Data

These datasets contain the attributes of individual trees located in and around Sequoia and Kings Canyon national parks. Attributes include remote sensing indices, terrain characteristics, and-- for the calibration data-- tree size and growth rates. Calibration data (mixedconifervulnerability_calibrationdataset.csv) were collected in long term research plots where trees are visited annually to che

The dataset includes three separate excel spreadsheets which provides waterbird (and predator) observations within individual survey units during the May 2019 breeding waterbird survey of south San Francisco Bay (2019WaterbirdSurveyFullData.xlsx), the total number of American avocets, black-necked stilts, and Forster's terns within each pond unit surveyed during the May 2019 survey (2019WaterbirdS

April through May (spring) and August through September (fall) locations for tule greater white-fronted geese (Anser albifrons elgasi) marked with GPS collars at Summer Lake Wildlife Area, OR in 2018 and 2019. Locations may include pre-migration and post-migration designations. Locations occurring between September 11, 2020, and arrival at, or within 50 kilometers of the Summer Lake Wildlife Area

This dataset records mortality-- including involvement of bark beetles-- and burn severity information for trees in long term forest dynamics plots in Sequoia National Park and Yosemite National Park that experienced fire. These data support the following publication: Furniss, T.J., Das, A.J., van Mantgem, P.J., Stephenson, N.L. and Lutz, J.A., 2021. Crowding, climate, and the case for soci

These data identify the mean population growth rate and ratio change in abundance of common raven (Corvus corax; ravens) populations from 1966 through 2018, delineated by ecoregions defined by the U.S. Environmental Protection Agency. This enables researchers and land managers to identify regions which may be more heavily affected by growing raven populations. These data support the following p

We generated a spatially explicit map of categories of expected occurrence and density associated with predicted concentrations of resident and transient common ravens (Corvus corax; ravens) using the residuals from a generalized linear regression between the occurrence and density parameters. These data support the following publication: Webster, S.C., O'Neil, S.T., Brussee, B.E., Coates, P.S.,

These data are from joint visual encounter and environmental DNA (eDNA) surveys conducted for northwestern pond turtles (Actinemys marmorata) in streams on privately-owned timberlands in northern California.

This dataset includes results from external skin swabbing of Anaxyrus williamsi and Lithobates catesbeianus to test for the fungus Batrachochytrium dendrobatidis from the Dixie Valley in Churchill County, Nevada, in 2019-2021.

These data were acquired from two native Mojave Desert species, Ambrosia dumosa and Larrea tridentata, from 3 sites (50 m x 50 m garden plots) distributed across the differing Mojave Desert ecotones. These sites were located on the Fort Irwin National Training Center (CA), north of Joshua Tree National Park (CA), and north of Saint George (UT). Growth and survivorship data were collected monthly f

These data were acquired from 7 study sites distributed across the range of Gopherus agassizii. Data were collected from 1997 to 2002 as part of three separate studies, although data were not collected at all sites in each year. Radio-transmitters were attached to the carapace of 151 females and VHF radio-telemetry was used to relocate animals to assess reproductive status. Egg production was dete

A long-term research project was conducted on Agassiz's desert tortoises (Gopherus agassizii) at a 7.77 square kilometer plot at the Desert Tortoise Research Natural Area, Western Mojave Desert, California, USA. The plot included tortoise populations and habitat both inside and outside the protective fence at the Research Natural Area. Databases used in the research and publications from the resea

These data support poscrptR (Wright et al. 2021). poscrptR is a shiny app that predicts the probability of post-fire conifer regeneration for fire data supplied by the user. The predictive model was fit using presence/absence data collected in 4.4m radius plots (60 square meters). Please refer to Stewart et al. (2020) for more details concerning field data collection, the model fitting process, an