Wet season range fidelity in a tropical migratory ungulate

Wet season range fidelity in a tropicalmigratory

ungulate

ThomasA.Morrison1* and Douglas T. Bolger2

1WyomingCooperative Fish andWildlife Research Unit, University ofWyoming,WY82071, USA; and 2Environmental Stud-

ies Program, Dartmouth College, Hanover, NH 03755, USA

Summary

1. In migratory populations, the degree of fidelity and dispersal among seasonal ranges is an

important population process with consequences for demography, management, sensitivity to

habitat change and adaptation to local environmental conditions.

2. Characterizing patterns of range fidelity in ungulates, however, has remained challenging

because of the difficulties of following large numbers of marked individuals across multiple migra-

tory cycles and of identifying the appropriate scale of analysis.

3. We examined fidelity to wet season (i.e. breeding) ranges in a recently declining population of

wildebeest Connochaetes taurinus Burchell in northern Tanzania across 3 years. We used com-

puter-assisted photographic identification and capture–recapture to characterize return patterns

to three wet season ranges that were ecologically discrete and topographically isolated from one

another.

4. Among 2557 uniquely identified adult wildebeest, we observed 150 recaptures across consecu-

tive wet seasons. Between the two migratory subpopulations, the probability of remaining faithful

to wet season areas ranged between 0Æ82 and 1Æ00. Animals from a non-migratory segment of the

population (near Lake Manyara National Park) were rarely observed in other wet season ranges,

despite proximity to one of the migratory pathways.

5. We found no effect of sex on an individuals’ probability of switching wet season ranges. How-

ever, the breeding status of females in year i had a strong influence on patterns of range selection in

year i + 1, with surviving breeders over three times as likely to switch ranges as non-breeders.

6. Social-group associations between pairs of recaptured animals were random with respect to an

individual’s wet season range during the previous or forthcoming wet seasons, suggesting that an

individual’s herd identity during the dry season does not predict wet season range selection.

7. Examining fidelity and dispersal in terrestrial migrations improves our understanding of the

constraints that migrants experience when they face rapid habitat changes or fluctuations in envi-

ronmental conditions.

Key-words: breeding, migration, reproduction, residency, Tarangire National Park

Introduction

Migratory wildlife often exhibits a remarkable tendency to

return to the same sites, ranges and routes each year, a behav-

iour termed ‘fidelity’ (Greenwood 1980;Waser & Jones 1983;

Sawyer et al. 2009). Examples are widespread and include

many birds (Greenwood 1980), fish (Thorrold et al. 2001),

amphibians (Reading, Loman & Madsen 1991) and

mammals (Dobson 1982). Fidelity is thought to benefit

individuals by increasing their familiarity with the location of

resources and predators in specific areas (Greenwood 1980;

Switzer 1997). An important consequence of fidelity is that it

generates demographic and genetic substructure within

populations, which promotes local adaptation and assorta-

tive mating (McNamara & Dall 2011). High fidelity, how-

ever, increases the vulnerability of migratory populations

when habitat quality declines in particular ranges (Wiens,

Rotenberry & Vanhorne 1986; Sutherland 1998; Cooch,

Rockwell & Brault 2001). For example, high site fidelity

to areas that were heavily exploited by humans likely

compounded the rapid declines observed in many whale*Correspondence author. E-mail: [email protected]

Journal of Animal Ecology 2012, 81, 543–552 doi: 10.1111/j.1365-2656.2011.01941.x

� 2012 TheAuthors. Journal ofAnimal Ecology� 2012 British Ecological Society

populations during the 20th century (Clapham, Aguilar &

Hatch 2008). Some species exhibit a more flexible migration

strategy in which some or all individuals appear capable of

switching sites, possibly in response to changing environmen-

tal conditions (Sutherland 1998). Because this flexibility can

have important demographic, genetic and conservation

implications for migratory populations (Doligez et al. 2003;

Bolger et al. 2008), there is considerable interest in first

characterizing patterns of fidelity and dispersal in migratory

populations, and secondly in identifying the mechanisms that

generate these patterns (Shuter et al. 2011).

Characterizing ‘fidelity’, however, remains challenging for

several reasons. First, it requires following a relatively large

number of marked individuals across multiple migratory

cycles in multiple sites or ranges (Belisle 2005). Even with

large samples, separating mortality from dispersal is difficult

or impossible if individuals move over large areas and if some

locations remain unsampled (Webster et al. 2002). Further-

more, defining the scale and boundaries of seasonal ‘sites’

can be arbitrary, particularly in highly mobile species that

have large, potentially overlapping, seasonal ranges

(Schaefer, Bergman & Luttich 2000). Because non-territorial

herbivores, such as many ungulates, move over relatively

large areas within any given season and are unattached to

nesting or denning sites, fidelity for these species may bemost

appropriately measured at the scale of the entire range (Han-

sen, Aanes & Sæther 2010). Linear displacement and range

overlap across years have been used as measures of fidelity

(Sweanor & Sandegren 1989) or as measures of interannual

habitat selection following disturbance (Faille et al. 2010).

However, many such approaches suffer from a lack of an

appropriate null model (Schaefer, Bergman & Luttich 2000).

Here, we quantify fidelity at the scale of the entire seasonal

range and measure it along a continuum of possible inter-

annual movement strategies, from active dispersal (i.e. a

lower probability of returning to the same range than chance

predicts), random movement (i.e. an equal proportion of

individuals from a given range in 1 year move to all ranges

the following year) and fidelity (i.e. a higher probability of

returning to the same range than chance predicts).

A number of hypotheses have been proposed to explain

the control of fidelity and dispersal in migratory species.

Extremely high fidelity is presumably caused by genetic con-

trols, memory or cultural imprinting (Sutherland 1998)

because animals fail to act adaptively when conditions

change. Several migratory shrubsteppe bird species, for

instance, continued to exhibit high fidelity to breeding sites

despite the experimental removal of 75% of their habitat,

presumably to the detriment of their fitness (Wiens,

Rotenberry & Vanhorne 1986). In many populations, how-

ever, only a portion returns to the same range or site in con-

secutive years. While this pattern does not preclude the

possibility that genetics or cultural imprinting play roles in

determining range selection, it suggests that animals may fol-

low conditional decision rules in which fidelity or dispersal is

based on past experiences or environmental or social cues

(Hoover 2003). Many birds (Switzer 1997; Hoover 2003) and

some ungulates (Tremblay et al. 2007) consistently return to

breeding sites or ranges in years following successful breeding

events (although see: Paton & Edwards 1996). Switzer (1993)

termed this the ‘win-stay: lose-switch’ strategy. An alterna-

tive to basing range selection decisions on past experiences is

to respond to reliable environmental cues that predict the

quality of habitats in the future. This strategy involves a

response to either extrinsic factors, such as recent rainfall and

plant growth (Holdo, Holt & Fryxell 2009), or intrinsic forces

such as density-mediated habitat selection (Morris 1987).

Ungulates have largely been viewed as employing this strat-

egy, enabling them to exploit resource gradients during the

growing season (‘summer’ in temperate latitudes and ‘wet’

season in tropical latitudes; Fryxell & Sinclair 1988; Albon &

Langvatn 1992; Sawyer et al. 2009). This view emphasizes

the importance of environmental cues in determining where

andwhen animals migrate (Albon&Langvatn 1992).

Individual attributes are also known to play important

roles in resource selection and local movement decisions

(Fischhoff et al. 2007). For example, sex and age often corre-

late with patterns of fidelity and dispersal (Greenwood 1980;

Harvey et al. 1984). In polygynous mammals, males and

juveniles (of either sex) tend to have greater probabilities of

dispersing than females and older individuals, presumably to

reduce inbreeding, lower mate competition or retain pre-

ferred sites (Dobson 1982). Identifying causes of fidelity and

dispersal becomes more complicated in gregarious species,

where attraction to social groups may override an individ-

ual’s directional bias (Gueron, Levin & Rubenstein 1996;

Couzin et al. 2005). For example, if an individual that selects

area ‘A’ in year i joins a social group composed mostly of

individuals from area ‘B’, the individual may be more likely

to migrate to area ‘B’ in year i + 1. This type of behaviour

could be facilitated by either leadership of a few well-

informed or experienced individuals or by group consensus

decision-making (Conradt &Roper 2005).

Wildebeest are one of the best-studied migratory ungulates

owing to long-term monitoring efforts in the Serengeti–Mara

Ecosystem in East Africa (Sinclair et al. 2007). Serengeti

wildebeest appear to respond to forage and nitrogen avail-

ability in new grass growth within the perceptual range of

individuals (80–100 km) (Holdo, Holt & Fryxell 2009). At

the large scale, migratory movements may be a strategy to

maximize energetic intake (Wilmshurst et al. 1999) or the

ingestion of new grass growth (Boone, Thirgood & Hopcraft

2006).Notably, these explanations focus on animal responses

to environmental cues as the key to understanding local and

regional scale habitat selection. Just east of the Serengeti eco-

system, wildebeest in the Tarangire–Manyara Ecosystem

(TME) migrate 40 to 120 km between seasonal ranges. The

TME provides a convenient location to study range fidelity

because wildebeest occupy three spatially and ecologically

discrete ranges during the wet season and congregate within

two discrete areas in the dry season (Fig. 1) (TCP 1998). The

TME population is also sufficiently small (c. 6000 individu-

als) that we can use photographic identification methods to

individually follow animals across annual cycles (Morrison

544 T. A.Morrison &D. T. Bolger

� 2012 TheAuthors. Journal ofAnimal Ecology� 2012British Ecological Society, Journal of Animal Ecology, 81, 543–552

et al. 2011). We characterize range fidelity across three

migration cycles and develop multistate capture–recapture

models (MCR) to quantify the probability of migrating to

alternative wet season ranges. MCR models are a robust

method for estimating transition probabilities while account-

ing for potential survival and recapture differences between

ranges (Brownie et al. 1993).We use these models to quantify

the degree of range fidelity and test four hypotheses. (i) If

environmental cues influence wet season range selection, we

would expect the probability of switching ranges to be greater

towards one range than others in particular years (here called

‘directionality’). We also test whether an individual’s (ii) sex

or (iii) a female’s breeding success during the previous wet

season influences their probability of returning to the same

wet season range in consecutive years. Finally, and (iv) we

examine the role of social forces in determining where indi-

viduals spend the wet season by comparing pairs of individu-

als recaptured within the same herd during the dry season

and testing whether they come from, or move to, the same

wet season ranges. If individuals associate at random during

the dry season, it supports the hypothesis that social-group

identity does not influence an individual’s range selection

decisions in the wet season.

Materials andmethods

STUDY AREA

The TME lies in the eastern branch of the Great Rift Valley in

northern Tanzania and encompasses roughly 20 000 km2 (Fig. 1).

Precipitation is highly variable across time and space (mean,

656 mm year)1; coefficient of variation, 36.4%) and largely falls

betweenNovember andMay (Foley & Faust 2010). Both calving and

mating are highly synchronous in wildebeest and occur within short

periods during the wet season (Fig. 2; Estes 1976). In most years,

calving and breeding occur in three distinct wet season ranges: the

Northern Plains (NP), the Simanjiro Plains (SP) and along the north-

ern shore of Lake Manyara (LM) (TCP 1998). Once surface water

dries out (c. July), animals migrate to Tarangire and Lake Manyara

National Parks, where they spend the dry season. SP and NP lie c.

140 km apart and are separated by a chain of forested volcanic

mountains (Losiminguri, Burko and Mondouli mountains). Thus,

direct movements between these ranges within the same wet season

are unlikely and would require passing through Tarangire National

Park (TNP) (Fig. 1). The LM subpopulation is thought to be non-

migratory (Borner 1985; Prins & Douglas-Hamilton 1990); though,

no quantitative data exists about mixing with animals from Tarang-

ire. On the western edge of TME, the Gregory Rift Wall forms a

major geographic barrier between TME wildebeest and adjacent

Ngorongoro–Loliondo wildebeest, preventing any significant recent

gene flow between populations (Georgiadis 1995). Connectivity with

other nearby ecosystems (e.g. Amboseli and Shompole) is possible,

although any movement that does occurs is likely at low levels

because of the considerable distance and lack of suitable habitat

(open grassland) between the areas.

Historically, the TME wildebeest inhabited four or five distinct

wet season ranges (Lamprey 1964; Borner 1985). However, since the

1940s, human population and agricultural expansion outside of Tar-

angire and LakeManyara National Parks have increased four to six-

fold (Mwalyosi 1991), reducing the connectivity in the ecosystem and

causing substantial habitat loss (TCP 1998). Between 1988 and 2001,

wildebeest in TME have experienced an estimated sixfold decline,

from roughly 40 000 to 6 000 individuals (TAWIRI 2001).

STUDY DESIGN AND DATA

The presence of natural variation in shoulder stripe patterns of adult

(>2 years old) wildebeest allowed us to use computer-assisted

photographic identification to compile encounter histories across

Fig. 1. Map of the Tarangire–Manyara Ecosystem, Tanzania. Solid

arrows denote the two primary pathways used by wildebeest as they

migrate between Tarangire National Park in the dry season and the

Simanjiro Plains and Northern Plains during the wet season.

Approximate seasonal ranges are outlined with dotted lines. The

non-migratory population inhabits LakeManyaraNational Park.

Fig. 2. Generalized annual cycle for wildebeest in the Tarangire–

Manyara Ecosystem, Tanzania. Calving and mating occur during

brief periods in the wet (i.e. breeding) season and are thought to be

highly synchronous across space (Estes 1976).

Wildebeest range fidelity 545

� 2012 TheAuthors. Journal ofAnimal Ecology� 2012 British Ecological Society, Journal of Animal Ecology, 81, 543–552

three wet and dry seasons of 2005–2007 (Fig. 2). In each study year,

we initiated photo sampling in May (i.e. c. 2Æ5 months after the calv-

ing pulse) when wildebeest were on their wet season ranges. Sampling

of each range took between 2 and 14 days, depending on conditions

and local animal densities. We sampled each wet season range twice

per year (except in 2005, which had only one sample) within a robust

design framework (Table S1; Pollock 1982).We also collected photo-

graphs in Tarangire and Lake Manyara National Parks during the

dry season (October–November). These dry season photographs

were used in the analysis of social-group associations and to establish

connectivity between dry season ranges.

To collect photographs, we drove all roads and main tracks within

the SP, NP and LM ranges once per secondary period and photo-

graphed individuals within herds that we encountered. We aged and

sexed animals on the basis of horn morphology and body size

(Watson 1967) and collected GPS locations of each herd. Herds were

defined as groups of wildebeest in which no individual was >100 m

from the next closest individual. We photographed individuals on

their right sides (stripe patterns were not symmetrical on both sides),

perpendicular to the length of the animal. Photographs were col-

lected from a stationary vehicle at a distance of�10–100 metres dur-

ing daylight hours using a 6Æ1 megapixel Pentax istD camera (Pentax

Corporation, Denver, CO, USA) with a 400-mm Sigma telephoto

zoom lens. For each herd, we attempted to collect as many photo-

graphs as there were adults in the group. In some cases, herds moved

away or joined other herds before we had collected the target number

of photographs. Because the identity of individuals was not known at

the time of photographing, some herds were unknowingly photo-

graphed multiple times while others were not photographed at all.

Overall, we aimed to photograph 40–50% of all adults within each

range to balance sample size and coverage. Actual capture rates (i.e.

the percentage of the population identified) were much lower (cap-

ture probabilities,P, ranged from 0.02 to 0.22) because some individ-

uals were photographed multiple times and c. 30% of images were

too poor in quality to be used for matching (Table S1).

COMPUTER-ASSISTED PHOTOGRAPH IDENTIF ICATION

We used two software platforms to identify individuals based on

stripe patterns: one for adult males and one for adult females. The

first program matched all males; however, the availability of a more

time-efficient program led us to switch platforms prior to identifying

females. Both platforms yielded similar probabilities of identifying

individuals. The ‘male’ platform was developed by Conservation

Research Ltd. (Hastings, Hiby & Small 2008) and involved three pre-

processing steps: (i) users digitally outlined the margins of each indi-

vidual within an image and placed reference markers on several key

features, such as the nose and base of the tail, (ii) The software used

these markers to fit a three-dimensional surface model of the animal,

which helped compensate for variation in viewpoint, posture changes

and body shape of the animal across photographs, and (iii) The soft-

ware extracted a standard region of the shoulder stripes and created a

planar black-and-white image, which was then used for pattern-rec-

ognition. For female images, we switched to a simpler identification

program (Wild-ID; http://dartmouth.edu/~envs/faculty/bolger.html)

that required only one preprocessing step: cropping a rectangular

region of the torso of each animal (Fig. 3). In both programs, the

main region of interest for pattern matching was along the torso

between the mid-neck and rump.

Both software programs matched and scored images using four

similar steps: (i) distinctive features within each processed image were

located using the SIFT operator (Scale Invariant Feature Transform;

Lowe 2004). These ‘SIFT features’ were invariant to scale and rota-

tion, (ii) The program identified candidate pairs of SIFT features

from each pair of images in the data base, (iii) A subset of geometri-

cally self-consistent matched image pairs obtained in step 2 was

selected, from which the program calculated a 2D affine transform,

mapping the first image to the second image, (iv). The program

assigned a standardized score between 0 and 1, describing the

strength of match between the two images, and (v) Images were

ranked based on the standardized score. For each photograph, the

user (T. Morrison) visually compared the top twenty ranking photo-

graphs and recorded anymatches.We then compiled the resulting set

of matched photographs into encounter histories that denoted

whether individuals were seen or not seen (1 or 0) during each

sampling period.

Photographic data often violate the capture–recapture assumption

that all marks (i.e. photographs) are correctly identified. False accep-

tances (i.e. falsely matching two photographs of different animals)

are relatively rare in the wildebeest encounter history data sets (esti-

mated false acceptance rate was 8Æ1 · 10)4, based on 100 test images;

Morrison et al. 2011), and we assume these errors did not have a sig-

nificant impact on data structure. However, encounter histories likely

contained moderate numbers of false rejections (i.e. failures to match

two photographs of the same individual), which inflate the number of

observed encounter histories (Yoshizaki et al. 2009; Morrison et al.

2011). We estimated the false rejection rate (FRR) for both male and

female identification programs using a test set of 198 images of

known-identity animals collected in both the dry and the wet seasons

(Morrison et al. 2011). The two software programs yielded similar

false rejection rates (‘FRR’; FRR: 0Æ06–0Æ08; Fig. S1). This was

unsurprising, given that both programs used the same pattern-char-

acterization algorithm (SIFT) and scored images in a similar manner.

Any slight differences between male and female data sets because of

the software would be reflected in recapture probabilities and not in

transition probabilities (i.e. the probability of migrating to alterna-

tive ranges the following year) of the capture–recapture models

because transition probabilities are already conditioned on individu-

als being available for capture at least twice. Thus, we combined both

male and female data sets and using them in a single ‘all-adults’

model.

RANGE FIDEL ITY MODELS

We fit two sets of wet season encounter history data to multistate

robust design capture–recapture (MSRD) models (Brownie et al.

1993): the ‘all-adults’ model and the ‘females-only’ model. MSRD

models provide estimates of transition probabilities among and

between different states across some sampling interval. The interval

between primary sampling periods was 1 year and ‘state’ in our mod-

els corresponded to the three wet season ranges that individuals occu-

pied at the time of sampling: Simanjiro (SP), NP or Lake Manyara

National Park (LM). However, because we observed very few

switches to, or from, LM, we excluded LM data from the model. We

report the observed transitions involving LM animals and discuss

this smaller population separately.

The ‘all-adults’ model examined the effect of sex and range in year

i on the probability of transition to an alternative wet season range

(SP orNP) in year i + 1.We definewSP�SPi andwNP�NP

i as the proba-

bilities that an animal present in SP and NP, respectively, in the wet

season of year i and alive in year i + 1, selects the same wet season

range in i + 1. We developed various parameterizations of these

transition probabilities. First, ‘non-Markovian transitions’ occurred

when range in year i was random with respect to the range selected



the previous year, following constraints: wSP)SP = wSP)NP, and

wNP)NP = wNP)SP (Nichols et al. 1994). These constraints implied

that individuals first captured in SP or NP have the same probability

of returning to those same ranges as they do of migrating to the alter-

native range (i.e. that movement between ranges is random). Models

lacking this constraint indicate that individuals exhibit either fidelity

(e.g. wSP)SP > wSP)NP) or dispersal (e.g. wSP)SP < wNP)SP). ‘Direc-

tionality’ occurs whenwSP)NP > wNP)SP orwSP)NP < wNP)SP.

The second model (‘female-only’) used female captures across two

wet seasons (2006 and 2007) to examine whether a female’s breeding

status [breeder (B) or non-breeder (N)] in year i influenced the proba-

bility of switching ranges in year i + 1. Breeding status was recorded

at the time of photo capture (i.e. May–July, 2Æ5–4Æ5 months post-

calving), and females occupied four possible states (SPB, SPN, NPB,

NPN). Breeding status was based on whether or not females had visi-

ble mammary glands (i.e. teats), indicating that they were nursing a

calf. Any adult female that had lost their calf within c. 10 days of

being photographed would likely still have visible teats and would

thus be recorded as a breeder (Watson 1967). Similarly, ‘non-breed-

ers’ included both females that had failed to breed and females that

had reproduced during that current breeding cycle but had lost their

calf or foetus c. 10 or more days before being photographed. In

24Æ5%of captures, we were unable to discern breeding status because

females moved away too quickly, so these females have unclassified

breeding statuses. All females that had unclassified states (always

because of unknown breeding status) were censored from the data set

(n = 41 individuals).We assumed that all recorded breeding statuses

were classified correctly and that unclassified females were random

with respect to transition probability and survival.

Other assumptions were similar for both the ‘all-adults’ and the

‘females-only’ models: (i) there was no heterogeneity in capture or

survival probabilities within ranges and sexes, (ii) within primary

periods, survival probability was 1Æ0 and individuals could not transi-tion between states, and (iii) that the population was open to transi-

tions between states, mortality and recruitment between primary

periods (Brownie et al. 1993).

MODEL SELECTION

In the ‘all-adult model’, we compared 20 candidate models where sur-

vival (u), transition (w), and capture probabilities (P) varied by sex

(g), state (s), primary periods (T) and secondary periods (t). In the

‘females-only’ model, 18 candidate models were developed in a simi-

lar fashion; though, in these we varied all model parameters by breed-

ing status (b) rather than sex. We developed parameterizations of u,w and P based on a priori model sets to reduce the number of

potential models to a manageable figure (Tables S2 and S3). The

‘all-adults’ model used a global model of {u(T,g,s), w(T,g,s),P(T,t,g,s)}, indicating variation in survival, transition probability,

and capture probability across sex, breeding states, primary periods

and secondary periods. The ‘females-only’ model used a global model

of {u(s,b), w(s,b), P(s,b,t)}. We assessed Goodness-of-fit tests on the

global models using the program MSSURVIV (Hines 1994). This

program estimates a pooled G2 Goodness-of-fit test statistic, which

can be used to assess the amount of dispersion in the data (c-hat) by

dividing G2 by model degrees of freedom (Lebreton et al. 1992). We

compared competing models using the quasi-Akaike Information

Criteria corrected for small sample sizes (‘QAICc’; Burnham &

Anderson 2002). QAICc weights determined the strength of support

for a particular model within a model set (Burnham & Anderson

2002). All model selection steps and estimation procedures were con-

ducted using the ‘Open Robust Design MultiState’ model with Hug-

gins Closed Capture data structure in Program MARK, ver 5.1

(White & Burnham 1999).

SOCIAL-GROUP ASSOCIATIONS IN THE DRY SEASON

We identified all pairs of individuals captured in the same herd in

TNP during the dry season that were both also photographed in

either the preceding or subsequent wet season. We classified each of

these pairs into one of three categories: (i) SP pair (i.e. both individu-

als used Simanjiro in the wet season), and (ii) NP pair (i.e. both indi-

viduals used the NP in the wet season) or 3) mixed pair (i.e. one

individual from SP and one from the NP). In all cases, herds con-

tained other unidentified individuals whose wet season range affilia-

tions were unknown. If social-group associations during the dry

season were random with respect to their wet season range, we

expected the number of herds in each of the three pair categories to

approximate a binomial distribution. We tested this hypothesis in

each of three transition periods using a chi-squared test. We gener-

ated the expected frequency of herd category in each transition using

the relative frequency of individuals from either wet season range. All

estimates are reported as mean ± SE.

Results

Overall, we collected 5657 high-quality images of 2557

unique wildebeest on wet season ranges between 2005 and

2007 (Table S1). We observed 150 recaptures (involving 136

unique individuals) among the wet season ranges in consecu-

tive years (Fig. 4). Wildebeest exhibited high but variable

fidelity to migratory wet season ranges. The most parsimoni-

ous model in the ‘all-adult’ data set {u(T,g,.)w(T,.,.)

(a) (b)

Fig. 3. Example of an adult female wildebeest photograph captured on two occasions in different wet season ranges: (a) in the Simanjiro Plains

in June 2006 and (b) in the Northern Plains in June 2007. Female was a breeder in both years. Dashed lines show the cropped region of the torso

used for pattern analysis and imagematching in adult females.



P(T,t,g,s)} estimated annual range fidelity as 1.0 from 2005

to 2006 (no SE because of estimates lying at the edge of

parameter space) and 0Æ82 ± 0Æ06 from 2006 to 2007

(Table 1).We found a strong effect of year on the probability

of switching wet season ranges between year i and year

i + 1, with switching more likely between 2006 and 2007,

but no effect of sex nor of directionality in year i [summed

QAICc weights for models with an effect of year = 0Æ94, formodels with an effect of sex = 0Æ28, and for models with

directionality (i.e. a state effect) = 0Æ08; Table S2]. The effect

of year may have been partially an artefact of lower power to

detect transitions in 2005 (unique captures: N2005 = 384,

N2006 = 1178 and N2007 = 1230; Table S1). The ‘all-adult’

model did not suffer from a significant lack of fit

(v2 = 11Æ60, d.f.=10,P = 0Æ37).In the ‘females-only’ data set, the probability of returning

to, or of switching, wet season ranges from years i to i + 1

depended on breeding status in year i (Table 2). The top eight

models ranked by QAICc included an effect of breeding sta-

tus on the transition probability (Table S3; summed QAICc

weights for models with an effect of breeding status = 0Æ99).Breeders in year iwere over three times more likely than non-

breeders to switch wet season ranges between year i and

i + 1. Total switching probability among breeders in year i

was 0Æ20 (i.e. 0Æ10 + 0Æ10), while non-breeders was only 0.06(i.e. 0Æ03 + 0Æ03; Table 2). Overall, we observed 9 of 32

breeders in year i switching wet season ranges in year i + 1,

while 0 of 10 non-breeders switched between years (Fig. 5).

While eight of nine observed range switches involved

breeders moving from SP to NP (all between 2006 and 2007),

the top model indicated that range ‘switching’ probabilities

were equal in both directions (i.e. wSP)NP = wNP)SP;

Table 2). The ‘female-only model’ did not suffer from lack of

fit (v2 = 38Æ54, d.f. = 28, P = 0Æ09) and had a c-hat value

of 1Æ38.Wildebeest around LakeManyara National Park were lar-

gely isolated from the migratory portion of the population

and exhibited near-absolute fidelity. We observed three tran-

sitions to, or away from, LM in consecutive wet seasons.

Across all encounters, including non-consecutive wet and dry

seasons, we observed 12 total transitions between LM and

other ranges (9 females and 3 males). This involved move-

ment to, or from, the NP (n = 7 transitions), TNP (n = 4

transitions) and SP (n = 1 transition), demonstrating an

underlying degree of connectivity between the migratory and

resident populations.

Individuals coming from, or going to, the two wet season

ranges appear to associate at randomwithin dry season herds

in TNP (Table 3). The distribution of pair-wise within-herd

associations in the dry season did not differ significantly from

a random null model of associations in all years, except in the

early dry season of 2007. During this early 2007 wet-to-dry

transition (‘3a’ in Table 3), pairs of animals in TNP were sig-

nificantly segregated by the identity of their wet season

ranges during the previous wet season. However, by the late

dry season sample (i.e. immediately prior to migrating to wet

season ranges), associations were random with respect to the

identity of their previous wet season (‘3b’ in Table 3).

Discussion

RANGE FIDEL ITY IN MIGRATORY UNGULATES

Wet season ranges provide tropical ungulates with seasonally

available, high-quality forage that is critical for reproductive

activities and play central roles in adaptive explanations of

the causes or the timing of migration (Fryxell & Sinclair

1988; McNaughton 1990; Holdo, Holt & Fryxell 2009).

Fidelity to these ranges constrains the ability of individuals

to respond to resource heterogeneity across the entire land-

scape, which increases sensitivity to habitat degradation or

loss (Owen-Smith 2004). High fidelity also promotes genetic

differentiation among population segments, which furthers

the importance of managing each segment independently.

Adult wildebeest in the TME exhibited high but variable

fidelity to wet season ranges. While patterns of dispersal and

fidelity have not been well-documented in other tropical

migratory ungulates (Bolger et al. 2008), many temperate

ungulates exhibit similarly high fidelity to seasonal ranges.

For example, in Yellowstone National Park, 96% of migra-

tory elk Cervus canadensis Erxleben (n = 52 individuals fol-

lowed for 2–4 breeding cycles) returned to the same summer

grounds in consecutive years across 12 possible summer

ranges (White et al. 2010). Additionally, pronghorn Antilo-

capra americana Gray (White et al. 2007), sika deer Cervus

nippon Temminck (Sakuragi et al. 2004) and barren-ground

Table 1. Estimates of transition probabilities between Simanjiro

Plains (SP) and the Northern Plains (NP), Tanzania between 2005–

2006 and 2006–2007. These estimates are derived from a time-

varying model of transition probability (Model 1, Table S2). Note

that we could not estimate SE for transition probabilities that fell

near the boundary of parameter space (0.0 and 1.0)

Year Behaviour Parameters Estimate SE

2006 Switch wSP)NP,wNP)SP 0Æ00 0Æ00Stay wSP)SP,wNP)NP 1Æ00 0Æ00

2007 Switch wSP)NP,wNP)SP 0Æ18 0Æ06Stay wSP)SP,wNP)NP 0Æ82 0Æ06

Fig. 4. Observed fidelity patterns to wet season ranges in Tarangire–

Manyara wildebeest, summed across two wet–wet season transitions

(2005–2006, and 2006–2007). Black squares indicate instances where

individuals returned to the same seasonal range, while white squares

are instances of switching seasonal ranges. Ranges include: Simanjiro

Plains, Northern Plains and LakeManyaraNational Park .



caribouRangifer tarandus groenlandicusBorowski (Cameron,

Whitten & Smith 1986) exhibit high fidelity to summer

ranges, while woodland caribou R. tarandus caribou Gmelin

(Schaefer, Bergman & Luttich 2000) and bighorn sheep

(Festa-Bianchet 1986) exhibit high winter site fidelity. Other

ungulate populations exhibit much lower fidelity to all or

portions of their ranges (Faille et al. 2010). In the Porcupine

caribou herd that migrates hundreds of kilometres each year

in northern Canada and Alaska, Fancy & Whitten (1991)

found low fidelity to calving sites when comparing occupied

calving sites to randomly selected calving sites in consecutive

years (n = 245 transitions across years). This variation in

strategies across seasons, populations and species suggests an

underlying degree of plasticity in range selection in ungulates

that is shaped by local environmental conditions. An impor-

tant aspect of future research, with high relevance to conser-

vation of migratory ungulates, will be to determine if and

how fidelity patterns change as local conditions change (Fa-

ille et al. 2010).

DETERMINANTS OF WET SEASON RANGE SELECTION

Surprisingly, breeding female wildebeest were responsible for

over three times as many range switches as non-breeders

within the two migratory ranges (Table 2). This result runs

contrary to past research that has found either no correlation

or a positive correlation between reproductive success and

fidelity (Schaefer, Bergman & Luttich 2000; Hoover 2003;

Tremblay et al. 2007). One possibility is that breeders were

more nutritionally stressed than other animals, making them

particularly sensitive to variation in food quality across the

landscape and more inclined to explore alternative ranges.

While we could not measure nutritional state or body condi-

tion, annual rainfall was below average in TME during the

course of our study (2005–2007) and was particularly low

during April in both 2005 and 2006 (Fig. S2), the period of

year when lactating wildebeest are under their greatest ener-

getic demands (Sinclair 1977). Given the rapid decline in wil-

debeest abundance in this ecosystem (TAWIRI 2001), we

suspect food limitation and resource competition are rela-

tively low on wet season ranges and that these factors could

not adequately explain the pattern of range switching among

breeding females, except perhaps at the very beginning of the

rainy season when resource patches are highly variable across

space. However, even if range switching by lactating females

is a response to spatial heterogeneity in resources at the

beginning of the rainy season, we would also have expected

directionality in range shifts (i.e. a greater proportion of indi-

viduals moving one direction than the other) among these

females. We found no statistical support for directionality in

range selection; though, a greater number of animals shifted

ranges from the SP to the NP. This lack of directionality

makes it difficult to implicate resource-driven explanations

for range switching above other possible explanations, such

as human disturbance (Faille et al. 2010) or predation risk

(Wittmer,McLellan&Hovey 2006).

Social-group associations during the dry season did not

appear to relate to wet season range selection (Table 3). Indi-

vidually based movement models in group-forming animals

have assumed that individual movement is a consequence of

social forces and directional biases of one or more leaders in

a herd (Gueron, Levin & Rubenstein 1996; Couzin et al.

2005). While our results cannot rule out the influence of

social forces in influencing wet season range selection, they

do suggest that wildebeest herds are not stable over the dry

season and that herd identity does not influence wet season

Table 2. Transition probabilities for the ‘Female-only’ model for breeders (B) and non-breeders (N) in the Simanjiro Plains (SP) and Northern

Plains (NP), Tanzania from June 2006 to June 2007

Breeding Status Transition type Parameters Estimate SE LCI UCI

Non-breeder at 1st capture Stay-do not breed wSPN�SPN ;wNPN�NPN 0Æ10*Stay-breed wSPN�SPB ;wNPN�NPB 0Æ84 0Æ11 0Æ51 0Æ97Switch-do not breed wSPN�NPN ;wNPN�SPN 0Æ03 0Æ03 <0Æ01 0Æ17Switch-breed wSPN�NPB ;wNPN�SPB 0Æ03 0Æ03 <0Æ01 0Æ17

Breeder at 1st capture Stay-do not breed wSPB�SPN ;wNPB�NPN 0Æ05 0Æ04 0Æ01 0Æ24Stay-breed wSPB�SPB ;wNPB�NPB 0Æ75*Switch-do not breed wSPB�NPN ;wNPB�SPN 0Æ10 0Æ04 0Æ05 0Æ21Switch-breed wSPB�NPB ;wNPB�SPB 0Æ10 0Æ04 0Æ05 0Æ21

Estimates were derived fromModel I (Table S3), which assumed transitions across wet season ranges from year i to i + 1 were dependent upon

breeding status of females at year i and that ‘switching’ was equal for breeders and non-breeders at year i + 1.Note that transition probabilities

of non-breeders and breeders at year i each sum to 1Æ0. Estimates with asterisks were calculated by subtraction; no SE or CI could be estimated.

Fig. 5. Observed fidelity patterns among adult females only, summed

across three consecutive wet seasons (2005–2007). Breeding status

was classified at year i. Black squares indicate instances where indi-

viduals returned to the same wet season range, while white squares

are instances of switching wet season ranges. Note these data are a

subset from Fig. 4A (i.e. females in which breeding status was

recorded in year i).



range selection. Early in the 2007 dry season, individuals in

TNP were still segregated according to their wet season

affiliations. However, this pattern disappeared by the late

dry season (October–November) and pair-wise associations

within herds become random with respect to the identity

of an individual’s previous or forthcoming wet season

range.

MIGRATORY VS. RESIDENCY STRATEGIES

Partial migration occurs when only a portion of a population

migrates in any given year (Kaitala, Kaitala & Lundberg

1993). Similar to fidelity, an individual’s migration strategy

(residency vs. migratory) may be inflexible such that they

either always migrate or always remain resident (Anderson

1991). Alternatively, decisions tomigrate may vary from year

to year and be conditioned on a combination of environmen-

tal, internal or social cues (White et al. 2007). In the LM sub-

population, only a small portion of individuals were

observed in other wet season ranges (c. 6% of observed con-

secutive-year transitions). This confirms earlier speculations

that the LM wildebeest form a relatively isolated resident

subpopulation (Borner 1985; Prins & Douglas-Hamilton

1990; Georgiadis 1995). Prins & Douglas-Hamilton (1990)

report that wildebeest went locally extinct in the LM basin in

the mid-1960s as a result of rising lake levels, but that a small

number of animals (n = 80) repatriated the area in the mid-

1970s. Their residency near the northern boundary of Lake

Manyara National Park is intriguing in the light of the fact

that the NP migration route lies within <5 km of the LM

population, near the vicinity of Manyara Ranch. While land

between the migration corridor and LM is inhabited by an

increasingly sedentarized pastoralist community, much of

the area remains open rangeland through which other large

herbivores are known to move (e.g. elephants). The availabil-

ity of year-roundwater and perennial grasses with long grow-

ing seasons near the lake edge (Prins 1988) likely allow

animals to move locally, rather than regionally, in search of

resources (Owen-Smith 2004). Nonetheless, dispersal events

to and away from LM demonstrate that this subpopulation

is, at least nominally, still connected to the large TME popu-

lation. The fact that the majority of their range is inside Lake

Manyara National Park may decrease their long-term

vulnerability to the changes happening elsewhere in the eco-

system. Nonetheless, the dichotomy in movement strategies

between LM and Tarangire animals highlights the need for

comparative analyses that quantify the costs and benefits of

migratory vs. residency strategies in animal populations

(Bolger et al. 2008; Hebblewhite & Merrill 2009), as well as

longitudinal studies that track migratory strategies over the

course of individuals’ lifetimes.

FIDEL ITY AND CONSERVATION

The TME ecosystem is undergoing rapid conversion of

rangelands for agricultural use (TCP 1998). Most historical

migratory pathways first described by Lamprey (1964) are no

longer available for use, and the population has declined

roughly eightfold between 1988 and 2001 (TAWIRI 2001).

The majority of wet season habitat of the Eastern white-

bearded wildebeest subspecies (C. taurinus albojubotus) in

southern Kenya and northern Tanzania lies outside of for-

mally protected areas (Estes & East 2009). If range fidelity

remains consistently high over many years, these wildebeest

will have a limited capacity to overcome rapid habitat

changes, severe fluctuations in environmental conditions or

barriers tomigration such as high traffic roads.Wildlife man-

agers, therefore, should not assume that wildebeest can easily

switch to new wet season ranges if previously inhabited

ranges deteriorate. Overall, high fidelity to wet season ranges

constrains migration patterns at the seasonal scale, promotes

genetic differentiation among population subunits (assuming

that breeding occurs on these ranges) and furthers the impor-

tance of managing each subunit independently (White et al.

2010). Understanding variation in fidelity and dispersal in

migratory ungulates is central to developing effective conser-

vation strategies in the face of habitat changes to seasonal

ranges.

Acknowledgements

We thank the Tanzania Wildlife Research Institute, the Commission for Sci-

ence and Technology and the Tanzania National Parks for permission to con-

duct research in Tanzania. We are grateful to R. Mollel, J. McGrew and N.

Brown for help collecting and analysing photographs, to J. Nichols and J.

Hines for input about the capture–recapture modelling, and to G. Hopcraft

and an anonymous reviewer for constructive comments on the manuscript.

The work was funded by the Wildlife Conservation Society, Dartmouth Col-

lege, the Marion and Jasper Whiting Foundation, the Nelson A. Rockefeller

Center andNSF grant DBI-0754773.

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Received 25 July 2011; accepted 21November 2011

Handling Editor: John Fryxell

Supporting Information

Additional Supporting Information may be found in the online ver-

sion of this article.

Table S1. The number of unique wildebeest identified per year per

range followed in parentheses by the total number of high quality

images used for identification. Wet season ranges include: Simanjiro

Plains (SP), Northern Plains (NP) and LakeManyara National Park

(LM). Dry Season ranges include: Tarangire National Park (TNP)

and Lake Manyara National Park (LM). Note that Lake Manyara

National Park serves as both a wet and dry season range, and that

the ‘wet’ season was defined as November–June, while the dry season

was defined as July–October.

Table S2. Summary of candidate models fit to ‘all-adults’ dataset.

Survival (u) and transition probabilities (w) varied across years (T),

sex (g) and wet season range (s). Capture probability (P) varied

within primary periods (t), across years (T), with sex (g) and with

range (s). ‘nonMARK’ indicates a model where transitions were ran-

dom with respect to wet season range identity in year i. All other

models followed a first-order Markovian process (i.e., wet season

ranges in year i + 1 dependent upon range in year i).

Table S3. Summary of candidate models fit to ‘females-only’ dataset.

Survival (u) and transition probabilities (w) varied across breeding

status (b) and wet season range (s). Capture probability (P) varied

across breeding status (b), wet season range (s) and secondary periods

(t). ‘Switch’ implied that transitions across breeding status was

dependent on whether animals were switching or remaining faithful

to wet season ranges.

Fig. S1. False reject rates of the two photograph identification sys-

tems as a function of the number of images that were visually

inspected within the ranked list of highest scoring potential matching

candidate images. Results for both ID systems are based on the same

test set of 198 true matching photograph pairs of adult wildebeest

from the TME (Morrison et al. 2011).

Fig. S2. Difference between observed monthly rainfall and mean

monthly rainfall (solid line) in Tarangire National Park, Tanzania.

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Wet season range fidelity in a tropical migratory ungulate