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Opposing Rainfall and Plant Nutritional Gradients Best Explain the Wildebeest Migration in the Serengeti


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Multiple hypotheses have been proposed to explain the annual migration of the Serengeti wildebeest, but few studies have compared distribution patterns with environmental drivers. We used a rainfall-driven model of grass dynamics and wildebeest movement to generate simulated monthly wildebeest distributions, with wildebeest movement decisions depending on 14 candidate models of adaptive movement in response to resource availability. We used information-theoretic approaches to compare the fits of simulated and observed monthly distribution patterns at two spatial scales over a 3-year period. Models that included the intake rate and nitrogen (N) concentration of green grass and the suppressive effect of tree cover on grass biomass provided the best model fits at both spatial scales tested, suggesting that digestive constraints and protein requirements may play key roles in driving migratory behavior. The emergence of a migration was predicted to be dependent on the ability of the wildebeest to track changes in resource abundance at relatively large scales (>80-100 km). When movement decisions are based solely on local resource availability, the wildebeest fail to migrate across the ecosystem. Our study highlights the potentially key role of strong and countervailing seasonally driven rainfall and fertility gradients--a consistent feature of African savanna ecosystems--as drivers of long-distance seasonal migrations in ungulates.
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vol. 173, no. 4 the american naturalist april 2009
Opposing Rainfall and Plant Nutritional Gradients Best
Explain the Wildebeest Migration in the Serengeti
Ricardo M. Holdo,
Robert D. Holt,
and John M. Fryxell
1. Department of Zoology, University of Florida, Gainesville, Florida 32611; 2. Department of Zoology, University of Guelph, Guelph,
Ontario N1G 2W1, Canada
Submitted June 20, 2008; Accepted October 9, 2008; Electronically published February 25, 2009
Online enhancements: appendixes, video.
abstract: Multiple hypotheses have been proposed to explain the
annual migration of the Serengeti wildebeest, but few studies have
compared distribution patterns with environmental drivers. We used
a rainfall-driven model of grass dynamics and wildebeest movement
to generate simulated monthly wildebeest distributions, with wil-
debeest movement decisions depending on 14 candidate models of
adaptive movement in response to resource availability. We used
information-theoretic approaches to compare the fits of simulated
and observed monthly distribution patterns at two spatial scales over
a 3-year period. Models that included the intake rate and nitrogen
(N) concentration of green grass and the suppressive effect of tree
cover on grass biomass provided the best model fits at both spatial
scales tested, suggesting that digestive constraints and protein re-
quirements may play key roles in driving migratory behavior. The
emergence of a migration was predicted to be dependent on the
ability of the wildebeest to track changes in resource abundance at
relatively large scales (180–100 km). When movement decisions are
based solely on local resource availability, the wildebeest fail to mi-
grate across the ecosystem. Our study highlights the potentially key
role of strong and countervailing seasonally driven rainfall and fer-
tility gradients—a consistent feature of African savanna ecosystems—
as drivers of long-distance seasonal migrations in ungulates.
Keywords: Connochaetes taurinus, dynamic model fitting, emergent
behaviors, resource landscapes, spatial autocorrelation.
Long-distance ungulate migrations constitute one of the
great spectacles of nature and have been documented
across a wide range of ecosystems (Craighead et al. 1972;
Fryxell and Sinclair 1988; Williamson et al. 1988; Fancy
et al. 1989; Berger 2004). By moving seasonally between
geographic locations that differ in terms of food intake,
survival, and fecundity, many species have evolved an ef-
fective life-history strategy for exploiting heterogeneous
* Corresponding author; e-mail:
Am. Nat. 2009. Vol. 173, pp. 431–445. 2009 by The University ofChicago.
0003-0147/2009/17304-50552$15.00. All rights reserved.
DOI: 10.1086/597229
environments (Fryxell et al. 1988; Berger 2004). The wil-
debeest (Connochaetes taurinus) migration in the Seren-
geti/Mara ecosystem of Tanzania and Kenya represents an
iconic example of ungulate migration and constitutes one
of the most thoroughly documented animal migrations in
one of the most intensively studied ecosystems on Earth
(Pennycuick 1975; Maddock 1979; Fryxell et al. 1988;
Mduma et al. 1999; Wilmshurst et al. 1999b; Wolanski and
Gereta 2001; Sinclair 2003; Musiega and Kazadi 2004;
Boone et al. 2006). Surprisingly, however, we still lack a
satisfactory mechanistic explanation of the Serengeti mi-
gration. The Serengeti wildebeest, by virtue of their large
numbers, are widely regarded as ecosystem engineers (Sin-
clair 1979, 2003; Holdo et al. 2007), so understanding
exactly which variables determine their migratory patterns
and how these might evolve over time (e.g., as a result of
global climate change) becomes of particular importance.
In the Serengeti migration, up to 1.4 million wildebeest,
plus large numbers of zebra (Equus burchelli) and Thom-
son’s gazelles (Gazella thomsoni), move seasonally between
dry and wet season ranges over a 30,000-km
area. It is
clear that this migration is ultimately driven by the marked
and strongly seasonal rainfall gradient that runs from the
southeastern short-grass plains to the tall-grass woodland
and savanna habitats in the north, center, and west of the
ecosystem (Pennycuick 1975; McNaughton 1979a; Sinclair
1979; Boone et al. 2006). This gradient imposes a con-
straint: at the end of the wet season, migrant species leave
the plains as the latter dry up and green grass and surface
water become confined to the wetter, northern reaches of
the ecosystem (McNaughton 1979a). These constraints
compel wildebeest and other migratory species to move
north. What is less clear is what the proximate factors are
that drive the wildebeest into the plains at the onset of
the wet season to begin with, since rainfall (the main de-
terminant of forage production) is higher in the woodlands
than in the plains throughout the year. Several explana-
tions have been postulated to explain this movement onto
the plains, including higher forage abundance and quality,
432 The American Naturalist
Figure 1: Map of the Serengeti ecosystem showing key protected areas
and geographic features (water bodies are shown in dark gray, and topo-
graphical features in lighter shades of gray). SNP pSerengeti National
Park, NCA pNgorongoro Conservation Area, MMGR pMasai Mara
Game Reserve (GR), MGR pMaswa GR, IGR pIkorongo GR, and
GGR pGrumeti GR.
surface water nutrient content, and escape from predation
(Jarman and Sinclair 1979; Fryxell et al. 1988; Murray
1995; Wolanski and Gereta 2001). So far, however, few
studies have systematically tested any of these hypotheses
by confronting wildebeest movement models with data.
Recently, Wilmshurst et al. (1999a) showed that wil-
debeest show a significant preference for grass patches of
short to medium height at the landscape scale. Because
energy intake shows a hump-shaped response as a function
of grass height (Wilmshurst et al. 1999b, 2000), wildebeest
movement patterns follow an energy maximization strat-
egy, a finding supported at a more restricted spatial scale
for Thomson’s gazelles, another migratory species (Fryxell
et al. 2004). Also in a recent article, Boone et al. (2006)
used evolutionary programming (EP) to show that sim-
ulated wildebeest evolve migratory pathways that resemble
those observed in nature when moving across a landscape
containing information on rainfall and the normalized dif-
ference vegetation index (NDVI), an index of plant pro-
ductivity, suggesting that seasonal fluctuations in primary
productivity can account for the migration (Boone et al.
2006). A potential difficulty with the analysis of Boone et
al. (2006), however, is that the wildebeest in the EP model
evolve to move toward areas that experience high rates of
change in NDVI in the plains during the wet season, but
these are also areas of lower absolute NDVI and, by in-
ference, lower peak green biomass than the woodlands.
Boone et al. (2006) suggest that changes in NDVI measure
new forage production, but it could be argued that these
changes track (rather than explain) wildebeest grazing pat-
terns, given that wildebeest grazing can more than double
grass primary production in the Serengeti plains (Mc-
Naughton 1985). In addition, NDVI generally does not
distinguish between woody and herbaceous green biomass
(Lu et al. 2003; Boone et al. 2006), so it quantifies different
resources in the treeless plains and the woodlands, which
consist of a tree-grass mixture (Sinclair 1979). Wildebeest
in the EP model also move toward areas of high relative
rainfall (measured as monthly rain expressed as a pro-
portion of the annual total), even though absolute rainfall
has been shown to be the best predictor of primary pro-
duction in the Serengeti (Sinclair 1975; McNaughton
1985). These derived variables allow the EP model to gen-
erate a reasonable approximation to the migration, but
they do not necessarily pinpoint the resources that drive
the migration and movement in general across the
Like Boone et al. (2006), in this article we focus pri-
marily on forage availability as the main driver of wil-
debeest spatial distribution patterns, but we expand on
their approach by teasing apart the effects of forage quality
and nutrient content, tree-grass competition, and abiotic
factors such as terrain characteristics and water availability
on habitat selection and assume that movement decisions
respond to seasonally fluctuating environmental condi-
tions. Following Wilmshurst et al. (1999a), we incorporate
realistic functional responses and digestive constraints into
a mechanistic model of food intake. We fit a series of
competing models for wildebeest movement driven by en-
vironmental variables to wildebeest distribution data, us-
ing geostatistical methods to allow the data to identify the
model providing the best fit to the observed distribution
patterns. Finally, we use our model to conduct atheoretical
investigation of the spatial scale of resource tracking used
by wildebeest, as reflected by the size of local movement
neighborhoods, to infer the minimum conditions under
which migration can occur within an adaptive movement
framework. We believe the approach we take here provides
a useful linkage among herbivore foraging decisions, land-
scape patterns, competitive interactions, and emergent
spatial distributions.
Material and Methods
Study System
The Serengeti ecosystem (fig. 1) can be broadly divided
into two main habitat types: the treeless, short-grass plains
Wildebeest Migration in the Serengeti 433
Figure 2: Environmental variables used to model wildebeest distribution patterns in the Serengeti ecosystem (defined by the extent of the wildebeest
migration), shown at 10-km resolution. A, Rainfall (varies monthly in the model; shown here as 1969–1972 annual mean); B, Percent tree cover;
C, Flow accumulation (shown here unweighted by rainfall) in km
;D, Terrain roughness, expressed as the standard deviation of elevation across
100 1-km
pixels, E, Plant N concentration; and F, Plant Na concentration. The total area covered represents 30,700 km
. The boundary of Serengeti
National Park is shown inset for reference.
in the southeastern portion of the ecosystem (“plains”
hereafter) and the tall-grass savanna and woodland
(“woodlands”) in the north and west (fig. 2B). Wildebeest
are the dominant herbivores in this system, with a pop-
ulation that has fluctuated between 1 and 1.4 million an-
imals over the past quarter-century. The Serengeti is char-
acterized by strong climatic and edaphic gradients and
discontinuities. A marked precipitation gradient runs in a
southeast to northwest direction, ranging from less than
400 mm in the rain shadow of the Ngorongoro volcano
to more than 1,200 mm near the shores of Lake Victoria
(fig. 1). In addition to this gradient, there is substantial
regional heterogeneity in soil properties. The plains soils
are shallow, nutrient-rich volcanic ashes with an almost
continuous hardpan layer that impedes tree growth (Sin-
clair 1979). In the north, soils tend to be dystrophic, with
a lower clay content than in the plains (Sinclair 1979). For
the purposes of our analysis, the Serengeti ecosystem is
regarded as the area bounded by the wildebeest migration,
as defined by Maddock (1979), covering a surface area of
30,700 km
(fig. 1).
Approach and Data Sources
A basic assumption of our model is that wildebeest move-
ments track real-time environmental conditions and re-
source availability and that we can use observed distri-
butions against the template of alternative resources to
make inferences about movement rules. The resources and
environmental drivers we included here as candidates for
informing movement decisions are forage biomass, intake,
and quality; water availability; and tree cover (which affects
forage biomass). We focused on plant nitrogen (N) and
sodium (Na) as key indicators of forage quality because
of the importance previously attached to crude protein
and Na for Serengeti herbivores (Sinclair 1977; McNaugh-
ton 1988; Murray 1995). We also included an index of
topographic roughness as a covariate, because this may
influence access to resources. To find the combination of
environmental drivers that best predict wildebeest distri-
bution patterns, we fit a wildebeest movement model to
detailed monthly wildebeest distribution data spanning a
3-year period over the entire Serengeti.
The main data set underpinning our analysis is a set of
monthly distribution maps of the Serengeti wildebeest
population based on monthly aerial surveys conducted
between August 1969 and August 1972, known as the
“recce” data (Norton-Griffiths 1973; Maddock 1979;
Boone et al. 2006). To this date, the recce data constitute
the only time series showing seasonal shifts in the spatial
distribution of the entire wildebeest herd in the Serengeti
and thus the only comprehensive data source detailed
enough to analyze the migration at the population level.
Given the importance attributed to the migration, it is
surprising both that such data have not been collected
since and that the recce data have not been analyzed in
greater detail.
The collection of the wildebeest recce data has been
described in detail elsewhere (Norton-Griffiths 1973; Mad-
dock 1979). For this study, we adopted two spatial scales:
434 The American Naturalist
the fine-scale resolution (10 km) used by Maddock (1979)
and a coarse-scale (25 km) resolution. We conducted the
analysis at the coarse resolution (25 km) to examine
whether misregistration of spatial information might lead
to erroneous rejection of particular models at the finer
scale. Misregistration occurs when two maps show similar
spatial patterns but do not “line up” (Costanza 1989), and
it occurs more frequently at finer spatial scales. Details on
the development of wildebeest distribution maps and the
geographical information system (GIS) layers of the en-
vironmental drivers are given in appendix A in the online
edition of the American Naturalist.
Wildebeest Movement Model
The wildebeest movement model is a discrete-time, spa-
tially explicit simulation model of grass and wildebeest
dynamics. The model is implemented across a lattice fol-
lowing the boundaries of the ecosystem as defined by the
recce data (fig. 2), at both fine (10 km) and coarse (25
km) resolutions. Both within-cell daily dynamics—grass
growth and decay (determined by monthly rainfall) and
herbivory—and between-cell weekly dynamics—wilde-
beest movement—occur in the model, which is described
in detail by Holdo et al. (2009) and summarized in ap-
pendix B in the online edition of the American Naturalist.
The model generates maps of wildebeest distribution pat-
terns, which can then be compared to observed distri-
bution patterns. Wildebeest emigration from a cell at each
time step is governed by the value of a resource variable
Zwithin the cell (Z
) in relation to its mean value evaluated
within a surrounding neighborhood of radius r(Z
) ac-
cording to the equation
ij ij
ij r
(following Fryxell et al. 2004), where V
(see eq. [B9])
represents the emigration of wildebeest W
from cell ij,
) is the expected value of Z
across the neighborhood,
and Jcontrols the shape of the migration function. An
increasingly strong dispersal response occurs as Jin-
creases. The variable rrepresents a circle of radius rsur-
rounding each cell, defined as the “resource tracking
neighborhood.” We assume that at any given time, wil-
debeest track resources across the landscape up to a dis-
tance rand then disperse within this local neighborhood.
Wildebeest that emigrate from a cell distribute themselves
proportionately throughout the subset of target cells
within the resource tracking neighborhood that are of
greater value than the cell they have left, as assumed by
Fryxell et al. (2004). Wildebeest distributions are updated
with a weekly rather than daily time step because of con-
straints imposed by the high computational burden of the
movement submodel. We show later that using a daily
time step does not alter our conclusions.
One of our primary objectives en route to developing
a movement model was to find the functional form of Z
that maximizes the fit between the model and the wil-
debeest distribution data. We selected 14 candidate models
that differed in terms of the combination of variables de-
fining Z: green grass biomass Gand intake I, tree cover
T(fig. 2B), surface water availability or flow accumulation
F(fig. 2C), terrain roughness R(fig. 2D), and grass ni-
trogen N(fig. 2E) and sodium Na (fig. 2F) concentrations
(table 1). Of these variables, some were directly obtainable
from GIS layers of the ecosystem (fig. 2), whereas others
(such as green grass biomass and intake) were derived
variables generated by the movement model. We used the
model ZpGas a “null” or base model, given that green
grass abundance is known to be one key driver of habitat
choice in the Serengeti (McNaughton 1979a). The inclu-
sion of green grass intake instead of green grass biomass
in several models allowed digestive constraints on green
grass intake imposed by the presence of dry grass biomass
and digestive capacity limitations to be incorporated in
the model. In several of the candidate models, we included
either GN
or IN
as terms of Z(table 1). This allowed
the simultaneous inclusion of the effects of grass biomass
(or intake) and quality (in terms of nutrient concentra-
tion) in the resource function; the model with ZpIN
implies that wildebeest maximize intake of green grass
multiplied by a power function of its N concentration.
The use of the exponent ballowed relatively flexible (i.e.,
nonlinear) functional forms to be fitted without having to
estimate a large number of parameters (cf. Pacala et al.
1994). It also allowed us to evaluate the comparative
strength of grass biomass or intake versus grass nutrient
concentration in informing movement decisions; for ex-
ample, when , wildebeest will respond dispropor-b11
tionately to grass N in comparison to grass biomass. We
also used power functions when testing the effects of flow
accumulation and terrain roughness on habitat choice (ta-
ble 1). To incorporate the effect of tree-grass competition,
we also included the term e
in some of the models; for
example, the model I,T,N, with ZpIe
, includes
intake, grass N, and suppressive effects of tree cover on
the amount of area occupied by grass (table 1).
Fitting the Dynamic Model to Distribution Data
The dynamic model generated maps of predicted wilde-
beest abundance (Y) across the Serengeti at monthly in-
tervals between August 1969 and August 1972. We used
the 33 months that matched the survey flights from the
recce data to obtain mean wildebeest abundances Y
model, ijs
Wildebeest Migration in the Serengeti 435
Table 1: Candidate models and ordinary least squares (OLS) and autoregressive
(AR) model fits for wildebeest movement models in the Serengeti ecosystem between
1969 and 1972
in model Zp
OLS 25 km AR 25 km OLS 10 km
GG 0 226.8 126.3 638.6
G,T Ge
1 123.9 117.2 548.5
1 55.3 71.6 232.2
G,T,N Ge
2 44.3 56.5 107.5
II 0 103.1 58.1 283.0
I,T Ie
1 72.4 72.2 162.8
1 25.8 53.9 143.9
I,T,N Ie
2 21.9 48.0 .0
I,T,Na Ie
2 78.0 80.9 107.5
I,T,N,Na Ie
4 26.6 53.5 42.7
I,T,N,F Ie
4.0 .0 .4
I,T,N,R Ie
4 12.2 46.3 4.1
3 2.6 3.8 135.4
I,F I gF
2 56.6 22.0 144.5
Note: Zrefers to the resources or environmental covariates that drive movement across the
landscape in each model (see eq. [1]). The quantity p(see eq. [2]) equals the number of movement
model parameters (Greek letters in function Z; it excludes k, the number of autocorrelation function
parameters). Variable definitions: Gpgreen grass biomass; Ipgreen grass intake; Tptree cover;
Npgrass N concentration; Na pgrass Na concentration; Fpflow accumulation; Rpterrain
Corrected Akaike Information Criterion value relative to best model.
(where iand jare cell coordinates and sis the season) for
the three distinct annual seasons. We then compared these
modeled abundances with the observed wildebeest abun-
dances Y
obs, ijs
. This is a regression problem in which either
the original data or the fitted residuals can exhibit spatial
and/or temporal autocorrelation, potentially leading to
problems of inference and parameter bias (Haining 1990,
pp. 40–41; Hoeting et al. 2006; Ives and Zhu 2006; Dor-
mann 2007). Taking account of both spatial and temporal
autocorrelation in this case would require computationally
intensive Monte Carlo methods that are beyond the scope
of this study (Ver Hoeff et al. 2001). We minimized the
problem of temporal autocorrelation by using seasonal
rather than monthly distributions, thereby sacrificing some
information. We tested for spatial autocorrelation by com-
puting Moran’s Ias a function of lag distance (Legendre
and Legendre 1998, p. 714; Lichstein et al. 2002) for the
fitted residuals after first fitting the model to data using
ordinary least squares (OLS) methods, and we then re-
peated the analysis using an autoregressive (AR) approach.
We assumed a lognormal distribution for wildebeest abun-
dances because this transformation improved the distri-
butional properties of the data and the residuals. Our pro-
cedure for calculating OLS and AR likelihoods is given in
appendix C in the online edition of the American
Following the estimation of the log likelihoods for each
model, we used a modified version of the corrected Akaike
Information Criterion (AIC
) to compare the goodness of
fit of competing models (Hoeting et al. 2006):
AIC p22ns ,(2)
ns pk2
where is the log likelihood, nis the number of cells in
the lattice, sis the number of seasons (and thus ns gives
the total number of observations), pis the number of
parameters fit to the movement model, and kis the num-
ber of parameters associated with the autocorrelation func-
tions, equal to 0 in the OLS models.
To test the robustness of our model fits to uncertainty
in the environmental data layers and model parameters,
we conducted an error analysis by running all of the
models with alternative realizations of the input GIS layers
and some key parameters (Pacala et al. 1996; Holdo 2007).
In particular, many of our environmental covariates (as
interpolated maps) were generated from observations col-
lected at a limited number of sites, and there was, there-
fore, considerable uncertainty associated with under-
sampled regions; this uncertainty can propagate through
the model (Kyriakidis 2001). To keep the error analysis
manageable, we focused the analysis on those layers and
parameters that were most likely to draw contrasts between
alternative models. For example, rainfall-driven grass
436 The American Naturalist
Video 1: Representation of movement model I,T,Nat fine scale. Figure
is the initial frame from a movie of the simulated migration, which is
available in the online edition of the American Naturalist.
growth is common to all models, so we kept the monthly
rainfall layers and grass growth parameters at their default
values. Of primary interest to us were the effects of plant
nutrients, tree cover, surface water, and intake versus bio-
mass as explanatory factors in the migration, so we in-
corporated uncertainty in plant Nand Na,treecoverT,
and four dynamic model parameters associated with intake
rates (a
, and daily voluntary intakes DVI
and DVI
described in app. B) in the error analysis. We did not model
uncertainty in flow accumulation because of the large
computational burden associated with producing these
monthly maps. For each of 1,000 random realizations of
these input maps and parameters, we fit all 14 models to
the data and computed AIC
values. We used the 25-km
resolution and OLS because of the prohibitive computa-
tional burden imposed by using either the AR approach
or the OLS analysis at 10-km resolution. The resampling
procedure we used to generate input maps and parameters
is outlined in appendix D in the online edition of the
American Naturalist.
Finally, to compare the performance of our model to
that of the only other model of the wildebeest migration
produced to date (the EP model of Boone et al. 2006), we
refit our best overall 10-km model at the higher 5-km
resolution used in EP. Although our model-fitting pro-
cedure (see below) differed from that used by Boone et
al. (2006), we used their classification system to compare
model fits. In the EP model, the raw census data (see app.
A) were used to produce a binary classification of lattice
cells as occupied (250 animals) or unoccupied (!250
animals). Unlike our seasonal approach, 12 monthly dis-
tribution maps averaged over the entire census period
(1969–1972) were produced and compared with the maps
generated by the model. An index of fit is provided by the
mean number of cells (averaged across all months) that
are occupied in both model and data as a fraction of the
total number of cells occupied in either model or data.
In addition to testing model fits with AIC
, we con-
ducted a graphical comparison of the ability of competing
models to generate a migration. We defined the term “mi-
gration distance” (d
) as the distance between the cen-
troids of the entire wildebeest herd averaged through the
wet and dry seasons (defined as the December–April and
August–November seasons). We calculated the migration
distance for the actual wildebeest distribution (the ob-
served value of d
) and for each of the candidate models.
We then graphically compared the observed and modeled
values of d
Resource Tracking Neighborhood
The default scenario in the model comparison is to assume
that wildebeest can track resources and disperse over a
radius ; that is, wildebeest can make movement de-rr
cisions based on the entire landscape and are therefore
“omniscient.” To investigate how migration and overall
model fit are affected by the size of the resource-tracking
neighborhood, we refit the best overall candidate model
to the data with fixed values of rranging between 10 km
(a neighborhood that would include only adjacent cells)
and 200 km and plotted d
and the log likelihood profile
as functions of r.
OLS Model Fits
In the OLS analysis, different models provided the best
fits to the data at 10- and 25-km resolutions. At the coarse
resolution, the model with the lowest AIC
value (and
therefore the best fit) included terms for intake, tree cover,
plant N, and flow accumulation (model I,T,N,F), whereas
at the fine resolution, model I,T,N(intake, tree cover, and
plant N) provided the best overall fit (table 1; video 1).
In both cases, green biomass intake Iwas a better predictor
of wildebeest distribution patterns than green biomass G,
and the inclusion of plant N in the function Zimproved
fit over simpler models (table 1). Although the inclusion
of tree cover did improve model fit at the 25-km resolution
over simpler models when flow accumulation (F) was ex-
cluded from the analysis, this covariate provided a mar-
ginal improvement to model fit only when water avail-
ability was taken into account at this scale (table 1). At
the 10-km resolution, tree cover greatly improved fit over
simpler models, and flow accumulation had little explan-
Wildebeest Migration in the Serengeti 437
Table 2: Maximum likelihood estimates (MLEs) and multivariate 95% confidence
intervals (lower and upper bounds) for best-fit model parameters
Model ZLattice (km) Parameter
MLE Lower Upper
25 b3.34 2.20 4.99
g9.50 1.49 99.94
d.34 .01 1.30
25 b3.09 1.62 4.31
g32.14 .82 99.93
d.50 .11 1.39
1.94 1.26 4.73
1.39 .57 4.97
10 q.031 .026 .033
b2.99 2.76 3.50
See table 1 for a list of parameters.
Parameter in the AR likelihood function (see app. C in the online edition of the American
atory power (table 1). At both spatial resolutions, neither
plant Na nor terrain roughness Rimproved model fits.
The error analysis strongly supported the conclusions
of the analysis conducted with the default layers and pa-
rameters (table D1). In most cases (particularly for the
best models), model ranks obtained in the default case
were preserved when error in the environmental layers
and parameters was included. In addition to generating
confidence limits for our AIC
values, we compared the
rank of any given model with the rank of the next-best
model for each realization of the error analysis, and we
found that our model rankings had very strong support
(table D1). For example, the inclusion of tree cover and
nitrogen consistently improved model fit, and intake mod-
els strongly outperformed biomass models (table D1). To
test whether surface water and tree cover may be somewhat
confounded with each other, we examined the bivariate
correlations between these and other input layers (table
E1 in the online edition of the American Naturalist). Gen-
erally, the correlations among the environmental variables
were weak, including that between tree cover and flow
accumulation. The only notable relationships were be-
tween plant N and Na, which showed a moderate positive
correlation, and plant N and rainfall, which were quite
strongly negatively correlated (table E1).
An examination of the maximum likelihood estimates
(MLEs) for the dynamic model parameters (table 2)
showed that variation in plant N concentration across the
landscape (fig. 2E) is more influential than tree cover (fig.
2B) or water availability (fig. 2C) in affecting wildebeest
distribution patterns in the model. An approximate in-
dication of the influence of a particular variable is given
by comparing the ratio of Zwith the variable in question
set at its maximum and minimum values (with parameters
set to their MLEs) across the ecosystem. This ratio can
then be compared across variables. Using this approach,
the estimated value of b, the power term in the function
, suggests a 40-fold difference in the “attrac-
tiveness” of areas with the lowest and highest plant N
content, whereas the estimated value of q(which controls
the effect of tree cover on grass biomass) produces a ratio
about half as large (see table 2 for parameter estimates).
Similarly, the estimated values of parameters gand d,
which determine the influence of flow accumulation on
habitat choice (table 2), result in a twofold variation in
the value of this term in the resource function Z(table
2). The multivariate confidence bounds for each of the
parameters estimated, drawn from the Metropolis sam-
pling distributions, are shown in figure E2. In addition to
showing confidence bounds, these plots indicate relation-
ships among the parameters. Figure E2Aand E2Bshows
that the coefficient gand exponent dfor the flow accu-
mulation term F(where ZpIN
; see table 2) in
the I,N,Fmodel appear to be strongly correlated, but nei-
ther of these parameters is correlated with the bexponent
of the nitrogen term N. This suggests that a simpler model
lacking a dparameter (i.e., ) would have provided
an equally good fit, but it also suggests that the contri-
butions of Nand Fin the model are independent and not
confounded. Figure E2Dalso suggests that the effects of
tree cover (parameter q) and N(parameter b) are inde-
pendent, which was to be expected given the weak cor-
relation between these two environmental covariates across
the landscape.
In terms of migration distance (d
), some models were
far more successful in generating a seasonal migration than
others (fig. 3A). The simplest model (G) performed par-
ticularly poorly, and replacing green biomass with green
biomass intake (I) substantially lengthened the migration
distance generated by the model (fig. 2A). Generally, mod-
els that included a plant N term resulted in migration
438 The American Naturalist
Figure 3: A, Observed and modeled migration distance d
of the wil-
debeest herd, defined as the distance between the centroids of the herd
averaged separately for the wet (December–April) and dry (August–
November) seasons. The seasonal values are means computed over the
period August 1969–August 1972. The observed value is compared with
that of 14 candidate models, identified by the environmental variables
that they include (see table 1 for model descriptions). B, Observed and
simulated (based on the best movement model I,T,N, which incorporates
green grass intake, grass N content, and tree cover) monthly locations
of the “center of mass” of the Serengeti wildebeest population averaged
over the 1969–1972 time period.
distances that most closely match the value of d
from the data (fig. 3A).
The predicted seasonal wildebeest distributions for the
best 10-km resolution OLS model (I,T,N) are shown
alongside the observed distributions in figures 3Band 4.
The fits between model and data were best at the extremes
of the migration (December–April and August–Novem-
ber) and poorer during the transition period (May–July).
During this transition period, wildebeest tend to move
toward the central woodlands and western corridor of the
Serengeti. Even in the best model, wildebeest tend to per-
sist in the southern portion of the ecosystem during this
period (figs. 3B, 4).
AR Model Fits
Since significant spatial autocorrelation was detected in
model residuals in even the best-fitting models for both
the 10- and 25-km resolutions (fig. E1), we used an au-
toregressive approach in addition to the OLS analysis. As
outlined in appendix C, we used an iterative procedure to
estimate the model and covariance function parameters.
At the fine resolution, this convergence was unsuccessful
for several of the candidate models, especially those pro-
viding poor fits in the OLS analysis, so we report results
only at 25-km resolution. The autoregressive approach
identified the same model (I,T,N,F) that provided the best
fit in the OLS case (table 1), suggesting that even after
correcting for the effects of spatial autocorrelation the
same variables emerge as the drivers of the wildebeest
migration and distribution patterns. The MLE for the au-
tocorrelation distance parameter (v
in app. C) was of the
order of 50 km (table 2; fig. E2).
Resource-Tracking Neighborhood
When the best overall model was run with different re-
source tracking neighborhood radii (r), we found that
model fit, in terms of both model likelihood and migration
distance, improved monotonically up to a distance of 80–
100 km, with no appreciable improvement in fit for larger
neighborhoods (fig. 5). This implies that wildebeest in the
dynamic model need to be able to track landscape con-
ditions over relatively large distances in order to replicate
the movement patterns that are actually observed. The plot
of migration distance in particular (fig. 5B) suggested that
when wildebeest move only within local neighborhoods
(!80 km), there is little or no emergent pattern of long-
distance movement, and the simulated population tends
to get “trapped” within certain portions of the landscape.
To test whether this effect was an artifact of our weekly
movement iteration or our choice of spatial scale, we re-
peated the simulation (i) with a daily movement step and
(ii) at a spatial resolution of 5 km and had similar results
in both cases (fig. 5A), suggesting that the perception ra-
dius threshold effect is not generated by the model struc-
ture or choice of spatial resolution but rather reflects the
spatial scale of seasonal environmental change and how it
needs to be tracked to be exploited effectively. Note that
although wildebeest are unlikely to be able to move across
the entire landscape in a single day (the maximum daily
distance recorded in the literature is 50 km; Talbot and
Wildebeest Migration in the Serengeti 439
Figure 4: Observed wildebeest distribution patterns (individuals km
) in the Serengeti ecosystem versus modeled output from the best-fit ordinary
least squares simulation model (I,T,N) at 10-km resolution. The distributions (both actual and modeled) represent seasonal means derived from
monthly layers spanning the period August 1969–August 1972.
Talbot 1963), our results show that increasing the fre-
quency of movements in our model without increasing
the size of the perception window does not increase the
ability of modeled wildebeest to track environmental gra-
dients efficiently enough to allow long-distance migration.
Grass Intake and Nutritional Quality
Explain the Migration
Our model suggests that green grass intake and protein
content both play a key role in determining the movement
and distribution patterns of migratory wildebeest in the
Serengeti. Green grass intake is a much better predictor
of the wildebeest migration than is grass biomass, and
maximizing this quantity rather than biomass helps to
explain not only local movement patterns but also long-
distance migration.
Previous studies on both wildebeest and Thomson’s ga-
zelle movements in the Serengeti suggested that ungulates
follow energy-maximizing rather than simply biomass-
maximizing strategies (Wilmshurst et al. 1999a; Fryxell et
al. 2004). Because the quality of grasses (as measured by
protein, fiber, and/or energy content) generally declines and
intake increases as a function of grass height or biomass,
energy intake is often optimized in areas of intermediate
grass height or biomass (Wilmshurst et al. 1999a; Fryxell et
al. 2004). Although a single variable (biomass) may in some
cases suffice to model both quantity and quality of forage
intake (Fryxell et al. 2004), here we partitioned grasses
into high- and low-quality (green and dry grass, respec-
tively) compartments. This allowed us to treat food quan-
tity and quality independently, which can be important in
distinguishing between growing and senescing swards that
may have equal biomass but differ in quality. Despite dif-
ferences in model details, however, our conclusions are
consistent with the previous finding (Wilmshurst et al.
1999a) that what is being optimized by the wildebeest is
440 The American Naturalist
Figure 5: A, Ordinary least squares likelihood profile (assuming weekly and daily movement intervals at 10-km resolution and weekly movement
at 5-km resolution), and B, migration distance d
(assuming weekly movement at 10-km resolution, the model default) for the best overall model
(I,T,N) as a function of the radius of the resource tracking neighborhood in the simulation model.
the rate of intake of the high-quality compartment. This
intake rate is not simply a monotonic function of biomass,
because (i) new green grass growth is inhibited by the
accumulation over time of dry grass due to senescence,
and (ii) digestive constraints on intake are imposed by the
inevitable consumption of some dry grass in a green-dry
grass mixture. This means that as the season progresses,
areas with abundant dry grass will tend to be avoided
because they reduce green grass intake. Green grass intake
thus follows an approximately unimodal relationship with
total grass biomass and may serve as a proxy for energy
An added aspect of food quality that we explicitly con-
sidered is grass crude protein or N concentration. Whereas
the intake variable incorporates a seasonally varying com-
ponent of food quality (in addition to quantity), our use
of landscape-level variation in plant N (or protein) content
adds an additional spatial component that is fixed over
time. This plant N gradient is strongly correlated with a
soil N gradient (McNaughton 1985; Ruess and Seagle
1994) that is opposite of the rainfall gradient in the Ser-
engeti: the nutrient-rich plains of the southeastern portion
of the Serengeti lie on volcanic soils and are at the low
end of the rainfall spectrum, whereas the sandier woodland
soils of the central and northern Serengeti receive more
rainfall but are less fertile (Sinclair 1979). Our results sug-
gest that this N gradient (or some other variable that is
correlated with N) further helps to explain the differences
in plant quality that drive the Serengeti migration. In par-
ticular, the high N content of grasses in the plains may be
the key factor driving the movement of the migratory
grazers into this habitat at the onset of the rains. When
the rainy season begins, green grass biomass rapidly in-
creases throughout the ecosystem, but the higher quality
of the food supply in the plains compared with the wood-
lands may explain why the wildebeest leave the woodlands
en masse at this time. As this supply dries up, green grass
persists only in the woodlands, and the wildebeest are
forced to move northward (McNaughton 1979b). The mi-
gration thus permits exploitation of an N-rich resource
pulse that arises predictably each year.
The EP model of Boone et al. (2006) used a different
approach to reach a broadly similar conclusion: the wil-
debeest migration is driven by a combination of rainfall
and NDVI that together comprise an index of forage avail-
ability. We note that in the EP model, when two areas have
equal NDVI, wildebeest do not choose areas with higher
rainfall (and thus grass production) at any given time, but
rather areas in which the ratio of current rainfall to total
annual rainfall for a site is highest (Boone et al. 2006). In
the wet season, this ratio is highest in the plains, so the
wildebeest move there, but it is difficult to see in practice
how assessing this ratio provides a plausible mechanism
to explain movement onto the plains. The comparison
between our model and that of Boone et al. (2006) in-
dicated a better fit (27.4% vs. 13.4% of blocks with wil-
debeest present or observed in agreement) of our model
at a 5-km resolution (fig. 6). This suggests that the in-
corporation of digestive constraints and plant protein con-
tent provide more reliable information about the resources
that wildebeest seek than NDVI. We show here that in-
corporating additional factors such as plant N concentra-
tion helps account more fully for the seasonal movement
into the plains, and the drive to exploit areas of high
protein intake (when available) could provide a concrete
mechanism to explain the migration. Given that Serengeti
ungulates tend to enter a period of protein deficiency to-
ward the end of the dry season (Sinclair 1977), it would
Wildebeest Migration in the Serengeti 441
Figure 6: Monthly wildebeest distributions from the recce data (A) and predicted by the best-fit ordinary least squares model (I,T,N) at 5-km
resolution (B), with filled cells representing mean values of 250 individuals calculated over the period August 1969–August 1972.
not be surprising if high-protein areas were rapidly sought
out at the onset of the wet season.
Tree-Grass Competition, Water Availability,
and Other Factors
Our conclusions regarding the potential importance of the
suppressive effect of tree cover on grass biomass in de-
termining wildebeest movement patterns are somewhat
scale dependent. There is a negative correlation between
tree and grass cover in savannas (Scholes and Archer
1997). All else being equal, the presence of trees in the
woodlands and their absence in the plains should therefore
result in a greater amount of grass biomass per unit area
in the latter, and this difference could play a role in at-
tracting wildebeest to the plains at times when grass is
available. Our results do show that including tree cover
in the model increases the modeled migration distance
(fig. 3A) compared with simpler grass biomass or intake
models, so tree-grass competition does play some role in
facilitating the migration. These results are less apparent
at the broader spatial scale, although it is possible that
heterogeneity in tree cover is more important in deter-
mining wildebeest distribution patterns at finer scales, and
loss of information that results from aggregating data at
the broader spatial scale masks these effects. An alternative
possibility is that at a local scale, woody cover is correlated
with risk factors (e.g., predation).
Conversely, the distribution of water supplies helps to
explain wildebeest distribution patterns at the broader
scale (25 km) but not at 10 km. The value of our water
availability index (flow accumulation) varies widely over
442 The American Naturalist
fine spatial scales, depending on the idiosyncratic location
of major rivers. Two very dry cells could differ greatly in
terms of their proximity to water but would be treated
identically in our model. This is more likely to lead to
poor fits at the finer scale, since at the broader scale this
heterogeneity becomes diluted. Regardless of these scale
differences, however, the quantitative contribution of flow
accumulation to model fit is minor. In the resource func-
tion Zwe use in the model, food and water are combined
additively (table 1). Our parameter estimates suggest far
greater variation in the effect of the food term than in the
water term (table 2), so the latter is likely to be influential
only at the end of the dry season, when the food term
tends to zero and water is scarce.
Past work has suggested than Na can play an important
role in the behavioral ecology of Serengeti ungulates (Mc-
Naughton 1990; Tracy and McNaughton 1995; Wolanski
and Gereta 2001). Ungulates often experience Na deficien-
cies, especially during pregnancy and lactation (Michell
1995), and select habitats and foods that offset these de-
ficiencies (Belovsky and Jordan 1981; Holdo et al. 2002),
so we hypothesized that the distribution of plant Na across
the landscape might influence wildebeest migratory move-
ments. In the end, this was not supported by our model.
We note, however, that our model fit is poor during the
transition period when wildebeest typically move into the
western corridor (fig. 4), where soil and plant Na levels
appear to be high (McNaughton 1988). It is also at this
time that lactation demands are high and Na stress likely
to be particularly important (Michell 1995). The lack of
fit could thus be due to uncertainty in our plant Na GIS
layer, leading to poor spatial agreement between the animal
distribution and plant Na layers. Unlike N, which shows
quite a consistent southeast to northwest gradient across
the Serengeti, Na appears to show a more complex spatial
pattern, with pockets of high Na within a low-Na matrix
(fig. 2F). As a consequence, the unbalanced sampling on
which the maps are based results in greater uncertainty in
Na than N mapping (since most cell values in the lattice
are based on interpolated quantities). Further sampling of
soil and plant nutrients across the landscape may clarify
this issue and shed light on the reasons for the preference
of wildebeest for the western corridor at the beginning of
the dry season.
Spatial Scale of Resource Tracking
A key finding of our model is that the size of the local
neighborhood that wildebeest track and move within is as
important as the overall spatial distribution of resources
and seasonality in determining the migration. In our
model, migratory behavior is an emergent property that
arises from an adaptive movement framework when wil-
debeest are able to track resources in a neighborhood with
a radius of at least 80 km and thus compare conditions
at this scale with conditions in their current location for
making movement choices. If their ability to track re-
sources remains highly localized, the model predicts that
they would not undertake the long-distance movements
that are necessary to generate the migration.
That wildebeest can respond in real time to environ-
mental cues to modify their migratory behavior has long
been known. Early studies of the Serengeti migration
showed that wildebeest and other species respond to both
intra- and interannual fluctuations in the spatial distri-
bution of resources, most likely forage or surface water
linked to rainfall events (Swynnerton 1958; Grzimek and
Grzimek 1960; Talbot and Talbot 1963; Pennycuick 1975;
McNaughton 1979a). These researchers noted that migra-
tion routes and the timing of movement are highly variable
from year to year (Swynnerton 1958; Talbot and Talbot
1963; Pennycuick 1975), prompting McNaughton (1979a,
p. 57) to prefer the term “nomadic” rather than “migra-
tory” to describe wildebeest movements. It must be
pointed out, however, that factors other than large-scale
perception may drive wildebeest to move out of local re-
source patches. We assumed in our model that the average
wildebeest makes adaptive “decisions” based on immediate
comparisons among sites, but our results could also fit
models with behavioral components involving memory
and genetically encoded movements.
We can outline several possibilities. At one extreme, only
real-time environmental cues and/or social information
drive the migration. It has long been noted that wildebeest
appear able to track stochastic rainfall events (and thus
flushes of green grass) over large distances (Talbot and
Talbot 1963; McNaughton 1979b). Moreover, a combi-
nation of environmental and social cues picked up by
animals in a widely dispersed herd could give rise to an
“effective range” of perception that is greater than the local
neighborhood of any given individual (Grunbaum 1998;
Couzin et al. 2005; Couzin 2007). Couzin et al. (2005)
and Grunbaum (1998) provided a theoretical framework
for such an expanded effective range of perception, show-
ing that when social information from even a few indi-
viduals is conveyed to the group, collective behavior
emerges that effectively tracks resources across the land-
scape. By combining local environmental information and
direct perception of long-distance cues with social cues
from the larger herd, wildebeest may be able to move
effectively up “noisy” resource gradients, leading to an
emergent seasonal migration. For an isolated individual,
this task may be impossible when conditions are variable
(Couzin 2007). Although our model does not directly in-
clude social cues, it strongly suggests that extended per-
Wildebeest Migration in the Serengeti 443
ception neighborhoods are a prerequisite for effective
landscape-level exploitation of resources by wildebeest.
At the other extreme, the timing of and route taken
during the migration are better understood as evolved
traits resulting from selective pressure imposed by a long-
term moving average in spatiotemporal patterns of re-
source availability (Boone et al. 2006). This seems unlikely
to us, given the demonstrated correlation between time
spent in wet and dry season ranges and rainfall abundance
across years (Pennycuick 1975). A third hybrid possibility
is that the migration is the result of simultaneous short-
term responses to local environmental conditions coupled
with navigation-driven movements (stemming from past
experience or genetically coded behavior) acting at larger
spatial scales (Bailey et al. 1996; Fritz et al. 2003; Mueller
and Fagan 2008). The latter coarse-scale driver would per-
mit wildebeest to explore areas beyond their local neigh-
borhoods. This would have a synergistic effect on social
information mechanisms and extend the effective percep-
tion range of the herd, allowing wildebeest to move up
noisy environmental gradients with greater efficiency.
Large-scale environmental trends, being more predictable
than small-scale fluctuations, may become internalized
over time by migrating herbivores, either through indi-
vidual experience or as a selective force (Boone et al. 2006),
and used as a tool for moving up noisy resource gradients.
The poor fit of the wildebeest distribution to environ-
mental factors during the transition period (May–July),
when wildebeest move to the western corridor for the rut
(Talbot and Talbot 1963), may be explained by a switch
from environmental cues to a navigational mode during
this period (Mueller and Fagan 2008). This switch could
allow wildebeest to find the rutting site at the appropriate
time. Memory (the past experience of an individual or
herd) has been identified as an important mechanism in
the movement behavior of large herbivores (Bailey et al.
1996; Dalziel et al. 2008) and may well play a role during
this period and cause a deviation from the short-term
resource-optimization strategy that operates at other times.
Further research is required to infer the extent to which
individually detected environmental versus social cues, and
opportunistic versus learned or genetically determined be-
havior (and the scales over which they operate), drive the
movement decisions of individual animals (Alerstam et al.
2003). Specifically, the simultaneous collection of data re-
lating to environmental factors, movement (as opposed to
the resulting distributional patterns), and social context
(behavior of members of the same or other species) would
provide a basis for teasing apart the contribution of social
cues after controlling for environment, for example; also,
a study of navigational skills and cues would provide a
basis for understanding the contribution of memory to
migratory patterns in wildebeest. It would be of particular
interest to understand the contribution of social cues and
memory and genetic factors in driving fluid “nomadic”
migrations such as that of the wildebeest versus other types
of large-scale movement behavior (e.g., strict to-and-fro
migration) more typical of migratory systems (Alerstam
et al. 2003; Mueller and Fagan 2008).
The two key elements in our model that unequivocally
give rise to a seasonal wildebeest migration are the coun-
tervailing rainfall and fertility gradients that cut across the
Serengeti. Rainfall acts as a seasonal “switch” that controls
the availability (and to some extent, the quality) of grass
biomass: the length of the growing season is positively
correlated with the amount of rainfall received across the
ecosystem (McNaughton 1985; Boone et al. 2006), so the
dry plains have forage available only for a few months of
the year, but that forage is of high nutritional value when
available. Such opposing rainfall and fertility gradients are
a common feature of African savanna ecosystems (Bell
1982; Ruess and Seagle 1994) and may thus play a role in
driving ungulate migrations elsewhere on the continent,
such as the white-eared kob (Kobus kob) migration of the
Sudan (Fryxell and Sinclair 1988) or the wildebeest mi-
gration of the Kalahari (Williamson et al. 1988). Given
strong enough gradients over short enough distances to
make seasonal migrations feasible, our model results sug-
gest that the rainfall-fertility correlation provides the nec-
essary conditions for a migration to emerge. The necessity
of a minimum “resource tracking window” threshold is a
key insight into our understanding of how complex emer-
gent phenomena such as migrations have at least the po-
tential to be explained by the application of simple rules
within an adaptive movement framework. Our results do
not refute the existence of genetically programmed factors
as contributing factors driving the migration but rather
suggest that they may not be strictly necessary to explain
We would like to thank S. J. McNaughton for making soil
and plant nutritional data available to the National Center
for Ecological Analysis and Synthesis Biocomplexity in the
Serengeti Working Group and M. Coughenour for facil-
itating data interpretation and providing Shuttle Radar
Topography Mission data. We also thank M. Norton-
Griffiths for providing permission to use canopy cover data
and T. Sinclair and K. Metzger for facilitating access to
these data sets. Many other data sets have been compiled
and maintained by the Tanzania Wildlife Research Institute
(TAWIRI). B. Bolker provided valuable assistance and dis-
444 The American Naturalist
cussions on the topic of fitting autoregressive dynamic
models to spatial data sets, M. Barfield provided comments
on an earlier version of the manuscript, and I. Couzin
provided valuable insights on the role of social cues in
driving movement behavior. We also thank three anony-
mous reviewers for their comments. We would like to
acknowledge the support of the National Science Foun-
dation Biocomplexity Program (DEB-0308486) and the
University of Florida Foundation.
Literature Cited
Alerstam, T., A. Hedenstrom, and S. Akesson. 2003. Long-distance
migration: evolution and determinants. Oikos 103:247–260.
Anderson, T. M., M. E. Ritchie, E. Mayemba, S. Eby, J. B. Grace,
and S. J. McNaughton. 2007. Forage nutritive quality in the Ser-
engeti ecosystem: the roles of fire and herbivory. American Nat-
uralist 170:343–357.
Bailey, D. W., J. E. Gross, E. A. Laca, L. R. Rittenhouse, M. B. Cough-
enour, D. M. Swift, and P. L. Sims. 1996. Mechanisms that result
in large herbivore grazing distribution patterns. Journal of Range
Management 49:386–400.
Bell, R. H. V. 1982. The effect of soil nutrient availability on com-
munity structure in African ecosystems. Pages 193–216 in B. J.
Huntley and B. H. Walker, eds. Ecology of tropical savannas.
Springer, Berlin.
Belovsky, G. E., and P. A. Jordan. 1981. Sodium dynamics and adap-
tations of a moose population. Journal of Mammalogy 62:613–621.
Berger, J. 2004. The last mile: how to sustain long-distance migration
in mammals. Conservation Biology 18:320–331.
Bocquet-Appel, J. P., and R. R. Sokal. 1989. Spatial autocorrelation
analysis of trend residuals in biological data. Systematic Zoology
Boone, R. B., S. J. Thirgood, and J. G. C. Hopcraft. 2006. Serengeti
wildebeest migratory patterns modeled from rainfall and new veg-
etation growth. Ecology 87:1987–1994.
Burrows, W. H., J. O. Carter, J. C. Scanlan, and E. R. Anderson.
1990. Management of savannas for livestock production in north-
east Australia: contrasts across the tree-grass continuum. Journal
of Biogeography 17:503–512.
Chib, S., and E. Greenberg. 1995. Understanding the Metropolis-
Hastings algorithm. American Statistician 49:327–335.
Costanza, R. 1989. Model goodness of fit: a multiple resolution pro-
cedure. Ecological Modelling 47:199–215.
Couzin, I. 2007. Collective minds. Nature 445:715.
Couzin, I. D., J. Krause, N. R. Franks, and S. A. Levin. 2005. Effective
leadership and decision-making in animal groups on the move.
Nature 433:513–516.
Craighead, J. J., G. Atwell, and B. O’Gara. 1972. Elk migrations in
and near Yellowstone National Park. Wildlife Monographs 29.
Cressie, N. A. C. 1993. Statistics for spatial data. Wiley, New York.
Dalziel, B. D., J. M. Morales, and J. M. Fryxell. 2008. Fitting prob-
ability distributions to animal movement trajectories: using arti-
ficial neural networks to link distance, resources, and memory.
American Naturalist 172:248–258.
Dormann, C. F. 2007. Effects of incorporating spatial autocorrelation
into the analysis of species distribution data. Global Ecology and
Biogeography 16:129–138.
Erdogan, E. H., G. Erpul, and I. Bayramin. 2007. Use of USLE/GIS
methodology for predicting soil loss in a semiarid agricultural wa-
tershed. Environmental Monitoring and Assessment 131:153–161.
Fancy, S. G., L. F. Pank, K. R. Whitten, and W. L. Regelin. 1989.
Seasonal movements of caribou in arctic Alaska as determined by
satellite. Canadian Journal of Zoology 67:644–650.
Fritz, H., S. Said, and H. Weimerskirch. 2003. Scale-dependent hi-
erarchical adjustments of movement patterns in a long-range for-
aging seabird. Proceedings of the Royal Society B: Biological Sci-
ences 270:1143–1148.
Fruhwirth-Schnatter, S. 2001. Markov chain Monte Carlo estimation
of classical and dynamic switching and mixture models. Journal
of the American Statistical Association 96:194–209.
Fryxell, J. M., and A. R. E. Sinclair. 1988. Seasonal migration by
white-eared kob in relation to resources. African Journal of Ecology
Fryxell, J. M., J. Greever, and A. R. E. Sinclair. 1988. Why are mi-
gratory ungulates so abundant? American Naturalist 131:781–798.
Fryxell, J. M., J. F. Wilmshurst, and A. R. E. Sinclair. 2004. Predictive
models of movement by Serengeti grazers. Ecology 85:2429–2435.
Fryxell, J. M., J. F. Wilmshurst, A. R. E. Sinclair, D. T. Haydon, R.
D. Holt, and P. A. Abrams. 2005. Landscape scale, heterogeneity,
and the viability of Serengeti grazers. Ecology Letters 8:328–335.
Gelfand, A. E., and K. Sahu. 1999. Identifiability, improper priors,
and Gibbs sampling for generalized linear models. Journal of the
American Statistical Association 94:247–253.
Grunbaum, D. 1998. Schooling as a strategy for taxis in a noisy
environment. Evolutionary Ecology 12:503–522.
Grzimek, M., and B. Grzimek. 1960. Census of plains animals in the
Serengeti National Park, Tanganyika. Journal of Wildlife Manage-
ment 24:27–37.
Haining, R. 1990. Spatial data analysis in the social and environ-
mental sciences. Cambridge University Press, Cambridge.
Hilborn, R., and M. Mangel. 1997. The ecological detective: con-
fronting models with data. Monographs in Population Biology 28.
Princeton University Press, Princeton, NJ.
Hoeting, J. A., R. A. Davis, A. A. Merton, and S. E. Thompson. 2006.
Model selection for geostatistical models. Ecological Applications
Holdo, R. M. 2007. Elephants, fire, and frost can determine com-
munity structure and composition in Kalahari woodlands. Eco-
logical Applications 17:558–568.
Holdo, R. M., J. P. Dudley, and L. R. McDowell. 2002. Geophagy in
the African elephant in relation to availability of dietary sodium.
Journal of Mammalogy 83:652–664.
Holdo, R. M., R. D. Holt, M. B. Coughenour, and M. E. Ritchie.
2007. Plant productivity and soil nitrogen as a function of grazing,
migration and fire in an African savanna. Journal of Ecology 95:
Holdo, R. M., R. D. Holt, and J. M. Fryxell. 2009. Grazers, browsers,
and fire influence the extent and spatial pattern of tree cover in
the Serengeti. Ecological Applications 19:95–109.
Hurtt, G. C., and R. A. Armstrong. 1999. A pelagic ecosystem model
calibrated with BATS and OWSI data. Deep-Sea Research I 46:27–
Ives, A. R., and J. Zhu. 2006. Statistics for correlated data: phylog-
enies, space, and time. Ecological Applications 16:20–32.
Jarman, P. J., and A. R. E. Sinclair. 1979. Feeding strategy and the
pattern of resource partitioning in ungulates. Pages 130–163 in A.
R. E. Sinclair and M. Norton-Griffiths, eds. Serengeti: dynamics
of an ecosystem. University of Chicago Press, Chicago.
Wildebeest Migration in the Serengeti 445
Kyriakidis, P. C. 2001. Geostatistical models of uncertainty for spatial
data. Pages 175–213 in C. T. Hunsaker, M. F. Goodchild, M. A.
Friedl, and T. J. Case, eds. Spatial uncertaintyin ecology: implications
for remote sensing and GIS applications. Springer, New York.
Legendre, P., and L. Legendre. 1998. Numerical ecology. Elsevier,
Lichstein, J. W., T. R. Simons, S. A. Shriner, and K. E. Franzreb.
2002. Spatial autocorrelation and autoregressive models in ecology.
Ecological Monographs 72:445–463.
Lu, H., M. R. Raupach, T. R. McVicar, and D. J. Barrett. 2003.
Decomposition of vegetation cover into woody and herbaceous
components using AVHRR NDVI time series. Remote Sensing of
Environment 86:1–18.
Maddock, L. 1979. The “migration” and grazing succession. Pages104–
129 in A. R. E. Sinclair and M. Norton-Griffiths, eds. Serengeti:
dynamics of an ecosystem. University of Chicago Press, Chicago.
McNaughton, S. J. 1979a. Grassland-herbivore dynamics. Pages 46–
81 in A. R. E. Sinclair and M. Norton-Griffiths, eds. Serengeti:
dynamics of an ecosystem. University of Chicago Press, Chicago.
———. 1979b. Grazing as an optimization process: grass ungulate
relationships in the Serengeti. American Naturalist 113:691–703.
———. 1985. Ecology of a grazing ecosystem: the Serengeti. Eco-
logical Monographs 55:259–294.
———. 1988. Mineral nutrition and spatial concentrations ofAfrican
ungulates. Nature 334:343–345.
———. 1990. Mineral nutrition and seasonal movements of African
migratory ungulates. Nature 345:613–615.
Mduma, S. A. R., A. R. E. Sinclair, and R. Hilborn. 1999. Food
regulates the Serengeti wildebeest: a 40-year record. Journal of
Animal Ecology 68:1101–1122.
Michell, A. R. 1995. The clinical biology of sodium: the physiology
and pathophysiology of sodium in mammals. Pergamon, Oxford.
Mueller, T., and W. F. Fagan. 2008. Search and navigation indynamic
environments: from individual behaviors to population distribu-
tions. Oikos 117:654–664.
Murray, M. G. 1995. Specific nutrient requirements and migration
of wildebeest. Pages 231–256 in A. R. E. Sinclair and P. Arcese,
eds. Serengeti II: dynamics, management, and conservation of an
ecosystem. University of Chicago Press, chicago.
Musiega, D. E., and S. N. Kazadi. 2004. Simulating the East African
wildebeest migration patterns using GIS and remote sensing. Af-
rican Journal of Ecology 42:355–362.
Norton-Griffiths, M. 1973. Counting the Serengeti migratory wil-
debeest using two-stage sampling. East African Wildlife Journal
———. 1979. The influence of grazing, browsing, and fire on the
vegetation dynamics of the Serengeti. Pages 310–352 in A. R. E.
Sinclair and M. Norton-Griffiths, eds. Serengeti: dynamics of an
ecosystem. University of Chicago Press, Chicago.
Olea, R. A. 1999. Geostatistics for engineers and earth scientists.
Springer, New York.
Pacala, S. W., C. D. Canham, J. A. Silander, and R. K. Kobe. 1994.
Sapling growth as a function of resources in a north temperate
forest. Canadian Journal of Forest Research 24:2172–2183.
Pacala, S. W., C. D. Canham, J. Saponara, J. A. Silander, R. K. Kobe,
and E. Ribbens. 1996. Forest models defined by field measure-
ments: estimation, error analysis and dynamics. Ecological Mono-
graphs 66:1–43.
Pebesma, E. J. 2004. Multivariable geostatistics in S: the gstatpackage.
Computers and Geosciences 30:683–691.
Pennycuick, L. 1975. Movements of the migratory wildebeest pop-
ulation in the Serengeti areas between 1960 and 1973. East African
Wildlife Journal 13:65–87.
Ruess, R. W., and S. W. Seagle. 1994. Landscape patterns in soil
microbial processes in the Serengeti National Park,Tanzania. Ecol-
ogy 75:892–904.
Sankaran, M., N. P. Hanan, R. J. Scholes, J. Ratnam, D. J. Augustine,
B. S. Cade, J. Gignoux, et al. 2005. Determinants of woody cover
in African savannas. Nature 438:846–849.
Schabenberger, O., and C. A. Gotway. 2005. Statistical methods for
spatial data analysis. Chapman & Hall, Boca Raton, FL.
Scholes, R. J., and S. R. Archer. 1997. Tree-grass interactions in sa-
vannas. Annual Review of Ecology and Systematics 28:517–544.
Shipley, L. A., J. E. Gross, D. E. Spalinger, N. T. Hobbs, and B. A.
Wunder. 1994. The scaling of intake rate in mammalian herbivores.
American Naturalist 143:1055–1082.
Sinclair, A. R. E. 1975. Resource limitation of trophic levels in tropical
grassland ecosystems. Journal of Animal Ecology 44:497–520.
———. 1977. The African buffalo: a study of resource limitation of
populations. University of Chicago Press, Chicago.
———. 1979. Dynamics of the Serengeti ecosystem: process and
pattern. Pages 1–30 in A. R. E. Sinclair and M. Norton-Griffiths,
eds. Serengeti: dynamics of an ecosystem. University of Chicago
Press, Chicago.
———. 2003. Mammal population regulation, keystone processes
and ecosystem dynamics. Philosophical Transactions of the Royal
Society B: Biological Sciences 358:1729–1740.
Sitati, N. W., M. J. Walpole, R. J. Smith, and N. Leader-Williams.
2003. Predicting spatial aspects of human-elephant conflict. Jour-
nal of Applied Ecology 40:667–677.
Swynnerton, G. H. 1958. Fauna of the Serengeti National Park.Mam-
malia 22:435–450.
Talbot, L. M., and M. H. Talbot. 1963. The wildebeest in western
Masailand, East Africa. Wildlife Monographs 12:1–88.
Tracy, B. F., and S. J. McNaughton. 1995. Elemental analysis of min-
eral lick soils from the Serengeti National Park, the Konza Prairie
and Yellowstone National Park. Ecography 18:91–94.
Ver Hoeff, J. M., N. A. C. Cressie, R. N. Fisher, and T. J. Case. 2001.
Uncertainty and spatial linear models for ecological data. Pages
214–237 in C. T. Hunsaker, M. F. Goodchild, M. A. Friedl, and T.
J. Case, eds. Spatial uncertainty in ecology: implications for remote
sensing and GIS applications. Springer, New York.
Williamson, D., J. Williamson, and K. T. Ngwamotsoko. 1988. Wil-
debeest migration in the Kalahari. African Journal of Ecology 26:
Wilmshurst, J. F., J. M. Fryxell, B. P. Farm, A. R. E. Sinclair, and C.
P. Henschel. 1999a. Spatial distribution of Serengeti wildebeest in
relation to resources. Canadian Journal of Zoology 77:1223–1232.
Wilmshurst, J. F., J. M. Fryxell, and P. E. Colucci. 1999b. What con-
strains daily intake in Thomson’s gazelles? Ecology 80:2338–2347.
Wilmshurst, J. F., J. M. Fryxell, and C. M. Bergman. 2000. The
allometry of patch selection in ruminants. Proceedings of theRoyal
Society B: Biological Sciences 267:345–349.
Wolanski, E., and E. Gereta. 2001. Water quantity and quality as the
factors driving the Serengeti ecosystem, Tanzania. Hydrobiologia
Associate Editor: Volker Grimm
Editor: Monica A. Geber
... green-up and senescence in response to rainfall or snow melt) and animal spatial distribution relate. For instance, migratory Serengeti wildebeest Connochaetes taurinus, zebra Equus quagga burchellii and Thomson's gazelles Eudorcas thomsonii move seasonally between wet and dry season ranges in response to plant phenology (Holdo et al., 2009). However, aside from season, the quality of the grass available to herbivores may also be determined by the species composition and architecture of the vegetation itself. ...
... ) is broadly characterized by two main habitat types: treeless short-grass plains in the southern region of the ecosystem and the tall-grass savannas and woodlands in the north and west of the ecosystem(Holdo et al., 2009). The ecosystem experiences a general gradient in rainfall ranging from 500 in the south east to 1300 mm/year in the north west, and a counter-gradient of soil fertility that is lowest in the north west to highest in the south east(Holdo et al., 2009;Morrison et al., 2019). ...
... ) is broadly characterized by two main habitat types: treeless short-grass plains in the southern region of the ecosystem and the tall-grass savannas and woodlands in the north and west of the ecosystem(Holdo et al., 2009). The ecosystem experiences a general gradient in rainfall ranging from 500 in the south east to 1300 mm/year in the north west, and a counter-gradient of soil fertility that is lowest in the north west to highest in the south east(Holdo et al., 2009;Morrison et al., 2019). The average temperature is 22 • C and fluctuates between 15 and 30 • C as minimum and maximum mean monthly temperature, respectively. ...
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Human activities are transforming landscapes and altering the structure and functioning of ecosystems worldwide and often result in sharp contrasts between human-dominated landscapes and adjacent natural habitats that lead to the creation of hard edges and artificial boundaries. The configuration of these boundaries could influence local biotic interactions and animal behaviours. Here, we investigate whether boundaries of different degrees of ‘hardness’ affect space utilization by migratory species in Serengeti National Park, Tanzania. We deployed camera traps along transects perpendicular to the national park boundary at three different locales. The transects were located in areas that consisted of two types of human–wildlife interface: a sudden transition from the national park into agro-pastoral land use (termed a ‘hard’ boundary) and a more gradual transition mediated by a shared usage area (termed a ‘soft’ boundary). Camera traps were placed at 2 km intervals along each 10 km transect from the edge towards the core of the park and were programmed to collect images hourly between dawn and dusk between June 2016 and March 2019. We used a deep neural network to detect the presence of wildlife within images and then used a Bayesian model with diffuse priors to estimate parameters of a generalized linear model with a Bernoulli likelihood. We explored the binomial probability of either wildebeest or zebra presence as a function of distance to the boundary, the rate of grass greening or drying (dNDVI) and the concentration of grass protein. There was a strong negative effect of distance to boundary on the probability of detecting wildebeest or zebra; however, this was only observed where the transition from human-dominated landscape to protected areas was sudden. Conversely, soft boundaries had little to no effect on the probability of detecting wildebeest or zebra. The results suggest that boundary type affects migratory species occurrence. The implications of these findings suggest that hard boundaries reduce the effective size of conservation areas; for many species, the area used by wildlife is likely less than the gazetted area under protection. The impacts may be severe especially for narrow protected areas or dispersal corridors.
... The available fuel load is determined by a number of interrelated factors. Firstly, savanna-woodlands can produce large amounts of grass biomass in the wet season (Holdo et al., 2009;Norton-Griffiths, 1979), and fuel loads are thus determined by rainfall in preceding wet seasons van Wilgen et al., 2004), as well as by soil fertility Higgins et al., 2000). Secondly, herbivores impact directly on available fuel by reducing grass biomass . ...
... Grass biomass, and thus available fuel loads, therefore exist as a function of rainfall (Higgins et al., 2000;van Wilgen et al., 2004), and fires become markedly less frequent in more arid savannas, or during droughts (Archibald et al., 2017(Archibald et al., , 2010. For example, in both the Serengeti and Kruger National Parks, the probability of fire occurrence dramatically increased following years with above-average rainfall and concomitant increased grass biomass accumulation (Holdo et al., 2009;van Wilgen et al., 2004). Mean annual rainfall varies substantially across savanna-woodland regions, as shown by Sankaran et al. (2005) for 854 savanna-woodland sites across Africa (132 -1 185 mm mean annual rainfall), and the amount that falls in any given year can also vary considerably. ...
... The frequency and intensity of fires have marked effects on the relative abundance of woody plant material in African savanna-woodlands (Holdo et al., 2009;Sankaran et al., 2008;Smit et al., 2016). A reduction in fire intensity and fire frequency results in an increase in tree height, canopy volume, and woody plant biomass (Trollope, 1980; Chapter 7). ...
Fire is an important process that shapes the structure and functioning of African savanna ecosystems, and frequently occurs as either prescribed burns or unintentional wildfires in protected areas. Though the level of understanding of the ecological effects of fires has grown substantially over the past century, comprehensive information on the practical application of fire is still restricted, and management information is scattered. Similarly, an improved understanding of how fire affects African mammals is important for the management of both fire regimes and mammal populations. This is also the case in Majete Wildlife Reserve (MWR), Malawi, where a lack of understanding of the past occurrence, determinants, features and effects of prevailing fire regimes prevents the development of appropriate fire management policies. Two separate reviews were conducted to describe the approaches to, and goals of, fire management in African savanna protected areas, as well as the response of large (>5 kg) mammals to fire. For MWR, combinations of remote-sensing and on-the-ground surveys were used to develop a spatially-explicit dataset of the recent fire regime (2001-2019), and to classify, describe and map the woody plant communities present. Additionally, the effect of long-term fire frequencies on vegetation composition, woody plant structure, and large mammal assemblages were assessed, as well as the immediate post-fire habitat selection of large herbivores in a comparative burnt and unburnt landscape. For protected areas, fifteen distinct fire management practices, used to achieve 10 broad ecological (e.g. reversing woody encroachment) and non- ecological (e.g. protecting infrastructure) goals, were identified. Additionally, the responses of 51 mammal species to fire were identified, showing that body size was strongly correlated with fire response, with smaller grazers more likely to respond positively to fire than larger browsers. In MWR, it was found that frequent fires dominate the landscape, with ~57% of MWR burning at intervals of two years or less, and an additional ~30% burning at intervals of 3-5 years. A current mismatch between intended fire management goals and actual trends was also highlighted. Five distinct woody plant communities, two of which were subdivided into three sub-communities each, were recognised, along with 118 woody species identified. Fire frequency had little effect on woody plant community composition, but did affect grass species composition. Mammal species clearly selected for either frequently-burnt or infrequently-burnt areas. Clear selection for either burnt (e.g. impala and warthog) or unburnt (e.g. elephant and bushbuck) habitats, that were unrelated to the availability of above-ground herbaceous biomass, were also shown post-fire. This information is intended to provide a basis for improved fire management planning and policy development, as well as providing a baseline against which to monitor change. Managers should re-evaluate fire policies based on these findings, setting clearly defined targets for the different vegetation types, and introducing flexibility in fire regimes to accommodate natural variation. Establishing a mosaic of patches exposed to different fire frequencies, intensities, seasons and sizes will likely be needed to create a range of habitat types that would best allow for the persistence of all facets of biodiversity in MWR.
... At the start of the dry season (in approximately June) as the nutritious short-grass plains in the southeast of the ecosystem dry out, the migratory herds move to the wetter northwest of the ecosystem where they remain until the start of the rains in approximately October, which initiates their return to the southeast of the ecosystem in November. Hence, the Serengeti migration is a response to spatial gradients of rainfall with higher annual mean rainfall with 1200 mm in the northwest than the southeast with 400 mm (Boone et al., 2006;Holdo et al., 2009), and higher soil fertility and nutritional content of forage in the southeast than northwest (Fryxell & Sinclair, 1988;McNaughton, 1988McNaughton, , 1990Pennycuick, 1975;Wilmshurst et al., 1999). ...
... The main rainfall peak during the wet season in March-April preceded the arrival of migratory herds 2 months later in clan territories (Figures 2a,b and 4a,b, Appendix S2: Table S1, Figure S1). This finding is consistent with previous studies focusing on drivers of migratory herd movements in the Serengeti, which revealed the importance of the rainfall gradient (Holdo et al., 2009;McNaughton, 1988McNaughton, , 1990). ...
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Little is known about potential cascading effects of climate change on the ability of predators to exploit mobile aggregations of prey with a spatiotemporal distribution largely determined by climatic conditions. If predators employ central-place foraging when rearing offspring, the ability of parents to locate sufficient prey could be reduced by climate change. In the Serengeti National Park, Tanzania, migratory species dominate mammalian herbivore biomass. These migratory herds exploit nutrient-rich vegetation on the southern plains in the rainy season and surface water in the northwest in the dry season. Female spotted hyenas Crocuta crocuta breed throughout the year and use long-distance central-place-foraging "commuting trips" to migratory herds to fuel lactation for ≥12 months. Changes in rainfall patterns that alter prey movements may decrease the ability of mothers to locate profitable foraging areas and thus increase their overall commuting effort, particularly for high-ranking females that have priority of access to food resources within their clan territory and thus less commuting experience. In hyena clan territories, this may be reflected by a decrease in migratory herd presence and a decrease in the presence of lactating females, as maternal den presence represents the opposite of commuting effort. We investigated the strength of the relationship between rainfall volume, migratory herd presence in three hyena clan territories , and the responses of lactating females to this climate/prey relationship in terms of maternal den presence, using an observation-based dataset spanning three decades. The probability of migratory herd presence in hyena clan territories increased with the amount of rainfall 2 months earlier, and maternal den presence increased with migratory herd presence. Rainfall volume substantially increased over 30 years, whereas the presence of migratory herds in hyena clans and the strength of the relationship between rainfall and migratory herd presence decreased. Hyenas thus adjusted well to the climate change-induced decreased the presence of migratory herds in their territories, since maternal den presence did not decrease over 30 years and still matched periods of high prey abundance, irrespective of female social status. These results suggest a high plasticity in the response of this keystone predator to environmental variability.
... Other species, however, migrate earlier as they track snowmelt (Laforge et al. 2021). In savanna environments, vegetation growth is associated with rainfall and, accordingly, many migrations towards wet season ranges occur at the beginning of the rainy season (Holdo et al. 2009; Bartlam-Brooks et al. 2013;Tshipa et al. 2017). ...
... Previous work has made clear that, as for many other species in tropical systems, departures from dry season ranges generally occur at the onset, rather than in the middle or the end, of the wet season (e.g. Bartlam-brooks et al. 2013;Holdo et al. 2009). This is also the case in the population studied here (Tshipa et al. 2017;this study). ...
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Migratory animals often use environmental cues to time their seasonal migrations. Local conditions may, however, differ from distant ones, and current conditions may poorly predict future conditions. This may be particularly true for early wet season conditions in tropical systems, as storms and associated rainfall events are generally not predictable at the scale of weeks or days and are heterogeneously distributed even at the scale of a few kilometres. How migratory animals cope with such challenges, and the consequences they may have, remain poorly known. We used time-to-event models based on GPS data from 19 African elephant herds from Hwange National Park (Zimbabwe) to study the effect of local and distant rainfall events on the elephants’ decision to initiate their wet season migration. Elephants relied more on distant rainfall events occurring along the future migration route than on local events when initiating their migration. Such ability to use distant cues does not, however, ensure an immediate migration success. In over 30% of the cases, the elephants came back to their dry season range, sometimes after having travelled > 80% of the expected migration distance. This happened particularly when there was little additional rain falling during the migration. All elephants successfully migrated later in the season. Our study improves the understanding of the migratory ecology of elephants. More broadly, it raises questions about the reliability of rainfall as a migratory cue in tropical systems, and shed light on one of its potential consequences, the poorly quantified phenomenon of migration false starts.
... For instance, collective attention in flocks of homing pigeons appears to enhance predator detection and improve navigation [61]. In addition to enhanced risk detection and predator dilution, large groups that migrate together to shared calving or spawning grounds might also benefit from collective sensing of forage resources, as hypothesized in wildebeest (Connochaetes taurinus) [62] and caribou (Rangifer tarandus) migration [15,63]. Similarly, collective sensing in massive flocks of passenger pigeons (Ectopistes migratorius) may have helped birds locate spatially and temporally unpredictable food patches [64]. ...
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Evidence of social learning is growing across the animal kingdom. Researchers have long hypothesized that social interactions play a key role in many animal migrations, but strong empirical support is scarce except in a few unique systems and species. In this review, we aim to catalyze advances in the study of social migrations by synthesizing research across disciplines and providing a framework for understanding when, how, and why social influences shape the decisions animals make during migration. Integrating research across the fields of social learning and migration ecology will advance our understanding of the complex behavioral phenomena of animal migration and help to inform conservation of animal migrations in a changing world.
... Grazers, such as large ungulates (Aikens et al., 2020;Bischof et al., 2012;Geremia et al., 2019;Merkle et al., 2016;Middleton et al., 2018) or birds (Kelly et al., 2016;Kölzsch et al., 2015;Shariatinajafabadi et al., 2014;Si et al., 2015;van Wijk et al., 2012), directly feed on the resource being measured by the normalized difference vegetation index (NDVI), a common remote sensing measure of greenness, and its derivatives. In less seasonal, tropical regions, these patterns are less well understood (Abernathy et al., 2019;Adamescu et al., 2018;Chapman et al., 2018), and phenological surfing is likely uncommon or at least under-explored in frugivorous tropical species, whose food availability is only indirectly linked to greenness (Branco et al., 2019;Giles et al., 2016Giles et al., , 2018Holdo et al., 2009). This may be because tropical phenologies are not as well studied , and the seasonality in the landscape is less pronounced (Feng et al., 2013). ...
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Migrating grazers and carnivores respond to seasonal changes in the environment and often match peaks in resource abundance. However, it is unclear if and how frugivorous animals use phenological events to time migration, especially in the tropics. The straw‐colored fruit bat (Eidolon helvum), Africa’s most gregarious fruit bat, forms large seasonal colonies throughout much of sub‐Saharan Africa. We hypothesized that aggregations of E. helvum match the timing of their migration with phenologies of plant growth or precipitation. Using monthly colony counts from across much of the species’ range, we matched peak colony size to landscape phenologies and explored the variation among colonies matching the overall closest phenological event. Peak colony size was closest to the peak instantaneous rate of green‐up, and sites with closer temporal matching were associated with higher maximum greenness, short growing season, and larger peak colony size. Eidolon helvum seem to time their migrations to move into highly seasonal landscapes to exploit short‐lived explosions of food and may benefit from collective sensing to time migrations. The link between rapid changes in colony size and phenological match may also imply potential collective sensing of the environment. Overall decreasing bat numbers along with various threats might cause this property of large colonies to be lost. Remote sensing data, although, indirectly linked to fruiting events, can potentially be used to globally describe and predict the migration of frugivorous species in a changing world.
... Emergent sensing is harder to observe and measure either in the wild or in an experimental setting (although see Holdo et al., 2009, andBerdahl et al., 2013, respectively). Consequently, most work on this process has been done on theoretical models and processes of animal behaviour (e.g. ...
... Long distance migrants may perceive that the environment is unfavorable on their present range and decide to migrate, but they are being pushed from their current location rather than pulled to a more favorable location, like the SNBS. Longdistance migrants may also use perception to follow a resource gradient to an alternate range (Holdo et al., 2015;Merkle et al., 2019). However, long-distance migrants cannot decide to move to a specific target destination without invoking nonperceptual cognitive processes. ...
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Seasonal migration is a behavioral response to predictable variation in environmental resources, risks, and conditions. In behaviorally plastic migrants, migration is a conditional strategy that depends, in part, on an individual’s informational state. The cognitive processes that underlie how facultative migrants understand and respond to their environment are not well understood. We compared perception of the present environment to memory and omniscience as competing cognitive mechanisms driving altitudinal migratory decisions in an endangered ungulate, the Sierra Nevada bighorn sheep ( Ovis canadensis sierrae ) using 1,298 animal years of data, encompassing 460 unique individuals. We built a suite of statistical models to partition variation in fall migratory status explained by cognitive predictors, while controlling for non-cognitive drivers. To approximate attribute memory, we included lagged attributes of the range an individual experienced in the previous year. We quantified perception by limiting an individual’s knowledge of migratory range to the area and attributes visible from its summer range, prior to migrating. Our results show that perception, in addition to the migratory propensity of an individual’s social group, and an individual’s migratory history are the best predictors of migration in our system. Our findings suggest that short-distance altitudinal migration is, in part, a response to an individual’s perception of conditions on alterative winter range. In long-distance partial migrants, exploration of migratory decision-making has been limited, but it is unlikely that migratory decisions would be based on sensory cues from a remote target range. Differing cognitive mechanisms underpinning short and long-distance migratory decisions will result in differing levels of behavioral plasticity in response to global climate change and anthropogenic disturbance, with important implications for management and conservation of migratory species.
... In addition, heterogeneity within and between rockshelter sequences may reflect dynamic changes in faunal distributions, animal migration patterns, or human hunting strategies in relation to these other variables. For example, the structure of major wildebeest migrations through this region is heavily shaped by rainfall patterns and grassland dynamics that may have changed markedly during the initiation and termination of the AHP (Holdo et al., 2009). ...
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The Holocene of eastern Africa saw extreme climatic fluctuations between hyper-humid and arid conditions, which manifested differently across the region's lake basins, coastal ecotones, and terrestrial biomes. Changes to resource availability, distribution, and predictability presented different constraints and opportunities to diverse hunter-gatherer communities. Major ongoing questions concern how humans reconfigured economic, social, and technological strategies in different regional settings. The role of more stable coastal environments in these processes remains especially under-studied. Here, we examine and compare relationships between environmental change and the organization of stone tool technology at the site of Panga ya Saidi Cave, eastern Kenya, in strata dating from c. 15-0.2 ka. Located near the Indian Ocean coast, this dataset provides the first insights into Holocene human-environmental relationships in a coastal forest zone of eastern Africa. Integrating the new Panga ya Saidi environmental and archaeological records with other high-resolution records from nearby terrestrial and lacustrine zones, we take a comparative approach to address how climatic fluctuations shaped trajectories of hunter-gatherer adaptations through the Holocene. We argue that lithic technologies deployed within lake basins and coastal zones reflect more stable land-use strategies with less residential mobility compared to those associated with terrestrial foraging. All regions exhibit technological reconfigurations with the arrival of pastoralism, except for the coastal forest which appear largely consistent across the study period. Results inform ongoing debates into the resilience of recent eastern African hunter-gatherers and food-producers and provide an analogical framework for examining human-environmental dynamics deeper in time.
Consumers must track and acquire resources in complex landscapes. Much discussion has focused on the concept of a ‘resource gradient’ and the mechanisms by which consumers can take advantage of such gradients as they navigate their landscapes in search of resources. However, the concept of tracking resource gradients means different things in different contexts. Here we take a synthetic approach and consider six different definitions of what it means to search for resources based on density or gradients in density. These include scenarios where consumers change their movement behavior based on the density of conspecifics, on the density of resources, and on spatial or temporal gradients in resources. We also consider scenarios involving non-local perception and a form of memory. Using a continuous space, continuous time model that allows consumers to switch between resource-tracking and random motion, we investigate the relative performance of these six different strategies. Consumers’ success in matching the spatiotemporal distributions of their resources differs starkly across the six scenarios. Movement strategies based on perception and response to temporal (rather than spatial) resource gradients afforded consumers with the best opportunities to match resource distributions. All scenarios would allow for optimization of resource matching in terms of the underlying parameters, providing opportunities for evolutionary adaptation, and links back to classical studies of foraging ecology.
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Migratory ungulates outnumber residents by an order of magnitude in several savanna ecosystems in Africa, as was apparently the case in other grasslands around the world before the intervention of modern man. Migrants may be more numerous because 1) they use a much larger area, 2) they make more-efficient use of resources, or 3) they are less vulnerable to regulation by predators. These hypotheses were examined using simulation models of migratory and sedentary wildebeest Connochaetes taurinus in the Serengeti ecosystem. Simulations suggest that realistic numbers of predators could regulate resident herbivores at low population densities, whereas such regulation is probably rare for migratory herds. When residents and migrants have overlapping ranges, migrants should always outcompete residents, reducing them to low numbers. Results suggest that differences in the modes of regulation explain the predominance of migratory herbivores in some grassland ecosystems. -from Authors
The huge growth in the use of geographic information systems, remote sensing platforms and spatial databases have made accurate spatial data more available for ecological and environmental models. Unfortunately, there has been too little analysis of the appropriate use of this data and the role of uncertainty in resulting ecological models. This is the first book to take an ecological perspective on uncertainty in spatial data. It applies principles and techniques from geography and other disciplines to ecological research. It brings the tools of cartography, cognition, spatial statistics, remote sensing and computer sciences to the ecologist using spatial data. After describing the uses of spatial data in ecological research, the authors discuss how to account for the effects of uncertainty in various methods of analysis. Carolyn T. Hunsaker is a research ecologist in the USDA Forest Service in Fresno, California. Michael F. Goodchild is Professor of Geography at the University of California, Santa Barbara. Mark A. Friedl is Assistant Professor in the Department of Geography and the Center for Remote Sensing at Boston University. Ted J. Case is Professor of Biology at the University of California, San Diego.
Spatial databases are increasingly used to “drive” environmental models by providing input parameters for spatially explicit process simulators. Global biogeochemical models, for example, are based on parameter maps of multiple spatially distributed variables, [e.g., temperature, precipitation, and land cover (Potter et al. 1993)]; these are typically provided in the form of information layers (coverages) in geographical information systems (GIS). Such parameter maps, however, are inherently uncertain because their elements represent derived, not directly measured, quantities.
Thesis (M.A. in Zoology)--University of California, Jan. 1963. "Food preferences of some East African wild ungulates" reprinted from the East African Agricultural and Forestry Journal, vol. 27, no. 3, January, 1962, l. 231. Bibliography: l. 203-220.
Three distinctive savannas of north-east Australia, originally dominated by unpalatable Eucalyptus L'Herit spp., the valuable fodder Acacia aneura F. Muell. and Astrebla F. Muell. ex Benth. spp. grasslands are described. In each case an imbalance of woody and herbaceous plants now poses a threat to successful pastoralism. Tree/shrub-grass relationships and the impact of fire are examined for each community. Utilization of these areas for beef and wool production is discussed and some approaches to predictive modelling of the systems are outlined.
The Serengeti ecosystem contains some of Africa's most geobotanically diverse landscapes and supports some of the highest primary and secondary production on Earth. In an attempt to characterize landscape patterns in soil microbial processes across the Serengeti, 17 study sites from nine landscape regions were sampled for soil physical/chemical characteristics and laboratory determination of soil microbial biomass, 20-d net turnover rates of carbon and nitrogen, and respiratory and nitrogen mineralization responses to carbon and nitrogen amendments. A large variation in soil physical/chemical characteristics across landscapes and a high degree of intercorrelation among these soil properties were found. Soil microbial biomass carbon ranged from 587 to 8971 @m/g soil dry mass, constituting between 3.4 and 9.4% of the total soil carbon at the Central Hills and Southern Plains landscape sites, respectively. Soil respiration rates (as carbon loss per unit soil dry mass) ranged from 9 @m.g^-^1.d^-^1 in the Northwest to 57 @mg.g^-^1.d^-^1 on the Southern Plains and were positively correlated with soil microbial biomass. Regression models incorporating percent water-holding capacity and total organic carbon were highly predictive of levels of microbial biomass and soil respiration across all landscapes. Net nitrogen mineralization rates per unit soil dry mass, averaging between @O0.48 and 1.09 @mg@?g^-^1@?d^-^1, were positively correlated with soil respiration rates, but unrelated to soil mineral nitrogen pools or soil microbial biomass. (NH"4)"2SO"4 additions significantly reduced both soil respiration rates and net nitrogen mineralization rates, but significantly increased net nitrate production, suggesting that nitrification is limited, in part, by ammonium availability. Low phosphorus availability may not only restrict nitrate production, but also limit ammonium production, thus having fundamental impacts on the nitrogen economy in this ecosystem. The interaction between N and P cycling is likely most significant in the tallgrass Northwest and Northeast landscapes, where granitic, P-deficient parent materials predominate, and nitrogen competition between plants and soil microbes limits aboveground nitrogen flow. On the Southern, Southeast, and Northwest Plains, where grazing intensity is greatest, soils have the highest levels of soil microbial biomass and lower C:P ratios, and microbial growth and nitrogen turnover rates appeared to be more C than N limited. The Eastern and Western Corridor, which support lower levels of sustained, intensive grazing, showed intermediate levels of soil microbial biomass and carbon and nitrogen turnover rates. The observation that mean annual rainfall is positively correlated with net primary production but negatively correlated with soil fertility across African savannas is supported by our data, which generally show that regional patterns of soil fertility and soil microbial processes are negatively correlated with mean annual rainfall across the Serengeti. We found soil microbial processes to be closely correlated with landscape patterns of herbivore use and intensity of herbivory. Herbivores track plant growth, which is highly variable both temporally and spatially, and in doing so, profoundly impact nutrient cycling processes where they feed. Thus, the bursts in soil microbial processes and plant growth that follow rainfall events are accompanied by the direct impact of herbivores on these processes.