The geographic mosaic in predispersal interactions and selection on Helleborus foetidus (Ranunculaceae)
P. J. REY, * C . M . H ER RERA, t J. GU ITI A´ N, X. CERD A´ , t A. M. S A´ NCHEZ-LAFUENTE,§ M. MEDRANO t & J . L . G A R R I D O
*Departamento de Biologı´a Animal, Biologı´a Vegetal y Ecologı´a, A´ rea de Ecologı´a, Universidad de Jae´n, Jae´n, Spain
tEstacio´ n Biolo´ gica de Don˜ ana, Consejo Superior de Investigaciones Cientı´ficas, Avenida de Marı´a Luisa s/n, Sevilla, Spain
Departamento de Bota´ nica, Universidad de Santiago, Santiago de Compostela, Spain
§Departamento de Biologı´a Vegetal y Ecologı´a, Universidad de Sevilla, Sevilla, Spain
differential phenotypic selection;
herbivores; path analysis; plant–animal interactions; pollinators;
We examine the hierarchical geographic structure of the interaction between a plant, Helleborus foetidus, and its floral herbivores and pollinators (interactors). Six populations from three distant regions of the Iberian Peninsula were used to examine intra- and inter-regional variation in plant traits, interactors and plant fecundity, and to compare, through selection gradient and path analyses, which traits were under selection, and which interactors were responsible for differential selection. Geographic and temporal congruency in interactor- mediated selection was further tested using a recent analytical approach based on multi-group comparison in Structural Equation Models. Most plant traits, interactors and fecundity differed among regions but not between populations. Similarly, the identity of the traits under selection, the selection gradients (strength and/or the direction of the selection) and the path coefficients (identifying the ecological basis for selection) varied inter- but not intra- regionally. Results show a selection mosaic at the broad scale and, for some traits, a link of differential selection to trait differentiation.
Understanding how the evolution of plant traits is influenced by interactions with animals is a major goal in the study of the evolutionary ecology of plant–animal interactions. Plants and animals interact in many differ- ent ways (see Herrera & Pellmyr, 2002, for a recent review) involving antagonisms (most frequently herbiv- ory, granivory and parasitism) and mutualisms (pollina- tion, seed dispersal and active defence). Most plant populations and individuals participate in several con- current interactions, but research programmes on plant– animal interactions have been largely devoted to just one kind (pollination, herbivory, granivory or seed dispersal). During the last decade, however, it has been explicitly recognized that the multiple interactions experienced by plants can lead them through ecological and
Correspondence: Pedro J. Rey, Departamento de Biologı´a Animal, Biologı´a Vegetal y Ecologı´a, A´ rea de Ecologı´a, Universidad de Jae´ n, E-23071 Jae´ n, Spain.
Tel.: +34 953 212145; fax: +34 953 212141;
evolutionary pathways that are not easily understood if the effects of each interaction are considered in isolation (Strauss & Armbruster, 1997; Go´ mez & Zamora, 2000; Herrera, 2000; Herrera et al., 2002a; Go´ mez, 2003; Strauss & Irwin, 2004). For example, some plant reproductive traits may have evolved as a compromise between conflicting selective pressures exerted by polli- nators and herbivores (Brody & Mitchell, 1997; Strauss,
1997; Galen & Cuba, 2001; Irwin et al., 2004). Several
mechanisms, including the cancellation of pollinator selection by overwhelming herbivore pressure (Quesada et al., 1995; Go´ mez & Zamora, 2000; Herrera, 2000; Mothershead & Marquis, 2000) and correlated evolution of traits enhancing pollination and herbivore avoidance (through pleiotropy, developmental associations or cor- relational selection, see Herrera et al., 2002a and refer- ences therein), may occur in plant-pollinator-herbivore systems, both at micro- and macroevolutionary levels.
However, the evolution of species interactions can only be fully understood by considering their variation in space and time (Thompson & Pellmyr, 1992; Thompson, 1994; Travis, 1996; Brody, 1997; Go´ mez & Zamora, 2000)
because this variation can limit species’ responses to selection. In fact, recent advances in coevolutionary theory have arisen from the recognition that the form and incidence of an interaction often vary across geo- graphic landscapes (Thompson & Pellmyr, 1992; Thompson, 1999: Benkman et al., 2001), forming a selec- tion mosaic. This geographic variation forms the basis of the geographic mosaic theory of coevolution (Thompson,
1994, 1997, 1999), and examples now exist that document selection mosaics in plant–animal interactions (Thompson, 1999: Benkman, 1999; Go´ mez & Zamora,
2000; Benkman et al., 2001; Parchman & Benkman, 2002;
Stinchcombe & Rausher, 2002; Zangerl & Berenbaum,
2003). More specifically, the selection mosaic in a specific plant–animal interaction may in part be generated by a concurrent interaction of one of the partners with a third species, which also varies geographically (see Thompson & Pellmyr, 1992; Go´ mez & Zamora, 2000; Benkman et al.,
2001; Stinchcombe & Rausher, 2002). For example, Benkman and co-workers (Benkman, 1999; Benkman et al., 2001; Parchman & Benkman, 2002) have shown that coevolution between crossbills and both lodgepole pine and black spruce (a seed predator–plant interaction) is altered in some regions by red squirrels (Tamiasciurus hudsonicus), another seed consumer that outcompetes crossbills. Similarly, in populations of the crucifer Hor- mathophylla spinosa subject to intense herbivory by ungu- lates, selection by pollinators is annulled or reduced, which leads to a selection mosaic in the interaction between the plant and its pollinators as a consequence of variation in the abundance of ungulates (Go´ mez & Zamora, 2000). Other aspects of the geographic structure of the plant–animal interactions are, however, poorly known. Thompson (2002) remarked that the hierarchical geographic structure of the species interactions (for example, the shape and scale of the geographic mosaic) have important consequences for the evolutionary dynamics of the interactions. However, we do not know of any attempt to explore such a structure in plant- pollinator-herbivore systems.
Here, we incorporate these new perspectives to the study of the effect of predispersal interactions with animals (pollination, herbivory by insects, and seed predation by rodents) on the maternal fecundity of the perennial herb Helleborus foetidus (Ranunculaceae), using the approach suggested by Brody (1997), i.e. using multiple study sites and -years to examine the magni- tude, direction and variability of the relationships between a plant and the different sets of animals with which it interacts (termed ‘interactors’ hereafter). To this end, we adopt a phenotypic selection approach at three widely separated regions spread over most of the geographic range of the species in the Iberian Peninsula. We investigated two populations in each region to examine intra- and inter-regional patterns and to assess the hierarchical geographic structure of these plant– animal interactions.
Phenotypic selection is commonly examined by stand- ard selection gradient analysis (Lande & Arnold, 1983). Standard tests of differences in direct, adaptive, selection involve interaction terms of trait · population (or region) for geographic comparisons, or trait · year for temporal comparisons (Dudley, 1996; Caruso, 2000). When dif- ferences in selection are found, path analysis is an excellent way of determining the ecological reasons for the differences (Conner et al., 1996; Conner, 1996). Here, we follow this scheme. First, we look for intra-regional (between populations), geographic (between regions) and temporal differences in phenotypic selection using selection gradient analysis, which will allow us to identify which plant traits are actually under selection and to asses whether differential selection (i.e. variation in the magnitude and direction of the selection) is happening at any of these levels. Then, we use path analysis to identify which interactors are responsible for such selection gradients. The study of selection mosaics caused by herbivores and pollinators in plant fecundity has recently been approached through Structural Equa- tion Models (SEM hereafter) and path analysis using nested models (Go´ mez & Zamora, 2000). Here, we propose a relatively novel analytical approach using multigroup comparisons of SEMs (Bentler, 1989; see Grace, 2003 for its use in evolutionary biology) that properly allows comparisons, among regions, of each causal link (i.e. paths) between plant traits, interactors and plant fecundity, while keeping the same SEM structure.
Three major questions will be addressed: (i) Does the geographic scale of differentiation in plant traits match the geographic scale of variation in the interaction between the plant and its herbivores and pollinators? (ii) Is there differential selection at regional or geographic level and, if this is the case, are differential selection and trait differentiation matched up? (iii) Does the interac- tion of H. foetidus with its animal interactors exhibit a selection mosaic across a broad geographic scale? We consider a selection mosaic to be caused by interactors when, first, differential selection occurs at some spatial or temporal level and, secondly, differential selection is a consequence of differences, at the same level, in the animal/s responsible for trait selection.
Plant natural history
Helleborus foetidus L. (Ranunculaceae) is a rhizomatous perennial herb distributed in western and south-western Europe (Werner & Ebel, 1994). In the Iberian Peninsula it typically occurs in clearings, forest edges, and the understory of montane forests. Each plant is formed by one or several ramets that develop a terminal inflores- cence after several seasons of vegetative growth. Each inflorescence produces 25–100 flowers over its
1.5–2.0 months flowering period, but these open gradu- ally and are extremely long-lived (up to 20 days). Details
of the floral biology and seed dispersal of the species in the Iberian Peninsula can be found in Herrera et al. (2001, 2002a,b) and Garrido et al. (2002). Flowering takes place in winter (January to March) and the species is mainly bumblebee-pollinated. Flowers are hermaphro- ditic, protogynous and self-compatible, although sub- stantial fruit set requires the participation of pollinators. Flowers are apocarpous, with one to five carpels (most commonly two to three) and each carpel contains 8–15 elaiosome-bearing seeds. Fruit maturation and seed shedding take place in June to early July. Flowers and, especially, developing fruits are frequently consumed by rodents and lepidopteran larvae (Noctua spp. and Trigon- ophora flammea, Noctuidae). Aphids (most frequently the monophagous Macrosiphum helebori, but also sometimes the polyphagous Brachycaudus cardui) also feed on flowers and fruits and, although their occurrence is rather unpredictable in time and space, they may become serious damaging agents in some plants, populations or years. Cryptic fruit or seed damage have never been detected.
This study was conducted during 1998 and 1999 in three widely separated regions, Sierra del Caurel (42°36¢N,
7°19¢W), Sierra de Cazorla (37°54¢N, 2°55¢ W) and Sierra
de Ma´ gina (37°44¢N, 3°15¢W), covering a considerable part of the geographic range of Helleborus in the Iberian Peninsula. Two of these regions, Sierra de Cazorla and Sierra de Ma´ gina, are south-eastern mountain systems,
100 km apart from each other. The third region was Sierra de Caurel, more than 600 km north-west of the other two localities. Two Helleborus populations, located a few kilometres apart from each other, were considered within each region. Data on vegetation type, elevation and climate at each region can be found in Herrera et al. (2002a,b) and Garrido et al. (2002).
Plant traits, interactors’ incidence and maternal fecundity
In each study year, 30–34 flowering plants were hap- hazardly chosen in each population. Total number of flowers was estimated as the total number of flower buds present at the beginning of the flowering season. Sim- ultaneously, we counted the number of ramets, and measured ramet (inflorescence + supporting stem) length. Plant size was estimated as the product of number of ramets by mean ramet length. The number of carpels per flower and flower size (estimated as the geometric mean of flower length and width) were also measured from five flowers per plant after anthesis. Data on flower traits for Cazorla in 1999 are not available.
We checked each plant on three to five occasions throughout the flowering-to-fruiting period in order to determine the effect of pollinator visitation and herbivore
damage on the final number of fruits releasing seeds from each plant. Pollinator visitation rate (pollinator service, hereafter) was evaluated as the probability of a plant being visited by pollinators, based on visitation data obtained during 20–30 3-min censuses per plant (see Herrera et al., 2001 for details). Three major types of herbivory were recorded: damage by rodents, which cut off flowers or fruits by the pedicels; damage by lepidop- teran larvae, which externally fed on flowers or, more frequently, carpels; and presence of aphids, which occur on both flowers and maturing carpels. Total fruit losses caused by rodents and lepidoptera were assessed as the cumulative number of flowers or fruits damaged by these herbivores recorded on different occasions. As direct effects of aphids on plants were not so evident, their incidence was evaluated as the maximum number of flowers infested in successive inspections. Fruit losses caused by abortion or abiotic damage were also recorded. At the end of the fruiting period, in late June and early July, and just before follicle dehiscence, we estimated for each plant the total number of fruits releasing seeds by counting the number of mature and intact fruits. These figures will be used here as estimates of maternal fecundity. Number of intact fruits per plant is strongly correlated with total final number of seeds (r-values ranging from 0.97 in Cazorla to 0.99 in Ma´ gina, P < 0.001).
To estimate variance components (between regions, between populations within regions and between plants within populations) in plant traits, interactors’ incidence and fecundity, we considered both region and population nested within region as random effects, as required for variance partitioning, and analyses were conducted with the MIXED procedure of SAS (SAS Institute, 1999). However, provided that only three regions were chosen for study, it is difficult to assume that they represent an adequate random sample of all regions in which the species appears. Therefore, to test for differences between regions and populations in the variables considered, we treated region as a fixed effect and conducted generalized linear mixed models. Error distributions and link func- tions were chosen according to the nature of data (Poisson error and log link function with variables involving counts, binomial error and logit link function with proportions or dichotomous variables, and normal error and identity with continuous variables). Analyses were conducted with SAS macro GLIMMIX (Littell et al.,
Study of phenotypic selection
Phenotypic selection at each region and year was examined by Lande-Arnold selection gradient models (Lande & Arnold, 1983). Multiple regressions for these models were conducted with relative fitness (estimated by dividing the maternal fecundity of each individual by the population mean) and standardized variables
(standardization of plant traits to mean 0 and variance 1). We obtained 95% confidence limits for selection coeffi- cients by bootstrapping (bias-corrected percentile limit estimates; Manly, 1997). The coefficients were consid- ered significant if their 95% confidence interval did not include zero. Regressions were conducted with SAS REG procedure and the bootstrap estimates with the SAS macro JACKBOOT (SAS Institute, 1999). Previous ana- lyses including nonlinear terms, show that quadratic partial regression coefficients were not significant at any region or year (data not shown), suggesting that, when present, selection on each particular trait was predomin- antly directional and linear. Similarly, bivariate terms for correlational selection were not significant in most cases, the only exception being Caurel in 1999, where a negative correlational gradient (b ¼ )0.15, P < 0.01) appeared for the interaction between plant size and number of flowers. Consequently, we consider here only direct linear selection components.
We looked at differential selection between popula- tions within region, between regions, and between years within region, in the traits being selected. In each case, we conducted an A N C O V A analysis including, as ade- quate, population · trait, region · trait or year · trait interactions. Traits were transformed as appropriate for normality in the A N C O V A analyses.
A causal model of the phenotypic selection by interactors To explain the ecological basis of the phenotypic selection found at each region and year, an overall SEM was built hypothesizing the causal relationships between three groups of variables: maternal fecundity (target variable), interactors’ incidence (intermediate variables directly influencing plant fecundity), and plant traits (independ- ent variables) (see Mitchell, 1992, 1993; Shipley, 1997; Go´ mez & Zamora, 2000 for similar approaches; see also Scheiner et al., 2000, for a recent review of the use of path analysis to measure natural selection). As all plant traits considered here might influence the incidence of each interactor on plant fitness, paths from each trait to each interactor were allowed in the model. We found each interactor to significantly affect relative fitness in some region or year (data not shown), thus paths from each interactor to maternal fecundity were included. Finally, as Helleborus is self-compatible and autonomous self-pollination occurs at all study regions (Herrera et al., 2001), we also included the number of flowers as a predictor of the maternal fecundity. We could not build the SEM for Cazorla in 1999 because of the absence of information on floral traits. A previous inspection of the correlation matrices showed poor correlations among interactors. Only three of 30 corre- lations (six from each path diagram) were significant: rodent damage was positively correlated with pollinator service in Cazorla in 1998 and Ma´ gina in 1999 (r ¼ 0.26, P < 0.05, n ¼ 62; r ¼ 0.29, P < 0.05, n ¼ 64 respect- ively), and aphid damage was positively correlated with
Lepidoptera damage in Cazorla in 1998 (r ¼ 0.43, P < 0.001). Entering paths for correlations between interactors only improved the model fit in Cazorla in
1998, while in the other regions the model became overspecified. By these reasons, we included such paths only in Cazorla.
The adequacy of the hypothesis expressed in the SEM model is obtained from a goodness-of-fit test comparing the observed covariance matrix with that expected if the model were true (Mitchell, 1994; Shipley, 1997). The SEM was analysed with SEPATH module (StatSoft,
2000). The variables were transformed for linearity and multinormality (log transformed or square root trans- formed as appropriate, Zar, 1999), although they some- times remained non-normal after transformation. We used Generalized Least Squares (GLS) as discrepancy function because GLS is preferred to maximum likeli- hood when multivariate normality is not met (see SAS/ STAT User’s guide, SAS Institute, 1990). In any case, we stress that the adequacy of the hypothesis of our causal model is not, in fact, a major issue in this paper. Instead, our interest is to build an overall model accounting for all the relevant relationships possibly occurring in any population, so that such relationships can be readily compared among populations, regions and years. The analytical method for such comparison is described below.
Spatial and temporal congruency in the causal model
An objective of this study was to evaluate the extent of spatial and temporal congruency in the constellation of selective pressures potentially exerted by animal inter- actors on plant traits. This implied comparing SEMs structure and path coefficients between years, regions and populations within region. The few studies that have so far assessed the spatio-temporal congruency of select- ive pressures by means of SEM have used nested models (Mitchell, 1994; Go´ mez & Zamora, 2000; Sa´ nchez-Lafu- ente, 2002). This approach looks for the most parsimo- nious model adequately explaining the causal relationships among variables. However, it does not explicitly compare the magnitude and direction of a path coefficient between population or years, a goal that can be accomplished by means of multigroup analysis of SEMs (Bentler, 1989; Grace, 2003; see Bishop & Schemske, 1998, for an application to plant-pollinator systems). A multigroup analysis allows to ask whether sets of parameters in the model differ between groups (Bentler, 1989). Multigroup analysis is carried out by imposing cross-group constraints on the path coefficients, and simultaneously fitting the model to the data from each group. The procedure is similar to fitting the model to a single group, except that the constrained paths must have the same coefficient in all groups (i.e. paths coefficients of interest are constrained to be equal in the compared groups). We first evaluated the most restrictive hypothesis of equality of all path coefficients,
relationships among plant traits, interactors and plant fecundity. If the chi-square value of the goodness of fit test shows significant departure of fit (i.e. P < 0.05), then the hypothesis of total equality can be rejected and it is pertinent to compare the path coefficients of different groups to identify the origin of overall between-groups heterogeneity. Multiple equality constraints containing n > 2 parameters or path coefficients are tested in n successive steps, each assuming the release of one of the equality-constrained parameters. Those constraints for which the relaxation of the equality assumption causes a significant decrease in the chi-square value indicate significantly different path coefficients (Bentler, 1989, see also SAS/STAT User’s Guide, SAS Institute, 1990). These multi-group analyses were conducted with the SEPATH module (StatSoft, 2000).
Geographical variation in plant traits, interactors’
incidence and fecundity
Among plants (error) Among populations Among regions
produced fewer carpels than in Caurel or Ma´ gina and, at least in 1998, flowers were larger in Ma´ gina than in the