DOI: 10.5061/dryad.80gb5mkrw
title: Data for behavioral state-dependent habitat selection analysis of translocated female greater sage-grouse, North Dakota 2018-2020

description: This dataset is associated with the article, "Behavioral
 state-dependent habitat selection and implications for animal
 translocations" (Picardi et al., 2021, Journal of Applied Ecology).
 Post-release monitoring of translocated animals can be used to inform
 future translocation protocols. In particular, quantifying habitat
 selection of translocated individuals may help identify features that
 characterize suitable settlement habitat and inform the choice of future
 release sites; however, because translocated animals undergo post-release
 behavioral modification, the underlying behavioral state needs to be taken
 into account. We analyzed behavioral state-dependent habitat selection in
 female greater sage-grouse (Centrocercus urophasianus) translocated from
 Wyoming to North Dakota, USA, using Hidden Markov Models in combination
 with Integrated Step Selection Analysis. This dataset includes GPS tracks
 (resampled at a 6-hour resolution using a Continuous Time Movement Model)
 for 48 individuals translocated between 2018 and 2020, along with
 environmental variables associated with each location. We segmented each
 track into behavioral phases corresponding to an exploratory state,
 characterized by broad and directed movements, and a restricted state,
 characterized by short and tortuous movements. Then, we quantified habitat
 selection in each state while also accounting for seasonality and
 individual reproductive status. Habitat selection of translocated
 sage-grouse differed between the post-release exploration and the
 settlement phase. While in the exploratory state, sage-grouse exhibited
 natal habitat preference induction by selecting for high sagebrush cover,
 typical of their natal area but not of the release area. In the restricted
 state, sage-grouse selected for gentle topography and also adjusted their
 habitat selection based on the season and their reproductive needs.
description: We equipped 48 female sage-grouse with rump-mounted Global
 Positioning System (GPS) Platform Transmitter Terminal (PTT) ARGOS-enabled
 tracking devices (23 g, GeoTrak, Inc.) scheduled to acquire 6 locations a
 day at irregular intervals. We resampled the data at regular 6-hour
 intervals using a Continuous Time Movement Model. For the first part of
 the analysis, we segmented the tracks into behavioral phases
 (corresponding to either an exploratory or a restricted state) using a
 Hidden Markov Model. For the second part of the analysis, we fit
 Integrated Step Selection models to individuals in each behavioral
 state. For each used step, we generated a set of 100 random steps drawn
 from the empirical population-level distribution of steps lengths and
 turning angles in the two behavioral states. We intersected each step with
 environmental covariates including aspect, percent sagebrush cover,
 percent herbaceous cover, sagebrush patch contiguity, slope, distance to
 roads, distance to well pads, and distance to mesic habitat. All habitat
 variables are scaled and centered. We removed geographical coordinates to
 fulfill our funding agreements not to disclose the location of our tracked
 individuals. We processed data in R (R Core Team, 2020) using the packages
 ‘amt’, ‘tidyverse’, ‘sf’, ‘raster’, and ‘lubridate’. For more details, see
 the associated manuscript (Picardi et al. 2021, Journal of Applied
 Ecology).
description: This dataset contains three objects:  1.
 Picardi-et-al_JApplEcol_HMM-Data.rds contains the tracks we used in input
 to the Hidden Markov Model for behavioral segmentation;
 2. Picardi-et-al_JApplEcol_iSSA-Data-State1.rds contains steps
 that were classified as "restricted state" by the Hidden Markov
 Model. We sampled 100 random steps per used step and associated each step
 with environmental predictors described in the manuscript. We used these
 data in input to the Integrated Step Selection Analysis.
 3. Picardi-et-al_JApplEcol_iSSA-Data-State2.rds contains steps
 that were classified as "exploratory state" by the Hidden Markov
 Model. We sampled 100 random steps per used step and associated each step
 with environmental predictors described in the manuscript. We used these
 data in input to the Integrated Step Selection Analysis.
 FILE Picardi-et-al_JApplEcol_HMM-Data.rds
 Object type: data.frame Fields:
 "ID": individual identifier
 "hen_name": name assigned to each individual
 "step": step length (m)
 "angle": turning angle (radians)
 "timestamp": date and time the location was
 acquired "with_brood": boolean variable
 describing whether the female had a brood at that time (1 if with brood, 0
 if without brood because the female didn't reproduce, the brood
 fledged, or the brood died) "status": status
 of the female at the time of release (PNH = pre-nesting hen; NBH =
 non-brood hen; TBH = brood hen) "dst": days
 since translocation "dst_scaled": days since
 translocation, scaled and centered FILES
 Picardi-et-al_JApplEcol_iSSA-Data-State1.rds
 & Picardi-et-al_JApplEcol_iSSA-Data-State2.rds
 Object type: nested data.frame
 Fields: "hen_name": name assigned to
 each individual "rsteps": tibble of data for
 each individual Fields within
 "rsteps": "ID": individual
 identifier "t1_": timestamp of the location
 at which the step begins "t2_": timestamp of
 the location at which the step ends "dt_":
 duration of the step "step_id_": incremental
 number identifying the steps "case_": boolean
 variable defining whether the step is used (TRUE) or random
 (FALSE) "sl_": step length (m)
 "ta_": turning angle (radians)
 "cos_ta_": cosine of the turning angle
 "log_sl_": logarithm of the step length
 "status": status of the female at the time of release
 (PNH = pre-nesting hen; NBH = non-brood hen; TBH = brood hen)
 "with_brood": boolean variable describing whether the
 female had a brood at that time (1 if with brood, 0 if without brood
 because the female didn't reproduce, the brood fledged, or the brood
 died) "state": behavioral state assigned via
 the Viterbi algorithm based on the fitted Hidden Markov Model (1 =
 restricted, 2 = exploratory)
 "dist_to_mesic_log_scaled": logarithm of the distance
 to mesic habitat (scaled and centered)
 "dist_to_roads_log_scaled": logarithm of the distance
 to roads (scaled and centered)
 "dist_to_well_pads_log_scaled": logarithm of the
 distance to well pads (scaled and centered)
 "sagebrush_scaled": percent sagebrush cover (scaled and
 centered) "perennial_herb_scaled": percent
 perennial herbaceous cover (scaled and centered)
 "sage_contig_scaled": sagebrush patch contiguity
 (scaled and centered) "slope_scaled": slope
 (scaled and centered) "asp_sin": sine of the
 aspect "asp_cos": cosine of the
 aspect