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Combining genetic and demographic monitoring better informs conservation of an endangered urban snake

  • Dustin A. Wood ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation U.S. Geological Survey, Western Ecological Research Center, San Diego Field Station, San Diego, California, United States of America

  • Jonathan P. Rose,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

    Affiliation U.S. Geological Survey, Western Ecological Research Center, Santa Cruz Field Station, Santa Cruz, California, United States of America

  • Brian J. Halstead,

    Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Writing – original draft, Writing – review & editing

    Affiliation U.S. Geological Survey, Western Ecological Research Center, Dixon Field Station, Dixon, California, United States of America

  • Ricka E. Stoelting,

    Roles Data curation, Resources, Writing – review & editing

    Affiliation Swaim Biological Incorporated, Livermore, California, United States of America

  • Karen E. Swaim,

    Roles Resources, Writing – review & editing

    Affiliation Swaim Biological Incorporated, Livermore, California, United States of America

  • Amy G. Vandergast

    Roles Conceptualization, Formal analysis, Funding acquisition, Writing – original draft, Writing – review & editing

    Affiliation U.S. Geological Survey, Western Ecological Research Center, San Diego Field Station, San Diego, California, United States of America


9 Sep 2021: Wood DA, Rose JP, Halstead BJ, Stoelting RE, Swaim KE, et al. (2021) Correction: Combining genetic and demographic monitoring better informs conservation of an endangered urban snake. PLOS ONE 16(9): e0257374. View correction


Conversion and fragmentation of wildlife habitat often leads to smaller and isolated populations and can reduce a species’ ability to disperse across the landscape. As a consequence, genetic drift can quickly lower genetic variation and increase vulnerability to extirpation. For species of conservation concern, quantification of population size and connectivity can clarify the influence of genetic drift in local populations and provides important information for conservation management and recovery strategies. Here, we used genome-wide single nucleotide polymorphism (SNP) data and capture-mark-recapture methods to evaluate the genetic diversity and demography within seven focal sites of the endangered San Francisco gartersnake (Thamnophis sirtalis tetrataenia), a species affected by alteration and isolation of wetland habitats throughout its distribution. The primary goals were to determine the population structure and degree of genetic isolation among T. s. tetrataenia populations and estimate effective size and population abundance within sites to better understand the present and future importance of genetic drift. We also used temporally sampled datasets to examine the magnitude of genetic change over time. We found moderate population genetic structure throughout the San Francisco Peninsula that partitions sites into northern and southern regional clusters. Point estimates of both effective size and population abundance were generally small (≤ 100) for a majority of the sites, and estimates were particularly low in the northern populations. Genetic analyses of temporal datasets indicated an increase in genetic differentiation, especially for the most geographically isolated sites, and decreased genetic diversity over time in at least one site (Pacifica). Our results suggest that drift-mediated processes as a function of small population size and reduced connectivity from neighboring populations may decrease diversity and increase differentiation. Improving genetic diversity and connectivity among T. s. tetrataenia populations could promote persistence of this endangered snake.


Contemporary expansion of urban environments (starting in the mid-20th century) and associated infrastructure into natural areas has been a major cause of habitat loss and fragmentation of wildlife habitat [1, 2]. As a consequence, wildlife populations often become restricted to smaller, isolated patches that are more vulnerable to extirpation [3, 4]. When connectivity across a species’ range is disrupted, the levels and distribution of genetic diversity across local populations can deteriorate as a result of genetic drift (chance loss of alleles through time), which can lead to reduced adaptive potential [57]. Genetic diversity provides the raw material for natural selection and is governed in part by several demographic determinants such as population size, gene flow and life history characteristics [8]. In fragmented landscapes, these demographic determinants can influence the magnitude of genetic drift within local populations. Loss of genetic diversity can be rapid when population sizes are small and lack gene flow from adjacent populations, increasing the frequency of inbreeding and resultant exposure of recessive deleterious mutations that can escalate a population’s extinction risk [911]. In contrast, larger population sizes and increased immigration and gene flow can counter the loss of genetic variation and promote population persistence [8, 12]. For species of conservation concern, monitoring and quantifying parameters related to local population size and connectivity across a species’ range provide crucial information to better manage isolated populations and implement effective mitigation measures to maintain species viability [13].

In conservation biology, genetic effective size (Ne) of a local population, rather than census size, can be used to measure the influence of genetic drift and provides a way to quantify the amount of genetic diversity that can be maintained in a population. This parameter is defined as the number of individuals in an idealized population that would show the same amount of genetic diversity as the population being measured [14]. Effective population size often deviates from the census size, as an idealized population excludes demographic determinants that increase genetic drift in real populations, such as nonrandom reproductive success, unequal sex ratios, inbreeding and changes in population size. These demographic changes may result in a reduction in Ne, and therefore, an increased loss of genetic diversity [11]. In this way, estimates of Ne measure the ability of vulnerable populations to maintain genetic diversity in future generations [14] and, when Ne is small, can prompt investigation into demographic factors that might result in low Ne estimates [15, 16]. When possible, estimating both effective size (Ne) and census size (N) can provide a robust understanding of population viability [17]. One common approach is to evaluate the ratio Ne/N across populations, given the direct connection of demographic and genetic processes represented in the ratio [18, 19], to help determine possible changes to local populations and inform management decisions.

The San Francisco Peninsula of California, USA has experienced substantial loss and modification of natural habitats over the past century as a result of urban and agricultural development [20, 21]. This region is home to the San Francisco gartersnake, Thamnophis sirtalis tetrataenia, which is endangered under both the U.S. and California Endangered Species Acts [22, 23] and classified as imperiled by NatureServe [24]. Although historically common, this species persists in the remnant and highly fragmented coastal marshes, wetlands, and forests of the San Francisco Peninsula from San Mateo County to northwestern Santa Cruz County, California, USA. Primary threats and impacts to T. s. tetrataenia survival have been alteration and isolation of habitats resulting from continued expansion of urbanization, decline of prey, and harvesting pressure by collectors. Six populations of T. s. tetrataenia are currently the focus of conservation efforts [25], although additional populations might occur on private lands. Recovery criteria include protecting and maintaining 10 populations with a minimum of 200 adults in a 1:1 sex ratio [26]. At present, too little is known about the size of local populations and the distribution of genetic diversity among them to adequately assess strategies for maintaining maximum genetic diversity within and across populations. Re-establishing or augmenting T. s. tetrataenia populations may increase the likelihood of their long-term persistence as additional populations would safeguard against extinction if one or more of the existing populations were extirpated due to stochastic events (e.g., wildfire, saline inundation of marsh habitat, and disease). In addition, captive breeding and translocations can play a vital role in the recovery of a species by providing a “rescue” mechanism to populations under immediate threat [27, 28]. Before establishing captive breeding and translocation programs, consideration of key factors is prudent: (i) how many founding populations would be necessary to capture the population structure and diversity present in the wild, (ii) which wild “source” populations have large and stable abundance and could best tolerate loss of individuals (for captive breeding or translocation efforts) without compromising viability [29, 30], and (iii) which life stages should be taken from source populations to minimize effects to those populations and conversely, which life stages could be used to most effectively establish or augment populations [31].

In this study, we used genome-wide SNP data and capture-mark-recapture models to provide estimates of effective size (Ne) and abundance (Na; an approximate estimation of census size (N)) to evaluate the population genetic diversity and size across the northern, central and southern range of T. s. tetrataenia along the San Francisco Peninsula. We use these two datasets to make conservation-relevant inferences about genetic and demographic characteristics of populations across the range of T. s. tetrataenia. The objectives of this study were to (1) determine the degree of population structure and genetic isolation among populations of T. s. tetrataenia using the genome-wide SNP dataset, (2) estimate current levels of Ne and Na within seven focal sites to better understand the present and future importance of drift in this subspecies; (3) use temporally sampled datasets to determine if there is evidence of genetic change over time, and (4) identify source populations that could be used in genetic rescue and captive breeding efforts, and conversely, populations that could benefit from genetic rescue. This study is a comprehensive genetic and demographic analysis of populations of T. s. tetrataenia and provides information that could be used as a basis for population management that is consistent with natural patterns of diversity across the range of T. s. tetrataenia.

Materials and methods

Field methods and sample collection

Between 2016–2018, we performed demographic surveys at seven sites (hereafter focal sites) that span the geographic range of T. s. tetrataenia (Fig 1). Four focal sites were located in northern San Mateo County: Pacifica, Skyline, Crystal Springs, and San Bruno. The remaining three focal sites were located in central and southern San Mateo County: Mindego, Pescadero, and Año Nuevo. During demographic surveys we also collected tissue samples for genetic analysis by removing a 5–10 mm tail tip from each snake and immediately preserved the tissue in 95-percent ethanol. At Pacifica, San Bruno, Pescadero, and Año Nuevo, tissue samples were also available from surveys conducted between 2004–2010 that were used in combination with the 2016–2018 tissues to investigate genetic change over time. We also obtained tissues from five sites (hereafter satellite sites; Fig 1) with small sample sizes (2 to 7 per site) that we included in phylogenetic and population structure analyses only.

Fig 1. Sampling map and cluster assignments at K = 2 across the San Francisco Peninsula in California, USA, with the focal sites (larger dots) and satellite sites (smaller, numbered dots) that were used in rangewide and phylogenetic datasets.

Light grey areas and dark grey lines respectively correspond to urban areas and major highways/streets (World Terrain Basemap source: USGS, ESRI, NOAA. Urban land coverage for San Francisco Bay Region source: Bay Area Open Space Council, GreenInfo Network, Conservation Lands Network, and San Francisco Bay Area Upland Habitat Goals Project. (2011). California Urban Lands: Farmland Mapping and Monitoring Project, 2006. Bay Area Open Space Council. Available at

We sampled Pacifica, Skyline, Crystal Springs, Pescadero, and Año Nuevo from 5 April to 8 June 2018, for 29–46 days per site per year (Table 1). At Mindego we sampled for 53 days from 2 April to 24 May 2016, and at San Bruno we sampled for 68 days from 3 April to 9 June 2017. We constructed 8–12 m long and 0.4 m high drift fences from tempered hardwood boards and installed them, buried approximately 5–7 cm into the soil, adjacent to wetlands and in nearby upland habitat. Each fence had four rectangular wooden funnel traps measuring 30 cm x 40 cm x 23 cm with a hardware cloth funnel facing towards the center of the fence. Two funnel traps were placed flush to each side of the fence at either end [32]. We installed 6–84 drift fences per site, resulting in 24–336 funnel traps deployed per site (Table 1). We checked traps once per day during the afternoon (starting at approximately 1400–1500 hours) at Año Nuevo, Pescadero, and Pacifica. At Crystal Springs, Mindego, San Bruno and Skyline we checked traps twice each day, once in the morning (starting between 0830 and 1000) and once in the afternoon (starting at or after 1400). At Pescadero, we also deployed 101 artificial cover objects (52 2 ft x 3 ft wooden boards, 49 2 ft x 3 ft corrugated metal) to aid in capturing snakes, following long-term survey protocols at this site. At all sites we made hand captures of T. s. tetrataenia when possible.

Table 1. Summary of field efforts at the seven focal sites including number of traps, duration of trapping effort, number of trap nights, captures, number of individuals (# Ind), number of females (F) and males (M), and unknown sex (U), total area (TA) of available habitat and effective area sampled (EAS) for each site.

Sites differed in size of available habitat and in the area sampled by traps and cover objects. To define the total area of available habitat for each site, we created polygons in ArcGIS version 10.7.1 [33] that encompassed all suitable habitat, whether wetlands or non-forested uplands. The effective area sampled was then calculated by using a fixed buffer of 200 m around all trap and artificial cover object locations for each site. We chose a 200 m buffer based on the maximum distance moved between captures for greater than 95 percent of individual T. s. tetrataenia at our study sites (S1 Fig). The total site area and effective area sampled for each site are given in Table 1. At Pacifica and San Bruno, nearly all of the available habitat for T. s. tetrataenia was within the area effectively sampled by traps, and the nearest known population of T. s. tetrataenia was more than 2.5 km away. In contrast, although the area sampled at Skyline and Crystal Springs was comparable to Pacifica, only 60–70 percent of the area was sampled because suitable wetland habitat was present nearby and additional habitat (not included in our calculations) is present along most of the 7 km corridor separating these two sites. Año Nuevo, Mindego, and Pescadero were much larger sites and the area sampled was less than 60 percent of the available habitat.

We identified all captured snakes to species and measured snout-vent length (SVL) and tail length to the nearest millimeter using a meter stick. We measured the mass of each captured snake in grams using a Pesola® spring scale and determined sex by cloacal probing. We were unable to determine the sex of a few snakes because wounds near the vent or tail prevented probing. At all sites except San Bruno, we gave each T. s. tetrataenia a unique ventral brand with a medical cautery unit that corresponds to a numerical code [34]; at San Bruno, we marked snakes with unique ventral scale clips and also implanted each with Passive Integrated Transponder (PIT) tags. We processed and released all snakes at their site of capture within one hour. Snakes were handled in accordance with IACUC Protocol WERC-2015-01, which was approved by the Western Ecological Research Center Animal Care and Use Committee in association with the University of California, Davis, and as stipulated in U.S. Fish and Wildlife Service Recovery Permits TE157216-4 and TE815537-10, California Department of Fish and Wildlife Scientific Collecting Permits/MOUs SC- 10779 and SC-002672.

Capture mark recapture modeling and census size estimation

We used population abundance estimates (Na) of adult T. s. tetrataenia to approximate census size (N) for each site during its respective survey period. We used a size threshold of 300 mm SVL to classify individuals as mature adults, following [35]. At all sites except San Bruno, we used a Bayesian multinomial N-mixture model [36] with random effects of date, site, and individual on capture probability (p) to estimate Na for the five sites monitored in 2018. By treating site as a random effect, this multi-site model shares information about the capture process among sites, allowing for more precise estimates of capture probability and abundance at each site. We also included fixed effects of air temperature, sex, SVL, and a behavioral response to previous capture on capture probability. The behavioral response tests whether there is an effect of being captured on day d-1 on the probability of being captured on day d. We centered and standardized the air temperature and SVL covariates by subtracting the mean from each measured value and dividing by the standard deviation to produce covariates with a mean = 0 and a standard deviation = 1. We evaluated the importance of covariates on capture probability by calculating the number of model iterations in which the parameter had the same sign as the median estimate and dividing by the total number of iterations. For example, if the median estimate for the effect of air temperature was positive, we calculated the posterior probability of a positive relationship as the proportion of posterior samples in which the air temperature coefficient was positive. We considered evidence for a positive (or negative) relationship between a covariate and capture probability to be strong if the posterior probability of the parameter being positive (or negative) was ≥ 0.9. For Mindego, we used the same model, except that no random effect of site was included because the model was fitted to data for a single site. For San Bruno, we used the similar Huggins closed capture modeling approach [37, 38] with results reported from a model with sex and random time effects retained for capture probability.

We estimated the number of T. s. tetrataenia that were present but not captured each year using data augmentation [39]. At all sites except Mindego and San Bruno, we augmented the observed capture histories of San Francisco gartersnakes with enough additional, all-zero capture histories for a total pool of 1000 individuals over all five sites. The model then estimated how many of these 1000 individuals were present and available to be captured, at each site, and this was the estimated abundance of T. s. tetrataenia. For Mindego, we augmented the observed capture histories to create a total pool of 500 individuals, and at San Bruno, we augmented the observed capture histories with an additional 2000 individuals, for a total pool of 2555 individuals. We fit Bayesian capture-mark-recapture (CMR) models using the software Just Another Gibbs Sampler [40] accessed through R version 3.6.1 [41] using the “runjags” package [42]. For the multi-site model, we used uninformative Uniform(lower = 0, upper = 1) priors for mean capture probability (p), weakly regularizing Normal(mean = 0, SD = 3.16) priors for covariate effects on capture probability and half-Cauchy(scale = 1) priors for the standard deviation of temporal and site random effects in the CMR model (S1 Table). Priors for Mindego and San Bruno are presented in S1 Table. We ran the CMR model on five independent chains for 200,000 sampling iterations each after discarding the first 10,000 iterations as burn-in, and thinned the samples by a factor of 10, resulting in a final posterior sample of 100,000. For Na estimates, we report the mode of the posterior distribution followed by the 95% Highest Posterior Density Interval (HPDI). We also estimated the distribution of males versus females at each of the focal sites, as recovery criteria include establishing populations with equal sex ratios, and sex ratio bias is known to influence Ne/N ratio estimates. To test for the correlation between the abundance (Na) estimates and sex ratio bias, we performed a regression of modal values of Na and sex ratio for each site.

Laboratory methods and bioinformatics

Genomic DNA was extracted using Qiagen DNeasy Blood and Tissue Kits (Qiagen, Valencia, California). Prior to next-generation sequencing (NGS) library preparation, we quantified DNA on a Qubit fluorometer (Life Technologies), and 500 nanograms (ng) of DNA were used for library preparation. We followed the double-digest restriction-associated DNA (ddRAD) sequencing protocol developed in [43] for NGS library preparation, with some modifications. We digested genomic DNAs using 20 units each of the restriction enzymes SbfI and MspI (New England Biolabs, U.S.A.) and used Agencourt AMPure beads (Beckman Coulter, Danvers, Massachusetts) to purify the digestions prior to ligating uniquely bar-coded adapters with T4 ligase (New England Biolabs). We quantified all ligation products on the Qubit fluorometer, pooled across 12 index groups in equimolar concentrations, and then size selected fragments between 400 and 530 base pairs (bp) using a Pippin Prep size fractionator (Sage Science, Beverly, Mass.). We amplified the recovered fragments from each pool using 5–12 ng of the recovered DNA, Phusion High-Fidelty Taq (New England Biolabs), and Illumina’s primers (Illumina, Inc., San Diego, California). Polymerase chain reaction (PCR) products were then cleaned with Agencourt AMpure beads (Beckman Coulter, Inc., Brea, California) and quantified using the Qubit fluorometer (Life Technologies) before being pooled for sequencing (100 bp single end reads) in a single lane on an Illumina HiSeq 4000 at the Genomics and Cell Characterization Core Facility at the University of Oregon.

We filtered and selected datasets using the stacks version 2.3e [44] bioinformatics pipeline. We used the process_radtag program to clean and filter raw reads following default settings. We conducted initial parameter testing following the recommendations of [45]. This involved examining a series of de novo RAD locus assemblies that used a range of values for the mismatch distance between loci within an individual (M), the number of mismatches between loci in the catalogue (n) from 1 to 6 (fixing n = M), and the minimum stack depth (m = 2–4). The final set of parameters chosen for analysis was based on the total number of polymorphic loci shared by 80% of samples and how the distribution of SNPs per locus was affected. Once optimal parameters were selected (m = 3, M = 2, n = 3), we then executed the ustacks, cstacks, sstacks, and gstacks modules using the denovo map wrapper and generated four different datasets: (1) a dataset comprised of the seven focal sites from the years 2016–2018, hereafter called the 2018 dataset; (2) the temporal dataset, comprised of individuals collected from four sites (Pacifica, San Bruno, Pescadero, and Año Nuevo) at two different time periods (2004–2010 and 2016–2018) and used to assess change in genetic diversity over time and temporal estimates of effective population size (Ne); (3) a dataset comprised of the seven sites as in the 2018 dataset with five additional satellite sites from throughout the range, hereafter called the rangewide dataset; and (4) a phylogenetic dataset comprised of a subset of samples from all focal and satellite sites, in addition to other subspecies of T. sirtalis. All datasets produced by stacks were subjected to a final filter approach that retained loci present across all sampled sites with at least 80 percent (population structure and genetic diversity) and 60 percent (phylogenetic analyses) of the individuals sequenced.

Population structure and genetic differentiation analyses

We evaluated population genetic structure with multiple analytical methods. First, we used the Bayesian clustering framework implemented in structure version 2.3.4 [46] to identify genetic groups and estimate admixture levels among them. structure uses a Markov Chain Monte Carlo (MCMC) method to simultaneously estimate population-level allele frequencies and group individuals into genetic clusters (K) that maximizes the within-cluster Hardy-Weinberg and linkage equilibria. Because there is often a large range of uncertainty in estimating K [4647] we used a hierarchical approach to identify the most probable number of clusters. First, we used the ΔK criterion to identify the highest hierarchical level of population structure [48]. Next, we tested for within-group structure by performing subsequent structure analyses on each of the highest hierarchical clusters that were previously identified. If individuals could not be unambiguously assigned to a cluster (i.e. membership coefficients were < 0.60 for any cluster) we removed them from the dataset prior to the hierarchical structure analyses. We also compared hierarchical ΔK results to the general (non-hierarchical) structure approach that uses log-likelihood values associated with each K [46] and considered the spatial distribution of the sampled sites in relation to various cluster assignments. For all analyses, we used the rangewide dataset and the admixture model with correlated allele frequencies and estimated the probability of K (1–15) clusters. For each K that was evaluated, we used 10 separate runs with 500,000 iterations of the MCMC algorithm following a burn-in of 500,000 iterations and calculated the mean log probability of the data (lnPr(X|K) in [46]) for the 10 runs combined. Results were compiled graphically in clumpak [49].

To complement the structure analyses, we used discriminant analysis of principal components (DAPC), a multivariate ordination approach implemented in the R package adegenet version 2.1.0 [50]. DAPC is a non-model based method and does not require the assumption of HWE or unlinked markers, as in the structure analyses. This method evaluates the optimal number of genetic clusters using PCA ordination to maximize the between-group variation while minimizing the variation found within groups. Given that DAPC relies on data transformation using PCA as a prior step, retaining too many PCs can lead to overfitting the discriminant functions. We used the cross-validation function xvalDapc in adegenet to identify the optimal number of PCs to retain. The DAPC procedures were replicated 100 times at each level of PC retention, and we selected the number of PCs associated with the lowest root mean squared error (RMSE) for the final analysis.

When a species’ distribution is characterized by isolation by distance (IBD), where genetic differentiation among populations is correlated with the geographic distance that separates them, it can be difficult for clustering methods to distinguish between genuine genetic clusters and artefacts due to IBD [47, 51]. To evaluate this relationship, we plotted pairwise estimates of differentiation by geographic distance using the rangewide dataset and used Mantel tests to assess correlation between pairwise genetic and geographic distances [52]. The magnitude of population differentiation (FST) was estimated using Weir & Cockerham’s θ [53], an unbiased estimator of FST. To visualize genetic distances with geography, individual point locations and pairwise genetic distances among individuals were analyzed with the package MAPI v. 1.0.0–62 [54] in R 3.6.1. The analysis highlights spatially connected cells with genetic distance values that are significantly high (genetically discontinuous) or low (genetically continuous) in comparison to random permutations of values among sample locations, which provides a graphical representation of genetic distances among samples without the confounding effects of IBD [53]. We calculated codominant genotypic distances [5556] among all individuals, georeferenced to their collection locations. Ellipses representing the pairwise genetic distances among points were overlaid on a grid, and grid cell values were computed as the average of overlapping ellipses. Cell sizes were computed in MAPI as a function of the study area and number of samples, using a beta = 0.25 for random (versus regular) sampling [57] and significance determined with 1000 permutations. We exported the results as ESRI shapefiles and visualized them in ArcMap 10.4.1. We also assessed whether selection may contribute to population structure across the San Francisco Peninsula by using the FST-outlier approach implemented in bayescan v2.1 [58]. This Bayesian method identifies putative outlier loci under selection because they show FST coefficients that are significantly more different than expected under neutrality. bayescan analyses consisted of 20 pilot runs of 5,000 iterations with a burn-in of 50,000 iterations. We used a thinning interval of 10 for a total number of 100,000 iterations, and ran two analyses, one with the prior odds for the neutral model set at 100 and the other analysis with the prior odds set at 500. To assess the contribution of neutral SNPs versus SNPs potentially under selection in delineating population structure, we repeated the structure analysis and pairwise estimates of differentiation (θ) using only the SNPs identified as outliers.

Finally, we used mrbayes 3.2 [59] to estimate a phylogenetic tree of genetic relationships between sites and genetic clusters. We randomly selected three samples per site from the rangewide T. s. tetrataenia dataset, with the exception of Site 5 where only two samples were collected. We also included a single sample of the red-sided gartersnake (Thamnophis sirtalis infernalis) from Lake Lagunita, Stanford University, Santa Clara County, California, USA (CAS 201525), and a single sample of the valley gartersnake (Thamnophis sirtalis fitchi; CAS 212588) from Colusa County, California, USA. A single sample of the undescribed south coast gartersnake (Thamnophis sirtalis ssp.), taken from the Santa Margarita River, San Diego County, California, USA, was used to root the tree because this sample represents the furthest geographic distance away from the San Francisco Peninsula and it is currently treated as an undescribed taxon [6062]. Tree searches consisted of two independent MCMC searches of tree space for 5 million generations each, sampling every 1,000 steps and discarded the initial 25% of samples from each run. We assessed evidence for convergence using tracer 1.7.1 [63] and considered lineages with posterior probabilities ≥ 0.95 to be strongly supported.

Effective size estimation and genetic rescue

We calculated summary statistics in stacks to compare genetic diversity among sites and genetic clusters with the 2018 and temporal datasets. Summary statistics include the following: allelic richness (Ar), mean observed heterozygosity (Ho), mean expected heterozygosity (He), and mean nucleotide diversity (𝜋). We estimated the contemporary effective size (Ne) of each site with the 2018 dataset and temporal dataset. We relied on the Ne thresholds outlined in Frankham et al. [64] to help guide management considerations. They conclude that a minimum Ne ≥ 100 as a short-term goal avoids the risk of extinction owing to inbreeding depression. We used the linkage disequilibrium method (LDNe) [65] and when possible a two-sample temporal method using moment-based F-statistics [66] within the program NeEstimator v2.1 [67] to obtain Ne values. The LDNe method has been shown to generally outperform coalescent-based methods for Ne estimation [68]. We assumed random mating at each site, calculated 95% confidence intervals for point estimates using the jackknife-across-samples method [69], and screened out rare alleles using a critical cut-off value (Pcrit) of 0.05. We used Ne values and the adult population size estimates (Na) to compute the Ne/Na ratio for each site for each year.

We evaluated whether the seven focal sites sampled in our 2018 dataset met criteria that would indicate that genetic erosion has occurred and whether genetic rescue could increase heterozygosity. Following the equation of Frankham et al. [13], we calculated the mean inbreeding coefficient (F) for each site as the ratio of average heterozygosity (H) of the receiver site (inbred) to the proposed heterozygosity of the source site (outbred). We used the suggested threshold of F > 0.01 [13] to identify receiver populations with genetic erosion in comparison to the source population that could benefit from assisted migration and augmentation management. To capture potential inbreeding effects of both genetic drift and non-random mating, we used observed heterozygosity estimates for each receiver site and expected heterozygosity estimates for the source site [13]. We split source-recipient comparisons according to the regional clusters identified from populations structure analyses and computed the mean inbreeding coefficient (F) for each site in relation to the following source population scenarios: (i) the closest source site to the receiver site, (ii) the source site with largest Ne, (iii) the source site with the highest He, and (iv) the source site with largest adult population size (Na).


Population size and sex ratio estimates

We made 1088 captures of 815 individual T. s. tetrataenia, over 34,576 trap-nights of sampling at the seven sites from 2016–2018 (Table 1). We captured 362 females, 392 males, and 7 snakes of unknown sex. The highest abundance estimate (Na) of T. s. tetrataenia was at San Bruno (modal = 1317, 95% HPDI = 1145–1487), followed by Mindego ( = 204, 95% HPDI = 144–328), Año Nuevo ( = 123, 95% HPDI = 93–161), and Pescadero ( = 73, 95% HPDI = 61–88) (Fig 2). Abundance estimates were nearly equal at Pacifica ( = 47, 95% HPDI = 33–63), Skyline ( = 48, 95% HPDI = 33–61), and Crystal Springs ( = 50, 95% HPDI = 35–64). In general, posterior distributions of sex ratios (males/females) were more female-biased in northern regional sites and male-biased in southern regional sites (Fig 3). Regression analysis of Na estimates and sex ratios were not significant when all sites were included (R2 = -0.19, p = 0.99). However, this was primarily due to the large Na estimate at San Bruno relative to all other sites. If San Bruno was treated as an outlier site and excluded from the analysis, then a significant correlation between Na and sex ratio bias was observed (R2 = 0.55, p = 0.05). However, the sex ratio did not significantly differ from 1 at any site, on the basis of whether the 95% HPDI overlapped 1.

Fig 2. Posterior distributions of estimated adult abundance (Na) at seven sites sampled for San Francisco gartersnakes (Thamnophis sirtalis tetrataenia) in 2016 (Mindego), 2017 (San Bruno), and 2018 (Año Nuevo, Pescadero, Pacifica, Skyline, and Crystal Springs).

The posterior distribution is displayed as a histogram of the frequency of possible Na values for each site. Red dashed lines and values presented in each panel represent the mode of the posterior distribution for adult abundance at that site.

Fig 3. Posterior distributions of estimated adult sex ratio (males/females) at seven sites sampled for San Francisco gartersnakes (Thamnophis sirtalis tetrataenia) in 2016 (Mindego), 2017 (San Bruno), and 2018 (Año Nuevo, Pescadero, Pacifica, Skyline, and Crystal Springs).

The posterior distribution is displayed as the probability density of different sex ratio values for each site. Red dashed lines in each panel represent a 1:1 sex ratio. Values to the left of the red dashed line represent a sex ratio biased towards females, values to the right of the red dashed line represent a sex ratio biased towards males. If the posterior distribution broadly overlaps both sides of the red dashed line, there is no evidence of a biased sex ratio in that population.

Summary of bioinformatics for genetic analyses

Using the stacks pipeline, our ddRAD sequencing effort yielded an average of 3,368,646 sequences per-individual (median: 3,276,428; min: 94,562; max: 8,250,652) across the 248 individuals sequenced. The mean coverage depth per-individual was 61.4X (min: 27.3X; max: 94.7X). After merging and calling final consensus sequences, we obtained 121,639 loci across 186 individuals sequenced. Once final filters were applied, we obtained 3,788 SNPs for the 2018 dataset, 2,747 SNPs for the temporal dataset, 3,029 SNPs in the rangewide dataset, and 7,036 SNPs in the phylogenetic dataset.

Population structure and genetic differentiation

Using a hierarchical approach and the ΔK statistic, structure partitioned T. s. tetrataenia sites into two regional clusters (Fig 4): (i) a “northern” regional cluster that extends from Pacifica and San Bruno southward along the San Andreas rift valley (Skyline, Site 1 & 2, and Crystal Springs), and (ii) a “southern” regional cluster that extends from Mindego westward to the coastal sites of Site 4, Pescadero, Año Nuevo, and Site 5. Membership coefficients (Q) of individuals from Site 3 showed nearly equal proportions to both northern and southern regional clusters (Q = 0.567/0.433, respectively) and were admixed between clusters up to K = 8, where they assigned to a distinct genetic cluster. Therefore, this site was excluded from the dataset before performing within-group analyses of regional clusters. Within the northern regional cluster, four miniclusters were supported (Fig 4): (i) Pacifica, (ii) Skyline, (iii) Sites 1 and 2 and Crystal Springs, and (iv) San Bruno. Snakes along the San Andreas rift valley showed some levels of admixture with each other, whereas assignment proportions for snakes from both Pacifica and San Bruno were exclusive. Similarly, within-cluster analyses across the southern regional cluster supported three additional miniclusters (ΔK = 3): (i) Mindego, (ii) Pescadero, Sites 4 and 5, and (iii) Año Nuevo. Some individuals sampled at Año Nuevo were admixed with the minicluster containing Pescadero, Site 4 and 5. The results of each K evaluated (K = 1–8) are presented in S2 Fig.

Fig 4. Cluster assignments of individuals from the sites sampled across the range of T. s. tetrataenia sites.

structure, assignments (a) show the two clusters identified at K = 2 that separate sites into northern and southern regional clusters, and (b) hierarchical analysis within each regional cluster that supported four miniclusters (northern) and three miniclusters (southern).

The DAPC cross-validation indicated 30 PCs provided the highest mean success of assignment at 0.953 with a lowest RMSE of 0.062. The final DAPC analysis resulted in seven discriminant functions, with a 0.536 proportion of conserved variance. The resulting DAPC plots largely discriminated the same seven miniclusters as the structure within-group analyses (Fig 5). The first two discriminant functions strongly discriminated among Pacifica, San Bruno and the San Andreas rift valley in the north (Fig 5A); however, there was minimal discrimination among sites within the San Andreas rift valley. The DAPC plot using the third and fourth discriminant functions showed Mindego separated from most other sites in the south (Fig 5B), while all other sites occupied distinct but similar linear space.

Fig 5.

DAPC scatterplots of sampled individuals using (a) discriminant functions 1 and 2, and (b) discriminant functions 3 and 4 (discriminant functions are indicated by the grey bars).

bayescan analyses (run with prior odds set at 100 and 500) identified 12 outlier SNPs with FST coefficients that were significantly more different than expected under neutrality. Population structure analyses using these 12 loci did not produce any meaningful patterns (S3 Fig) indicating that there was little effect of selection in our dataset. We detected a significant pattern of increasing genetic isolation with geographic distance among sites using the rangewide dataset (Fig 6A; R2 = 0.17, p = 0.008). The inter-individual genotypic distances mapped using the software MAPI identified two areas associated with significantly higher genetic dissimilarity (Fig 6B). The first area of higher genetic discontinuity is broadly centered along the Santa Cruz Mountains and separates the north and south regions, similar to the cluster analyses. In northern San Mateo County, cells separating San Bruno from other sites received “hotter’ values of genetic discontinuity, highlighting the isolation of individuals at this site. Global Fst among the sites sampled based on the rangewide dataset was 0.148, Fst between the northern and southern regional clusters was 0.077, and pairwise Fst estimates between sites with ≥ 5 samples were significant and ranged from 0.039 to 0.218 (Table 2). Temporal pairwise estimates of genetic differentiation also increased between the 2004–2010 and 2016–2018 sample periods (Fig 7), with site comparisons involving Pacifica and San Bruno showing the largest changes.

Fig 6. Isolation by distance of pairwise genetic differentiation (θ) estimates and MAPI inter-individual pairwise distance surface using genotypic distances.

Mantel tests confirmed a significant positive correlation (R2 = 0.173; p = 0.008). World Terrain Basemap source: USGS, ESRI, NOAA. Urban land coverage for San Francisco Bay Region source: Bay Area Open Space Council, GreenInfo Network, Conservation Lands Network, and San Francisco Bay Area Upland Habitat Goals Project. (2011). California Urban Lands: Farmland Mapping and Monitoring Project, 2006. Bay Area Open Space Council. Available at

Fig 7. Pairwise genetic differentiation estimates plotted by geographic distance among four sites (Pacifica, San Bruno, Pescadero, and Año Nuevo) in kilometers.

Light grey diamonds are pairwise estimates between sites measured in 2005, and black diamonds from 2018: (1) Pacifica vs. San Bruno, (2) Pescadero vs. Año Nuevo, (3) San Bruno vs. Pescadero, (4) Pacifica vs. Pescadero, (5) San Bruno vs. Año Nuevo, and (6) Pacifica vs. Año Nuevo. All values increased over time.

Table 2. Thamnophis sirtalis tetrataenia pairwise genetic differentiation estimates (θ; Weir & Cockerham [51]) for sampled sites with > 5 samples/site.

Phylogenetic tree estimation recovered two regional clades corresponding to northern and southern T. s. tetrataenia that are consistent with cluster analyses (Fig 8). We find strong support for relationships within the Northern clade. Pacifica and Skyline form distinct groups that are sister to a group corresponding to the remaining San Andreas rift valley sites, and San Bruno forms a distinct group that is sister to all other sites nested in the Northern clade. With the exception of Pescadero, sites included in the southern clade tended to form distinct groups, but the relationships among sites were not well supported. A third clade, comprised of different T. sirtalis subspecies, renders the two T. s. tetrataenia clades paraphyletic. This clade is sister to the northern clade and includes individuals from two sites that are often treated as T. s. tetrataenia-T.s.infernalis intergrades (Site 3 and Lake Lagunita, Stanford University) as well as a single sample of T. s. fitchi from Colusa County.

Fig 8. Phylogenetic tree of Thamnophis sirtalis tetrataenia based on 7,036 loci using MrBayes.

The northern and southern clades within T. s. tetrataenia are colored according to population cluster analyses, and samples are labeled by sites sampled. Black dots indicate branch support of ≥ 0.95 posterior probability.

Genetic diversity, effective size, Ne/Na ratios, and genetic rescue options

Diversity estimates were generally similar across sites, with the exception of Pacifica where measurably lower values were observed (Table 3). The highest estimates of diversity were found at Crystal Springs and Pescadero. Regional cluster diversity estimates were highest in the southern cluster for all diversity indices except allelic richness. Temporal estimates of diversity decreased between the 2004–2010 and 2016–2018 samples at Pacifica and Pescadero, while San Bruno and Año Nuevo increased in genetic diversity (Table 3). Effective population size (Ne) estimates were below the short-term threshold recommendation to limit inbreeding depression (≥ 100) for most sites with point estimates ranging from 9 to 60 (Table 4). The only exception was San Bruno, where Ne was estimated at 254. Effective population size estimates were particularly low at Pacifica and Crystal Springs (Ne = 9–13 and Ne = 10, respectively). Estimates using the two-sample temporal method provided similar point values and smaller confidence intervals than the LDNe method (Table 4). Using the point estimates for Ne and Na, we estimated an Ne/Na ratio for each site. The highest ratio was at Pescadero (Ne/Na = 0.78), followed by Skyline (Ne/Na = 0.64). The remainder of the sites were lower and ranged from 0.16–0.34.

Table 3. Diversity statistics based on SNPs from the “2018 dataset” and “Temporal dataset”.

Table 4. Ne estimates using single sample (LDNe) and two-sample temporal methods, year(s) sampled, and Ne/Na ratios across the seven focal sites.

We used estimates of inbreeding coefficients (F) to investigate evidence of genetic erosion. Within northern sites, we estimated mean inbreeding coefficients (F) greater than 10 percent at Pacifica across all four genetic rescue scenarios (Table 5). This population also met other criteria that suggest genetic erosion (small, Na & Ne < 100 and isolated). For San Bruno, the mean inbreeding coefficient (F) was high using the nearest source site (Skyline) and the source site with highest heterozygosity (Crystal Springs), but F did not exceed the 10% threshold and San Bruno did not meet other genetic erosion criteria except being isolated. Within the southern sites, only Mindego showed evidence of genetic erosion. Estimates of F for Mindego were marginally greater than 10% using Pescadero as the source site and this site is geographically isolated with a low effective size (Ne < 100). Overall, these results suggest that several sites may be suffering from genetic erosion.

Table 5. Assessment of genetic erosion and the expected impact of genetic rescue scenarios on recipient populations using different donor sites and scenarios based on the mean inbreeding coefficient (F) calculated following the equation of Frankham et al. [13].


Using a combination of genomic and demographic data, we show evidence of regional partitioning and differences in the amount of genetic diversity across the range of an endangered vertebrate within a landscape that includes both urban and natural barriers. Genetic partitioning is consistent with the effects of distance in a movement-limited organism and degree of isolation, as well as local demographic artifacts that drive differentiation in the absence of gene flow. Below we detail these findings and discuss their implications for management.

Regional population structure

Phylogenetic, clustering, and genetic differentiation analyses all supported two regional groups of T. s. tetrataenia, a northern and southern group. While connectivity throughout most of the San Francisco Peninsula has waned over the last century due to urban expansion (Fig 1), long standing environmental gradients along the Santa Cruz Mountains may have also presented movement barriers for T. s. tetrataenia even prior to urbanization. For example, freshwater wetlands with heterogeneous grassland-scrub upland communities are key habitat components that support perennial T. s. tetrataenia populations and their co-occurring amphibian prey species [7073], but they predominantly occur at lower elevations along the east and west sides of the Santa Cruz Mountains. In contrast, higher elevations along the crest of the mountains are dominated by the cooler, redwood-douglas fir forests [7475]. Where wetland habitat is available at high elevation, such as Mindego (~550m), it tends to be naturally isolated from other such sites (Fig 1). The upland grassland-scrub communities at Mindego are bounded by the mixed evergreen forests that separate this population by several kilometers from other permanent wetlands in the region [72]. Although isolated, our genetic data demonstrate that this population is more closely allied to the southern regional group compared to the northern group, suggesting that a reduction in gene flow in the middle of the species range is due to a combination of habitat limitations and geographic isolation. Further sampling in geographically intermediate areas would provide a better assessment of genetic interactions between the two major groups, but nonetheless these data support the recognition of these historically distinctive groups as separate management units.

Our results also corroborate past findings of a putative boundary between T. s. tetrataenia and T. s. infernalis, another subspecies of the common gartersnake (T.sirtalis) that borders the southern range of T. s. tetrataenia [7677]. Studies of color pattern have found a high proportion of intermediate color patterns between the two subspecies along the east side of the Santa Cruz Mountains, from Site 3 to as far south as Palo Alto [7677]. Our phylogenetic analyses grouped individuals sampled at Site 3 into a ‘mixed clade’ that included other samples of T. sirtalis and was sister to the northern T. s. tetrataenia clade (Fig 5). In light of both morphological and genetic patterns, the southern San Andreas rift valley, from the region of Site 3 southward, likely represents a region of gene exchange between these recently diverged taxa. Previous investigations of relationships among T. sirtalis subspecies across western North America using mtDNA [78] and both mtDNA and five microsatellites [79] have provided little evidence of diversification among T. sirtalis subspecies in this region. Although outside the scope of our study, future investigations using extended sampling of T. sirtalis subspecies and genome-wide SNP data would provide greater number of markers and may help to better evaluate the extent of gene flow and taxonomic limits within this region.

Population size and genetic diversity

Given the protected status of T. s. tetrataenia and the possibility of isolation among populations due to human alterations of the landscape, we evaluated effective population size (Ne) and adult abundance (Na) (as an approximation of census size) among the sites sampled to assess whether populations in different parts of the range vary across these parameters. One of our major findings is that estimates of both Ne and Na are small (≤ 100) for a majority of the sampled sites. Low empirical estimates for both Ne and Na were more pronounced among the northern regional populations, with the exception of San Bruno (which we discuss below), where anthropogenic habitat alteration and landscape fragmentation have increasingly reduced connectivity among populations. Long-term maintenance of at least 10 populations with a minimum of 200 adults with equal sex ratios is a U.S. Fish and Wildlife Service recovery criterion for T. s. tetrataenia [26]. Of the seven focal sites we monitored, five have adult population size estimates below this recovery threshold and the smallest sites appear to have shifted sex ratios, suggesting that this goal has not been met. Furthermore, recent empirical studies suggest minimum Ne thresholds for endangered species should be maintained above 100 to avoid short-term inbreeding depression and fitness loss and above 1000 to retain genetic diversity over longer time scales [64]. Our empirical findings (Ne < 60 in 6 of 7 focal sites) suggest that most populations are at risk of inbreeding depression and that more effective management of this species might be achieved by taking this into account. San Bruno was the only site that exceeded the minimum short-term threshold with an Ne of 254, which was coupled with highest abundance estimate (Na = 1317). Habitat enhancement activities over the past two decades at San Bruno may have facilitated the higher abundance estimates at this site despite being embedded in an urban matrix since the 1960s [35, 77], suggesting that habitat restoration and management may alleviate local loss of genetic diversity in this species.

Our results of the effective to census size ratio (Ne/N), where adult abundance (Na) was used to approximate census size (N), are a tangible contribution towards conservation actions of this endangered snake. Given the connection of genetic and demographic processes in the effective to census size ratio (Ne/N) ratio, researchers have recommended using these parameters as interchangeable surrogates of each other [17, 80]. On the basis of theoretical expectations, effective population size (Ne) is typically smaller than census size (N), and Ne/N ratios in real populations should range between 0.25–0.50 [18]. Comparing estimates for populations from a wide range of taxa, including some that are stable and some of conservation concern, Palstra & Ruzzante [80] reported empirical Ne/N ratio estimates that ranged from 0.16 to 0.20, while slightly lower estimates (median values ~0.1) were recovered by Frankham [80]. Few studies have estimated Ne/N ratios in snakes (especially across multiple populations) of a single species to document the magnitude of variation. However, Madsen et al. [82] reported effective size and census size estimates for an isolated population of adders (Vipera berus) that led to Ne/N ≈ 0.34, and Bradke et al. [83] report similar Ne/N ratios for two populations (Ne/N = 0.27 and 0.30) of the threatened Eastern massasauga (Sistrurus catenatus). Across our seven focal sites, Ne/N ratios varied widely but generally ranged between 0.16–0.40; however, two estimates were much higher at Skyline and Pescadero (0.64 and 0.78 respectively). Several factors are known to influence the Ne/N ratio at the population level, such as fluctuations in census size, unequal sex ratio and variance in reproductive success [81], so it is not surprising that we recovered a range of values across the different focal sites. We found some evidence that lower abundance might be associated with sex-ratios shifted towards more females, although this relationship was only significant if San Bruno (a site that has received intense habitat enhancements, as mentioned above) was treated as an outlier population (R2 = 0.55, p = 0.05). Nonetheless, sex-ratios shifted towards females in smaller populations may be related to the divergent reproductive strategies of males and females of T. sirtalis [8485]. Males typically invest more energy in mate-searching and courtship than females and this investment is likely to increase in populations with lower abundance, which may lead to reduced survival in males. However, other factors may also be at play. For some sites, the area of potential habitat was larger than what could be adequately surveyed, which could lead to a lower census estimate relative to expectations based on the Ne estimate from that same site (which does not rely on a thorough sampling of all individuals in the population). This might explain the higher ratios reported at Pescadero and Skyline. Nonetheless, these represent reasonable baseline estimates of the effective to census size ratio (Ne/N) in T. s. tetrataenia and can be used to evaluate temporal and spatial differences in Ne/N ratios as future conservation actions are implemented.

Theoretical studies indicate that an ideal, genetically healthy population is one that has a sufficiently large Ne coupled with moderate connectivity to other populations to maintain gene exchange and counter the effects of genetic drift [14]. When connectivity is disrupted, genetic differentiation between populations tends to increase and effective population sizes tend to decrease [16, 86]. We found greater population structure, lower Ne, and lower diversity within the northern regional populations than in the southern region, possibly because of the greater loss of physical and genetic connectivity due to anthropogenic disturbances over the last century (Fig 1). Genetic analyses of temporal datasets show that genetic differentiation has increased over time and that sites that are more isolated (Pacifica and San Bruno) show a greater change in differentiation over time. Pacifica in particular, had the lowest genetic diversity estimates of all sites sampled, small effective population size, the highest mean inbreeding coefficient (F), and decreased genetic diversity over time. Population surveys over the past several decades at this site have suggested that bottlenecks were likely the result of amphibian prey base declines from saltwater intrusion into wetlands from an eroded seawall during the 1980s [8789]. Our genetic results also suggest that drift-mediated processes, as a function of small population size and reduced connectivity from neighboring populations, may also be contributing to decreasing trends in diversity at Pacifica.

Concluding remarks

When contemporary disturbances are the driver of genetic differentiation, conservation strategies may focus on enhancing and reconnecting populations by reducing ecological threats and restoring habitat to pre-disturbance conditions [90]. Assisted gene flow, termed “genetic rescue,” has also been a useful tool for slowing the decline of small, at-risk populations [9194]. Following criteria developed by Frankham et al. [13], our evaluations of local population heterozygosity indicate that Pacifica may benefit from assisted gene flow from any other population in the northern region. San Bruno and Mindego also had marginally high inbreeding coefficients and both are isolated from other populations. However, for these sites the high abundance of adult snakes (Na ≥ 200) may buffer against the effects of genetic drift despite their isolation, given that the effects of drift are less pervasive in larger populations. Continued monitoring (both genetic and demographic) at these two sites may be necessary to assess population stability and whether assisted gene flow would help to retain genetic diversity over time.

Should a genetic management and recovery strategy be adopted for T. s. tetrataenia, the population genetic results provided here, along with estimates of abundance, can help to establish source and recipient populations. This information can be used with captive husbandry research programs to curb further loss of genetic diversity and strengthen fitness and (or) adaptive potential across the range of this remarkable gartersnake species. Although the risk of outbreeding depression is generally low between recently diverged populations [95], it is important to evaluate the potential loss of local adaptation for admixed populations [96]. Captive breeding efforts could be used to investigate whether outcrossing from the most divergent groups (northern versus southern regional groups) have favorable enough population-level fitness responses (e.g., increased genetic variation and fecundity) to outweigh concerns of outbreeding depression [94, 9798]. To assist assessment of status and trends in this subspecies, we also suggest continued demographic and genetic monitoring of the populations that were evaluated in this study. Genetic monitoring of any focal populations where genetic rescue efforts take place will also enable quantifying any genetic changes that may be associated with observable fitness effects (such as changes in population growth and size), assisted gene flow efforts, or further environmental change. Prior to our study there was little information published that pertained to genetic diversity and population demography across the range of T. s. tetrataenia. Our study shows how combining genetic and demographic monitoring of rare species can provide a more complete picture of the connectivity and viability of extant populations.

Supporting information

S1 Fig. Frequency histogram of distances moved by San Francisco garter snakes (Thamnophis sirtalis tetrataenia) between captures at five sites sampled in 2018.


S2 Fig. Structure assignments of individuals for K = 2–8 across all sites using the T. s. tetrataenia rangewide dataset.


S3 Fig. Structure assignment of individuals for K = 2 across all sites using the 12 putative outlier loci identified with BayeScan.

However, no meaningful patterns resulted from this analysis.


S1 Table. Priors for covariate effects on capture probability for sites sampled, listed by years sampled.

For all sites we used a model that does not include individual heterogeneity in capture probability (p). Percentiles are lower and upper 95% Highest Posterior Density Interval limits.


S2 Table. Pairwise genetic differentiation estimates using the 12 putative outlier loci identified with BayeScan for sampled sites with > 5 samples/site.

No values were significant using the α < 0.002 after Bonferroni correction.



We thank the California Department of Parks and Recreation, San Francisco Public Utilities Commission, Golden Gate National Recreation Area, San Francisco Recreation & Parks, Peninsula Open Space Trust, and Midpeninsula Regional Open Space District for providing access to their lands. We thank Natalie Reeder and Nixon Lam for access to San Bruno and for permission to share data. Many thanks to Elliot Schoenig for providing the photo of T. s. tetrataenia used in Fig 1 and to Tammy Lim for her time related to the 2004–2010 tissue organization. The manuscript benefitted much from comments by Jonathan Richmond, Shawna Zimmerman, and two anonymous reviewers. We thank Josh Hull (U.S. Fish and Wildlife Service) for his support of this research. We also wish to acknowledge the many agency staff, technicians, university students, and volunteers who assisted with capture-mark-recapture studies and collection of tissue samples, including Elizabeth Armistead, William Bauer, Rachael Burnham, Ryan Byrnes, Andrea Colton, Joie DeLeon, Julia Ersan, Jessica Gonzales, Ashley Estacio, Tianna Hanna, Richard Kim, John Kunna, Zach Leisz, Patrick Lien, William McCall, Daniel Macias, Haley Mirtz, Nathan Moy, David Muth, Sean Parnell, Hailey Pexton, Natalie Reeder, Elliot Schoenig, Glenn Woodruff. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. government.


  1. 1. Davies KF, Gascon C, Margules CR. Habitat fragmentation: consequences, management, and future priorities. In: Soulé ME, Orians GH, editors. Conservation Biology, Research priorities for the next decade. Washington, DC: Island Press, 2001. p.81–97.
  2. 2. Haddad NM, Brudvig LA, Clobert J, Davies KF, Gonzales A, Holt RD, et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Science Advances. 2015; 1:e1500052. pmid:26601154
  3. 3. Allendorf FW, Luikart GH, Aitken SN. Conservation and the genetics of populations. 2nd ed. Hoboken, NJ: Wiley; 2013.
  4. 4. Keyghobabi N. The genetic implications of habitat fragmentation for animals. Canadian Journal of Zoology. 2007;85: 1049–1064.
  5. 5. Miles LS, Rivkin LR, Johnson MTJ, Munshi-South J, Verrelli BC. Gene flow and genetic drift in urban environments. Molecular Ecology. 2019; 29(18):4138–4151.
  6. 6. Reed DH, Frankham R. Correlation between fitness and genetic diversity. Conservation Biology. 2003; 17:230–237.
  7. 7. Willi Y, Van Buskirk J, Hoffman AA. Limits to the adaptive potential of small populations. Annual Review of Ecology Evolution and Systematics. 2006; 37:433–458.
  8. 8. Ellegren H, Galtier N. Determinants of genetic diversity. Nature Reviews Genetics. 2016; 17 (7):422–433. pmid:27265362
  9. 9. Lynch M, Conery J, Burger R. Mutation accumulation and the extinction of small populations. American Naturalist. 1995; 146:489–518.
  10. 10. Frankham R, Ralls K. Conservation biology—inbreeding leads to extinction: Nature. 1998; 392:441–442.
  11. 11. Frankham R, Briscoe DA, Ballou JD. Introduction to conservation genetics. Cambridge: Cambridge University Press; 2002.
  12. 12. Elgar MA, Clode D. Inbreeding and extinction in island populations: a cautionary note. Conservation Biology. 2001; 15:284–286.
  13. 13. Frankham R, Ballou JD, Ralls K, Eldridge MDB, Dudash MR, Fenster CB, et al. Genetic management of fragmented animal and plant populations. 1st ed. Oxford: Oxford University Press; 2017.
  14. 14. Wright S. Evolution in Mendelian populations. Genetics. 1931; 16:97–159. pmid:17246615
  15. 15. Charlesworth B. Effective population size and patterns of molecular evolution and variation. Nature. 2009; 10:195–205.
  16. 16. Hare MP, Nunney L, Schwartz MK, Ruzzante DE, Burford M, Waples RS, et al. Understanding and estimating effective population size for practical application in marine species management. Conservation Biology. 2011; 25:438–449. pmid:21284731
  17. 17. Luikart G, Ryman N, Tallmon D, Schwartz M, Allendorf F. Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conservation Genetics. 2010; 11:355–373.
  18. 18. Nunney L, Elam DR. Estimating the effective population-size of conserved populations. Conservation Biology. 1994; 8:175–184.
  19. 19. Waples RS, Luikart G, Faulkner JR, Tallmon DA. Simple life-history traits explain key effective population size ratios across diverse taxa. Proceedings of the Royal Society of London B: Biological Sciences. 2013; 280:20131339.
  20. 20. Goal Project. Baylands Ecosystem Habitat Goals. A report of habitat recommendations prepared by the San Francisco Bay Area Wetlands Ecosystem Goals Project. 1999. U.S. Environmental Protection Agency, San Francisco, California, and San Francisco Bay Regional Water Quality Control Board, Oakland, California. Available from:
  21. 21. U.S. Fish and Wildlife Service. Recovery Plan for tidal marsh ecosystems of Northern and Central California. U.S. Fish and Wildlife Service, Sacramento, CA; 2013:p 77.
  22. 22. Office of the Secretary. Native fish and wildlife: endangered species. Federal Register. 1967; 32:4001.
  23. 23. California Fish and Game Commission. 1971Animals of California declared to be endangered or threatened: California Code of Regulations, title 14, section 670.5.
  24. 24. NatureServe. NatureServe Web Service. Arlington, VA. U.S.A. Available (Accessed: 30, October 2019).
  25. 25. U.S. Fish and Wildlife Service. San Francisco Garter Snake (Thamnophis sirtalis tetrataenia) 5-Year Review: Summary and Evaluation. U.S. Fish and Wildlife Service, Sacramento, California; 2006.
  26. 26. U.S. Fish and Wildlife Service. Recovery Plan for the San Francisco garter snake (Thamnophis sirtalis tetrataenia). U.S. Fish and Wildlife Service, Portland, OR; 1985.
  27. 27. Ralls K, Ballou JD. Captive breeding programs for populations with a small number of founders. Trends in Ecology and Evolution. 1986; 1:19–22. pmid:21227773
  28. 28. McGowan PJk, Traylor-Holzer K, Leus K. IUCN guidelines for determining when and how ex situ management should be used in species conservation. Conservation Letters. 2017; 10:361–366.
  29. 29. Mace G, Pemberton JM, Stanley H. Conserving genetic diversity with the help of biotechnology-desert antelopes as an example. In: Moore HDM, Holt WV, Mace GM, editors. Biotechnology and the Conservation of Genetic Diversity: Symposium of the Zoological Society of London;1992; Oxford University Press;1992. p. 123–134.
  30. 30. Willis K, Willis RE. How many founders, how large a population? Zoo Biology. 2010; 29:638–646 pmid:20127659
  31. 31. Folt B, McGowan CP, Steen DA, Piccolomini S, Hoffman M, Godwin JC, et al. Modeling strategies and evaluating success during repatriations of elusive and endangered species. Animal Conservation. Forthcoming 201.
  32. 32. Halstead BJ, Wylie GD, Amarello M, Smith JJ, Thompson ME, Routman EJ, et al. Demography of the San Francisco Gartersnake in coastal San Mateo County, California. Journal of Fish and Wildlife Management. 2011; 2(1):41–48.
  33. 33. ESRI. ArcGIS Desktop version 10.7.1, 2019; Redlands, California, Environmental Systems Research Institute.
  34. 34. Winne CT, Willson JD, Andrews KM, and Reed RN. Efficacy of marking snakes with disposable medical cautery units. Herpetological Review. 2006; 37:52–54.
  35. 35. Reeder NMM, Byrnes RM, Stoelting RE, Swaim KE. An Endangered Snake Thrives in a Highly Urbanized Environment. Endangered Species Research. 2015; 28:77–86.
  36. 36. Kéry M, and Royle JA. Applied Hierarchical Modeling in Ecology. 2016. Academic Press, San Diego, CA.
  37. 37. Huggins R. M. 1989. On the statistical analysis of capture-recapture experiments. Biometrika, 1989; 76:133–140.
  38. 38. Kéry M, Schaub M. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective: 2012; Academic Press; San Diego, CA. 535 p.
  39. 39. Royle JA, Dorazio RM, Link WA. Analysis of multinomial models with unknown index using data augmentation. Journal of Computational and Graphical Statistics. 2007; 16(1); 67–85.
  40. 40. Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing; 2003; Vienna, Austria; 2009.
  41. 41. R Code Development Team. R: a language and environment for statistical computing; 2019; Vienna, Austria, R Foundation for Statistical Computing
  42. 42. Denwood MJ. runjags—An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software. 2016; 71(9):1–25.
  43. 43. Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. Double Digest RADseq—an inexpensive method for de novo SNP discovery and genotyping in model and non-model species: PLoS ONE. 2012; 7:e37135. pmid:22675423
  44. 44. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks—an analysis tool set for population genomics. Molecular Ecology. 2013; 22:3124–3140. pmid:23701397
  45. 45. Rochette NC, Catchen JM. Deriving genotypes from RAD-seq short-read data using Stacks. Nature Protocols. 2017; 12:2460–2659.
  46. 46. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155:945–959. pmid:10835412
  47. 47. Meirmans PG. Seven common mistakes in population genetics and how to avoid them. Molecular Ecology. 2015; 24:3223–3231. pmid:25974103
  48. 48. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology. 2005; 14:2611–2620. pmid:15969739
  49. 49. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Molecular Ecology Resources. 2015; 15(5): 1179–1191. pmid:25684545
  50. 50. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008; 24:1403–1405. pmid:18397895
  51. 51. Frantz AC, Cellina S, Krier A, Schley L, Burke T. Using spatial Bayesian methods to determine the genetic structure of a continuously distributed population: cluster or isolation by distance? Journal of Applied Ecology. 2009; 46:493–505.
  52. 52. Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Research. 1967; 27:209–220. pmid:6018555
  53. 53. Weir BS, Cockerham CC. Estimating F-Statistics for the analysis of population structure. Evolution. 1984; 38:1358. pmid:28563791
  54. 54. Piry S, Chapuis MP, Gauffre B, Papaïx J, Cruaud A, Berthier K. Mapping Average Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure. Methods in Ecology and Evolution. 2006; 7(12):1463–1475.
  55. 55. Peakall R, Smouse PE. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes. 2006; 6:288–295.
  56. 56. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012; 28:2537–2539. pmid:22820204
  57. 57. Hengl T. Finding the right pixel size. Computers & Geosciences. 2006; 32:1283–1298.
  58. 58. Foll M, Gaggiotti OE. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics. 2008; 180:977–993. pmid:18780740
  59. 59. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S. MRBAYES 3.2: Efficient Bayesian phylogenetic inference and model selection across a large model space. Systematic Biology. 2012; 61:539–542. pmid:22357727
  60. 60. Jennings MR, Hayes MP. Amphibian and Reptile Species of Special Concern in California. California Department of Fish and Game, published Nov. 1, 1994.
  61. 61. Stebbins RC. A Field Guide to Western Reptiles and Amphibians. Houghton Mifflin Company: Boston: New York; 2003.
  62. 62. Thomson RC, Wright AN, Shaffer HB. California Amphibian and Reptile Species of Special Concern. University of California Press: Oakland: Califorina; 2016.
  63. 63. Rambaut A, Drummond AJ, Suchard M. Tracer v1.6: Molecular evolution, phylogenetics and epidemiology. [cited 2014; downloaded 2017] Available from:
  64. 64. Frankham R, Bradshaw CJA, Brook BW. Genetics in conservation management—revised recommendations for 50/500 rules, Red list criteria and population viability analyses. Biological Conservation. 2014; 170:56–63.
  65. 65. Waples RS, Do C. LDNE—a program for estimating effective population size from data on linkage disequilibrium. Molecular Ecology Resources. 2008; 8:753–756. pmid:21585883
  66. 66. Jorde PE, Ryman N. Unbiased estimator for genetic drift and effective population size. Genetics. 2007; 177(2):927–935. pmid:17720927
  67. 67. Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Molecular Ecology Resources. 2014; 14:209–214. pmid:23992227
  68. 68. Nunziata SO, Weisrock DW. Estimation of contemporary effective population size and population declines using RAD sequence data. Heredity. 2017; 120:196–207. pmid:29269932
  69. 69. Jones AT, Ovenden JR, Wang Y-G. Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 2016;117(4): 217–23. pmid:27005004
  70. 70. Barry SJ. The San Francisco garter snake and the San Francisco watershed: San Francisco. University of California, Davis; 1996; p. 1–121.
  71. 71. McGinnis SM. The current distribution and habitat requirements of the San Francisco garter snake (Thamnophis sirtalis tetrataenia) in Coastal San Mateo County. Final Report of Work Conducted Under Interagency Agreement C-673, California Department of Fish & Game, Sacramento, CA; 1984.
  72. 72. Kim R, Halstead BJ, Wylie GD, Casazza ML. Distribution and demography of San Francisco gartersnakes (Thamnophis sirtalis tetrataenia) at Mindego, Russian Ridge Open Space Preserve, San Mateo County, California. U.S. Geological Survey Open-File Report 2018–1063, 80 p.
  73. 73. Halstead HJ, Thompson ME, Amarello M, Smith JJ, Wylie GD, Routman EJ, et al. Effects of prescribed fire on San Francisco gartersnake survival and movement. The Journal of Wildlife and Management. 2019; 83:231–240.
  74. 74. Thomas JH. 1961. Flora of the Santa Cruz Mountains of California: a manual of the vascular plants. Stanford University Press, Stanford, California. 444 p.
  75. 75. Zinke PJ. The redwood forest and associated north coast forests. In: Barbour MG, Major J, editors. Terrestrial vegetation of California. California Native Plant Society, Sacramento, USA; 1988. p. 679–698.
  76. 76. Fox W. The status of the gartersnake, Thamnophis sirtalis tetrataenia. Copeia.1951; 4: 257–267.
  77. 77. Barry SJ. The distribution, habitat and evolution of the San Francisco garter snake, Thamnophis sirtalis tetrataenia [Master’s thesis]. Davis: University of California, Davis 1994.
  78. 78. Janzen FJ, Krenz JG, Haselkorn TS, Brodie ED Jr, Brodie ED III. Molecular phylogeography of common garter snakes (Thamnophis sirtalis) in western North America: implications for regional historical forces. Molecular Ecology. 2002; 11:1739–1751. pmid:12207724
  79. 79. Lim T. Phylogeography of the San Francisco garter snake (Thamnophis sirtalis tetrataenia) [Master’s thesis]. San Francisco: San Francisco State University, 2004.
  80. 80. Frankham R. 1995. Effective population size/adult population size ratios in wildlife: a review. Genet Res 66:95–107.
  81. 81. Palstra FP, Ruzzante DE. Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence? Molecular Ecology. 2008; 17:3428–3447. pmid:19160474
  82. 82. Madsen T, Stille B, Shine R. Inbreeding depression in an isolated population of adders Vipera berus. Biological Conservation 75. 1996; 113–118.
  83. 83. Bradke DR, Hileman ET, Bartman JF, Faust LF, King RB, Kudla N, et al. Implications of small population size in a Threatened pitviper species. Journal of Herpetology. 2018; 51(4):387–397.
  84. 84. Shine R, Elphick MJ, Harlow PS, Moore IT, LeMaster MP, Mason RT. Movement, mating, and dispersal of red-sided gartersnakes (Thamnophis sirtalis parietalis) from a communal den in Manitoba. Copeia. 2001; 1:82–91.
  85. 85. Shine R, Mason RT. Do a male garter snake’s energy stores limit his reproductive effort? Canadian Journal of Zoology. 2005; 83:1265–1270.
  86. 86. Lowe WH, and Allendorf FW. What can genetics tell us about population connectivity? Molecular Ecology. 2010; 19: 3038–3051. pmid:20618697
  87. 87. Philip Williams and Associates, Ltd. Laguna Salada resource enhancement plan. Prepared for the City of San Francisco and the State of California Coastal Conservancy; 1992: p. 67.
  88. 88. McGinnis SM. The distribution and feeding habitat requirements of the San Francisco garter snake (Thamnophis sirtalis tetrataenia). Final Report for California Department of Fish & Game conducted under Interagency Agreement C-673 and C-1376; 1987.
  89. 89. Swaim Biological Inc. Sharp Park Conceptual Restoration Alternatives. Final Report for San Francisco Recreation and Parks Department; 2009.
  90. 90. Doak DF, Himes Boor GK, Bakker VJ, Morris WF, Louthan A, Morrison SA, et al. Recommendations for improving recovery criteria under the US Endangered Species Act: Bioscience. 2015; 65:189–199.
  91. 91. Madsen T, Shine R, Olsson M, Wittzell H. Restoration of an inbred adder population. Nature. 1999; 402:34–35.
  92. 92. Frankham R. Genetic rescue of small inbred populations—meta-analysis reveals large and consistent benefits of gene flow. Molecular Ecology. 2015; 24:2610–2618. pmid:25740414
  93. 93. Whiteley AR, Fitzpatrick SW, Funk WC, Tallmon DA. Genetic rescue to the rescue. Trends in Ecology and Evolution. 2015; 30:42–49. pmid:25435267
  94. 94. Weeks AR, Heinze D, Perrin L, Stoklosa J, Hoffmann AA, van Rooyen A, et al. Genetic rescue increases fitness and aids rapid recovery of an endangered marsupial population. Nature Communications. 2017; 8:1071–1077. pmid:29057865
  95. 95. Frankham R, Ballou JD, Eldridge MDB, Lacy RC, Ralls K, Dudash MR, et al. Predicting the probability of outbreeding depression. Conservation Biology. 2011; 25: 465–475. pmid:21486369
  96. 96. Hedrick PW, Fredrickson R. Genetic rescue guidelines with examples from Mexican wolves and Florida panthers. Conservation Genetics. 2010; 11:615–626.
  97. 97. Madsen T, Ujvari B, Olsson M. Novels genes continue to enhance population growth in adders (Vipera berus). Biological Conservation. 2004; 120:145–147.
  98. 98. Fitzpatrick SW, Gernerich JC, Angeloni LM, Bailey LL, Broder ED, Torres-Dowdall J, et al. Gene flow from an adaptively divergent source causes rescue through genetic and demographic factors in two wild populations of Trinidadian guppies. Evolutionary Applications. 2016; 9: 879–891. pmid:27468306