Environmentally-driven dynamic parameters in mechanistic movement models reveal complex migratory pacing in a soaring bird

1 Long distance migration can increase lifetime fitness, but can be costly, incurring in2 creased energetic expenses and higher mortality risks. Stopover and other en route be3 haviors allow animals to rest and replenish energy stores and avoid or mitigate other 4 hazards during migration. Some animals, such as soaring birds, can subsidize the ener5 getic costs of migration by extracting energy from flowing air. However, it is unclear how 6 these energy sources affect or interact with behavioral processes and stopover in long7 distance soaring migrants. To understand these behaviors and the effects of processes 8 that might enhance use of flight subsidies, we developed a flexible mechanistic model to 9 predict how flight subsidies drive migrant behavior and movement processes. The novel 10 modelling framework incorporated time-varying parameters informed by environmental 11 covariates to characterize a continuous range of behaviors during migration. This model 12 framework was fit to GPS satellite telemetry data collected from a large soaring and op13 portunist foraging bird, the golden eagle (Aquila chrysaetos), during migration in western 14 North America. Fitted dynamic model parameters revealed a clear circadian rhythm in 15 eagle movement and behavior, which was directly related to thermal uplift. Behavioral 16 budgets were complex, however, with evidence for a joint migrating/foraging behavior, 17 resembling a slower paced fly-and-forage migration, which could facilitate efficient refu18 eling while still ensuring migration progress. In previous work, ecological and foraging 19 conditions are normally considered to be the key aspects of stopover location quality, 20 but taxa that can tap energy sources from moving fluids to drive migratory locomotion, 21 such as the golden eagle, may pace migration based on both foraging opportunities and 22 available flight subsidies. 23

subsidies. Now, were apparent pre-migration staging areas. These were not considered part of migration 240 and excluded from the analysis here.  This also helped reduce zero inflation, particularly for thermal uplift, which often decays 261 to zero after sunset due to heat flux and atmospheric boundary layer dynamics. To 262 introduce the interaction, we used a dummy variable z 0 , such that z 0,i = 0 when t i fell 263 after sunset but before sunrise and z 0,i = 1 when t i fell after sunrise but before sunset.

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This assumed behavior was not dependent on the covariates at night, which is sensible behavioral process for the full model was: We compared candidate models with leave-one-out cross-validation approximated by  (Table 1).

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The model revealed that eagles changed their behavior on multiple scales. First,    Table S1). Intense thermal uplift was often associated with the peaks in daily migration 418 bouts (Fig. 1). The larger magnitude of the thermal uplift effect, relative to orographic 419 uplift, was somewhat surprising, as many individuals in our sample followed the Rocky

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The second behavioral pattern revealed was a general stopover pattern, whereby ea-503 gles changed behavior for one to several days while en route (Figs. 1-3). These changes theoretical movement process to infer behavior from movement patterns along tracks on 517 a spectrum ranging from stopped to rapid, directionally-persistent movement.

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Our analyses, however, showed that eagles still tended to continue along their mi-519 gration route during periods of movement most resembling stopover, but with reduced 520 movement rate and directional persistence (Figs. 1-3). This pattern suggests a joint mi- ton, 2008), however, as true stops during the migrations we observed were rare (Fig. 3), 528 except for expected nightly stops. Rather, migrants seemed to change their pace-either 529 by slowing down, moving more tortuously, or both-but still generally moved toward 530 their migratory destination (Figs. 1-3). Thus, instead of a discrete behavioral frame-531 work, whereby migrants switch between two migratory phases (migration and stopover) 532 with very different movement and behavioral properties, we propose that for certain taxa, in food and that lack the morphological specialization to maximally exploit the energetic 546 subsidies available in moving fluids (Piersma, 2007;Gill, 2007).

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Our model results revealed seasonal variability in migratory pacing by golden eagles.

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The tendency for eagles to exhibit movements matching fly-and-forage behavior, and 549 pace their migrations more slowly was most apparent during fall migration. In contrast, 550 spring migration was usually composed of much more punctuated events of slower-paced 551 movements but these were still extended over space (Fig. 3), indicating the eagles pace 552 their migration and employ a mixed behavioral strategy to some extent in spring as well.

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During spring, hibernating mammalian prey would be minimally available, leaving car- is a mechanism driving changes in movement patterns and thus behavior.

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In the behavioral budgets of migrating golden eagles, we identified an expected daily 588 rhythm, as well as evidence for behavioral dynamics that would allow nearly simulta-589 neous foraging and migration, which is greater complexity than the traditional stopover   Table 2: Number of golden eagle migration tracks recorded by GPS transmitters that each candidate formulation of the behavioral process in the correlated random walk model fit the best, according to approximate leave-one-out cross-validation (Table S1).