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Population Biology |
Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912 USA
Received for publication November 5, 2002. Accepted for publication February 28, 2003.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: achene • allometry • Asteraceae • germination • Liatris • morphological constraints • New England • plant conservation • propagule mass • regional differention • wind dispersal
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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), although this may not be true in all cases (e.g., Thompson et al., 1999
). In a rare composite species, Centaurea corymbosa, loss of dispersal capability has resulted in an inability of propagules to reach nearby suitable habitat areas, increasing the probability of extinction (Colas et al., 1997
). Dispersal may be particularly crucial for rare species that are adapted to disturbance due to their dependence on dispersal for persistence (Hodgson, 1991
; Pavlovic, 1994
; Menges, 1995
). Additionally, for species that have metapopulation dynamics, the maintenance of dispersal ability assumes a special importance (Kalisz et al., 1997
).
In addition to escape from suboptimal habitat patches, dispersal can have other evolutionary advantages, such as reduction of competition between close relatives (Dieckmann et al., 1999
), avoidance of potential costs of inbreeding (Dieckmann et al., 1999
), avoidance of disproportionate propagule and seedling mortality near the parent plant (Howe and Smallwood, 1982
), and location of microhabitats suitable for establishment and growth (Howe and Smallwood, 1982
). However, high dispersal capability may also come with important costs, especially for wind-dispersed species. Investments in dispersal morphology can divert resources from other functions in the maternal plant (Dieckmann et al., 1999
), and offspring with high capability for dispersal may have poor germination success (e.g., Morse and Schmitt, 1985
; Strykstra et al., 1998
). Assessment of differences in dispersal capability among populations and species of rare plants may thus enable identification of the relatively more important threats for each (e.g., poor colonization ability vs. poor germination and establishment).
Here we investigate the dispersal biology of Liatris scariosa var. novae-angliae, northern blazing star, a rare New England perennial variety. This taxon has disappeared from 69% of its former occurrences and is continuing to decline (Kane and Schmitt, 2001
; Vickery, 2002
), predominantly as a result of the decline of its preferred habitat, New England's scarce sandplain grassland ecosystem (Vickery, 2002
). Dispersal biology remains a crucial avenue for study in this taxon for several reasons. First, northern blazing star exists in both island and mainland habitats. A previous study (Cody and Overton, 1996
) has shown that some composite species can rapidly lose dispersal capability as a result of island habitation. In that study, populations on islands were closer to open water and were hypothesized to lose dispersal ability to keep propagules from dispersing into the water. Potentially, then, island populations of northern blazing star may have significantly reduced dispersal capability relative to their mainland counterparts, suggesting the need for different management strategies for island and mainland regions. This potential loss of dispersal capability could also further imperil island populations of northern blazing star, because Frankham (1997)
has argued that genetic adaptations to island environments may contribute to higher extinction rates of island populations. In addition, the taxon's adaptation to disturbance and possible metapopulation structure (Kane and Schmitt, 2001
; E. Steinauer, Massachusetts Audubon Society, personal communication) suggest that dispersal research could make a valuable contribution to management strategies.
Dispersal capability of wind-dispersed propagules can often be successfully predicted from their morphological characteristics (e.g., Sheldon and Burrows, 1973
; Morse and Schmitt, 1985
; Matlack, 1987
; Cody and Overton, 1996
). This method is particularly attractive because it also permits the comparison of dispersal-related morphologies across populations and closely related species. In addition, a predictive model, once developed, can be used for rapid assessment of additional populations and/or time points. Such models also make general contributions to the as-yet inconclusive literature on dispersal modeling methods for wind-dispersed propagules (e.g., Matlack, 1987
; Okubo and Levin, 1989
; Andersen, 1991
; Niklas, 1992, 1994
; Murren and Ellison, 1998
; Bullock and Clarke, 2000
).
We address the following questions for northern blazing star. First, which propagule morphological traits predict dispersal capability? Second, do propagule dispersal traits differ significantly among maternal families, populations, and/or groups of populations from island, coastal, and inland regions? Finally, is there a trade-off between dispersal capability and germination success in this species, and, if so, does this trade-off differ among populations?
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). Of 214 reported historical and extant occurrences of this plant, 82 may have extant status today (Kane and Schmitt, 2001
). A map of confirmed extant occurrences of this taxon was recently presented in Vickery (2002)
. Current evidence supports the classification of the taxon as a variety of Liatris scariosa (P. Somers, Massachusetts Department of Environmental Management, personal communication). Analysis of chloroplast marker DNA showed no evidence of differentiation from Indiana L. scariosa var. nieuwlandii populations (Mason-Gamer, 1998
), while RAPD markers placed Indiana and Long Island populations in a separate clade from New England L. scariosa populations (Kesseli et al., 1998
). Taxonomic synonyms include L. borealis and L. novae-angliae (Kane and Schmitt, 2001
).
Northern blazing star is an herbaceous, iteroparous perennial that typically overwinters as a corm, emerges in May or June, flowers in August and September, and sets seed in late September or October. Reproductive plants produce one to several flowering stalks 0.25–1 m high (Fernald, 1950
). Three to 30 rose-purple flowering heads are typically produced during a growing season, each of which may contain 35–60 flowers (Fernald, 1950
). Pollination is mediated by a variety of insects, potentially including bees, flies, butterflies, and moths (Kane and Schmitt, 2001
). Although other Liatris species are predominantly outcrossing (e.g., L. helleri, Godt and Hamrick, 1995
), preliminary data (Kane, 2001
; E. J. von Wettberg, unpublished data) suggests that northern blazing star may be capable of self-fertilization. Propagules (achene-pappus units) are wind-dispersed.
Northern blazing star is usually found in early successional habitats characterized by sandy, nutrient-poor soils (Hamilton, 1991
; Collins, 1999
). Within its range, the taxon's preferred habitat includes sandplain grasslands, xeric heathlands, roadsides, and pine barrens (Hamilton, 1991
). Currently, New England grasslands are declining from disturbance suppression, which has resulted in successional takeover of these sites (Vickery and Dunwiddie, 1997
). Northern blazing star is threatened by this habitat loss, as well as by land development, propagule predation, deer grazing, and low juvenile recruitment (Kane, 2001
; Kane and Schmitt, 2001
). Fire has been a particularly successful management tool (Peteroy, 1996
; Vickery, 1996
), as it serves to create habitat, reduce propagule predation rates, and provide a suitable substrate for seedling establishment (Vickery, 1996
, 2002
).
Propagule collection
We collected northern blazing star propagules (achene-pappus units) from mid-September through early November 2001. Appropriate permission agreements were obtained from state agencies and landowners prior to collection. Our 14 sample sites included four occurrences on Block Island, Rhode Island; two on Nantucket, Massachusetts; two on Martha's Vineyard, Massachusetts; two on the Massachusetts mainland; two on the Connecticut mainland; one on the Maine mainland (Kennebunk plains); and one on Long Island, New York (Fig. 1).
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Dispersal modeling
To investigate whether variation in morphological characters could predict propagule dispersal capability, we selected 100 "parameter" propagules from a pool of extra propagules collected from the sites. Parameter propagules were selected based on a visual assessment of intact pappus bristle number and pappus geometry. An attempt was also made to select propagules representing as much of the range of variation of the total propagule pool as possible.
We conducted two independent measures of dispersal capability for each parameter propagule. For the first measurement, drop time in dead air space, the time required for a propagule to fall from the top to the bottom of a 1.75-m-high acrylite/paper cylinder, was recorded with a stopwatch. As propagules were assumed to reach terminal velocity almost instantaneously (Sheldon and Burrows, 1973
), our drop time is a measure of dispersal capability because of its inverse relationship with terminal velocity (Morse and Schmitt, 1985
). At least three drop trials were conducted for each propagule, and the mean of these trials was used as the final drop time measurement.
For the second dispersal capability measurement, we released propagules into a low-speed wind tunnel with test section dimensions of 225 cm (length) by 60 cm (width) by 60 cm (height) in the Prince Engineering Laboratory, Brown University. The wind speed in the tunnel during these trials was measured with a Pitot tube to be approximately 3.4 m/s. We selected this speed to ensure that most of the propagules would land on the floor of the test section, allowing accurate measurements of their flight distances. Weather station data indicate that this speed is of a similar magnitude to that experienced by propagules during the dispersal season (
5 m/s). Before the trials, a hole was drilled in the tunnel roof at the beginning of the test section to allow consistent propagule release from the top of the tunnel. This was equivalent to a release height of 60 cm, roughly correspondent to the mean height of flower heads in the field (Fernald, 1950
; K. Gravuer and E. J. von Wettberg, personal observation). We lined the bottom of the test section with textured shelf-lining material to promote fixation of propagules to the section bottom. In addition, we attached a 60 cm by 60 cm section of window screening to the end of the test section to prevent propagule loss during the runs. Although this screening may have increased turbulence at the end of the tunnel, performing the runs without it would have caused unacceptable damage to both the tunnel and the propagules. While we acknowledge that the potentially increased turbulence at the end of the tunnel may have had differential impacts on the dispersal behavior of the more dispersive propagules, we nonetheless chose to retain all measurements to ensure that a sufficiently wide range of propagule variation was captured. Propagules were released into the test section with forceps, and the distance they traveled before contacting the section bottom was recorded. In a small number of trials (5%), propagules traveled further than 225 cm and adhered to the window screening. In these cases, we recorded the height of the propagules above the section bottom and extrapolated their flight distance assuming a parabolic flight path. A minimum of three flight trials was conducted for each propagule, and the mean of these trials was used as the final flight distance measurement. Our results did not suggest that the propagules were damaged in flight or that subsequent flights were altered by the impact of landing on the shelf lining.
We used PROC GLM in SAS 8.0 (SAS Institute, Cary, North Carolina, USA) to conduct multiple regression analyses using morphological measurements as independent variables and the drop time and flight distance measurements as dependent variables for each propagule. In the first set of analyses, predictor variables (achene length, achene width, pappus length, and mass) were included with all possible interaction terms, and interaction terms were eliminated sequentially until only significant predictors remained (Littell et al., 1996
). We included all of these effects because we a priori expected them to affect dispersal ability based on their function, although we did not know their relative importance. We also assessed models using the ratio of pappus volume (assumed spherical) to achene volume (assumed elliptical and conical for two separate analyses) as the predictor variable (Cody and Overton, 1996
). In addition, the square root of the wing loading ratio (= propagule mass/pappus surface area, assuming pappus to be conical with a 45° vertex angle) was assessed as a predictor variable (Matlack, 1987
). The models explaining the greatest proportion of the variation were then used to estimate dispersal capability (drop time and flight distance) for the full set of 1400 propagules.
Measurement and analysis of variation in propagule traits
Ten propagules (achene + pappus) from each maternal plant were weighed to the nearest 0.1 mg. In addition, we digitally photographed each propagule to allow measurement of morphological traits (Fig. 1). From the photographs, measurements of achene length, achene width, and length of the longest pappus bristle were made using Adobe Photoshop 6.0 (Adobe Systems, San Jose, California, USA).
We used PROC GLM in SAS 8.0 to test for family, population, and regional differences in mass, achene length, achene width, pappus length, drop time, and flight distance. To meet the assumptions of the statistical analysis, mass was square-root transformed and achene width was log transformed for all analyses. Estimated drop time was transformed using the Box Cox transformation method in JMP (JMP version 4.0.4, SAS institute, Cary, North Carolina, USA), while estimated flight distance was not transformed.
Family and population differences were analyzed using nested ANOVA, with population and family nested within population as random effects. Although populations were not chosen entirely at random, we considered population as a random effect because we chose populations at random within the constraints of the legal protection provided the taxon, and because our purpose was to develop a model applicable to other populations from which we could not sample. To evaluate regional effects, we performed a second analysis in which we classified each source population as either island, coastal, or inland. Nested ANOVA for regional effects were performed on family means, with the model specifying region as a fixed effect and population nested within region as a random effect. Tukey's studentized range (honestly significant difference [HSD]) test was used to compare regional means.
Allometric relationships between family means of propagule morphological measurements were assessed using analysis of covariance in PROC GLM. Each model included a main effect of population and an interaction term of covariate x population. To test for potential variation in wing loading among populations, we performed an ANCOVA with pappus length as the dependent variable and propagule mass as the covariate. In this model, a significant main effect of population would indicate that populations differed in wing loading, i.e., in pappus length for a given propagule mass. A significant interaction term (here, pappus length x population) would indicate that the slope of the relationship between pappus length and propagule mass differed among populations, i.e., that a unit gain in propagule mass would result in differential gains in pappus length in the different populations. Similarly, to assess the degree of achene shape variation among populations, we used an ANCOVA model with achene length as the dependent variable and achene width as the covariate. Likewise, in this model, a significant main effect of population would indicate that populations differed in achene shape, and a significant interaction term would indicate that populations differed in the slope of the relationship between achene length and width. However, an important caveat to the use of PROC GLM for these analyses is that, in order to test for homogeneity of slopes (represented by the significance test of the interaction term), one of the variables had to be specified as the explanatory variable (covariate) and the other as the response. To achieve this configuration, we assigned variable designations arbitrarily. Thus, the arrangement of variables we used is not meant to imply that pappus bristle was inferred to be causal with regard to differences in mass, nor that achene length was inferred to be causal with regard to differences in achene width.
We also assessed the potential contribution of proximity to a body of water in shaping dispersal-related propagule traits. For this analysis, we measured the distance from each collection site to the nearest body of water using ArcView GIS software (ESRI, Redlands, California, USA). We employed regression analysis (PROC GLM) to evaluate the relationship between distance to water and population means for propagule traits. An additional regression was performed with a log transformation of distance to water because of the curvilinear nature of the distance data.
Germination assessment
Collected propagules were stored at room temperature. On 10 January 2002, they were sown into 8.9-cm-square pots containing a mixture of 60% coarse-grained sand and 40% MetroMix (Scotts-Sierra Horticultural Products, Marysville, Ohio, USA). Seeds were not stratified, as the New England Wildflower Society has had success germinating seeds without stratification (C. Mattrick, personal communication). Pots were randomized on two benches in a heated greenhouse at Brown University. We censused the pots every other day and recorded emergence of seedlings.
We used logistic regression in JMP to evaluate family and population effects on germination success, using a model specifying population and family nested within population as random effects. Likelihood ratio tests were used to assess significance of effects. To evaluate regional effects, we first arcsine-square-root transformed family mean germination success to meet analysis assumptions. The regional analysis was then performed using SAS PROC GLM, with the model specifying region as a fixed effect and population nested within region as a random effect.
Trade-offs between germination and dispersal capability were assessed using SAS PROC CORR to determine the degree and direction of correlations between family-mean-germination percentage, initial propagule mass, and estimated dispersal capability. To test whether the relationships among these variables were consistent across populations, we used ANCOVA in PROC GLM with either propagule mass or dispersal capability as covariates. Each model included a main effect of population, the covariate, and a covariate x population interaction term.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Achene width was significantly greater for propagules from the coastal populations as compared to the inland and island populations (Tables 3 and 4). A trend for propagule mass to be greater in coastal populations was also suggested (Tables 3 and 4). Other traits did not differ significantly among regions, and inland and island populations did not differ significantly from each other for any trait (P > 0.05, Tukey's HSD). Island, coastal, and inland populations also did not differ significantly in predicted dispersal capability as measured by either estimated drop time or estimated flight distance (Tables 3 and 4). Populations that were closer to water (log-transformed distance) appeared to have propagules with longer pappus bristles (df = 1,111, F = 5.72, P = 0.03, r = 0.568). However, the relationship was entirely driven by one coastal population with long bristles, suggesting that its applicability to the system as a whole is limited at best. No other traits were significantly associated with proximity to water.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The percentage of variance explained by the best-fitting models, 34.4% and 49.4% for drop time and flight distance, respectively, may seem low given the established importance of characters such as mass and pappus length in determining the dispersal capabilities of propagules (e.g., Sheldon and Burrows, 1973
; Werner and Platt, 1976
; Green, 1980
; Venable and Levin, 1985
; Matlack, 1987
; Hensen and Muller, 1997
; Strykstra et al., 1998
; Fenner et al., 2001
). However, compared to other authors who have used a similar approach, our model achieves a reasonable prediction. The results of Cody and Overton (1996)
indicate that the predictive capability of morphological characters can vary widely among species (r2: 0.23–0.95), and Matlack (1987)
reports an r2 value (0.50) very similar to our results. Factors that we did not account for, such as pappus geometry (Sheldon and Burrows, 1973
), pappus width, or achene curvature, may have decreased the predictive capability of the model.
The model coefficients indicate that light, short, wide propagules with a large pappus will be the best dispersers. Among the model coefficients, the coefficient for mass was the largest and most significant. This is reasonable given the conditions we used, in which air was either still or moving in a steady horizontal stream. The generally reduced turbulence (vertical mixing) in our modeling conditions could potentially account for the supremacy of mass over shape factors and pappus, which may be more important under field conditions (e.g., Venable and Levin, 1985
).
Pappus length was significantly related to flight distance, but not to drop time. Thus, it seems likely that pappus shape (the "parachute" function of the pappus) is most important in determining drop time (Sheldon and Burrows, 1973
), while pappus area (the "sail" function of the pappus) may be more important in the wind tunnel because it increases the surface area from which wind resistance can be generated (Green, 1980
; Augspurger and Franson, 1987
; Matlack, 1987
). Additional data on pappus width and geometry may aid in further clarifying these relationships.
An important caveat to applying our dispersal findings is that differences in dispersal capability in the laboratory may not adequately represent dispersal differences in the field. A number of additional factors, including variation in maternal plant height, wind speed, height and structure of surrounding vegetation, and site topography, may impact dispersal of propagules in the field, such that their true relative dispersal capabilities may differ dramatically from those we observed under controlled conditions. However, as field study of dispersal in multiple populations would be very labor intensive and could cause unacceptable damage to these plants, our data provide a reasonable basis for rapid assessment of population differences in dispersal ability to inform management.
Differences among families, populations, and regions
Propagule morphological traits and estimated dispersal capability varied significantly among populations and among families within populations. Although we cannot partition the relative contributions of genetic and maternal environmental components to the variation observed in our field-collected seed sample, the pattern of variation allows some limited inferences about possible causes. In a companion study, variation among populations relative to within-population variation for achene length, achene width, and pappus length (QST) was significantly lower than a comparable index of population differentiation (FST) estimated from allozymes, which were assumed to be selectively neutral (K. Gravuer et al., unpublished manuscript). If the observed pattern of variation in propagule dispersal traits has a genetic basis, this result would indicate that natural selection has maintained less variation among populations and/or more variation within populations than would be expected if genetic drift was the only force shaping variation in these traits (Prout and Barker, 1993
; Spitze, 1993
). In this case, it seems that optimal exploitation of the trade-off we observed between germination and dispersal capability may result in greater fitness gains than the production of a dispersal phenotype specific to a particular site. Alternatively or additionally, the maintenance of sufficient variation in dispersal traits within populations, presumably allowing persistence in the face of changing successional conditions, may assume a greater importance than adaptation to the overall habitat type at each site. It seems unlikely that this pattern of variation could be driven by maternal environmental effects, which would be expected to increase among-population variation relative to within-population variation in propagule traits (Podolsky and Holtsford, 1995
). Thus, the observed pattern of variation in dispersal traits probably has been shaped by natural selection.
Similarly, if selection has acted on propagule traits in northern blazing star, our results indicate that it has not led to reduced dispersal capability in island populations. In contrast, Cody and Overton (1996)
found reduced dispersal of island populations relative to their mainland counterparts for several species of Asteraceae, in accordance with theoretical predictions regarding the selective influence of proximity to water. Likewise, in a field study of coastal and inland ragwort plants (Senecio jacobaea), McEvoy and Cox (1987)
found greater dispersal of propagules at an inland site than at a coastal site. Clearly, however, the availability of open habitat when a species arrives on an island can also be important (Carlquist, 1966
). As Cody and Overton (1996)
pointed out, proximity to water and local habitat availability may have acted in concert to promote reduced dispersal in their island populations. At our study sites, however, island populations of northern blazing star often had access to the most extensive habitat areas, while the habitat available to coastal populations appeared most limited. The absence of a relationship between proximity to water and dispersal capability of our propagules further illustrates the potential importance of additional factors, such as local habitat availability or heterogeneity among habitat types within regions, in shaping the regional patterns we observed.
Although we observed no regional differences in dispersal ability, coastal propagules were wider and marginally heavier than island and inland propagules. This result is consistent with the observation of Harper et al. (1970)
that maritime ecotypes frequently have larger propagules than their inland counterparts, possibly to facilitate extension of the radicle to zones below those influenced by sea water. Further study is needed to determine whether this regional differentiation is the result of adaptive evolution. However, the regional differences we observed in propagule morphology did not translate into differences in predicted dispersal capability, presumably because the correlated increases in mass and achene width for coastal propagules exerted opposing effects on dispersal capability. If the observed correlation between propagule mass and achene width is genetic, it may constrain the evolution of dispersal ability in northern blazing star (Lande, 1979
). Similarly, the positive correlation we detected between mass and pappus length is seen across numerous taxa in the Asteraceae and may represent a genetic constraint on the evolution of propagule shape and architecture in this family (Matlack, 1987
).
Dispersal–germination trade-offs
The trade-off we observed between germination success and dispersal capability was expected based on previous experimental work (e.g., Morse and Schmitt, 1985
; Strykstra et al., 1998
) and the importance of propagule mass to both of these attributes (e.g., Schaal, 1980
; Morse and Schmitt, 1985
; Rees, 1993
). The fact that the trade-off did not differ significantly among populations attests to its potential ubiquity at our study sites. However, populations differed in germination percentage for propagules of the same dispersal capability. It is possible that our measure of germination ability is influenced by variation in seed dormancy or intrinsic viability in different populations. If so, however, we would expect populations to differ significantly in germination ability relative to propagule mass, contrary to our observations. It therefore seems more likely that the observed population differences in germination relative to dispersal ability may result from population differences in achene shape. That is, for a given propagule mass and germination ability, populations with wider achenes would have greater dispersal capability and therefore apparently reduced germination relative to dispersal.
Implications for management
The model we have developed can be extrapolated to assess dispersal capability for other northern blazing star populations and may help to determine which of these additional populations are most likely to be dispersal-limited and to benefit from efforts to promote colonization. This approach may also prove valuable for rapid assessment of dispersal ability in other rare wind-dispersed species. The results of the present study demonstrate that several northern blazing star populations have a particularly diminished ability to disperse. If further experimental work determines that propagules from these populations are incapable of reaching potentially favorable habitat patches in their proximity, as with Centaurea corymbosa (Colas et al., 1997
), in situ management should be undertaken to enhance colonization at those sites. Conversely, the observed trade-off between dispersal and germination success may limit recruitment in populations with high dispersal ability. At these sites, management regimes promoting germination and establishment, most notably controlled burning (Vickery, 1996
), may be particularly important. It will also be important to determine whether the observed population differences in germination ability (independent of propagule size) are due to differences in dormancy or in intrinsic viability, to devise strategies for ex-situ seed banking as well as to understand the potential risks to extant populations.
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FOOTNOTES |
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2 Current address: Ecology and Entomology Group, P.O. Box 84, Lincoln University, Canterbury, New Zealand
3 Author for reprint requests (e-mail: Eric_von_Wettberg{at}Brown.edu)
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