DOI: 10.5061/dryad.573n5tbf3
title: Supporting data for assessing impacts of satellite GPS transmitters on survival, nesting propensity, and nest success of greater sage-grouse

description: Telemetry technology and data are commonly used to study behavior and
 demography of wildlife. Satellite-based, global positioning system (GPS)
 telemetry allows researchers to remotely collect a high volume of
 fine-resolution animal location data but may also come with hidden costs.
 For example, recent studies suggested GPS transmitters attached via
 backpacks may reduce survival of greater sage-grouse (Centrocercus
 urophasianus) relative to very high frequency (VHF) telemetry transmitters
 attached via collars. While some evidence suggests GPS backpacks can
 reduce survival, no studies examined their effects on sage-grouse breeding
 behavior and success. We compared survival, breeding behavior, and nest
 success for sage-grouse hens marked with either VHF collars or GPS
 backpack transmitters in central Idaho, USA. GPS backpacks reduced
 spring-summer survival relative to VHF collars, yet GPS backpacks did not
 consistently affect nest success or the likelihood or timing of nest
 initiation relative to VHF collars. Daily nest survival varied annually
 and with timing of nest initiation and nest age, but marginal effects of
 transmitter type were statistically insignificant, and interactions
 between transmitter type and study year were inconsistent. These results
 demonstrate the effect of GPS backpacks on sage-grouse survival but also
 suggest GPS backpacks do not appear to affect components of
 fecundity. 
description: # Supporting data for assessing impacts of satellite GPS transmitters on
 survival, nesting propensity, and nest success of greater sage-grouse
 The data files contain
 data used to conduct the following analyses: 1) multi-state modeling of
 daily mortality probabilities for hen sage-grouse in program MARK
 (MultiState.inp), 2) nest survival modeling to estimate daily nest
 survival probabilities for greater sage grouse in program MARK
 (NestSurvival.inp), 3) mixed effects logistic regression modeling of
 nesting propensity for hen sage-grouse (i.e., proportion of females
 initiating a nest) in program R (nest_propensity.csv), and 4) mixed
 effects Gaussian regression modeling of timing of nest initiation for hen
 sage-grouse in program R (NestTiming.R). In addition, analysis scripts,
 written in R language, are provided for analyses 3 (LogisticReg.R) and 4
 (GaussianReg.R) above. These R scripts are self contained and annotated
 within to explain each piece of code. ## Description of the data and file
 structure ### Description of the data and file structure for file
 MultiState.inp for multi-state model analyses in MARK Each row of data
 represents a single encounter history observation and the corresponding
 covariates. The first column of data is a string of data for n = 154
 encounter occasions, with each observation consisting of either:
 "A" (observed alive), "D" (observed dead),
 "." (no attempt to monitor and therefore no data), "0"
 (attempted to monitor but failed to detect). Columns 2-4 represent animal
 group number, transmitter type (0 = VHF transmitter, 1 = PTT transmitter),
 and age of hen (1 = yearling, 0 = adult). Columns 5-9 represent individual
 covariates (binary indicator variables) for each corresponding year of
 study (year 2016 is indicated by all other year columns being set to 0).
 All remaining columns contain time-specific individual covariate data for
 each observation day (days 1-153), which were used to fit the linear and
 quadratic time effects to model changes in daily mortality risk across the
 spring-summer period. ### Description of the data and file structure for
 file NestSurvival.inp for nest survival model analyses in MARK Nesting
 data are organized for analysis according to two groups: 1) hens marked
 with VHF transmitters, and 2) hens marked with PTT transmitters. Each row
 of data represents a single nest and its corresponding nest check data and
 covariates. The first column is a nest identification code that does not
 get used at all in the statistical analysis; it's there simply to
 indicate which nest is being observed. Columns 2-5 contain the standard
 nest survival data (going left to right): day of the nesting season on
 which the nest was found, last day the nest was checked when alive, last
 day the nest was checked, and nest fate (0 = successful, 1 = depredated).
 Column 6 is all 1s and indicated the number of nests each row represents.
 Columns 7-13 are individual covariates for each corresponding year of
 study. Column 14 is ordinal day of nest initiation, and column 15 is nest
 age on the first observation day of each season (used to model nest age as
 an individual covariate). ### Description of the data and file structure
 for file nest\_propensity.csv for mixed effects logistic regression
 analyses in R This csv file contains 6 columns: 1) transmitter type, 2)
 Year, 3) number of nests initiated (No_nests), and 4-5) total number of
 hens available for nesting under the conservative (Total_method_1) and
 liberal (Total_method_2) definitions of hens available for nesting (see
 methods section in manuscript text). ### Description of the data and file
 structure for file nest\_initiation\_data.csv for mixed effects Gaussian
 regression analyses in R This csv file contains 5 columns of data: 1)
 individual nest ID, 2) year, 3) ordinal day of nest initiation, 4)
 transmitter type (VHF or PTT), and 5) age of hen (adult [A], yearling [Y],
 or not available [NA]). ## Sharing/Access information NA ## Code/Software
 Code scripts described above are written and executed in the R statistical
 computing language.