Telemetry in A High Altitude Partial Migration System
Nawang Norbu1, 2, 3, Joshua Golberg4, Martin C Wikelski1,2
(Target Journal: Biology Letters)
1 International Max-Planck Research School for Organismal Biology, University of Konstanz, Germany, 2 Max-Planck Institute for Ornithology, Radolfzell, Germany, 3 Ugyen Wangchuck Institute for Conservation and Environment, Lamai Gompa, Bhutan 4 College of Forestry and
Conservation, University of Montana, USA
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57 ABSTRACT
Tradeoffs in partial migration systems, determining the decision whether to stay or to go, remain poorly understood and the proximate consequences of individual decisions have not been examined in detail. Using accelerometer-enabled GPS telemetry, we investigated differences in energetic status and home range sizes for residents and migrants over a migratory season in a partially migratory population of Stayr Tragopans in the Bhutan Himalayas. Winter home ranges for residents overlapped between
conspecifics and were significantly larger than those of migrants whose home ranges were located at discrete non-overlapping sites. Over the course of the entire migratory season, we did not find significant differences between migrants and residents in energy expenditure as estimated by dynamic body acceleration (DBA). Nevertheless, for
migrants, we observed higher DBA scores and activity states associated with
running/flying and walking during migration. We conclude that an individual’s migratory status does not significantly affect its overall energy expenditure status despite
manifesting differently in terms of space use. This may explain the maintenance of a partial migration system within a relatively small geographic area where the better strategy (i.e. of either being resident or a migrant) could be mediated by fluctuating micro-habitat conditions across time.
INTRODUCTION
Most migration systems are cases of partial migration (Chapman et al. 2011) where only a portion of the population migrates (Lundberg 1988). This is also the case in some
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altitudinal migrants (Gillis et al. 2008; Boyle 2010; Norbu et al. 2013). Though partial migration is ubiquitous and influences ecosystem functioning (Brodersen, Nicolle &
Nilsson 2011), the ultimate drivers and the proximate consequences in terms of energetic status and home range sizes of individuals remain poorly understood.
The rates at which animals expend energy determine important parameters associated with growth, reproduction and acquisition of food amongst others (Berthold, Gwinner &
Sonnenschein 2003; Brown et al. 2004). As such the allocation of energy budgets has proximate consequences on an individual’s life history with ultimate implications for its fitness. Migration has generally been considered as an adaptive strategy wherein an individual seeks to avoid seasonally difficult conditions by travelling to energetically less challenging areas (Klaassen 2003; Fort et al. 2013). It has also been shown that
conditions at non-breeding sites can influence fitness outcomes at the breeding grounds (Marra, Hobson & Holmes 1998). This assumption may hold true for most long distance latitudinal migrants. However, whether wintering sites are also energetically benign in altitudinal migration systems has not been assessed so far.
Very few studies have quantified the energetic costs (Wikelski et al. 2003) associated with migration and the related costs and benefits of remaining resident or adopting migration (Fort et al. 2013). However, migration is perceived as energetically
demanding and costly for individuals who undertake it (Alexander 1998; Wikelski et al.
2003; Newton 2008), although migrants potentially benefit from access to better quality habitats (Fryxell & Sinclair 1988). Within partial migration systems, it is still not clear as to which strategy (i.e. being a resident or a migrant) provides better fitness outcomes
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and the possibility of a suite of evolutionary stable strategies to exist has been suggested (Newton 2008).
Amongst other aspects, the energetic status of an individual may depend on food availability and amounts of foraging required (Pyke 1984; Murray 1991). Home range sizes have been shown to correlate with the availability of food (Newton 2008). Also in ungulates, it has been shown that home range sizes are larger where habitat conditions are poor (Mysterud 1999). However, it has also been suggested that territory sizes may not necessarily be regulated by food availability (Adams 2001) and can be influenced by the presence of conspecifics in ways other than by pure food competition. Nonetheless, we presume that home range sizes can have a bearing on the energetic status of
individuals through the extent of movement required to meet foraging needs. It is known that for many (but not all) different foraging guilds, a major portion of an animal’s energy expenditure can be attributed to movement (Alexander 2003; Wilson et al. 2006).
The accurate measurement of movement-related energy expenditure in the wild together with estimation of home range sizes is now possible with accelerometer informed GPS telemetry (Cooke, Hinch & Wikelski 2004; Wilson et al. 2006; Wilson, Shepard & Liebsch 2007; Cagnacci et al. 2010; Brown et al. 2013). Animal-attached accelerometers which measure body acceleration over time can provide proxies of energy expenditure and their reliability as indices for energy expenditure has been demonstrated in a wide range of animals (Halsey & White 2010; Halsey, Shepard &
Wilson 2011; Brown et al. 2013).
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Altitudinal migrations occur across most montane regions of the globe (Dixon & Gilbert 1964; Powell & Bjork 1995, 2004; Burgess & Mlingwa 2000; Chaves-Champos, Arevalo
& Araya 2003; Laymon 2009; Boyle 2010) and a few such altitudinal migrants have also been shown to be partial migrants (Boyle 2008; Norbu et al. 2013). The fact that these altitudinal migration systems occur over relatively restricted geographic areas enable us to monitor migrants and residents in tandem across a migratory season thereby
allowing us to test for the drivers and consequences of migration. So far, we are not aware of any study that has assessed differences of space use and energy expenditure in a partial migration system across an entire migratory season.
Here, through the use of high resolution GPS/accelerometer telemetry, we track energy expenditure and home range size changes in a partially migrating population of the Satyr tragopan (Tragopan satyra), hereafter referred to as tragopan/s in the Bhutan Himalayas. We assess whether the decision to migrate or to remain a resident has implications for home range sizes and energy expenditure across time. We discuss our findings with regards to possible tradeoffs and mechanisms which drive such partial migration systems and offer pointers on how such a partial migraton system could be maintained within small altitudinally graded geographic areas.
MATERIALS AND METHODS
Study Area
Tragopans were studied in Thrumshingla National Park of Bhutan (27° 22'46'' N, 91°01'46''E). The elevation in the study area ranged from 1500 masl to 4500 masl and
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average daily temperatures ranged from a maximum of 25 °c to a minimum of -8 °c. The area has four distinct seasons with most rainfall occurring between the months of May to August as part of the Asian monsoons. The study area is mostly conifer forests dominated by fir (Abies densa) with rhododendron understory at higher elevations (>3000 masl) transiting to mixed conifer forests (2400 – 3000 masl) comprising of spruce (Picea spinulosa), hemlock (Tsuga dumosa) and larch (Larix griffithii). Below 2400 masl, conifer forests give way to conifer-broadleaf mixed forests, and to cool
broadleaved forests comprising mostly of oak (Quercus glauca and Q. lamellosa). There are also a few patches of open grazing areas used by nomadic cattle herders in the region.
Study Species
The Satyr tragopan (Tragopan satyra) is a pheasant species endemic to the central and eastern Himalayas covering the countries of Nepal and Bhutan. They are also found in the state of Arunachal Pradesh in India, and some lower valleys of Xizang in China (Sibley & Monroe 1990). Only an estimated 20,000 individuals (about 6000 – 15000 adults) are extant in the wild (BirdLife International 2012). The tragopans are classified as Near Threatened by the IUCN (IUCN 2008) and listed on Appendix III of CITES (www.cites.org). Such listings while important may not adequately reflect the actual threat to a species. In many parts of its range, it has been suggested that the tragopans face increasing threats from habitat loss, forest fires and poaching (BirdLife International 2012).
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Tragopans are omnivorous and feed on seeds, fresh leaves, moss, bamboo shoots, berries and insects (BirdLife International 2012). Adults perform elaborate courtship displays and breeding starts from April and lasts till June. About 3 – 5 eggs are laid per clutch which are then incubated for about 28 days (BirdLife International 2012). Little is known of the biology of tragopans in the wild, although it has recently been shown to be a partial altitudinal migrant (Norbu et al. 2013).
We trapped tragopans in 2009, 2010 and 2011 using neck noose traps laid along known haunts following ridges which we barricaded with bamboo and other shrub species. We flushed tragopans towards traps during early mornings and evenings. All animal
trapping were approved by the Ministry of Agriculture and Forests in Bhutan. In 2009, in order to reduce any handling related fatality given that these pheasants were trapped for the first time, all captured pheasants were released immediately after attaching GPS tags. Pheasants captured in 2010 and 2011 were weighed (to the nearest gm) and measurements were also taken of tarsus length (mm) and beak size (mm).
GPS Tags and Data Acquisition
We used GPS/accelerometer tags (www.e-obs.de, Munich, Germany) to record the location (GPS) and activity (accelerometer) of our tragopans. Tags with harnesses weighed 45 gms. These tags save the recorded data (i.e., location, elevation, date, time and acceleration) onboard to be remotely downloaded via a handheld base station after the tragopan is relocated via the tag VHF radio pulse (ping). To help locate tagged birds, tags were programmed to ping once every 2 seconds for 2 hours every day.
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Tags deployed in 2009 were programmed to take a GPS reading every 2 hours from 0400 hrs to 2200 hrs. Given battery power constraints; tags in 2010 were programmed to take only 2 GPS readings everyday at 0600 hrs and 1400 hrs; while in 2011 tags were programmed to take 3 GPS readings everyday at 0800 hrs, 1400 hrs and 2000 hrs. In order to optimize battery performance, tags were further programmed with GPS
‘give up times’ of 2 minutes, after which the tag does not try to obtain a GPS fix for that particular location.
Classifying Migrants and Residents
We defined migration as a clear shift of an individual between non-overlapping ranges (Dingle 1996), regardless of the actual distance between those ranges. As such, we classified all birds which showed distinct summer and winter ranges as migrants and the rest as residents. We measured migration distance as the length between the locations on the day when migration was initiated to the first location of the day when migration was terminated using the ‘show elevation profile’ tool in Google Earth 5.2. We visually assessed GPS fixes in Google Earth 5.2 to determine the date of migration initiation and the date of termination.
Home Range Analyses
We calculated home range sizes using the kernel density estimator (Worton 1987) in R (version 3.0.2) using the ‘adehabitatHR’ package (Calenge 2011). To capture temporal changes, for each individual, we further calculated home range sizes for every two weeks beginning from the start of the month for which a given individual had data.
64 Dynamic Body Acceleration (DBA)
Axial acceleration was measured at 10Hz with 54-bit resolution. In previous studies, OBDA (overall dynamic body acceleration) have been calculated using all 3-axes on a moving animal to estimate energy expenditure. However, single-axis derived energy expenditure proxies have been shown to be equally reliable as the ODBA, and have been recommended for use to enable logging of data over longer periods (Halsey et al.
2009). Single axes reading along the z-axis has also been shown to be a reliable proxy for energy expenditure in humans (Brage et al. 2005). Hence, we calculated DBA values in a similar way to the ODBA (Qasem et al. 2012), but using only the z-axes.
First, we calculated acceleration scores by subtracting a running mean from the 36 raw values sampled within every 2-minute period. The acceleration scores thus obtained were summed for each 2-minute period to provide a DBA estimate for the 2 minute interval. We then summed up all the 2-minute DBA values in a day to provide an estimate for total daily energy expenditure.
Quantification of Activity States (Flying/Running, Walking and Resting Bouts) Similar to the DBA, we used the 36 raw acceleration values obtained every 2 minutes to quantify activity states. We calculated the difference between each successive raw observation within each 2-minute interval to indicate the extent of movement between each successive reading. We then computed variances for these differences. We visually inspected plots of variances against standard deviations and ranges and
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observed strong correlation between them. We classified all variances less than 125 as resting; between 125 to 1500 as walking/ foraging; and greater than 1500 as flying/
running. We could not separate flying and running because activity signatures for the two were similar. We assessed the accuracy of our classifications by cross checking with classifications obtained using the ‘accelviewer’ software provided by
movebank.org. To calibrate activity signatures for the ‘accelviewer’, we observed a tagged chicken for 2 days and noted the time when it walked, ran, flew and rested and inspected its patterns in ‘accelviewer’. See Figure 1.
Statistical Analyses
All statistical analyses were carried out in R (version 3.0.2). We performed ANOVA and fitted linear mixed models (LMMs) (package lme4 in R (Bates, Maechler & Bolker 2012)) to examine whether home range sizes, DBA scores and activity patterns were
influenced by migratory status and sex and whether they changed over time. To
account for repeated measures, we included individuals as random effects in the mixed models. We used the ‘lmerTest’ package (Kuznetsova, Brockhoff & Christensen 2012) in R to compute p-values based on Satterthwaite’s approximation and Kenward-Roger’s approximation (Kuznetsova et al. 2012) for LMMs. All other standard tests were also performed in R.
RESULTS
Out of a total of 24 individuals for which we gathered data over 3 years, 14 were
migrants and 10 residents. We had complete home range and acceleration data for the
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months of October to April for 8 sedentary individuals (7 males and 1 female). And for 11 migratory individuals (3 males and 8 females), we had complete data from October to March. See Figure 5.
More females migrated than males (n=24, Fisher’s exact test, p = 0.047), with males (n=11, median weight=1.68kg) being heavier (n=20, Mann-Whitney U Statistic= 4.000, T
= 49.000, p = <0.001) than females (n=9, median weight=1.16kg). For all birds we tracked, fall migration started by the 21st of September and ceased with the last migrant reaching winter quarters by the 19th of November (See (Norbu et al. 2013)).
Home Range Size Dynamics
Winter (December, January, February) home range sizes ranged from 0.12 to 4.15km2 with a median value of 0.91km2 for males; while female sizes ranged from 0.10 to 0.94km2 with a median value of 0.14km2. An ANOVA on log transformed winter home range sizes showed marginal influence of migratory status (F(1,18)=3.982, p=0.0633) and sex (F(1,18)=3.567, p=0.0772) in predicting winter home range sizes. However, when fortnightly winter home range sizes were tested, we found that residents had
significantly larger home ranges (LMM, F=9.7113, p=0.0026, Figure 2). Fortnightly average home range sizes translated to 0.078km2 for migrants and 0.256km2 for residents. The interaction between sex and migratory status also had a marginal effect on home range size (LMM, F=9.7113, p=0.0582). Standard deviations of fortnightly home range sizes were also significantly higher for residents (LMM, F=11.6690, p=0.0011). However, body size (weight) and sex within migrants (GLM; sex, p=0.732;
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weight, p=0.922) and residents (GLM; sex, p=0.395; weight, p=0.91) did not influence winter home range sizes.
Out of the 11 migrants for which we mapped winter home ranges (Figure 3), apart from 2 females whose home ranges overlapped to a small extent, all other migrants
maintained discrete home ranges which did not overlap. In contrast, during the same period, there was significant overlap in the home ranges of sedentary individuals (n=8).
Home range sizes did not show discernable changes (LMM, F=0.7214, p=0.3984) across seasons (October to April) for residents. For migrants, we observed a marginal significance for the effect of time (October to April) on home range sizes (LMM,
F=3.3556, p=0.072). As expected, home range sizes were significantly larger during migration than other periods (LMM, p=0.0378, Figure 4).
Dynamic Body Acceleration (DBA) and Activity Patterns
During migration, DBA scores were significantly higher than during non-migratory period for migrants (LMM, p<0.0001). However, there was no significant difference (LMM, p=0.176) between migrants and residents (data for residents taken only from 15
September to 30November to match migratory period). Even during winter (December to February), DBA scores for migrants and residents were not significantly different (LMM, p=0.322). We also did not find significant differences between residents and migrants across an entire season (LMM, p=0.45). See Figures 5 and 6.
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Confirming our findings from the DBA scores, we found that activity states differed significantly only during the migratory period (Table 1). In particular, during migration, flying/running bouts were significantly higher compared to non-migratory periods (LMM, F=18.404, p<0.0001). For all 3 activity states (resting, walking, flying/running), we
observed no significant differences between migrants and residents during winter (LMM;
resting, p=0.2537; walking, p=0.297; flying/running, p=0.276).
DISCUSSION
We investigated differences in space use and energy expenditure in a partially migrating population of tragopans in the high altitude Bhutan Himalayas. Migration is often
perceived as an energetically expensive life history stage (Wikelski et al. 2003; Newton 2008) albeit with potential benefits accruing in terms of better food availability (Newton 1998) and lowered costs of thermoregulation. All the migrants we tracked traversed less than 15km (Norbu et al. 2013) taking multiple days (with one individual taking up to 32 days) between summer breeding and winter grounds. Though travelling across a relatively small geographic area, we found that migration is indeed energetically costly.
This increased energetic expenditure was accounted for by heightened activity states associated with walking and running/flying during migration and also showed up as an increase in home range size over the migratory period.
In contrast, during the non-migratory periods and also during winter, we found no differences in energy expenditure between migrants and residents. Except for the period of migration, overall energetic expenditure balanced out over the course of a season. As such, we suggest that there are no clear tradeoffs between strategies (i.e.
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residency and migratory) in terms of energy expenditure. The enhanced energy expenditure during the active parts of migration is apparently compensated for by a slightly, but not significantly, reduced energy expenditure during the non-active phases of the migratory period.
The question then arises as to why migrants tolerate and seek out the additional cost associated with migration? The significant differences in winter home range sizes, with sizes being significantly smaller for migrants than residents perhaps suggests that migrants seek out better quality habitats during winter. While we do not have any knowledge on the habitat quality of these winter sites and therefore cannot predict energy intake rates, it is clear that migrants are able to meet their dietary needs within these relatively smaller home ranges. Given that both residents and migrants expend equal amounts of energy during winter and assuming greater costs for residents to compensate for thermoregulation (because of the generally higher altitude of resident home ranges), we suggest that migrants may accrue positive energetic balances at the end of the winter period. It has been shown that resident tits do have higher basal metabolic rates during winter suggesting higher energy expenditure (Nilsson, Nilsson &
Alerstam 2011). Thus, similar to other long distance (Marra et al. 1998) and altitudinal migrants (Gillis et al. 2008; Mackas et al. 2010), the consequences of winter habitat occupancy could affect success on the breeding grounds for our migrants as well.
For females where the need to retain a breeding territory is not there, the choice to migrate appears to be a preferable option. However, we also found that some females migrate higher up in winter (Chapter 1). Such migrations may seek out areas that are
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either preferred by their microclimate, by the seasonal occurrence of food, or by lowered predation pressure. Future studies will assess these possibilities to determine the
relative importance of each of these factors in the partial migration systems of tragopans.
For males, it appears that the maintenance of breeding grounds is often important to ensure mating success (Alatalo, Lundberg & Glynn 1986; Kokko 1999). In some species it has even been shown that the occupation of breeding sites is more important than access to food (Aebischer, Perrin & Krieg 1996). As such density dependence (Kokko &
Lundberg 2001) at breeding sites could also be affecting the migratory status for males.
While migrant males occupy possibly discrete winter home ranges, resident home ranges overlapped significantly. Given the different tradeoffs for males and females, it could well be that female tragopans are obligate migrants while males are facultative migrants. This has been shown to so in the case of partially migrating European
While migrant males occupy possibly discrete winter home ranges, resident home ranges overlapped significantly. Given the different tradeoffs for males and females, it could well be that female tragopans are obligate migrants while males are facultative migrants. This has been shown to so in the case of partially migrating European