41Winter 2011

Assessing the Utility of Microsatellites for Assigning MaternityIn a Wild Population of Anolis sagrei Lizards

eliZAbeth pArker ‘12

ecology

To study selection in the wild, one must be able to measure both survival and reproductive success in situ. Brown anole lizards (Anolis sagrei) are an ideal

model species for studying natural selection in the wild (i.e., survival), but studies of sexual selection in the wild (i.e., mating success) require genetic techniques for assigning pa-ternity. Previous studies have assigned paternity using mi-crosatellite genetic markers, but only when maternity was already known. In the present study, each of 29 dams and their collective 103 progeny were genotyped at seven mic-rosatellite loci and the program cERVuS was used to esti-mate maternity assuming no prior knowledge of parentage. By comparing these maternity assignments to the true dam for each offspring, we determined that this methodology yields 87% success in maternity identification. Most incor-rect assignments of maternity occurred when multiple dams matched an offspring at all loci, suggesting that including additional loci might improve success rates. The applica-tion of this methodology to wild populations of anoles of unknown parentage should allow for successful maternity assignments in situ, thereby alleviating some of the difficul-ties associated with studying sexual selection in the wild.

IntroductionAn animal’s fitness is related to both survival and re-

productive success. To study selection in the wild, one must therefore measure both natural and sexual selection, ideally, by measuring survival, fecundity, and mating success in situ. Many studies have measured selection in the wild (1-4); how-ever, only a handful have measured total fitness, defined as including survival, mating success, and fecundity (5-8). These studies indicate that focusing on individual components of fit-ness often yields misleading conclusions of the total strength and form of selection. Thus, understanding how survival, fe-cundity, and mating success interact is an important step to-wards understanding how selection shapes wild populations.

Brown anole lizards are an ideal model species for studying natural selection in the wild due to their abun-dance, ease of capture, high site fidelity, and relatively short lifespan (9-11). These features allow for relatively straight-forward measurements of natural selection arising from differential survival. Measuring fecundity and mating suc-cess in situ, however, presents several challenges. First, female anoles repeatedly lay single eggs at 1-2 week inter-vals throughout a breeding season that can last for up to six months (12-14). this makes it difficult to measure the total annual fecundity of individual females without transferring them to captivity and thereby defeating the goal of measuring selection in situ. Second, female anoles typically mate and produce offspring with more than one male (15). This also makes it difficult to determine the true reproductive success

of wild males without performing genetic paternity analyses.Genetic analyses can be used to estimate the reproduc-

tive success of adults in a population by assigning parent-age and then counting the number of viable progeny each adult produces. Methodologies using microsatellite genetic markers for paternity analyses have proven successful in brown anoles (16-18); however, these previous studies have ensured that the maternal genetic contribution and, hence, maternal identity, was known a priori by taking females into captivity following mating and collecting their offspring. While transferring females into captivity allows one to iden-tify maternity with certainty, it also alters subsequent mat-ing dynamics and prevents simultaneous studies of natural selection. If neither parent is known ahead of time, assign-ing paternity from these same microsatellite genetic mark-ers may be significantly more difficult depending on the genetic structure of the population at these particular loci.

Ideally, both natural and sexual selection can be studied in the wild. Here, I test a methodology that aims to reduce the challenges associated with studying sexual selection in the wild via the use of microsatellites to assess maternity with no prior information on parentage. If maternal identity can be assigned with confidence, this will facilitate estimates of fe-male fecundity and male reproductive success in future work. In anticipation of these future studies, I genotyped eight mi-crosatellite loci for each of 329 tissue samples that were col-lected from an entire island population of brown anole dams and sires (Regatta Point, Great Exuma, The Bahamas) and for 318 of their captive-bred progeny. My specific goal in the present study was to use a subset of this large data set to test our ability to determine maternity when assuming no prior knowledge of parentage. I did this by comparing maximum-likelihood estimates of the most likely dam for 103 individual progeny hatched in captivity from 29 individual dams against the true identity of the dam, which was known with certainty.

Materials and MethodsSampling of adults in the wild

At the beginning of the reproductive season (May 2010), all adult male and female brown anoles were captured from an isolated island population on Regatta Point, near Georgetown, Great Exuma, Bahamas (23°30’N, 75°45’W). A tissue sample (2 mm, tail tip) was collected from each individual and stored at -20 °F. Snout-vent length (SVl, nearest mm) and body mass (nearest 0.1 g) were measured for each individual using a ruler and a 10-g Pesola spring scale. A subset of adult females (n = 92) from the south end of the island were transported to a captive breeding facil-ity at Dartmouth College so that their progeny could be collected as they hatched. The remainder of the females and all adult males were released at their site of capture.

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Collection of progeny in captivity Gravid females were individually housed in 10-gal-

lon glass cages in the breeding facility at Dartmouth Col-lege. Each cage contained a potted plant into which females oviposited their eggs. Plants were located directly under a 40-W incandescent bulb for warmth, and all cages were situated under two Repti Glo 5.0 fluorescent bulbs for uVB light. Females were fed an ad libitum diet of crickets (Acheta domestica) that were dusted weekly with vitamin and mineral supplements (Repta-Vitamin, Fluker Farms, Port Allen, LA). Cages and plants were watered daily.

Previous studies have shown that brown anoles store sperm for several months and repeatedly lay single eggs at 11-day intervals in captivity (15, 12, 13). Although the fe-males in this study mated only in the wild prior to capture, they produced an average of 3.46 offspring (range 0-8) over a period of four months following capture. All cages were searched on a weekly basis and new hatchlings were sexed, measured for SVl (nearest 0.5 mm) and mass (nearest 0.2 g), and transplanted to new cages. A tissue sample (2 mm, tail tip) was collected from each hatchling and stored at -20°F.

DNA extraction I used a sterile scalpel to obtain a thin slice of tissue from

each tail sample. Each tissue sample was placed in a 200 µl strip tube containing 150 µl of 5% chelex in purified water and 1.0 µl of Proteinase-K. DNA was extracted by incubating samples for 180 minutes at 55 °C and then for 10 minutes at 99 °C on a thermocycler. Samples were then centrifuged for 15 minutes at 3000 rpm and 30 µl of the supernatant was collected and stored at -20 °F until PcR amplification.

Amplification of microsatellite loci Each individual hatchling, dam, and candidate sire was

genotyped at eight microsatellite loci: AAAG-70, AAAG-68, AAAG-91, AAAG-61, AAGG-38, AAAG-77, AAAG-76, and AAAG-94 (19). I performed PCR reactions using a total vol-ume of 10 µl with 1 µl template DNA, 1 µl 10x Buffer, 0.6 µl MgCl2, 0.8 µl dNTPs, 0.25 µl of each primer (forward and reverse), and 0.06 µl of Taq polymerase. PCR cycles consisted of an initial denaturation step at 94°C for 5 min followed by 29 or 35 cycles of 45 sec at 94 °C (Table 1), 1 min at primer-specific annealing temperatures (t

a, Table

1), and 1 min at 72 °c, followed by a final extension for 5 min at 72 ° C. See Table 1 for a details on PCR conditions and number of cycles for each respective locus. All PCRs were performed on a DNAEngine Thermal Cycler (Bio Rad).

Locus Ta (°C) # of cyclesPool 1 AAAG-70 56 29 AAAG-68 56 35 AAAG-91 54 35 AAAG-61 55 35Pool 2 AAGG-38 44 35 AAAG-77 55 35 AAAG-76 54 35 AAAG-94 55 35

Sequencing and microsatellite analysisLoci were pooled into two sets for genotyping (Table

1) on an ABI 3730 Genetic Analyzer (Applied Biosystems) using ABI multiplex dye-labeled primers. All genotypes were scored by visual inspection of electro-pherogram traces using GeneMapper (version 3.7) software against a GeneScan 500 LIZ size standard (Applied Biosystems). One locus (AAAG-76) was difficult to score and was, there-fore, conservatively omitted from subsequent parent-age analyses, which were, as a result, based on a total of seven loci. For the parentage analyses described below, only those progeny and dams that were successfully geno-typed at five or more of these seven loci were included.

Maternity analysisi used the computer program cERVuS (version 3.0; 20)

to estimate allele frequencies at each locus (Fig. 1) and con-ducted parentage analysis. i first confirmed that levels of ob-served heterozygosity for each locus were similar to expected levels of heterozygosity using the Hardy-Weinberg Equilib-rium test as implemented in cERVuS. i also confirmed that the frequency of null alleles was less than 5% for each locus, as recommended for inclusion in parentage analyses using cERVuS. i then ran a simulation analysis on the genotype data set to determine confidence levels at both 80% and 95% for the assignment of maternity. I did this by simulating 10,000 offspring genotypes and assuming that the propor-tion of sampled dams was 100% and that the proportion of loci that were typed and mistyped were 98% and 1% respec-tively. i then used cERVuS to assign maternity by compar-ing the genotype of each offspring to all of the 29 potential dams in the population using a maximum likelihood analysis.

ResultscERVuS assigned maternity to all but two of the 103

progeny included in our analysis. Of the 101 progeny for which cERVuS assigned maternity, 90 of these individuals were correctly assigned maternity when compared to known dams. Thus, maternity was successfully assigned in 90 of 103 progeny (87%) using these seven microsatellite loci (Fig. 2a).

To gain further insight into the sources of error in maternity assignment, I separately assessed the success of cERVuS when assigning maternity at the 95% and 80% con-fidence levels. cERVuS assigned maternity with 95% confi-dence for 77 progeny, of which 74 (96%) were correct when compared against known dams (Fig. 2a). Inspection of the three individual progeny that were incorrectly assigned at the 95% confidence level revealed clear mismatches between the genotypes of known dams and those of their progeny. This indicates either an error in genotyping or in recording which progeny came from which dam rather than an error due strictly to insufficient genetic variation at these seven loci. cERVuS assigned maternity with 80% confidence for 24 progeny, of which 16 (67%) were correct (Fig. 2a). The eight incorrect assignments all occurred in situations where multi-ple candidate dams matched progeny at each locus (Fig. 1b).

to assess the ability of cERVuS to identify maternity in situations where multiple candidate dams matched progeny,

Table 1: PCR conditions and sequencing pools. Ta = annealing temperature.

43Winter 2011

I performed a second set of analyses on the subset of proge-ny for which multiple candidate dams matched at each locus (Fig. 2b). This situation occurred in 43 of the 103 progeny (42%). Of these 43 cases, cERVuS accurately identified ma-ternity 35 times (81%). When cERVuS assigned maternity with 95% confidence in these situations, it was correct in all 21 assignments. By contrast, when cERVuS assigned mater-nity with 80% confidence in these situations, it was only cor-rect in 14 out of 22 cases (64%). In each of these eight cases of incorrect assignment, the incorrect designations can be explained by cERVuS taking allele frequencies into account and favoring an incorrect dam that shared rare alleles with the progeny over the true dam that passed on the common allele.

DiscussionOur analysis indicates that the methodology presented

here is successful in determining maternity with 87% success by using seven microsatellite loci and no information about paternal genotypes. However, with only seven loci used for this population there was a relatively high frequency of situ-ations in which at least two dams matched the progeny at all loci (43/103 = 42%). Although these seven loci were suf-ficient to accurately resolve maternity in 81% of these cases (35/43), this nonetheless represents a considerable source of error in the measurements. In particular, measurements made at the 80% confidence interval were only correct in 36% of the cases (8/22). To reduce the frequency of wrong assignments one would have to throw out all cases in which multiple candidate dams match the progeny at all seven loci and the match is made with 80% confidence. under these criteria, 21% of the present data set would be considered un-reliable. However, the rest of the data set would be identi-fied with 95 to 100% confidence and the only errors would be due to genotyping errors. Thus, if the goal is to have full confidence in maternity, our analysis suggests that 21% of the data set should be considered unreliable - namely those situations in which there are multiple potential dams that match at all seven loci and maternity is assigned with only 80% confidence. this methodology, therefore, allows for 74% of data to be successfully called with >95% confi-dence, with the only error due to inaccurate genotyping.

Our data suggest that, for a reasonable fraction of a population, one can estimate maternity and paternity when both are unknown using these seven microsatellite loci. Fu-ture use of the methodology presented here has the potential to allow for in situ measurements of sexual selections via the

assignation of maternity using these seven microsatellite loci.

Fig. 1: Frequency of alleles by locus.

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AcknowledgementsThank you to R. M. Cox for his mentorship and help

with analysis, R. Calsbeek for suggestions about experi-mental design and manuscript clarifications, and M. c. Duryea for help with PCR, sequencing, GeneMapper analy-sis, and troubleshooting. Thanks to J. McLaughlan and K. Pinson for assistance collecting progeny tissue samples. I conducted this research as a recipient of the James O. Freedman Presidential Scholar Research Assistantship.

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Fig. 2: Accuracy of CERVUS maternity calls.