Using dynamic Brownian bridge movement modelling to measure temporal patterns of habitat selection

Abstract

Summary1. Accurately describing animal space use is vital to understanding how wildlife use habitat. Improvements in GPS technology continue to facilitate collection of telemetry data at high spatial and temporal resolutions. Application of the recently introduced dynamic Brownian bridge movement model (dBBMM) to such data is promising as the method explicitly incorporates the behavioural heterogeneity of a movement path into the estimated utilization distribution (UD). 2. Utilization distributions defining space use are normally estimated for time-scales ranging from weeks to months, obscuring much of the fine-scale information available from high-volume GPS data sets. By accounting for movement heterogeneity, the dBBMM provides a rigorous, behaviourally based estimate of space use between each set of relocations. Focusing on UDs generated between individual sets of locations allows us to quantify fine-scale circadian variation in habitat use. 3. We used the dBBMM to estimate UDs bounding individual time steps for three terrestrial species with different life histories to illustrate how the method can be used to identify fine-scale variations in habitat use. We also demonstrate how dBBMMs can be used to characterize circadian patterns of habitat selection and link fine-scale patterns of habitat use to behaviour. 4. We observed circadian patterns of habitat use that varied seasonally for a white-tailed deer (Odocoileus virginianus) and coyote (Canis latrans). We found seasonal patterns in selection by the white-tailed deer and were able to link use of conifer forests and agricultural fields to behavioural state of the coyote. Additionally, we were able to quantify the date in which a Rio Grande wild turkey (Meleagris gallopavo intermedia) initiated laying as well as when during the day, she was most likely to visit the nest site to deposit eggs. 5. The ability to quantify circadian patterns of habitat use may have important implications for research and management of wildlife. Additionally, the ability to link such patterns to behaviour may aid in the development of mechanistic models of habitat selection.

Space Use and Habitat Selection by Resident and Transient Red Wolves (Canis rufus)

“…We used dynamic Brownian bridge movement models (dBBMM) to estimate space use of resident and transient red wolves [48–49]. We used dBBMMs from the R package ‘moveud’ [50] in Program R [51] to estimate utilization distributions (UD) along the full movement track of each red wolf.…”

“…The dBBMM is a continuous-time stochastic movement model that incorporates time and distance, location error, and an estimate of the Brownian motion variance ( between 2 successive locations) [52–53]. The dBBMM identifies changes in movement speed and direction using a sliding window along the movement path to calculate a for each time step that correspond to different movement patterns, which are then averaged to produce a final, independent for each path step [48–49,52,54]. For full tracks of each wolf, we used a window size of 3 locations (equivalent to 15 hours) and chose a margin of 3 locations on each end of the window in which no breaks in sequential points could occur.…”

Movement analysis of free-grazing domestic ducks in Poyang Lake, China: a disease connection

“…We used the R package moveud (Collier 2013) to extract the σ 2 m estimate for each individual time step within a bird’s overall dBBMM and create a 50% UD contour for every pair of sequential locations (Byrne et al . 2014).…”

An analytical description of the time-integrated Brownian bridge

“…It has been used to perform home range and animal movement analyses. For example, the migration route of caribou (Rangifer tarandus) (Horne et al 2007) and mule deer (Odocoileus hemionus) (Sawyer et al 2009), as well as the utility distribution of the herbivorous fish Sarpa sarpa (Pàges et al 2013) and the home ranges of black and turkey vultures (Coragyps atratus and Cathartes aura) and other species (Buchin et al 2012;Bullard 1991;Byrne et al 2014;Kranstauber et al 2012;Yan et al 2014), have been investigated through the use of the BBMM or one of its extensions. The latter are implemented and available within the 'BBMM' and 'move' packages for R, which makes the model accessible and easy to use Nielson et al 2012).…”