Optimal foraging theory suggests animals will minimize energetic costs while maximizing their net energy gain (Pyke et al. 1977). Energy is thus a key currency by which we can examine a proxy of animal fitness at individual and population levels (Brownscombe et al. 2017). Although internal metabolism constitutes the largest portion of energy expenditure, physical activity can result in the greatest energetic fluctuations (Wilson et al. 2020). Predation risk, intraspecific competition, resource distribution, landscape structure and temperature can impact an animal’s physical activity levels and subsequent energetics (Brownscombe et al. 2017). As these factors can vary across space and time, quantifying an animal’s energetic landscape allows us to identify the biological and physical constraints underpinning their movement ecology (Carnahan et al. 2021).
Among large carnivores, energetic demands related to movement can account for extensive portions of daily energy allocation (Karasov, 1992; Steudel, 2000; Weibel et al. 2004; Scantlebury et al. 2014). In addition, energetic demand increases with body size (Blanckenhorn, 2000) and carnivory (Carbone et al. 2007), and there is a selective advantage to minimize locomotor costs (Bryce and Williams, 2017). Carnivore movement decisions are affected by biotic and abiotic factors, and they will often minimize travel costs (Bryce and Williams, 2017). For example, many species of large felids travel along human roads and trails (Davis et al. 2011), while wolves (Canis Lupus) travel along anthropogenic and natural linear features to reduce energetic costs (Bryce and Williams, 2017). However, an efficient movement strategy must be assessed in relation to the environment the animal is traversing, as movement costs can vary greatly depending on temporal and spatial factors (Shepard et al. 2013). This environmental variation in the cost of transport, driven by features including slope, vegetation, and anthropogenic barriers has been termed the energy landscape (Wilson et al. 2012), where animals are expected to adapt their decisions based on these variable landscapes. In landscapes where resources are spatially heterogeneous, animals are predicted to forage in areas that offer the greatest cost minimization and net energetic uptake (Masello et al. 2017). The energy landscape, along with internal and external factors that interact to drive animal movement decisions, remains poorly understood (Shepard et al. 2013) (Fig. 1)
Studying the energetic expenditures of free-ranging wildlife is challenging (Halsey and Bryce, 2021). Energetic variation has been estimated using fluctuations in measurements of attached heart monitors (Green, 2011) or doubly-labelled water (Westerterp, 2017; Pagano and Williams, 2019). Recent advances in technology, specifically tri-axial accelerometers, are now used to study energetic ecology (Pagano et al. 2018a; Pagano et al. 2020). Accelerometers can be calibrated to measures of oxygen consumption from captive animals, providing estimates of overall energy expenditure (Wilson et al. 2006). The conversion of accelerometer data to a unit of energetic measurement has been referred to as dynamic body acceleration (DBA) and represents fluctuations in velocity due to animal movements (Wilson et al. 2006; Gleiss et al. 2011).
Advantages of accelerometers include their relatively low cost and minimal invasiveness to the host animal (Brown et al. 2013). However, obtaining the stored data typically requires collection of the accelerometer upon completion of the study (Brown et al. 2012), which can be challenging due to the remote locations and wide-ranging behavior of some species, such as large carnivores. An alternative method to estimate energetic expenditure from wild animals fitted with global positioning system (GPS) radio collars involves the use of locomotor speeds across a range of slopes from successive GPS locations, which can be linked to the energy expenditure from captive individuals moving at varying speeds and slopes (Dunford et al. 2020, Carnahan et al. 2021). While this technique does not gather energetic data at the same resolution as an accelerometer, it has an advantage over accelerometers since the data can be downloaded remotely from the animal and thus, does not require retrieval of the device (Thomas et al. 2011). Specifically, GPS-derived estimates of energy expenditure should be effective at measuring movement-based energetic costs that result from point-to-point-based movements, while accelerometer-derived estimates should reflect all movement-based energetic costs regardless of whether the animal changed its spatial location. It is important to note however, that neither method can account for non-movement based energy expenditure, such as lactation, growth, thermoregulation and digestion.
Our objectives were to evaluate the movement-based daily energetic expenditures (MDEE) of brown bears (Ursus Arctos) on the Kodiak Archipelago, Alaska, USA and to evaluate the use of GPS-derived estimates of energy expenditure relative to more intensively collected accelerometer-derived (ACC) estimates. Furthermore, using the GPS method we assessed brown bear movement-based energetic expenditures in relation to intrinsic (reproductive status, age, movement rate), spatial (terrain roughness, distance to salmon [Oncorhynchus spp.] streams) and temporal (food abundance period, temperature) factors on the Kodiak Archipelago. We predicted that bears would increase energetic expenditures in the high food abundance period due to increased movements and predation on spawning salmon. We further predicted that reproductive status would affect movement-based energetics, where females with dependent young would constrict space use to reduce risk and thus have lower energetic expenditures compared to males and solitary females.