maura), a semi-aquatic species in the Natricinae sub-family native to southwestern Europe and 

northwestern Africa that inhabits rice growing regions in Spain. Reproduction is delayed in N. 

maura inhabiting rice fields compared to snakes in natural wetlands, likely because prey are not 

available in fields and canals until after they are flooded in the spring (Santos and others, 2005). 

It is possible that the timing of reproduction in giant gartersnakes will vary between sites and 

among years in response to the timing of rice planting and canal flooding. This would be an 

interesting future research question to study in giant gartersnakes.
41

Summary and Conclusions

The 4 years of study at the 8 demographic monitoring sites have shown that it is possible 

to measure important demographic rates in giant gartersnake populations with the amount of 

sampling effort expended and that greater sampling effort produces more precise estimates. Sites 

differed in their estimated abundance, survival, and the size distribution of giant gartersnakes in 

the population. It is also apparent that there is variation in the amount of aquatic habitat available 

for giant gartersnakes because the amount of active rice growing fluctuated over time and varied 

among sites. Continued sampling in the future would allow for estimation of temporal trends in 

abundance and vital rates and for investigation of how the amount of rice grown affects the 

demography and health of giant gartersnake populations.

• We trapped for giant gartersnakes at eight sites in the Sacramento Valley, California, 

from 2014–17.

• Trapping in 2014 and 2015 was not extensive and was designed to capture snakes for 

radio-transmitter implantation as part of an earlier study. 

• Sampling effort increased in 2016 and 2017 to capture more snakes for capture-markrecapture analysis and estimation of abundance, growth, survival, and fecundity. 

• We made 2,995 captures of 1,011 snakes from 2014–17.

• The majority of snakes were captured for the first time in 2016 or 2017, when trapping 

effort increased at the start of this study.

• Abundance was highest at site 8 at lowest at sites 5 and 7.

• The area undergoing active rice growing varied among sites and over time.

• The probability of capturing an individual snake at least once during the sampling period 

increased in 2016 and 2017, the 2 years with full trapping effort designed for capturemark-recapture.

• Estimates of abundance, survival, and population growth rate were more precise in 2016 

and 2017 than previous years in which trapping effort was low.

• Both female fecundity (litter size) and the probability a female was gravid exhibited a 

positive relationship with female snout-vent length.
42

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44

Glossary

Apparent survival (φ): The joint probability that an individual survives and does not permanently 

leave the study area in a capture-mark-recapture study. Capture-mark-recapture studies, based on 

marking individuals within a defined study area, estimate apparent survival, not true survival, 

because it is not possible to know with certainty whether an individual that is not captured for 

several years died or emigrated offsite. To estimate true survival, additional data, such as the 

recovery of dead individuals or sighting of emigrants out of the capture-mark-recapture study 

area, are needed.

Asymptotic size (LL∞): The theoretical maximum size an individual approaches as it grows with 

age in a von Bertalanffy growth model. The asymptotic size describes a population level average, 

and some individuals may reach maximum sizes greater than the asymptotic size for the 

population or species.

Bayesian analysis: A method of statistical inference in which Bayes’ rule is used to update 

probability distributions as data are acquired.

Capture-mark-recapture: A method of estimating demographic parameters of populations for 

which individuals cannot be directly counted that involves capturing, marking, and recapturing 

individuals. Not all capture-mark-recapture methods require physical capture of individuals (for 

example, camera traps or hair snares for DNA).

Capture probability: The per-survey probability that a specific individual is captured or observed.

Convergence: The meeting and overlapping of independent Markov chains in a Markov Chain 

Monte Carlo simulation used to assess whether the stationary posterior distribution has been 

reached.

Covariate: A (predictor) variable thought to be correlated with another (response) variable used 

in a regression analysis.

Credible interval: A specified domain of the posterior probability distribution used for interval 

estimation. Credible intervals can be interpreted as the probability, confidence, or belief that a 

parameter or value falls within the specified region.

Emigration: The movement of individuals outside the sampled area. Emigration can be temporary 

or permanent.

Fecundity: The number of offspring produced by a female in a given year (litter size).

Gravid: Pregnant or full of eggs.

Informative prior: A prior probability distribution that expresses prior information or knowledge

about a variable based on data from previous studies or expert knowledge.

Jolly-Seber model: An open capture-mark-recapture model that simultaneously estimates survival 

between sampling periods and the abundance of the population during each sampling period. The 

Jolly-Seber model attempts to estimate how many individuals were present in the population but 

not captured during each sampling period.

Linear: Involving the effect of a predictor variable that is raised to the first power in a regression 

model.

Litter: The number of young brought forth by an animal with multiple reproductive cycles at one 

parturition.

Markov Chain Monte Carlo (MCMC): A class of algorithms for sampling from a probability 

distribution that is based on constructing a Markov chain that has the desired distribution as its

equilibrium distribution.

Neonate: A newborn.

Parturition: The act or process of giving birth to offspring.
45

Population growth rate (λ): The change in abundance of a population in year t+1 relative to year t. 

A value of λ = 1 represents a stable population (no change in abundance), λ less than 1 indicates 

a declining population, and λ greater than 1 indicates an increasing population. λ can be 

calculated from a time series of abundances, for example, λ = Nt+1 / Nt. 

Posterior distribution: The probability distribution of an unknown quantity conditional on the 

evidence obtained from an experiment or survey, possibly taking into account relevant 

information available prior to the experiment or survey.

Posterior probability: The conditional probability assigned to a random event after the relevant 

evidence or background has been taken into account.

Precinctive: Restricted to a defined geographic area.

Prior probability: The probability distribution that expresses uncertainty about an unknown 

quantity before some evidence (experiment, survey, observation) is taken into account.

Quadratic: Involving the square of a predictor variable in a regression model.

Recruitment: The process by which offspring born in year t survive and grow to join the censused 

population in year t+1. Only a fraction of the neonates produced in year t will survive to recruit 

to the censused population in year t+1

Somatic growth: The rate of change in an individual animal’s body size (for example, length).

Snout-vent length (SVL): A measure of body length commonly used in reptiles. Length is 

measured from the anterior tip of the snout to the vent or opening of the cloaca. SVL is generally 

preferred over total body length because in reptiles many individuals lose parts of their tail 

during their life (likely from attempted predation); thus, SVL is comparable among individuals.

Survival: The rate or probability at which individuals alive at time t remain alive until time t+1.

Uninformative prior: A prior probability distribution that expresses vague or general information 

about a variable, typically with the goal of assigning equal probabilities to all possibilities.

Vital rate: Also called a demographic rate; the rates of survival, growth, and reproduction in a 

population. Vital rates are often linked to individual states or characteristics, such as size, age, or 

sex, to allow researchers to simulate the way in which the population will change over time.

von Bertalanffy growth model: A model of growth in body size where individuals initially 

increase rapidly in size. Then growth slows as individuals approach an asymptotic size. This 

model is commonly used to describe the growth of fish, reptiles, and other ectotherms.
46

Appendix A. Details of Bayesian Models

The models of female reproductive state (gravid or not) and fecundity were each run on 

five independent chains. The five chains were run for 50,000 iterations each after an initial burnin of 1,000 iterations. The resulting chains were thinned by a factor of 10, resulting in 5,000 

iterations per chain and a total posterior sample of 25,000.

The Jolly-Seber open-population capture-mark-recapture model was run on four 

independent chains for 75,000 iterations each, following an initial burn-in of 10,000 iterations. 

The resulting chains were thinned by a factor of three, resulting in 25,000 iterations per chain, 

and a total posterior sample of 100,000.

The von Bertalanffy growth model was run on five independent chains for 30,000 

iterations each after an initial burn-in of 3,000 iterations. The model of giant gartersnake growth 

rates was run on five independent chains for 1,000,000 iterations each, after an initial burn-in of 

10,000 iterations. The chains were thinned by 500, resulting in 2,000 samples per chain, and a 

total posterior of 10,000 samples.

Table A1. Definition of priors for parameters in the reproductive state model. This model estimates the 

relationship between reproductive state and size (snout-vent length) for female giant gartersnakes from 

eight sites in the Sacramento Valley, California, in 2017.

[SD = standard deviation; SVL = snout-vent length]

Parameter Symbol Prior distribution

Mean probability a female is gravid pg Uniform(0, 1)

Linear effect of maternal SVL on reproductive status βgSVL Normal(0, 10)

Linear effect of exam date on reproductive status βdate1 Normal(0, 10)

Quadratic effect of exam date on reproductive status βdate2 Normal(0, 10)

SD of site random effect on α σsite Half-Cauchy(0, 1)

Table A2. Definition of priors for parameters in the fecundity model. This model estimates the relationship 

between size (snout-vent length) and fecundity for female giant gartersnakes from eight sites in the 

Sacramento Valley, California, in 2017.

[SD = standard deviation; SVL = snout-vent length]

Parameter Symbol Prior distribution

Intercept of maternal size-fecundity relationship αf Normal(0, 10)

Linear effect of maternal SVL on litter size βfSVL Normal(0, 10)

SD of site random effect on α σsite Half-Cauchy(0, 1)
47

Table A3. Definition of priors for parameters in the Jolly-Seber model. This model estimates abundance 

and survival from capture-mark-recapture data of giant gartersnakes at eight sites in the Sacramento 

Valley, California, from 2014–17.

[SD = standard deviation]

Parameter Symbol Prior distribution

Mean daily capture probability p Uniform(0, 1)

Mean apparent survival probability φ Uniform(0, 1)

SD of site random effect on p σp,site Half-Cauchy(0, 1)

SD of year random effect on p σp,t Half-Cauchy(0, 1)

SD of individual random effect on p σp,ind Half-Cauchy(0, 1)

SD of site random effect on φ σφ,site Half-Cauchy(0, 1)

SD of year random effect on φ σφ,t Half-Cauchy(0, 1)

Entrance probability η Gamma(1, 1)
48

Table A4. Definition of priors for parameters in the growth models. This includes a von Bertalanffy growth 

model and a model of growth rates (in mm/day) for giant gartersnakes at eight sites in the Sacramento 

Valley, California, from 2014–17.

[SD = standard deviation; SVL = snout-vent length]

Model Parameter Symbol Prior distribution

von 

Bertalanffy Mean asymptotic size (mm) L∞ Normal(1,000, 100)

growth Mean growth coefficient k Uniform(0, 0.01)

Individual variability in growth λ Uniform(0, 100)

SD of year random effect on k σky Uniform(0, 0.01)

SD of site random effect on k σks Uniform(0, 0.01)

SD of site random effect on L∞ σL Uniform(0, 200)

SD of measurement error σe Gamma(0.001, 0.001)

Sex effect on L∞ (mm) mL Normal(0, 316)

Sex effect on k mk Normal(0, 0.01)

Sex effect on λ mλ Normal(0, 100)

Growth rate Mean growth rate αGR Normal(0, 1)

Effect of sex on growth rate βsex Normal(0, 1)

Effect of SVL on growth rate βSVL Normal(0, 1)

Effect of sex × SVL interaction on growth rate βsex × SVL Normal(0, 1)

SD of residual variation in growth rate σGR Half-Cauchy(0, 1)

SD of year random effect on αGR σsite Half-Cauchy(0, 1)

SD of site random effect on αGR σyear Half-Cauchy(0, 1)

SD of individual random effect on αGR σind Half-Cauchy(0, 1)
Publishing support provided by the U.S. Geological Survey

 Science Publishing Network, Sacramento Publishing Service Center

For more information concerning the research of this report, contact the

 Director, California Water Science Center

 U.S. Geological Survey

 6000 J Street, Placer Hall

 Sacramento, California 95819

 http://ca.water.usgs.gov
Rose and others—Findings from a Preliminary Investigation of the Effects of Aquatic Habitat (Water) Availability on Giant Gartersnake (Thamnophis gigas) 

Demography and Reproduction in the Sacramento Valley, California, 2014–17 —Open-file Report 2018–1114

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20181114