1 Organismic and Evolutionary Biology Program, University of Massachusetts, Amherst, MA 01003, USA (Phone: (413)545-4357; Fax: (413)545-3243; E-mail: [email protected]); 2Plant Biology Program, University of Massachusetts, Amherst, MA 01003, USA (E-mail: [email protected])
Key words: darwins, global change, haldanes, introductions, life history, microevolution, rates of evolution
Abstract
Evidence for rapid evolutionary change in plants in response to changing environmental conditions is widespread in the literature. However, evolutionary change in plant populations has not been quantified using a rate metric that allows for comparisons between and within studies. One objective of this paper is to estimate rates of evolution using data from previously published studies to begin a foundation for comparison and to examine trends and rates of microevolution in plants. We use data gathered from studies of plant adaptations in response to heavy metals, herbicide, pathogens, changes in pH, global change, and novel environments. Rates of evolution are estimated in the form of two metrics, darwins and haldanes. A second objective is to demonstrate how estimated rates could be used to address specific microevolutionary questions. For example, we examine how evolutionary rate changes with time, life history correlates of evolutionary rates, and whether some types of traits evolve faster than others. We also approach the question of how rates can be used to predict patterns of evolution under novel selection pressures using two contemporary examples: introductions of non-native species to alien environments and global change.
Introduction
Plant populations can change rapidly in response to changed environmental conditions. Populations growing in the vicinity of heavy metal mines have evolved metal tolerances in as few as 150 years (Antonovics & Bradshaw, 1970; Wu & Kruckeburg, 1985; Bradshaw & McNeilly, 1991a; Nordal et aI., 1999). Many initially susceptible populations of crop weeds evolve resistance to the herbicides used to control them within as few as two generations (Heap, 1997; Powles et aI., 1998; Mallory-Smith, Hendrickson & Mueller-Warrant, 1999). Many plant species have evolved in response to recent environmental changes, such as ozone pollution and atmospheric C02 increase (Davison & Reiling, 1995; Ward et aI., 2000).
Although it is clear that plant populations can evolve rapidly, it would be exciting to expand this information to an evolutionary rate metric that would allow comparisons between and within studies (Hendry & Kinnison, 1999). This would allow us to seek quant-
itative answers to important questions: such as how life history, time, anthropogenic changes, and trait type (e.g., physiological or morphological) affect the rate of evolution.
Evolutionary rate metrics do exist, and they are used in the animal literature. Carroll et aI. (2001) used rates of evolution of numerous traits to examine adaptation of an insect to a new plant host, and the tradeoffs associated with the adaptation. Reznick et aI. (1997) transferred guppies from high to low predation sites and found that the observed rates of evolution were consistent with natural selection as a driving force behind macroevolution. Huey et al. (2000) and Gilchrist, Huey, and Serra (2001) found clines in fly body size and wing size along latitudinal gradients only two decades after introduction to North America, which mirrored clines in their native range. Hendry and Kinnison (1999) reviewed the animal literature and generated a database of rates of evolution. We have generated a similar database of rates of evolution in plants.
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We see many benefits of the application of these rate metrics in examining trends and rates of rnicroevolution in plants. Many plant species in the wild are useful indicators of change (Davison & Reiling, 1995; Davison & Barnes, 1998; Barnes et aI., 1999), particularly when measuring rates of response to anthropogenically induced environmental change. For example, rates of evolution may be helpful in predicting the resilience of plant populations in the face of rapid environmental change by demonstrating which species in a community will be able to evolve in step with global change. Economically important species such as crop plants and their associated weeds are also useful models of microevolution as a frame of reference for potential rates under artificial selection (Barrett, 1983; Erskine, Smartt & Muehlbauer, 1994).
We did not find any published rates of evolution in the plant literature, most likely because a precedent had not been set. Hence, one of the objectives of this paper is simply to make widely available rates of evolution estimated from previously published studies to begin a foundation for comparison (Table 1). A second objective of the paper is to examine what these rates may add to our current methods of inference. We do this by presenting the rates from Table 1 in the context of some basic microevolutionary questions. Our database is small and broad and cannot in itself fully answer these questions, but rather can demonstrate how these questions can be approached using rates.
The rates
Microevolutionary rates require information about ancestor and descendant populations. When such data are available, the study is referred to as 'allochronic' and the differences closely reflect evolutionary change (Hendry & Kinnison, 1999). Often, however, information about ancestral populations is not available, and in these cases two or more contemporary populations sharing a common ancestry are compared, with one population given status as the 'ancestor', or source population, and the other populations considered descendants. In these cases, it is assumed that the 'contemporary ancestral' population has changed very little. For example, in studies of plants adapted to heavy metals, plants on contaminated soils are considered the descendant population and those on the adjacent uncontaminated soil are considered the ancestral, or source, population (Antonovics & Brad-
shaw, 1970). Because it is possible, even likely, that the source population has also been subject to some change, the amount of difference between the two populations is most accurately referred to as divergence and the study is considered 'synchronic' (Hendry & Kinnison, 1999). Few studies compare allochronic and synchronic rates but Gilchrist, Huey and Serra (2001) did so for their study on clines in flies and found the two rates to be very similar. We use the term 'rates of evolution' to refer to both allochronic and synchronic studies, though we make the distinction between the two in our table of rates.
We estimated rates of evolution in two forms, as proposed by Hendry and Kinnison (1999). One form, the darwin, estimates the rate of change based on the number of years elapsed since the ancestral population gave rise to the descendant population and is expressed in powers of e per million years (Haldane, 1949). The second form is the haldane (Gingerich, 1983), which estimates rates based on the number of generations elapsed since the ancestral population gave rise to the descendant population and is expressed as standard deviation units per generation
. In(x2) -In (Xl) darwms = . (1)
t
(2)
in which Xl and X2 are the mean trait values of the ancestor and descendant populations, respectively, t is time in millions of years, sp is the pooled standard deviation of the populations' trait values, and g
is the number of generations since the separation of the populations. Number of generations is estimated as number of years divided by the generation length.
Both metrics effectively estimate the slope of a line between two points, which is the rate. For time-series data, provided there is a linear relationship, the slope of a least square regression line, In(xi) or (xJsp ) regressed against t or g, where i represents the different temporal samples (1, 2, 3 ... n), can be estimated along with its confidence interval.
For darwins (Eq. (1)), the traits are expressed in natural logs so that rates are proportional and comparisons can be made across different traits and taxa. For example, a mean increase in the height of a tree population of 4 cm would give the impression of a much greater change than a 1 cm increase in a small herb, but taking the natural log of the values makes each trait change proportional to the mean trait values.
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Table 1. Estimated rates of evolution estimated from published and unpublished studies
Study type Species Years Gen. Trait Darwins Haldanes (xl0-3)
Resistance and tolerance
to abiotic factors
Zinc tolerance I Anthoxanthum odoratumP 100 Zn tolerance index 20.42
Zinc tolerance2 Anthoxanthum odoratumP 68 34 Zn tolerance index 28.62
*Denotes studies which are allochronic; all other studies are synchronic. Numbers after study type refer to the cited work, which can be found in the appendix along with specific information about the sources of data from each study. P = perennial; A = annual; B = biennial.
Haldanes (Eq. (2)) incorporate trait standard deviations, which scale with mean values. Hendry and Kinnison (1999) found no differences between natural log transformed and untransformed data when estimating haldanes. Here we present rates based on untransformed data.
We estimated rates of evolution using individual traits such as biomass, seed number, tolerance indices, and percent germination. The term 'trait' refers to any measured character in a given study. Trait values usually encompass many individual traits; for example, biomass could change in response to changed root mass, leaf surface area, or leaf architecture, among other traits, which themselves may be controlled by a suite of genes.
Generation length is commonly defined as the average age of an individual in a population at the time of
reproduction (Stearns, 1992). In all studies for which generation length was not reported, we estimated this parameter as the length of time to the first reproductive event. Generation length for annuals is 1 year, except in cases where few or no seeds germinate in stressful years, such as dry years for desert annuals. In the case of seed dormancy, generation length was estimated as number of years divided by number of years with appropriate germinating conditions. Biennials have a generation length of 2 years. Perennial species have variable generation lengths, and because of this, generation length can be a potential source of error in estimating rates in perennials. Estimating generation length using life tables would help to reduce this error. However, information on longevity and fecundity are often unavailable, especially in species that are of no economic importance or are long-lived, such as trees.
Some clonal plants are known to live for a 100 years or longer, reproducing both sexually and asexually, and are an extreme example of the difficulties in estimating generation length.
Because of the difficulties in estimating generation lengths, our rates may be underestimated, particularly for perennials. Underestimating generation length may lead to an inflated estimate of the number of generations of evolution, consequently underestimating rates of evolution per generation. For example, although a perennial herb may have low seed set in its first year of reproduction, it may continue to increase its reproductive output over subsequent years. Future evolutionary rate studies should endeavor to report precise estimates of generation length.
We used data from published literature, and correspondence via personal communications, to estimate rates of microevolution (Table 1). Where possible we have estimated both haldanes and darwins, but in some cases standard deviations were not available for estimating haldanes. We did not draw randomly from the literature, but instead chose studies whose topics were environmental differentiation among plant populations. The result may be that our survey is biased toward high rates of evolution.
The rates we estimated are displayed in Figures lea) and l(b), showing the range and distribution of both haldanes and darwins (0-0.808 haldanes, 0-1372 darwins). Rates tend to be most common at the low end of the spectrum, with relatively few at the high end. Kingsolver et al. (2001) found a similar distribution in selection strengths across a variety of studies. The rate histograms suggest a need to refine our use of the word 'rapid' when referring to contemporary evolution so that only those evolutionary changes that move at an exceptional pace are called rapid. Certainly the highest rates in these graphs are 'rapid', but the rates in the lowest category, though the changes are measurable, may be commonplace and relatively sluggish.
The studies
In this section we provide background information for the studies included in our database. This is not an exhaustive survey of the published literature, but we have covered a broad range of topics. None of these studies were originally conducted for the purposes of estimating rates of microevolution, and we often focused on only one aspect of a broader study.
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We chose studies in which populations were compared in a common environment in the hopes of observing phenotypic differences caused by genetic differences rather than phenotypic plasticity, with one exception (Cody & Overton, 1996). Hendry and Kinnison (1999) referred to these as 'genetic' rates. We have included reference numbers as superscripts within the text and in the table to allow for cross-referencing.
Resistance and tolerance to abiotic factors
Some of the pioneering work on plant population differentiation involves resistances and tolerances to heavy metals (usually mine tailings) and chemicals. In each mine study, seeds and shoots were collected from transects perpendicular to abrupt boundaries separating contaminated mine soil and uncontaminated soil. Many of these studies report tolerance indices, measured as root length in contaminated medium divided by root length in uncontaminated medium, unless otherwise noted. In every case, plants originating from contaminated sites evolved increased tolerance to the target metal or chemical compared to plants from uncontaminated sites.
Many of the mine studies share basic attributes and are described together here. The rates for the mine studies for a variety of traits associated with metal tolerance are relatively low (0.06-28.62 darwins, 0.0004-O.0614haldanes). These low rates could be because selection for metal tolerance is strong on the mines and most evolution happens in the first few generations (Wu, Bradshaw & Thurman, 1975; Ernst, 1999). The time frame for divergence in the studies we reviewed here is between 20 and 1000 years, which means that our rates may reflect a long period of stasis after the initial change. Because rate metrics average the amount of change over the time interval, they don't necessarily reflect the peak rate of evolution. If the rates in these mine studies could be estimated after very few generations, the rate would have likely been much higher.
A review of mine spoil data by Jain and Bradshaw (1966)1 considered adaptation of Anthoxanthum odoratum to zinc, and Agrostis tenuis to both copper and lead. Antonovics and Bradshaw (1970)2 also investigated tolerance of A. odoratum to zinc. Wu and Kruckeburg (1985)4 tested both Lupinus bicolor and Lotus pershianus for tolerance to copper. Shaw, Antonovics and Anderson (1987)3 studied zinc and copper tolerance in a moss, Funaria hygrometrica. The evolutionary rates of the moss were relatively low
170
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(0.88-14.82 darwins), and are similar to the rates for vascular plants in mine polluted habitats.
Macnair, Smith and Cumbes (1993)5 tested for copper tolerance in Mimulus guttatus, but did not use a tolerance index. Plants were tested at two copper concentrations: 0.5[Lg/cm3 to differentiate between tolerant and intolerant individuals, and at 20 [Lg/cm3
to test for degree of tolerance among tolerant individuals. Because root lengths could only be measured on plants that were tolerant enough to grow roots, the rate represents change in tolerance using tolerant individuals. Wu and Antonovics (1976)6 tested for tolerance to lead in Plantago lanceolata collected from three roadside sites. The site nearest to the roadside was highly contaminated (365 ppm lead) and all other sites, including the control, had low lead (4.0, 0.9, 0.4 ppm). The tolerance index was estimated as increase in length per day of the longest root in solution with lead divided by increase in length per day of the longest root in solution without lead. The rate of evolution for lead tolerance was highest in the population collected from the most lead contaminated area, showing that stronger selection pressures increase the rate of evolution. Kiang (1982)7 tested for tolerance to road deicing salts in A. odoratum. The rates of evolution for tolerance of deicing salt are similar to those of tolerance to metals (0.70-12.43 darwins, 0.0066-0.0456 haldanes).
Powles et al. (1998)8 measured resistance of Lotium rigidum to an herbicide, glyphosate. MallorySmith, Hendrickson and Meuller-Warrant (1999)9 tested for degree of resistance to another herbicide, sulfosulfuron, in a population of Bromus tecto rum showing signs of resistance after only 2 years of
exposure. Resistances to herbicides evolved at a relatively rapid pace (138.82-605.88 darwins, 0.2966-0.6096 haldanes), which is not surprising given the short time intervals over which the rates were estimated. In these studies, no period of stasis would be averaged into the final rate.
Resistance to biotic factors
Burdon and Thompson (1995)10 tested the evolution of resistance of Linum marginale to various isolates of a rust fungus, Melampsora lini, over a 6 year period. Two outbreaks of M. lini occurred during the study but otherwise the incidence of fungi was low. There was an overall decline in resistance during the study, as reflected by the negative rates of evolution (219.52-440.90 darwins).
Adaptation to changes in pH
R.W. Snaydon and collaborators conducted a series of experiments at the Park Grass Plots at Rothamsted, u.K. examining the effects of various fertilizer and liming treatments on an old field after decades of treatment (e.g., Snaydon, 1970; Snaydon & Davies, 1972; Davies & Snaydon, 1976; Snaydon & Davies, 1982). Here we have used the two representatives of their many studies that best lend themselves to rate estimation (Snaydon & Davies, 197211 ; Davies & Snaydon, 197612). A. odoratum was assessed for adaptation to various pH levels in both studies. In the 1972 study, they found increased adaptation to high pH. In the 1976 study, they demonstrated that plants always performed better in their native plots relative to a plot
with contrasting pH. The evolutionary rates for these plants were relatively low (0-7.92 darwins). Again, these rates were estimated over a fairly long time interval.
Global change
Human induced global change also can generate conditions for microevolutionary change. Davison and Reiling (1995)13 compared ozone resistance among populations of P. major. Ozone levels were fairly low initially but increased significantly late in the study, 3-6 years later. Results showed significant differences in ozone resistances for two of three populations. Changes in ozone resistance were associated with significant physiological differences. Estimated rates were relatively high (0.2981-0.6506 haldanes, 31.05-67.14 darwins, absolute values), but again were over short time intervals. Ward et al. (2000)14 used Arabidopsis thaliana to test for artificial selection to both high (70 PaC02) and low (20 PaC02) atmospheric C02. In both selection regimes, performance (as measured by seed number) increased each generation. Rates estimated from selection under high C02 (0.4989-0.5256 haldanes, 977.33-1058.25 darwins) tended to be higher than those under low C02 (0.2336-0.4226 haldanes, 676.92-1372.09darwins). These were some of the highest rates in our database, due in part to the artificial selection regime and the short time interval.
Introductions
Another aspect of human induced global change is the widespread introduction of non-native species to novel environments. Blossey and Notzold (1995)15 tested the hypothesis that plants in alien environments have evolved increased competitive ability by comparing native and non-native populations of Lythrum salicaria grown with and without natural herbivory. Release from herbivory in North America may have led to shifts in biomass allocation patterns, resulting in more vigorous growth. Willis, Memmott and Forrester (2000)16 tested for the evolution of increased size after introduction using four invasive species (Carduus Ill/tans, Digitalis purpl/rea, Echiu111 vulgare, Senecio jacobaea). Both studies found no significant differences in the measured traits between native and nonnative popUlations, despite the long time interval. As a result, the rates we estimated were relatively low
Daehler and Strong (1997)17 tested for the evolution of decreased herbivore resistance between three different populations of a clonal grass, Spartina alternifiora, that have historically experienced different levels of herbivory. Both the native population and one non-native population introduced to San Francisco Bay (SF) have undergone similar herbivory levels, whereas the other non-native population introduced to Willapa Bay (WB) has been free of herbivory for over a century. Rates estimated were much higher compared to the previous introduction studies (0.3510-0.8082 haldanes, 9.34-9.44 darwins, absolute values), despite the similar time intervals. Surprisingly, the SF population has evolved less herbivore resistance than the WB population, despite experiencing more herbivory. The SF population changed nearly half as much as the WB population (8.08 and 17.55 standard deviation units, respectively) since their initial introduction, which was 20 years ago for the SF population, and 100 years ago for the WB population. One possible factor contributing to the apparently higher rate of evolution in the SF population is that it was estimated over a shorter time interval. These absolute rates should be interpreted with caution since the study involved a very small sample size, and because estimating generation length for clonal plants is problematic.
Artificial selection
Rates derived from artificial selection experiments can be used as a frame of reference for how fast and for how long plants can potentially evolve. Lambert et al. (1997)18 selected for high oil content in com (Zea mays) for 28 generations. Frey and Holland (1999)19 selected for high oil content in oats (Avena sativa) for nine generations. Another study selected for high oil and high protein content in corn for 100 generations (Dudley, 1977; Dudley & Lambert, 1992; lW. Dudley, personal communcation2o). In all studies, oil or protein yield rose continuously, although not always linearly throughout the experiment. Although these species exhibit a high stamina for evolution, the rates are relatively modest (9.44-53.1 darwins, 0.106-0.247 haldanes), with one exception. That exception is selection in A. sativa for high oil content, whose rate in haldanes is one of the highest in our database (0.750 haldanes).
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Other studies of interest
Carney, Gardner and Rieseberg (2000)21 investigated degree of hybridization between Helianthus bolanderi and H. annuus since the two species first came into contact in an old field. Investigators used morphological hybrid index scores, which measure the amount of trait similarity between the hybrid and one or the other parent species. Hybrid populations in eastern and western sides of the field changed at different rates because of environmental heterogeneity. The rates for hybridization fall in the low end of the rate spectrum (6.42-19.68 darwins, 0.0220-0.0486 haldanes). Because hybridization is a powerful force in plant evolution (Abbott, 1992; Arnold, 1997; Warwick & Small, 1999), rates documenting the pace of change as a result of hybridization can be of great interest.
C. Vanier (personal communication)22 examined change in Amsinckia tessellata, following a fire event, uncommon in the study area. Comparisons in traits were made between an unburned site and two sites (B1 and B2), burned 6 and 4 years earlier, respectively. Although both populations from the burned sites showed change, the magnitude of the evolutionary rate for each trait measured for population B 1 (2.64-12.77 darwins, 0.0193-0.0522ha1danes) is less than that for population B2 (l 0.51-70.49 darwins and, 0.0886-0.4069 haldanes), the more recently burned site.
Cody and Overton (1996)23 showed evidence of reduced dispersal ability in island populations over five generations in three biennial species, Hypochaeris radicata, Lactuca muralis, and Senecio sylvaticus. Island populations of wind-dispersed species were expected to evolve reduced dispersal ability due to strong selection for reduced accidental and passive dispersal. Their results showed a reduction of the pappus, a plume-like structure that aids in wind dispersal. This change was associated with larger achenes (small, one-seeded fruits), whose size increase also reduces dispersal ability. However, rates we estimated were some of the lowest in our database (0.0013-0.0104 haldanes, 3.58-41.95 darwins, absolute values), despite the short time interval.
Inferences from rate database
Rates of evolution over time
Rates of evolution can progress over time in three basic patterns; rates can either remain constant, increase, or decrease. Evolutionary rate patterns can be
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depicted by plotting changes in trait values against time. Changes in trait value represent the differences between a trait value at a given time and the initial value, which is simply the numerator of haldanes or darwins plotted against the denominator of the same. If the relationship is linear, the rate is constant with time, and the rate can be estimated by the slope of the least squares regression line (Figure 2(a)). If the rate decreases or increases with time, a logarithmic shaped curve (Figure 2(b)) or an exponential shaped curve (Figure 2( c)) may result, respectively. Rates may be expected to decrease with time if genetic potential is exhausted, if a local adaptive peak is approached, or if rate is variable over time (Lynch, 1990; Kinnison & Hendry, 2001). Reznick et al.'s (1997) guppy work suggested that within their system rates decreased over time. Schluter (2000), however, argues that rates do not decrease with time in adaptive radiations. If the data set is large enough to examine across many time scales, we may discriminate among these patterns. For example, Figure 2(d) shows hypothetical data set which can best be described as showing no net change over the time interval. However, looking at smaller scales, other patterns can be discerned. Variation such as this could be a result of random effects, sampling error, or reversals in the direction of selection (Grant & Grant, 1995). We have estimated rates for a number of studies that can further address the effects of time interval on rate.
Artificial selection experiments are excellent sources of time series data. In Lambert et al. (1997),
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Figure 3. (a) Rate of evolution of corn selected for high oil content over 28 generations. From Lambert et al. (1997), y = 0.044x + 0.318; rate =0.044 ± 0.004. (b) Rate of evolution of oats selected for high oil content over 9 generations. From Frey and Holland (1999), y = 0.750x + 0.253; rate = 0.750 ± 0.111. (c) Rate of evolution of corn selected for high oil content over 100 generations. From J. Dudley (personal communication), generations 0--40: y = 0.226x + 1.329; rate =0.226 ± 0.030; generations 41-100: y = 0.1I8x +4.108; rate =0.042. (d) Rate of evolution of corn selected for high protein content over 100 generations. From J. Dudley (personal communication), y = 0.119x + 1.705; rate = 0.119 ± 0.015.
Zea mays (Figure 3(a)) and in Frey and Holland (1998) Avena sativa (Figure 3(b)) were subjected to artificial selection for 28 and nine generations, respectively. Both studies show continued increases in high oil content throughout the study, with data that fit least squares regression lines (p < 0.001 for both), suggesting a nearly constant rate of evolution throughout. A similar study, selecting for high oil content in Zea mays over 100 years (Dudley, 1977; Dudley & Lambert, 1992; l.W. Dudley, personal communication) shows that, although the trend for rate (22.77 darwins, 0.247 haldanes) was constant initially, the rate began to decrease after about 30 generations (9.44 darwins, 0.106 haldanes, Figure 3(c)). This highlights the risk of making extrapolations about rates beyond the time interval. Although the data on selection for high oil content show a decrease in rate after long periods of time, data from the same experiment selecting for high protein content in Zea mays (l.W. Dudley, personal communication) show a constant rate
of change over the full 100 generations of selection (Figure 3(d)).
The Ward et al. (2000) study of artificial selection in response to high and low atmospheric C02 in A. thaliana illustrates the effect of time on rate over a relatively short interval (Figure 4). There were trends for individual lineages but no significant trend across lineages. Some of the lineages show decreased rate with time whereas others show increased rate with time. Regardless of the mechanisms behind variation in the patterns, it appears that different trajectories can result from the same selection pressures, at least in the first few generations following initiation of novel selection.
To provide a wider look at the effect of time on the rate of evolution across a variety of plant species, traits, and selection pressures, we used all of our studies together in a single trait change versus time plot (Figure 5). Only haldanes were plotted because they are robust with respect to variation in trait dimensionality (e.g., length vs. area vs. mass). This plot does
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Figure 4. Patterns of evolution in seed number of six populations of Arabidopsis thalialla in response to selection in high and low C02. Solid lines and dashed lines denote high and low C02 selection lineages respectively. From Ward et al. (2000).
not show a clear trend of rate with time across a variety of studies and species. However, if least square regression lines are constructed across increasingly long time intervals, the slopes of the lines, which correspond to the rates, decrease as the time interval is lengthened. This suggests that there may be a decrease in rate with time.
Life history correlates
A potentially useful application of estimated rates is inferring how life history characteristics may be correlated with rates of evolution. Do different life history strategies confer different potentials for evolution? For example, annual plants may be expected to adapt more quickly in absolute time because of their short generation length, especially when compared to longlived perennial plants such as trees or clonal plants. Another consideration is how the evolutionary rates of annuals and perennials compare when generation length is standardized using a metric such as haldanes. Long-lived species may experience stronger selection pressures per generation (assuming constant selection pressures), and therefore may evolve more quickly when rates are expressed in haldanes.
We used a subset of our database to compare rates estimated in darwins and haldanes between annuals and perennials (Figure 6). In our analysis, we grouped biennials with perennials because species representing both life history strategies in our database had a generation length of 2 years. We chose to compare rates for traits that were most closely associated with biomass
(e.g., leaf length) when possible to make traits more comparable across disparate studies. To avoid oversampling errors, we included only one rate for each study unless the rates in question involved different popUlations or different species. We did not include studies for which we could only estimate darwins. Also not included were rates resulting from artificial selection because we sought a comparison of rates under natural selection. One outlier was excluded.
Our results (Figure 6) show that there are no differences in rates of evolution between annuals and perennials. However, annuals are greatly underrepresented in our database and it is therefore difficult to ascertain a trend. It is possible that underestimating generation length for some perennials may have resulted in underestimated rates of evolution, leading to the lack of observed differences between annuals and perennials.
It would be useful for future studies to collect more information on rates of evolution in annuals as well as in long-lived species of perennials, trees, shrubs, and clonal plants. Another factor to consider in long-lived species with lengthy juvenile periods and delayed reproduction is the environmental variability they may be subjected to over a longer time scale. This spatial and temporal variability may generate enhanced phenotypic plasticity in certain traits, which would oppose selection for genetic differentiation in those traits (Linhart & Grant, 1996). In contrast, annuals experience environmental variability over a period of only 1 year. On the other hand, some annual species that are ephemeral or persist as dormant seeds for some time may evolve more slowly because they do not experience the variety of selection pressures that perennials do.
Do some types of traits change faster than others?
We compared rates of evolution in haldanes between morphological traits and physiological traits using the same data set as in the previous section. Morphological traits were categorized as any metric character that was not a function of growth rate (Mousseau & Roff, 1986; Houle, 1992) and comprised leaf length, biomass, shoot mass, achene volume, and hybrid index (an index of morphological traits). Physiological traits consisted of changes in lead tolerance, copper tolerance, ozone resistance, and herbicide resistance. Tolerance and resistance were inferred from comparisons of root length in contaminated versus uncontaminated medium.
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•
100 120 140
Figure 5. Rate of evolution for all traits, studies, and species from Table I from which haldanes were estimated. Lines represent least square regression lines over progressively longer intervals.
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-0.5 0 0.5 I 1.5 2 2.5 3 Rate (log darwins)
Figure 6. Comparison of log transformed rate estimates in darwins and haldanes for annuals versus perennials. Data point labels refer to reference numbers from Table I.
Our results suggest that physiological traits accumulate more change per generation than do morphological traits (Figure 7). ANCOVA showed differences in the amount of change (elevation of the line) per unit time were marginally significant (p = 0.063). Differences in rate of evolution (the slope of the regression line) between the two trait types are significant
(p = 0.006), with physiological traits evolving faster than morphological traits. One possible explanation for this difference is that in the studies we reviewed, changes in physiological traits (such as ion uptake and transport), were inferred from changes in morphological traits (such as root length or biomass). Therefore, cumulative changes in physiological traits were not
Figure 7. Rates of evolution for physiological traits versus morphological traits over time. The solid line and dashed line represent the rate of evolution for physiological traits and morphological traits. respectively. Physiological traits: y = 0.082x + 1.453. rate = 0.082 ± 0.136: Morphological traits: y = 0.014 + 0.187, rate = 0.014 ± 0.018. Data point labels refer to reference numbers from Table I.
detected until they had resulted in changes in morphological traits. For example, changes in heavy metal tolerance are eventually manifested in changes in root length.
If we exclude the two exceptionally high rates estimated from Daehler and Strong (1997, large leverage), a slightly different pattern emerges. Differences in the accumulated amount of change between physiological and morphological traits (i.e., elevation) become more significant (p = 0.025), with differences in rate of evolution (i.e., slope) becoming slightly less significant (p = 0.014). Taking into account the two possible outliers, results consistently show that physiological traits accumulate more change over time, and evolve faster over time, compared to morphological traits.
Of course physiological changes are not necessarily manifested in morphological changes. In some cases, the same morphological phenotype can exhibit quite different physiological adaptations in different environments. It would be useful for future work to contrast rates of evolution in other trait types, such as life history traits, and to add a greater breadth of traits measured, or species and growth forms compared. Kingsolver et aI. (2001) found that selection strength was twice as great for morphological traits than for life history traits. Their findings raise the question of whether morphological traits evolve faster than life history traits. Comparisons could also be made across different hierarchical groupings. For example, do spe-
cies within species-rich genera evolve more rapidly than species within species-poor genera? How do rates of evolution compare across different families? Or different growth habits such as herbs, woody shrubs, vines, epiphytes, and trees?
Global change
From a conservation perspective, rates of evolution could be useful for predicting the consequences of global change for plant populations. Can plant populations change and adapt to increasing atmospheric pollution, C02 levels, climate change, and nitrogen deposition, or will physiological, developmental, or genetic constraints become limiting? To more accurately predict the probability of local extinctions and shifts in vegetation distributions, it is important to consider a plant species' capacity for evolution, and how this may buffer the impact of global change.
Extensive empirical work has shown that plant populations can evolve in response to elevated C02 (e.g., Curtis et aI., 1996; Thomas & Jasienski, 1996; Ward et aI., 2000). For example, artificial selection experiments have demonstrated considerable evolutionary potential under selection in response to both low and high levels of C02 (Ward et aI., 2000), with rates ranging from 0.2336 to 0.5256 haldanes and from 676.92 to 1372.09 darwins. However, it is uncertain whether this capacity to evolve is widespread among natural populations and species, or whether it
can be sustained. Global change can indirectly influence rates of evolution, such as in the case of elevated atmospheric C02, by increasing plant invasion, productivity, and consequently altering the fire regime (Smith et aI., 2000; C. Vanier, personal communication). Plant populations must possess sufficient genetic variability to respond to different selection pressures, the limits of which can be species-specific (Bradshaw & McNeilly, 1991b; Burger & Lynch, 1995). Given that a population does possess the necessary variability, it may only be able to evolve in response to increased C02 initially, before it becomes constrained by other limiting factors such as availability of nitrogen or water (Bazzaz et aI., 1995).
Plant populations can also adapt to atmospheric pollution such as ozone (Davison & Reiling, 1995; Davison & Barnes, 1998) and sulfur dioxide (Barnes et aI., 1999). Rates of evolution of ozone tolerance estimated here are some of the highest in our database (0.2981-0.6506 haldanes, 31.05-67.14 darwins, absolute values). The demonstration of appropriate heritable genetic variation in ozone resistance within and between plant populations has lent support to evidence of its rapid evolution (Whitfield, Davison & Ashenden, 1997; Davison & Barnes, 1998; Barnes et aI., 1999). However, it remains uncertain whether this evolved resistance was due to increased ozone concentrations, or to other environmental factors exhibiting similar spatial and temporal patterns, such as light intensity (Davison & Reiling, 1995). Interactions between atmospheric pollution and other environmental variables, such as climate, soils, water deficit, nutrient regime, disease, herbivory, and human impact (Davison & Barnes, 1998; Barnes et aI., 1999), make it difficult to predict how natural populations will be affected by pollution and how they will respond in an evolutionary sense. Resulting interactions may produce different rates of evolution across different geographic regions. Gene flow between these areas could constrain the overall rate of evolution (Merila, Sheldon & Kruuk, 2001). Also, adaptive responses may carry a cost. For example, some resistant populations have been found to be competitively inferior to non-resistant populations at low air pollution levels (Bell, Ashmore & Wilson, 1991). Evolved resistance may result in an overall negative effect on fitness if pollution levels decline, or may be constrained if pollution levels and consequently, selection pressures, vary substantially among years (Merila, Sheldon & Kruuk, 2001). Finally, the mechanism of evolved resistance is unknown, and the necessary physiological
177
and biochemical mechanisms may not be universal among species (Barnes et aI., 1999).
The rapid increase in greenhouse gases such as C02 and ozone is expected to lead to rapid global climatic change, with a substantial increase in temperature over the next several 100 years (Bradshaw & McNeilly, 1991b). The immediacy of this change highlights the need to examine evolutionary change on a contemporary time scale. For obvious reasons, we did not find studies involving plant population differentiation due to climatic change from which to estimate rates. Although ecotypes do evolve in response to minor changes in climate, a widely held view is that historical evidence shows that many species' ranges moved considerable distances to cope with major climatic shifts (e.g., Sauer, 1988), implying a lack of sufficient evolution (Bradshaw & McNeilly, 1991b). However, some recent evidence may show support for adaptation to climate change occurring in conjunction with range shifts (Davis & Shaw, 2001).
Other aspects of global change invite further questions. For example, habitat fragmentation is often cited as one of the leading threats to biodiversity (e.g., Baur & Erhardt, 1995; Debinski & Holt, 2000). How does habitat fragmentation, and its consequent effects on dispersal and gene flow, affect rates of evolution? Limited dispersal ability can restrict the reproduction and growth of populations. Resulting small population sizes can lead to reduced genetic variability and restrict the potential for evolutionary change (Bradshaw & McNeilly, 1991b). Fragmented habitats are akin to islands and might be expected to impose similar selection pressures on characters such as dispersal ability. Our estimated rates of evolution of decreased dispersal ability in island plant popUlations of three species were among the lowest rates in our database (0.0013-0.0104 haldanes, 3.58-41.95 darwins, absolute values), despite the short time interval and the authors' conclusion that selection was potentially strong. This might suggest that in general, traits related to dispersal ability might not evolve very quickly. If so, increased dispersal ability may also not evolve quickly enough, leaving fragmented populations susceptible to local extinctions.
The speed and extent at which global change is occurring provides an interesting and important context in which to frame future work on evolutionary rates. Despite the difficulties discussed above, consideration of an organism's potential to evolve is important with respect to conservation, and studies that estimate rates
178
in response to global change can increase our ability to make conservation decisions.
Introductions
Human transport of plants to regions where they are not native generates numerous novel selection pressures. This scenario of potentially rapidly evolving populations not only provides an excellent opportunity to observe microevolution at work, but also fuels many questions about the dynamics of invading populations (Neuffer & Linde, 1999; Barrett, 2000). One such question concerns how the rate of evolution corresponds with observed trends in temporal patterns of invasions. Often species are observed in new sites at low densities for as long as a century before they finally spread rapidly (Kowarik, 1995; Crooks & Soule, 1996). This lag time may be due to density dependent forces, such as propagule pressure or pollen limitation. Alternatively, the population may be poorly adapted to the new environment initially, but after a period of selection it may finally be able to expand. A good test for an evolutionary pattern during this lag time would be to gather time series data on recently introduced populations of a known invasive species. Rates estimated from time intervals would nicely illustrate the trajectory of evolution following introduction. Similar work on animal introductions have shown that evolution can proceed rapidly in the initial stages, but then decline as the population approaches a new optimum (e.g., Reznick et aI., 1997; Haugen & V~'Jllestad, 2001; Reznick & Ghalambor, 2001).
Most of the plants introduced into a new region do little more than persist, or thrive only in disturbed areas. However, a small fraction of introduced plant species become invasive and spread to the detriment of the native flora (Williamson & Fitter, 1996). Comparisons between invasive and non-invasive plant species have discovered differences between the two groups (Mack, 1996; Pysek, 1998; Goodwin, McAllister, & Fahrig, 1999; Rejmanek, 2000). Another technique, which may prove fruitful, is to compare rates of evolution of various traits between invasive and non-invasive introduced species to see if, perhaps, certain traits of invasive plants evolve more quickly and enable them to be invasive. Our estimated rates of evolution of invasive plants, with the exception of Spartina alterniflora (Daehler & Strong, 1997), are relatively low (0.64-8.04 darwins, 0.002-0.032 haldanes), many of which are unlikely to significantly differ from zero. However, S. alterniflora has
a relatively rapid rate of evolution of traits associated with loss of herbivore resistance (9.34-9.44 darwins, 0.808-0.351 haldanes), which could suggest that rapid evolution is a part of invasive plants' successes at least in some situations. We have no comparable data for non-invasive introduced species. Although most invasive species in our database show very low rates of evolution, a more extensive survey of both invasive and non-invasive species and traits would be necessary to ascertain a robust trend. Another possibility is that invasive plants actually evolve more slowly than non-invasive plants because some trait that confounds evolutionary change, such as phenotypic plasticity, confers invasiveness.
Conclusions
The rates we estimated allowed us to address some fundamental questions about micro evolution. Although it is difficult to draw definitive conclusions from our small and broad data set, we hope that the emergent trends we found provide the impetus for future work. Similar to Kinnison and Hendry's (2001) review of animals, the frequency distribution of our estimated rates follows a negative exponential distribution with progressively higher rates occurring toward the tail end of the distribution curve. Progressively higher rates tend to be associated with shorter time intervals. Although our review of the literature is biased toward studies demonstrating population differentiation due to novel selection pressures, our resultant rates do not show a bias toward high or 'rapid' rates of evolution. The majority of rates in our database were relatively low «0.05 haldanes), with only four studies showing exceptionally high rates (>0.5 haldanes).
Artificial selection experiments illustrate that consistently high rates of evolution can be sustained under constant selection pressures, with rates either decreasing or remaining constant over time. However, selection pressure is more likely to fluctuate in the wild and accordingly, studies of natural populations show rates either decreasing or fluctuating over time. A comparison of life history strategies showed that there are no differences in rate of evolution between annuals and perennials, though annuals are underrepresented in our database, and rates for perennials may be underestimated. When contrasting different trait types, similar to Mousseau and Roff's (1986) and Houle's (1992) classifications, we found that physiological traits accumulated more change over time, and evolved faster, compared to morphological traits.
We also addressed questions about global change and plant introductions, and how rates might be used to express patterns and mechanisms of evolution in a rapidly changing environment. Rates of evolution can be a useful tool for predicting which populations are more susceptible to extinctions, for example, and can be used to guide conservation decisions.
The issues we explored and the questions raised highlight the need for studies whose aim is to estimate rates. Such studies would find good company among the innumerable studies seeking differentiation among plant populations (e.g., Bradshaw & McNeilly, 1991a, b; Linhart & Grant, 1996). The database of rates could be greatly enhanced if future studies regarding population differentiation published estimated rates. Not only would this increase the number of rates for comparison, it may also result in more accurate rates by improving the accuracy of estimated generation lengths and time intervals.
The rate of evolution database summarized in this paper (Table 1) has breadth in topic, but this could be further improved in future work. Selection pressures such as habitat fragmentation, S02 pollution, competition, grazing, and nutrient stress, would add more scope to the existing pool of rates. Also of great interest would be increased taxonomic breadth. All of the studies we reviewed address angiosperms, with one exception, (Shaw, Antonovics & Anderson, 1987). Inclusion of evolutionary rates for more taxonomic groups would incorporate a wider variety of life histories, and may even open the door for such provocative questions as: do animals and plants evolve at different rates?
Although evolutionary rates currently have little presence in the plant literature, they show promise in helping to answer some compelling evolutionary and conservation questions. It is our hope that the database we have begun will lay a foundation and become a catalyst for further estimation and use of rates in plant studies.
Acknowledgements
We thank A.P. Hendry for help throughout the preparation of this manuscript; S.E. Carney, c.c. Daehler, lW. Dudley, lB. Holland, RJ. Lambert, M.R Macnair, C. Preston, C. Vanier, J.K. Ward, and AJ. Willis for use of unpublished data; and P. Alpert, C. Holzapfel, M. T. Kinnison, and two anonymous reviewers for review of the manuscript. Elizabeth Bone was supported by a Graduate Assistantship in Areas
179
of National Need from the U.S. Department of Education, and Agnes Farres was supported by a Gilgut Fellowship in Plant Biology.
Notes
l. Jain and Bradshaw (1966): Data used from this paper were all from previously unpublished data of other investigators. Zinc tolerance data were estimated from Figure 3, (data of P.D. Putwain). Copper tolerance data were estimated from Figure 2 (data of T.S. McNeilly). Lead tolerance data were estimated from Figure 1 (dataofT.D. Bradshaw).
2. Antonovics and Bradshaw (1970): Trait values were estimated from Figure 1, from transects 6 and 3.
3. Shaw et al. (1987): Trait values were estimated from Figures 2(a)-(d).
4. Wu and Kruckeberg (1985): Indices and standard errors were taken directly from Table 2. Time span of evolution was estimated to be l30 years, which is the length of time since mine activity began; however, the authors conjecture that colonization could not have begun earlier than 30 years prior to collection.
5. Macnair et al. (1993): Trait values were obtained from Table 1 and from M.R Macnair (School of Biological Sciences, University of Exeter, UK). For the purposes of these calculations, we pooled both the sites with the five highest and the five lowest copper concentrations.
6. Wu and Antonovics (1976): For our calculations we used data from cloned plants rather than seed grown plants. Lead tolerance indices and standard errors, were estimated from Figure 1.
7. Kiang (1982): Means, variances, and N's were taken directly from Table 1, using 1973 data on seed grown material. Data for percent germination were taken directly from Table 4 using the two median salt concentrations to calculate rates. For 'days to heading' only haldanes were estimated because interval data are not applicable to darwins.
8. Powles et al. (1998): Trait values and standard errors were obtained from C. Preston (Department of Applied and Molecular Ecology, University of Adelaide, Waite Campus, Australia). We used data from 450 g a.e./ha concentration glyphosate.
9. Mallory-Smith et al. (1999): Mean dry weights of resistant and non-resistant plants, expressed in dry
180
weight of treated plants as a percent of dry weight of untreated controls, were estimated from Figure 1, sulfosu1furon treatments only.
10. Burdon and Thompson (1995): Rates were calculated from this paper using two of the four M. lini isolates (K18 and MD1), chosen randomly, and over the interval of the entire study. Percent resistances were estimated from Figure 4.
11. Snaydon and Davies (1972): Trait values were taken directly from Table 2, and two plot pairs (3U, 3L and 9U, 9L) were chosen randomly.
12. Davies and Snaydon (1976): Data from a plot pair that varied by pH only was used (1 U and 1 L). Other plot pairs were not used because they differed by more than one selection event, fertilizing in 1856 and liming in 1903.
13. Davison and Reiling (1995): Trait values, standard errors, and N were taken from Table 3.
14. Ward et al. (2000): Mean seed numbers and standard errors were estimated from Figure 3. N values and generation length (62 days) were obtained directly from 1.K. Ward (Department of Biology, University of Utah, USA).
15. Blossey and Notzold (1995): Trait values, standard errors, and N were taken from Table 1.
16. Willis et al. (2000): Trait values, standard errors, and N were obtained directly from A.l. Willis (NERC Centre for Population Biology, Imperial College of Science, Technology, and Medicine, Silwood Park, UK).
17. Daehler and Strong (1997): We estimated rates using the author's index for herbivore resistance expressed as biomass ratios (herbivory: no herbivory). Means, variances, and N were obtained directly from c.c. Daehler (Department of Botany, University of Hawaii, USA).
18. Lambert et al. (1997): Trait values were obtained from R.l. Lambert (Department of Crop Sciences, University of Illinois at Urbana-Champaign, USA).
19. Frey and Holland (1999): Oil content was taken directly from Table 1; standard deviations and N were obtained from 1.B. Holland (Department of Crop Science, North Carolina State University, USA).
20. Dudley (personal communication): All data were obtained directly from 1.W. Dudley (Department of
Crop Sciences, University of Illinois at UrbanaChampaign, USA).
21. Carney et al. (2000): Means and standard deviations were taken directly from Figure 2. N values were obtained from S.E. Carney (Department of Biology, Colorado State University, USA).
22. Vanier (unpublished): All data were obtained from C. Vanier (Department of Biological Sciences, U niversity of Nevada Las Vegas, USA) and were the result of common garden experiments.
23. Cody and Overton (1996): Data were taken directly from Table 2.
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