Capture-mark-recapture data from San Francisco Gartersnakes at five sites in San Mateo County, California in 2018. These data include capture histories, snout-vent lengths, and sex for individual snakes. R files included with the data fit closed capture-mark-recapture models to estimate the abundance of adult snakes at each site in 2018.

Capture-mark-recapture data from San Francisco Gartersnakes at five sites in San Mateo County, California in 2018. These data include capture histories, snout-vent lengths, and sex for individual snakes. R files included with the data fit closed capture-mark-recapture models to estimate the abundance of adult snakes at each site in 2018.

This dataset includes records of captures of San Francisco gartersnakes (Thamnophis sirtalis tetrataenia) collected at five sites from 2007 to 2020. The data include measurements of snake snout-vent length to estimate growth patterns, snake sex, and the record of all captures of individual snakes for estimating capture probability, availability for capture, and survival.

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 population structure, genetic diversity and abundance of 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. We also used temporally sampled datasets to examine the magnitude of genetic change over time.

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 population structure, genetic diversity and abundance of 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. We also used temporally sampled datasets to examine the magnitude of genetic change over time.

A study comparing reintroduction scenarios for the San Francisco gartersnake (Thamnophis sirtalis tetrataenia), an endangered subspecies native to San Mateo County and Santa Cruz County in northern California. Models for snake survival, growth, fecundity, and reproductive status were used to construct a demographic population model. Data are posterior distributions for demographic parameters from Markov Chain Monte Carlo sampling in hierarchical Bayesian models.

A study comparing reintroduction scenarios for the San Francisco gartersnake (Thamnophis sirtalis tetrataenia), an endangered subspecies native to San Mateo County and Santa Cruz County in northern California. Models for snake survival, growth, fecundity, and reproductive status were used to construct a demographic population model. Data are posterior distributions for demographic parameters from Markov Chain Monte Carlo sampling in hierarchical Bayesian models.

The dataset consists of two csv files one for home range and net displacement analysis for adult snakes with at least 20 locations collected during the study period and one for the analysis of movement metrics for all adult snakes in the study. The home range data contains the calculated 100 percent and 95 percent minimum convex polygons (MCP) and 95 percent adaptive local convex hull (a-LoCoH) home range estimates, 3 measures of net displacement from the release location of the snake as well as other pertinent information about individual snakes (year included in study, id, treatment group, site, snout to vent length (SVL)). The movement data contains calculations of the following movement metrics: sinuosity, start-to-end distance, and total distance traversed of seasonal movement paths as well as information about individual snakes described above.

These data are from capture-mark-recapture studies of the giant gartersnake, Thamnophis gigas, in the Sacramento Valley of California, USA from 1999-2016. The data are primarily capture histories (number of captures per year) for 1891 total snakes from 10 sites. Additional data include environmental covariates including precipitation, vegetative cover, and prey abundance from 8 sites from 2011-2016. The files also include R code to reproduce the analyses of giant gartersnake survival presented in Rose et al. (2018) Ecosphere. NOTE: Data are in Supporting Information section of publication (https://esajournals.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fecs2.2384&file=ecs22384-sup-0001-DataS1.zip).

The dataset consists of two csv files one for adult snakes and one for captive-reared juvenile snakes. The data contains start times (when snakes entered the study) and end times (when snakes died or were censored from the study) for survival analysis. The data also contains general information about the individual snakes (id, snout-vent length, treatment group, whether or not they were translocated), sex (juveniles only since adults were all female), surface activity (and data needed to calculate surface activity), and average distance moved between telemetry resighting. The above information was used as covariates in the survival models.