Dynamic-Parameter Movement Models Reveal Drivers of Migratory Pace in a Soaring Bird

Abstract

Long distance migration can increase lifetime fitness, but can be costly, incurring increased energetic expenses and higher mortality risks. Stopover and other en route behaviors allow animals to rest and replenish energy stores and avoid or mitigate other hazards during migration. Some animals, such as soaring birds, can subsidize the energetic costs of migration by extracting energy from flowing air. However, it is unclear how these energy sources affect or interact with behavioral processes and stopover in long-distance soaring migrants. To understand these behaviors and the effects of processes that might enhance use of flight subsidies, we developed a flexible mechanistic model to predict how flight subsidies drive migrant behavior and movement processes. The novel modeling framework incorporated time-varying parameters informed by environmental covariates to characterize a continuous range of behaviors during migration. This model framework was fit to GPS satellite telemetry data collected from a large soaring and opportunist foraging bird, the golden eagle (Aquila chrysaetos), during migration in western North America. Fitted dynamic model parameters revealed a clear circadian rhythm in eagle movement and behavior, which was directly related to thermal uplift. Behavioral budgets were complex, however, with evidence for a joint migrating/foraging behavior, resembling a slower paced fly-andforage migration, which could facilitate efficient refueling while still ensuring migration progress. In previous work, ecological and foraging conditions are usually considered to be the key aspects of stopover location quality, but taxa, such as the golden eagle, that can tap energy sources from moving fluids to drive migratory locomotion may pace migration based on both foraging opportunities and available flight subsidies.

Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds

“…The population we studied summers primarily in the western Alaska Range and Talkeetna and Chugach Mountains of Alaska, USA and overwinters in the Rocky Mountain West, including Colorado, Utah, Wyoming, Montana, Idaho, Oregon, and Washington, in the US and mid to southern Alberta and British Columbia in Canada (Fig. 1; Eisaguirre et al, 2019; Bedrosian et al, 2018).…”

“…Irregular observations are often handled in the observation equation in a state-space model; however, with GPS data, we can typically assume negligible observation error and save considerable model complexity by modeling the observations directly with the movement equation (Patterson et al, 2008, 2017; Hooten et al, 2017). Notably, parameterizing a CRW in terms of displacement vectors allows for straightforward relationships with time without an observation equation (Auger-Méthé et al, 2017; Gurarie et al, 2017; Eisaguirre et al, 2019; Jonsen et al, 2019).…”

“…To account for behavioral heterogeneity, its predictors, and irregular observations, we implemented our SSF with the following movement model representing ϕ ( · ) (Auger-Méthé et al, 2017; Eisaguirre et al, 2019; Jonsen et al, 2019): Where Here, Δ t i = t i − t i− 1 represents the time interval between Cartesian coordinate vectors x i and x i− 1 for the observed locations of the animal at times t i and t i− 1 , and i = 1, 2, …, N for a track with N observations. Note that the movement parameters in equation 1 are θ i = ( γ i , Σ i ).…”

“…Note that the movement parameters in equation 1 are θ i = ( γ i , Σ i ). The latent variable γ i correlates displacements (or ‘steps’) and can be interpreted to understand the type of movement, and thus behavior, of migrating individuals: estimates of γ i closer to one indicate directionally-persistent, larger-scale migratory movement, while estimates of γ i closer to zero indicate more-tortuous, smaller-scale stopover movement (Breed et al, 2012; Auger-Méthé et al, 2017; Eisaguirre et al, 2019; Jonsen et al, 2019). The behavioral process can be written where and S i is the vector of environmental covariates at location x i and time t i .…”

“…In contrast, while stopped over during migration for foraging or resting, an animal’s movements and habitat selection should change substantially due to differences in energy and habitat requirements during stopover; such a behavioral change would result not only in different use of habitat, but also change in habitat preferences as well. How soaring birds budget behavior and movements, ranging from stopover to migratory, each day across a migration has been shown to be driven by the spatiotemporally-explicit state of the atmosphere (Eisaguirre et al, 2019; Miller et al, 2016), so it is reasonable to suspect that the state of the atmosphere, in part, also influences a soaring migrant’s time-dependent use of the landscape, step selections, and movement decisions. Within such behaviorally-specific use of the landscape, use of linear features could also emerge as being behavioral state- and weather-dependent.…”