Movement and selection are inherently linked behaviors that form the foundation of a species space-use patterns. Anthropogenic development in natural ecosystems can result in a variety of behavioral responses that can involve changes in either movement (speed or direction of travel) or selection (resources used) behaviors which in turn may cause differential population level consequences including loss of landscape level connectivity. Understanding how a species alters these different behaviors in response to human activity is essential for effective conservation. In this study, we investigated the effects of anthropogenic development such as roads, power lines and oil wells on the greater prairie-chicken (Typanuchus cupido) in the post-nesting and nonbreeding season. Our first objective was to assess if greater prairie-chickens alter their movement behaviors or their selection patterns when encountering oil wells, power lines, or roads using integrated step selection analysis (iSSA). Our second objective was to determine if prairie-chickens avoided crossing linear features such as roads or power lines by comparing the number of crossing events in greater prairie-chicken movement tracks to the number of movements that crossed these features in simulated movement tracks. Based on the iSSA analysis, we found that greater prairie-chickens avoided oil wells, power lines, and roads in both seasons, but found little evidence for changes in speed or direction of movement at the population-level. However, at the individual level we observed individuals using a number of strategies near development including avoidance and increased rates of movements. Furthermore, prairie-chickens traveled across roads and power lines at much lower rates than expected. Consistent avoidance of development resulted in indirect habitat loss for greater prairie-chickens. These behaviors also resulted in a potential loss of landscape connectivity for this species. By considering both movement and selection we were able to develop an ecological understanding of how increasing human activity may influence the space-use of this species of conservation concern. This research provides insight into the decision-making process by animals when they encounter anthropogenic development by considering multiple behavioral responses. --

We captured prairie-chickens in March and April of each year from 2014 – 2019 using walk-in funnel traps at lek sites (communal courtship arenas). We aged and sexed all captured individuals based on plumage and the presence of secondary sex characteristics (enlarged air sacs and eye combs in males; Henderson et al. 1967), and we marked males and females with a uniquely numbered aluminum leg band to aid in identification. We attached rump-mounted, 22-gram ARGOS GPS transmitters (PTT-100, Microwave Telemetry, Columbia, Maryland, USA) to all captured female prairie-chickens. The GPS transmitters were programmed to record one GPS location every hour from 700 to 1900 from 1 March to 31 August and every two hours during the remainder of the year. All females were monitored remotely via data downloads from the ARGOS server as data became available. The GPS transmitters were estimated to have an average error of less than 20 meters (personal communication Microwave Telemetry).

We focused on two seasons for our analysis, the post-nesting and the nonbreeding season. We defined the post-nesting season as the period after a female concluded nesting activity for the year (typically late May to early June) until 14 September when the last broods are likely to have broken up prior to the fall/winter season. The start of the post-nesting period was determined separately for each individual based on when individual females were observed leaving the nest site (Londe et al. 2019). This period corresponds to the time when females are either raising newly hatched chicks or are recovering from previous reproductive efforts. Due to a lack of data about brood presence and survival in the early parts of our study, we combined data for all females during this period regardless of reproductive status. Previous studies suggest habitat selection patterns are likely similar between brooding and nonbrooding female prairie-chickens (Londe et al. 2019, Londe et al. 2021). The nonbreeding season was defined as the period from 15 September to 15 March of the subsequent year. This period corresponds to the fall and winter period when females are not engaged in any reproductive activity. We did not include telemetry locations from the period when females are attending leks (mid-March to late April) and nesting (early April to nest hatch or failure) as movements during these periods are limited and tend to be concentrated on leks or nest locations. For prairie-chickens that were monitored in multiple years in the study, we treated each year (hereafter, prairie-chicken year) as a separate individual to account for changing habitat conditions between years of the study (Hovick et al. 2015).

Integrated step selection analysis 

Integrated step selection analysis (iSSA) allows for the simultaneous modelling of selection and movement processes by comparing environmental and habitat variables observed during a step (straight line connecting two GPS locations) and variables that describe an animal’s movement pattern, such as step length and turning angles (change in direction of travel between two steps), to those same variables recorded on random steps (Fortin et al. 2005, Thurfjell et al. 2014). For our analysis, we used observed prairie-chicken steps where the beginning and ending locations were at GPS telemetry locations that were recorded 2 hours apart, and were part of a series of steps that included ≥ 3 telemetry locations (this is the minimum number of consecutive relocations required to calculate valid turning angles; Avgar et al. 2016). For each observed step, we generated 10 random steps that shared a starting location with the observed step, but where step length and turning angles of the random step was randomly selected from a gamma and von Mises distribution, respectively (Avgar et al. 2016, Signer et al. 2019). We assigned habitat attributes to each step by extracting environmental variables from GIS rasters at the beginning and ending location for each step. Each set of observed and ten random steps was defined as a choice set and were compared using conditional logistic regression models in program R using the survival package (Therneau and Lumley 2015). Prior to analysis, we removed six individuals from the post-nesting season analysis and nine individuals from the nonbreeding season analysis that had established home ranges in areas where there was no oil and gas development, and the only roads were private ranch roads that received little traffic.

All data needed to perform the integrated step selection analysis in the two files titled as "summer_ssf.csv" (for the post-nesting analysis) and "winter_ssf.csv" (for the nonbreeding analysis). Columns within each file is the same.  ID contains a unique id number for each bird/year combination. Case_ indicates if a step was used (1) or unused (0). Step_id_2 is the step ID used in the random intercept for all models. time since fire is measured in years post fire (0=0-12 months, 1=13-24 months, 2= >24 months). Oil_start, oil end, road_start, road_end, powerline_start, power_line end indicate the log-transformed distance to each structure at the start and end of a step respectively. Ln_step and cos.turning.angle is the log-transformed step length, and cosine transformed turning angle. 

Summary data needed to recreate the crossing analysis are contained in the two files titled "summer_crossing.csv" (post-nesting) and "winter_crossing.csv" (nonbreeding). Columns are the same in both files. ID is the bird/year ID. Case indicates if a movement track was used (1) or not (0). rd.cross.count and pl.cross.count indicate the number of crossing events that occurred in a movement track. Day.cnt is the number of days a bird was monitored. hr.dist.road and hr.dist.powerlines indicate the distance from the movement track centroid to the nearest road or powerline respectively.