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Energy landscapes of Kodiak brown bears: a comparison of accelerometer and global positioning system-derived estimates

Energy landscapes of Kodiak brown bears: a comparison of accelerometer and global positioning... Within optimal foraging theory animals should maximize their net energy gain while minimizing energetic costs. Energetic expenditure in wild animals is therefore key to measure proxies of fitness. Accelerometers are an effective tool to study animal movement-based energetics, but retrieval of the device is usually required and often difficult. Accelerometers measure movement across three axes (x, y, and z) and can be calibrated to measures of oxygen con- sumption from captive animals, providing estimates of overall energy expenditure. Measuring energetic expenditures using a global positioning system (GPS) approach could provide an alternative method to study energetic ecology. This technique uses locomotor speeds across a range of slopes from successive GPS locations, which can be linked to the energy expenditure from captive individuals. We compared accelerometer and GPS methods of energetic expenditures in free-roaming brown bears (Ursus arctos) on the Kodiak Archipelago, Alaska, USA. We then applied the GPS method to examine how multiple factors influenced brown bear movement-based daily energetic expen- ditures (MDEE). We found that while the two energetic measurements differed ( Wilcoxon signed rank test: V = 2116, p < 0.001), they were positively correlated (r = 0.82, p < 0.001). The GPS method on average provided 1.6 times greater energy estimates than the accelerometer method. Brown bears had lower MDEE during periods of high food abun- dance, supporting optimal foraging theory. Reproductive status and age did not influence MDEE, however movement rates had a positive linear relationship. Energetic ecology is important for understanding drivers of animal move- ments. Data from GPS collars can provide useful information on energetic expenditures, but should be validated for the specific taxa, ecosystem, and GPS sampling rate used. Additionally, while movement-based estimates of energy expenditure can elucidate the mechanisms driving habitat use decisions, they may not fully reflect an animal’s overall energy demands. Brown bear movement-based energetic expenditure was influenced by food abundance and movement rates, which highlighted the importance of access to prime foraging sites to enhance energetic efficiency. Keywords Accelerometers, Large carnivore, Daily energetic expenditure, Ecological energetics, GPS, Optimal foraging theory, Ursus arctos *Correspondence: Present Address: U.S. Geological Survey, Alaska Science Center, S. P. Finnegan Anchorage, AK, USA shannonfinnegan8@yahoo.com Illinois Natural History, University of Illinois Urbana-Champaign, 615 E Global Wildlife Conservation Center, State University of New York, Peabody Drive, Champaign, IL 61820, USA College of Environmental Science and Forestry, Syracuse, NY, USA Koniag, 194 Alimaq Dr, Kodiak, AK 99615, USA School of the Environment, Washington State University, Pullman, WA, USA Alaska Department of Fish and Game, Kodiak, AK, USA Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 2 of 11 Advantages of accelerometers include their relatively Background low cost and long battery life [5]. However, obtain- Optimal foraging theory suggests animals will mini- ing the stored data typically requires collection of the mize energetic costs while maximizing their net energy device upon completion of the study [6], which can gain [49]. Energy is attained through consumption of be challenging due to the remote locations and wide- resources and used to meet metabolic demands, growth, ranging behavior of some species, such as large carni- reproduction and activity, it is therefore a key currency vores. A supplementary approach to estimate energetic by which we can examine a proxy of animal fitness at expenditure from wild animals fitted with global posi - individual and population levels [7]. Although internal tioning system (GPS) radio collars involves the use of metabolism and heat production account for the larg- locomotor speeds across a range of slopes from succes- est portions of energy expenditure, physical activity can sive GPS locations, which can be linked to the energy result in the greatest energetic fluctuations [68]. The vari - expenditure from captive individuals moving at varying ation in environmental factors which continually impact speeds and slopes [12, 17], what we refer to as the “GPS an animal’s cost of transport, such as vegetation type, method”. While this technique does not gather ener- slope and speed, have been termed the ‘energy landscape’ getic data at the same resolution as an accelerometer, [69]. Quantifying an animal’s energetic landscape allows it has an advantage over accelerometers since the data us to identify the biological and physical constraints can be downloaded remotely from the animal and thus, underpinning their movement ecology [12]. does not require retrieval of the device [61]. This GPS Among large carnivores, which are often required to method potentially provides researchers, already study- travel large distances to obtain food and mates, energetic ing movement ecology using GPS collars, the additional demands related to movement can account for exten- capability of investigating the energetic ecology of their sive portions of daily energy allocation ([35, 56, 64]. In study species. Specifically, GPS-derived estimates of addition, energetic demand increases with body size [4] energy expenditure should be effective at measuring and there is a selective advantage to minimize locomo- movement-based energetic costs that result from point- tor costs [8]. Carnivore movement decisions are affected to-point-based movements, while accelerometer-derived by many landscape factors, and they will often minimize estimates should reflect all movement-based energetic travel costs where possible [8]. For example, many species costs regardless of whether the animal changed its spa- of large felids travel along human roads and trails [16], tial location. It is important to note however, that neither while wolves (Canis lupus) often travel along anthropo- method can account for non-movement based energy genic and natural linear features to reduce energetic costs expenditure, such as lactation, growth, thermoregulation [8]. An efficient movement strategy must be assessed in and digestion. relation to the environment the animal is traversing, as Brown bears (Ursus arctos) are a generalist species movement costs can vary greatly depending on temporal which occupy a wide variety of habitats [2]. They display and spatial factors [53]. In landscapes where resources extensive variation in diet across seasons, which influ - are spatially heterogeneous, animals are predicted to for- ences home range sizes and energetic expenditures [43]. age in areas that offer the greatest cost minimization and For instance, brown bears on the Kodiak Archipelago, net energetic uptake [40]. The role of energy landscapes Alaska, USA, occupy larger home range sizes during in driving animal movement decisions remains poorly summer when salmon and berries are highly abundant [1, studied across many taxa [53]. 19]. However, in late fall when food is less available, bears Studying the energetic expenditures of free-rang- may increase movements in search of food [19] or reduce ing wildlife is challenging [30]. Energetic variation has movement before denning [20]. Brown bears exhibit sex- been estimated using fluctuations in measurements of ual size dimorphism, where larger-bodied males often attached heart monitors [29] or doubly labeled water [48, occupy larger home ranges than smaller females [15, 19]. 65]. Recent advances in technology, specifically tri-axial Although likely due to greater energetic demands associ- accelerometers, are now used to study energetic ecology ated with larger body sizes, these space use differences [45, 46]. Accelerometers measure movement across three may also be a result of mate-seeking behavior, whereby axes (x, y, z) and can be calibrated to measures of oxygen males use larger ranges to increase reproductive success consumption from captive animals, providing estimates [14]. of overall energy expenditure [70], what we refer to as the Infanticide is the killing of conspecific offspring for “accelerometer method”. The conversion of accelerom - reasons including enhanced reproductive opportunities, eter data to a unit of energetic measurement is known as competition and cannibalism, and is commonly reported dynamic body acceleration (DBA) and represents fluctua - in brown bears [59]. Female bears with dependent tions in velocity due to animal movements [28, 70]. young may restrict their space use to reduce the risk of F innegan et al. Animal Biotelemetry (2023) 11:7 Page 3 of 11 infanticide [14], or alternatively they may increase move- Raspberry Island. The archipelago has a subarctic mari - ments to obtain more resources to support increased time climate, with a mean annual temperature of 2.1  °C energetic demands associated with cub-rearing [42]. Age (−  0.9° to 12.9  °C monthly mean range) and total mean can also play an important role in home range dynamics annual precipitation and snowfall of 198 and 175  cm, due to age-related dominance [15]. Age is closely related respectively [50]. Sitka spruce (Picea sitchensis) is the to body size, with older individuals often reaching greater dominant tree species on Afognak, while devil’s club body sizes until reaching an asymptote and can display (Oplopanax horridus), blueberry (Vaccinium ovalifo- greater dominance and hold larger home ranges [15]. As lium), salmonberry (Rubus spectabilis), and willow (Salix brown bears must gain enough fat reserves to survive spp.) are dominant understory species [62]. Chum (O. denning, understanding their energetic ecology and the keta), coho (O. kisutch), pink (O. gorbuscha), and sock- potential biological and behavioral factors which may eye (O. nerka) salmon migrate and spawn throughout influence it, are important considerations for conserva - the island’s streams and lakes (Alaska Department of Fish tion and management. and Game, unpublished data). Because of close proximity We first aimed to compare the use of GPS-derived esti - and presumed inter-island movements of brown bears, mates of energy expenditure relative to more intensively we considered Afognak and Raspberry islands a single collected accelerometer-derived (ACC) estimates in wild, study site, hereafter referred to as Afognak Island. Sitka- free-ranging brown bears on the Kodiak Archipelago, lidak Island (57.1030° N, 153.2356° W, Fig. 1) (300  km ) is Alaska, USA. Previous research has applied the use of separated from Kodiak Island by a 320- to 3200-m-wide GPS energy estimates to wild brown bears in the Yel- strait and is located about 91 km south of Afognak Island. lowstone ecosystem [12], however no prior work to our Sitkalidak Island has steep mountains with elevations to knowledge has simultaneously compared this method 672 m. Several streams provide spawning habitat for four to accelerometer-derived estimates in brown bears. We species of Pacific salmon. On Afognak and Sitkalidak then applied the GPS method as a case study on a larger islands, brown bears rely seasonally on salmon, vegeta- sample size of brown bears to determine movement- tion and berries, and to a lesser degree, ungulate prey and based daily energetic expenditures (MDEE) in relation other marine-derived food [1, 62]. to intrinsic (reproductive status, age, movement rate), spatial (terrain roughness, distance to salmon [Onco- Animal handling rhynchus spp.] streams) and temporal (food abundance We captured bears during 2019–2020 using standard aer- period, ambient temperature) factors. We predicted that ial darting techniques with an R44 helicopter and rifle- bears would have increased energetic expenditures in the fired (CapChur SS cartridge-fired rifle) darts containing high food abundance period due to increased movements Telazol (Zoetis Services LLC; Parsippany, USA) [57]. We to monopolize food resources during this time [19]. fitted animals with global positioning system (GPS) col - Alternatively, due to the effects of mate-seeking behavior lars (model Vertex Plus-4, Vectronic, Berlin, Germany), on bear movement in spring, bears may exhibit higher with built in tri-axial accelerometers sampling continu- energetic costs during these low food abundance peri- ously at 32  Hz (± 8  g range). We programmed collars to ods. We further predicted that reproductive status would attempt a relocation every 60  min then release from the affect movement-based energetics, where females with animal 21–24  months post-capture. We also inserted dependent young would constrict space use to reduce a leather link designed to degrade after 2  years as a sec- risk of infanticide and thus have lower movement-based ondary drop-off mechanism [22]. We extracted a ves - energetic expenditures compared to males and solitary tigial upper premolar from bears to estimate age using females. Lastly, we predicted older individuals with larger cementum annuli counts [11]. Body weight data were body sizes would move greater distances as they can unavailable for this study, however as age is closely asso- dominate resource rich areas over younger individuals, ciated with body size, with older individuals commonly and thus incur greater energetic costs. larger until reaching an asymptote [58], we used age as a proxy for body weight. We assigned each bear to an Methods age category (young adult, adult and mature adult) to Study area examine how age and associated body mass may influ - Afognak (58.3279° N, 152.6415° W) (1809  km ) and ence energetics. Young adults were 3–6  years old and Raspberry (58.0708° N, 153.1876° W) (197  km ) islands unlikely to reproduce (although age of first reproduction are in the Kodiak Archipelago, Alaska, USA, 5 km north in brown bears varies) [41]. Adult bears were 7–12 years of Kodiak Island and separated by a 1.5-km-wide strait old and considered breeding adults, while mature adults (Fig.  1). Both islands contain rolling mountains, with were > 12  years old and likely to have reached full skull elevations to 739  m on Afognak Island and 732  m on size [32]. We recorded sex and for females, evidence Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 4 of 11 Fig. 1 Location of Afognak, Raspberry and Sitkalidak islands, Kodiak Archipelago, Alaska, USA of lactation and presence of young. No females with Forestry Institutional Animal Care and Use (IACUC) dependent young of the year (< 1  years old) were cap- (protocol 180503) and Alaska Department of Fish and tured, therefore dependent young in this study refers to Game (ADFG; IACUC protocol 0030-2017-37). cubs 1–3  years old. We positioned bears sternal follow- ing handling to recover at their capture sites. All animal ACC and GPS energy calculation handling procedures were approved by the State Univer- To derive energetic expenditure from accelerometer data, sity of New York College of Environmental Science and we calculated overall dynamic body acceleration (ODBA) F innegan et al. Animal Biotelemetry (2023) 11:7 Page 5 of 11 from eight brown bears and converted values to rates of energetic measurements for each individual brown bear −1 −1 oxygen consumption (Vo ) based on relationships derived (J  kg  h ). from captive brown bears walking on a motorized tread- mill, where Vo (ml/kg/min) = 0.069 + 31.972 × ODBA Environmental variables [47]. We converted V o to joules by multiplying by 20.08. We created a terrain roughness index (TRI) from a We excluded data from all individuals for 5  days post- 30-m-resolution digital elevation model for the Kodiak capture to account for potential recovery effects on Archipelago (Kodiak Island Borough GIS and Map movement behavior [60]. We used a 2-s running mean Center, unpublished data) using ArcGIS (10.7.1, ESRI of the raw acceleration data to determine static accelera- 2018, Redlands, CA). This index calculates the differ - tion and then subtracted from the raw data to estimate ence in elevation from a center cell value to the eight dynamic acceleration [47, 54, 70]. The ODBA value was surrounding cells by squaring each of the eight elevation calculated as the absolute sum of dynamic acceleration difference values to make them all positive, summing across all three axes (surge, heave, and sway) (Additional them, and taking the square root [52]. This index char - files 2, 3): acterizes irregularity in elevation within a given unit [55], ODBA = |A | + A + |A |, and can be an important factor that impedes or facili- x y z tates animal movement and subsequent energetic expen- where A , A and A are the derived dynamic accelera- x y z ditures. We assigned a TRI value for each brown bear tions at any point in time corresponding to the three location and then determined the circular average daily orthogonal axes of the accelerometer. TRI value to assess its effect on energetic expenditure. Using data collected from GPS collars, we calculated Due to the importance of salmon to bears and poten- −1 movement rates (km  h ) between successive hourly tial energetic implications of foraging, we calculated the locations. To reduce uncertainty, we only considered distance from each GPS location to the nearest anadro- successive locations < 63  min apart and removed fixes mous salmon stream (Alaska Department of Fish and with poor dilution of precision rates. We determined Game, unpublished data), and determined the average the minimum distance between each location as the daily distance for each animal. We divided GPS data into great-circle distance (accounting for the curvature of the two periods to assess how fluctuations in food availabil - Earth’s surface), and derived a movement rate by dividing ity influenced MDEE. The high food abundance period the distance by the duration between locations [47]. We was 1 July–31 September, corresponding with spawning measured the slope between locations using the R pack- salmon and ripe berries, important foods for brown bears age elevatr [34]. [1, 3, 62]. The low food abundance period was 1 April–30 We derived the ACC measure of energetic expendi- June and 1 October–31 November, reflecting when dom - ture using ODBA combined with the equations for inant foods were less available. We excluded data col- moving up or downhill based on associated GPS loca- lected during December–March as many animals enter tions. The GPS method used the relationships between denning during this period. Ambient temperature read- −1 slope and speed (m  s ) with energy expenditure ings were recorded at each GPS location via the GPS col- derived from nine captive brown bears [12] to meas- lar and we calculated daily means to test its influence on ure energy expenditure based on the hourly move- MDEE. Temperature readings collected from GPS collars ment rate and slope derived from successive GPS are strongly correlated with temperature readings from locations. At horizontal slopes (i.e., 0°), energy expendi- nearby weather stations and considered suitable for these −1 −1 ture J  kg  s = 2.81 + 2.45 × speed; at inclines > 0° purposes [18]. and < 15°, energy expenditure = 2.93 + 6.05 × speed; at inclines ≥ 15°, energy expenditure = 2.76 + 10.63 × speed; Energy landscape and at declines (i.e., < 0°), energy expendi- To create visual examples of energetic expenditures we ture = 2.71 + 4.74 × speed − 2.48 × speed [12]. used the inverse distance weighted interpolation tool in ArcGIS [13] to construct energy landscapes for an indi- Application to wild bears vidual male and female brown bear on Afognak Island. Using GPS collars retrieved from wild brown bears we We created maps for both animals that displayed ener- determined and compared the ACC- and GPS-derived getic measurements across the landscape from the ACC energetic expenditure. We then applied the GPS method and GPS methods (4 maps total). We used the inverse to a dataset from 28 brown bears on Afognak and Sit- distance weighted interpolation tool as we had a large kalidak islands. We determined movement-based daily sample of location data that represented the range of energetic expenditure (MDEE) by averaging hourly observed values for that energy surface [39]. Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 6 of 11 Statistical analyses our top model based on the lowest Akaike Information We conducted all analyses using R statistical software Criterion, adjusted for small sample sizes (AICc), and [51]. We tested our data for normality and found the only models with Δ ≤ 2 were selected for further con- ACC and GPS measures of energetic expenditure were sideration [10]. Unlike generalized linear models, the not normally distributed. We used a Wilcoxon signed likelihood ratio statistic of a GAM does not follow a rank test [24] to test whether GPS and ACC measure- Chi-square distribution and consequently, p-values are ments differed. We used the non-parametric Spearman’s only approximate [71, 74]. However, if observed p-val- rank correlation test to measure the strength and direc- ues are approximately in the upper 2.5%, results can be tion of the association between the two energy measure- interpreted with more confidence [74]. Consequently, ments [23]. We constructed 11 a priori models and a null we considered only model variables with p values < 0.025 model to assess the influence of internal (reproductive as strongly significant, and variables with p values 0.05– status [M = male, F = female, F Y = female with depend- 0.025 as marginally significant. −1 ent young], age and movement rate [km  h ]), spatial (TRI, distance from nearest salmon spawning streams), and temporal (food abundance period, temperature) fac- Results tors on brown bear MDEE derived from the GPS method We estimated hourly energetic data using the (Additional file  1: Table  S1). We used a Pearson’s prod- GPS and ACC method for eight brown bears uct-moment correlation coefficient (r) to diagnose mul - (M = 3, F = 3, F Y = 2) from Afognak and Sitkali- ticollinearity among dependent variables, and assumed it dak islands during September 2019–November 2020 did not influence model results if |r|< 0.70 ). (n = 23,024, Additional file  1: Table  S2). The ener - We used generalized additive mixed models (GAMMs) getic expenditure was greater using the GPS method −1 −1 to examine factors influencing MDEE using the ‘mgcv’ (median = 10,198 J  kg  h ) compared to the ACC −1 −1 package in R [72]. This model type allowed flexibility to method (median = 5351 J  kg  h ) (V = 2116, handle nonlinear predictor variables. We applied a cubic p < 0.001), with paired daily measurements positively spline smoothing factor to nonlinear variables and set correlated (r = 0.82, p = < 0.001; Fig .  2). Visual inspec- individual bear ID as a random factor [71]. We selected tion of the inverse distance weighted energy landscapes Fig. 2 Correlation between daily average GPS- (GPS method) and accelerometer- (ACC method) derived measures of energetic expenditure −1 −1 (J kg m ; r = 0.82, p = < 0.001) for eight brown bears (F = female, FY = female with young, M = male), Afognak and Sitkalidak islands, Alaska, USA, September 2019–November 2020 F innegan et al. Animal Biotelemetry (2023) 11:7 Page 7 of 11 for a male and female bear suggested more energetic Table 1 Parameter estimates for generalized additive mixed models (GAMM) on daily movement-based energetic variation with the ACC method (Fig. 3). expenditure of 28 brown bears, Afognak and Sitkalidak islands, We applied the GPS method of energetic expendi- Alaska, USA, September 2019–November 2020 ture to data collected from 28 brown bears (M = 6, F = 9 and FY = 13) during September 2019–November Covariate edf Parameter t/f score p-value estimate 2020 for a total of 3509 bear days (M = 769, F = 1079, FY = 1661). The median MDEE was 10,303 (standard Intercept 5.124 4326.987 0.001 −1 −1 deviation = 33,432) J  kg  h for males, 10,301 (stand- Sex—female with young − 4.830 − 0.647 0.517 −1 −1 ard deviation = 19,201) J  kg  h for solitary females Sex—male 7.526 0.087 0.930 −1 −1 and 10,307 (standard deviation = 6470) J  kg  h for Age—mature 2.569 0.035 0.972 females with young. Our full model was most supported Age—young 5.060 0.563 0.573 (AIC = − 21,726, model weight = 1, R = 0.96) with no Food period—high − 2.443 − 5.333 0.001 competing models. We found internal (movement rate) Temperature − 1.026 − 2.559 0.010 and temporal (high food abundance period) factors had Movement rate 3.911 276.628 0.001 the greatest effects on brown bear movement-based Terrain roughness 1.000 4.317 0.037 energetic expenditure (Table 1). Brown bears had lower Distance to salmon streams 1.000 4.621 0.031 energetic expenditures in the high food abundance Effective degrees of freedom = edf, values in bold are significant (p < 0.05) period (p = 0.001), and movement rate was positively related to MDEE (R = 0.96, p = 0.001; Fig .  4). Decreas- ing ambient temperatures were associated with greater Discussion movement-based energetic costs (p = 0.010). Increasing High-frequency accelerometer data can measure instan- terrain roughness was associated with marginally sig- taneous energetic costs as animals move across changing nificant increases in energetic expenditures (p = 0.037, landscapes in search of resources [17, 46, 66]. Although edf = 1.000), while closer proximity to salmon streams the benefits of this technology are immense, chal - (p = 0.031, edf = 1.000) was also associated with mar- lenges remain, in particular the collection of the device ginally significant increases in expenditure. upon study completion [6]. Accelerometers are also data intensive due to their continuous high-frequency Fig. 3 Estimated energy landscapes for a male (top) and female (bottom) brown bear using accelerometer- and GPS-derived measures of energetic −1 −1 expenditure (J kg m ), Afognak Island, Alaska, USA, 1 July–4 August 2020 Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 8 of 11 constraints of wild animals. However, this method can- not account for metabolic energy demands and may only be suitable for comparisons of movement-based energy expenditure among individuals [9]. We applied the GPS technique to a larger sample size of wild brown bears on the Kodiak Archipelago and found that bears had greater energy expenditure with increased movement rates and lower energetic expenditure dur- ing the high food abundance period, when salmon and berries were abundant. Our strong positive relationship between brown bear movement rate and energetic cost was similar to previous studies [31, 37, 48]. An animal’s metabolic rate and speed are fundamentally linked to their dynamic body acceleration [28]. Bears are intrinsi- cally sensitive to increased locomotor speeds due to their Fig. 4 Relationship between GPS-derived daily movement-based −1 −1 plantigrade posture, large body sizes and higher resting energetic expenditure (MDEE; J kg m ) and movement rate −1 (km h ; parameter estimate = 3.911, p = 0.001) for 28 brown bears, metabolic rate compared to similar-sized animals [47]. Afognak and Sitkalidak islands, Alaska, USA, September 2019– These higher energetic demands during locomotion may November 2020 explain why some brown bears, like polar bears (U. mar- itimus), often employ a sit-and-wait predation strategy, particularly along salmon streams [21, 36, 46]. However, measurements, making analysis computationally this strategy is likely only efficient in areas with access demanding. A GPS-derived measure of energy expendi- to anadromous salmon. The higher energetic demands ture offers an alternative to study animal energetic ecol - associated with increased movement rates may also ogy when accelerometer data may not be available [9, 12]. explain the importance of a social dominance structure We compared these two techniques of measuring animal among brown bears at prime foraging sites [3, 25]. Cer- energetics and applied the GPS technique to wild brown tain areas along anadromous fish streams may provide bears. We found that GPS-derived estimates of MDEE bears the best access to migrating salmon, with reduced were on average 1.6 times greater than the ACC method, costs in obtaining this resource [26]. Under an optimal- displayed less overall variation and likely overestimated ity framework, such locations would be favored due their true energetic expenditure. This finding contrasted with increased net energy uptake per unit time of effort [27]. Bryce et  al. [9], who noted the energetic costs derived Bears had reduced MDEE during the high food abun- from accelerometers fitted to wolves were on average dance period (July–September) when spawning salmon 1.3 times higher than the GPS method. Discrepancies and ripe berries are abundant throughout the Kodiak between our results and that of Bryce et  al. [9] may be Archipelago [62]. This finding contradicted our predic - due to differences in ecology between wolves and brown tions given that bears on the Kodiak Archipelago have bears and highlight the importance of species-specific larger range sizes during this period [1, 19], and there- considerations when examining GPS-derived energetic fore would be expected to experience greater energetic expenditures. It is also important to note that the rela- demands associated with locomotion. This reduced tionships between oxygen consumption and speed, and MDEE in the high food abundance period supports oxygen consumption and ODBA in brown bears were optimal foraging behavior among brown bears as it sug- devised from a small number of captive animals through gests that bears minimize energetic cost while maximiz- two separate studies, and may not fully reflect the ener - ing food resource gains in times of increased abundance. getic demands of free-ranging individuals. Our move- Despite traveling greater distances to use resource rich −1 −1 ment-based energetic expenditures (2.7–8.9  J  kg  s ) areas, bears reduced energetic costs likely by altering were similar to ranges reported for brown bears in the movement and foraging behavior. Brown bears often Yellowstone ecosystem, with expenditure values of 3.0– choose movement paths that offer reduced resistance −1 −1 10.6 J  kg  s [12]. While energetic costs derived from [12] and can employ sit-and-wait hunting strategies GPS locations with infrequent resampling should be along salmon streams [36]. Such behavioral choices likely interpreted with caution, the high correlation between attribute to the reduced energetic costs we found in this ACC- and GPS-derived estimates of MDEE suggests the study. GPS technique can be useful to study energetic ecology Our finding of no effect of age and reproductive status [12], particularly for providing insights into the energetic on brown bear movement-derived energetic expenditure F innegan et al. Animal Biotelemetry (2023) 11:7 Page 9 of 11 was surprising because of previously reported differ - anadromous salmon, increased anthropogenic distur- ences in bear movements between males and females, bance at these locations may limit bears from maximizing and between younger and older individuals [14, 15]. energetic gain. Such energetic considerations may inform Larger body sizes incur increased energetic demands, designating areas of high importance for bear conserva- thus species such as brown bears would be expected to tion and management [67]. We suggest future research have higher energetic costs in older, larger-bodied males examine the GPS method using finer resolution reloca - [33]. Although we found no such relationship, it is impor- tion data to improve its accuracy. We also recommend tant to note that our energy calculations do not account comparisons between methods in a laboratory setting for internal energetic costs where differences as a result on a larger sample of captive animals. However, as with of sex and age may be significant, and our sample of 28 all studies that involve animal handling, we encourage bears included few males which may have limited our researchers to consider ethics and animal welfare when ability to detect sex-specific differences. Additionally, as designing and implementing energetic ecology research. we did not have females with dependent young under the age of 1  year in this study, it may have affected our Abbreviations ability to fully examine how energetic expenditure may ACC Accelerometer have differed between reproductive classes as a result of GPS Global positioning system MDEE Movement-based daily energetic expenditures risk avoidance behavior [3]. We found that lower ambi- ODBA Overall dynamic body acceleration ent temperatures were associated with greater energy Vo R ates of oxygen consumption costs, potentially attributed to the increased thermoreg- TRI Terrain roughness index GAMMs Generalized additive mixed models ulatory demands on mammals in cooler temperatures AICc Akaike Information Criterion, adjusted for small sample sizes [63]. Brown bears may respond to lower temperatures by increasing movements and thus experienced increased Supplementary Information energetic costs associated with locomotion. Although, it The online version contains supplementary material available at https:// doi. is also possible that cooler temperatures affected energet - org/ 10. 1186/ s40317- 023- 00319-0. ics simply due to the time of year that they occurred, as colder temperatures are more common in late fall when Additional file 1: Table S1. List of 11 a priori models and a null model to assess the influence of internal (reproductive status, age and movement food resources are less available, and likely impact bear rate), spatial ( Terrain roughness, distance from nearest salmon spawning movements. We found a marginal influence of increased streams), and temporal (food abundance period and temperature) factors terrain roughness and proximity to salmon streams on brown bear GPS-derived movement-based energetic expenditure, on the Kodiak Archipelago, Alaska, USA, September 2019–November 2020. resulting in higher energetic cost. Large carnivores can −1 −1 Table S2. Average hourly energetic expenditure (J kg m ) from global be more susceptible to increased costs of movement in position system (GPS) and accelerometer (ACC)-derived estimates for mountainous terrain [17]. Although these factors may eight brown bears (F = female, FY = female with young, M = male) on the Kodiak Archipelago, Alaska, USA, September 2019–November 2020. have influenced bear energetic expenditures, we are (N = number of hourly locations). cautious to infer such relationships with marginal sig- Additional file 2. Bear ACC energy data. nificance in our GAMM due to the likelihood of model Additional file 3. Bear GPS daily energy data. overfitting [73]. Acknowledgements Conclusions We thank the Alaska Department of Fish and Game for logistical and financial The study of animal energetics continues to provide new support. We thank Afognak Native Corporation, Koniag Native Corporation, Koncor, Natives of Kodiak Native Corporation, Ouzinkie Native Corporation, insights into ecosystem-scale resource requirements, and Old Harbor Native Corporation for logistical support and land access. We and how animals adapt to spatiotemporal heterogeneous are thankful to the Brown Bear Trust for project support. We thank K. Kellner environments [67]. We demonstrated that brown bear and T. Jackson for statistical advice and J. Crye for field support. We thank the many individuals who contributed to this project including L. Van Daele, movement-based energetic expenditure was sensitive to D. Dorner, A. Hopkins, K. Kruger, P. Olsen, K. Wandersee, K. Wattum and T. M. intrinsic and extrinsic factors, particularly movement Witteveen. rate and food abundance. Human disturbance and altera- Author contributions tion of bear habitat may lead to risk-aversion behaviors SPF, AMP, JLB and NJS conceived the ideas and designed methodology; SPF, [38], such as increased movement rates when traversing JLB, NJS and SLS collected the data; SPF and AMP analyzed the data; SPF led areas perceived as higher risk [44]. As movement rate the writing of the manuscript. All authors contributed critically to the drafts. All authors read and approved the final manuscript. increases brown bear cost of transport, such risk avoid- ance behavior could impede bears from maintaining Funding optimal energetic efficiency. Additionally, as brown bears This project was financially supported by the Federal Aid in Wildlife Restora- tion Act under Pittman-Robertson project AKW-12 and the Alaska Department reduce energetic expenditure when food is abundant, of Fish and Game. likely by selecting foraging areas with greatest access to Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 10 of 11 Availability of data and materials 18. Ericsson G, Dettki H, Neumann W, Arnemo JM, Singh NJ. Offset Data are available upon request from the authors. between GPS collar-recorded temperature in moose and ambient weather station data. Eur J Wildl Res. 2015;61:919–22. 19. Finnegan SP, Svoboda NJ, Fowler NL, Schooler SL, Belant JL. Variable Declarations intraspecific space use supports optimality in an apex predator. Sci Rep. 2021;11:1–3. Ethics approval and consent to participate 20. Friebe A, Swenson JE, Sandegren F. 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Energy landscapes of Kodiak brown bears: a comparison of accelerometer and global positioning system-derived estimates

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Abstract

Within optimal foraging theory animals should maximize their net energy gain while minimizing energetic costs. Energetic expenditure in wild animals is therefore key to measure proxies of fitness. Accelerometers are an effective tool to study animal movement-based energetics, but retrieval of the device is usually required and often difficult. Accelerometers measure movement across three axes (x, y, and z) and can be calibrated to measures of oxygen con- sumption from captive animals, providing estimates of overall energy expenditure. Measuring energetic expenditures using a global positioning system (GPS) approach could provide an alternative method to study energetic ecology. This technique uses locomotor speeds across a range of slopes from successive GPS locations, which can be linked to the energy expenditure from captive individuals. We compared accelerometer and GPS methods of energetic expenditures in free-roaming brown bears (Ursus arctos) on the Kodiak Archipelago, Alaska, USA. We then applied the GPS method to examine how multiple factors influenced brown bear movement-based daily energetic expen- ditures (MDEE). We found that while the two energetic measurements differed ( Wilcoxon signed rank test: V = 2116, p < 0.001), they were positively correlated (r = 0.82, p < 0.001). The GPS method on average provided 1.6 times greater energy estimates than the accelerometer method. Brown bears had lower MDEE during periods of high food abun- dance, supporting optimal foraging theory. Reproductive status and age did not influence MDEE, however movement rates had a positive linear relationship. Energetic ecology is important for understanding drivers of animal move- ments. Data from GPS collars can provide useful information on energetic expenditures, but should be validated for the specific taxa, ecosystem, and GPS sampling rate used. Additionally, while movement-based estimates of energy expenditure can elucidate the mechanisms driving habitat use decisions, they may not fully reflect an animal’s overall energy demands. Brown bear movement-based energetic expenditure was influenced by food abundance and movement rates, which highlighted the importance of access to prime foraging sites to enhance energetic efficiency. Keywords Accelerometers, Large carnivore, Daily energetic expenditure, Ecological energetics, GPS, Optimal foraging theory, Ursus arctos *Correspondence: Present Address: U.S. Geological Survey, Alaska Science Center, S. P. Finnegan Anchorage, AK, USA shannonfinnegan8@yahoo.com Illinois Natural History, University of Illinois Urbana-Champaign, 615 E Global Wildlife Conservation Center, State University of New York, Peabody Drive, Champaign, IL 61820, USA College of Environmental Science and Forestry, Syracuse, NY, USA Koniag, 194 Alimaq Dr, Kodiak, AK 99615, USA School of the Environment, Washington State University, Pullman, WA, USA Alaska Department of Fish and Game, Kodiak, AK, USA Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 2 of 11 Advantages of accelerometers include their relatively Background low cost and long battery life [5]. However, obtain- Optimal foraging theory suggests animals will mini- ing the stored data typically requires collection of the mize energetic costs while maximizing their net energy device upon completion of the study [6], which can gain [49]. Energy is attained through consumption of be challenging due to the remote locations and wide- resources and used to meet metabolic demands, growth, ranging behavior of some species, such as large carni- reproduction and activity, it is therefore a key currency vores. A supplementary approach to estimate energetic by which we can examine a proxy of animal fitness at expenditure from wild animals fitted with global posi - individual and population levels [7]. Although internal tioning system (GPS) radio collars involves the use of metabolism and heat production account for the larg- locomotor speeds across a range of slopes from succes- est portions of energy expenditure, physical activity can sive GPS locations, which can be linked to the energy result in the greatest energetic fluctuations [68]. The vari - expenditure from captive individuals moving at varying ation in environmental factors which continually impact speeds and slopes [12, 17], what we refer to as the “GPS an animal’s cost of transport, such as vegetation type, method”. While this technique does not gather ener- slope and speed, have been termed the ‘energy landscape’ getic data at the same resolution as an accelerometer, [69]. Quantifying an animal’s energetic landscape allows it has an advantage over accelerometers since the data us to identify the biological and physical constraints can be downloaded remotely from the animal and thus, underpinning their movement ecology [12]. does not require retrieval of the device [61]. This GPS Among large carnivores, which are often required to method potentially provides researchers, already study- travel large distances to obtain food and mates, energetic ing movement ecology using GPS collars, the additional demands related to movement can account for exten- capability of investigating the energetic ecology of their sive portions of daily energy allocation ([35, 56, 64]. In study species. Specifically, GPS-derived estimates of addition, energetic demand increases with body size [4] energy expenditure should be effective at measuring and there is a selective advantage to minimize locomo- movement-based energetic costs that result from point- tor costs [8]. Carnivore movement decisions are affected to-point-based movements, while accelerometer-derived by many landscape factors, and they will often minimize estimates should reflect all movement-based energetic travel costs where possible [8]. For example, many species costs regardless of whether the animal changed its spa- of large felids travel along human roads and trails [16], tial location. It is important to note however, that neither while wolves (Canis lupus) often travel along anthropo- method can account for non-movement based energy genic and natural linear features to reduce energetic costs expenditure, such as lactation, growth, thermoregulation [8]. An efficient movement strategy must be assessed in and digestion. relation to the environment the animal is traversing, as Brown bears (Ursus arctos) are a generalist species movement costs can vary greatly depending on temporal which occupy a wide variety of habitats [2]. They display and spatial factors [53]. In landscapes where resources extensive variation in diet across seasons, which influ - are spatially heterogeneous, animals are predicted to for- ences home range sizes and energetic expenditures [43]. age in areas that offer the greatest cost minimization and For instance, brown bears on the Kodiak Archipelago, net energetic uptake [40]. The role of energy landscapes Alaska, USA, occupy larger home range sizes during in driving animal movement decisions remains poorly summer when salmon and berries are highly abundant [1, studied across many taxa [53]. 19]. However, in late fall when food is less available, bears Studying the energetic expenditures of free-rang- may increase movements in search of food [19] or reduce ing wildlife is challenging [30]. Energetic variation has movement before denning [20]. Brown bears exhibit sex- been estimated using fluctuations in measurements of ual size dimorphism, where larger-bodied males often attached heart monitors [29] or doubly labeled water [48, occupy larger home ranges than smaller females [15, 19]. 65]. Recent advances in technology, specifically tri-axial Although likely due to greater energetic demands associ- accelerometers, are now used to study energetic ecology ated with larger body sizes, these space use differences [45, 46]. Accelerometers measure movement across three may also be a result of mate-seeking behavior, whereby axes (x, y, z) and can be calibrated to measures of oxygen males use larger ranges to increase reproductive success consumption from captive animals, providing estimates [14]. of overall energy expenditure [70], what we refer to as the Infanticide is the killing of conspecific offspring for “accelerometer method”. The conversion of accelerom - reasons including enhanced reproductive opportunities, eter data to a unit of energetic measurement is known as competition and cannibalism, and is commonly reported dynamic body acceleration (DBA) and represents fluctua - in brown bears [59]. Female bears with dependent tions in velocity due to animal movements [28, 70]. young may restrict their space use to reduce the risk of F innegan et al. Animal Biotelemetry (2023) 11:7 Page 3 of 11 infanticide [14], or alternatively they may increase move- Raspberry Island. The archipelago has a subarctic mari - ments to obtain more resources to support increased time climate, with a mean annual temperature of 2.1  °C energetic demands associated with cub-rearing [42]. Age (−  0.9° to 12.9  °C monthly mean range) and total mean can also play an important role in home range dynamics annual precipitation and snowfall of 198 and 175  cm, due to age-related dominance [15]. Age is closely related respectively [50]. Sitka spruce (Picea sitchensis) is the to body size, with older individuals often reaching greater dominant tree species on Afognak, while devil’s club body sizes until reaching an asymptote and can display (Oplopanax horridus), blueberry (Vaccinium ovalifo- greater dominance and hold larger home ranges [15]. As lium), salmonberry (Rubus spectabilis), and willow (Salix brown bears must gain enough fat reserves to survive spp.) are dominant understory species [62]. Chum (O. denning, understanding their energetic ecology and the keta), coho (O. kisutch), pink (O. gorbuscha), and sock- potential biological and behavioral factors which may eye (O. nerka) salmon migrate and spawn throughout influence it, are important considerations for conserva - the island’s streams and lakes (Alaska Department of Fish tion and management. and Game, unpublished data). Because of close proximity We first aimed to compare the use of GPS-derived esti - and presumed inter-island movements of brown bears, mates of energy expenditure relative to more intensively we considered Afognak and Raspberry islands a single collected accelerometer-derived (ACC) estimates in wild, study site, hereafter referred to as Afognak Island. Sitka- free-ranging brown bears on the Kodiak Archipelago, lidak Island (57.1030° N, 153.2356° W, Fig. 1) (300  km ) is Alaska, USA. Previous research has applied the use of separated from Kodiak Island by a 320- to 3200-m-wide GPS energy estimates to wild brown bears in the Yel- strait and is located about 91 km south of Afognak Island. lowstone ecosystem [12], however no prior work to our Sitkalidak Island has steep mountains with elevations to knowledge has simultaneously compared this method 672 m. Several streams provide spawning habitat for four to accelerometer-derived estimates in brown bears. We species of Pacific salmon. On Afognak and Sitkalidak then applied the GPS method as a case study on a larger islands, brown bears rely seasonally on salmon, vegeta- sample size of brown bears to determine movement- tion and berries, and to a lesser degree, ungulate prey and based daily energetic expenditures (MDEE) in relation other marine-derived food [1, 62]. to intrinsic (reproductive status, age, movement rate), spatial (terrain roughness, distance to salmon [Onco- Animal handling rhynchus spp.] streams) and temporal (food abundance We captured bears during 2019–2020 using standard aer- period, ambient temperature) factors. We predicted that ial darting techniques with an R44 helicopter and rifle- bears would have increased energetic expenditures in the fired (CapChur SS cartridge-fired rifle) darts containing high food abundance period due to increased movements Telazol (Zoetis Services LLC; Parsippany, USA) [57]. We to monopolize food resources during this time [19]. fitted animals with global positioning system (GPS) col - Alternatively, due to the effects of mate-seeking behavior lars (model Vertex Plus-4, Vectronic, Berlin, Germany), on bear movement in spring, bears may exhibit higher with built in tri-axial accelerometers sampling continu- energetic costs during these low food abundance peri- ously at 32  Hz (± 8  g range). We programmed collars to ods. We further predicted that reproductive status would attempt a relocation every 60  min then release from the affect movement-based energetics, where females with animal 21–24  months post-capture. We also inserted dependent young would constrict space use to reduce a leather link designed to degrade after 2  years as a sec- risk of infanticide and thus have lower movement-based ondary drop-off mechanism [22]. We extracted a ves - energetic expenditures compared to males and solitary tigial upper premolar from bears to estimate age using females. Lastly, we predicted older individuals with larger cementum annuli counts [11]. Body weight data were body sizes would move greater distances as they can unavailable for this study, however as age is closely asso- dominate resource rich areas over younger individuals, ciated with body size, with older individuals commonly and thus incur greater energetic costs. larger until reaching an asymptote [58], we used age as a proxy for body weight. We assigned each bear to an Methods age category (young adult, adult and mature adult) to Study area examine how age and associated body mass may influ - Afognak (58.3279° N, 152.6415° W) (1809  km ) and ence energetics. Young adults were 3–6  years old and Raspberry (58.0708° N, 153.1876° W) (197  km ) islands unlikely to reproduce (although age of first reproduction are in the Kodiak Archipelago, Alaska, USA, 5 km north in brown bears varies) [41]. Adult bears were 7–12 years of Kodiak Island and separated by a 1.5-km-wide strait old and considered breeding adults, while mature adults (Fig.  1). Both islands contain rolling mountains, with were > 12  years old and likely to have reached full skull elevations to 739  m on Afognak Island and 732  m on size [32]. We recorded sex and for females, evidence Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 4 of 11 Fig. 1 Location of Afognak, Raspberry and Sitkalidak islands, Kodiak Archipelago, Alaska, USA of lactation and presence of young. No females with Forestry Institutional Animal Care and Use (IACUC) dependent young of the year (< 1  years old) were cap- (protocol 180503) and Alaska Department of Fish and tured, therefore dependent young in this study refers to Game (ADFG; IACUC protocol 0030-2017-37). cubs 1–3  years old. We positioned bears sternal follow- ing handling to recover at their capture sites. All animal ACC and GPS energy calculation handling procedures were approved by the State Univer- To derive energetic expenditure from accelerometer data, sity of New York College of Environmental Science and we calculated overall dynamic body acceleration (ODBA) F innegan et al. Animal Biotelemetry (2023) 11:7 Page 5 of 11 from eight brown bears and converted values to rates of energetic measurements for each individual brown bear −1 −1 oxygen consumption (Vo ) based on relationships derived (J  kg  h ). from captive brown bears walking on a motorized tread- mill, where Vo (ml/kg/min) = 0.069 + 31.972 × ODBA Environmental variables [47]. We converted V o to joules by multiplying by 20.08. We created a terrain roughness index (TRI) from a We excluded data from all individuals for 5  days post- 30-m-resolution digital elevation model for the Kodiak capture to account for potential recovery effects on Archipelago (Kodiak Island Borough GIS and Map movement behavior [60]. We used a 2-s running mean Center, unpublished data) using ArcGIS (10.7.1, ESRI of the raw acceleration data to determine static accelera- 2018, Redlands, CA). This index calculates the differ - tion and then subtracted from the raw data to estimate ence in elevation from a center cell value to the eight dynamic acceleration [47, 54, 70]. The ODBA value was surrounding cells by squaring each of the eight elevation calculated as the absolute sum of dynamic acceleration difference values to make them all positive, summing across all three axes (surge, heave, and sway) (Additional them, and taking the square root [52]. This index char - files 2, 3): acterizes irregularity in elevation within a given unit [55], ODBA = |A | + A + |A |, and can be an important factor that impedes or facili- x y z tates animal movement and subsequent energetic expen- where A , A and A are the derived dynamic accelera- x y z ditures. We assigned a TRI value for each brown bear tions at any point in time corresponding to the three location and then determined the circular average daily orthogonal axes of the accelerometer. TRI value to assess its effect on energetic expenditure. Using data collected from GPS collars, we calculated Due to the importance of salmon to bears and poten- −1 movement rates (km  h ) between successive hourly tial energetic implications of foraging, we calculated the locations. To reduce uncertainty, we only considered distance from each GPS location to the nearest anadro- successive locations < 63  min apart and removed fixes mous salmon stream (Alaska Department of Fish and with poor dilution of precision rates. We determined Game, unpublished data), and determined the average the minimum distance between each location as the daily distance for each animal. We divided GPS data into great-circle distance (accounting for the curvature of the two periods to assess how fluctuations in food availabil - Earth’s surface), and derived a movement rate by dividing ity influenced MDEE. The high food abundance period the distance by the duration between locations [47]. We was 1 July–31 September, corresponding with spawning measured the slope between locations using the R pack- salmon and ripe berries, important foods for brown bears age elevatr [34]. [1, 3, 62]. The low food abundance period was 1 April–30 We derived the ACC measure of energetic expendi- June and 1 October–31 November, reflecting when dom - ture using ODBA combined with the equations for inant foods were less available. We excluded data col- moving up or downhill based on associated GPS loca- lected during December–March as many animals enter tions. The GPS method used the relationships between denning during this period. Ambient temperature read- −1 slope and speed (m  s ) with energy expenditure ings were recorded at each GPS location via the GPS col- derived from nine captive brown bears [12] to meas- lar and we calculated daily means to test its influence on ure energy expenditure based on the hourly move- MDEE. Temperature readings collected from GPS collars ment rate and slope derived from successive GPS are strongly correlated with temperature readings from locations. At horizontal slopes (i.e., 0°), energy expendi- nearby weather stations and considered suitable for these −1 −1 ture J  kg  s = 2.81 + 2.45 × speed; at inclines > 0° purposes [18]. and < 15°, energy expenditure = 2.93 + 6.05 × speed; at inclines ≥ 15°, energy expenditure = 2.76 + 10.63 × speed; Energy landscape and at declines (i.e., < 0°), energy expendi- To create visual examples of energetic expenditures we ture = 2.71 + 4.74 × speed − 2.48 × speed [12]. used the inverse distance weighted interpolation tool in ArcGIS [13] to construct energy landscapes for an indi- Application to wild bears vidual male and female brown bear on Afognak Island. Using GPS collars retrieved from wild brown bears we We created maps for both animals that displayed ener- determined and compared the ACC- and GPS-derived getic measurements across the landscape from the ACC energetic expenditure. We then applied the GPS method and GPS methods (4 maps total). We used the inverse to a dataset from 28 brown bears on Afognak and Sit- distance weighted interpolation tool as we had a large kalidak islands. We determined movement-based daily sample of location data that represented the range of energetic expenditure (MDEE) by averaging hourly observed values for that energy surface [39]. Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 6 of 11 Statistical analyses our top model based on the lowest Akaike Information We conducted all analyses using R statistical software Criterion, adjusted for small sample sizes (AICc), and [51]. We tested our data for normality and found the only models with Δ ≤ 2 were selected for further con- ACC and GPS measures of energetic expenditure were sideration [10]. Unlike generalized linear models, the not normally distributed. We used a Wilcoxon signed likelihood ratio statistic of a GAM does not follow a rank test [24] to test whether GPS and ACC measure- Chi-square distribution and consequently, p-values are ments differed. We used the non-parametric Spearman’s only approximate [71, 74]. However, if observed p-val- rank correlation test to measure the strength and direc- ues are approximately in the upper 2.5%, results can be tion of the association between the two energy measure- interpreted with more confidence [74]. Consequently, ments [23]. We constructed 11 a priori models and a null we considered only model variables with p values < 0.025 model to assess the influence of internal (reproductive as strongly significant, and variables with p values 0.05– status [M = male, F = female, F Y = female with depend- 0.025 as marginally significant. −1 ent young], age and movement rate [km  h ]), spatial (TRI, distance from nearest salmon spawning streams), and temporal (food abundance period, temperature) fac- Results tors on brown bear MDEE derived from the GPS method We estimated hourly energetic data using the (Additional file  1: Table  S1). We used a Pearson’s prod- GPS and ACC method for eight brown bears uct-moment correlation coefficient (r) to diagnose mul - (M = 3, F = 3, F Y = 2) from Afognak and Sitkali- ticollinearity among dependent variables, and assumed it dak islands during September 2019–November 2020 did not influence model results if |r|< 0.70 ). (n = 23,024, Additional file  1: Table  S2). The ener - We used generalized additive mixed models (GAMMs) getic expenditure was greater using the GPS method −1 −1 to examine factors influencing MDEE using the ‘mgcv’ (median = 10,198 J  kg  h ) compared to the ACC −1 −1 package in R [72]. This model type allowed flexibility to method (median = 5351 J  kg  h ) (V = 2116, handle nonlinear predictor variables. We applied a cubic p < 0.001), with paired daily measurements positively spline smoothing factor to nonlinear variables and set correlated (r = 0.82, p = < 0.001; Fig .  2). Visual inspec- individual bear ID as a random factor [71]. We selected tion of the inverse distance weighted energy landscapes Fig. 2 Correlation between daily average GPS- (GPS method) and accelerometer- (ACC method) derived measures of energetic expenditure −1 −1 (J kg m ; r = 0.82, p = < 0.001) for eight brown bears (F = female, FY = female with young, M = male), Afognak and Sitkalidak islands, Alaska, USA, September 2019–November 2020 F innegan et al. Animal Biotelemetry (2023) 11:7 Page 7 of 11 for a male and female bear suggested more energetic Table 1 Parameter estimates for generalized additive mixed models (GAMM) on daily movement-based energetic variation with the ACC method (Fig. 3). expenditure of 28 brown bears, Afognak and Sitkalidak islands, We applied the GPS method of energetic expendi- Alaska, USA, September 2019–November 2020 ture to data collected from 28 brown bears (M = 6, F = 9 and FY = 13) during September 2019–November Covariate edf Parameter t/f score p-value estimate 2020 for a total of 3509 bear days (M = 769, F = 1079, FY = 1661). The median MDEE was 10,303 (standard Intercept 5.124 4326.987 0.001 −1 −1 deviation = 33,432) J  kg  h for males, 10,301 (stand- Sex—female with young − 4.830 − 0.647 0.517 −1 −1 ard deviation = 19,201) J  kg  h for solitary females Sex—male 7.526 0.087 0.930 −1 −1 and 10,307 (standard deviation = 6470) J  kg  h for Age—mature 2.569 0.035 0.972 females with young. Our full model was most supported Age—young 5.060 0.563 0.573 (AIC = − 21,726, model weight = 1, R = 0.96) with no Food period—high − 2.443 − 5.333 0.001 competing models. We found internal (movement rate) Temperature − 1.026 − 2.559 0.010 and temporal (high food abundance period) factors had Movement rate 3.911 276.628 0.001 the greatest effects on brown bear movement-based Terrain roughness 1.000 4.317 0.037 energetic expenditure (Table 1). Brown bears had lower Distance to salmon streams 1.000 4.621 0.031 energetic expenditures in the high food abundance Effective degrees of freedom = edf, values in bold are significant (p < 0.05) period (p = 0.001), and movement rate was positively related to MDEE (R = 0.96, p = 0.001; Fig .  4). Decreas- ing ambient temperatures were associated with greater Discussion movement-based energetic costs (p = 0.010). Increasing High-frequency accelerometer data can measure instan- terrain roughness was associated with marginally sig- taneous energetic costs as animals move across changing nificant increases in energetic expenditures (p = 0.037, landscapes in search of resources [17, 46, 66]. Although edf = 1.000), while closer proximity to salmon streams the benefits of this technology are immense, chal - (p = 0.031, edf = 1.000) was also associated with mar- lenges remain, in particular the collection of the device ginally significant increases in expenditure. upon study completion [6]. Accelerometers are also data intensive due to their continuous high-frequency Fig. 3 Estimated energy landscapes for a male (top) and female (bottom) brown bear using accelerometer- and GPS-derived measures of energetic −1 −1 expenditure (J kg m ), Afognak Island, Alaska, USA, 1 July–4 August 2020 Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 8 of 11 constraints of wild animals. However, this method can- not account for metabolic energy demands and may only be suitable for comparisons of movement-based energy expenditure among individuals [9]. We applied the GPS technique to a larger sample size of wild brown bears on the Kodiak Archipelago and found that bears had greater energy expenditure with increased movement rates and lower energetic expenditure dur- ing the high food abundance period, when salmon and berries were abundant. Our strong positive relationship between brown bear movement rate and energetic cost was similar to previous studies [31, 37, 48]. An animal’s metabolic rate and speed are fundamentally linked to their dynamic body acceleration [28]. Bears are intrinsi- cally sensitive to increased locomotor speeds due to their Fig. 4 Relationship between GPS-derived daily movement-based −1 −1 plantigrade posture, large body sizes and higher resting energetic expenditure (MDEE; J kg m ) and movement rate −1 (km h ; parameter estimate = 3.911, p = 0.001) for 28 brown bears, metabolic rate compared to similar-sized animals [47]. Afognak and Sitkalidak islands, Alaska, USA, September 2019– These higher energetic demands during locomotion may November 2020 explain why some brown bears, like polar bears (U. mar- itimus), often employ a sit-and-wait predation strategy, particularly along salmon streams [21, 36, 46]. However, measurements, making analysis computationally this strategy is likely only efficient in areas with access demanding. A GPS-derived measure of energy expendi- to anadromous salmon. The higher energetic demands ture offers an alternative to study animal energetic ecol - associated with increased movement rates may also ogy when accelerometer data may not be available [9, 12]. explain the importance of a social dominance structure We compared these two techniques of measuring animal among brown bears at prime foraging sites [3, 25]. Cer- energetics and applied the GPS technique to wild brown tain areas along anadromous fish streams may provide bears. We found that GPS-derived estimates of MDEE bears the best access to migrating salmon, with reduced were on average 1.6 times greater than the ACC method, costs in obtaining this resource [26]. Under an optimal- displayed less overall variation and likely overestimated ity framework, such locations would be favored due their true energetic expenditure. This finding contrasted with increased net energy uptake per unit time of effort [27]. Bryce et  al. [9], who noted the energetic costs derived Bears had reduced MDEE during the high food abun- from accelerometers fitted to wolves were on average dance period (July–September) when spawning salmon 1.3 times higher than the GPS method. Discrepancies and ripe berries are abundant throughout the Kodiak between our results and that of Bryce et  al. [9] may be Archipelago [62]. This finding contradicted our predic - due to differences in ecology between wolves and brown tions given that bears on the Kodiak Archipelago have bears and highlight the importance of species-specific larger range sizes during this period [1, 19], and there- considerations when examining GPS-derived energetic fore would be expected to experience greater energetic expenditures. It is also important to note that the rela- demands associated with locomotion. This reduced tionships between oxygen consumption and speed, and MDEE in the high food abundance period supports oxygen consumption and ODBA in brown bears were optimal foraging behavior among brown bears as it sug- devised from a small number of captive animals through gests that bears minimize energetic cost while maximiz- two separate studies, and may not fully reflect the ener - ing food resource gains in times of increased abundance. getic demands of free-ranging individuals. Our move- Despite traveling greater distances to use resource rich −1 −1 ment-based energetic expenditures (2.7–8.9  J  kg  s ) areas, bears reduced energetic costs likely by altering were similar to ranges reported for brown bears in the movement and foraging behavior. Brown bears often Yellowstone ecosystem, with expenditure values of 3.0– choose movement paths that offer reduced resistance −1 −1 10.6 J  kg  s [12]. While energetic costs derived from [12] and can employ sit-and-wait hunting strategies GPS locations with infrequent resampling should be along salmon streams [36]. Such behavioral choices likely interpreted with caution, the high correlation between attribute to the reduced energetic costs we found in this ACC- and GPS-derived estimates of MDEE suggests the study. GPS technique can be useful to study energetic ecology Our finding of no effect of age and reproductive status [12], particularly for providing insights into the energetic on brown bear movement-derived energetic expenditure F innegan et al. Animal Biotelemetry (2023) 11:7 Page 9 of 11 was surprising because of previously reported differ - anadromous salmon, increased anthropogenic distur- ences in bear movements between males and females, bance at these locations may limit bears from maximizing and between younger and older individuals [14, 15]. energetic gain. Such energetic considerations may inform Larger body sizes incur increased energetic demands, designating areas of high importance for bear conserva- thus species such as brown bears would be expected to tion and management [67]. We suggest future research have higher energetic costs in older, larger-bodied males examine the GPS method using finer resolution reloca - [33]. Although we found no such relationship, it is impor- tion data to improve its accuracy. We also recommend tant to note that our energy calculations do not account comparisons between methods in a laboratory setting for internal energetic costs where differences as a result on a larger sample of captive animals. However, as with of sex and age may be significant, and our sample of 28 all studies that involve animal handling, we encourage bears included few males which may have limited our researchers to consider ethics and animal welfare when ability to detect sex-specific differences. Additionally, as designing and implementing energetic ecology research. we did not have females with dependent young under the age of 1  year in this study, it may have affected our Abbreviations ability to fully examine how energetic expenditure may ACC Accelerometer have differed between reproductive classes as a result of GPS Global positioning system MDEE Movement-based daily energetic expenditures risk avoidance behavior [3]. We found that lower ambi- ODBA Overall dynamic body acceleration ent temperatures were associated with greater energy Vo R ates of oxygen consumption costs, potentially attributed to the increased thermoreg- TRI Terrain roughness index GAMMs Generalized additive mixed models ulatory demands on mammals in cooler temperatures AICc Akaike Information Criterion, adjusted for small sample sizes [63]. Brown bears may respond to lower temperatures by increasing movements and thus experienced increased Supplementary Information energetic costs associated with locomotion. Although, it The online version contains supplementary material available at https:// doi. is also possible that cooler temperatures affected energet - org/ 10. 1186/ s40317- 023- 00319-0. ics simply due to the time of year that they occurred, as colder temperatures are more common in late fall when Additional file 1: Table S1. List of 11 a priori models and a null model to assess the influence of internal (reproductive status, age and movement food resources are less available, and likely impact bear rate), spatial ( Terrain roughness, distance from nearest salmon spawning movements. We found a marginal influence of increased streams), and temporal (food abundance period and temperature) factors terrain roughness and proximity to salmon streams on brown bear GPS-derived movement-based energetic expenditure, on the Kodiak Archipelago, Alaska, USA, September 2019–November 2020. resulting in higher energetic cost. Large carnivores can −1 −1 Table S2. Average hourly energetic expenditure (J kg m ) from global be more susceptible to increased costs of movement in position system (GPS) and accelerometer (ACC)-derived estimates for mountainous terrain [17]. Although these factors may eight brown bears (F = female, FY = female with young, M = male) on the Kodiak Archipelago, Alaska, USA, September 2019–November 2020. have influenced bear energetic expenditures, we are (N = number of hourly locations). cautious to infer such relationships with marginal sig- Additional file 2. Bear ACC energy data. nificance in our GAMM due to the likelihood of model Additional file 3. Bear GPS daily energy data. overfitting [73]. Acknowledgements Conclusions We thank the Alaska Department of Fish and Game for logistical and financial The study of animal energetics continues to provide new support. We thank Afognak Native Corporation, Koniag Native Corporation, Koncor, Natives of Kodiak Native Corporation, Ouzinkie Native Corporation, insights into ecosystem-scale resource requirements, and Old Harbor Native Corporation for logistical support and land access. We and how animals adapt to spatiotemporal heterogeneous are thankful to the Brown Bear Trust for project support. We thank K. Kellner environments [67]. We demonstrated that brown bear and T. Jackson for statistical advice and J. Crye for field support. We thank the many individuals who contributed to this project including L. Van Daele, movement-based energetic expenditure was sensitive to D. Dorner, A. Hopkins, K. Kruger, P. Olsen, K. Wandersee, K. Wattum and T. M. intrinsic and extrinsic factors, particularly movement Witteveen. rate and food abundance. Human disturbance and altera- Author contributions tion of bear habitat may lead to risk-aversion behaviors SPF, AMP, JLB and NJS conceived the ideas and designed methodology; SPF, [38], such as increased movement rates when traversing JLB, NJS and SLS collected the data; SPF and AMP analyzed the data; SPF led areas perceived as higher risk [44]. As movement rate the writing of the manuscript. All authors contributed critically to the drafts. All authors read and approved the final manuscript. increases brown bear cost of transport, such risk avoid- ance behavior could impede bears from maintaining Funding optimal energetic efficiency. Additionally, as brown bears This project was financially supported by the Federal Aid in Wildlife Restora- tion Act under Pittman-Robertson project AKW-12 and the Alaska Department reduce energetic expenditure when food is abundant, of Fish and Game. likely by selecting foraging areas with greatest access to Finnegan et al. Animal Biotelemetry (2023) 11:7 Page 10 of 11 Availability of data and materials 18. Ericsson G, Dettki H, Neumann W, Arnemo JM, Singh NJ. Offset Data are available upon request from the authors. between GPS collar-recorded temperature in moose and ambient weather station data. Eur J Wildl Res. 2015;61:919–22. 19. Finnegan SP, Svoboda NJ, Fowler NL, Schooler SL, Belant JL. Variable Declarations intraspecific space use supports optimality in an apex predator. Sci Rep. 2021;11:1–3. Ethics approval and consent to participate 20. Friebe A, Swenson JE, Sandegren F. 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Journal

Animal Biotelemetry – Springer Journals

Published: Feb 20, 2023

Keywords: Accelerometers; Large carnivore; Daily energetic expenditure; Ecological energetics; GPS; Optimal foraging theory; Ursus arctos

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