Spatial patterns of acorn dispersal by rodents: do acorn crop... ungulate presence matter?

Oikos 119: 179187, 2010
doi: 10.1111/j.1600-0706.2009.17793.x,
# 2009 The Authors. Journal compilation # 2009 Oikos
Subject Editor: Jordi Bascompte. Accepted 29 June 2009
Spatial patterns of acorn dispersal by rodents: do acorn crop size and
ungulate presence matter?
Carolina Puerta-Piñero, José Marı́a Gómez and Eugene W. Schupp
C. Puerta-Piñero (carol@ugr.es) and J. M. Gómez, Depto Ecologı́a, Facultad de Ciencias, Univ. de Granada, ES18071 Granada, Spain.
E. W. Schupp, Dept of Wildland Resources and the Ecology Center, Utah State Univ., Logan, UT 84322-5230, USA, and Estación Biológica
de Doñana (CSIC), Calle Americo Vespucio s/n, ES 41092 Sevilla, Spain.
Seed dispersal is qualitatively effective when it increases recruitment probability. A poorly studied factor likely affecting
recruitment is the spatial distribution of dispersed seeds. Seed-caching animals are thought to disperse seeds in a way that
reduces clumping and density to impede cache pilfering. Furthermore, dispersal might differ depending on whether the
seed is immediately consumed versus cached for later consumption, and might differ depending on the ecological context.
The main objectives of this study were to determine: 1) the spatial pattern of seed dispersal by rodents in a heterogeneous
environment; 2) whether the patterns differ among years and among acorn competitor exclosure treatments, and
3) whether rodents create different spatial patterns of dispersal for acorns that are cached versus consumed immediately
following dispersal. We studied the degree of spatial aggregation of acorn dispersal by rodents using two different
estimators derived from the Ripley K and the Diggle G functions. We also analyzed various metrics of dispersal distances.
For both analyses we used observed acorn dispersal patterns in two years differing in crop size and inside versus outside
exclosures restricting access to acorn-consuming ungulates. During 2003, a year with a larger crop size, maximum seed
dispersal distances were less, and the pattern of dispersed seeds was more clumped, than in 2004, a year with a smaller
crop size. Median dispersal distances did not differ between years. In the presence of ungulates, seed dispersal was
marginally sparser than in their absence. Cached acorns were dispersed more sparsely than acorns eaten immediately.
These results have important implications for the quality of seed dispersal for oak recruitment that are likely relevant to
other systems as well.
Dispersal effectiveness, the contribution of a disperser to
the recruitment of a plant, has both a quantitative and a
qualitative component (Schupp 1993, Jordano and Schupp
2000). Quantitatively, a disperser is effective if it disperses
many seeds. Qualitatively, an effective disperser is one that
increases the recruitment probability of dispersed seeds.
Several aspects of the qualitative component of dispersal
effectiveness have been emphasized in empirical studies. Of
these, the distances seeds are dispersed (Vander Wall 1990,
Jordano and Schupp 2000, Forget et al. 2005, Jordano et al.
2007, Spiegel and Nathan 2007, Gómez et al. 2008), and
the microsite, microhabitat, or habitat where seeds are
deposited ( Jordano and Schupp 2000, Vander Wall 2001,
2002, Wenny 2001, Hollander and Vander Wall 2004,
Muñoz and Bonal 2007, Gómez et al. 2008), have probably
received the most attention.
In contrast, a variety of potentially important measures
of the quality of dispersal have received much less
consideration. Among these, the spatial pattern of dispersed
seeds has only begun to be assessed quantitatively (Russo
and Augspurger 2004, Moore et al. 2007), despite having important consequences for plant fate (Satterthwaite
2007). For example, seeds deposited in clumps are more
likely to suffer from intraspecific competition, postdispersal seed and seedling predation, pathogen attack,
and allelopathy (Augspurger and Kelly 1984, Howe 1989,
Harms et al. 2000, Schupp et al. 2002). However, if the
habitat is intrinsically heterogeneous in conditions and
resources, clumped dispersal to favorable microsites could
benefit establishment (Muller-Landau et al. 2002). Thus,
the spatial pattern of the seed shadow after dispersal is
potentially a critical determinant of disperser effectiveness.
In addition, because the potential negative effects of clumping are driven by near-neighbour interactions and smallscale density-dependent effects, it is important to consider
the distances between dispersed propagules independent
of the pattern of dispersion.
Seed-caching animals are thought to distribute seeds in a
way that reduces clumping and local density of seeds in
order to impede the pilfering of caches by other seed
consumers (Stapanian and Smith 1978, 1984, Vander Wall
1990, Male and Smulders 2007, 2008). However, the
spatial patterns of dispersal and the distances between
dispersed seeds are not fixed characteristics of a species of
disperser, but instead depend on the ecological context in
which dispersal occurs. Since caching incurs both costs and
179
benefits, optimal hoarding should vary depending on the
ecological context. Thus, the spatial pattern of seed dispersal
might vary depending on the abundance and composition
of the community of competitors potentially pilfering
caches (Muñoz and Bonal 2007). Spatial patterns of
dispersal can also vary depending on whether the seeds
will be immediately consumed or will be cached for later
consumption (Vander Wall 1990, Li and Zhang 2003,
Jansen et al. 2004). Finally, it is predicted that with
increasing seed crop size, seeds will be dispersed further
before caching in order to maintain a low cache density and
reduce pilfering (Stapanian and Smith 1978, Vander Wall
2002, Jansen et al. 2004, Moore et al. 2007). However,
increasing seed dispersal distances does not guarantee
reduced local small-scale density and cache isolation since
long-distance caches can still be quite clumped.
Plant spatial patterns result from many processes that
can operate simultaneously and even interact with each
other (Nathan and Muller-Landau 2000). Seed dispersal is
the first process that creates the template for plant spatial
distributions (Schupp and Fuentes 1995, Schupp et al.
2002, Russo and Augspurger 2004). After seed deposition,
intra- and interspecific competition, post dispersal predation, secondary dispersal, herbivory, pathogens, and more
can restructure this primary spatial distribution (Jordano
and Herrera 1995, Schupp and Fuentes 1995, Muller
Landau et al. 2002). Many of these interacting processes can
lead to substantially similar final observed spatial patterns.
Thus, it is important to consider the mechanisms actually
shaping spatial patterns. The seed dispersal kernel, a
measure of the frequency distribution of seeds as a function
of distance, has been extensively investigated in a wide
variety of systems (Nathan and Muller-Landau 2000,
Jordano and Godoy 2002, Nathan et al. 2002, Kwit
et al. 2004, Nathan 2007). However, spatially-explicit
approaches focused on the actual point patterns of seed
dispersal provide a more powerful tool for investigating the
processes creating plant spatial patterns (Nathan and
Muller-Landau 2000). Nonetheless, to date there has been
little success linking the spatial pattern of the seed shadow
with the spatial distribution of the plants (but see Russo and
Augspurger 2004).
In this study we analyze the dispersal distances and the
spatial patterns of acorn dispersal by rodents under different
ecological contexts: 1) two years that differed in acorn crop
size and 2) with and without ungulate competitors for
acorns. We discuss the implications of our results for rodent
caching behaviour, for plant recruitment, and for seed
dispersal effectiveness. In particular, the objectives of this
study were to determine: 1) the spatial pattern of seed
dispersal by rodents in a heterogeneous oak woodland;
2) whether dispersal distances and spatial patterns differ
among years which differed in crop sizes, and among
exclosure treatments that alter the community of competitors for acorns, and 3) whether rodents create different
spatial patterns of dispersed acorns depending on whether
the dispersed acorns are cached or consumed immediately
following dispersal. For analyses of spatial patterns, we used
spatially-explicit models considering two functions simultaneously. First, Ripley’s K function (Ripley 1981) tests the
spatial distribution by counting the number of points
within an area (density) at different radii from the origin.
180
Second, the Diggle’s G function (Diggle 1979) considers
the distance between each point and its nearest neighbour.
Methods
Study species and sites
As a model system, we used a heterogeneous landscape
composed of oak woodland patches (Quercus ilex, the holm
oak) intermingled with pine woodlands (Pinus spp.) and
shrubland patches (Gómez 2003). The study site is located
in the Sierra Nevada protected area, southeastern Spain
(3785?N, 3828?W), between 1550 and 1800 m a.s.l.
Climate is continental Mediterranean, with cold winters,
hot summers, and severe summer drought. Mean annual
temperature is 11.58C. Precipitation, mostly as rain in
autumn and spring, totals 825 mm year 1. Oak woodland
patches, the focus of this study, are internally heterogeneous
with Q. ilex clumps or clones mixed together with different
species of shrubs, pines, and open spaces. Quercus ilex is a
masting evergreen tree abundant in the Mediterranean
region. Acorns are dispersed from late October through
early December by rodents, mainly the woodmouse
Apodemus sylvaticus (Muñoz and Bonal 2007, Gómez et
al. 2008), and by the Eurasian jay Garrulus glandarius
(Gómez 2003). Acorns at the site are also consumed by
ungulates, especially wild boars Sus scrofa, and to a lesser
extent Spanish ibex Capra pyrenaica (Gómez and Hódar
2008); these ungulates are acorn predators that compete
with rodents.
This study was conducted during 2003 and 2004, years
differing in acorn production. Acorn production was
estimated each year on 50 Q. ilex trees within the study
plots. Using a semi-quantitative scale ranging from 0 (no
acorns) to 4 (more than 90% of the branches full of acorns),
acorn production (n 50 trees) was 1.8391.28 (mean9
SD) in 2003 and 0.8290.52 in 2004 (Levene F 85.84;
pB0.0001).
Experimental design
Experimental acorns were placed on the ground and
covered with a 0.51.0 0.1 m (w l h) metal cage
with 1-cm2 mesh which was open on two opposing sides to
allow the entrance of rodents but not ungulates or jays
(supply point, hereafter). Acorns were individually numbered and attached to a metal wire (8 cm long, 0.6 mm ø)
with a flag (see Gómez et al. 2008 for a complete
description of the methods). This marking method does
not appear to affect seed dispersal by rodents (Xiao et al.
2006). Experimental acorns were set out in November
during the natural dispersal period, and censused periodically to determine fate. All supply points were located
beneath Q. ilex adult trees, the microhabitat in which
rodents naturally encounter acorns.
When rodents cached acorns in the soil or beneath leaf
litter, the flagging was exposed on the surface making
relocation easy (Xiao et al. 2006). We began censuses at
supply points and searched outwards in expanding circles.
As in Gómez et al. (2008), we considered an acorn to be
dispersed only when it was moved at least 50 cm from its
initial location, independent of the fate of the acorn (cached
vs consumed immediately). When a dispersed acorn was
found, we recorded the distance (in cm) and direction (in
degrees) from the supply point and the fate of the acorn
(eaten or cached). Note that the number of surviving caches
at the end of the season was too small for the spatial
statistical analyses; therefore, our analyses use data from the
first census as it was the one with the highest number of
cached acorns.
In 2003, we monitored a total of 450 acorns in 18
supply points (25 acorns/supply point), 10 located inside a
12-ha exclosure constructed in 1982 with a 2-m tall fence
that excludes ungulates but allows the passage of rodents,
and eight located outside the exclosure. In 2004, we
monitored 1500 acorns in 50 supply points (30 acorns/
supply point), 20 inside the exclosure and 30 outside the
exclosure. Overall, we relocated 70% of the acorns
dispersed from supply points (Gómez et al. 2008).
Quantification of spatial patterns of dispersed acorns
We first determined the Cartesian coordinate position of
each dispersed acorn within a circular plot centered on the
supply point (n 68 supply points) that acorn came from
(see Fig. 1 for an example and Supplementary material
Appendix 1 for all the maps). Each plot had a radius equal
to the maximum dispersal distance for the relocated acorns
from that supply point, so each plot had a different radius
and area. Thus, each plot included all dispersed acorns that
were relocated in the field.
As the observed dispersal patterns showed evident lack of
stationarity (i.e. the probability distribution varies in space),
we then fit the spatial trend of each supply point (see Fig. 1
on the right for an example of this trend) and used this
trend to compute the inhomogeneous Ripley’s K and
Diggle’s nearest neighbor G for the total observed acorns
dispersed from each supply point (Diggle 1979, Ripley
1981, Baddeley and Turner 2005). The tests involve two
complementary functions based on: (1) the average number
of points located within an area for a given distance from
the supply point (Ripley’s K function, K(d), hereafter) and
(2) the distance between each point of the observed sample
and its nearest neighbour (Diggle’s G function, G(w)).
Deviations between the empirical and theoretical curves
give insight into the spatial dispersion of the points. The
two tests have different sensitivities to different types of
spatial distributions (Diggle 1979); for example, G(w) is a
better detector of regularity while K(d) has the advantage of
being density-independent (Barot et al. 1999). Therefore,
using various methods simultaneously, as we have, is highly
recommended (Diggle 1979, Ripley 1981). Finally, as we
considered all relocated acorns per supply point, we did not
compute an edge correction for either the inhomK(d) or the
G(w) functions (Wiegand et al. 2007).
The Ripley’s K function, K(d), is a second order measure
of spatial association. The K(d) function is defined so that
lK(d) equals the expected number of additional points of
the spatial pattern resulting from a random point process,
X, within a distance d of a point of X, where l is the
intensity (expected number of points per unit area), as in
the equation:
K(d)p d2
where d is the distance between any two points of the
sample. To attain constant variance and easier interpretation
of the results, K(d) is often transformed (Fortin and Dale
2005) into the function:
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
L(d) K(d) =p
The observed point pattern x is assumed to be a realization of a random point process X (Baddeley and Turner
Figure 1. Example of the observed acorn dispersal pattern from a supply point (left) and its corresponding fitted trend (right). Note that
the sums of raw residuals on both dimensions (X and Y) are cumulative.
181
2005). The null model is a homogeneous Poisson point
process. In our case, because of clear deviations from
stationarity of the observed patterns (see Supplementary
material Appendix 1 for visual inspection), we used the
conditional intensity of an inhomogeneous Poisson process
on the spatial pattern W. We assume X has a probability
density f(x; U) with respect to the distribution of the Poisson
process with intensity 1 on W. The distribution is governed
by a p-dimensional parameter U. We then used the
Papangelou conditional intensity which is defined (Baddeley
and Turner 2005) for a location u W and observed point
pattern x as:
lU (u; x)
f (x@ fug; U
f (x =u; U)
This has an intensity function l:W 0D, which has a
conditional intensity of l (u, x)l(u), where l is the
intensity of points. The conditional intensity lU (u) will
depend on U to reflect the ‘spatial trend’ (a change in
intensity across the region of observation) (Baddeley and
Turner 2005). This resulting function will be referred to
hereafter as inhomK(d).
The Diggle nearest neighbor function, G(w), is the
cumulative distribution function of the distance from a
point of the pattern X to the nearest other point of X. G(w)
summarizes the clustering of near-neighbour pairs of points,
and is defined as:
G(w) 1exp(l p w 2 )
where w indicates the distance between a point and its
nearest neighbour.
We then compared the observed distribution at each
supply point to an inhomogeneous Poisson process
with the same area and number of acorns (H0: complete
spatial randomness, CSR) to see if the spatial pattern
followed a random, clumped, or homogeneous distribution. Tests of significance were estimated by a Monte
Carlo procedure using 200 permutations (Wiegand et al.
2007). Rejection limits were estimated as the envelopes of
the simulation. If the observed distribution was above the
envelopes the distribution was considered as clumped,
within the envelopes as random, and below the envelopes
as homogeneous.
Finally, when the null hypothesis (CSR) was rejected,
we followed the suggestion of Barot et al. (1999) and
computed the maximum discrepancy distance (dmax)
between the theoretical and observed Ripley’s inhomogeneous K and Diggle’s G functions (dmaxK and dmaxG,
respectively) and subsequently used them as dependent
variables. These maximum discrepancy distances were
computed as:
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
dmaxK sup j K(d)=p K + (d)=pj
d
dmaxG sup jG(w)G+ (w)j
w
where the first part of the equations corresponds to the
observed empirical functions for each plot while the second
part corresponds to the theoretical functions (H0: complete
spatial randomness following an inhomogeneous Poisson
distribution).
182
For clumped patterns, dmax can be viewed as a measure
of the ‘compactness’ of the clump. For example, in the case
of G(w) it measures the average distance between points
within a clump (Barot et al. 1999). Thus, the larger the
value of dmax the less ‘compact’ the spatial distribution of
the acorns and vice versa (Barot et al. 1999).
Each point, or dispersed acorn, can be classified by fate
as cached or eaten. We included this information on fate
(‘marks’ in geostatistical terminology and hereafter) for each
point and then calculated inhomK(d) including the
information of this mark to compute a maximum discrepancy distance (dmaxKF) as described above. Finally, we
divided the acorns into eaten and cached and separately
computed the inhomK(d) and G(w) functions to calculate
dmaxK and dmaxG for each acorn category (dmaxKEATEN,
dmaxKCACHED, dmaxGEATEN, dmaxGCACHED, respectively) in order to investigate whether acorn fate affected
patterns of dispersion.
We performed all spatial computations using Spatstat
1.86 under R 2.5.0 (Baddeley and Turner 2005,
R development Core Team 2007).
Statistical analysis
We used two-way ANOVAs to compare median and
maximum dispersal distances between years, exclosure
treatments, and the interaction year exclosure. In these
analyses we included the calculated median and the
measured maximum distance for each supply point as
dependent variables. Medians rather than means were
calculated for each supply point because the distributions
of distances were highly skewed. Only dispersed seeds (i.e.
moved ]50 cm) were used in these analyses.
The effects of exclosure and year on the spatial patterns
of dispersed acorns as measured by dmax were explored
using general linear mixed models (GLMM):
dmaxK Yearr Exclosure
dmaxG Yearr Exclosure
We then fit the same GLMMs described above, but also
including acorn fate, in order to see the effect of exclosure
and year on the spatial patterns of acorns after controlling
for their fate.
dmaxKF Yearr Exclosure
Subsequently, we divided the data into cached and eaten
acorns to separately compute the following GLMMs for
cached
dmaxK CACHED Yearr Exclosure
dmaxGCACHED Yearr Exclosure
and eaten acorns
dmaxK EATEN Yearr Exclosure
dmaxGEATEN Yearr Exclosure
Finally, we analyzed whether the spatial pattern of the
acorns differed as a function of fate by comparing the
dmaxK and dmaxG between cached and eaten acorns. For
this, we fit the general linear models (GLM)
dmaxK Fate
dmaxG Fate
In all models, the supply point was the experimental
unit. Response variables were previously transformed to
improve normality using log or arcsin(square root) transformations. Year was considered a random factor and
exclosure a fixed factor. Significance of the random factor
was calculated by comparing models with and without the
random factor (Pinheiro and Bates 2000, Pinheiro et al.
2006). All analyses were performed using R (R Development Core Team 2007).
Results
Although mean dmax values appeared to be greater
outside than inside the exclosure, results were only marginally significant for dmaxK and not close to significant for
dmaxG (Table 1, Fig. 3). Thus, dispersed acorns were
marginally more sparsely distributed outside the exclosure
in the presence of ungulates than inside the exclosure in
the absence of ungulates.
The percentage of the total variance of the whole model
(Table 1) explained by the random factor (year) for dmaxK
and dmaxG was 20.5% and 29.7%, respectively.
Acorn fate effect
Dispersal distances
The median dispersal distances did not differ significantly
between years or exclosure treatments (Fig. 2). However,
maximum dispersal distances were significantly less in the
higher acorn year 2003 (370.94 cm9350.20, mean9SE
of maximum dispersal distance) than in the lower acorn
year 2004 (1591.86 cm9210.06). The interaction was not
significant in either analysis.
Spatial pattern of acorn dispersal
For all supply points in both years all observed distributions and distance classes were above the envelopes of the
simulations, whether considering the inhomK(d) or the
G(w) function. Thus, the observed point patterns of
dispersed acorns were all significantly clumped at a B
0.05, (see Supplementary material Appendix 1 for visual
inspection of the patterns).
Year and exclosure effects
The inhomK(d) and G(w) functions showed analogous
results. Both dmaxK and dmaxG were significantly greater
in 2004 than in 2003 (Table 1, Fig. 3). Thus, in 2004, the
year with lower acorn production, the spatial pattern of
dispersed acorns appears to be less compact, or less
clumped, than in 2003, a year with more acorns.
There was no significant effect of either year or exclosure on
dmaxKF (Table 1). Despite the lack of significance of year
on dmaxKF, the AIC in all cases demonstrates that the best
models were those containing the random factor (year). The
percentage of the variance of the whole model explained by
the random factor (year) was 14.6% for dmaxKF. Thus,
there is still strong evidence that spatial patterns were more
compact in 2003 than in 2004, even when acorn fate was
controlled for.
Finally, there were significant differences in dmaxG
between acorns that were eaten immediately following
dispersal and those that were cached (Table 2, Fig. 4); the
distances between neighbouring cached acorns were greater
than the distances between neighbouring eaten acorns.
Though not significant, there were also noticeable differences for dmaxK between eaten and cached acorns, with a
tendency for cached acorns to be dispersed in a less clumped
distribution (Table 2, Fig. 4).
When splitting the acorns into eaten versus cached, the
year factor was highly significant in all cases (Table 3).
Thus, as in the overall combined analysis, acorns were
more clumped in 2003 than in 2004 for both cached and
eaten acorns (Fig. 4). Exclosure significantly affected the
spatial pattern of eaten acorns but not the clumping of
cached acorns (Table 3). Eaten acorns were less clumped
outside the ungulate exclosure (824.069150.98; mean9
SE dmaxKEATEN) than inside the exclosure (388.649
150.98). The opposite pattern was found with distances among nearest neighbour acorns, which were greater
Figure 2. Results of the two-way Anovas for year and exclosure effects on median and maximum dispersal distances. DFmodel 67,
DFfactors 1, DF. *p B0.01.
183
Table 1. Effects of year and exclosure on spatial patterns of seed dispersal. GLMM considering the factor Year as random and Exclosure as
fixed. In all cases the best model in terms of Akaike information criterion (AIC) was the model containing the random factor (pB0.05
contrasting models with and without the random factor). R2 correspond to the total variance explained by the whole model. dmax variables
consist of the maximum discrepancy distance between the observed and theoretical spatial pattern (dmax) for Ripley’s K and Diggle’s G.
dmaxKF, corresponds to dmax considering the mark of the fate of the acorns (eaten vs cached).
DF
dmaxK
F
Year
Exclosure
R2
1
1
66
3.793
0.146
dmaxG
p
B0.05
0.056
inside the exclosure (0.4190.07; mean9SE dmaxGEATEN)
than outside (0.3690.07).
Discussion
The spatial pattern of holm oak acorn dispersal by rodents
differed between the two years, whether we considered all
dispersed acorns together or considered eaten and cached
acorns separately. During 2003, the year with a larger crop,
seed dispersal was more clumped than in 2004. Interestingly, these changes in spatial patterns occurred without a
change in median dispersal distances, although the maximum dispersal distances were greater in 2004. More acorns
were put out per supply point in 2004 (30) than in 2003
(25), and with more acorns tracked one would expect to
find greater maximum dispersal distances by chance.
However, a 20% increase in the number of acorns should
not result in a four-fold increase in observed maximum
dispersal distances. Additionally, acorn dispersal in this
system is highly biased to particular microhabitats (e.g. oaks
and shrubs), but this behaviour is unlikely to affect our
results since there was no difference between 2003 and
2004 in the distribution of dispersed acorns among
microhabitats (Gómez et al. 2008).
A number of studies have shown that differences in seed
crop size can result in differences in some aspect of seed
dispersal patterns, such as the proportion of seeds dispersed (Jordano and Schupp 2000, Jansen et al. 2004),
F
1.207
0.155
dmaxKF
p
B0.05
0.276
F
2.586
0.118
p
ns
0.113
the speed of seed dispersal (Vander Wall 2002), or the
proportion of dispersed seeds placed in primary (Jansen
et al. 2004) or secondary (Vander Wall 2002) caches.
Furthermore, a number of studies also have shown that crop
size affects distances seeds are dispersed (Vander Wall 2002,
Jansen et al. 2004, Li and Zhang 2007, Moore et al. 2007).
Our results contradict somewhat the conventional theoretical optimization models that predict that with increasing
seed abundance rodents will cache further from the seed
source (parent) in order to maintain a low density and high
spacing of cached seeds and reduce pilfering (Stapanian
and Smith 1978, Moore et al. 2007). However, our results
are compatible with other studies that have documented
shorter dispersal differences with increased seed abundance
(Jansen et al. 2004, Moore et al. 2007). Though they are to
some extent contrary to the model prediction, these results
are not necessarily surprising. As noted by Moore et al.
(2007), the original prediction failed to consider that food
abundance can directly affect the behaviour and impact
of potential cache pilferers. In smaller crop years it
should be even more important to have widely-spaced
caches that are more isolated from parents because
with fewer seed resources available, pilfering pressure will
be much greater than in larger crop years. In years with
smaller crops, rodents are expected to be more dependent
on stored supplies which should favor more effective
hoarding (Broding et al. 2001) and increased cache spacing.
Other important factors not considered in this study,
such as among-year variation in rodent populations, may
Figure 3. Box-plots showing the median (points), 2575% quantiles (boxes), and 1090% (whiskers) values between years (2003 had
higher acorn production than 2004) and exclosure treatments. Values consist of the maximum discrepancy distance between the observed
and theoretical spatial pattern (dmax) for Ripley’s K (left) and Diggle’s G (right). Larger dmax values mean a greater distance between
neighboring seeds (dmaxG) and a less clumped distribution of dispersed seeds (dmaxK).
184
Table 2. Effect of initial fate of acorns (eaten versus cached) on
spatial patterns of seed dispersal. dmax variables consist of the
maximum discrepancy distance between the observed and theoretical spatial pattern (dmax) for Ripley’s K and Diggle’s G. R2
correspond to the total variance explained by the whole model.
DF
Fate
R2
1
dmaxK
dmaxG
F
p
F
p
1.18
0.001
0.28
76.19
0.22
B0.0001
also contribute to the differential dispersal patterns we
found. Since the low crop year 2004 was preceded by high
crop years, the population of rodents might have increased
at the same time that there was a reduction in the
availability of resource. The resulting increase in intraspecific competition would further accentuate the need to
better protect cached resources in the low crop year, further
contributing to a greater spacing of acorns and a greater
maximum dispersal distance.
Additionally, because heavier acorns tend to be dispersed
greater distances than lighter acorns (Gómez et al. 2008),
our results might be due at least partly to having heavier
acorns in the low crop year of 2004. However, acorns used
in our experiments were actually significantly heavier in
2003 (4.7390.05, mean9SE), the year with shorter
maximum dispersal distances and more clumped distributions, than in 2004 (3.4690.04; R2 0.12, F 358.56,
pB0.0001). Thus, the differences in acorn weight cannot
explain our results.
While it has generally been assumed that greater dispersal distances result in less clumped patterns, actual
patterns of dispersal have not been previously quantified.
Our results are at least partially compatible with the
assumption, but not entirely. Although maximum dispersal distances were greater in the year with less clumped
dispersion, the median dispersal distances did not differ
at all. This might suggest that rodents responded in
multiple ways, altering distances dispersed to some extent,
but perhaps also altering dispersion of caches independent
of distance.
Although our study cannot directly quantify the effect
of dispersal patterns on cache survival due to the very low
survival of caches, more hyperdispersed distributions of
caches have been experimentally demonstrated to reduce
cache pilfering by birds (Male and Smulders 2007, 2008).
Although it is often assumed that greater dispersal distances
result in greater cache survival, we could not detect a
significant effect of distance on survival in this system,
probably because of the extremely low survival of caches
(Gómez et al. 2008). Thus, there might be a variety of
ways that rodent caching behaviour can respond in order
to reduce pilfering and increase survival of caches.
In a related result, we found strong effects of acorn fate
on dispersal pattern. That is, rodents dispersed the acorns
in different patterns depending on whether they were to
consume or cache them. As expected, acorns cached for
future consumption were spaced further apart than were
acorns that were eaten immediately after dispersal. Because
there was no significant effect of fate on the densityindependent dmaxK, our result might be due to a large
extent to the much lower numbers of cached than of
consumed seeds (Gómez et al. 2008). Nonetheless, it is
still an important result in that acorns were not being
aggregated in distinct caching locations to any greater
degree than expected based on acorn dispersal in general.
Further, the increased spacing of cached acorns, no matter
the cause, is expected to decrease pilfering.
Although not significant, our results suggest that
rodents might disperse acorns slightly more sparsely in
the presence of ungulates. Such behaviour is expected to
reduce loss to these interspecific competitors. In a similar
study in central Spain, the presence of ungulates did not
affect the distance A. sylvaticus dispersed acorns of Q. ilex,
but did alter the microhabitat destination of caches
(Muñoz and Bonal 2007). In this study we also found
no difference in median or maximum dispersal distances
inside and outside exclosures. A possible explanation of the
overall results is that rodents only subtly alter their caching
Figure 4. Box-plots showing the median (points), 2575% quantiles (boxes), and the 1090% (whiskers) values between years (2003 had
higher acorn production than 2004) and fate of the acorns. Values consist of the maximum discrepancy distance between the observed
and theoretical spatial pattern (dmax) for Ripley’s K (left) and Diggle’s G (right). Larger dmax values mean a greater distance between
neighboring seeds (dmaxG) and a less clumped distribution of dispersed seeds (dmaxK).
185
Table 3. Effects of year and exclosure on the spatial pattern of seed dispersal for cached and eaten acorns.
DF
Cached acorns
Eaten acorns
dmaxK
2
Year
Exclosure
R2
Variance (%)
1
1
61
dmaxG
x
p
17.64
1.91
0.27
42.94
B0.0001
0.17
dmaxK
2
p
x
8.39
0.63
0.004
0.428
0.12
10.30
7.40
0.42
48.61
x
23.61
2
dmaxG
2
p
x
p
0.001
0.006
16.19
4.14
0.46
63.02
B0.0001
0.042
GLMM considering the factor Year as random and Exclosure as fixed. dmax variables consist of the maximum discrepancy distance between
the observed and theoretical spatial pattern (dmax) for Ripley’s K and Diggle’s G. R2 correspond to the total variance explained by the whole
model.
behaviour in response to interspecific competitors. Alternatively, rodents may not have responded to competitors
at all, but rather to other factors that obscured the
response to competitors. For example, the ungulate exclosures likely increased the amount of acorns available inside
the exclosures relative to outside, which, based on our
results on annual variation in patterns of dispersal, might
have directly resulted in the slightly greater dispersion of
caches outside relative to inside exclosures. Either way, our
results overall tentatively suggest that rodents consider
other acorn consumers with similar predation and foraging
behaviors, such as conspecifics and perhaps even jays, as
more of a pilfering threat than ungulates.
Although our study cannot document that the plastic
caching behaviour found here leads to greater plant
recruitment, results suggest that it is likely to affect the
qualitative component of dispersal effectiveness. First, as
previously described in Gómez et al. (2008) and noted
above, 2003 and 2004 did not differ in frequencies of
acorns dispersed to different microhabitats. Thus, greater
dispersal distances and greater clumping did not result in
more acorns being dispersed to lower quality microsites.
On the positive side, although greater acorn mortality is
expected in the low crop year of 2004 due to large numbers
of rodents feeding on the few acorns (Jansen et al. 2004),
the shift in the spatial pattern of caching is expected
to increase acorn survival over what would occur if the
rodents did not cache more sparsely in low acorn years.
That is, the decreased clumping and increased spacing of
seeds in 2004 is expected to have positive consequences for
further establishment (Russo and Augspurger 2004) that
can help alleviate to some extent the expected greater loss
due to the lower acorn crop. First, the decreased clumping
is expected to result in a reduction in cache pilfering, which
in turn should lead to an increased chance of surviving to
germination. In addition, many post-dispersal processes are
to some extent dependent on density or spacing among
individuals. For example, intraspecific seedling competition, pathogen attack, and predation sensu lato of seeds
or seedlings likely depend on the density and distances
among dispersed propagules (Augspurger and Kelly 1984,
Harms et al. 2000, Nathan and Muller-Landau 2000,
Schupp et al. 2002, Gómez and Hódar 2008). Thus, from
the perspective of spatial patterns of dispersed seeds, the
plastic caching behaviour of rodents appears to some extent
to have increased the quality of dispersal in low crop
years over what would be expected if caching behaviour
was constant. That is, although few acorns survived in
186
the low crop year, without changes in rodent caching
behaviour it is likely that even fewer would have survived.
To a minor extent, the same appears to have been true in
the presence of acorn competitors.
In conclusion, we have demonstrated the importance
of considering the actual spatial pattern of seed dispersal,
not simply the distance-based dispersal kernel. We show
that in a smaller crop year acorns were dispersed to greater
maximum distances and in a less tightly clumped seed
dispersal pattern without altering the median dispersal
distance. Although with only two years of data we cannot
unequivocally demonstrate that the change in crop size
caused the shifts in dispersal patterns, the results are compatible with our present understanding of how caching
strategies should change in order to reduce pilfering in
years where stored items are more valuable. This plastic
caching behaviour likely has important consequences
for seed dispersal effectiveness by affecting post-dispersal
density-dependent processes including cache pilfering.
Acknowledgements We deeply appreciate the guidance of
Marcelino de la Cruz through the statistical analysis of spatial
point patterns. Janis Boettinger, Manuel López, Ángel Navarra,
and Joaquı́n Sánchez provided important help in the field. We
thank Pierre-Michel Forget for suggestions that greatly improved
the manuscript. Financial support was provided by the Spanish
Ministry of Science and Education grant REN2003-07048 and a
predoctoral fellowship AP2003-344. Red de Jardı́nes Botánicos
de Andalucı́a (Junta de Andalucı́a) and the headquarters of the
Sierra Nevada National Park offered support for field work.
References
Augspurger, C. K. and Kelly, C. K. 1984. Pathogen mortality of
tropical tree seedlings experimental studies of the effects of
dispersal distance, seedling density and light conditions.
Oecologia 61: 211217.
Baddeley, A. and Turner, R. 2005. Spatstat: an R package for
analyzing spatial point patterns. J. Stat. Software 12: 142.
Barot, S. et al. 1999. Demography of a savanna palm tree:
predictions from comprehensive spatial pattern analyses.
Ecology 80: 19872005.
Broding, A. et al. 2001. The effect of dominance on food
hoarding: a game theoretical model. Am. Nat. 157: 6675.
Diggle, P. J. 1979. On parameter estimation and goodness-of-fit
testing for spatial point patterns. Biometrics 35: 87101.
Forget, P. M. et al. 2005. Seed fate: predation, dispersal and
seedling establishment. CABI Publ.
Fortin, M. J. and Dale, M. 2005. Spatial analysis: a guide for
ecologists. Cambridge Univ. Press.
Gómez, J. M. 2003. Spatial patterns in long-distance dispersal of
Quercus ilex acorns by jays in a heterogeneous landscape.
Ecography 26: 573584.
Gómez, J. M. and Hódar, J. A. 2008. Wild boars (Sus scrofa) affect
the recruitment rate and spatial distribution of holm oak
(Quercus ilex). For. Ecol. Manage. 256: 13841389.
Gómez, J. M. et al. 2008. Effectiveness of rodents as local seed
dispersers of Holm oaks. Oecologia 155: 529537.
Harms, K. E. et al. 2000. Pervasive density-dependent recruitment
enhances seedling diversity in a tropical forest. Nature 404:
493495.
Hollander, J. L. and Vander Wall, S. B. 2004. Effectiveness of six
species of rodents as dispersers of single leaf piñon pine (Pinus
monophylla). Oecologia 138: 5765.
Howe, H. F. 1989. Scatter-dispersal and clump-dispersal and
seedling demography: hypothesis and implications. Oecologia 79: 417426.
Jansen, P. A. et al. 2004. Seed mass and mast seeding enhance
dispersal by a neotropical scatter-hoarding rodent. Ecol.
Monogr. 74: 569589.
Jordano, P. and Herrera, C. M. 1995. Shuffling the offspringuncoupling and spatial discordance of multiple stages in
vertebrate seed dispersal. Ecoscience 2: 230237.
Jordano, P. and Schupp, E. W. 2000. Seed disperser effectiveness:
the quantity component and patterns of seed rain for Prunus
mahaleb. Ecol. Monogr. 70: 591615.
Jordano, P. and Godoy, J. A. 2002. Frugivore-generated
seed shadows: a landscape view of the demographic and
genetic effects. In: Levey, D. J. et al. (eds.), Seed dispersal
and frugivory: ecology, evolution and conservation. CABI,
pp. 305321.
Jordano, P. et al. 2007. Differential contribution of frugivores to
complex seed dispersal patterns. Proc. Natl Acad. Sci. USA
104: 32783282.
Kwit, C. et al. 2004. Contagious seed dispersal beneath heterospecific fruiting trees and its consequences. Oikos 107:
303308.
Li, H. J. and Zhang, Z. B. 2003. Effect of rodents on acorn
dispersal and survival of Liaondong oak (Quercus liatungensis
Koidz.). For. Ecol. Manage. 176: 387396.
Li, H. J. and Zhang, Z. B. 2007. Effects of mast seeding and
rodent abundance on seed predation and dispersal by rodents
in Prunus armeniaca (Rosaceae). For. Ecol. Manage. 242:
511517.
Male, L. H. and Smulders, T. V. 2007. Hyper-dispersed cache
distributions reduce pilferage: a field study. Anim. Behav.
73: 717726.
Male, L. H. and Smulders, T. V. 2008. Hyper-dispersed cache
distributions reduce pilferage: a laboratory study. J. Avian
Biol. 39: 170177.
Moore, J. E. et al. 2007. Determinants of seed removal distance by
scatter-hoarding rodents in deciduous forests. Ecology 88:
25292540.
Muller-Landau, H. C. et al. 2002. Assessing recruitment limitation: concepts, methods, and case studies from a tropical
forest. In: Levey, D. J. et al. (eds.), Seed dispersal and
frugivory: ecology, evolution and conservation. CABI, pp.
3553.
Muñoz, A. and Bonal, R. 2007. Rodents change acorn dispersal behaviour in response to ungulate presence. Oikos 44:
157164.
Nathan, R. 2007. Total dispersal kernels and the evaluation of
diversity and similarity in complex dispersal systems. In:
Dennis, A. J. et al. (eds), Seed dispersal: theory and its
application in a changing world. CABI, pp. 252276.
Nathan, R. and Muller-Landau, H. 2000. Spatial patterns of
seed dispersal, their determinants and consequences for
recruitment. Trends Ecol. Evol. 15: 278285.
Nathan, R. et al. 2002. Mechanisms of long-distance dispersal of
seeds by wind. Nature 418: 409413.
Pinheiro, J. C. and Bates, D. M. 2000. Mixed-effects models in S
and S-PLUS. Springer.
Pinheiro, J. et al. 2006. nlme: linear and nonlinear mixed effects
models. R package ver. 3: 179.
Ripley, B. D. 1981. Spatial statistics. Wiley Series in probability
and statistics. Wiley-IEEE.
Russo, S. E. and Augspurger, C. K. 2004. Aggregated seed
dispersal by spider monkeys limits recruitment to clumped
patterns in Virola calophylla. Ecol. Lett. 7: 10581067.
Satterthwaite, W. H. 2007. The importance of dispersal in
determining seed versus safe site limitation of plant populations. Plant Ecol. 193: 113130.
Schupp, E. W. 1993. Quantity, quality and the effectiveness of
seed dispersal by animals. Vegetatio 107/108: 1529.
Schupp, E. W. and Fuentes, M. 1995. Spatial patterns of
seed dispersal and the unification of plant population ecology.
Ecoscience 2: 267275.
Schupp, E. W. et al. 2002. Dissemination limitation and the
origin and maintenance of species-rich tropical forests. In:
Levey, D. J. et al. (eds), Seed dispersal and frugivory: ecology,
evolution and conservation. CABI, pp. 1934.
Spiegel, O. and Nathan, R. 2007. Incorporating dispersal distance
into the disperser effectiveness framework: frugivorous birds
provide complementary dispersal to plants in a patchy
environment. Ecol. Lett. 10: 718728.
Stapanian, M. A. and Smith, C. C. 1978. A model for seed scatterhoarding: coevolution of fox squirrels and black walnuts.
Ecology 59: 884898.
Stapanian, M. A. and Smith, C. C. 1984. Density-dependent
survival of scatterhoarded nuts: an experimental approach.
Ecology 65: 13871396.
Vander Wall, S. B. 1990. Food hoarding in animals. Univ. of
Chicago Press.
Vander Wall, S. B. 2001. The evolutionary ecology of nut
dispersal. Bot. Rev. 67: 74117.
Vander Wall, S. B. 2002. Masting in animal-dispersed pines
facilitates seed dispersal. Ecology 83: 35083516.
Wenny, D. G. 2001. Advantages of seed dispersal: a re-evaluation
of directed dispersal. Evol. Ecol. Res. 3: 5174.
Wiegand, T. et al. 2007. Analyzing the spatial structure of a
Sri Lankan tree species with multiple scales of clustering.
Ecology 88: 30883102.
Xiao, Z. et al. 2006. Using seed-tagging methods for assessing
post-dispersal seed fate in rodent-dispersed trees. For. Ecol.
Manage. 223: 1823.
Supplementary material (available online as Appendix
O17793 at www.oikos.ekol.lu.se/appendix). Appendix 1.
187