Ecography 33: 545 555, 2010 doi: 10.1111/j.1600-0587.2009.06052.x # 2010 The Authors. Journal compilation # 2010 Ecography Subject Editor: Kevin Burns. Accepted 21 July 2009
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, Estacio´n Biolo´gica de Don˜ana (CSIC), Avda. Ame´rico 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 Don˜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 recovering this ‘‘hidden’’ information (Wiegand et al. aggregated or dispersed, co-determines how a species uses 2003, 2007, 2009, Wiegand and Moloney 2004, Grimm resources, how it is used as a resource, and how it reproduces et al. 2005, McIntire and Fajardo 2009). However, too (Condit et al. 2000, Wiegand et al. 2007). For instance, the simple or imprecise analytical tools have often hindered spatial distribution of plants can influence the movements of linking the observed patterns to processes (McIntire and frugivores leading to neighbourhood-specific dispersal pat- Fajardo 2009 and references therein). terns and fruit-removal rates where isolated plants are less One of the most important methodological issues in this often visited than plants growing in clusters (Carlo and respect is the use of oversimplified null models which does Morales 2008). In turn, seed dispersers often influence the not allow the characterization of the different features of spatial distribution of plants by establishing the initial spatial patterns in enough detail for meaningful inference template on which post-dispersal processes act (e.g. seed (Schurr et al. 2004, Wiegand and Moloney 2004, Wiegand survival, germination, seedling survival, establishment; et al. 2007, McIntire and Fajardo 2009). However, spatial Fragoso 1997, Russo and Augspurger 2004). Therefore, patterns have many features that can be revealed when plant-frugivore interactions can be seen as a dynamic two- appropriate techniques are used. For example, earlier way process in which the interacting organisms (plants and applications of spatial pattern analysis in ecology have seed dispersers) mutually affect their spatial patterns at a compared the observed patterns only with random patterns. range of scales. The emerging spatial patterns, e.g. the This approach can reveal the range of scales with significant pattern of adult plants and the pattern of seed dispersal, are aggregation, but does not provide further information such therefore expected to conserve signals from the underlying as the number of clusters, the average size of clusters, or if processes, and precise spatial pattern analysis can help the pattern is likely to be a superposition of independent
545 patterns with different characteristics (Wiegand et al. 2007, consecutive fruiting seasons to address four objectives: 2009). However, clustered patterns may be the rule rather 1) to characterize the spatial distribution of adult trees, than the exception (Condit et al. 2000, Wiegand et al. 2) to characterize the spatial distribution of seeds delivered 2007) and, especially in the case of plant populations by each disperser, 3) to assess the spatial relationship dispersed by several frugivores with contrasting behaviors between the distribution of adult trees and the seed rain (e.g. scatter- and clump-dispersal; Howe 1989), seed rain generated by each disperser, and 4) to find out if P. is expected to show a superposition of patterns. bourgaeana seeds were more frequent in mammal deposi- Spatial point pattern analysis (SPPA; Diggle 2003, Illian tion sites closer to adult P. bourgaeana trees. We then et al. 2008) deals with the statistical analysis of mapped discuss in light of our pattern analysis several hypotheses on point patterns, which comprise the coordinates and addi- the processes acting in this plant-frugivore system and the tional features of ecological objects. The assumption is that consequences of our results for dispersal service resilience the objects can be approximated as points (but see Wiegand and implications for conservation. et al. 2006) and that either all points are mapped within a given study site or a random sample of all points. Second- order statistics such as the pair correlation function or Methods Ripley’s K are the summary statistics of choice for describing the characteristics of point patterns (e.g. cluster- Study system and site ing) over a range of spatial scales (Stoyan and Stoyan 1994, Wiegand and Moloney 2004, Law et al. 2009). Addition- Pyrus bourgaeana (Rosaceae) is a monoecious small tree ally, they can be applied in conjunction with realistic null (typically 3 6 m in height) distributed across the southern models to help in the identification of the underlying Iberian Peninsula and northern Morocco (Aldasoro et al. patterns (Schurr et al. 2004, Wiegand and Moloney 2004, 1996). Our focal population is located in the Don˜ana Wiegand et al. 2007, McIntire and Fajardo 2009). National Park (510 km2;3789?N, 6826?W; elevation In this study, we applied recent extensions of SPPA to 0 80 m), on the west bank of the Guadalquivir River better understand the processes that determined the spatial estuary in southwestern Spain. In the Don˜ana area, pattern of adult trees in the Iberian pear, Pyrus bourgaeana, P. bourgaeana distribution is very fragmented, with trees in southwester Spain. In our study region, P. bourgaeana occurring at low densities (generallyB1 individual ha 1) appears to be distributed in clusters (Fedriani and Delibes in several Mediterranean scrubland patches that are isolated 2009a) though no formal analyses of the spatial patterns from each other by marshes, sand dunes, or cultivations. have been undertaken for this species. The spatial structure The climate is Mediterranean sub-humid, characterized by of P. bourgaeana is likely to be the result of several non- dry, hot summers (June September) and mild, wet winters exclusive processes, including edaphic variables (Clark et al. (November February). Annual rainfall varies widely, ran- 1999), establishment beneath mother trees (Chapman and ging during the last twenty-five years from 170 to 1028 mm Chapman 1995), and the pattern of seed delivery by its seed (mean9SD 583.09221.1 mm). Though most rain dispersers (Russo and Augspurger 2004). Therefore, we also (Â80%) falls between October March, there is a marked assessed the spatial patterns of P. bourgaeana seed rain interannual seasonal variability in rainfall. For example, the generated by its dispersers, as well as its potential relation- coefficients of variation for summer and winter rainfall were ship with the local patterning of adult tree distribution. The 93.3 and 54.4%, respectively, between 1984 and 2005. main local dispersers of P. bourgaeana are the Eurasian In our study population the understory is dominated by badger Meles meles, the wild boar Sus scrofa, and the red fox Pistacia lentiscus shrubs growing singly or in small clumps Vulpes vulpes (Fedriani and Delibes 2009a). Disperser- separated by unvegetated sandy substrate or sparse Hali- specific mobility (Spiegel and Nathan 2007), habitat mium halimifolium, Ulex spp., and Chamaerops humilis preferences (Jordano and Schupp 2000), and fecal marking (Fedriani and Delibes 2009a). Scattered across the area behavior (Fragoso 1997) are likely to lead to differences in there are Quercus suber, Olea europaea var sylvestris and the pattern and scale of seed delivery, which may contribute Fraxinus angustifolia trees. The fieldwork (see below) was to the persistence of the tree population despite disturbance undertaken in a tetragonal plot of 72-ha (Â0.6 0.9 (Frost et al. 1995, Peterson et al. 1998). Thus, seed 0.8 1.0 km) where the locations of all 75 P. bourgaeana dispersers that apparently seem redundant often operate at reproductive trees were known (Fig. 1B). Most of the plot contrasting spatial scales, providing uniqueness and com- (49 ha) is occupied by Mediterranean scrubland as detailed plementariness (sensu Spiegel and Nathan 2007) to their above, whereas its southwestern side (23 ha), delimited by a services and, thus, cross-scale resilience in terms of seed fire-break, is occupied by a ‘‘dehesa’’, i.e. the habitat dispersal (Fragoso 1997, Loiselle et al. 2007). Nonetheless, resulting of a traditional form of management of the even when different dispersers operate at the same spatial Mediterranean forests, in which native trees (e.g. Q. suber scales, they may provide within-scale resilience to the and O. europaea var sylvestris) are spaced out or inserted in a dispersal service (Peterson 2000). Therefore, differentiating continuum of grasslands with no or sparse understory of among the seed rain generated by different disperser species Mediterranean scrubs (P. bourgaeana does not occur in this is important not only in assessing their potential as side of the plot). underlying mechanism of tree spatial patterns, but also in In Don˜ana, P. bourgaeana flowers during February estimating the resilience of disperser’s service. March, with each individual producing 200 450 fruits More specifically, we used spatial data of P. bourgaeana that ripen during the autumn (September November; adult trees and dispersed seeds (within fecal samples) by the Jordano 1984). Developed fruits are globose pomes three main dispersers within a 72-ha plot during two (2 3 cm diameter; Â9.5 g wet weight; Fedriani and
546 120 (B) P. bourgaeana (A) ) r ( 0.8 700
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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 1m 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 y15 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 which were regularly distributed. During each survey, an (Herrera 1987). Each fruit includes 1 5 viable seeds (46 observer walked from a starting point (which changed 91 mg each; Fedriani and Delibes 2009b) with easily among consecutive surveys by rotating them clockwise) to breakable coats. Several medium- and large-sized mammals the opposite side of the plot following a non-fixed consume the fruit of P. bourgaeana; whereas badger, fox, zigzagging trajectory and, once there, came back following and the wild boar ingest the whole fallen fruit and thus a different path to a changing location of the departure plot potentially disperse their seeds over long distances, red deer side (Fedriani and Delibes 2009a). Cervus elaphus also ingest whole fruits but act strictly as seed Each survey took about two hours and overall we predators (Fedriani and Delibes 2009a). Also, small undertook 104 different surveys (Â208 h-observers). On mammals such as the European rabbit Oryctolagus cuniculus average, each starting point was used 8.7 times. Based on and up to four species of rodents eat P. bourgaeana seeds twenty-one surveys for which distance travelled was (and some fruit pulp), but they destroy and grind into tiny measured (using a Global Position System; GPS), the pieces all ingested seeds; thus, act as seed predators minimal lengths of surveys were, on average, 2.849 (Fedriani and Delibes 2009a). Because in P. bourgaeana 0.21 km (mean91SE). Thus, overall, we walked a mini- seed germination requires that attached fruit pulp be mum of 295 km searching for mammal feces within the removed, fruit processing by mammals is an important plot (whose longest side is 1.0 km). In each survey, the service for the tree (i.e. most seeds within fallen fruits decay observer collected any fresh feces of foxes and badgers at the end of the dispersal season; Fedriani and Delibes (which were locally scarce) and up to five samples of boar unpubl.). Birds (e.g. azure-winged magpie Cyanopica (which were most abundant). A GPS-reading was attained cianea, blackcap Silvia atricapilla) mostly act as pulp- for each mammal feces, recorded into a geographic predators rather than as seed dispersers (Fedriani and information system to establish their map location (using Delibes 2009a). ArcView software). Mammal feces were identified at the species level on the basis of shape, odor, and color (Fedriani et al. 1999). For Fecal collection and seed quantification example, fox feces are cylindrical, tapered at one of the extremities, with a strong repulsive smell, and deposited Collection of fecal samples was carried out weekly during over the substrate (plants, raised spots, etc). Badger feces are the dispersal seasons (September February) of 2005 2006 deposited in small dugs (or latrines) often buried shallowly and 2006 2007. During the surveys, sampling effort was with loose substrate and, typically, with a distinctive musky distributed homogeneously across the plot. We set a total of smell. Boar droppings are usually cylinder shaped, with twelve ‘‘starting points’’, three along each side of the plot, disc-shaped sections fused together. Overall sample sizes
547 were 121, 118, and 19 for the boar, badger, and fox, Nearest neighbor statistics respectively. Also, for comparative purposes, we collected feces (n 81) delivered by the red deer, a strict predator The ‘‘shortsighted’’ accumulative distribution function G(y) (not disperser) of P. bourgaeana seeds. Feces were air dried of distances y to the nearest neighbor provides important and stored individually in paper bags. Each fecal sample was additional information about the nature of clustered thoroughly washed under running water on a sieve univariate patterns (Illian et al. 2008). It serves as an (0.50 mm mesh size) and air-dried. Seeds and other fruit independent test statistic to assess how well fitted point remains (skin, pulp, pedicels, etc) were identified using a processes describe the data (Jacquemyn et al. 2007, 2009, reference collection. Further, seeds were examined with a Wiegand et al. 2007). We calculated G(y) without edge 20 40 magnification glasses and the number intact seeds correction (Diggle 2003). This is a reasonable approxima- was recorded (for details, see Fedriani and Delibes 2009a). tion because the scales we investigate (i.e. rB100 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 Test of significance (Ripley 1981, Stoyan and Stoyan 1994, Diggle 2003, Illian The empirical test statistics were contrasted to appropriate et al. 2008) to investigate the distribution of adult trees, null models (see below), which represent a ‘‘benchmark’’ mammal feces, and dispersed seeds. To quantify the uni and point process with known structure adapted to our bivariate spatial patterns we employed the pair-correlation questions. We used a Monte Carlo approach for construc- function (Stoyan and Stoyan 1994, Illian et al. 2008), a tion of simulation envelops of a given null model and test normalized neighborhood density function, and the dis- statistic. Each of the 199 simulations of the point process tribution function of the distances to the nearest neighbor underlying the null model generates a g(r) function (or (Diggle 2003, Illian et al. 2008). another appropriate test statistic), and simulation envelopes For estimation of these summary statistics we followed with an approximate a 0.05 were calculated for the test the grid-based approach of Wiegand and Moloney (2004) statistic using its 5th highest and 5th lowest values. and used an adapted grid size of 1 1 m. This is a fine Note that we cannot interpret the simulation envelopes resolution compared with the size of our study plots as confidence intervals because we tested the null hypothesis (72 ha), but necessary to study the apparently small-scale at several scales of r simultaneously. This may cause type clustering of the trees and the feces and the accuracy of their I error (Stoyan and Stoyan 1994, Diggle 2003, Loosmore recorded coordinates. To avoid problems in estimating and Ford 2006). To test overall departure of the data from some spatial patterns due to environmental heterogeneity the null model without type I error inflation we used a within our study plot (i.e. scrubland vs ‘‘dehesa’’), we Goodness-of-Fit (GoF) test that collapses the scale-depen- conducted the corresponding point pattern analyses only in dent information contained in the test statistics into a single a rectangle (Â52 ha) centred over the homogeneous part of test statistic ui which represents the total squared deviation the study region covered by Mediterranean scrubland. 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 Pair-correlation function 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 To characterize the spatial patterns of P. bourgaeana trees is a significant departure from the null model with a 0.05 and feces, and potential associations between them, we used over the scales of interest (i.e. 0 50 m). Details can be uni- and bivariate pair-correlation functions, respectively. found in Diggle (2003), Loosmore and Ford (2006), and The univariate pair-correlation function g(r) is based on the Illian et al. (2008). 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 Null models for analysis of univariate patterns expected intensity of points at distance r of a representative point of the pattern (Wiegand and Moloney 2004, Illian For univariate patterns that did not show apparent et al. 2008), where l is the intensity of the pattern in the clustering we used a homogeneous Poisson process, which study region (i.e. number of points divided by area of is sometimes called ‘‘complete spatial randomness’’ (CSR) the study region). In practice the mean number of points as null model. To this end we assigned the points of the within a ring (r dw, r dw) with an adapted width (or pattern to random coordinates within the study plot. bandwidth) dw 2 m around the points of the pattern is Because most univariate patterns were apparently not determined and divided by the mean area (i.e. the number random but strongly clustered (e.g. Fig. 1), we fitted a of cells) of these rings that fall within the study region simple cluster process to the data. Note that this process is (Wiegand and Moloney 2004). Bivariate extensions of the phenomenological, not mechanistic, and does not provide a pair correlation function for patterns composed of type 1 direct link to the underlying processes. However, the cluster and type 2 points follow intuitively (Wiegand and Moloney process serves as ‘‘benchmark’’ processes with known 2004); in this case O12(r) l2g12(r) is the expected structure and directly interpretable parameters, which intensity of type 2 points at distance r of a representative allows us to characterize fully the key properties of type 1 point where l2 is the intensity of type 2 points. clustering. To this end we used the Thomas process
548 (Thomas 1949, Stoyan and Stoyan 1994, Seidler and wrapped as a torus, one pattern is fixed, and the other is Plotkin 2006, Jacquemyn et al. 2007, 2009, Wiegand shifted as a whole random vector (Dixon 2002, Wiegand et al. 2007, 2009, Morlon et al. 2008) which is based on a and Moloney 2004). simple stochastic construction principle; it consists of a number of randomly and independently distributed ‘‘clus- ters’’ with properties specified below. The pair-correlation Results function of the Thomas process yields: 1 exp( r 2=4s2) Distribution of adult trees g(r; s; r) 1 : r 4ps2 Adult P. bourgaeana trees showed a strong clustering, with The parameter r is the intensity of clusters in the study local densities at distances 2 7 m being ca 40 times higher region of area A (i.e. Ar is the number of clusters) and the than expected by a complete random distribution [Fig. 1A; parameter s described the cluster size. The points are note that the expectation of the pair correlation function for randomly assigned to the clusters, i.e. the number of points a random pattern is g(r) 1 and that g(r) 40 means that per cluster follows a Poisson distribution with mean m the neighborhood density at scale r is 40 times higher than l/r where l is the intensity of the pattern. Note that the under CSR]. The data at scale 0 50 m could be fitted well Thomas process also describes situations were the number with the Thomas process (rank 37), yielding Ar* 10.7 of clusters is higher than the number of points (i.e. lBr). randomly distributed clusters and a cluster size of rC : In this situation a given cluster may be empty (i.e. there is 2s 22.6 m (Fig. 1B). However, the distribution G(y)of no point assigned to the cluster) or comprise only one distances y to the nearest neighbor y indicated some point. This is a consequence of the stochastic construction departure from this benchmark process: nearest-neighbor principle underlying the Thomas process in which the distances y10 m were less frequent than expected (small definition of a cluster is not related to the number of points inset Fig. 1A; rank 199). This departure was caused by belonging to a given cluster. The probability for a given nine adult P. bourgaeana trees (12%) which had no nearest cluster to be empty yields P[n 0, m] Exp( m), and the neighbor within 40 m (grey disks in Fig. 1B); thus growing probability that a given cluster has just one point yields clearly outside the clusters. It is therefore likely that the P[n 1, m] mExp( m). Low values of m thus indicate pattern of P. bourgaeana is a superposition pattern, that the pattern is basically a random pattern but with a few comprising a clustered and a non-clustered component ‘‘paired’’ points that imprint clustering. The proportion of pattern. points occurring in clusters with only one point yields In case of an independent superposition of a random mExp( m)/[1 Exp( m)]. pattern and a pattern generated by a Thomas process the The distribution of the locations of points belonging to a estimate of the parameter r is influenced by the super- 2 given cluster, relative to the center of the cluster, is assumed position. In this case the ‘‘true’’ value yields r r*pC to be a two-dimensional normal distribution with variance (Wiegand et al. 2007) were pC is the proportion of trees 2 2 s . The cluster size rC can be defined as rC :2s includes ca belonging to the cluster component pattern (i.e. pC 87% of the points belonging to a given cluster, and the (62/71)2 0.76) and r* the initial estimate based on the approximate area covered by one cluster is AC fit of the entire (superposition) pattern. Thus, the number 2 2 p rC 4ps . Note that this definition of a cluster size is of clusters should be Ar* 10.7*0.76 8.2. Indeed, not directly related to properties of individual clusters (e.g. repeating the analysis without the nine isolated points the number of points belonging to a given cluster, or the (Fig. 1C) yields an estimate of 8.3 clusters comprising on area covered by an identifiable cluster), but based on the average m 62/8.3 7.5 trees. The distribution function stochastic construction principle of the clusters underlying G(y) of distances y to the nearest neighbor are now well in the Thomas process (Wiegand et al. 2009). accordance with the benchmark cluster process (small inset The unknown parameters r and s were determined Fig. 1C; rank 160). However note that the number of using minimum-contrast methods based on the pair- points is relatively low and that the stochastic nature of the correlation function (Stoyan and Stoyan 1994, Wiegand Thomas process prevents a further reconstruction of the et al. 2007, 2009). To test if the fit with the Thomas cluster structure. 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. Spatial patterns of mammal fecal delivery
Badger feces showed extreme clustering, with local densities Null model for analysis of association between approximately 1000 times higher than expected by a patterns completely random distribution (Fig. 2D). Nonetheless, by analyzing the distribution of the distances to the nearest To find out if there was an association between the spatial neighbor, we found that 24 feces had no nearest neighbor patterns of the trees and that of feces we contrasted the data within 15 m (small inset Fig. 2D), but 76 feces were to a null model of independence (Wiegand and Moloney clustered in a few (say five) small clusters (Fig. 2A). This 2004). A test for independence requires conservation of the resulted in extremely high values of the pair-correlation spatial structure of the individual univariate patterns, but to function at scalesB10 m (Fig. 2D). The pattern is thus a break their dependence (Dixon 2002). We achieved this by superposition of an extreme cluster pattern with a less using a toroidal shift null model where the study rectangle is clustered component. Because of this complex structure and
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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 1m and the ring width dw 3m.
the relatively low number of feces we did not conduct more (i.e. some 92 feces are distributed in clusters with only one detailed analyses for this pattern. feces), but a few feces (ca 15) are aggregated in small clusters The distribution of wild boar feces (Fig. 2B) showed a of two or tree (Fig. 2B). significant clustering up to scales r 5 (Fig. 2E). To Limited sample size prevented SPPA for the red fox (see approximate the small-scale clustering we fitted a Thomas below); however, for comparative purposes, we also assessed process to the data that reproduced the observed clustering the spatial patterns of feces delivered by the red deer, a of the data well, both for the pair correlation function (Fig. species that act strictly as predator of P. bourgaeana seeds. In 2E; rank 142) and for the nearest neighbor distribution contrast to the clustered patterns of badger and wild boar, function (small inset Fig. 2E; rank 162). The fit yielded a we found that the distribution of deer feces (Fig. 2C) cluster size of rC :2s 4 m and 353 clusters. Thus, the did not differ significantly from a random pattern (Fig. 2F; expected number of feces per cluster were m 107/353 rank 132). As a consequence of their fecal marking 0.295 (there were 107 feces in the study rectangle). Thus, behaviors (Fig. 2), the three dispersers noticeably differed the proportion of feces occurring in clusters with only one in their patterns of seed delivery (Fig. 3). Badger and point yields mExp( m)/[1 Exp( m)] 0.86 or 92 feces boar generated the highest and lowest seed aggregations, indicating that most boar feces are randomly distributed respectively (Fig. 3).
550 Red fox Vulpes vulpes 4
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Badger Meles meles 22 Figure 3. Number of P. bourgaeana seeds per square cell (50 m 20 side) within the study plot for each of the three legitimate 18 dispersers of this tree in Don˜ana. Boxes and error bars show 16 mean9SE and 95% bootstrap confidence intervals, respectively. 14 12 Spatial relationship between the distribution of adult 10 trees and the mammal-generated seed rain 8
No. of observations 6 The spatial pattern of badger feces was independent from 4 that of adult trees (Fig. 2G; rank 175); however, when 2 randomizing the badger feces not with a torus shift null 0 model that conserves the strong clustering of feces, but 25 75 125 175 225 275 325 using a random pattern (CSR) as null model, we found significant and positive association (results not shown), Wild boar Sus scrofa which highlight the importance of accounting for 35 the specific distribution of feces in assessing second 30 Feces with seeds order patterns. Conversely, the pattern of wild boar feces Feces without seeds was significantly and positively associated with adult 25 P. bourgaeana trees, especially at distances 4 9 m from the trees (Fig. 2H; rank 199). The pattern of red deer feces 20 was independent from the pattern of adult trees (Fig. 2I; 15 rank 57). 10 No. of observations
5 Distance between dispersers deposition sites with and without P. bourgaeana seeds and the nearest 0 25 75 125 175 225 275 adult tree 50 100 150 200 250 300 Distance to the nearest neigbour Finally we had a closer look at whether the distribution of P. bourgaeana seeds within deposition sites of the dispersers Figure 4. Frequency distribution of distances between fecal was dependent on the distance to the nearest P. bourgaeana deposition sites (with and without seeds) and the nearest tree. Because the radius of statistically significant tree P. bourgaeana conspecific for the three legitimate seed dispersers clusters was Â25 m, whereas clusters were generally in Don˜ana. 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 fox, the feces with P. bourgaeana seeds were significantly movements within the typical cluster radius), long distance closer to adult trees than feces without seeds (Fisher exact (100 m; i.e. seed movements among different tree test, p 0.002), but because of the small sample size for this clusters), and intermediate (i.e. 25 m and 5100 m). species, SPPA were not performed for the fox. Conversely, Though all three legitimate dispersers (fox, badger, and for the badger and the boar, feces with and without seeds boar) delivered some feces up to three hundred meters away were mostly delivered at intermediate distances from the from the nearest P. bourgaeana tree (Fig. 4), they differed nearest tree (25 m and 5100 m; Fig. 4) and no clearly in the frequencies of distances to the nearest tree for significant differences were found in their respective both feces with (x2 13.78, DF 4, pB0.010) and distribution of distances (Fisher exact test, p0.129). without seeds (x2 30.57, DF 4, pB0.001). For the Therefore, for these two species, all fecal deposition sites
551 appear to be potential dispersal sites. Though these results Second, dispersal limitation sometimes leads to seedling indicate that most badger and boar-dispersed seeds are establishment beneath mother trees, resulting in an delivered at intermediate distances away from the nearest aggregated patterning (Chapman and Chapman 1995, adult tree, this metric does not fully characterize the spatial Bustamante and Simonetti 2000, Cordeiro and Howe pattern of seed delivery in relation to the whole conspecific 2003). This is a likely possibility since a fraction of the fruit neighborhood, which is an important point due to the fallen beneath adult trees are not taken by mammals. Many strong aggregated pattern of P. bourgaeana. However, our seedlings emerge beneath mother trees every season and, data do not allow for a more refined analysis (i.e. trivariate eventually, some of those seedlings get established (Fedriani random labeling; De la Cruz et al. 2008, Biganzoli et al. et al. unpubl.). Finally, rhizome sprouting in response to 2009) to find out if the presence of seeds in feces depends disturbance (Barnes et al. 1998, Bond and Midgley 2001) on the distance to adult P. bourgaeana trees. could result in clustering if different sprouts emerge from a single individual and eventually produce fruit. In Don˜ana, P. bourgaeana experience heavy browsing by red deer and Discussion sprouts of a range of sizes emerge beneath some trees; thus, the possibility that those shoots grow and eventually reach the SPPA allowed us to characterize in detail the spatial patterns adult size leading to tree clusters needs to be evaluated. of adult P. bourgaeana trees, dispersed seeds, and the On the other hand, the aggregated patterning of adult relationship between trees and seeds at a range of spatial P. bourgaeana trees might have important consequences for scales. We used the Thomas cluster process to characterise their interaction with fruit consumers (Blendinger et al. important properties of our empirical data with much 2008, Carlo and Morales 2008). Both simulation models higher precision than, for example, a null model of and empirical evidence suggest that as fruiting plants complete spatial randomness. The key properties (average become aggregated, inequality among individuals in fruit- number of points per cluster, cluster size, number of removal rates increases and seed dispersal distance decreases and, in turn, both of these processes could help create and clusters, and assessment of potential superposition patterns) maintain plant aggregation (Carlo and Morales 2008). Our fully characterize the features of the observed spatial data on fruit removal for P. bourgaeana during the autumn patterns. In addition, to evaluate the independence between of 2005 (Fedriani and Delibes 2009a) suggest that isolated the spatial pattern of adult trees and feces of different individuals (i.e. those with no neighbors within 25 m) are dispersers, we preserved the spatial structure of the visited by legitimate dispersers less frequently (0.5090.29 individual univariate patterns, but broke their dependence visits per night, n 4) than individuals located within tree by using a toroidal shift null model (Wiegand and Moloney clusters (1.0890.19 visits per night, n 12). Furthermore, 2004). All these features enhance the accuracy of our results, nearest neighbor distances between adult P. bourgaeana and improving our understanding of the distribution of adult badger, fox, and boar deposition sites were always lower for trees and the potential underlying processes. 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, Distribution of P. bourgaeana in Don˜ana our preliminary data support the hypothesis that the distribution of P. bourgaeana in Don˜ana interacts with Pyrus bourgaeana trees in our study plot were clearly the foraging behavior of its mammalian seed dispersers, aggregated, with a critical spatial scale of ca 25 m leading to neighbourhood-specific dispersal patterns and (Fig. 1). A similar aggregated pattern appears to occur for fruit-removal rates (Aukema and Martinez del Rı´o 2002, neighboring trees located outside of our study plot Carlo and Morales 2008, Levey et al. 2008). (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 Spatial patterns of mammal feces and seed delivery habitat factors (Fedriani et al. unpubl.). Therefore, the strong aggregated patterning found is likely to be the result As expected based on disperser’s marking behaviors, the of one or several other non-exclusive processes. First, by spatial patterns of fecal deposition varied widely among creating the initial template on which post-dispersal mammal species at a range of spatial scales. Badger feces and processes act, seed dispersers can be responsible, at least dispersed seeds were clearly clustered at small spatial scales, partially, for P. bourgaena aggregation (Fragoso 1997, which is consistent with their intensive usage of shared Wenny 2000, Russo and Augspurger 2004). Nonetheless, defecation sites (latrines) which are preferentially placed in the sequence of concatenated post-dispersal events (seed the vicinity of main setts and along their territory germination, seedling survival, establishment) could erase boundaries (Kruuk 1989, Revilla and Palomares 2002). the initial patterns in seed distribution imposed by Boar feces were only lightly clustered, which may be related P. bourgaena dispersers (Jordano and Herrera 1995, Schupp with the small boar group size in Don˜ana (Ferna´ndez-Llario and Fuentes 1995, Rey and Alca´ntara 2000; but see Garcı´a et al. 1996). The few fox feces found were located on et al. 2005). Thus, our ongoing research examining conspicuous sites (plants, raised spots) scattered across the emergence, survival, and establishment of seedlings from plot, which is a typical pattern in this species (Lloyd 1980). mammal-dispersed and non-dispersed seeds is required to Furthermore, badger and boar differed in their fecal fully assess the role of mammals on P. bourgaeana deposition behavior with respect to P. bourgaeana neighbor- distribution. hoods. If the initial pattern of seed delivery persists beyond
552 subsequent ontogenic stages, boars likely contribute to the factors (Schupps 2007), and P. bourgaeana seed dispersers maintenance and enlargement of existing tree clusters. differ in their microsites of seed deposition. Whereas Badgers, however, delivered feces and seeds away from the Pistacia shrubs were the most frequent microhabitat of tree neighborhoods; thus, they are more likely to create new deposition for badgers (43% of cases; n 140), for foxes tree clusters. Conversely, the pattern of fecal delivery by the and boars the most frequent microhabitat was open red deer, a seed predator of P. bourgaeana seeds, was neither interspaces among Pistacia shrubs (82% [n 22] and clustered nor associated with adult trees. Interestingly, the 45% [n 112], respectively; Fedriani and Delibes 2009a). spatial patterns of both badger feces and adult trees were a Given the unpredictability of the Mediterranean weather superposition of an extremely clustered pattern, in con- (Thompson 2005), which diversifies the environmental junction with a less clustered component, which further conditions at seed deposition sites, the assemblage of suggests a role of badger dispersal on tree distribution. P. bourgaeana dispersers likely spreads the risk (sensu Furthermore, badgers were the only dispersers that delivered Cohen 1966) of countering conditions particularly unsui- seeds in the ‘‘dehesa’’, a habitat where at present table for survival, resulting in a complementary dispersal P. bourgaeana does not occur. Badgers frequently use service that enhances recruitment. However, the loss of any dehesa habitat (Fedriani et al. 1999) and may play an one dispersal agent could lead to a more stereotyped seed important role in colonization of new areas (Nathan and rain (i.e. lesser variability in the microhabitat of deposition), Muller-Landau 2000). Therefore, even if badgers and boars which, under some circumstances, could lessen recruitment. were redundant in other aspects of the dispersal process (see In Don˜ana, fox, badger, and boar disperse at least, ten, below) they show important disparities in seed delivery as a nine, and six species of fleshy fruits (Fedriani and Delibes result of their spatial and fecal marking behaviors. Thus, 2008), respectively, and thus some of our results are also they provide complementary dispersal services (Spiegel and relevant to the overall local community of fleshy-fruited Nathan 2007), which may confer resilience to P. bour- shrubs. As is generally true (Janson 1983), large-fruited gaeana dispersal (Peterson et al. 1998). Nonetheless, these species are dispersed more frequently by mammals inferences should be taken with caution since our analyses (P. bourgaena, C. humilis, Juniperus oxycedrus subsp. ‘‘only’’ evaluate seeds and adult trees. To interpret fully the macrocarpa) than small-fruited species (Rubus ulmifolius, influence of seed dispersal on plant demography other Juniperus phoenicea, Daphe gnidium). For instance, J. stages (seedling, sapling) should be taken into account. 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 Dispersal service resilience and conservation (Mun˜oz-Reinoso 2003). In Don˜ana, its only known implications dispersers are the red fox, the badger, and the wild boar (Mun˜oz-Reinoso 2003, Fedriani et al. unpubl.) and thus In humanized landscapes, such as the Don˜ana area, it is these mammals are of paramount importance for the important to account for the susceptibility of frugivores to dispersal of this endangered species. Therefore, preserving anthropogenic disturbance when assessing the resilience of a diversity of highly mobile mammalian seed dispersers is the dispersal service (Wright et al. 2000). Intense human central for the resilience of the Don˜ana ecosystem (Lunberg activities in Don˜ana have lead to increases of red fox and and Moberg 2003). wild boar populations during the last four decades (Rau In conclusion, SPPA is a powerful tool that allowed us to et al. 1985, Gorta´zar et al. 2008), whereas hunting has characterize in detail the distribution of P. bourgaeana trees decreased the population of badgers (Revilla et al. 2001) and the mammal-generated seed rain in Don˜ana. Pyrus and led to extinction of other potential dispersers such as bourgaeana dispersers delivered ingested seeds in contrasting wolves Canis lupus (Valverde 1967) and, long ago, bears spatial patterns, providing complementary dispersal and Ursus arctos (Swenson et al. 2000). Thus, dispersers more suggesting lack of redundancy and expendability (sensu tolerant to human activities (fox, boar) may replace to Kareiva et al. 2003) of any one disperser species. The some extent susceptible species (wolf, badger) when and singularities of the dispersal services provided by each where levels of humanization decrease their populations, disperser likely affords a degree of resilience to their overall which could provide resilience to P. bourgaeana dispersal service (Peterson et al. 1998) in an area under high levels of (Wright et al. 2000). For instance, because foxes and boars human disturbance. The strongly aggregated pattern of trees are highly mobile and P. bourgaeana distribution in Don˜ana might be an outcome of seed dispersal and, in turn, likely is very fragmented, with trees occurring at low densities in has a strong effect on disperser behavior and the resulting several isolated patches, they can potentially replace human- patterns of seed delivery. The interaction between sensitive species (e.g. badgers) in terms of (re)colonization at P. bourgaeana and its mammalian seed dispersers emerges vacant patches, connecting different subpopulations, and as a dynamic two-way process in which the interacting enhancing the genetic flux among them (Nathan and organisms (trees and dispersers) mutually affect their spatial Muller-Landau 2000, Levin et al. 2003). patterns at a range of scales. 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 Acknowledgements We are indebted to Gemma Calvo, Mo´nica main dispersers (Chapman and Russo 2006). For instance, Va´z, Magdalena Zywiec and innumerable volunteers for their contrasting microhabitats often lead to varying seed and enthusiastic field and lab assistance. Gene Schupp, Kevin Burns, seedling survival (Rey and Alca´ntara 2000, Traveset et al. and two anonymous reviewers provided useful comments that 2003), depending on a myriad of variable biotic and abiotic improved the manuscript. The Spanish Ministerio de Medio
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