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Crocodilian Biology and Evolution

Genetic structure of six populations of Americanalligators: a microsatellite analysis

L. M. Davis, T. C. Glenn, R. M. Elsey, I. L. Brisbin Jr, W. E.Rhodes, H. C. Dessauer and R. H. Sawyer

Surrey Beatty & Sons43 Rickard Road, Chipping Norton, New South Wales, 2170 Australia

Telephone: (02) 9602 3888 Facsimile: (02) 9821 1253Email: [email protected]

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CHAPTER 4

calls annually from people who have hadencounters, either directly or indirectly, withalligators. In response, nuisance control agentsmust relocate or destroy the animals inquestion. Each of these management practicesrequires consideration of the most appropriatemethods for achieving a balance betweenmaintaining alligators as a sustainableresource and the preservation of healthy wildalligator populations.One component considered critical to the

successful management of healthy wildpopulations is the maintenance of geneticvariation. In fact, one of the three specificobjectives outlined by the World ConservationStrategy of the IUCN/SSC Crocodile SpecialistGroup is the preservation of genetic diversity(Thorbjarnarson 1992). To manage a specieswith that goal in mind, appropriate geneticmarkers should be developed that allowinsight into the genetic structure of thepopulations of interest. Such data can provideimportant clues about the evolutionary history

Genetic structure of six populations ofAmerican alligators: a microsatellite analysis

LISA M. DAVIS,1 TRAVIS C. GLENN,I.2.6 RUTH M. ELSEY,3 1. LEHR BRISBIN ]R,2WALTER E. RHODES,4 HERBERT C. DESSAUER5 and ROGER H. SAWYERI

The American alligator Alligator mississippiensis was once listed as an endangered species but now thrivesin many wetland ecosystems of the southeastern United States. As a result of its present abundance, state andfederal wildlife agencies must manage alligator populations in a number of ways including handling nuisancecalls, overseeing controlled harvests and regulating trade in meat and hides. To date, few genetic data havebeen available for consideration when developing management plans for alligator populations. This study usesfive microsatellite loci to examine the genetic structure of six populations of American alligators throughout theirgeographic range. A total of 178 individuals were analysed from 1) southwest Louisiana, 2) Marsh Island,Louisiana, 3) Mobile, Alabama, 4) Savannah River Site, South Carolina, 5) Santee Coastal Reserve, SouthCarolina, and 6) Everglades National Park, Florida. The amount of genetic variation detected by these microsatelliteloci represents the highest found by any study of this species to date. Observed mean heterozygosity across allloci for all populations ranged from 0.52 to 0.76. Measures of genetic distance (delta mu squared, ~~2) revealedsignificant population differentiation among all populations and a significant correlation between genetic andgeographic distance (P = 0.01). Analyses of molecular variance (AMOVAs) failed to demonstrate higher levelsub-structuring of groups of populations although there was a striking degree of among population variation(26.46%). The Savannah River Site population, the only inland population in the study, had unique geneticcharacteristics relative to coastal populations. Each population had distinct alleles in at least one of the fiveloci, some of which occurred in relatively high frequency, providing possible location-specific genetic markers.Additionally, assignment tests utilizing a variety of genetic distance measurements allowed assignment ofindividuals to their correct population of origin 72-83% of the time, though they were assigned to their ownpopulation of origin exclusively, only 35-45% of the time.

Key words: Alligator, Microsatellites, Population genetic structure.

INTRODUCTION

Background and management of Americanalligator populations

IN spite of severe population declines through-out much of its range during the first halfof this century, the American alligator thrivesin large numbers today. This rise in numberhas resulted in the need for management ofalligator populations at various levels. Stateregulated public and private harvests areoverseen by wildlife agencies who monitor thetaking of a limited number of animals fromspecific harvest areas Uoanen and McNease1987; Louisiana Dept. of Wildlife and Fisheries,unpub!. report; Woodward 1998). Trade inalligator meat and hides is regulated at state,federal and international levels in an attemptto curtail illegal harvest and trade of productsfrom several wild crocodilian species, many ofwhich are listed as threatened or endangered(Thorbjarnarson 1992). Additionally, statewildlife managers must address hundreds of

'DepartmentofBiologicalSciences.UniversityofSouthCarolina.Columbia.SC29208USA.'SavannahRiverEcologyLaboratory.DrawerE. Aiken.SC29802USA.'LouisianaDepartmentofWildlifeandFisheries.RockefellerWildlifeRefuge.GrandChenier.LA70643USA."South Carolina Department of Natural Resources, P.O. Drawer 190. Bonneau. SC 29431 USA."Department of Biochemistry and Molecular Biology, Louisiana State University Medical Center, New Orleans. 70119 LA, USA."To whom correspondence should be addressed.Pages38-50in CROCODILIANBIOLOGYANDEVOLUTIONedbyGordonC.Grigg.FrankSeebacherandCraigE. Franklin.SurreyBeatty 8<Sons.ChippingNorton.2000.

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DAVIS ET AL.: GENETIC STRUCTURE OF AMERICAN ALLIGATORS 39

of species such as recent or ancient bottle-necks, patterns of gene flow and relationshipsbetween sub-populations (Menotti-Raymondand O'Brien 1995; Ramey 1995). Further,genetic markers that elucidate the populationstructure and reproductive dynamics of localpopulations can be used in conjunction withwhat is known about the organism's overallgenetic structure, ecology, physiology, etc., tomake more educated management decisionsfor the species as a whole (Paetkau et al. 1995;Sugg et al. 1996; Wenburg et al. 1998).

Much is already known about alligatorecology and behavior that would allow certainhypotheses of genetic structure to be proposedand tested. First, observations of matingbehavior indicate that large males dominatebreeding by forming mating territories fromwhich smaller males are excluded (Garrick andLang 1977; Vliet 1989). Additionally, studiesof movement patterns in American alligatorsand freshwater crocodiles (Crocodylus johnstoni)show that males, both as adults and as sexuallyimmature juveniles, tend to disperse greaterdistances than females (joanen and McNease1972; Brisbin et al. 1992; Tucker et al. 1998).In light of these observations, it could behypothesized that population genetic structurewould reflect a male-mediated gene flowpattern within certain geographic regions.

In addition to the behavioral aspects ofthe American alligator, several importantthings should be considered when forminghypotheses about population genetic structure.The first of these is the long evolutionaryhistory of this species. As a group, crocodilianshave existed for approximately 200 millionyears (Alderton 1991). Many crocodilianspecies were able to survive the changes inglobal climate that caused the eradicationof many terrestrial and marine species 65million years ago (Sues 1989). Today, theAmerican alligator has a southeastern distribu-tion in the United States, though fossil recordsindicate that their range extended into morenorthern regions prior to the Ice Age (Sues1989). Alligators have undergone dramaticpopulation fluctuations since the Europeancolonization of the Americas, particularly inthis century. During the early 1960s whenthere was the greatest decline in numbers,populations in portions of Texas, Louisianaand Florida remained relatively high whilepopulations in other areas of the alligator'srange suffered dramatic declines (Ross andRoberts 1979). Over the past three decadesthere has been a tremendous recovery dueto a number of factors including protectivelegislation, sustained utilization managementprogrammes and restocking of manythousands of alligators into depleted habitats

(Ross and Roberts 1979). Given these factors,it would be possible to hypothesize thatpresent day alligator population geneticstructure reflects a historical, higher levelstructuring involving differentiation of easternand western populations.

Objective of study

The purpose of this study was to examinesix populations of American alligators usingfive polymorphic microsatellite loci to: 1)discover the allele frequencies and distribu-tions of these markers for each population,2) elucidate the population genetic structureamong and within all six populations, 3)perform statistical analyses to test for popula-tion differentiation against a null hypothesisof random mating across all populations,and 4) discuss the use of these data inthe management of alligators, including thepotential for developing location-specificgenetic markers.

Alligator population genetic studies

Several previous studies sought to quantifygenetic variation in American alligatorpopulations. Three studies examined proteinpolymorphisms in wild populations fromLouisiana, Florida and South Carolina(Gartside et al. 1977; Menzies et al. 1979;Adams et al. 1980). All studies reported lowlevels of genetic variation. For example, ofthe 49 loci examined in the Louisiana popula-tion (n = 80), only lactate dehydrogenase-2,catalase and peptidase-2 were found to bepolymorphic (Gartside et al. 1977). Averageheterozygosity for this sample (0.021) wasamong the lowest reported for a vertebratespecies (Nevo et al. 1984). Additionally, Nei'sindex of genetic distance (D) between thethree populations examined by Adams et al.(1980) was quite low (Louisiana and SouthCarolina, D = 0.003; South Carolina andFlorida, D = 0.008; Louisiana and Florida,D = 0.004).

Four other methods have been employed todetect variation in American alligators. Thefirst of these is a Southern DNA hybridizationanalysis that utilized M13 DNA, a markerthat has been shown to reveal extensivepolymorphism in many organisms (Vassartet al. 1987). This approach yielded only fourbands, the patterns of which were identicalacross all alligators examined (n = 10;Dessauer, unpubl. data). Second, randomlyamplified polymorphic DNA (RAPD) analysishas also been attempted (Dessauer, unpubl.data). Of the 43 arbitrary primers used todetect variation in the alligator, 34 yieldedamplicons and of those, only 23% werepolymorphic. A study by Demas and Wachtel

microsatellite loci reported in that study arethe most polymorphic genetic markers yetavailable for American alligators. Therefore,they hold great promise for providing insightinto the reproductive and social structure ofalligators, levels of gene flow, and developinglocation-specific markers for use in wildlifeforensics.

40 CROCODILIAN BIOLOGY AND EVOLUTION

(1991) utilized the banded krait minor satelliteDNA probe (Bkm) to examine DNA finger-prints in six species of chelonians and fivespecies of crocodilians. Interestingly, individual-specific DNA fingerprints were distinguishablefor all chelonians examined whereas onlythree fragments were polymorphic in 45American alligators (but see Lang et al,1993; Aggarwal et al, 1994). Finally, our labsequenced two regions of the mitochondrialgenome of the American alligator, cytochromeb (cyt b) and the control region or D-loop.These regions have been used in a numberof studies because they tend to be easy toamplify and have been found to be variablein many organisms (Kocher et al. 1989). Yet,of the 25 individuals sampled from acrossthe American alligator's geographic range,we found only one polymorphism at thecyt b locus and two polymorphisms in thecontrol region (Glenn, unpub!. data). The lowvariation revealed by these studies makes mostof the genetic markers examined of limiteduse in management programmes.

Recently, Glenn et al. (1998) characterized11 polymorphic micro satellite loci forAmerican alligators. Preliminary microsatelliteanalyses of alligators sampled from southwestLouisiana (SWL) and Everglades NationalPark, Florida (ENP) revealed 4-17 alleles perlocus for 43 individuals (Glenn et al. 1998).The average heterozygosity for these twosample sites was 0.47. More importantly,the level of variability demonstrated by theseloci was extensive enough to distinguishbetween all individuals sampled. Additionally,the SWL and ENP populations were signifi-cantly differentiated at all loci examined. The

MATERIALS AND METHODS

Samples and study sites

Six populations of American alligators (Fig.1) were sampled: southwest Louisiana (SWL,n = 53), Marsh Island, Louisiana (n = 24),Mobile Bay (Mobile), Alabama (n = 23), theSavannah River Site (SRS), South Carolina(n = 45), Santee Coastal Reserve (Santee),South Carolina (n = 19), and EvergladesNational Park (ENP), Florida (n = 14). Bloodwas collected from the anterior dorsal sinus,caudal vein or by cardiac puncture. In the caseof nuisance and harvested animals, a musclesample was taken in addition to the blood. Asubset (n = 19) of the SWL population usedin this study and the entire ENP sample werepreviously characterized by Glenn et al. (1998).These samples were added to this studydue to a slight modification in techniquesused and evaluated in analyses of all six popu-lations. Most of the additional 34 alligatorsfrom SWL were sampled from RockefellerWildlife Refuge in 1997. The 24 individualsfrom Marsh Island were taken in the 1997harvest. It should be noted that Marsh Island,although a freestanding island, is in closeproximity to mainland Louisiana marshes.The 24 alligators from Mobile were taken

Mississippi River

Southwest, LA(incl. RockefellerWildlife Refuge)(S='\E

Santee CoastalReserve (Santee,

n=19)

Everglades National Park(ENP, n=14)

Fig. 1. Range of the American alligator (shaded, adapted from R. Coulson and T. Hernandez 1983) with samplingsites and sizes indicated. Dashed lines indicate the approximate routes of geographic distance measurements.Bold line is the Mississippi River.


as nuisance animals in 1997 and 1998. TheSRS samples consisted of 45 individualssampled between 1996 and 1998. Nineteenindividual hatchlings, one each from 19 nests,were sampled in 1997 from Santee. Previouslypublished descriptions of the study areas (SWL- Joanen and McNease 1987; SRS - Murphy1981; Brandt 1989; Santee - Rhodes andLang 1995, 1996; Marsh Island - Tayloret at. 1991) provide additional informationon the characteristics of the habitats andalligator populations.

DNA isolationDNA was isolated using a minor modifi-

cation of methods described by Carter andMilton (1993). Blood or muscle was placed inlysis buffer (100mM Tris pH 8.0, 100mMEDTA, 1% SDS) and stored at -20°C. DNAwas isolated by adding approximately 100 f.LIof blood or 40 mg muscle and 4 f.LI ofproteinase K (10 mg/ml) to 500 f.LI TENSsolution (12 mM Tris pH 8.0, 12 mM EDTA,120 mM NaCI, 1% SDS). This was incubatedat 55°C overnight. Of this digest, 200 f.LIwasadded to 900 f.LI of guanidine thiocyanate(80 mM Tris pH 8.0; 8M GuSCN; 35 mMEDTA, 0.5% Triton X-100) and 75 f.LIMUD(1:1 w]v diatomaceous earth in water) andplaced in a rocking incubator at 55°C from15 min. to overnight. The mixture was thenvortexed and centrifuged at 10 000 X g forone min. and the supernatant removed. TheDNA-containing MUD pellet was then washedtwo times with 70% ethanol, dried, andresuspended in 200 f.LITE (10 mM Tris pH8.0, 1 mM EDTA). After incubation at 55°Cfor at least 15 min., the MUD/DNA mixturewas centrifuged at 10 000 x g for five min.and the DNA-containing supernatant removed.DNA quality and quantity were estimated byelectrophoresis through a 1% agarose gel andvisualized by ethidium bromide staining andultraviolet light transillumination.

Multiplex PCRDetailed protocols on the development and

characterization of the microsatellite loci usedin this study can be downloaded from theInternet at: http://gator.biol.se.edu and fudLonyx.si.edu/protocols. The five loci used inthis study, Amiu-fi, Amiu-S, Amif.L-15,Amif.L-17and Amif.L-18contain at least 10 uninterruptedAC repeats and were chosen for their varyingdegrees of polymorphism. One primer perpair was tagged with a fluorescent label(Table 1) for detection on an ABI Prism 377automated DNA sequencer (Perkin Elmer,Applied Biosystems). Fluorescent labels werechosen such that loci tagged with the samelabel did not have overlapping size ranges(Fig. 2). Differential fluorescent emissions andspectral overlap of the ABI labels were alsotaken into account. Multiplex PCR amplifica-tions were performed in a Techne Geniusthermocycler and carried out in 25 f.LIvolumeswith a final concentration of 250 f.Lg/mlBSA, 150 f.LM of each dNTp, 2 mM MgCI2,

and either 1.25 units of Promega Taq DNApolymerase or 1 unit AmpliTaq Gold (withappropriate buffer from the supplier), and0.2-1.0 f.LMof each primer (Table 1) and 25 ngDNA. Thermocycling parameters were asfollows for multiplex reactions using PromegaTaq: 95°C for 3 min. after a 3 min. delayto heat the lid, followed by 30 cycles of 95°Cfor 30 see., 55°C annealing for 15 see., and72°C extension for 30 see. A 72°C extensionfor 5 min. was added as a final step. Whenusing AmpliTaq Gold the parameters were thesame except for an initial 95°C incubation for10 min. prior to cycling. PCR products werequantified by electrophoresis through a 1.2%agarose gel stained with ethidium bromideand UV transillumination.

ABI 377 gel electrophoresisA cocktail of 3.0 f.LI Dextran/formamide

loading buffer, 0.65 f.LI Pro mega CXR

Table 1. Characteristics of PCR primers used to amplify microsatellite loci from American alligators. Primer sequencesare 5' to 3'.

Locus Primer sequences Repeat! Temp" Size" Label' PCR5

Amiu-Ba TTCTTTCCCAGATACACACTT (AC).o 55 126 TET 0.4 p.MAmiu-fib AGTAGAAGGGGACAGGTTATT 0.4 p.M

Amiu-Sa CCTGGCCTAGATGTAACCTTC (AC»lO 55 > 115 FAM 0.2 p.MArn iu-Sb AGGAGGAGTGTGTTATTTCTG 0.2 p.M

Amip.-15a CACGTACAAATCCATGCTTTC (AC)l6 60 159 HEX 0.4 p.MAmip.-15b GGGAGGGTTCAGTAAGAGACA 0.4 p.M

Amip.-17a GCTGACCTTGGTTGGAAACTCTA (ACb(AT), 55 234 FAM 1.0p.MAmip.-17b CCTGTTCTTGCATAAANCTGATA (ATGT) l2(AGAT)g 1.0 p.M

Amie-LBa ATCTCCGAGGGGAAAAATACA (ACb 60 188 FAM 0.4 p.MAmip.-18b AATAGATGGAGTGATGTTATAGTCAG 0.4 p.M

1 repeat motif. 2optimal annealing temperature for PCR. 3 size of cloned fragment. 'ABI fluorescent label used for eachprimer pair. 5 concentration of primer used in multiplex amplifications.

1,@tllUl: _>I'-

""AmijJ:1jl78~

17 J~90 187,84242,79 256.47

-=~ ...,c..!!- ./1.•....

Amiu-S AmifJ.-18 AmifJ.-17

o'"0o124,56 136,530t:l

It·, JJl __L~ -r-sz18OJ-0. ~a , = ~~ r-Amiu ..6 0Q

147,24 >z

b6t:le:a MH! <:0r-c:...,08 ,""-, z

AmifJ.-1S (converted from6 r\MI yellow to black)

Fig. 2. GeneScan image (left) and Genotyper data (colored lines) from five fluorescently labelled microsatellite loci for one American alligatorhatchling. Numbers above peaks indicate fragment sizes in base pairs.

DAVIS ET AL.: GENETIC STRUCTURE OF AMERIGAN ALLIGATORS 43

fluorescent ladder, and 25 ng of PCR productwas prepared, denatured by incubation at 95°Cfor 5 min. and placed on ice. Of this, 1.2 f.LIwas loaded into the wells of a 0.2 mm thick4.5% polyacrylamide gel (12 or 36 cm well-to-read length) and the amplicons separatedover a 1.5-2.0 hour period. GeneScan andGenotyper programmes (Perkin Elmer, AppliedBiosystems) were then used to identify andquantify micro satellite peaks.

Statistical analyses

Allele frequencies, observed heterozygosity(Ho) and expected heterozygosity (HE) werecalculated using Genepop 3.lc (updated fromRaymond and Rousset 1995). Deviations fromHardy-Weinberg expectations of panmixiawere tested using the exact test of Guo andThompson (1992) which employs the Markovchain method. This test was performed underthe alternative hypothesis of heterozygotedeficiency to test for violations of randommating that would result in an excess ofhomozygotes. Tests for linkage disequilibriumby pair-wise locus comparisons for all popu-lations were performed by calculating exactprobabilities of generating a Type I errorunder the null hypothesis that all pairs of lociwere unlinked.

Several statistical analyses were employed todetect differentiation among populations.First, to detect differentiation of genotypesamong populations, the G-based exact testof Gaudet et al. (1996) was employed inwhich the null hypothesis assumes that thegenotypic distributions are identical acrossall populations (Genepop 3.1 c). Similarly,genic differentiation among populations wastested using the method of Raymond andRousset (1995) which assumes identical alleledistributions across populations (Genepop3.1c). Arlequin 1.1 (Schneider et al. 1997) wasused to calculate the P-values of the Fsr' Sfor all pair-wise population comparisons todete.rnine which populations were significantlydifferentiated from one another. Three analysesof molecular variance (AMOVAs, Excoffieret al. 1992) were then performed usingArlequin 1.1 which utilized different ways ofsubdividing the six populations (Schneideret al. 1997). The first AMOVA, performed withno subdivision of the six populations, wasbased on the null hypothesis of randommating among all populations. The secondAMOVA subdivided the six populations into awestern group comprised of SWL and MarshIsland, and an eastern group comprised ofthe remaining four populations. The thirdAMOVA was similar to the second one, butplaced the Mobile population within the westernsub-grouping. The last two sub-groupings were

used to test the hypothesis that a higher levelstructuring exists between eastern and westernpopulations with the Mississippi Riverrepresenting a major barrier to gene flow.This hypothesis was proposed by Neill (1971)and supported by differences in scalemorphology between the two regions as notedby Ross and Roberts (1979).

Genetic distance measurements of delta musquared (D.f.L2) were calculated using RSTCalc(Goodman 1997) according to the method ofGoldstein et al. (1995). This distance is basedon a sum of squared size differences in allelesizes and has been shown to be a superiormeasure of genetic distance for microsatellitesbecause it increases linearly with time ofseparation between populations (Goldstein etal. 1995). Delta mu squared genetic distanceswere plotted against geographic distances andtested for an isolation by distance effect. Itshould be noted that geographic distancemeasures were not straight-line measurementsbetween populations, but followed alligatorhabitats of higher density within the range(Fig. 1).Pair-wise D.f.L2 genetic distances among thesix populations were subjected to a Multi-dimensional Scaling (MDS) analysis usingSYSTAT 9.0 (Wilkinson et al. 1992). The MDSanalysis spatially models all genetic distancesbetween populations simultaneously andprovides a measure of goodness of fit (R2and stress values) between the data and themodel. Lastly, to test the inherent geneticvalue of the loci chosen in this study inassigning individuals to one of the sixpopulations, assignment tests were performedusing GeneClass (Cornuet et at. 1999). Thesetests calculate a probability of correctlyassigning an individual to a population thatis closest to it genetically. These data can beparticularly useful in law enforcement issueswhen the origin of alligators, meat or skins isin question.

RESULTSSummary statistics

None of the populations studied showedsignificant deviation from Hardy-Weinbergequilibrium based on tests of heterozygotedeficiency (P > 0.05). Locus Amif.L-17 did,however, show significant deviation in theSWL, Marsh Island and Mobile populations(P < 0.05). This was probably due in large partto the presence of null, or non-amplifying,alleles in those populations. A previous studyof female alligators and their offspring inthe SWL population confirmed the presenceof null alleles, all of which were in locusAmif.L-17 (Davis, unpubl. data). The inclusion

44

Alleles, allele sizes and frequencies for allpopulations are given in Table 2. These allelesizes differ from those given in Glenn et al.(1998) because our study utilized the sameinternal size standard for each sample whereastheir study compared sample fragments todifferent size markers run adjacent to thesample. Our data can be compared directlywith Glenn et al. (1998) by: adding 4 bp toAmiu-fi, adding 2 bp to Amif.L-17and 18 andsubtracting 2 bp from Amiu-S and 15.

Mean number of alleles detected per locusover all populations was 6.1 (range = 4.3-9.1,Table 3). Mean Ho for all loci among allpopulations was 0.64 (range = 0.52-0.76).

CROCODILIAN BIOLOGY AND EVOLUTION

of a locus with null alleles results III anunderestimate of allele numbers, hetero-zygosity, and genetic distance. However, sinceAmif.L-17was the most polymorphic locus inthis study, we chose to perform many of ourcalculations both with and without that locus.

Of the 60 pair-wise locus comparisonsmade including Amif.L-17,58 showed no signof linkage disequilibrium (P > 0.05). Theremaining two comparisons as well as 10 outof 10 pair-wise locus comparisons acrossall populations were not significant afterBonferroni correction. Therefore, these locican be used to test for population geneticstructure without concern for linkage effects.

Table 2. Alleles, allele sizes and allele frequencies across all alligator populations for all loci. 2N is two times the numberof individuals sampled.

Locus: AmiJ.L-6

2N

allele sizePopulationSWLMarshMobileSRSSanteeENP

Locus: AmiJ.L-8

Alleles

I 2 3 4 5 6 7 8 9 10110 118 122 124 126 128 130 132 134 136

0.000 0.000 0.238 0.190 0.010 0.381 0.057 0.105 0.019 0.000 1040.000 0.000 0.271 0.271 0.000 0.354 0.063 0.000 0.042 0.000 480.000 0.068 0.000 0.886 0.000 0.023 0.000 0.023 0.000 0.000 440.000 0.000 0.100 0.189 0.000 0.000 0.000 0.000 0.311 0.400 900.000 0.000 0.895 0.000 0.000 0.000 0.000 0.000 0.026 0.079 380.179 0.143 0.000 0.679 0.000 0.000 0.000 0.000 0.000 0.000 28

Alleles 2N


Locus: AmiJ.L-15

I 2 3 4 5 6 7 8 9 10 11 12 13 14128 130 132 134 136 138 140 142 144 146 148 152 154 156

0.000 0.000 0.321 0.009 0.462 0.009 0.000 0.028 0.000 0.028 0.038 0.094 0.009 0.000 1060.000 0.000 0.333 0.063 0.125 0.021 0.000 0.042 0.021 0.042 0.083 0.208 0.063 0.000 480.000 0.283 0.304 0.130 0.239 0.000 0.022 0.000 0.000 0.000 0.000 0.000 0.000 0.022 460.023 0.000 0.080 0.295 0.182 0.420 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 880.000 0.000 0.342 0.079 0.211 0.368 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 380.107 0.036 0.321 0.357 0.107 0.036 0.000 0.036 0.000 0.000 0.000 0.000 0.000 0.000 28

Alleles 2N


1 2 345 678147 149 151 153 157 159 161 163

0.000 0.113 0.000 0.009 0.698 0.123 0.057 0.000 1060.000 0.042 0.000 0.000 0.542 0.250 0.167 0.000 480.261 0.065 0.000 0.000 0.609 0.022 0.043 0.000 460.533 0.289 0.000 0.000 0.178 0.000 0.000 0.000 900.553 0.368 0.000 0.000 0.079 0.000 0.000 0.000 380.107 0.464 0.143 0.000 0.107 0.036 0.036 0.107 28

Locus: AmiJ.L-18

Alleles


2N

I 2 345 6 7 8 9164 172 180 182 188 190 192 194 196

0.311 0.160 0.123 0.000 0.208 0.000 0.170 0.019 0.009 1060.375 0.083 0.104 0.042 0.167 0.000 0.208 0.021 0.000 480.283 0.109 0.022 0.000 0.283 0.000 0.196 0.109 0.000 460.148 0.045 0.000 0.000 0.295 0.000 0.477 0.034 0.000 880.132 0.053 0.000 0.000 0.211 0.000 0.447 0.105 0.053 380.000 0.000 0.000 0.000 0.571 0.250 0.143 0.036 0.000 28


Table 2 - continued

Locus: Ami~-17

Alleles

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15allele size 221 229 233 237 241 243 245 249 253 255 257 259 261 265 267PopulationSWL 0.000 0.029 0.057 0.038 0.029 0.000 0.010 0.000 0.038 0.000 0.038 0.019 0.029 0.260 0.038Marsh 0.000 0.071 0.024 0.071 0.024 0.000 0.095 0.095 0.000 0.000 0.000 0.000 0.048 0.095 0.071Mobile 0.000 0.071 0.238 0.119 0.071 0.000 0.000 0.000 0.000 0.000 0.095 0.000 0.143 0.214 0.000SRS 0.000 0.000 0.000 0.000 0.000 0.182 0.239 0.034 0.352 0.000 0.182 0.000 0.011 0.000 0.000Santee 0.000 0.000 0.000 0.000 0.000 0.000 0.737 0.026 0.105 0.132 0.000 0.000 0.000 0.000 0.000ENP 0.036 0.000 0.000 0.000 0.143 0.000 0.464 0.214 0.036 0.000 0.071 0.036 0.000 0.000 0.000

Alleles 2N

16 17 18 19 20 21allele size 269 273 277 281 285 289PopulationSWL 0.221 0.096 0.048 0.019 0.010 0.000 104Marsh 0.167 0.119 0.071 0.024 0.000 0.024 42Mobile 0.000 0.048 0.000 0.000 0.000 0.000 42SRS 0.000 0.000 0.000 0.000 0.000 0.000 88Santee 0.000 0.000 0.000 0.000 0.000 0.000 38ENP 0.000 0.000 0.000 0.000 0.000 0.000 28

Interestingly, the Ho in both the Mobile andSantee sites at Ami,u-6 is low compared tothe other populations. This is because oneallele is very common in each population(Table 2). Santee has the lowest overall Hofollowed by Mobile and SRS. In contrast 'to thelower variability in the populations fromMobile and Santee for Ami,u-6, there are highvalues of both Ho and HE in the Marsh Islandpopulation at this locus. This populationexhibits the highest overall Ho of allpopulations examined and has very highallelic diversity.

Close examination of the frequencies anddistributions of alleles (Table 2) reveals severalinteresting trends that imply a potential formaking population-specific designations forindividuals. Across all five loci, there are 16alleles that are unique to specific populations.In most cases, these alleles occur in lowfrequency. Allele 110 at locus Ami,u-6 in ENp,however, is not only unique to that population,but also occurs in relatively high frequency(17.9%). This is also true for allele 190 atlocus Ami,u-lS for this population and allele243 at Ami,u-17 in the SRS sample. Thesedifferences in allele frequency can serveas a basis for population differentiation andassignment tests.

Population genetic structure

Considerable population differentiation wasapparent. Tests of genotypic and genicdifferentiation (which test for random matingamong populations based on differences ingenotype and allele frequencies) showed that72 of the 75 pair-wise comparisons betweenpopulations were significantly differentiated(P < 0.05). The few insignificant comparisons

were scattered across loci and populations.FSTvalues for all pair-wise population com-parisons were significant (P < 0.05) afterBonferroni correction, demonstrating that allpopulations are significantly differentiatedfrom one another.To test the hypothesis of a higher level

structuring of western and eastern popula-tions, three AMOVAs were carried out withand without Ami,u-17 (Table 4). The result ofthe first AMOVA,which included Amiu-l ? andgrouped all populations together, partitionedthe total variation into 26.46% amongpopulations and 73.54% within populations.The exclusion of Ami,u-17 produced a similarresult with an among population variationof 24.79%. The second AMOVA divided thesix populations into groups east and west ofthe Mississippi River. The amount of amonggroup variation remained high (25.92%) withthe inclusion of Ami,u-17 but dropped to17.29% without it. Additionally, the 7.S5%variation within groups (with Ami,u-17, Table4) was significant based on permutation tests(P < 0.01). The third AMOVAwas similar tothe second but placed Mobile into the westerngroup. With Ami,u-17, this sub-structuringresulted in a reduction in the amount of amonggroup variation and an increase in the amongpopulations/within group variation, indicatingthat the Mobile population was more similarto the South Carolina and Florida alligatorsthan the Louisiana alligators. But whenAmi,u-17 was excluded, this sub-structuringproduced a partitioning of variation that wasonly slightly different than when Mobile wasplaced in the western group.Pair-wise !:",u2 genetic distances and geo-

graphic distance measurements are given in


Table 5. To test whether the populations followed an isolationby distance model, geographic distance was plotted against/::,.J.l2 genetic distance and a least-squares regression line drawn(Fig. 3). When AmiJ.l-17 was included in the genetic distancecalculations, the correlation between geographic and geneticdistance was not statistically significant in a Mantel test(R2 = 0.357, P = 0.09). However, when Ami,u-17 was excluded,the correlation was significant (R2 = 0.407, P = 0.01).Pair-wise /::,.J.l2 genetic distances subjected to a MDS analysis

in two dimensions (2D) yielded a strong clinal variation, orgeographic sub-structuring, in two directions (R2 = 0.999,Kruskal stress = 0.006, Fig. 4). Similar to the AMOVA results,the two Louisiana populations formed a western groupingwhile Santee, ENP and SRS fell into a eastern group. TheMobile population showed a genetic orientation intermediatebetween the eastern and western groups. Additionally, withinthe eastern group, the MDS showed a clinal variation in anorth-south direction for the SRS, Santee and ENP popula-tions. This result can be predicted given their geographiclocations and differences in habitats.If allele frequencies are distinct enough among populations,

the combined power of many microsatellite loci can be usedto assign individuals to specific populations. Because there aresix populations, the probability of correct assignment is 16.7%by chance alone. In performing assignment tests both withand without Amin-I 7, individuals could be assigned to theircorrect population of origin from 73-82% of the time usingmost genetic distance measurements (GeneClass, Table 6).Likelihood based measurements using the Bayesian methodproduced similar results with probabilities greater than 79%(P = 0.05). Goldstein's !::.j.L2 genetic distance allowed only abouta 48% probability of correctly assigning the population oforigin. This value reflects this measurement's inherently largevariance. Nevertheless, the probability of assigning anindividual solely to its population of origin ranged from 35to 45%. In most cases individuals were assigned to theircorrect population and to additional populations as well(Table 6). But on average, only one additional populationcould not be excluded as the correct population of origin.In most cases the additional population was the one closestto it geographically (Table 6). For example, in the twopopulations that were closest to one another geographically(and genetically), SWL and Marsh Island, the low probabilityof assigning sole correct origin was due to the inclusionof the other population as a second population of origin(Table 6).

DISCUSSION

Population differentiation and patterns of gene flow

Each of the six populations examined in this studyhad significant genetic differentiation. Moreover, with theexclusion of the locus that was known to contain null alleles,Amiu-I 7, the populations followed an isolation by distancemodel. Analyses of molecular variance, while suggestinga higher level structuring of western and eastern populationswith the inclusion of AmiJ.l-17, fail to support that hypothesiswithout it. Genetic distance measurements suggested a stronggene flow pattern along coastlines. This was well illustratedby the small genetic distance between both ENP and Santee(0.287) and ENP and Mobile (0.268). Interestingly, the genetic

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Table 4. Results of three analyses of molecular variance (AMOVAs,Excoffier et al. 1992) for six populations of Americanalligators. Values are based on a sum of squared differences in allele sizes (RST)' All values are significant atP < 0.01.

All populationsgrouped together

SWL+ Marsh/ SWL + Marsh + Mobile/Mobile + SRS+ Santee + ENP SRS + Santee + ENP

Percentage of variationw/Ami/L-17 w/o Ami/L-17 w/Ami/L-17 wlo Ami/L-17

25.92 17.29 19.93 18.78

7.85 12.47 12.13 11.40

66.23 70.25 67.94 69.83

Source of variation w/Ami/L-17 w/o Ami/L-17

Among populations

Among populationswithin groups

Within populations

26.46 24.79

73.54 75.21

Table 5. Matrix of genetic and geographic distance measurements. The values above the upper diagonal aregeographic distances in kilometres and those below the diagonal are values of f¥,2 distances calculated withoutlocus Ami/L-17.

A. SWL Marsh Mobile SRS Santee ENP

SWL X 80 525 1410 1465 1450Marsh 0.116 X 370 1290 1330 1320Mobile 0.246 0.605 X 970 980 970SRS 0.894 1.332 0.857 X 210 900Santee 0.898 1.285 0.416 0.606 X 900ENP 0.845 1.223 0.268 1.218 0.287 X

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Fig. 3. Genetic versus geographic distance for all pair-wise popula-tions excluding locus Ami/L-I7. Least-squares regression lineis significant (P = 0.01).

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Fig. 4. Multidimensional Scaling analysis(20) of six alligator populations basedon D./L2 genetic distances.

Table 6. Proportion of individuals assigned to populations using GeneClass (Cornuet et al. 1999) based on Cavalli-Sforza genetic distance measurements including locus Ami/L-17. Simulations were for I 000 individuals perpopulation and rejected populations with P < 0.05. Population totals exceed 1.00 because individuals may beassigned to more than one population (see text). Values in parenthesis indicate the proportion of individuals inwhich only the correct population of origin was assigned.

Assigned PopulationTruePop. of Origin SWL Marsh Mobile SRS Santee ENP

SWL (0.1321) 0.9245 0.8302 0.0943 0 0 0Marsh (0.2083) 0.8416 0.9167 0.0833 0 0 0Mobile (0.5217) 0.3478 0.2609 0.8696 0.0434 0 0.0434SRS (0.7333) 0.0222 0.0222 0.0667 0.8444 0.0889 0.0222Santee (0.3684) 0 0 0 0.5625 0.8421 0ENP (0.7148) 0 0 0 0 0 0.7148

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distance between Mobile and Santee wassmaller than between Mobile and SWL whenAmiJ.L-17was included, but not when it wasexcluded. It is apparent, however, that geneflow has been occurring between SWL andMobile because the genetic distance betweenthem was still relatively small compared toother pair-wise combinations of SWL andeastern populations.In contrast to the coastal gene flow patternssuggested by these data, genetic distancesimply limited gene flow between coastalpopulations and the inland SRS population.This is apparent from the MDS analysis as theSRS is primarily separated in dimension 2whereas the coastal populations are primarilydifferentiated along dimension 1 (Fig. 4).There are a number of factors that maybe contributing to this phenomenon. First,SRS is an inland population located at anorthern extreme of the alligator's range.Although this site is connected to downstreamaquatic ecosystems via the Savannah River, itremains relatively isolated from other largeaquatic habitats which are the main conduitsof gene flow for this species. Therefore, itmight be less likely to receive migrantsfrom other populations. Also as a result ofits northern location, SRS may be subject tocold winter temperatures which may producedifferent selection pressures (Brisbin et al.1982). It is believed that the SRS populationwas founded by a relatively low number ofindividuals (Murphy 1981), so the presentgenetic structure may also reflect a foundereffect. Too few loci, however, are examined inthis study to test this hypothesis. Thus, werecommend examination of additional inlandpopulations with similar characteristics in anyfurther analyses of gene flow between coastaland inland populations.

Location-specific genetic markers andassignment tests

There are several alleles that occur in highfrequency in specific populations or geographicareas. The occurrence of unique alleles inhigh frequencies in one geographic area andnot in others, may enable the development oflocation-specific genetic markers. Similarly,combining populations which are in closegeographic proximity and which share allelesthat are in high frequency in that area couldalso enhance diagnostic power. For example,allele 128 at locus AmiJ.L-6occurred in highfrequency in both SWL (38.1%) and MarshIsland (35.4%) but it was in low frequencyor absent in the other populations (Table 2).This was also true for allele 138 at AmiJ.L-8from SRS and Santee. More importantly, thepresence of such diagnostic alleles when

combined with a comprehensive analysis of thetotal range-wide genetic structure, could helpwildlife forensic agents to identify illegallyharvested animals. This is illustrated by theability of the assignment tests to predict withhigh probability the population of origin formany of the individuals sampled in this study.

Several interesting trends are worthy ofnote from the assignment analyses. First, theinclusion of AmiJ.L-17does little to increasethe probability of assigning an individual toits population of origin, but it does increasethe chance of assigning an individual solelyto its original population (i.e., excluding allother populations) by approximately 10%.Secondly, the highest probability of assigningsole correct origin to individuals was in theSRS population (0.7333). Secondary popula-tion assignments were made to all otherpopulations in low proportion. In Santee,while the probability of assigning sole originto individuals was 37%, all of the secondarypopulation assignments were to SRS. Interest-ingly, in ENP with the inclusion of AmiJ.L-17,no secondary populations were assigned, but4 of the 14 individuals could not be assignedto any population, including ENP, due tothe presence of rare alleles. When AmiJ.L-17was excluded, Marsh Island and Mobile wereassigned as possible origins as well as ENP.Also, Santee was never assigned as a popu-lation of origin for Mobile individuals andSRS was only assigned once. What is clearfrom these assignment tests is that individualalligators from ENP can be distinguished fromalligators west of the Mississippi River inSWL and Marsh Island. ENP was neverincluded as a possible population of origin forany SWL or Marsh Island alligator and viceversa. In fact, the ENP population was rarelyassigned as a secondary population of originfor any other alligator outside that populationwhen AmiJ.L-17was included. While correctassignment of individuals to their populationsof origin was relatively high, the inclusion ofadditional loci will undoubtedly increase theprobability of correct assignment substantially.

Sample size and considerations for micro-satellite data

One point of consideration for the micro-satellite data in this study is the difference insample sizes among the populations. Thenumber of alleles increases with increasingsample size and SWL, which had the highestmean number of alleles, also had the highestsample size. However, ENP had the thirdhighest mean number of alleles even thoughit had the smallest sample size. This amountof variation is consistent with the fact thatFlorida population densities of alligators have


remained high throughout historical times.One interesting finding from a comparisonbetween these data and those of Glenn et al.(1998) was the increase in number of alleleswith an increase in sampling effort for theSWL population. The average number ofalleles across all loci from the previous study(n = 19) was 6.0. With the addition of 34individuals, the average number of alleleswent up to 8.8. Not surprisingly, most of thisincrease was due to AmiJ.L-17.This findingunderscores the need to calculate the numberof individuals necessary to capture a desiredamount of variation in a population. Suchcalculations are often performed for mtDNAstudies (Dueck and Danzmann 1996), butare not yet common for micro satellite studies,in part because appropriate models mustbe determined empirically. Such statisticalinformation is mandatory for genetic data tobe used in making management decisions.Increasing and evening out the sample sizesamong populations would provide a strongerbasis for assignment tests.

Future directions

Alligator management today is concernedwith issues of nuisance animals, controlledharvests, and regulation of trade in meat andhides. These are the issues that must bedealt with "hands-on" on an everyday basis bywildlife managers and these are the issues wewould like to address with our genetic data-base. Each year more than 40 000 alligatorsare taken from Louisiana and Florida fromnuisance programmes and controlled harvests,resulting in over $17 million in alligatorproducts (LA Dept. of Wildlife and Fisheries,unpubl. report; Woodward 1998). Results fromthis study demonstrate that microsatellitemarkers reveal a great deal of variation ineach of the six American alligator populationssurveyed. Moreover, the populations showa significant degree of differentiation thatcan be used to assign individuals to thepopulations or geographic regions from whichthey came. Further, as the type of datagenerated in this study are in the form ofindividual genotypes, we can begin tounderstand in detail the reproductive andsocial dynamics of local populations. Usingthis information to determine the effectivepopulation size, breeding structure and otherrelevant measures of local populations willassist in determining harvest quotas andaddressing other management issues.

ACKNOWLEDGEMENTSLisa Davis would like to graciously acknow-

ledge the following: Bob Lawther, Joe Quattroand Ken Jones for comments and suggestions

on the original manuscript; Thomas WoodsBrown and Daniel Hallman for assistancein the lab; Warren "Cub" Stephens and JayCumbee for many memorable nights of gatortrapping and Joe Staton and Regina Wetzerfor much needed advice and support. KenJ ones performed the MDS analysis as well.Special thanks go to Mike and Gary Casperfor providing the Mobile samples and a bigdose of Southern hospitality. Louisiana StateUniversity Museum of Zoology providedsamples from ENP. Ruth Elsey would like toacknowledge the trapping efforts of DarrenRichard, Eric Richard and Scooter Trosclair.This project was funded in part by theUniversity of South Carolina Water Center(Grant DE-FG02-97EW09999 of the USDepartment of Energy Office of Environ-mental Management), a USC Research andDevelopment Grant, the Howard HughesMedical Institute, the Louisiana Departmentof Wildlife and Fisheries, the South CarolinaDepartment of Natural Resources andSavannah River Ecology Laboratory ContractNo. DE-FC-09-96SR18546.

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