Hierarchical Analysis of Genetic Differentiation

A Hierarchical Analysis of Genetic Differentiation in a Montane Leaf Beetle Chrysomela aeneicollis (Coleoptera: Chrysomelidae) Author(s): Nathan Egan Rank Source: Evolution, Vol. 46, No. 4 (Aug., 1992), pp. 1097-1111 Published by: Society for the Study of Evolution Stable URL: http://www.jstor.org/stable/2409759 Accessed: 01/02/2010 19:13

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http://www.jstor.org Evolution, 46(4), 1992, pp. 1097-1111

A HIERARCHICAL ANALYSIS OF GENETIC DIFFERENTIATION IN A MONTANE LEAF BEETLE CHRYSOMELA AENEICOLLIS (COLEOPTERA: CHRYSOMELIDAE)

NATHAN EGAN RANK' Department of Zoology, University of California, Davis, CA 95616 USA

Abstract. -Herbivorous insects that use the same host plants as larvae and adults can have a subdivided population structure that corresponds to the distribution of their hosts. Having a subdivided population structure favors local adaptation of subpopulations to small-scale environmental differences and it may promote their genetic divergence. In this paper, I present the results of a hierarchical study of population structure in a montane willow leaf beetle, Chrysomela aeneicollis (Coleoptera: Chrysomelidae). This species spends its entire life associated with the larval host (Salix spp.), which occurs in patches along high-elevation streams and in montane bogs. I analyzed the genetic differentiation of C. aeneicollis populations along three drainages in the Sierra Nevada mountains of California at five enzyme loci: ak-l, idh-2, mpi-J, pgi-J, and pgm-J, using recent modifications of Wright's F-statistics. My results demonstrated significant differentiation (FST = 0.043) among drainages that are less than 40 kilometers apart. One locus, pgi-1, showed much greater differentiation than the other four (FST = 0.412), suggesting that it is under natural selection. C. aeneicollis populations were also subdivided within drainages, with significant differentiation 1) among patches of willows (spanning less than three kilometers) and 2) in some cases, among trees within a willow patch. My results demonstrate that this species has the capacity to adapt to local environmental variation at small spatial scales.

Key words. -Allozymes, Chrysomela aeneicollis, F-statistics, gene flow, genetic differentiation, insect population structure, natural selection, phosphoglucose isomerase.

Received February 4, 1991. Accepted November 20, 1991.

All speciesconsist of populationsthat vary ample, if a herbivorous insect lives on trees in size and degree of isolation from other that grow in distinct patches separated by populations. Local population sizes, migra- geographical barriers, a study of that insect's tion rates, and the patternsof matings with- population structure should include sam- in a population all affect a species' popu- ples from individual trees from several lation structure (Hartl and Clark, 1989; host-tree patches. The advantage of a hi- McCauleyand Eanes, 1987; Slatkin, 1987). erarchical study is that one can distinguish In many cases, local environmental varia- evolutionary processes such as isolation due tion causes naturalselection to operate dif- to geographic barriers, isolation by distance, ferently among local populations, and pop- and isolation resulting from the organism's ulations may differ genetically in response behavior itself. Until recently however, few to this naturalselection. Even in the absence studies have attempted to measure popu- of selection, genetic drift among small-sized lation structure in this way. populationsincreases their likelihood of be- Willow leaf beetles (Family Chrysomeli- coming genetically differentiated. On the dae) occur on willow shrubs or trees that other hand, gene flow tends to make pop- commonly grow in separated patches of ulations more genetically homogeneous boggy or moist habitat. These beetles feed (Slatkin, 1987). To measure the genetic on the same host species as adults and as structureof populations,Wright (1978) pro- larvae, and they usually overwinter below posed that studies should use a hierarchical their host tree or under the bark of dead sampling design that correspondsto spatial branches (Brown, 1956; Raupp and Denno, subdivisions of naturalpopulations. For ex- 1983; Rowell-Rahier, 1984; Pasteels et al., 1988; Smiley and Rank, 1986). Although ' Present address: Institute fuirterrestrische okologie, they can fly, most willow leaf beetles do so ETH-Zirich, Grabenstr. 3, CH-8952 Schlieren, SWIT- rarely (Brown, 1956). ZERLAND. Fax 41-1-731-0783. Because of their low vagility and because

1097 1098 NATHAN EGAN RANK

C. aeneicollis populations are potentially RC ~ ~ ~ R subdivided at several levels. First, populations are localized in isolated drainages that are separated by high-elevation ridges. Sec- ond, C. aeneicollis populations occur on SL000m separated patches of willows in bogs, on ta-

5km. 1. MapofthelBP B lus slopes, or along streams. Finally, within N LA these patches, the willows grow as distinct shrubs supporting populations of C. aeneicollis larvae and adults. Thus, for this species, which has a limited vagility, physical SL BP G barriers and the patchiness of its host plants may favor genetic subdivision at each spatial scale. I tested this hypothesis by sam-

D E pling C. aeneicollis populations hierarchi- cally 1) among isolated drainages, 2) among patches of willows within a drainage (localities), and 3) among individual willows within a locality. FIG. 1. Maps of the localities. Top /eft-locations MATERIALSAND METHODS of the three drainages in the eastern Sierra Nevada. The areas shaded in black indicate regions higher than Collection. - Chrysomela aeneicollis 3500 meters. Top right-locations of the three locali- adults from the previous year can be found ties in the Rock Creek drainage. Bottom left -locations in May and June, but adults from the pres- of the five localities near South Lake in the Bishop Creek drainage. Bottom right-locations of the seven ent generation do not emerge from their pu- localities in the Big Pine Creek drainage. pae until late August or early September. For this study, I collected newly emerged adult beetles on September 3-7, 1988. The they occur in distinct patches of host plants, beetles were collected along three drainages: one may predictthat willow leaf beetles have Big Pine Creek (37?7'N/118?29'W), Bishop a subdivided population structure. This Creek (370 1'N/118?32'W, populations subdivision may enhance their potential for sampled near South Lake), and Rock Creek local adaptationto small scale environmen- (37025'N/1 18044'W). These drainages are tal variation. Indeed, an electrophoretic isolated by barren ridges that constitute part study of a willow leaf beetle, Plagioderaver- of the Sierra crest. Big Pine Creek is sepa- sicolor, which was introduced to North rated from Bishop Creek by about 10 ki- America at the turn of the century, found lometers and one ridge, but populations in significantdifferentiation among patches of both of these drainages are separated from willows separated by several kilometers the Rock Creek populations by several ridg- (McCauley et al., 1988). This study also es and about 40 kilometers. I sampled bee- demonstrated significant variation among tles in a stepping stone fashion within each patches in average relatedness of individ- drainage at localities that were 0.3 to 5 ki- uals within larval feeding groups, which lometers apart (Fig. 1). In each locality, I suggests that genetic differentiationin av- sampled beetles from two to eight willows erage relatedness has occurred. separated by 2 to 150 meters (Table 1). Be- In this study, I used gel electrophoresisto cause these willows are shrubs, all of the quantify the population structureof a wil- branches could be sampled. All of the bee- low leaf beetle, Chrysomela aeneicollis, tles were collected from a single, salicin-rich which occurs at high elevations in the east- willow species, S. orestera,except for those ern Sierra Nevada of central California collected from six S. boothi individuals at (Smiley and Rank, 1986). Chrysomela locality BPf. The two willow species grew aeneicollisis native to westernNorth Amer- together at this locality. ica from the Yukon to California (Brown, Electrophoresis. -After collection, I 1956). At this southernextreme of its range, brought the beetles to the University of Cal- LEAF BEETLE GENETIC DIFFERENTIATION 1099

TABLE 1. Description of localities. N refers to the average sample size per willow, and the distance is the average distance between any two willows at the site.

Locality Trees N Elevation m Distance m Site description BPCa 6 27.2 3182 94 Large clones on talus slope BPCb 3 15.3 3268 13 Bog by 4th lake BPCc 3 27.3 3219 29 Open bog BPCd 7 31.9 3127 32 Talus and bog BPCe 3 31.7 3106 13 Atedgeofshadedpond BPCf 8 28.8 2974 31 Bog and pine woodland BPCg 4 13.0 2877 19 Shaded bog RCa 3 12.3 3365 68 Stream-side clones RCb 7 20.9 3264 90 Stream-side in steep canyon RCc 3 16.3 3064 34 Bog in campground SLa 3 51.0 3170 6 Shaded bog SLb 3 45.0 3194 8 Shaded bog SLc 2 38.5 3219 10 Shaded bog SLd 2 21.0 3005 2 At edge of open pond SLe 4 23.8 2883 74 Open boggy area near road

ifomia, Davis for long-term storage at the bands were not clearly readable for scor- - 80?C. I prepared the samples for electro- ing of putative genotypes. Thus, five loci phoresis by homogenizing them with a glass were used in the complete survey. rod in 0.1 ml of distilled water in a micro- For these five loci, I used two buffer sys- centrifuge tube. Electrophoresis was con- tems. For ak-1, idh-2, and mpi-1, I used a ducted according to the methods outlined continuous tris-citrate buffer (pH 8.0), run in Harris and Hopkinson (1976), using a at 100 mA for four hours. I added about 5 12.5% potato starch solution (Sigma Chem- mg of NADP to the gel buffer just before ical Co., St. Louis, Mo.). To determine which pouring the gel. For pgi- 1 and pgm- 1, I used loci were polymorphic, I first screened C. a discontinuous tris-citrate buffer (pH 7.1) aeneicollis (10 individuals per drainage) run at 75 mA for six hours (buffer recipe in for polymorphism among 22 enzyme loci: Ayala et al., 1972). Agar overlays were used 6-phosphogluconatedehydrogenase (6pgd; in staining all of the enzymes except for idh- E.C. 1.1.1.49), aconitase (acon-1; E.C. 2. For all five loci, I reran individuals to 4.2.1.3), adenylate kinase (ak-1; E.C. homologize alleles and to resolve scores that 3.5.4.4), alcoholdehydrogenase (adh- 1, adh- had been questionable on the first run. 2, adh-3; E.C. 1.1.1.1.), alpha-gpd (alpha- Analyses. -I used the BIOSYS (Swofford glycerophosphate dehydrogenase E.C. and Selander, 1981; version 1.6 for personal 1.1.1.8.), aspartate aminotransferase(aat- computers) program on single-individual 1, aat-2; E.C. 2.6. 1. 1), creatinekinase (ck- 1; genotypic data to obtain allele frequencies E.C. 2.7.3.2), esterase (est-2; nonspecific), and to test for significant deviations from isocitratedehydrogenase (idh- 1, idh-2; E.C. Hardy-Weinberg equilibrium for each wil- 1.1.1.42), malate dehydrogenase (mdh-1, low. Before testing for HWE, I pooled the mdh-2; E.C. 1.1.1.37), malic enzyme (me- 1; genotypes into homozygotes for the most E.C. 1.1.1.40), mannose-6-phosphateisom- common allele, heterozygotes between the erase (mpi-1; 5.3.1.8), peptidase (pep-b], most common allele and other alleles, and pep-c, pep-d; uncertain E.C. assignment, homozygotes and heterozygotes for the oth- peptidase nomenclature based on Murphy er alleles. I used the exact test option in et al., 1990), phosphoglucomutase(pgm-i; BIOSYS to obtain exact probabilities of the E.C. 5.4.2.2), phosphoglucose isomerase deviations, and I report the direct count av- (pgi-1; E.C. 5.3.1.9), superoxide dismutase erage heterozygosities and their standard er- (sod-i; E.C. 1.15.1.1). Of these 22 loci, rors given in BIOSYS's step VARIAB. acon-1, ak- 1, idh-2, me- 1, mp-1, pgi- 1, and To analyze differentiation among sub- pgm- I showed polymorphism. I subse- populations, I used Weir and Cockerham's quently dropped acon-I and me-I because (1984) modification of Wright's F-statistics 1100 NATHAN EGAN RANK

(Wright, 1978). The F-statistics are calcu- To determine the statistical significance lated from the genotypicfrequencies at each of the genetic variation, I have taken two locus. FIS measures the degree of nonran- approaches: G tests for heterogeneity of al- dom matingwithin a subpopulation.It takes lele frequencies at each locus and t-tests of a positive value when the observed hetero- the jackknifed FSTestimates. For the G tests, zygosity is less than the expected hetero- I pooled the rarer alleles into the next most zygosity, or a negative value when there is common allelic classes when the expected a heterozygoteexcess. FSTmeasures the rel- values in any cell were less than 5. To test ative fixation of alternatealleles in different whether the jackknifed estimates were sig- subpopulations by comparing the average nificantly different from zero, I used simple of the subpopulationheterzygosities to the t-tests (cf. McCauley et al., 1988). Because total heterozygosityexpected under random FIS and FIT can take both negative and pos- mating. The magnitudeof FSTtherefore de- itive values, I evaluated their t-values with pends on the amount of among-subpopu- two-tailed probabilities, but I used one- lation divergence in allele frequencies tailed tests to determine the significance of (Wright, 1978; Hartl and Clark, 1989). The FST. Additionally, I used the sequential F-statistics are relatedby the equation (1 - Bonferroni procedure to control for Type I FIS) = (1 -FIT)(1 -FST). error resulting from multiple significance In a multiallelesystem, the estimatesfrom tests (Rice, 1989). Finally, I reported neg- each allele are weightedby their frequencies ative FSTvalues as zero. The lowest value I to obtain the estimate for each locus. A jack- obtained at an individual locus was - 0.072, knife procedureis used to combine the in- and for a jackknife estimate the lowest value formation across alleles within a locus and was -0.012. across loci and generatea mean and a stan- Another way of describing the geograph- dard errorfor the F-statistics. Because Weir ical pattern of allele frequencies is to use and Cockerham's (1984) modification of principal components analysis. One can plot Wright'sF-statistics uses estimatorsthat are the principal components against other geo- corrected for sample size differences, FST graphic variables (cf. Langercrantz and Ry- may assume slightlynegative values in their man, 1990). In this analysis, I used the an- formulation, which indicates a lack of any gular transformed allele frequencies on each differentiationamong subpopulations(Slat- willow. I included all alleles that had an kin and Barton, 1989). overall frequency greater than 0.09, result- In this paper, I calculated FST separately ing in a total of 11 variables: ak-I allele 1, at each level in the spatial hierarchy.At the idh-2 alleles 1, 3, 4, mpi-l alleles 1, 2, pgi- lowest level, FSTwas calculated using the 1 alleles 1, 4, and pgm-1 alleles 1, 4, 6. I genotype frequencieson each willow within then plotted the first versus the second prin- a locality, and this calculationwas repeated cipal component and elevation versus the 15 times reflectingthe total number of lo- first principal component separately for each calities sampled (Table 1). I refer to this drainage. level as FWLdenoting differentiationamong willows in a locality. Then I calculated an RESULTS FSTamong localities within each drainage In most cases, the loci that were poly- (FLD), using the genotype frequencies for morphic in the original screening were poly- each locality. Three values were calculated morphic on each willow (Appendix 1). at this level, one for each drainage.Finally, However, at the level of drainages, popu- I calculated a single FST from the overall lations in Rock Creek and South lake pos- genotypic frequencies in each drainage, sessed alleles at idh-2 (allele 2), mpi- 1 (allele which I referto as FDT. This type of notation 3), and pgi- 1 (alleles 2, 3) that were absent was used by Wright (1978) and McCauley from Big Pine Creek, and the Big Pine Creek et al. (1988). I have calculated the F statis- and South Lake populations possessed four tics for each level in the spatial hierarchy alleles at pgm- 1 (alleles 2, 3, 5, 7) that were separatelybecause the individual measure- absent from Rock Creek. Additionally, at ments show more clearly how the patterns pgi-1, the most common allele at Big Pine of differentiationvary at differentlocations. Creek was quite rare at Rock Creek and LEAF BEETLE GENETIC DIFFERENTIATION 1101

TABLE 2. FST values among drainages (FDT) and among localities within each drainage (FLD). Table-wide significance levels obtained from G tests for allele frequency heterogeneity as described in the text.

Locus F-statistic ak-i idh-2 mpi-i pgi-i pgm-l FLD (BPC) 0.008 0.014*** 0.01 1* 0.012** 0.01 1** FLD (RC) 0.022 0.007 0.018 0.016 0.000 FLD (SL) 0.000 0.047*** 0.0 16** 0.058** 0.026*** FDT 0.016*** 0.030*** 0.040*** 0.412*** 0.067*** $p < 0.05, ** P < 0.0 1, *** P < 0.005. conversely, the most common allele at Rock enced by naturalselection, I made two jack- Creek was rare at Big Pine Creek. The South knife estimates for FDT, one that included Lake populations had intermediate fre- pgi- I and one without pgi- 1. In both cases, quencies of the two alleles at pgi- 1 (Appen- the overall among-drainagedifferentiation dix 1). was significant (Table 3). However, when There were no pervasive departures from pgi-I is included in the jackknife estimate HWE in the sample. After the sequential the standarderror is proportionatelygreat- Bonferroni adjustment of the P values for er, and the significancelevel is reduced to the number of tests made at each locus, only some degree. two of the deviations from HWE were sta- Among-Locality Differentiation. - Local- tistically significant. Both of these devia- ities within a drainagewere genetically dif- tions occurred on willow RV2 at locality ferentiatedas well (Tables 2 and 3). The loci SLe. At pgm- 1, there was a heterozygote within each drainageare in basic agreement, excess (Fi = -0.82, P < 0.001), while at except for the zero estimates of FLDat ak-I idh-2, there was a deficiency of heterozy- in South Lake and at pgm- 1 at Rock Creek. gotes (Fi = 0.32, P < 0.001). It is worth noting that ak-I shows the least If a population has experienced a pro- polymorphism at South Lake and pgm-1 longed bottleneck, its average heterozygos- shows much less polymorphism at Rock ity should be lower than others (Hartl and Creek than at the other two drainages(Ap- Clark, 1989). Average heterozygosities in pendix 1). In all three drainages, the jack- this study were very similar for Big Pine knife estimates of FLDwere lower than FDT, Creek (0.430 ? 0.084) and for South Lake indicating that the ridges indeed act as bar- (0.430 ? 0.097), but they were somewhat riers to gene flow. Nevertheless, the jack- smaller for Rock Creek (0.322 ? 0.099). knife estimate of FLD was substantially Much of this difference results from the greaterin South Lake than in the other two greatly reduced heterozygosity at pgi-1 (H drainages. = 0.078) in Rock Creek relative to Big Pine Among- Tree Diferentiation.-The esti- Creek (H = 0.322) and South Lake (H= mates for FWLvary considerablyamong lo- 0.431). calities (Tables 4 and 5). At this level, there Among-Drainage Differentiation. -The is greatervariability in the estimates across frequencies at each locus were significantly heterogeneous among drainages (Table 2). The FDTvalues for four of the five loci agree TABLE 3. Jackknifeestimates of C. aeneicollis genetic reasonably well, and the degree of poly- differentiationamong localities within each drainage morphism at the locus probably accounts and among drainages.Significance levels were deter- for some of the variation in FDT (Slatkin mined by t-tests. and Barton, 1989). However, the value of F-statistic F SE P FDTfor pgi- 1 is nearly 10 times greater than any of the other four loci. This discrepancy FDT with pgi-1 0.135 0.041 * FDT without pgi- 1 0.043 0.005 ** suggests that natural selection is acting on FLD (BPC) 0.012 0.001 * one or several of the alleles at pgi-1 at the FLD (RC) 0.010 0.002 ** level of drainages (Slatkin, 1987). Because FLD (SL) 0.037 0.004 ** differentiation at pgi- 1 appears to be influ- Table-wide significance levels, * P < 0.05, ** P < 0.01, *** P < 0.001. 1102 NATHAN EGAN RANK

TABLE 4. FWL values among trees in a locality for each locus. Column-wide significances were determined separately for each locus after conducting G tests of allele frequency heterogeneity as described in the text. Dashes indicate that a locus was not polymorphic at that locality.

Locus Locality ak-i idh-2 mpi-i pgi-l pgm-l FWL FWL FWL FWL FWL BPCa 0.000 0.001 0.000 0.000 0.000 BPCb 0.066 0.253** 0.000 0.068 0.107 BPCc 0.033 0.005 0.000 0.036 0.133 BPCd 0.018 0.038** 0.051** 0.049** 0.015 BPCe 0.000 0.001 0.000 0. 100*** 0.014 BPCf 0.009 0.033 0.018 0.128*** 0.027 BPCg 0.042 0.000 0.000 0.000 0.004 RCa 0.000 0.045 0.001 0.063 0.000 RCb 0. 167*** 0.049* 0.025 0.051 0.161 RCc 0.102 0.036 0.249** - 0.268*** SLa 0.015 0.030* 0.000 0.097*** 0.010 SLb 0.067** 0.104*** 0.016 0.018 0.061 SLc 0.005 0.008 0.000 0.004 0.022 SLd 0.023 0.000 0.000 0.000 0.000 SLe 0.980 0.386* 0.181 0.367*** 0.397 *P < 0.05, **P < 0.01, ***P < 0.001. loci, probably resulting from relatively The jackknife estimates of FWL indicated greater sampling error. Despite this vari- significant substructuring at the 95% col- ability, the FWLvalues for individual loci umn-wide significance level for 4 of the 15 were generally consistently high or low localities sampled, with significant values within a population and usually if there was ranging from 0.039 to 0.427. Additionally, significant differentiation at any one locus, another three localities were significantly dif- other loci indicated significant differentia- ferentiated at the 90% significance level (Ta- tion as well (Table 4). FWLwas greatest at ble 5). In four cases, (BPb, BPf, RCb, RCc), locality SLe, but the high value for ak-I at the among-willow differentiation occurred SLe resulted from its fixation at several wil- in localities where the sample size on at least lows. one tree was small (N < 10), and the highest value of FWLoccurred at locality SLe, where TABLE 5. Jackknife estimates of FIW, the inbreeding the subpopulations were clearly out of HWE. coefficient, and FWL among willows within a locality. On the other hand, at locality BPd, where All estimates are based on five loci except for RCc, FWLwas also significant, sample sizes were which was based on four loci because pgi- 1 was fixed consistently large. Finally, FWL was not re- for one allele. Statistical significance was determined lated to the average distance between wil- by t-tests. lows within a locality (m = 0.00 1, r = 0.35,

Locality FIW SE FWL SE P = 0.20, N = 15 localities). Neither the jackknifed estimates of at BPCa 0.000 ? 0.024 0.000 ? 0.001 FIS BPCb 0.002 ? 0.034 0.113 ? 0.022* the drainage level (FID) nor the estimates BPCc -0.047 ? 0.034 0.043 ? 0.016 for localities within a drainage (FIL) were BPCd -0.012 ? 0.015 0.036 ? 0.003** significantly different from zero. Addition- BPCe 0.015 ? 0.009 0.015 ? 0.008 ally, at the level of individual willows BPCf -0.030 ? 0.007 0.039 ? 0.007* (FIW), BPCg -0.005 ? 0.018 0.000 ? 0.004 most of the values were not significant (Ta- RCa -0.108 ? 0.028 0.023 ? 0.008 ble 5). This result suggests that there was no RCb 0.033 ? 0.025 0.079 ? 0.015* substantial genetic structuring within pop- RCc 0.015 ? 0.064 0.165 ? 0.041' ulations, and it corresponds with the finding SLa -0.064 ? 0.018 0.034 ? 0.009 that most of the SLb -0.097 ? 0.013* 0.016 ? 0.005 individual populations were SLc -0.053 ? 0.026 0.009 ? 0.0021 in HWE. For the one locality where FIWwas SLd -0.153 ? 0.058 0.000 ? 0.002 significant (locality SLb), it was negative SLe 0.024 ? 0.080 0.427 ? 0.048** (Table 5). It seems clear that C. aeneicollis I P < 0.1, * P < 0.05, ** P < 0.01. populations are not generally inbred and LEAF BEETLE GENETIC DIFFERENTIATION 1103

4 0

0 v v 0 2 T V T~T L

LO) v 0 0 00~~~

a_ 0 v~~~~~~ o -T

2800 3000 3200 3400 ELEVATION (m) PC1 (47X) FIG. 3. Plot of PCI versus elevation. Each point FIG. 2. Scatterplot of the first (PC 1) and second representsthe least-squaresmean for that locality and (PC2) principal components of the allele frequencies the errorbars representthe standarderror. The sym- at the five polymorphic loci. Each point represents a bols referto the differentdrainages as in Figure 2. different willow. Hollow circles refer to BP populations, filled triangles refer to SL populations, and filled circles refer to the RC populations. The percentages regressions showed that there was no sig- refer to the proportion of the variance explained by the nificant relationship in BP (m = 0.001 ? principal component. 0.001, r2 = 0.05, P = 0.22) or in RC (m = 0.001 ? 0.002, r2 = 0.01, P = 0.70), but there is no Wahlund effect resulting from the relationship between elevation and PC I genetic structure below the lowest level of was highly significant in SL (m = -0.007 the sampling design. ? 0.002, r2 = 0.58, P = 0.002). Thus, the In the principal components analysis, the upper-elevation populations in SL were first principal component (PC1) explained more genetically similar to the BP popula- 47% of the variation in the 11 allele fre- tions than the lower-elevation populations quencies, and the second principal com- in SL. These populations are the closest to ponent (PC2) explained an additional 15% the BP populations (Fig. 1). of the variation. However, a plot of PCI Differentiationamong WillowSpecies. - versus PC2 shows that PC1 accounts for At one of the localities in Big Pine Creek much of the variation among drainages (Fig. (BPf), I collected C. aeneicollis individuals 2). The drainages differ in their PCI scores from four neighboring willows of two spe- (Nested ANOVA with drainages and local- cies: two S. boothi individuals and two S. ities as random factors F2,14 = 102.8, P = orestera individuals. To determine whether 0.0001), but PCI also varied significantly C. aeneicollis populations on different spe- among localities within a drainage (F12,46 = cies were genetically differentiated, I used a 2.0, P = 0.04). PCI is most closely corre- different hierarchical F statistic (FSL). First, lated to the two most common alleles at I calculated FWLfor each species separately, pgi-I (r = 0.41 for allele 1 and -0.42 for then I pooled the genotype frequencies for allele 2). Nevertheless, it is also fairly highly each species and obtained FSL for the pooled correlated to alleles 1 and 6 at pgm- 1 (r = estimate. Although the sample size for any -0.41 and 0.35, respectively) and to allele comparison of among-willow differentia- 1 at mpi- 1 (r = -0.3 1). Of the alleles in- tion versus among-species differentiation is cluded in the PC analysis, these five differ small, this technique would detect substan- the most in frequency among the drainages. tial genetic divergence. For the beetles col- There was a linear relationship between lected on S. boothi, the jackknife estimate PC 1 and elevation in drainage SL, but not of FWL was 0.053 ? 0.010, and for the bee- in the other two drainages (Fig. 3). Linear tles collected on S. orestera, the estimate 1104 NATHAN EGAN RANK was 0.000 ? 0.003. The hierarchical FSL locus is highest in the SL populations be- was 0.040 + 0.009, which is smaller than cause both of the common alleles are pres- the jackknife estimate of FWL for S. boothi ent at high frequencies there. alone. If populations on different species Previous studies have indicated that nat- were subdivided, one would expect the hi- ural selection is acting on enzyme loci in erarchical FSL values to exceed the values herbivorous insects; Slatkin (1987) discuss- for FWL within each species (McPheron et es an example in the butterfly Euphydryas al., 1988). Thus, my results indicated no editha, where FsT at hexokinase was sub- differentiation among host species for C. stantially greater than at other loci. Addi- aeneicollis. tionally, natural selection on pgi has been demonstrated for Colias butterflies (Watt, DISCUSSION 1977; Watt et al., 1983, 1985). However, Chrysomela aeneicollis populations are this is the first observation that natural se- indeed subdivided among drainages. The lection is probably acting on pgi in a beetle. level of genetic subdivision (FDT = 0.043) In the case of Colias, differential tempera- among these nearby drainages is higher than ture sensitivities of the different pgi en- the FST values across broad geographic scales zymes form the basis for natural selection. for many flying insects, including bark bee- The pgi genotype affects the timing of tles, Drosophila spp., and several lepidop- flight activity. It is possible that different terans (McCauley and Eanes, 1987). The FsT pgi- 1 genotypes vary in their temperature value for Plagiodera versicolor populations sensitivity for C. aeneicollis. Further inves- from Virginia to Illinois is 0.057 (McCauley tigation is required to determine the signif- et al., 1988). Chrysomela aeneicollis's rel- icance of this unusual pattern of differen- atively higher levels of differentiation may tiation at pgi- 1. result from the fact that this is a native spe- Differentiation among localities within a cies that has presumably been in the eastern drainage has implications for local adap- Sierra Nevada for much longer than the tation in C. aeneicollis. Environmental con- length of time that the introduced P. ver- ditions vary with elevation along all three sicolor has been in North America. How- of these drainages. For example, Smiley and ever, it is also clear that C. aeneicollis's pop- Rank (1986) documented a reduction in ulation subdivision is also greatly affected levels of predation on C. aeneicollis with by the topographic relief of the eastern Si- increasing elevation along Big Pine Creek. erra Nevada. In addition, Smiley and Rank (1986) re- Despite the among-drainage differentia- ported phenotypic differences in larval tion, the PC analysis suggested that there is growth rates among populations from high gene flow over the Sierra crest. Although versus low elevations. Similarly, McCauley the streams are connected by nearly contin- et al. (1988) demonstrated spatial and tem- uous stands of willow at the lower eleva- poral differences in average relatedness tions, these areas appear to be very unsuit- among populations of P. versicolor.The re- able habitats for C. aeneicollis; over seven sults of this study suggest that C. aeneicollis years of research I never discovered a pop- populations are sufficiently isolated to allow ulation below 2,300 meters. Thus, it ap- for local adaptation to this environmental pears that most of the gene flow among variation. Genetic divergence among these drainages occurs over the ridges rather than populations at other loci that are under nat- along the streams. This gene flow has an- ural selection may be even greater than di- other result; it is probably responsible for vergence at the relatively neutral allozyme the greater FLD values observed in the SL loci (Slatkin, 1987). drainage than in the other two drainages. If At the lowest level of population struc- this drainage had been examined alone, the ture, differentiation among trees within a high FLD among the SL populations may locality, these results parallel those of have been erroneously attributed to genetic McCauley et al. (1988), who observed sig- drift. Finally, these results illustrate that gene nificant differentiation among trees in two flow can be a "creative force" as described of eight cases for P. versicolor.In both stud- by Slatkin (1987). The variability at the pgi-l ies, the among-tree FST varied considerably LEAF BEETLE GENETIC DIFFERENTIATION 1105 among localities. It is possible that among- plants. This phenomenon has been ob- tree differentiation may reflect differentia- served over a broad range of taxonomic tion at an even smaller scale, i.e., within groups, including treehoppers (Guttman and small groups of trees in a locality. This seems Weigt, 1989; Guttman et al., 1989), milk- unlikely however, because there was no re- weed beetles (McCauley and Eanes, 1987), lationship between the distance between willow leaf beetles (McCauley et al., 1988), willows in a locality and FWL. It is also un- and Rhagoletis fruit flies (McPheron et al., likely that the among-tree differentiation re- 1988; Feder et al., 1990a, 1990b). It appears flects among-branch differentiation as Gutt- that the association with patchily distrib- man et al. (1989) observed for treehoppers, uted host plants has important conse- or a Wahlund effect resulting from the com- quences for the subsequent evolution of her- bination of samples of several breeding col- bivores whose vagility is limited. Indeed, if onies, or inbreeding, as Crouau-Roy (1 9 8 8) a subdivided population structure is com- suggested for cave-dwelling beetles. Among- bined with phenological differences among branch differentiation is unlikely because the host plants, it may lead to host-race for- willows are relatively small, and because C. mation and sympatric speciation (Courtney aeneicollis individuals move about consid- Smith, 1988; Feder et al., 1990a, 1990b; erably within a willow. A Wahlund effect is Wood and Keese, 1990; Wood et al., 1990). unlikely because most of the willows appear In the absence of differences in willow phe- to be in HWE, and inbreeding appears to nology and host-preference among C. aenei- be rare in C. aeneicollis. collis populations that occur on different In some cases, the among-tree differen- hosts (Rank, 1990), host-race formation ap- tiation may have been based on sampling a pears unlikely in this species despite its pro- few families on individual willows. As not- nounced genetic subdivision. ed above, the willows sampled were occa- sionally relatively small, and because fe- ACKNOWLEDGMENTS males lay eggs in clutches of 30 eggs, some I thank B. Shaffer for suggesting this study, trees may have consisted of only a few fam- and J. Smiley for getting me interested in ilies. This was apparently the case on willow willow beetles. J. Clark, B. Shaffer, and A. RV2 at locality SLe, where one locus indi- Guerrera assisted the author with the elec- cated a deficit of heterozygotes and another trophoresis. H. Dingle, B. Shaffer, and J. indicated an excess, which would occur if Smiley made useful comments on an earlier many of the individuals came from an in- version of this manuscript, and J. Schmitt, dividual family. Family group sampling may W. Watt, and an anonymous reviewer's also have occurred at localities (BPb, BPf, comments improved it greatly. T. Holtsford RCb, RCc) where some of the willows were kindly provided his FORTRAN program to relatvely small and beetle population sizes calculate the F statistics. This research was were low. On the other hand, other localities supported by a NSF dissertation improve- with among-tree differentiation (BPd, BPf, ment grant (BSR-8714734), by the White SLc) consisted of larger willows with larger Mountain Research Station, and by the population sizes. In these cases, it is most Center for Population Biology at the Uni- likely that the among-tree differentiation re- versity of California, Davis. flects genetic drift among C. aeneicollispop- ulations on different willows. Thus, overall LITERATURECITED genetic subdivision at this scale may be in- AYALA, F. J., J. R. POWELL,M. L. TRACEY,C. A. fluenced by founder effects as well as some MOURAO,AND S. PEREZ-SALAS.1972. Enzyme genetic drift following establishment, as variability in the Drosophilawillistonii group. IV. Genetic variation in natural populations of Dro- McCauley and Eanes (1987) concluded for sophila willistonii.Genetics 70:113-139. the milkweed beetle Tetraopestetraophthal- BROWN,W. J. 1956. The New World species of Chry- mus. somela L. (Coleoptera: Chrysomelidae). Can. En- In conclusion, these results represent yet tomol. 88, Suppl. 3. 1-54. COURTNEYSMITH, D. 1988. Heritable divergence of another example of an herbivorous insect Rhagoletispomonella host races by seasonal asyn- that shows significant genetic subdivision at chrony. Nature 336:66-67. small spatial scales down to individual host CROUAU-ROY,B. 1988. Genetic structure of cave- 1106 NATHAN EGAN RANK

dwelling beetles populations: significant deficien- (Schaeffer). Ph.D. Diss. Univ. of California, Davis, cies of heterozygotes. Heredity 60:321-327. USA. FEDER,J. L., C. A. CHILCOTE,AND G. L. BUSH. 1990a. RAUPP,M. J., AND R. F. DENNO. 1983. Leaf age as Regional, local, and microgeographic allele fre- a predictor of herbivore distribution and abun- quency variation between apple and hawthorn pop- dance, pp. 91-124. In R. F. Denno and M. S. Mc- ulations of Rhagoletispomonella (Diptera: Tephrit- Clure, (eds.) Variable Plants and Herbivores in Nat- idae) in the Eastern United States and Canada. ural and Managed Systems. Academic Press, N.Y., Evolution 44:570-594. USA. 1990b. The geographic pattern of genetic dif- RIcE,W.R. 1989. Analyzingtablesofstatisticaltests. ferentiation between host-associated populations of Evolution 43:223-225. Rhagoletispomonella (Diptera: Tephritidae) in the RoWELL-RAHIER,M. 1984. The food plant prefer- Eastern United States and Canada. Evolution 44: ences of Phratora vitellinae (Coleoptera: Chryso- 595-608. melidae). A: Field observations. Oecologia 64:369- GUTTMAN, S. I., AND L. A. WEIGT. 1989. Macrogeo- 374. graphic genetic variation in the Enchenopabinotata SLATKIN,M. 1987. Gene flow and the geographic complex (Homoptera: Membracidae). Ann. Ento- structure of natural populations. Science 236:787- mol. Soc. Am. 82:225-231. 792. GUTTMAN, S. I., T. WILSON, AND L. A. WEIGT. 1989. SLATKIN, M., AND N. H. BARTON. 1989. A compar- Microgeographic genetic variation in the Encheno- ison of three indirect methods for estimating av- pa binotatacomplex (Homoptera: Membracidae). erage levels of gene flow. Evolution 43:1349-1368. Ann. Entomol. Soc. Am. 82:156-165. SMILEY, J. T., AND N. E. RANK. 1986. Predator pro- HARRIS, H., AND D. A. HOPKINSON. 1976. Handbook tection versus rapid growth in a montane leaf beetle. of Enzyme Electrophoresis in Human Genetics. El- Oecologia 70:106-112. sevier, N.Y., USA. SWOFFORD, D. L., AND R. B. SELANDER. 1981. BIO- HARTL, D. L., AND A. G. CLARK. 1989. Principles of SYS- 1: A Fortran computer program for the anal- Population Genetics. Sinauer, Sunderland, MA ysis of allelic variation in genetics. J. Hered. 72: USA. 281-283. LANGERCRANTZ, U., AND N. RYMAN. 1990. Genetic WATT, W. B. 1977. Adaptation at specific loci. 1. structure of Norway Spruce (Picea abies):Concor- Natural selection on phosphoglucoseisomerase of dance of morphological and allozymic variation. Colias butterflies: Biochemical and population as- Evolution 44:38-53. pects. Genetics 87:177-194. MCCAULEY, D. E., AND W. F. EANES. 1987. Hierar- WATT,W. B., P. A. CARTER,AND S. M. BLOWER. 1985. chical population structure analysis of the milk- Adaptation at specific loci. IV. Differential mating weed beetle, Tetraopestetraophthalmus (Forster). success among glycolytic allozyme genotypes of Heredity 58:193-201. Colias butterflies. Genetics 109:157-175. MCCAULEY, D. E., M. J. WADE, F. J. BREDEN, AND M. WATT, W. B., R. C. CASSIN, AND M. S. SWAN. 1983. WOHLTMAN. 1988. Spiral and temporal variation Adaptation at specific loci. III. Field behavior and in group relatedness: Evidence from the imported survivorship differences among Colias pgi geno- willow leaf beetle. Evolution 42:184-192. types are predictable from in vitro biochemistry. MCPHERON, B. A., D. COURTNEY SMITH, AND S. H. Genetics 103:725-729. BERLOCHER. 1988. Genetic differences between WEIR,B. S., ANDC. C. COCKERHAM.1984. Estimating host races of Rhagoletispomonella. Nature 336:64- F-statistics for the analysis of population structure. 66. Evolution 38:1358-1370. MURPHY, R. W., J. W. SITES, D. G. BUTH, AND C. H. WOOD,T. K., AND M. C. KEESE. 1990. Host-plant- HAUFLER. 1990. Proteins I: Isozyme electropho- induced assortative mating in Enchenopatreehop- resis, pp. 45-126. In D. M. Hillis and C. Moritz pers. Evolution 44:619-628. (eds.), Molecular Systematics. Sinauer, Sunderland, WOOD,T. K., K. L. OLMSTEAD,AND S. I. GUTTMAN. MA USA. 1990. Insect phenology mediated by host-plant PASTEELS, J. M., M. RowELL-RAHIER, AND M. J. RAUPP. water relations. Evolution 44:629-636. 1988. Plant-derived defense in Chrysomelid bee- WRIGHT, S. 1978. Evolution and the genetics of pop- tles, pp. 235-271. In P. Barbosa and D. Letourneau ulations. Vol. 4. Variability Within and Among (eds.), Novel Aspects of Insect-plant Interactions. Natural Populations. Univ. of Chicago Press, Chi- Wiley, N.Y., USA. cago, IL USA. RANK, N. E. 1990. Ecological determinants of host plant use and migrogeographic genetic differentia- Corresponding Editor: J. Schmitt tion in a willow leaf beetle Chrysomelaaeneicollis LEAF BEETLEGENETIC DIFFERENTIATION 1107

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