Spatial pattern of adult trees and the mammal-generated seed rain in
the Iberian pear
Jose M. Fedriani, Thorsten Wiegand and Miguel Delibes
J. M. Fedriani and M. Delibes, Estación Biológica de Doñana (CSIC), Avda. Américo Vespucio s/n, Isla de la Cartuja, ES-41092 Sevilla.
— T. Wiegand, UFZ Helmholtz Centre for Environmental Research — UFZ, Dept of Ecological Modeling, PF 500136, DE-04301 Leipzig,
Germany.
The degree to which plant individuals are aggregated or dispersed co-determines how a species uses resources, how it is
used as a resource, and how it reproduces. Quantifying such spatial patterns, however, presents several methodological
issues that can be overcome by using spatial point pattern analyses (SPPA). We used SPPA to assess the distribution of
P. bourgaeana adult trees and their seeds (within fecal samples) dispersed by three mammals (badger, fox, and wild boar)
within a 72-ha plot across a range of spatial scales. Pyrus bourgaeana trees in our study plot (n =75) were clearly
aggregated with a critical spatial scale of ca 25 m, and approximately nine randomly distributed tree clusters were
identified. As expected from their marking behaviors, the spatial patterns of fecal deposition varied widely among
mammal species. Whereas badger feces and dispersed seeds were clearly clustered at small spatial scales ( B10 m), boar
and fox feces were relatively scattered across the plot. A toroidal shift null model testing for independence indicated that
boars tended to deliver seeds to the vicinity of adult trees and thus could contribute to the maintenance and enlargement
of existing tree clusters. Badgers delivered feces and seeds in a highly clumped pattern but unlike boars, away from
P. bourgaeana neighborhoods; thus, they are more likely to create new tree clusters than boars. The strong tree
aggregation is likely to be the result of one or several non-exclusive processes, such as the spatial patterning of seed
delivery by dispersers and seedling establishment beneath mother trees. In turn, the distinctive distribution of
P. bourgaeana in Doñ ana appeared to interact with the foraging behavior of its mammalian seed dispersers, leading to
neighbourhood-specific dispersal patterns and fruit-removal rates. Our study exemplifies how a detailed description of
patterns generates testable hypotheses concerning the ecology of zoochorous. Pyrus bourgaeana dispersers were unique and
complementary in their spatial patterning of seed delivery, which likely confers resilience to their overall service and
suggests lack of redundancy and expendability of any one species.
Patchiness, or the degree to which plant individuals are
aggregated or dispersed, co-determines how a species uses
resources, how it is used as a resource, and how it reproduces
(Condit et al. 2000, Wiegand et al. 2007). For instance, the
spatial distribution of plants can influence the movements of
frugivores leading to neighbourhood-specific dispersal patterns and fruit-removal rates where isolated plants are less
often visited than plants growing in clusters (Carlo and
Morales 2008). In turn, seed dispersers often influence the
spatial distribution of plants by establishing the initial
template on which post-dispersal processes act (e.g. seed
survival, germination, seedling survival, establishment;
Fragoso 1997, Russo and Augspurger 2004). Therefore,
plant-frugivore interactions can be seen as a dynamic twoway process in which the interacting organisms (plants and
seed dispersers) mutually affect their spatial patterns at a
range of scales. The emerging spatial patterns, e.g. the
pattern of adult plants and the pattern of seed dispersal, are
therefore expected to conserve signals from the underlying
processes, and precise spatial pattern analysis can help
recovering this ‘‘hidden’’ information (Wiegand et al.
2003, 2007, 2009, Wiegand and Moloney 2004, Grimm
et al. 2005, McIntire and Fajardo 2009). However, too
simple or imprecise analytical tools have often hindered
linking the observed patterns to processes (McIntire and
Fajardo 2009 and references therein).
One of the most important methodological issues in this
respect is the use of oversimplified null models which does
not allow the characterization of the different features of
spatial patterns in enough detail for meaningful inference
(Schurr et al. 2004, Wiegand and Moloney 2004, Wiegand
et al. 2007, McIntire and Fajardo 2009). However, spatial
patterns have many features that can be revealed when
appropriate techniques are used. For example, earlier
applications of spatial pattern analysis in ecology have
compared the observed patterns only with random patterns.
This approach can reveal the range of scales with significant
aggregation, but does not provide further information such
as the number of clusters, the average size of clusters, or if
the pattern is likely to be a superposition of independent
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patterns with different characteristics (Wiegand et al. 2007,
2009). However, clustered patterns may be the rule rather
than the exception (Condit et al. 2000, Wiegand et al.
2007) and, especially in the case of plant populations
dispersed by several frugivores with contrasting behaviors
(e.g. scatter- and clump-dispersal; Howe 1989), seed rain
is expected to show a superposition of patterns.
Spatial point pattern analysis (SPPA; Diggle 2003, Illian
et al. 2008) deals with the statistical analysis of mapped
point patterns, which comprise the coordinates and additional features of ecological objects. The assumption is that
the objects can be approximated as points (but see Wiegand
et al. 2006) and that either all points are mapped within a
given study site or a random sample of all points. Secondorder statistics such as the pair correlation function or
Ripley’s K are the summary statistics of choice for
describing the characteristics of point patterns (e.g. clustering) over a range of spatial scales (Stoyan and Stoyan 1994,
Wiegand and Moloney 2004, Law et al. 2009). Additionally, they can be applied in conjunction with realistic null
models to help in the identification of the underlying
patterns (Schurr et al. 2004, Wiegand and Moloney 2004,
Wiegand et al. 2007, McIntire and Fajardo 2009).
In this study, we applied recent extensions of SPPA to
better understand the processes that determined the spatial
pattern of adult trees in the Iberian pear, Pyrus bourgaeana,
in southwester Spain. In our study region, P. bourgaeana
appears to be distributed in clusters (Fedriani and Delibes
2009a) though no formal analyses of the spatial patterns
have been undertaken for this species. The spatial structure
of P. bourgaeana is likely to be the result of several nonexclusive processes, including edaphic variables (Clark et al.
1999), establishment beneath mother trees (Chapman and
Chapman 1995), and the pattern of seed delivery by its seed
dispersers (Russo and Augspurger 2004). Therefore, we also
assessed the spatial patterns of P. bourgaeana seed rain
generated by its dispersers, as well as its potential relationship with the local patterning of adult tree distribution. The
main local dispersers of P. bourgaeana are the Eurasian
badger Meles meles, the wild boar Sus scrofa, and the red fox
Vulpes vulpes (Fedriani and Delibes 2009a). Disperserspecific mobility (Spiegel and Nathan 2007), habitat
preferences (Jordano and Schupp 2000), and fecal marking
behavior (Fragoso 1997) are likely to lead to differences in
the pattern and scale of seed delivery, which may contribute
to the persistence of the tree population despite disturbance
(Frost et al. 1995, Peterson et al. 1998). Thus, seed
dispersers that apparently seem redundant often operate at
contrasting spatial scales, providing uniqueness and complementariness (sensu Spiegel and Nathan 2007) to their
services and, thus, cross-scale resilience in terms of seed
dispersal (Fragoso 1997, Loiselle et al. 2007). Nonetheless,
even when different dispersers operate at the same spatial
scales, they may provide within-scale resilience to the
dispersal service (Peterson 2000). Therefore, differentiating
among the seed rain generated by different disperser species
is important not only in assessing their potential as
underlying mechanism of tree spatial patterns, but also in
estimating the resilience of disperser’s service.
More specifically, we used spatial data of P. bourgaeana
adult trees and dispersed seeds (within fecal samples) by the
three main dispersers within a 72-ha plot during two
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consecutive fruiting seasons to address four objectives:
1) to characterize the spatial distribution of adult trees,
2) to characterize the spatial distribution of seeds delivered
by each disperser, 3) to assess the spatial relationship
between the distribution of adult trees and the seed rain
generated by each disperser, and 4) to find out if P.
bourgaeana seeds were more frequent in mammal deposition sites closer to adult P. bourgaeana trees. We then
discuss in light of our pattern analysis several hypotheses on
the processes acting in this plant-frugivore system and the
consequences of our results for dispersal service resilience
and implications for conservation.
Methods
Study system and site
Pyrus bourgaeana (Rosaceae) is a monoecious small tree
(typically 3—6 m in height) distributed across the southern
Iberian Peninsula and northern Morocco (Aldasoro et al.
1996). Our focal population is located in the Doñ ana
National Park (510 km2; 3789?N, 6826?W; elevation
0—80 m), on the west bank of the Guadalquivir River
estuary in southwestern Spain. In the Doñ ana area,
P. bourgaeana distribution is very fragmented, with trees
occurring at low densities (generally B1 individual ha—1)
in several Mediterranean scrubland patches that are isolated
from each other by marshes, sand dunes, or cultivations.
The climate is Mediterranean sub-humid, characterized by
dry, hot summers (June—September) and mild, wet winters
(November—February). Annual rainfall varies widely, ranging during the last twenty-five years from 170 to 1028 mm
(mean9SD =583.09221.1 mm). Though most rain
(‘80%) falls between October—March, there is a marked
interannual seasonal variability in rainfall. For example, the
coefficients of variation for summer and winter rainfall were
93.3 and 54.4%, respectively, between 1984 and 2005.
In our study population the understory is dominated by
Pistacia lentiscus shrubs growing singly or in small clumps
separated by unvegetated sandy substrate or sparse Halimium halimifolium, Ulex spp., and Chamaerops humilis
(Fedriani and Delibes 2009a). Scattered across the area
there are Quercus suber, Olea europaea var sylvestris and
Fraxinus angustifolia trees. The fieldwork (see below) was
undertaken in a tetragonal plot of 72-ha (‘0.6 x0.9 x
0.8 x1.0 km) where the locations of all 75 P. bourgaeana
reproductive trees were known (Fig. 1B). Most of the plot
(49 ha) is occupied by Mediterranean scrubland as detailed
above, whereas its southwestern side (23 ha), delimited by a
fire-break, is occupied by a ‘‘dehesa’’, i.e. the habitat
resulting of a traditional form of management of the
Mediterranean forests, in which native trees (e.g. Q. suber
and O. europaea var sylvestris) are spaced out or inserted in a
continuum of grasslands with no or sparse understory of
Mediterranean scrubs (P. bourgaeana does not occur in this
side of the plot).
In Doñ ana, P. bourgaeana flowers during February—
March, with each individual producing 200—450 fruits
that ripen during the autumn (September—November;
Jordano 1984). Developed fruits are globose pomes
(2—3 cm diameter; ‘9.5 g wet weight; Fedriani and
(B) P. bourgaeana
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Figure 1. Analysis of the spatial pattern of adult P. bourgaeana. (A) Point pattern analysis of the pattern adult P. bourgaeana shown in (B)
using a Thomas process. The fit of the pair-correlation function with the Thomas process (eq. 3) yielded approximately nine clusters and a
cluster size of rC :2s =22.6 m (shown as circles in B). The pair correlation function of the data (dots), the expected pair-correlation
function of the Thomas process (grey solid line), and simulation envelopes of the Thomas process (solid lines) being the 5th lowest and
highest values of the pair correlation functions the 199 simulated of the null model. The cell size was 1 x1m and the ring width dw =
2 m. The small inset shows the corresponding accumulative distribution function of the distances y to the nearest neighbor. Note that the
pattern of P. bourgaeana comprises several ‘‘isolated’’ trees with nearest neighbor distances y >15 m that are farther away than expected by
the Thomas process. (B) The spatial pattern of adult P. bourgaeana, isolated trees (gray circles), and a visual reconstruction of the clusters
(circles). The trees outside the clusters are isolated trees. (C) same as (A) but analysis of pattern without isolated points. The fitted cluster
size yields rC :2s =22.2 m and the estimated number of clusters was 8.26.
Delibes 2009b) that comprise a sugary water-rich pulp
(Herrera 1987). Each fruit includes 1—5 viable seeds (46—
91 mg each; Fedriani and Delibes 2009b) with easily
breakable coats. Several medium- and large-sized mammals
consume the fruit of P. bourgaeana; whereas badger, fox,
and the wild boar ingest the whole fallen fruit and thus
potentially disperse their seeds over long distances, red deer
Cervus elaphus also ingest whole fruits but act strictly as seed
predators (Fedriani and Delibes 2009a). Also, small
mammals such as the European rabbit Oryctolagus cuniculus
and up to four species of rodents eat P. bourgaeana seeds
(and some fruit pulp), but they destroy and grind into tiny
pieces all ingested seeds; thus, act as seed predators
(Fedriani and Delibes 2009a). Because in P. bourgaeana
seed germination requires that attached fruit pulp be
removed, fruit processing by mammals is an important
service for the tree (i.e. most seeds within fallen fruits decay
at the end of the dispersal season; Fedriani and Delibes
unpubl.). Birds (e.g. azure-winged magpie Cyanopica
cianea, blackcap Silvia atricapilla) mostly act as pulppredators rather than as seed dispersers (Fedriani and
Delibes 2009a).
Fecal collection and seed quantification
Collection of fecal samples was carried out weekly during
the dispersal seasons (September—February) of 2005—2006
and 2006—2007. During the surveys, sampling effort was
distributed homogeneously across the plot. We set a total of
twelve ‘‘starting points’’, three along each side of the plot,
which were regularly distributed. During each survey, an
observer walked from a starting point (which changed
among consecutive surveys by rotating them clockwise) to
the opposite side of the plot following a non-fixed
zigzagging trajectory and, once there, came back following
a different path to a changing location of the departure plot
side (Fedriani and Delibes 2009a).
Each survey took about two hours and overall we
undertook 104 different surveys (‘208 h-observers). On
average, each starting point was used 8.7 times. Based on
twenty-one surveys for which distance travelled was
measured (using a Global Position System; GPS), the
minimal lengths of surveys were, on average, 2.849
0.21 km (mean91SE). Thus, overall, we walked a minimum of 295 km searching for mammal feces within the
plot (whose longest side is 1.0 km). In each survey, the
observer collected any fresh feces of foxes and badgers
(which were locally scarce) and up to five samples of boar
(which were most abundant). A GPS-reading was attained
for each mammal feces, recorded into a geographic
information system to establish their map location (using
ArcView software).
Mammal feces were identified at the species level on the
basis of shape, odor, and color (Fedriani et al. 1999). For
example, fox feces are cylindrical, tapered at one of the
extremities, with a strong repulsive smell, and deposited
over the substrate (plants, raised spots, etc). Badger feces are
deposited in small dugs (or latrines) often buried shallowly
with loose substrate and, typically, with a distinctive musky
smell. Boar droppings are usually cylinder shaped, with
disc-shaped sections fused together. Overall sample sizes
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were 121, 118, and 19 for the boar, badger, and fox,
respectively. Also, for comparative purposes, we collected
feces (n =81) delivered by the red deer, a strict predator
(not disperser) of P. bourgaeana seeds. Feces were air dried
and stored individually in paper bags. Each fecal sample was
thoroughly washed under running water on a sieve
(0.50 mm mesh size) and air-dried. Seeds and other fruit
remains (skin, pulp, pedicels, etc) were identified using a
reference collection. Further, seeds were examined with a
20—40 xmagnification glasses and the number intact seeds
was recorded (for details, see Fedriani and Delibes 2009a).
Nearest neighbor statistics
The ‘‘shortsighted’’ accumulative distribution function G(y)
of distances y to the nearest neighbor provides important
additional information about the nature of clustered
univariate patterns (Illian et al. 2008). It serves as an
independent test statistic to assess how well fitted point
processes describe the data (Jacquemyn et al. 2007, 2009,
Wiegand et al. 2007). We calculated G(y) without edge
correction (Diggle 2003). This is a reasonable approximation because the scales we investigate (i.e. r B100 m) are
much smaller than the smallest side of the plots (roughly
1 km; Stoyan 2006).
Generalities of point pattern analysis
We used recent techniques of spatial point-pattern analysis
(Ripley 1981, Stoyan and Stoyan 1994, Diggle 2003, Illian
et al. 2008) to investigate the distribution of adult trees,
mammal feces, and dispersed seeds. To quantify the uni and
bivariate spatial patterns we employed the pair-correlation
function (Stoyan and Stoyan 1994, Illian et al. 2008), a
normalized neighborhood density function, and the distribution function of the distances to the nearest neighbor
(Diggle 2003, Illian et al. 2008).
For estimation of these summary statistics we followed
the grid-based approach of Wiegand and Moloney (2004)
and used an adapted grid size of 1 x1 m. This is a fine
resolution compared with the size of our study plots
(72 ha), but necessary to study the apparently small-scale
clustering of the trees and the feces and the accuracy of their
recorded coordinates. To avoid problems in estimating
some spatial patterns due to environmental heterogeneity
within our study plot (i.e. scrubland vs ‘‘dehesa’’), we
conducted the corresponding point pattern analyses only in
a rectangle (‘52 ha) centred over the homogeneous part of
the study region covered by Mediterranean scrubland.
Pair-correlation function
To characterize the spatial patterns of P. bourgaeana trees
and feces, and potential associations between them, we used
uni- and bivariate pair-correlation functions, respectively.
The univariate pair-correlation function g(r) is based on the
distribution of distances r between pairs of points. It can be
defined for homogeneous patterns using the quantity
O(r) =lg(r) which has the intuitive interpretation of the
expected intensity of points at distance r of a representative
point of the pattern (Wiegand and Moloney 2004, Illian
et al. 2008), where l is the intensity of the pattern in the
study region (i.e. number of points divided by area of
the study region). In practice the mean number of points
within a ring (r —dw, r+dw) with an adapted width (or
bandwidth) dw =2 m around the points of the pattern is
determined and divided by the mean area (i.e. the number
of cells) of these rings that fall within the study region
(Wiegand and Moloney 2004). Bivariate extensions of the
pair correlation function for patterns composed of type 1
and type 2 points follow intuitively (Wiegand and Moloney
2004); in this case O12(r) =l2g12(r) is the expected
intensity of type 2 points at distance r of a representative
type 1 point where l2 is the intensity of type 2 points.
548
Test of significance
The empirical test statistics were contrasted to appropriate
null models (see below), which represent a ‘‘benchmark’’
point process with known structure adapted to our
questions. We used a Monte Carlo approach for construction of simulation envelops of a given null model and test
statistic. Each of the 199 simulations of the point process
underlying the null model generates a g(r) function (or
another appropriate test statistic), and simulation envelopes
with an approximate a =0.05 were calculated for the test
statistic using its 5th highest and 5th lowest values.
Note that we cannot interpret the simulation envelopes
as confidence intervals because we tested the null hypothesis
at several scales of r simultaneously. This may cause type
I error (Stoyan and Stoyan 1994, Diggle 2003, Loosmore
and Ford 2006). To test overall departure of the data from
the null model without type I error inflation we used a
Goodness-of-Fit (GoF) test that collapses the scale-dependent information contained in the test statistics into a single
test statistic ui which represents the total squared deviation
between the observed pattern and the theoretical result
across the scales of interest. The ui were calculated for the
observed data (i =0) and for the data created by the i =
1, . . . 199 simulations of the null model and the rank of u0
among all ui is determined. If the rank of u0 is >190 there
is a significant departure from the null model with a =0.05
over the scales of interest (i.e. 0—50 m). Details can be
found in Diggle (2003), Loosmore and Ford (2006), and
Illian et al. (2008).
Null models for analysis of univariate patterns
For univariate patterns that did not show apparent
clustering we used a homogeneous Poisson process, which
is sometimes called ‘‘complete spatial randomness’’ (CSR)
as null model. To this end we assigned the points of the
pattern to random coordinates within the study plot.
Because most univariate patterns were apparently not
random but strongly clustered (e.g. Fig. 1), we fitted a
simple cluster process to the data. Note that this process is
phenomenological, not mechanistic, and does not provide a
direct link to the underlying processes. However, the cluster
process serves as ‘‘benchmark’’ processes with known
structure and directly interpretable parameters, which
allows us to characterize fully the key properties of
clustering. To this end we used the Thomas process
(Thomas 1949, Stoyan and Stoyan 1994, Seidler and
Plotkin 2006, Jacquemyn et al. 2007, 2009, Wiegand
et al. 2007, 2009, Morlon et al. 2008) which is based on a
simple stochastic construction principle; it consists of a
number of randomly and independently distributed ‘‘clusters’’ with properties specified below. The pair-correlation
function of the Thomas process yields:
g (r ; s; r) = 1 +
1 exp(—r2=4s 2)
r
4ps2
wrapped as a torus, one pattern is fixed, and the other is
shifted as a whole random vector (Dixon 2002, Wiegand
and Moloney 2004).
Results
Distribution of adult trees
:
The parameter r is the intensity of clusters in the study
region of area A (i.e. Ar is the number of clusters) and the
parameter s described the cluster size. The points are
randomly assigned to the clusters, i.e. the number of points
per cluster follows a Poisson distribution with mean m =
l/r where l is the intensity of the pattern. Note that the
Thomas process also describes situations were the number
of clusters is higher than the number of points (i.e. l Br).
In this situation a given cluster may be empty (i.e. there is
no point assigned to the cluster) or comprise only one
point. This is a consequence of the stochastic construction
principle underlying the Thomas process in which the
definition of a cluster is not related to the number of points
belonging to a given cluster. The probability for a given
cluster to be empty yields P[n =0, m] =Exp( —m), and the
probability that a given cluster has just one point yields
P[n =1, m] =mExp( —m). Low values of m thus indicate
that the pattern is basically a random pattern but with a few
‘‘paired’’ points that imprint clustering. The proportion of
points occurring in clusters with only one point yields
mExp( —m)/[1 —Exp( —m)].
The distribution of the locations of points belonging to a
given cluster, relative to the center of the cluster, is assumed
to be a two-dimensional normal distribution with variance
s2. The cluster size rC can be defined as rC :2s includes ca
87% of the points belonging to a given cluster, and the
approximate area covered by one cluster is AC =
p r2C =4ps2 . Note that this definition of a cluster size is
not directly related to properties of individual clusters (e.g.
the number of points belonging to a given cluster, or the
area covered by an identifiable cluster), but based on the
stochastic construction principle of the clusters underlying
the Thomas process (Wiegand et al. 2009).
The unknown parameters r and s were determined
using minimum-contrast methods based on the paircorrelation function (Stoyan and Stoyan 1994, Wiegand
et al. 2007, 2009). To test if the fit with the Thomas
process was reasonable, we evaluated this null model using
the accumulated distribution function G(y) of the distances
y to the nearest neighbor as additional test statistic.
Null model for analysis of association between
patterns
To find out if there was an association between the spatial
patterns of the trees and that of feces we contrasted the data
to a null model of independence (Wiegand and Moloney
2004). A test for independence requires conservation of the
spatial structure of the individual univariate patterns, but to
break their dependence (Dixon 2002). We achieved this by
using a toroidal shift null model where the study rectangle is
Adult P. bourgaeana trees showed a strong clustering, with
local densities at distances 2—7 m being ca 40 times higher
than expected by a complete random distribution [Fig. 1A;
note that the expectation of the pair correlation function for
a random pattern is g(r) =1 and that g(r) =40 means that
the neighborhood density at scale r is 40 times higher than
under CSR]. The data at scale 0—50 m could be fitted well
with the Thomas process (rank =37), yielding Ar* =10.7
randomly distributed clusters and a cluster size of rC :
2s =22.6 m (Fig. 1B). However, the distribution G(y) of
distances y to the nearest neighbor y indicated some
departure from this benchmark process: nearest-neighbor
distances y >10 m were less frequent than expected (small
inset Fig. 1A; rank =199). This departure was caused by
nine adult P. bourgaeana trees (12%) which had no nearest
neighbor within 40 m (grey disks in Fig. 1B); thus growing
clearly outside the clusters. It is therefore likely that the
pattern of P. bourgaeana is a superposition pattern,
comprising a clustered and a non-clustered component
pattern.
In case of an independent superposition of a random
pattern and a pattern generated by a Thomas process the
estimate of the parameter r is influenced by the superposition. In this case the ‘‘true’’ value yields r =r*pC 2
(Wiegand et al. 2007) were pC is the proportion of trees
belonging to the cluster component pattern (i.e. pC 2 =
(62/71)2 =0.76) and r* the initial estimate based on the
fit of the entire (superposition) pattern. Thus, the number
of clusters should be Ar* =10.7*0.76 =8.2. Indeed,
repeating the analysis without the nine isolated points
(Fig. 1C) yields an estimate of 8.3 clusters comprising on
average m =62/8.3 =7.5 trees. The distribution function
G(y) of distances y to the nearest neighbor are now well in
accordance with the benchmark cluster process (small inset
Fig. 1C; rank 160). However note that the number of
points is relatively low and that the stochastic nature of the
Thomas process prevents a further reconstruction of the
cluster structure.
Spatial patterns of mammal fecal delivery
Badger feces showed extreme clustering, with local densities
approximately 1000 times higher than expected by a
completely random distribution (Fig. 2D). Nonetheless,
by analyzing the distribution of the distances to the nearest
neighbor, we found that 24 feces had no nearest neighbor
within 15 m (small inset Fig. 2D), but 76 feces were
clustered in a few (say five) small clusters (Fig. 2A). This
resulted in extremely high values of the pair-correlation
function at scales B10 m (Fig. 2D). The pattern is thus a
superposition of an extreme cluster pattern with a less
clustered component. Because of this complex structure and
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(A) Badger
(B) Wild boar
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Spatial scale r [m]
40
50
0
10
20
30
Spatial scale r [m]
Figure 2. Analysis of feces and its relationship of P. bourgaeana. (A—C): the pattern of feces (open disks) and P. bourgaeana (grey disks).
Note that, for badgers, we show isolated feces (those with no neighbors within 15 m) as open squares. (D—F): univariate point pattern
analysis of the feces using a CSR null model for red deer feces and a Thomas process for wild boar feces. (G—I) bivariate point pattern
analysis testing for independence between feces and trees using a torodial shift as null model. The pair correlation function of the data
(dots), the expected pair-correlation functions of the null model (grey solid line), and simulation envelops of the null model (solid lines).
The cell size was 1 x1m and the ring width dw =3 m.
the relatively low number of feces we did not conduct more
detailed analyses for this pattern.
The distribution of wild boar feces (Fig. 2B) showed a
significant clustering up to scales r =5 (Fig. 2E). To
approximate the small-scale clustering we fitted a Thomas
process to the data that reproduced the observed clustering
of the data well, both for the pair correlation function (Fig.
2E; rank =142) and for the nearest neighbor distribution
function (small inset Fig. 2E; rank =162). The fit yielded a
cluster size of rC :2s =4 m and 353 clusters. Thus, the
expected number of feces per cluster were m =107/353 =
0.295 (there were 107 feces in the study rectangle). Thus,
the proportion of feces occurring in clusters with only one
point yields mExp( —m)/[1 —Exp( —m)] =0.86 or 92 feces
indicating that most boar feces are randomly distributed
550
(i.e. some 92 feces are distributed in clusters with only one
feces), but a few feces (ca 15) are aggregated in small clusters
of two or tree (Fig. 2B).
Limited sample size prevented SPPA for the red fox (see
below); however, for comparative purposes, we also assessed
the spatial patterns of feces delivered by the red deer, a
species that act strictly as predator of P. bourgaeana seeds. In
contrast to the clustered patterns of badger and wild boar,
we found that the distribution of deer feces (Fig. 2C)
did not differ significantly from a random pattern (Fig. 2F;
rank 132). As a consequence of their fecal marking
behaviors (Fig. 2), the three dispersers noticeably differed
in their patterns of seed delivery (Fig. 3). Badger and
boar generated the highest and lowest seed aggregations,
respectively (Fig. 3).
Red fox Vulpes vulpes
No. of observations
4
3
2
1
0
25
75
125
175
225
275
325
Badger Meles meles
22
The spatial pattern of badger feces was independent from
that of adult trees (Fig. 2G; rank 175); however, when
randomizing the badger feces not with a torus shift null
model that conserves the strong clustering of feces, but
using a random pattern (CSR) as null model, we found
significant and positive association (results not shown),
which highlight the importance of accounting for
the specific distribution of feces in assessing second
order patterns. Conversely, the pattern of wild boar feces
was significantly and positively associated with adult
P. bourgaeana trees, especially at distances 4—9 m from
the trees (Fig. 2H; rank 199). The pattern of red deer feces
was independent from the pattern of adult trees (Fig. 2I;
rank 57).
Distance between dispersers deposition sites with
and without P. bourgaeana seeds and the nearest
adult tree
Finally we had a closer look at whether the distribution of
P. bourgaeana seeds within deposition sites of the dispersers
was dependent on the distance to the nearest P. bourgaeana
tree. Because the radius of statistically significant tree
clusters was ‘25 m, whereas clusters were generally
>100 m apart (Fig. 1B), we categorized all distances
between fecal deposition sites and the nearest neighbor
into three categories: short distance (525 m; i.e. seed
movements within the typical cluster radius), long distance
( >100 m; i.e. seed movements among different tree
clusters), and intermediate (i.e. >25 m and 5100 m).
Though all three legitimate dispersers (fox, badger, and
boar) delivered some feces up to three hundred meters away
from the nearest P. bourgaeana tree (Fig. 4), they differed
clearly in the frequencies of distances to the nearest tree for
both feces with (x2 =13.78, DF =4, p B0.010) and
without seeds (x2 =30.57, DF =4, p B0.001). For the
18
No. of observations
Spatial relationship between the distribution of adult
trees and the mammal-generated seed rain
20
16
14
12
10
8
6
4
2
0
25
75
125
175
225
275
325
Wild boar Sus scrofa
35
30
No. of observations
Figure 3. Number of P. bourgaeana seeds per square cell (50 m
side) within the study plot for each of the three legitimate
dispersers of this tree in Doñ ana. Boxes and error bars show
mean9SE and 95% bootstrap confidence intervals, respectively.
Feces with seeds
Feces without seeds
25
20
15
10
5
0
25
75
50
125
100
175
150
225
200
275
250
300
Distance to the nearest neigbour
Figure 4. Frequency distribution of distances between fecal
deposition sites (with and without seeds) and the nearest
P. bourgaeana conspecific for the three legitimate seed dispersers
in Doñ ana.
fox, the feces with P. bourgaeana seeds were significantly
closer to adult trees than feces without seeds (Fisher exact
test, p =0.002), but because of the small sample size for this
species, SPPA were not performed for the fox. Conversely,
for the badger and the boar, feces with and without seeds
were mostly delivered at intermediate distances from the
nearest tree ( >25 m and 5100 m; Fig. 4) and no
significant differences were found in their respective
distribution of distances (Fisher exact test, p >0.129).
Therefore, for these two species, all fecal deposition sites
551
appear to be potential dispersal sites. Though these results
indicate that most badger and boar-dispersed seeds are
delivered at intermediate distances away from the nearest
adult tree, this metric does not fully characterize the spatial
pattern of seed delivery in relation to the whole conspecific
neighborhood, which is an important point due to the
strong aggregated pattern of P. bourgaeana. However, our
data do not allow for a more refined analysis (i.e. trivariate
random labeling; De la Cruz et al. 2008, Biganzoli et al.
2009) to find out if the presence of seeds in feces depends
on the distance to adult P. bourgaeana trees.
Discussion
SPPA allowed us to characterize in detail the spatial patterns
of adult P. bourgaeana trees, dispersed seeds, and the
relationship between trees and seeds at a range of spatial
scales. We used the Thomas cluster process to characterise
important properties of our empirical data with much
higher precision than, for example, a null model of
complete spatial randomness. The key properties (average
number of points per cluster, cluster size, number of
clusters, and assessment of potential superposition patterns)
fully characterize the features of the observed spatial
patterns. In addition, to evaluate the independence between
the spatial pattern of adult trees and feces of different
dispersers, we preserved the spatial structure of the
individual univariate patterns, but broke their dependence
by using a toroidal shift null model (Wiegand and Moloney
2004). All these features enhance the accuracy of our results,
improving our understanding of the distribution of adult
trees and the potential underlying processes.
Distribution of P. bourgaeana in Doñana
Pyrus bourgaeana trees in our study plot were clearly
aggregated, with a critical spatial scale of ca 25 m
(Fig. 1). A similar aggregated pattern appears to occur for
neighboring trees located outside of our study plot
(Fedriani et al. unpubl.). In addition, both within and
outside our study plot, the distribution of this tree within
Mediterranean scrubland does not seem to correlate with
habitat factors (Fedriani et al. unpubl.). Therefore, the
strong aggregated patterning found is likely to be the result
of one or several other non-exclusive processes. First, by
creating the initial template on which post-dispersal
processes act, seed dispersers can be responsible, at least
partially, for P. bourgaena aggregation (Fragoso 1997,
Wenny 2000, Russo and Augspurger 2004). Nonetheless,
the sequence of concatenated post-dispersal events (seed
germination, seedling survival, establishment) could erase
the initial patterns in seed distribution imposed by
P. bourgaena dispersers (Jordano and Herrera 1995, Schupp
and Fuentes 1995, Rey and Alcántara 2000; but see Garcı́a
et al. 2005). Thus, our ongoing research examining
emergence, survival, and establishment of seedlings from
mammal-dispersed and non-dispersed seeds is required to
fully assess the role of mammals on P. bourgaeana
distribution.
552
Second, dispersal limitation sometimes leads to seedling
establishment beneath mother trees, resulting in an
aggregated patterning (Chapman and Chapman 1995,
Bustamante and Simonetti 2000, Cordeiro and Howe
2003). This is a likely possibility since a fraction of the fruit
fallen beneath adult trees are not taken by mammals. Many
seedlings emerge beneath mother trees every season and,
eventually, some of those seedlings get established (Fedriani
et al. unpubl.). Finally, rhizome sprouting in response to
disturbance (Barnes et al. 1998, Bond and Midgley 2001)
could result in clustering if different sprouts emerge from a
single individual and eventually produce fruit. In Doñ ana,
P. bourgaeana experience heavy browsing by red deer and
sprouts of a range of sizes emerge beneath some trees; thus,
the possibility that those shoots grow and eventually reach the
adult size leading to tree clusters needs to be evaluated.
On the other hand, the aggregated patterning of adult
P. bourgaeana trees might have important consequences for
their interaction with fruit consumers (Blendinger et al.
2008, Carlo and Morales 2008). Both simulation models
and empirical evidence suggest that as fruiting plants
become aggregated, inequality among individuals in fruitremoval rates increases and seed dispersal distance decreases
and, in turn, both of these processes could help create and
maintain plant aggregation (Carlo and Morales 2008). Our
data on fruit removal for P. bourgaeana during the autumn
of 2005 (Fedriani and Delibes 2009a) suggest that isolated
individuals (i.e. those with no neighbors within 25 m) are
visited by legitimate dispersers less frequently (0.5090.29
visits per night, n =4) than individuals located within tree
clusters (1.0890.19 visits per night, n =12). Furthermore,
nearest neighbor distances between adult P. bourgaeana and
badger, fox, and boar deposition sites were always lower for
aggregated trees (38.4991.0, 40.094.1, and 22.592.2,
respectively; n =63) than for isolated ones (91.0914.8,
70.799.6, and 39.297.3, respectively; n =12). Therefore,
our preliminary data support the hypothesis that the
distribution of P. bourgaeana in Doñ ana interacts with
the foraging behavior of its mammalian seed dispersers,
leading to neighbourhood-specific dispersal patterns and
fruit-removal rates (Aukema and Martinez del Rı́o 2002,
Carlo and Morales 2008, Levey et al. 2008).
Spatial patterns of mammal feces and seed delivery
As expected based on disperser’s marking behaviors, the
spatial patterns of fecal deposition varied widely among
mammal species at a range of spatial scales. Badger feces and
dispersed seeds were clearly clustered at small spatial scales,
which is consistent with their intensive usage of shared
defecation sites (latrines) which are preferentially placed in
the vicinity of main setts and along their territory
boundaries (Kruuk 1989, Revilla and Palomares 2002).
Boar feces were only lightly clustered, which may be related
with the small boar group size in Doñ ana (Fernández-Llario
et al. 1996). The few fox feces found were located on
conspicuous sites (plants, raised spots) scattered across the
plot, which is a typical pattern in this species (Lloyd 1980).
Furthermore, badger and boar differed in their fecal
deposition behavior with respect to P. bourgaeana neighborhoods. If the initial pattern of seed delivery persists beyond
subsequent ontogenic stages, boars likely contribute to the
maintenance and enlargement of existing tree clusters.
Badgers, however, delivered feces and seeds away from the
tree neighborhoods; thus, they are more likely to create new
tree clusters. Conversely, the pattern of fecal delivery by the
red deer, a seed predator of P. bourgaeana seeds, was neither
clustered nor associated with adult trees. Interestingly, the
spatial patterns of both badger feces and adult trees were a
superposition of an extremely clustered pattern, in conjunction with a less clustered component, which further
suggests a role of badger dispersal on tree distribution.
Furthermore, badgers were the only dispersers that delivered
seeds in the ‘‘dehesa’’, a habitat where at present
P. bourgaeana does not occur. Badgers frequently use
dehesa habitat (Fedriani et al. 1999) and may play an
important role in colonization of new areas (Nathan and
Muller-Landau 2000). Therefore, even if badgers and boars
were redundant in other aspects of the dispersal process (see
below) they show important disparities in seed delivery as a
result of their spatial and fecal marking behaviors. Thus,
they provide complementary dispersal services (Spiegel and
Nathan 2007), which may confer resilience to P. bourgaeana dispersal (Peterson et al. 1998). Nonetheless, these
inferences should be taken with caution since our analyses
‘‘only’’ evaluate seeds and adult trees. To interpret fully the
influence of seed dispersal on plant demography other
stages (seedling, sapling) should be taken into account.
Dispersal service resilience and conservation
implications
In humanized landscapes, such as the Doñ ana area, it is
important to account for the susceptibility of frugivores to
anthropogenic disturbance when assessing the resilience of
the dispersal service (Wright et al. 2000). Intense human
activities in Doñ ana have lead to increases of red fox and
wild boar populations during the last four decades (Rau
et al. 1985, Gortázar et al. 2008), whereas hunting has
decreased the population of badgers (Revilla et al. 2001)
and led to extinction of other potential dispersers such as
wolves Canis lupus (Valverde 1967) and, long ago, bears
Ursus arctos (Swenson et al. 2000). Thus, dispersers more
tolerant to human activities (fox, boar) may replace to
some extent susceptible species (wolf, badger) when and
where levels of humanization decrease their populations,
which could provide resilience to P. bourgaeana dispersal
(Wright et al. 2000). For instance, because foxes and boars
are highly mobile and P. bourgaeana distribution in Doñ ana
is very fragmented, with trees occurring at low densities in
several isolated patches, they can potentially replace humansensitive species (e.g. badgers) in terms of (re)colonization at
vacant patches, connecting different subpopulations, and
enhancing the genetic flux among them (Nathan and
Muller-Landau 2000, Levin et al. 2003).
Nevertheless, the overriding influence of post-dispersal
processes makes it very difficult to predict accurately how a
particular tree species will respond to the reduction of its
main dispersers (Chapman and Russo 2006). For instance,
contrasting microhabitats often lead to varying seed and
seedling survival (Rey and Alcántara 2000, Traveset et al.
2003), depending on a myriad of variable biotic and abiotic
factors (Schupps 2007), and P. bourgaeana seed dispersers
differ in their microsites of seed deposition. Whereas
Pistacia shrubs were the most frequent microhabitat of
deposition for badgers (43% of cases; n =140), for foxes
and boars the most frequent microhabitat was open
interspaces among Pistacia shrubs (82% [n =22] and
45% [n =112], respectively; Fedriani and Delibes 2009a).
Given the unpredictability of the Mediterranean weather
(Thompson 2005), which diversifies the environmental
conditions at seed deposition sites, the assemblage of
P. bourgaeana dispersers likely spreads the risk (sensu
Cohen 1966) of countering conditions particularly unsuitable for survival, resulting in a complementary dispersal
service that enhances recruitment. However, the loss of any
one dispersal agent could lead to a more stereotyped seed
rain (i.e. lesser variability in the microhabitat of deposition),
which, under some circumstances, could lessen recruitment.
In Doñ ana, fox, badger, and boar disperse at least, ten,
nine, and six species of fleshy fruits (Fedriani and Delibes
2008), respectively, and thus some of our results are also
relevant to the overall local community of fleshy-fruited
shrubs. As is generally true (Janson 1983), large-fruited
species are dispersed more frequently by mammals
(P. bourgaena, C. humilis, Juniperus oxycedrus subsp.
macrocarpa) than small-fruited species (Rubus ulmifolius,
Juniperus phoenicea, Daphe gnidium). For instance, J.
oxycedrus subsp. macrocarpa (1.5 cm of fruit diameter) is
an endangered and protected species in Spain, with a range
limited to some coastal dunes of the Mediterranean basin
(Muñ oz-Reinoso 2003). In Doñ ana, its only known
dispersers are the red fox, the badger, and the wild boar
(Muñ oz-Reinoso 2003, Fedriani et al. unpubl.) and thus
these mammals are of paramount importance for the
dispersal of this endangered species. Therefore, preserving
a diversity of highly mobile mammalian seed dispersers is
central for the resilience of the Doñ ana ecosystem (Lunberg
and Moberg 2003).
In conclusion, SPPA is a powerful tool that allowed us to
characterize in detail the distribution of P. bourgaeana trees
and the mammal-generated seed rain in Doñ ana. Pyrus
bourgaeana dispersers delivered ingested seeds in contrasting
spatial patterns, providing complementary dispersal and
suggesting lack of redundancy and expendability (sensu
Kareiva et al. 2003) of any one disperser species. The
singularities of the dispersal services provided by each
disperser likely affords a degree of resilience to their overall
service (Peterson et al. 1998) in an area under high levels of
human disturbance. The strongly aggregated pattern of trees
might be an outcome of seed dispersal and, in turn, likely
has a strong effect on disperser behavior and the resulting
patterns of seed delivery. The interaction between
P. bourgaeana and its mammalian seed dispersers emerges
as a dynamic two-way process in which the interacting
organisms (trees and dispersers) mutually affect their spatial
patterns at a range of scales.
Acknowledgements — We are indebted to Gemma Calvo, Mó nica
Váz, Magdalena Zywiec and innumerable volunteers for their
enthusiastic field and lab assistance. Gene Schupp, Kevin Burns,
and two anonymous reviewers provided useful comments that
improved the manuscript. The Spanish Ministerio de Medio
553
Ambiente (15/2003 grant) and Ministerio de Educación y Ciencia
(CGL2007-63488/BOS) supported this study.
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