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1998 Gene flow at a snail's pace: phylogeography and conservation genetics of relict populations of the Iowa Pleistocene snail Tamara Kay Ross Iowa State University
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UMI
Gene flow at a snail's pace: Phylogeography and conservation
genetics of relict populations of the Iowa Pleistocene snail
by
Tamara Kay Ross
A dissertation submitted to the graduate faculty
in partial fultlllment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Major: Ecology and Evolutionary Biology
Major Professors: Brent J. Danielson and Richard J. Hoffmann
Iowa State University
Ames, Iowa
1998
Copyright © Tamara Ross, 1998. All rights reserved. UMI Number: 9 841082
UMI Microform 9841082 Copyright 1998, by UMI Company. All rights reserved.
This microform edition is protected against unauthorized copying under Title 17, United States Code.
UMI 300 North Zeeb Road Ann Arbor, MI 48103 ii
Graduate College Iowa State University
This is to certify that the Doctoral dissertation of
Tamara Kay Ross has met the thesis requirements of Iowa State University
Signature was redacted for privacy.
Co-major Professor
Signature was redacted for privacy. Co-major Proressor
Signature was redacted for privacy.
For the Major Prograpi
Signature was redacted for privacy. iii
TABLE OF CONTENTS
CHAPTER 1. GENERAL INTRODUCTION 1
CHAPTER 2. POPULATION ESTIMATES FOR THE IOWA PLEISTOCENE SNAIL, DISCUS MA CCLINTOCK! (BAKER) 18
CHAPTER 3. PHYLOGEOGRAPHY AND CONSERVATION GENETICS OF THE IOWA PLEISTOCENE SNAIL 60
CHAPTER 4. EVOLUTION AT A SNAIL'S PACE: VARIATION IN PARTIAL 16S RDNA SEQUENCES 106
CHAPTER 5. NUCLEAR VERSUS MITOCHONDRIAL VARIATION DISPARITIES AS A FUNCTION OF DISPERSAL STRATEGIES 135
CHAPTER 6. GENERAL CONCLUSION 156
APPENDIX A. SEQUENCE ALIGNMENT 158
APPENDIX B. MORPHOLOGICAL DATA AND ANALYSES 175
ACKNOWLEDGEMENTS 179 1
CHAPTER 1. GENERAL INTRODUCTION
Dissertation Organization
This dissertation is organized into five chapters. This first chapter contains a general introduction/overview of the Iowa Pleistocene snail and a literature review- relevant to this work. Chapter 2 is a paper that investigates the population sizes of fourteen Discus macclintocki populations and is to be submitted to the Biological Bulletin.
Chapter 3 investigates the phylogeography of the Iowa Pleistocene snail and will be submitted as a paper to Molecular Ecology. The fourth chapter is a paper to be submitted to Molecular Biology and Evolution that compares the rate of variation in snails to other ta.xonomic groups at the 16s rDNA locus. Chapter 5, a paper to be submitted to
Evolution, is a meta-analysis of genetic studies which use both mitochondrial and nuclear markers. Following Chapter 5 are a General Conclusions chapter and appendices containing DNA sequence data and an analysis of morphological measurements.
Taxonomy and Description of Discus macclintocki
Discus maccliniocki (Baker 1928), the Iowa Pleistocene snail, is a small terrestrial snail ranging from five to eight millimeters in diameter and having 5 to 6.5 whorls as an adult. Other defining features are the presence of fine ribs on the top of the outermost whorl, which are indistinct on the underside of the shell, and the absence of teeth in the aperture. Two different color morphs have been observed, the common light brown morph and the rare olive-green colored morph. The shape of the shell ranges from almost 2 flat to nearly globose, so the height of the shell ranges from three to four millimeters.
The Iowa Pleistocene snail currently exists only in the northeastern comer of Iowa and in a single county in Illinois. For many years this species was known only from fossil records before turning up in museum collections of living specimens made by
Bohemil Shimek in 1928 (Pilsbry 1948). Hubricht (1955) rediscovered live populations in
Iowa in 1955. In 1977, it was declared endangered and is included on the endangered species list (U.S. Fish and Wildlife Service 1993). Today, just over thirty known populations of this species are known to exist, all located in a unique type of habitat called algific talus slopes.
Discus macclintocki is a pulmonate (lung-breathing) snail in the Order
Stylommatophora of the Class Gastropoda (Pilsbry 1948). D. macclintocki is classified in the Family Endodontidae characterized by a wide umbilicus, strong ribbing, and a flat to globose shape (Burch 1962). This family includes the uniquely North American genera
A ngiiispira and Helicodiscus, as well as the more widespread genera such as Discus. The phylogenetic relationships among the major groups within Class Gastropoda are still very much in dispute (see Bieler 1992; Boore and Brown 1994; Rosenberg et al. 1994; Tillier et al. 1994; Ponder and Lindberg 1996; Taylor 1996; Winnepennickx et al. 1998).
Endodontidae species possess ovotestis and hermaphroditic ducts (Solem 1976a). This suggests that they are hermaphrodites as are most other pulmonates (Duncan 1975). No
Discus species are known to undergo self-fertilization. However, specific tests for selfmg in these species have not been reported in the literature.
Several other species of snails belong to the Discus genus. Four of these are also 3
found in the same range as Discus maccliiitocki: Discus calskillensis. Discus cronkheti.
Discus shimeki, and Discus patulus, which can all be distinguished from D. maccliniocki by shell characteristics (Burch 1962; Frest 1984). Nothing is known about the genetic relationships among any of these groups.
Pilsbry (1948) lists two fossil subspecies: D. macclintocki macclintocki and D. m. angulatus. He distinguishes D. ni. angulatus as being slightly larger with a more sharply angled outer whorl. Frest (1981) suggests that D. m. angulatus is present at one location, but later (1984) determines that distinct subspecies cannot be identified on the basis of morphological measurements. The present study found no evidence for the existence of the D. m. angulatus subspecies based on morphology or genetics (Chapter 3 and Appendix
B).
Species Range
Fossil evidence suggests that D. macclintocki has existed for 400,000 years (Frest
1984). Historically, it was present in Iowa, Nebraska, Missouri, Illinois, Indiana, and
Ohio before being reduced to its present range (Frest 1984). No record of D. macclintocki in its present range exists before 20,000 years ago (Frest 1984). The oldest state fossil record was dated at 20,000 years ago in Clayton County (Frest 1984). In
Jackson County, the oldest fossil of D. macclintocki is between 8,000 and 10,000 years old (Frest 1984).
Currently, D. macclintocki is known only from Iowa and Illinois. The range is probably limited today by temperature and food source. They prefer to eat leaves from a 4
limited number of trees, primarily Betula papyiifera (paper birch), Acer spicalum
(mountain maple), and Betula alleghaniensis (yellow birch) (Frest 1984). Their food
sources and temperature requirements are met on unique habitats called algific talus
slopes. They are likely restricted to these slopes unless passive dispersal occurs via an
outside mechanism, such as wind (Rees 1965; Kirchner et al. 1997), water, or other
animals (Boag 1986).
Geologic Histoiy of the Region
The northeast comer of Iowa is a part of the 'Driftless Area', which encompasses
portions of Wisconsin, Minnesota, and Illinois where Pleistocene glaciers were diverted
for an unknown reason (Prior 1991). The most recent glacial advance to cover
northeastern Iowa occurred sometime in the pre-Illinois period over 500,000 years ago
(Prior 1991). During the Pleistocene years, temperatures were much cooler (Frest and Fay
1980). Deciduous forest dominated the region at that time (Baker et al. 1990; Chumbley
ct al. 1990).
The landscape of northeast Iowa is dominated by bedrock that has been eroded
into steep slopes by many rivers draining into the Mississippi (Prior 1991). The
Mississippi River currently divides the single Illinois population of Discus macclintocki
from the other populations in Iowa. However, Anderson (1988) speculates that the
Mississippi River may have had a more easterly course before settling into its present course in the pre-Illinoisan period. Algiflc Talus Slopes
The term, algific (cold-air) talus (loose rock) slope, was first used by Frest (1981) to describe a particular type of geological formation which creates a cool, moist environment characterized by particular plants and animals including Discus macclintocki.
These slopes may be similar to a slope in Virginia, referred to by Hayden (1843) as "ice mountain". However, no current records of the Virginia slopes are known.
Algific talus slopes consist of dolomite layers over thin limestone with shale at the bottom (Frest 1981). Approximately 18,000 years ago during the Wisconsin period of the Pleistocene Epoch these slopes formed along major drainages where glacial runoff would accumulate and there would be large amounts of ice (Frest 1991). The frost and ice on the cliffs expanded and pushed apart chunks of the underlying carbonate rock resulting in many vents and small sinkholes within the cliff (Frest 1991). Water collected within the slopes and, in some cases, formed springs where the water would run out at the base of these slopes (Frest 1991). Gradually the talus began to be vegetated and then stabilized somewhat with the formation of soil (Frest 1991).
Several groups of slopes, which differ in the particular arrangement of the talus and fissures, are found in different geological formations (Silurian, Galena, and Prairie du
Chien) (Frest 1991). All types of slopes are believed to function similarly in keeping temperatures cool. The explanation for this phenomenon, as presented by Prior (1991) and Frest (1991), is as follows; In the spring, as the ground thaws, water enters and filters down to the ice frozen within the slope. This begins to melt some of the ice and drains out through the loose talus. Cold air enters now that some of the ice has melted. 6
further melts the ice, and then flows out over the talus keeping the talus cool throughout the summer. In about October or November, when outside temperatures dip below freezing, dense winter air remains underground, freezing the water once again.
Temperature Tolerance of Discus macclintocki versus other Snail Species
Soil temperatures at air vents on one slope ranged from -7 to +-10 "C in 1981
(Frest 1982). Air temperatures measured at ground level on the talus of one slope ranged from 3 to 9 °C year-round, whereas ground temperatures off-slope measured -1 to +27 "C
(Solem 1976b). The normal air temperatures in this region average 20 to 21 T in June through August, but temperatures over 38''C are not unusual (Midwest Climate Center, http://mcc.sws.uiuc.edu/Summary/Data/132603.txt).
Laboratory studies have shown that D. macclintocki has a narrow temperature tolerance. This limited tolerance is important because the areas between algific slopes reach temperatures above this range during much of the most active breeding period for these snails. (Average high temperatures during Iowa summers are over 27 "C according to the Midwest Climate Center.) With a limited temperature tolerance, snails would be isolated in the areas within their acceptable temperature range.
Other pulmonale gastropods show varying patterns in temperature tolerance.
Rossetti el al. (1989) studied a freshwater snail, Lvmnaea auricularia, and determined that it preferred temperatures at 19.8 °C when kept in a laboratory and temperatures around
19.3 "C when freshly obtained from their natural habitat. Sensitive species of terrestrial snails, such as Arianta arbustonim, have been shown to have reduced egg hatching 7
success when temperatures rose to 25 "C (Baur and Baur 1993). Egg survival of a more
robust species, Cepaea nemoralis, was not negatively affected until temperatures reached
29 "C. From these data, Baur and Baur (1993) suggest that temperature plays an
important role in where snail species are located and speculate that an increase in
temperature in one area actually led to the extinction of A. arhustonun from that location.
Many snails have a much higher temperature tolerance. Cowie (1985) showed that Theba
pisana, a European terrestrial snail, climbs up vegetation to avoid lethal ground
temperatures. His study also showed that the upper lethal limit for T. pisana was 42 to
50 "C.
The restricted temperatures are important for the survival of D. macclintocki, as
they appear to have a limited temperature tolerance. Laboratory studies show that above
21 "C, the snails are inactive (Frest 1981). Field studies by Ostlie (1992) show that snails
are most active at soil temperatures from 10 to 15.6 "C and are not found when soil
temperatures were below 3.3 °C or above 20 °C. This appears to be a slightly different
temperature range than that for related species. A study of Discus cronkhiiei in Canada showed species to be most active from 0 to 25 "C and temperature affected activity below
3°C and above 25 "C (Boag 1985).
Snail Dispersal
Snail movement has been studied in a variety of ways, from mark-recapture methods to radio tagging. In a mark-recapture study, Ostlie (1992) recorded Discus macclintocki moving up to 1 meter per week. This is a higher rate of movement than 8
Goodhart (1962) recorded for Cepaea nemoralis (average 0.18 meters in 4 weeks). The maximum recorded distance moved by Troclioidea geyeri was 13.09 meters over a 210 day period (Pfenninger et al. 1996). Woodruff (1978) estimated that one generation of
Ceiion species disperses only 0.2 meters. Chondiina clienta dispersed a ma.ximum distance 0.342 meters over a si.x-month period in a study by Baur (1988).
Some species appear to move much more readily. Cepaea nemoralis was studied in a grassland area by Cameron and Williamson (1977), who found 31-38% of snails per year migrated from their initial plot. Tomiyama and Nakane (1993) used radio tags to study giant African snails (Achatina fulica) and found they move on average 0.54 to 3,77 meters per day and juveniles are known to disperse up to 500 meters over a four month period.
Genetic Variation and Genetic Structure
A llozym es
Snail studies have long been used to examine questions regarding gene flow and genetic structure. Most of the earlier studies used allozymes as molecular markers to determine the genetic diversity and structure of populations. Many allozyme studies examine genetic patterns in snails over large spatial scales. For example, genetic variation in Cepaea nemoralis, perhaps the most-studied snail species, has been examined across
Britain (Johnson 1979), across Britain, France, Switzerland, and Spain (Ochman ei al.
1987), and across the eastern United States (McCracken and Brussard 1980). Arter (1990) used allozyme frequency distribution patterns in Ariania arbusionim to test two post- 9
glaciation recolonization theories in the Swiss Alps. Genetic distances from allozyme
frequencies combined morphological measures have also been used to support the existence of a subspecies (Lazaridou-Dimitriadou et al. 1994). Guiller el al. 1994 compared populations of Heli.x aspersa across northern Africa and found little support for the existance of certain subspecies.
At a smaller spatial scale, allozyme variation has also been examined within populations. Early work at this spatial scale includes Selander and Kaufman's (1975) study of variation in Heli.x aspersa in a single block in Texas and Brussard and
McCracken's (1974) study of a Cepaea nemoralis colony in Virginia. Many studies have found extremely low levels of variation within populations, presumably due to selfmg (or apomixis) in Rumina decollaia (Selander and Kaufman 1973), Deroceras laeve (Nicklas and Hoffmann 1981), Ligiius fasciatus (Hillis et al. 1987; Hillis 1989), and Chondrina clienta (Baur and Klemm 1989). Johnson (1988) found reduced variation in an island populations of Tlieha pisana compared to the mainland population in Australia and attributed the reduced variation to a founder effect. In contrast. Woodruff and Solem
(1990) found high levels of variation in Cristilabnim primum which reside only on small hills which are remnants of a Devonian reef. Schilthuizen and Lombaerts (1994) found high levels of genetic differentiation among rock-piles in Albinaria coirugaia, suggesting even at small spatial scales subdivision occurs. 10
Mitochondrial markers
Mitochondrial DNA sequencing has the advantage of showing all variation which
exists in that segment of DNA. The high resolution allows the determination of
population structure at a finer scale than, for example, allozyme analysis, which may not
detect all mutations. A vise (1987) points out several advantages in using mtDNA.
Mitochondrial DNA does not recombine so haplotypes are easier to trace among
populations. In addition, mitochondrial DNA tends to mutate at a faster rate, so more
variable characters are available for analyses.
Avise (1994) summarizes the use of mitochondrial markers in a variety of studies
across taxonomic groups. Although technology has allowed the use of mitochondrial
DNA for population studies since the 1970's (Avise el al. 1979), difficulties in extracting clean DNA precluded extensive snail studies until recent work provided new methods
(Stine 1989; Terrett 1994). Many studies have now been conducted that use actual base- pair sequences of mitochondrial DNA loci to examine intraspecific variation in snails
(Thomaz et al. 1996; Remigio and Blair 1997; Douris et al. 1998).
Mitochondrial DNA studies of snails often show surprising amounts of genetic variability, especially given the isolated, patchy nature of their distribution. Pfenninger ei al. (1996) suggest that the high level of subdivision in snails contributes to a high genetic diversity. Thomaz et al. (1996) found one of the highest level of mitochondrial diversity among populations of Cepaea nemoralis in Europe. Populations of Discus macclintocki show even higher levels of diversity (Chapter 3). Comparing the levels of diversity within snails to diversity within other taxa at the same locus (16s rDNA) indicates that the DNA 11
of snails is more free to vary {Chapter 4).
Comparison of Mitochondrial Mariners to Nuclear Maricers
Although this study examines only mitochondrial variation, many organisms have
been examined for variation in the nuclear genome as well. These studies sometimes give conflicting results of how much variation is present within a population. One potential reason for differences in levels of nuclear variation and mitochondrial variation is due to differential dispersal rates and/or distances between the sexes. These ideas are explored in
Chapter 5 through an analysis of the existing data in the literature.
Implications for Consers'ation
Discus macclintocki is listed on the federal endangered species list (U.S. Fish and
Wildlife Service 1993), so this work has important implications for conser\-ation of this species. Moritz (1994) reviews the utility of mitochondrial DNA studies in conser%'ation and states that these analyses are especially useful in identifying evolutionarily significant management units and in estimation of demographic parameters. Understanding how gene flow historically or currently connects populations is essential for conser\'ation of species of concern in order to maximize the genetic diversity and overall health of the species.
Although demographic factors may be more important than genetic diversity for the preser\'ation of many species (Lande 1988), reduced genetic diversity has been shown to be linked to reduction in fitness characteristics in some species (for example, butterflies, as in Saccheri el al. 1998). Issues of connectedness and intraspecific variation have 12
become especially important as populations become more and more isolated in today's
fragmented environments. Studies such as this one. which combine demographic and
genetic approaches, are extremely important for the management of rare species.
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Solem, A. 1976a. Endodontoid Land Snails from Pacific Islands (Mollusca: Pulmonata: Sigmurethra) Part 1: Family Endodontidae. Field Museum of Natural History. Chicago, Illinois.
Solem, A. 1976b. Final Report, Contract no. 14-16-0008-965. Investigate the ecology and identify potential threats to the endangered land snail. U. S. Department of the Interior, Office of Endangered Species.
Stine, O.C. 1989. Cepaea nemoralis from Lexington, Virginia: the isolation and characterisation of their mitochondrial DNA, the implications for their origin and climatic selection. Malacologia 30: 305-315.
Taylor, J.D. 1996. Origin and Evolutionary Radiation of the Mollusca. Oxford University Press, Oxford.
Terrett, J.A., S. Miles, R. H. Thomas. 1994. The mitochondrial genome of Cepaea nemoralis (Gastropoda: Stylommatophora): Gene order, base composition, and heteroplasmy. The Nautilus, Supplement 2: 79-84.
Thomaz, D., A. Guiller, and B. Clarke. 1996. Extreme divergence of mitochondrial DNA within species of pulmonate land snails. Proc. R. Soc. Lond. B 263: 363-368. 17
Tillier, S., M. Masselot, J. Guerdoux, and A. Tiiiier. 1994. Monophyly of major gastropod taxa tested from partial 28S rRNA sequence, with emphasis on Euthyneura and hot-vent limpets Peltospiroidea. The Nautilus, Supplement 2: 122-140.
Tomiyama, K. and Nakane, M. 1993. Dispersal patterns of the Giant African Snail, Achatina fidica (Ferussac) (Stylommatophora: Achatinidae), equipped with a radio- transmitter. J. Moll. Stud., 59; 315-322.
U.S. Fish and Wildlife Service. 1993. Endangered and threatened wildlife and plants. United States Fish and Wildlife Service, Department of the Intererior.
Winnepennickx, B., G. Steiner, T. Backeljau, and R. de Wachter. 1998. Details of gastropod phylogeny inferred from 18s rRNA sequences. Mol. Phyl. Evol. 9: 55- 63.
Woodruff, D.S. 1978. Evolution and adaptive radiation of Cerion: a remarkably diverse group of West Indian land snails. Malacologia, 17: 223-239.
Woodruff, D.S. and A. Solem. 1990. Allozyme variation in the Australian Camaenid land snail Cristilabnwi phniiim: A prolegomenon for a molecular phylogeny of an extraordinary radiation in an isolated habitat. Veliger 33: 129-139. 18
CHAPTER 2. POPULATION SIZE ESTIMATES FOR
DISCUS MACCLINTOCKI (BAKER)
A paper to be submitted to Biological Bulletin
Tamara Kay Ross
Abstract
Population sizes were estimated for fourteen Discus niacclintocki populations and ranged from 182 to 22,097 individuals. Estimation procedures from both Bayesian and more common methods gave similar results, although the Baysian method allowed estimation of some populations that could not be estimated with common methods due to small sample sizes. The snails rarely moved over the surface, and never more than eight meters. A comparison of two methods of sampling, visual counts of quadrats and cover boards, showed that using cover boards resulted in much higher probabilities of capture, number of recaptures, and percent recaptures. Snail activity was highly variable over time and space, which makes precise estimation difficult.
Introduction
Discus maccliniocki is a glacial relict species found only on algific (cold-air) talus slopes in northeastern Iowa and nonhwestem Illinois. These snails are small (5 to 8 millimeters in diameter) and either brown or olive in color. Due to their size, coloration, and the nature of their habitat, they are difficult to sample. This species is included on the United States Endangered Species list (U.S. Fish and Wildlife Service 1993), and the 19
need for conservation and management plans makes estimates of population sizes
especially important. However, studying the population dynamics of terrestrial snails
presents a challenge to malacologists. The difficulties include: e.xtreme variability in
microhabitat (and therefore species density) from quadrat to quadrat, low densities, and
little movement between sampling times (Goodhart 1962; Ausden 1996).
Various methods of sampling have been used for all types of studies with
terrestrial snails. Greenwood (1974) used a modeling approach to determine the effective
population size. Most malacologists have relied on more direct sampling schemes in their
studies. Goodhart (1962) used transects to estimate population density. Grab samples
have often been used to survey snails (Van Es and Boag 1981; Locasciulli and Boag
1987). Wooden cover boards that serve as refugia for snails have also been used
(Leonard 1959; Boag 1982; Ostlie 1992, 1993). The most common method has been
visual searching of some sort of grid, quadrat, etc. (Cain and Currey 1968; Williamson et al. 1977; Cowie 1984; Baur and Baur 1990).
Factors such as night-time movements, very slow dispersal rates, and excellent camouflage make normal methods difficult for studying dispersal in snails (Tomiyama and
Nakane 1993). The most common method for these types of studies has been marking snails and introducing them at a site and recording the locations of their subsequent recaptures (Goodhart 1962; Baur 1988). Radio transmitters were used by Tomiyama and
Nakane (1993) to study movement in Giant African snails. Several studies have attempted to use genetic markers to trace movement (Murray and Clarke 1984; Dillon 1988). These studies introduced individuals with unique genotypes and traced the amount of gene flow. 20
An important issue is how effective the sampling methods are relative to one another. Rarely are multiple approaches compared in the same malacological study
(although see Williamson et al. 1977). Knowledge of the directional biases of these studies is important to understand the precision of estimates. See Sutherland (1996) for a discussion of sampling methods and their biases.
In this study, I examine the population dynamics of Discus macclinlocki using quadrat sampling and cover boards. 1 estimate the population size and dispersal rates for several populations and discuss the spatial and temporal variation associated with this species.
Methods and Materials
Study Organism and Habitat
Discus macclintocki resides only on algific talus slopes. Algific talus slopes consist of loose, porous rock with underlying deposits of ice. Cold-air currents circulating through the slopes keep the talus cool. The temperatures on these slopes stay much cooler than surrounding areas throughout the summer. Soil temperatures at air vents on one slope ranged from -7 to +10 °C during 1981 (Frest 1982). Air temperatures measured at ground level on the talus of one slope ranged from 3 to 9 °C year-round, whereas ground temperatures off-slope measured -I to +27 T (Solem 1976). The normal air temperatures in this region average 20 to 21 "C in June through August, but temperatures over 38"C are not unusual (Midwest Climate Center, http://mcc.sws.uiuc.edu /Summary/Data/I32603.txt).
The restricted temperatures are important for the survival of the snails, because 21
they appear to have a limited temperature tolerance. Laboratory studies show that above
2rC, the snails are inactive (Frest 1981). This appears to be a slightly different
temperature range than a closely related species. A study of Discus cronkhitei in Canada
showed species to be most active from 0 to 25 "C and that temperature affected activity
below 3°C and above 25°C (Boag 1985).
The cool air flows out of the talus at vents arranged haphazardly along the slope,
depending on the underlying openings in the rock. The snails are found in areas with
suitable temperature, moisture, and vegetation, becoming active at the surface to feed and
mate. They prefer vegetation consisting of deciduous leaves, and tend to avoid mossy
areas and areas covered by yews, which are common on these slopes (Solem 1976; Frest
1981). These preferences make for an extremely uneven distribution of individuals on the
algific area. The portions of the surface area of the slopes with cold-air drainage are
themselves often small and separated, so distribution over the whole slope is distinctly
patchy.
Population sizes for most populations of Discus macclinlocki were previously
estimated by e.xtrapolating from their densities in grab samples taken as part of the
original studies to survey the snail community (Frest 1981-1987). Due to the extreme
patchiness of the areas where snails are found, estimates are difficult to replicate. In addition, grab sampling is not desirable in the algific talus habitat where removing talus can destabilize the slope, causing minor rockslides and potentially killing many of the snails or destroying valuable habitat. Additionally, Ostlie (1993) expressed concern over compaction of the soil. In 1992, Wallendorf and Clark conducted a mark-recapture study of one population using basswood (Tilia amehcana) boards as "trapping stations". One advantage of this method is the ease with which snails can be viewed as they collect on the underside of the boards. The boards provide an attractive microhabitat for the snails, keeping them at the surface. However, this method is labor intensive, since large numbers of boards must be hauled to these steep slopes. In addition, some evidence suggests that leaving boards on the slopes for extended periods of time reduces the snail populations under the boards
(Wallendorf and Clark 1992). The boards block light to the underlying vegetation, thereby temporarily making these areas qualitatively different from uncovered areas.
To combat these problems, I used quadrat sampling, where each sampling plot was searched by eye for snails. This eliminated potential bias and habitat disturbance from the boards. In addition, I used cover boards on a subset of slopes to determine differences in the effectiveness of the two methods.
Study Sites
In June of 1997, I marked out transects on twelve algific talus slopes in northeastern Iowa. At two meter inter\'als, I measured 0.5 by 0.5 meter quadrats and marked them with flags. Transects were different lengths on different slopes due to the different configurations of the algific areas. However, each slope (population) contained a total of twenty quadrats in which snails were counted. (The one exception is a large slope, #99, on which three distinct algific areas exist. On this slope, I set up one transect of ten quadrats in each of the three areas, refered to as B, C, and X. The estimates for 23
each section were calculated separately.)
Three minutes were spent searching for D. macclintocki in the surface litter at each
quadrat. Any snails found during that time period were marked either with white
fingernail polish and an individual number written in ink or with a unique colored and
numbered bee tag (BeeWorks, Inc.) glued to the shell with superglue. Care was taken to
minimize the disturbance of the underlying loose rocks and to maintain the substrate
structure for continued air flow. I then returned the snails to the quadrat where they were
found.
In order to compare my results with those of a previous study (Wallendorf and
Clark 1992) which used sampling boards instead of searching quadrats, I used sampling
boards in addition to quadrats on a subset of slopes. Weathered basswood {Tilia
americana) boards approximately 0.5 meters by 0,25 meters were placed in the same areas
as the transects. At these points. 1 marked any individuals which were discovered under
the boards. These snails were readily visible, either attached to the board itself or at the surface of the ground underneath, so I did not spend three minutes searching at these
points. Marking and handling was identical to the snails found at the quadrats.
I visited each site a minimum of four times, counting new individuals and
recording recaptures. For each slope, I measured the minimum area, which included all of
the sampling quadrats. (Due to the geometrical arrangement of the sampling transects and
the unique structure of each slope, the sampled areas are not the same on each slope.) 1 also estimated the total algific area (i.e., potential snail habitat) on the slopes in order to
test for a relationship between population size and habitat area. To estimate the total area, 24
1 measured what appeared visually to be habitat of similar structure to that where the
snails were sampled. Estimates were made of areas where direct measure was not
possible (i.e. cliff faces). Dry areas or areas with extensive yews {Taxus canadensis) or
other undesireable vegetation were not included as total habitat available. Obviously if
the snails are able to use these areas as well, the total population estimates would be even
higher.
Statistical A nalysis
The data were compiled and analyzed using the program CAPTURE (White et al.
1978). CAPTURE uses the probability of recapturing marked individuals to estimate the
total population size over the area sampled (White et al. 1978). Depending on the model
chosen, the trapping probability can be either the same over the entire sampling period, or
the probability may vary due to differences between sampling times or individual animals
or some combination of these (White et al. 1978). The model used to describe these data
was Chao's M(th) (Chao 1989). The M(th) model makes intuitive sense in this situation,
because the model assumes the trapping probability varies by time and individual animal.
This is a realistic assumption of what is occurring in the snail populations, because micro- scale environmental conditions determine whether the snails are active at any particular time or location. CAPTURE gave high support for this model (normalized selection criteria value = 0.83 for both slopes 33 and 99B), for the populations on which the model selection criteria could be run (several of the populations had such low capture numbers that the model selection procedure would stop because it could not compute the null 25
model results). Other models received high support in one or the other slopes (Bumham's
M(tb) or M(tbh) or the null model), but they could not be run on all of the populations with such small sample sizes and low numbers of recaptures. Therefore, I decided to use the same model on all populations, and the model with the highest uniform support was
M(th).
In contrast to the methods used in CAPTURE, Gazey and Staley's (1986) method of population estimation uses a Bayesian approach. This method uses prior probability methods to calculate the probability of obtaining the data for specific population sizes
(Gazey and Staley 1986). Bayesian methods can be useful with small sample sizes
(Gazey and Staley 1986). Since I had very low sample sizes, I also calculated the mean population size using their method. I also used the Bayesian approach to calculate the estimated minimum population sizes. The estimates of population size in the quadrats were then extrapolated to the entire habitat area. The suitable habitat area was determined as explained above. Where I had not made measurements, I used those estimated by Frest
(1985a, l9S5b).
In order to test for independent sampling, I plotted captures according to quadrat location on semivariograms using the GSLIB Geostatistical Software package (Deutsch and Joumel 1992). Locations of the recaptured individuals were noted and compared to the locations of the previous capture to determine the amount of movement occurring within the populations. 26
Results
Chao's M(th) estimates of the population size of the sampled area are given in
Table 1 for each slope. These estimates range from 59 to 2,333. Gazey and Staley's
estimates range from 76 to 2,973 and are shown in Table 2. Minimum sample sizes as
determined from Gazey and Staley's Bayesian method are given in Table 2. These are the
95% confidence level (determined from graphs such as Figure 1) that populations are at
least this large. Estimates for each slope from both methods (Bayesian and Chao's M(th))
are plotted against one another in Figure 2. Although Bayesian estimates tend to be
higher, the slope of the regression line is 1, indicating the methods hold the same
relationship over the range of population sizes found in this study.
The total population estimates extrapolated from these data are compared to
previous estimates from Frest (1981-1985) (Table 3). These estimates range from a low
of 193 individuals on slope 247 to a high of 297,346 individuals on slope 246 using
Gazey and Staley estimates. The proportion of the total area that was sampled, multiplied
by the sample-size estimate, resulted in the total population estimates. The sampled areas,
along with the total suitable snail area, are given in Table 4. For these calculations, I
assumed that the area sampled effectively included the area within the quadrats, between
the quadrats, and a small border around the edge of the transects (up to 0.5 m which is
less than half the distance between quadrats) when this border contained similar habitat.
The calculated sample area may be larger than the actual area sampled because movement
is limited (at least at the surface), but if the sampled area is considered only to be the area contained within the quadrats, the population estimates would be even higher. Therefore, Table 1. Estimates of the population size in the areas sampled using Chao's M(th) method. These estimates are extrapolated to obtain total population estimates over the entire habitat area shown in Table 3. Population sizes could not be estimated for all slopes with this method due to the small sample sizes and low recapture rates. Slope numbers refer to numbers assigned in original reports by Frest (1981-1987). Slope 99 is divided into three distinct areas B, C, and X which were studied separately. Probability of capture (p) is the probability of capture taken from the average p from all capture occasions calculated by Chao's M(th) model. Total number of snails captured (n), number of snails recaptured (r), percentage of individuals recaptured (%r), and number of sampling occasions at that site (s) are also listed.
Slope number Chao's M-th (S.E., 95% CI) P n r %r s
297 405 (326, 117-1,667) 0.016 32 2 6.25 5
207 59 (45, 24-247) 0.078 16 1 0 4
103 149 (73, 71-393) 0.047 35 5 14.29 6
247 101 (96, 30-516) 0.043 16 1 6.25 4
120 296 (171, 120-886) 0.032 45 3 6.67 5
99B (total) 367 (63, 272-528) (boards only) 100 (15, 80-144) 0.156 63 30 47.6 8 (quadrats only) 432 (187, 209-1,008) 0.023 69 6 8.70 8
99C (total) 136 (42, 85-263) (boards only) 70 (19, 48-131) 0.105 35 8 22.86 8 (quadrats only) not estimated — 14 0 0 8
99X (total) 157 (37,110-267) — (boards only) 90 (19, 68-152) 0.230 56 22 39.29 4 (quadrats only) not estimated — 19 0 0 4
62 539 (540, 125-2,829) 0.010 34 I 2.94 6
98 570 (270, 256-1,418) 0.022 78 4 5.13 6
121(total) 654 (115,478-937) — (boards only) 75 (15, 57-119) 0.127 44 11 25.00 6 (quadrats only) 831 (252, 486-1,521) 0.030 142 11 7.75 6 28
Table 1. Continued.
Slope number Chao's M-th (S.E., 95% CI) p n r %r s
33 1,874 (411, 1,252-2,901) 0.024 297 23 7.74 7
119 2,333 (964, 1,111-5,152) 0.013 175 6 3.43 6
246 not estimated — 47 0 0 5 29
Table 2. Means, minimum, and maximum population sizes for the sampled area calculated using Bayesian method from Gazey and Staley (1986). Slope numbers refer to numbers assigned in original reports by Frest (1981-1987). Slope 99 is divided into three distinct areas B, C, and X which were studied separately. Only snails observed in quadrats were considered for these analyses. These estimates are extrapolated over the entire potential habitat area (Table 4) to obtain the estimates in Table 3.
Slope Mean Minimum'* Maximum''
297 135 78 182
207 76 28 138
103 100 53 171
247 107 32 231
120 216 132 281
99B 771 330 1,210
99C 230 30 880
99X 1,074 225 1,825
62 704 192 1,292
98 740 347 1,216
121 1,378 700 2,500
33 1,832 1,320 2,500
119 2,787 1,325 5,100
246 2,973 815 4,850
"These values correspond to the 95% confidence intervals as determined visually from graphs such as Figure 1. Minimum and Maximum Population Size Estimates (or Hypothetical Population
>
i •i' •ii hi N Maximum = 261 Minimum = 115
Figure 1. Minimum population size estimation plot for a hypothetical slope. Plots like this one were generated in the Gazey and Staley estimation process and used to determine 95% confidence intervals for the minimum population size. The dotted line shows the points where there is a 95% and a 5% probability of having a population at least that large. 31
Comparison of Population Estimate Methods
3000
(Q E
(fi lU c *(/j2 0) 2000 - >» (Q CQ Q) x: o c D
= 1000
(A UJ c o S 3 a o Q.
500 1 000 1 500 2000 2500 Population Estimate Using Chao's (\/l(th)
Figure 2. Relationship between mean population estimates obtained from two different statistical methods (Chao's M(th) and Gazey and Staley's Bayesian methods). The solid line represents the relationship between the estimates (y-l.00x-r243.i0, r^=.646, p<.05). 32
Table 3. Comparison of total populations estimates from this study and those previously reported by Frest (1982, 1985, 1986). The estimates in the first two columns are calculated by extrapolating estimates for the sampled area (Tables 2 and 3) over the total potential habitat area (Table 4). Slope numbers refer to numbers assigned in original reports by Frest (1981-1987). Slope 99 is divided into three distinct areas B, C, and X which were studied separately. Slopes marked with — could not be estimated under Chao's M(th) model.
Population Chao M(th) Gazey and Staley Frest's estimates
297 816 272 4-6,000
207 22,097 28.442 2,000
103 298 200 2,000
247 182 193 500
120 740 540 4,000
99total 16,854 2,000 99C — 4,287 99X — 19,690 99B 940 1,677
62 981 1,281 400-600
98 16,286 22,769 1,000
121 11,946 19,809 2,000
33 20,775 20,310 600-1.000
119 21,601 25,806 2,000
246 297,346 1,000 33
Table 4. Area sampled and potential snail habitat at each site. Slope numbers refer to numbers assigned in original reports by Frest (1981-1987). Slope 99 is divided into three distinct areas B, C, and X vvhich were studied separately.
Population Area Sampled (m') Potential Snail Area (m")
297 150 300
207 80 30,000'
103 69 138
247 70 126
120 186 465
99B 157 342
99C 77 1,427
99X 36 660
62 78 142
98 84 240
121 72 1,035
33 67 740
119 108 1,000 o o o 246 150
' Slope size estimate from Frest (1983).
• Slope size estimate from Frest (1985). I think the sample-size areas that I used are appropriate for minimum estimates.
The snails moved very little during the survey. Table 5 lists the individuals that
moved, how far away from their original location that they were subsequently found, and
their speed. Low inter-sample movement rates mean that the sampled area is not
underestimated, because few snails even moved to the ne.xt quadrat.
Semivariograms show no relationship between number of captures and capture
location (Figure 3). Semivariograms measure the relationship between the variation
(semivariance) of some item of interest (in this case the number of initial captures) at one
point, and the same item of interest at points at increasing distances, termed lag distances,
from that point. Lag distances ranged from two meters (the distance between the quadrats
in this study) to six meters, for this analysis. If samples were spatially non-independent,
the semivariograms would show a slowly increasing slope. Instead, most semivariograms
show rapid increases before leveling off, which means there is no relationship. Therefore,
the individual quadrats can be considered independent.
A few semivariograms (Figures 3 a, m, n) seem to show a slight spatial
relationship, indicating that on some slopes captures are concentrated in smaller areas than
the entire area sampled. This is reinforced in Figure 4 which shows the number of
individuals captured at each quadrat on each slope. On some slopes, captures occurred only in a few quadrats, whereas other slopes showed a more even distribution of captures.
No single pattern is seen on all of the slopes, reinforcing the idea that each slope has a
unique arrangement of vents and desirable vegetation.
Figure 5 shows the total captures (new and recaptures combined) at each sampling 35
Table 5. Individuals that were recaptured at a quadrat or board other than where they were initially captured and how far they moved.
Individual Distance Moved Between Captures/meters Rate/meters per day
Slope 33 #97 2.8 0.029 #60 8.0 0.086 #112 2.0 0.667
Slope 121 #53 2.0 0.667
Slope 119 #53 2.0 0.667
Slope 99X #R32 movement a 2.0 0.667 movement b 2.0 0.667 #R80 2.0 0,667 (a) Scmivaiiogram lor Slope 103 (b) Seniivaiioyiani for Slope 11')
12 25 OJ o 10 g 20 c ra 0 c • 2 15 ro > 6 ra / E 4 I 10 / m CO 2 C/3 O 0 0 • 0 2 4 6 0 2 4 0 Lag Distance, meters Lag Distance, meters
(e) Semivarioj-ram lor Slope 121) Transeel (il) Semivariogram for Slope 120 Transei l H A 6
80 12 0) Q) o o 10 c GO c ro ro 8 ro 40 > >03 6 E E 4
(g) Scmivariograin for Slope 33 (h) Seniivariograni for Slope 62
100 50 S 80 -• 2 40 •§ 60 - 30 ra / • ICTJ > 20 I l i}^iire 3, (Onlimieil (i) Scinivaiii)gnini lor Slope (j) Semivariogram lor Slope 9'J Transect H 0) o (Uc 4 > ^ §2 (/) 2 4 0 2 4 6.162 Lag Distance, meters Lag Distance, meters (k) SLMiiivarinyrnni lor Slope •)'-> (I) Semivariogram lor Slope '] raiisect (' Trausei t X ira 6 ra 4 > ^ Q)E 2^ CO 0 2 4 2 4 6.162 Lag Distance, meters Lag Distance, meters I'lfAiire .V ('oiiliiuicil (m) Scmivariograni for Slopc24() (n) Seinivariograin lor Slope 2')7 Traii.secl A 25 2 g 20 0) o c 1.5 § 15 eg (U ro 1 I «> > 0) E c/3 5 0) 0.5 CO 0 2 4 6.182 0 2 4 6.162 Lag Distance, meters Lag Distance, meters (o) Seniivariograin lor Slope 297 Transccl B 0) 8 o c ro 6 ro > 4 EQ) 2 OD 0 0 2 4 6.162 Lag Distance, meters ( onliniicd 40 (a) Slope 103 20 15 li 10 II 5 0 L t- cN n T m (b) Slope 119 0 ^ 25 1 2 20 S §• 15 10 s '5 5 w 0 liiiiiiillliill. <-cNco'Cin (c) Slope 120 o^ «QJ o« 5 5. ^ 4 5 ® ^ O 2 I. i.iiii •• iiL. t-cNnTTtflcDr^oooO'-cNn'c-LncorscoaiO <<<<<<<<<'-cncQCDCQcQCQmmci:'- < 03 Quadrat Figure 4. Histograms showing how captures are distributed among the quadrats for each population. Individual quadrats are listed on the x-axis by transect. For example. A3 refers to the third quadrat along transect .A.. Transects do not follow linearly, so B1 is not adjacent to .A. 10. 41 (d) Slope 121 40 (/) o 30 u w £1 20 E Q. 3 (Q 2 a 10 0 •IbIIJI. -.•1. ^cNCOTLncoi^ooOTO'-cNnM-Lncor^ooaio <<<<<<<<<'-CQCQCQCDCDCQCDCDCDr- < CQ Quadrats (e) Slope 207 «-(Nn^incDr^ooa)0'-cNCO>Tix)tDr^oooio CQQQCQCQCQCQCOCQCQ'— •— CQ Quadrats (f) Slope 246 15 "o irt 5 i 10 i ^ 5 z " ilii IIf... <-fNf0^tf)CDr^000iO'-c\in'3'Ln»-cNf0'<3-LT <<<<<<<<<'-a3CQCQCQcnucjcjuu < Quadrat ure 4. Continued Number of Number of Total Number of Number of Captures Captures Snails Captured O M 4^ 0> CO Individuals —» K) to 4^ O cn o cn O O O O O Captured A1 I O to •t' O) to A1 i Ai i AI A3 ! A2i A2 I A3 i A3 1 A3 A4 1 A5 i A5 I A5 • A6 A7 A7 A8 A9 A9 D W D AS • o c c O O) Ql A10 • •D o o C o R> Q. •o O. •o Q) 0) 0) 0) •o (£> A11 • (D 3 B1 • (D 00 (/) o> (Ji LJ N> NJ A12 i CO B3 ^ I CO A13 I 83• A14 i I B5 81 85 • 82 87 83 87 84 89 85 89 86 (k) Slope 99, Transect B 25 'o « 20 5 5 15 E ^ 10 I" 5 >-cNnrrir:cop>>00CT)OII I CQCDCQCQCflCDCQCQCQ'— m Quadrat (I) Slope 99, Transect C S 7 ^5 I?,O 3 = 0 -• tt-CNnrrifitor^oao) .uuuuuuuuu . ..I u Quadrats (m) Slope 99, Transect X O 0 2 5 a. (3 0 3 £ 2 X) £ 1 1 0 1 <- CN CO Lfl tD 00 (T) o XXXX X X XXX X Quadrat ure 4. Continued 44 (a) Slope 103 24-Jun 27-Jun 30-Jun 3-Jul 6-Jul 20-Sep Date (b) Slope 119 12-Jun 15-Jun 18-Jun 21-Jun 11-Jul 20-Sep Date (c) Slope 120 20 • 15 10 5 0 24-Jun 27-Jun 30-Jun 3-Jul 20-Sep Sampling Date Figure 5. Plots of total number of individuals captured (new captures and recaptures combined) at each sampling occasion for each slope. 45 (d) Slope 121 80 a 60 • £ re = o 40 • E J2 i2 '5 20. o = 0 12-Jun 15-Jun 18-Jun 21-Jun 10-Jul 20-Sep Date (e) Slope 207 10*. O 3 CP ^Q z" fO *<5 o c H W 7-Jul 10-Jul 13-Jul 16-Jul Sampling Date (f) Slope 246 8-Jul 1 1-Jul 14-Jul 17-Jul 20-Sep Sampling Date Figure 5. Continued. 46 (g) Slope 297 §=<3 ^ c-JljI 5-Ju! I2-JuI 1 5-JuI 20-Sep Sampling Date (hi Slope 33 13-Jun 16-Jun 19-Jun 22-Jun 10-Jul IS-Jui 20-Sep Sampling Data (i| Slope 62 f S 15 14-Jun 17-Jun 20-Jun 23-Jun 1-Jul 20-Sen Sampling Date (j) Slope 98 20 ' 1 7-Jl." 20-Jun 23-Jur! 1-l-Jul 20-Seo Sampling Date ure 5. Continued. 47 time on each slope. Notice the extreme variability between sampling times and slopes. Figure 6 shows the total captures at each sampling time on slope 99 where boards and quadrats are treated separately. In these graphs, the number of snails found in the quadrats is consistantly lower than the number found under the boards. Discussion Population estimates The population estimates calculated in this study show large confidence intervals. This is expected with small sample sizes and low numbers of recaptures. Bayesian methods are sometimes useful for small data sets, so I also calculated mean estimates using a Bayesian estimator (Table 2). The Bayesian and normal estimation methods show similar results except for a few slopes (Figure 2). The Bayesian method is also able to calculate an estimate for populations 99C, 99X. and 297 where Chao's estimator did not have enough data to generate an estimate. Difficulties do exist with the Bayesian methods. As pointed out in Chao (1989), estimates calculated using the method of Gazey and Staley (1986) can be influenced by the size of the prior distribution assumed. For example, a distribution with an extremely long tail with low probability would affect the 95% confidence intervals. I minimized the tails by running test distributions to find a suitable range as Gazey and Staley suggest (1986). All of the Bayesian estimates fell within the standard errors of the Chao estimates for populations where both estimates were available. From Figure 2, the Bayesian estimates tend to be larger than the Chao estimates, but the slope of the 4S Slope 99, Transect B Quadrats 25 c z Boaras 15?^ I? 10 5 0 25- 28- 1 - 4- 9- 12- 15- 20- Jun Jun Jul Jul Jul Jul Jul Sep Date Slope 99, Transect C Quadrat Boards 5 5 10 Date Slope 99, Transect X " Quadrat 9-Jul 12-Jul 15-Jul 20-Sep Date Figure 6. Plots of the total number of individuals captured at each sampling occasion on slope 99. Snails caught under boards and those found in quadrats are displaved separately. 49 relationship between the two does not differ significantly from 1, so both methods appear to be equally consistant. Although there are no standard errors available for Frest's population size estimates, eight of twelve of my estimates are much higher than his estimates (Table 3). Using a sign test, my estimates are not significantly higher than Frest's estimates as a whole (p=0.121). Some of the differences can probably be explained by the structure of the particular slope. For example, slope 247 consists of a small patch of talus and a large overhanging cliff, which could not be sampled. The potential snail habitat was calculated only as the talus area and the area immediately at the bare base of the cliff where many shells and a few live snails were found, presumably having fallen from the cliff. Discus macclinlocki has been found living in cracks in bare cliff faces on other slopes (personal observation). If the entire cliff face is counted as habitat, the estimate would be greatly increased. Thus, most error in the estimates is probably due to area effects, not counting error. Other slopes which have low population estimates are also complex in structure. Slopes 103 and 297 are extremely complex with many outcrops interspersed along the slope, so areas of potential snail habitat may have been overlooked and not included in my estimates. The relatively low estimate on slope 120 is an anomaly, however, because it appeared to be relatively uniform over the area sampled. Estimates for slopes 207 and 246 are larger than those of Frest by two orders of magnitude, which is due to the large habitat areas given by Frest. Comparing Frest's area estimate to my estimate of area for slope 297 (the only slope where both estimates are 50 available) shows his estimate (52,000 m") is larger than mine (300 m"). It is likely that Frest was estimating the entire hillside area rather than just suitable habitat area. Wallendorf and Clark (1992) used sampling boards for their mark-recapture study of slope 99 (encompassing the three separate areas B, X, and C). Using CAPTURE, their population estimate of 205,000 (95% c.i. = 47,000 to 885,000) is significantly higher than the estimate from my study. However, they assumed uniform density over the entire slope area to obtain this value. The assumption of uniform density is obviously not realistic, as can be seen from the results from the estimates from the three distinct sections used in this study. Extrapolating a high density over areas which do not contain many (if any) snails would result in a higher estimate. Using their density estimate of 86 individuals per m" over the total area I sampled on slope 99 (270 m"), results in a population estimate of 23,220 individuals which is still much larger than the Bayesian ma.ximum estimates for the three areas (B, C, X) combined (3915 individuals). Overall estimates from the boards and quadrats are not directly comparable because fewer boards v/ere used than quadrats. However, sampling boards produced some very different dynamics in the snails' behavior. Capture probabilities were much higher for the boards than for the quadrats (0.127 vs. 0.03 on slope 121; 0.156 vs. 0.023 on 99B). Snails were recaptured at much higher rates under the boards than in the quadrats (25.0%, 47.6%, 39.29%, and 22.86% vs. 7.75%, 8.70%, 0%, and 0%). These rates are consistent with previous monitoring studies using boards, where the recapture rate was 54% (Ostlie 1992). The total number of individuals under the boards also appeared to be more stable than the number in the quadrats (Figure 6). In effect, the snails are "trap 51 happy", a classic problem in population estimation. Because the estimates are based on the proportion of captures which are recaptures, this could severely bias the population estimates to be lower than the actual population size (White el al. 1982). These behaviors suggest that the snails are more likely to come to the surface under a board than in an open quadrat, and once under a board, they are more likely to remain under a board. The snails' behavior is probably because the boards are maintaining cover over the area and keeping more moisture on the ground, which results in higher rates of recapture when using the boards. Variograms from some of the slopes show a slight relationship between the number of individuals captured at any one site and the number captured at the next closest site — at a lag distance of two meters (see Figure 3). On the slopes where this relationship occurs, the snails are heterogeneously distributed across the areas sampled. The number of snails captured at each quadrat in this study shows clumping on some slopes as well (see Figure 4). On some slopes most of the captures are from just a few sampling sites (Figure 4), which would appear to coincide with Frest's reference to colonies of snails (Frest 1984). Or, the snails may only be more uniformly distributed beneath the surface layer and only funneled to the surface at certain places, i.e. those areas cooled just enough by air flow from vents or with sufficient vegetation to maintain desirable humidity levels. The number of captures also varied among sampling occasions (Figures 4 and 5). Many factors could influence this result including moisture levels and temperature over the season. Ostlie (1992) showed that peaks in the number of individuals at each board 52 varied over time and with temperature, but the peaks were not the same over the entire slope. Wallendorf and Clark (1992) found that temperature did not determine the number of snails captured at temperatures from 6 to 12 "C, but these temperatures are below the temperature (15.6 °C) at which Ostlie (1992) noted reduced activity . The patterns of variation are not consistant among slopes, further emphasizing the uniqueness of each slope. Capture probabilities varied across slopes (as in the three sections of slope 99) so an average density should not be applied to the entire slope. Rough calculations of sampling estimates divided by sample area (surface area only) give results ranging from 0.74 individuals per m" on slope 207 to 28.07 individuals per m" on slope 33. Wallendorf and Clark (1992) recorded average densities of Discus maccliniocki of 86 individuals per m\ The density estimates from my study and Wallendorf and Clark's (1992) study of D. macclintocki are comparable to densities determined in other studies of terrestrial snails, which show a wide range of densities. Pfenninger et al. (1996) used visual searching of quadrats to study the small (5-8 mm) Trochoidea geyeri and obtained estimates of 6.89 individuals per m'. Studies of Cepaea nemoralis analyzed by Greenwood (1974) had densities of 0.1 to 1.5 per m" while Goodhart (1962) found densities of 3.5 per m% for that same species. Discus cronkhitei have been found in densities as high as 48.9 per m" (Van Es and Boag 1981). High densities were also found in Theba pisana (39-202 individuals per m"), and Ariania arbuslonim (10.1-42.9 individuals per m") although the variation between quadrats was large (Cowie 1984; Baur and Baur 1990). Woodruff (1978) recorded average densities of 13 individuals per m' for Cerion sp. Baur (1988) 53 recorded densities of Chondiina clienta reaching 794 individuals per m\ Movein ent Recaptures showed that very few snails moved to a different quadrat or board during this study. The percentage of recaptured snails that moved at all was 0.13% on slope 33, 0.09% on slope 121, 0.17% on slope 119, and 0.05 on 99X (under the boards). A low percentage of movement is not unusual for snail studies and illustrates the difficulty in studying motility in an animal with such slow movement. Schilthuizen and Lombaerts (1994) found that 90% of adult Albinaria comtgaia, which also resides in a patchy habitat, moved less than 2 meters. Of the Discus macclintocki that moved, the most common rate of movement was 0.7 meters per day. The longest recorded movement was 8 meters. These rates are similar to the rates of movement shown in other studies. Ostlie (1992) recorded Discus macclintocki moving up to 1 meter per week. This is a higher rate of movement than Goodhart (1962) recorded for Cepaea nemoralis (average 0.18 meters in 4 weeks). The maximum recorded distance moved by T rocho idea geyeri was 13.09 meters over a 210 day period (Pfenninger el al. 1996). Woodruff (1978) estimated that one generation of Cerion species disperses only 0.200 meters. Chondrina clienta dispersed a ma.ximum distance 0.342 meters over a six-month period in a study by Baur (1988). Some species appear to move much more readily. Cepaea nemoralis was studied in a grassland area by Cameron and Williamson (1977), who found 31-38% of snails per year migrated from the initial plot. Giant African snails (Achatinafulica) move on average 54 0.54 to 3.77 meters per day and juveniles are known to disperse up to 500 meters over a four month period (Tomiyama and Nakane 1993). Some studies have shown a relationship between the number of dispersers or the distance of dispersal in snails and either the density of the population (Greenwood 1974), the type of habitat (Baur 1988; Baur and Baur 1990), or the amount of handling (Cameron and Williamson 1977). I cannot test for similar effects in this study of Discus macclintocki with such small sample sizes and a limited ability to detect dispersal. Spatial and temporal factors This study illustrates several factors that create difficulties when attempting to estimate snail populations. First, the three-dimensional nature of the problem must be considered. Locasciulli and Boag (1987) examined the vertical distribution of snails in forest litter using soil cores. The snails tended to move upward in the summer. However, 67% of the Discus cronkhitei (a close relative of Discus macclintocki) were found deeper than 5 centimeters from the surface. This result suggests that only a small portion of the snail population is active at the surface at any one time. Boag (1985) found that humidity level is the most important factor regulating the presence of snails above the litter. Boards and quadrats could be measuring only a very small fraction of the actual population. Because snails can move vertically as well as horizontally, sampling methods which assume that all snails will be observed in a certain two-dimensional area will result in biased estimates since, snails below ground at a given plot are not observed. Counts that reflect fewer than the actual number of snails at a site will result in low estimates. If 55 this is the case, the population estimates given here could be severe underestimates of the actual populations, but they will not be overestimates. Another important factor to be emphasized is that temporal variation is extremely important in these populations. Both day-to-day and year-to-year fluctuations are evident. Number of captures varied between sampling occasions in this study (Figure 5) showing fluctuating activity levels. Year-to-year variation was formerly attributed to population decline (Frest 1984). Frest compared population sizes between years at five slopes and found three of them to be lower in the second year. However, extrapolating from the fluctuations seen between sampling occasions in this study, year to year variation could be a normal occurrence. Additional evidence exists for yearly fluctuation in an individual's presence above ground. In May and June of 1996, while collecting snails for a separate study (Ross, in prep), I observed ten Discus macclintocki marked during previous monitoring work on slope 99 where 122 adults were marked with fingernail polish in unique color patterns (Ostlie 1992; Wallendorf and Clark 1992). However, during the 1997 season, I observed only one snail from that study, and it was not one of the individuals observed in 1996. Thus, long-term monitoring is necessary to determine just how much snail populations fluctuate. Finally, snails live at much finer spatial scales than scientists, and unrecognized differences in microhabitat can make large differences in the presence or absence of a snail in one area or another, even on the same slope. Figure 4 illustrates the presence of snails in some quadrats and not others over the sampling season. Although assuming habitat uniformity even within "snail habitat" will tend to 56 overestimate the population, it is clear that sizeable populations exist at all of these sites. Estimates presented here are probably even biased low due to error in sampled-area estimates and the inability to monitor snails underground. Population sizes appear much larger at most sites than previously thought. Despite the difficulties in sampling, this study provides baseline data for monitoring efforts and future studies of Discus macclintocki. Acknowledgements I would like to thank Tawnya Cary, Katherine Holger, Jessemine Fung, and Adam Remsen for their help in the field. Funding for this project was provided by the Iowa Department of Natural Resources. I appreciate the cooperation of the Iowa Department of Natural Resources (especially Daryl Howell), the Algific Slope National Refuge, the U.S. Fish and Wildlife Service, and the Iowa Chapter of The Nature Conservancy. This study was conducted under the Iowa Department of Natural Resources Permit for research on Discus macclintocki. References Ausden, M. A. 1996. Invertebrates. In; Ecological Census Techniques: A Handbook (W. J. Sutherland, ed.), 139-177. Cambridge University Press, Cambridge. Baur, B. 1988. Microgeographical variation in shell size of the land snail Chondrina clienta. Biol. J. of the Linn. Soc. 35: 247-259. Baur, A. and Baur, B. 1990. Are roads barriers to dispersal in the land snail Ariania arbustoruml Can. J. Zool. 68: 613-617. 57 Boag, D.A. 1985. Microdistribution of three genera of small terrestrial snails (Stylommatophora: Pulmonata). Can. J. Zool. 63; 1089-1095. Boag, D.A. 1982. Overcoming sampling bias in studies of terrestrial gastropods. Can. J. Zool. 60: 1289-1292. Cain A.J. and Currey, J.D. 1968. Studies on Cepaea. III. Ecoigenetics of a population of Cepaea nemoralis (L.) subject to strong area effects. Phil. Trans. R. Soc. London, Series B, 253: 447-482. Cameron, R.A.D. and Williamson, P. 1977. Estimating migration and the effects of disturbance in mark-recapture studies on the snail Cepaea nemoralis L. J. Animal Ecol. 46: 173-179. Chao, A. 1989. Estimating population size for sparse data in capture-recapture experiments. Biometrics 45: 427-438. Cowie, R.H. 1984. Density, dispersal and neighbourhood size in the land snail Theba pisana. Heredity 52: 391-401. Deutsch, C.V. and Joumel, A.G. 1992. GSLIB Geostatistical software library and user's guide. Oxford University Press, New York. Dillon, R.T. 1988. Evolution from transplants between genetically distinct populations of freshwater snails. Genetica 76: 111-119. Frest, T.J. 1981. Project SE-1-2. Iowa Pleistocene snail. Report to the Iowa State Conservation Commission, Des Moines. Frest, T.J. 1982. Project SE-1-4. Iowa Pleistocene snail. Report to the Iowa State Conservation Commission, Des Moines. Frest, T.J. 1984. National Recovery Plan for Iowa Pleistocene snail. United States Fish and Wildlife Service. Frest, T.J. 1985a. Final report Iowa Pleistocene snail project 1983. Report to the Iowa State Conservation Commission, Des Moines. Frest, T.J. 1985b. Final report Iowa Pleistocene snail survey 1985. Report to the Iowa State Conservation Commission, Des Moines. Frest, T.J. 1986. Iowa Pleistocene snail survey. Project E-1-6. Report to the Iowa State Conservation Commission, Des Moines. 58 Fresl, T.J. 1987. Final report. Iowa Pleistocene snail project, 1987. Project E-1-7. Report to the Iowa State Conservation Commission, Des Moines. Gazey, W.J. and Staley, M.J. 1986. Population estimation from mark-recapture experiments using a sequential Bayes algorithm. Ecology 67: 941-951. Goodhart, C.B. 1962. Variation in a colony of the snail Cepaea nemoralis (L.). J. Animal Ecol, 31; 207-237. Greenwood, J.J.D. 1974. Effective population numbers in the snail Cepaea nemoralis. Evolution 28: 513-526. Leonard, A.B. 1959. Handbook of gastropods in Kansas. Univ. Kansas Mus. Nat. Hist. Pub. 20, 224 pgs. Locasciulli, O. and Boag, D.A. 1987. Microdistribution of terrestrial snails (Stylommatophora) in forest litter. Can. Field-Nat. 101; 76-81. Murray, J. and Clarke, B. 1984. Movement and gene flow in Partula taeniata. Malacologia 25; 343-348. Ostlie, W.R. 1992. Development of a monitoring methodology for Discus macclintocki, 1991 field season (with notes on the life history of the species). Unpublished report to the Iowa Department of Natural Resources, Des Moines. Ostlie, W.R. 1993. A monitoring method for the Iowa Pleistocene snail {Discus macclintocki) 1992 field season. Unpublished report to the Iowa Chapter of The Nature Conservancy, Des Moines. Pfenninger, M., Bahl, A., and Streit, B. 1996. Isolation by distance in a population of a small land snail Trochoidea geyeri: evidence from direct and indirect methods. Proc. R. Soc. London, Series B 263; 1211-1217. Schilthuizen, M. and Lombaerts, M. 1994. Population structure and levels of gene flow in the Mediterranean land snail Albinaria corrugata (Pulmonata; Clausiliidae). Evolution 48: 577-586. Solem, A. 1976. Final Report, Contract no. 14-16-0008-965. U. S. Department of the Interior, Office of Endangered Species. Sutherland, W. J. 1996. Ecological Census Techniques; A Handbook. Cambridge University Press, Cambridge. 59 Tomiyama, K. and Nakane, M. 1993. Dispersal patterns of the Giant African Snail, Achatina fiilica (Ferussac) (Stylommatophora: Achatinidae), equipped with a radio- transmitter, J. Moll. Stud. 59: 315-322. U.S. Fish and Wildlife Service. 1993. Endangered and threatened wildlife and plants. United States Fish and Wildlife Service, Department of the Intererior. Van Es, J. and Boag, D.A. 1981. Terrestrial molluscs of Central Alberta. Can. Field-Nat. 95; 75-79. Wallendorf, M.J. and Clark, W.R. 1992. Evaluation of a population monitoring methodology for Discus macclintocki: 1992 field season. Unpublished report to the Iowa Chapter of The Nature Conservancy. White, G.C., Bumham, K.P., Otis, D.L., and Anderson, D.R. 1978. User's Manual for Program CAPTURE Utah State University Press, Logan, Utah. White, G.C., Anderson, D.R., Bumham, K.P., and Otis, D.L. 1982. Capture-Recapture and Removal Methods for Sampling Closed Popualtions. Los Alamos National Laboratory, Los Alamos, New Mexico. U.S. Department of Energy. Williamson, P., Cameron, R.A.D.. and Carter, M.A. 1977. Population dynamics of the landsnail Cepaea nenwralis L.: a si.x-year study. J. Animal Ecoi. 46: 181-194. Woodruff, D. S. 1978. Evolution and adaptive radiation of Cerion: a remarkably diverse group of West Indian land snails. Malacologia 17: 223-239. 60 CHAPTER 3. PHYLOGEOGRAPHY AND CONSERVATION GENETICS OF THE IOWA PLEISTOCENE SNAIL A paper to be submitted to Molecular Ecology Tamara Kay Ross Background Understanding how gene flow historically or currently connects populations is essential for conservation of species of concern in order to maximize the genetic diversity and overall health of the species. Issues of connectedness and intraspecific variation have become especially important as populations become more and more isolated in today's fragmented environments. Terrestrial snails are often ideal for studying the effects of isolation on genetic variation, because they commonly show low rates of migration. Many factors affect the amount and distribution of genetic variation within a species, including molecular mechanisms (e.g., mutation rate), as well as ecological factors (e.g., dispersal). (For a complete discussion of these factors see Avise 1994.) The geographical arrangement of populations can also affect the amount of variation. For example, in most species that migrate passively in water, populations downstream should receive migrants from populations upstream, but not vice versa. Therefore, populations downstream have the opportunity for more new genotypes to be introduced and may show a higher level of diversity than the upstream populations, which only obtain new genotypes through new mutations arising from within the population (at least in non- recombining loci). 61 Snail studies have often been used to examine questions regarding gene flow and genetic structure (including Selander & Kaufman 1973; Brussard & McCracken 1974; Brussard 1975; Schilthuizen & Lombaerts 1994; Selander & Kaufman 1975; and many others summarized in Fretter & Peake 1978). Most of the earlier snail studies used allozymes as molecular markers to determine the genetic diversity and structure of populations. Although technology has allowed the use of mitochondrial DNA for population studies since the 1970s (Avise et al. 1979), difficulties in extracting pure DNA precluded extensive snail studies until recent work provided new methods (Stine 1989; Terrett 1994). Many studies have now been conducted that use sequences of mitochondrial DNA loci to examine intraspecific variation in snails (Thomaz et al. 1996; Remigio & Blair 1997; Douris et al. 1998). Mitochondrial DNA sequencing has the advantage of showing all existing variation in that segment of DNA. The high resolution allows the determination of population structure at a finer scale than, for example, allozyme analysis, which may not detect all mutations. Mitochondrial DNA also does not recombine so haplotypes are easier to trace among populations. In addition, mitochondria tend to mutate at a faster rate than nuclear DNA (Avise 1987), so more variable characters are available for analyses. Discus macclintocki is a small terrestrial snail (5 to 8 millimeters in diameter) that is a federally listed endangered species (United States Fish and Wildlife Service 1993). Currently, it is an extreme habitat specialist inhabiting only algific (cold-air) talus slopes in northeastern Iowa and northwestern Illinois. The species appears to have a limited temperature tolerance (Frest 1981) that prevents its expansion into the areas between 62 slopes, which have harsher climates. Because of these temperature requirements, D. macclintocki is believed to be a relict species, surviving only on these isolated algific talus slopes since the last glacier receded and the temperatures increased approximately 16,500 years ago (Prior 1991). The initial work on this species (Baker 1928) described two subspecies, D. macclintocki macclintocki and D. macclintocki angulatus, from fossils differing in height and angle of the outer whorl. The existence of distinct subspecies was not confirmed in a later study (Frest 1984). In addition, two color morphs exist, tan and olive/gray, but whether there is any genetic difference between the morphs is unknown. No previous information is available on the genetics of this species, and a unique opportunity presented itself to examine the genetic diversity and genetic structure of a species that is highly fragmented and isolated in remnant populations. Several questions are addressed in this study. First, how is geological history and geographical separation reflected in the genetic relationships among populations observed today? For example, are populations along the same watershed more closely related to one another than to populations in another watershed? Secondly, has the isolation of these populations diminished the amount of genetic diversity within the populations? Third, does the arrangement of these populations influence the amount of variation within populations (i.e., are downstream populations more diverse)? Fourth, do particular slopes represent a greater amount of the total genetic diversity, and therefore should be given higher conservation priority? And, finally, do the genetic data support the designation of the subspecies? 63 Methods Field work Snails were collected from nine algific slopes from three watersheds in Iowa from May through July of 1996 and from one slope in Illinois in July 1997 (Figure 1. Table 1), The snails collected included individuals of both color morphs. These slopes had been identified previously as having D. macclintocki populations (Frest 1981-1986). I collected most of the snails by placing wooden collecting boards on the slopes. The snails took refuge in the moist microclimate under these boards, so I could easily return to the boards and find snails. On one slope (f?232), the steepness of the cliff face precluded the "board method", but snails were easily picked from the rock cliff face by hand. Snails were frozen in liquid nitrogen or maintained alive on ice for transport to the laboratory where they were stored at -70''C. Samples of Discus catskillensis and Discus cronkhiiei were collected for use as outgroups. Morphological measurements of all snails collected were taken for a separate analyses (Ross, in prep). Table 1. Number of snails collected from each site. Slope numbers refer to the numbers assigned by Frest (1981-1987) in original surveys. Slope Number County Number of Snails Collected 33 Dubuque 30 62 Clayton 5 99 Clayton 30 103 Clayton 30 119 Clayton 30 120 Clayton 30 121 Clayton 30 213 Jo Davies 1 232 Jackson 20 297 Jackson 30 64 But.)' Ci»JeK uck Creek V TurkeyA/olga River >jC^'A SsLis \ Wisconsin / illinoi •'.•213 i:: 232 Maquoketa Rivpr ' Figure 1. Map of northeastern Iowa and northwestern Illinois showing the locations of the collecting populations. Numbers refer to slope designations from Frest (1981-198/). 65 Laboratory' Methods DNA was extracted from the snails using the protocols of Frazer & Collins (1994). I amplified a 485 base-pair segment of the 16s rDNA region of the mitochondrial genome using the polymerase chain reaction (PCR). The 16s rDNA region was chosen because it amplified consistently with available primers and an initial screen showed variation was present. I used the primers 16sar-L (cgcctgtttaacaaaaacat) and 16sbr-H (ccggtctgaactcagatcacgt) originally designed for use in Drosophila (Palumbi et al. 1991). Each 50jil PCR reaction contained: 5|il MgCK, 5|il Promega (lOX Mg-free) buffer, 2.5 nl of each primer, 5 jil dNTPs, 28 ^1 sterile water, 0.2 )il Promega Taq polymerase (or one Promega TaqBead), and 1 ^l of diluted DNA. DNA was diluted 1:10 with sterile water for use in PCR. (The 1:10 dilutions were used because they amplified most reliably in an initial test of different dilution factors.) The thermal program consisted of thirty-four cycles of three steps: 95 "C for one minute, 43 to 47 °C for one minute, and 72 °C for 1.5 minutes. These cycles were followed by 95 °C for one minute, 43 to 47 °C for one minute, 72 °C for seven minutes, and an indefinite hold at 4 °C. In reactions where liquid Taq was used, a hot-start step was added to the beginning of the procedure, where the reactions were heated to 93 °C for thirty seconds before the Taq was added. The annealing temperatures were adjusted from 43 to 47 °C because of differences between individual samples, which amplified better at lower or higher temperatures. The PCR reactions were conducted in a PTC-100 thermocycler (MJ Research). PCR products were purified using Amicon Microcon-50 or Microcon-100 microconcentrators and then directly sequenced on Perkin Elmer Applied 66 Biosystems ABI 377 and 373 machines at the Iowa State University Nucleic Acid Sequencing Facility. A nalyses Forward and reverse sequencing reactions were run for each individual and checked against one another using Sequence Navigator version 1.01 software. In the few instances where the software's base-pair determinations were still ambiguous, base-pairs were called conservatively (i.e. the base-pair was assumed to agree with the common haplotype for that population at that position). Sequences are deposited in GenBank under accession numbers AF064400 to AF064438 . Sequences were aligned using ClustalW- (1.4)-big&fat. Sequences for D. catskillensis (accession ffAF063140) and D. cronkhitei (accession iTAF063141) were aligned and used as outgroups. Using PAUP*4 (Swofford 1997), I calculated the genetic distance using the following algorithms; absolute, Tajima-Nei, Jukes-Cantor, Kimura-2, and maximum likelihood. A representative of each unique haplotype in each population was used to create neighbor-joining and UPGMA trees. Parsimony methods were also used to construct a phylogenetic tree. These trees were visualized using MacClade version 3.05. Results The phylogenetic trees that result from the data generally support clades grouped according to watershed. The parsimony method generated 200 trees. The strict consensus of all of these trees is shown in Figure 2. Trees constructed using different distance Illinois liiick Creek Mnqiioketa River l iirkcy , •' Outgr(Hips rovD\r — cMr^ro^tcTiviDCMoaD — mooor^cM — — r^cMco — mvocTir^ — o'^m ^ CM m vo o OO'-OOO — — --•^orooooo — — OCAOO — CMCMOOOO — — CM-^ — o v£) — cr> Ln ^ d O CvJ (\J CM c\j ^ O O CM O '-oooo Gv l iyiiic 2. IMiylogciictic tree sliowing tlic relalionsliip between haplotypes ol" Disc its ludcclinlocki. Strict consensus tree ol 200 niost-parsinionioiis trees (2S4 steps, consistency iiuiex = ().S7, retention index 0.85). Ilaplotypes from the same watersheils are iiulicateil hy brackets. 68 measures differ slightly in topology (see Figure 3). Of the 355 base pairs that can be aligned across all haplotypes, there are 62 variable sites. Seven changes exhibit homoplasy on the trees. The parsimony tree method is the shortest tree having just 284 steps (consistency index = 0.87). Neighbor-joining trees constructed from maximum likelihood, Kimura-2, and Jukes-Cantor matrices are only 2 steps longer (286 steps, consistency index = 0.86). The neighbor-joining tree generated from absolute differences is 289 steps (consistency index = 0.85). The Tajima-Nei matrix produces a tree of 301 steps (consistency index = 0.82). UPGMA trees were slightly longer, using both absolute differences (290 steps, consistency index = 0.85) and maximum likelihood (289 steps, consistency index = 0.85). All trees suppon the monophyletic group of haplotypes from the Maquoketa watershed (including individuals from slopes 297 and 232) combined with individuals from slope 213. Haplotypes from slope 232 form a monophyletic group using all methods except the Tajima-Nei distance matrix. Two different haplotype clusters exist on slope 297, and they are more distant from one another than from some of the haplotypes from slope 232 and 213. The parsimony method and all distance methods except the Tajima-Nei and absolute differences methods support the monophyletic group of individuals from Buck Creek (slopes 99, 103, 119, 120, and 121). None of the methods indicate any that any individual slope within the Buck Creek watershed is monophyletic. Individuals from slope 33 are monophyletic in trees constructed using parsimony, neighbor joining with any of the distance methods, and UPGMA using absolute distance Illinois line k ('ii-i-k Hiii k ('ivc k MiKjnokcIa River Tiirki'v liii key — fO — M vn ou ro o M r- —a.Of\jin^cof\i — — r^O'jeD'-inr^ — *i3a»o^r^i/) ool I l )nl};iiiii|)s vt o o o (XI m — — O O — vD — a>or-o--K>oo«~oooo — f^jfNJOo — »~oo I'iyiire 3. IMiylogcnclic trees showing the rehitionship between haplotypes of Discus macclintocki. a) Neiglibnr-joining tree constnicteii using absolute distances (280 steps, consistency index - ().S3, retention index = 0.83), b) neighbor-joining tree constructed using .Fukes-Cantor distances (286 steps, CI - t).S6, Rl = 0.84), c) neighbor-joining tree using i ajima-Nei ilistances (301 steps, CI 0.82, RI = 0.79), d) neighbor-joining tree using Kiniura-2 distances (286 steps, CI ^ 0.86, 1 or maximum-likelihood. The haplotype from slope 62 is outside all the other populations on all trees with very strong character support. However, several changes still link it with the haplotypes from slope 33, the other slope on the Turkey River watershed. Fifteen of the variable sites showed gaps (insertions or deletions) in some individuals which were coded as missing data and not included in the distance calculations. Including the gaps in the analysis, coded as a fifth character state, did not change the overall structure of the trees (Figure 4). Four of these gaps support the clade consisting of populations 33 and 62, which groups the Turkey River watershed as a monophyletic group. The gaps are included in the haplotype network where each base pair added is counted as a separate step change (Figure 5). The branch leading to the haplotype from slope 62 shows a large number of changes compared with any of the other branches. The locations of the slopes can be seen in Figure 1. Within slopes, no spatial structuring of haplotypes was evident. Haplotype diversities varied considerably among populations (Table 2). Population 103 shows a much higher diversity than the other populations on Buck Creek. Three haplotypes occur in more than one population (Table 3). Common haplotype A is found in all 5 populations on Buck Creek. The frequencies decrease in population 103. The number of individuals with each haplotype is given in Table 4. I calculated the relationship between average Juke's-Cantor genetic distance and linear geographic distance (Figure 6a) and stream distance (Figure 6b). Average genetic distances were determined by averaging the Juke's-Cantor distance from each haplotype Figure 4. Strict consensus of 200 most parsimonious trees when gaps are coded as a fifth character state. (Treeiength is 333 steps, consistency mde.x = 0.S6. retention index = 0.S5). Haplotypes from the same watershed are indicated by brackets. -zr-'Vi 99,103, ' ."tr 119,120, 'r— !7 steps rurkeyjVclga River MaquoKeta River Figure 5. Haplotype network for Discus macclintocki populations investigated in this study. Hash marks represent DNA sequence changes from one haplotype to the ne.xt. .-Ml changes (transitions, transversions. and indels) are included. The size of the circles represents the number of individuals with that haplotype. (For the e.xact number of individuals with each haplotype, see Table 5.) Haplotypes from the same population cluster together and are labeled by the population number. Populations on the same watershed are designated bv similar shadina as shown in the leuend. 7S Table 2. Haplotype diversities within populations. Population Haplotype Diversity (h= 1-Zf,") 119 0.435 121 0.410 120 0.420 99 0,455 103 0.730 62 0.000 33 0.355 232 0.663 297 0.676 Table 3. Haplotypes that occur in multiple populations. No other haplotypes listed in Table 4 are found in more than one population. Haplotype Populations Frequency A 12! 0.75 120 0.75 119 0.60 99 0.73 103 0.25 B 121 0.15 119 0.20 C 120 0.05 103 0.05 79 Table 4. Number of individuals with each haplotype. Letters identify unique haplotypes. Slope 99 Slope 213 Haplotype Number of Individuals Haplotype Number of Individuals A 16 U 1 D 2 E 1 Slope 297 F 1 Haplotype Number of Individuals G 1 V 11 HI W 3 X 3 Slope 121 Y I Haplotype Number of Individuals Z 1 A 15 AA 1 B 3 AB 1 I 1 J I Slope 232 Haplotype Number of Individuals Slope 119 AC 12 Haplotype Number of Individuals AD 2 A 12 AE 2 B 4 AF 1 K 2 AG I LI AH 1 Ml AI 1 Slope 103 Slope 33 Haplotype Number of Individuals Haplotype Number of Individuals A 5 AJ 19 N 8 AK 3 0 4 AL 1 PI AM 1 Q 1 C 1 Slope 62 Haplotype Number of Individuals Slope 120 AN 5 Haplotype Number of Individuals A 15 R 2 S 1 T 1 C 1 80 within a population to each haplotype within another population. Each population was assigned zero within-population genetic distance. Linear geographic distance was determined by measuring the shortest straight-line distance between populations. Stream distance was determined by measuring the distance between populations by following the route of the waterway. These relationships show a significant positive correlation (p=0.0043 for linear distance and p=0.00026 for stream distance), but explain very little of the variation (r^= 0.144 and 0.224 for linear and stream distance respectively). Slope 62 could bias these results since the haplotype found there is extremely different than haplotypes on the other slopes. Therefore, I reran the analyses without including distances to slope 62 (Figure 6c and d). With these analyses, the relationships remain significant (p«0.05) and a larger portion of the variation is explained (r^=0.517 and 0.630 for linear distance and stream distance respectively). Since these points are not all independent, a Mantel's test was conducted to determine the significance (Smouse 1986; as analyzed in Hellberg 1994; Tilley ei al.\990). For this test, average genetic distances for each population were randomized among all populations and the observed slope was compared against the slopes obtained from ten thousand replications to determine the probability of obtaining the observed slope by chance. The relationship between genetic distance and watershed distance was significant (p=0.018), but the relationship between genetic distance and linear distance was not (p=0.11). I also investigated the relationship between haplotype diversity and population size, habitat area, and number of other snail species present. The population sizes were SI Linear Distance vs. Genetic Distance 0.10- 0.09- 0.08- O O 0.07 i ctQ - ° 0.06- O S 0.05- O - 0.04 - 0.02- 0.01 - 0,00 C 10 20 30 40 50 60 70 8 0 9 0 100 Linear Distance, kilometers Figure 6. Relationship beuveen genetic distance and a) linear geographical distance (y=0.014-t-0.00024.\. r^=0.144) and b) stream distance (v-0.012+0.000 16.k. r^=0.224j. Linear distance was determined by measuring the shonest straight-line distance between the populations from a topographical map. Stream distance was determined by measuring the shonest route between populations following waterways only. Removing data from population 62 results in the relationships shown in c) for linear geographical distance (y=0.0063-0.00021x. r=0.517) and d) for stream distance (y=0.0057-0.00013.\. r=0.630). 82 Stream Distance vs. Genetic Distance 0.10 0.09- 0.Q8- 0) u 0.07- R]c to O 0.06- c 0.05- 0.04- 0.03- 0.02- 0.01 - 0.00 D- 0 2 5 5 0 7 5 1 00 1 25 1 5 0 175 200 Stream Distance, kilometers Figure 6. Continued. Linear Distance vs. Genetic Distance (with population 62 excluded) 0.040 0.035 - 0.030 - a u c CO in Q 0.025 - o 01 c o 0.020 - O w o 0.015 - 0.010 - 0.005 0.000 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 Linear Distance, kilometers Figure 6. Continued. S4 Stream Distance vs. Genetic Distance (with population 62 excluded) 0.040 0.005 0.000 50 75 100 125 1 50 1 75 200 Stream Distance, kilometers Figure 6. Continued. 85 determined during a mark-recapture study conducted in 1997 (Ross, in prep). The number of other snail species present was taken from original surveys of the slopes (Frest 1981, 1982). Frest (1986) did not list all species present in his survey of slope 232 or 297, so these slopes are not included in the analyses of species number and diversity. Potential snail habitat areas were measured in relation to the mark-recapture study. Neither population size nor habitat area could be estimated for the snail population on slope 232, so it was not included in either of these analyses. The sample size (n=l) from population 213 did not allow calculation of haplotype diversity. None of these factors showed a slope that was significantly different from zero using standard least squares methods (Figure 7). Population size and habitat area described only a small portion of the variation (r^= 0.014 and 0.005). Species number did describe a large portion of the variation (r^=0.499). but the slope was not significantly different from zero (p=0.076). A reduced major a.\is test (see Clarke 1980; LaBarbera 1989) increased the steepness of the slopes for all three comparisons, but again none were significantly different from zero (population size -42988 +/- 5113, area 3427 -^/- 18.23, species diversity -27.69 +/- 19.56). The sample size is so low for all of these comparisons that the power of the test may not be large enough to determine if a relationship exists. Discussion This study provides a description of genetic structuring at various spatial scales. No genetic structure e.xists within slopes, as the haplotypes were distributed among the S6 Population Size vs. Haplotype Diversity 0.8 0.7 • 0.5 • 0)w a 0.4 - 0) a. £ a to X 0.0 0 SQQO 10000 15000 20000 25000 30000 Population Size Figure 7. Correlation benveen haplotype diversity and a) population size (y=0.46-2.77e- 6.\, r^=0.0I4), b) habitat area (y=0.43-rI.65e-6.\, r^=0.000028), and c) number of snail species co-habitating that slope (y=0.75-2.55e-2x. r=0.499). 87 Habitat Area vs. Haplotype Diversity 500 1000 1500 2000 2500 3000 Habitat Area, meters'*2 Figure 7. Continued. 88 Haplotype Diversity vs. Species Diversity 0 5 1 0 1 5 2 0 2 5 3 0 Number of Snail Species 89 sampling boards on each slope. However, among slopes, there is extensive structuring of haplotypes, (except on Buck Creek as explained below). Individuals from slopes 33. 62, 297, and 232 cluster together reliably, illustrating slope-level differences. Two separate clusters of haplotypes exist in population 297, most likely due to the survival of two polymorphic haplotypes from the ancestral population. At the largest scale investigated in this study, all watersheds are genetically distinct from one another. The geological history of Iowa is dominated by multiple glacial advances. The northeast comer is part of the 'Driftless Area' because of the peculiar situation where several of the Pleistocene glacial advances did not cover the entire area (Prior 1991). The fossil record of Discus maccliniocki suggests a population existed to the south and west during the most recent glaciation (Frest & Fay 1980; Frest 1984; Hubricht 1985). These snails appeared in the geologic record 300,000 ybp and have existed in their current geographical area as far back as 20,000 ybp (Frest 1984). The current populations on the algific slopes may be remnants of a population(s) which existed in the driftless area during the glacial period and subsequently became isolated on the cold-air slopes. Alternatively, the snails may have migrated into the area from the southern population as the glacier receded and the area to the south warmed, successfully colonizing only the algific slopes. The data from this study cannot distinguish between these two scenarios. The population on slope 213 is the only population located on the eastern side of the Mississippi River (Jo Davies County, Illinois). Despite this seemingly large barrier to migration, the single sample from this population clusters within those populations found on the Maquoketa watershed at roughly the same latitude on the western side of the river. This clustering suggests that the course of the Mississippi River may have changed after the populations were initially founded, subsequently separating the populations. Some geological theories do suggest that the Mississippi River existed farther east prior to being diverted to its present course (Anderson 1988). However, the Mississippi River has been in its present course at that location for at least 700,000 years (Anderson 1988), which is before the first appearance of Discus macclintocki in the fossil record (400,000 ybp, Frest 1984). The phylogenetic relationships are also consistent with the hypothesis that flooding events have been the main method of dispersal. Although other animals and even wind have been implicated in dispersal of other snail species (Boag 1986; Kirchner ei al. 1997), dispersal by (looding seems most likely in these habitat specialists. The stream distance has a higher correlation with genetic distance than linear distance, and when average genetic distances are randomized, the relationship between genetic distance and linear distance is no longer significant. Therefore, being downstream from other populations may be more important than the actual linear geographical distance for determining genetic divergence. Individuals from slope 62 show extreme divergence from all haplotypes from all of the other slopes. Thirty-three steps separate the individuals from slope 62 from the individuals on the next closest slope (# 33) on the same watershed. This divergence seems extreme for the distance between slopes 62 and 33 (14 miles), because it is much larger even than pairwise divergences between watersheds. For example, individual haplotypes from Buck Creek slopes and Maquoketa River slopes over 45 miles are only 91 separated by six steps. The morpiioiogy of the individuals from slope 62 does not differ significantly from individuals on the other slopes (Ross, in prep), so it is unlikely that they represent a separate species. Mis-identification is also not a likely explanation because the DNA sequence is not similar to the sister taxa found in the area {Discus cronkhitei and Discus catskillensis). Slope 62 is the westernmost slope of those sampled and could perhaps represent a different colonization event than that which founded the other populations. Without information from populations farther west, support for a separate founding event cannot be assessed. Slope 62 is located right at the edge of the Niagara Escarpment and the Iowa Erosion Surface as determined from maps of landform regions of Iowa (Prior 1991). How this might have affected colonization of this area is unclear. However, because populations on different watersheds do not share any hapiotypes, it is clear that we cannot assume a stepping-stone model of colonization whereby southern populations were founded first, followed by successive colonization of more northerly populations. Based upon DNA sequences, the five populations along Buck Creek (Slopes 99, 103, 119, 120, and 121) could not be differentiated from one another. This pattern contrasts with the patterns of divergence seen among all of the remaining populations. If a constant rate of divergence is assumed, the Buck Creek populations must have been connected to one another by a common ancestor much more recently than the other populations. A difference exists between the frequency of the common haplotype A between slope 103 and the remaining four Buck Creek populations, however, suggesting some differentiation is occurring among sites. 92 Overall, the genetic distance was related to the linear geographic distance and the watershed distance, supporting the idea that geography did play an important role in the distribution of the species. This isolation by distance is consistent with a study by Pfenninger et al. (1996), which found genetic distance and geographic distance were highly correlated in a similarly sized snail, Trochoidea geyeri. The snails from slope 103 also exhibit a larger variety of haplotypes than the snails from other populations. Slope 103 is downstream from all the other populations on Buck Creek and could potentially receive immigrants during flooding events from all of the upstream populations (those currently in existence and those which might have existed historically on other slopes in the area). Slope 33 also has a higher level of diversity than the upstream population on the same watershed (slope 62). This evidence supports the hypothesis that populations further downstream would have higher genetic diversities than populations upstream. However, unique genotypes exist in the downstream populations as well, so migration is not the only way they are generating diversity. Total levels of genetic diversity within populations are extremely high. At least four haplotypes exist in all of the sampled populations where at least 20 individuals were sampled. The amount of polymorphism in some of these populations is even higher than that observed by Thomaz et al. (1996), who commented on the extreme levels of variation found in 300 base pairs of the 16s region in Cepaea nemoralis. In their study, they reported up to five haplotypes in a population and a maximum intraspecific divergence of 12.9%. Calculating the haplotype diversity from their reported haplotype frequencies results in a range of 0.00 to 0.709. The haplotype diversities in my study range from 0.00 93 to 0.730 with maximum intrapopulation genetic distance of 1.4%. Between populations, the maximum divergence in this study was 11,9% (between haplotypes P and AN). Intraspecific comparisons of Albina/ia species from different islands around Crete give distances of 1.5 to 9.5% for the 16s region (Douris et ai 1998). Remigio & Blair (1997) found distances between populations of various snail species ranging from 0.2% to 9.7% at the 16s locus. These distance are higher than distances found in other molluscs. Mulvey et al. (1997) found a sequence difference of 0.2 to 2,5% between species within the same genera of freshwater mussels, Thomaz et al. (1996) point out that this level of divergence is as large as that contained within some orders, such as the 7.19% divergence (using transversions only) between suborders within Order Cetacea which Milinkovitch et al. (1994) found at the 12s and 16s regions. Bargelloni et al. (1994) describe a maximum 16.2% genetic difference within Order Perciformes. One factor that may affect genetic diversity in snails differently than in some other species is the possibility of self-fertilization. Although Discus macclintocki is not known to undergo self-fertilization, the potential effect of selfing on genetic diversity must be considered. Selfing should reduce diversity within populations, with up to an 80% reduction possible (Charlesworth et al. 1993). Other land snails do show reduced levels of diversity in self-fertilizing populations (Njiokou et al. 1993; Viard et al. 1997). However, Viard et al. (1997) note that demography outweighed any effects of selfing in several Bulinus tnmcatus populations that exhibited high levels of variability. Self- fertilization cannot be ruled out from the current study, but, if present, other factors are clearly much more important in determining the amount of genetic variation within 94 populations of D. niacclinlocki. Thomaz et al. (1996) suggest several reasons why terrestrial snails may exhibit such high genetic diversities including: 1) rapid mitochondrial mutational rate, 2) very old isolation events, 3) selection for variability, and 4) population structure conducive to high variation. They conclude that the most support is for hypothesis number four, because terrestrial snails tend to have numerous demes with low migration rales. However, they could not completely eliminate the first or third hypotheses. The second explanation is not supported in their system, since C. nemoralis show no geographical structuring with allozymes. In the present system, no allozyme data for Discus macclintocki are available to eliminate the second possibility. However, the geological history of the region would suggest that these populations may have been isolated on the algific talus slopes for at least 16,000 years. Whether this is sufficient time to allow for the observed variation is questionable if mutation rates are similar to those for other species. Mutation rate estimates determined for other snail species of approximately 0.6% per million years (Rumbak et al. 1994) would put the separation of populations 33 and 62 at more than 19 million years ago (using the method shown in Li & Graur 1991). This split would be older than the land surfaces in the area which were last glaciated around 700,000 to 2 million ybp (Prior 1991), so it is unlikely that these mutation rates are correct. A faster mutation rate seems likely for pulmonale snails at this locus, but the reasons for this pattern are unclear. Selection for the maintenance of variability is an interesting possibility explored in 95 Nevo (1988). In snails, some work has suggested that there may be selection for diversity in shell banding patterns (Jones 1973), Thomaz et al. (1996) state they are further investigating selection for diversity in mitochondrial DNA haplotypes based on interactive function v/ith nuclear ribosomal regions. My study cannot address this possibility. In addition to the possible explanations discussed above, several ecological and environmental factors may affect haplotype diversity. Population size is one potential influence because large populations are less likely to drift to fixation. However, regression analyses indicate mark-recapture estimates of the population size are not significantly correlated with the haplotype diversity exhibited by the populations (p»0.05), suggesting that the size of the population has little effect on the variation in these snail populations. Habitat area is also related to species diversity (MacArthur & Wilson 1963; Rosenzweig 1995; among many others) and by corollary could be related to genetic diversity. However, habitat area does not show a relationship with the haplotype diversity either (linear regression test, p»0.05). Another potential influence on the maintenance of high levels of variability within populations is the complexity of the snail community. This hypothesis is an extension of traditional niche theory. Niche theory is based on the idea that organisms have the ability to do best within certain parameters of a resource, and will outcompete other organisms at this optimum for which they are best suited morphologically, behaviorally, etc. (Hutchinson 1965; MacArthur & Levins 1967; Abrams 1983). Competition with other species forces a narrowing (stabilizing selection) towards that optimum. Patterns to support this prediction have been shown in studies of finch species in the Galapagos 96 where Geospiza difficilis and Geospiza fuliginosa have more similar diets when found on separate islands, but partition the food resources when living sympatrically (Schluter & Grant 1982). Davidson (1977) showed that ant species adjusted their foraging strategies depending on the species that made up the ant community. If the ability to exploit the resource gradient is at least partially heritable, then the underlying genotypes must be involved in the dynamics of niche compression. Several studies have examined how genotypes within one species may partition that species' niche width (Garbut & Zangerl 1983; Zangerl & Bazzaz 1984; Garbut et al. 1985), but this approach does not address the question of how species diversity is related to genotype diversity. Vrijenhoek (1985) reasoned that if the niche space is at least partially dependant on some heritable trait, a population with more genotypes will have a wider niche. However, the idea that the level of intrapopulation variation is related to niche width has been extensively debated (Rothstein 1973; Loeschcke 1984). Noy ei al. (1987) found that in two Liltorina species, heterozygosity was correlated with niche width. Huston (1994) hypothesized that with increased species diversity in a given habitat, each species should comprise a narrower niche and, therefore, exhibit lower genetic diversity. Assuming similar resource gradients. Discus macclintocki have more niche space available on slopes with few competitors, which means more intraspecific genetic diversity can be developed and maintained. The number of other snail species found on a slope did show a negative relationship with the haplotype diversity (r=.449), but again the slope of the regression line is not significantly different from zero (linear regression, p=0.07). More work is needed on this topic to support this theory further. 97 Some slopes appeared to have more distinct patches of high quality habitat separated by less suitable habitat. Bacteria have been shown to increase in genetic diversity as habitat variability increases (McArthur et al. 1988). Complex habitat variation could influence the number of haplotypes on a slope due to the maintenance of local variation. However, no distinct structuring within a slope was observed. Little surface movement was noted (Ross, in prep), suggesting that perhaps the population was uniformly connected underground. No evidence was found to support Discus macclintocki angulaius as a separate species with a more angular shape as originally described (Baker 1928; Pilsbry 1948). For the most part, snails clustered together by slope. If subspecies existed sympatrically with strong assortative mating, angular individuals from one slope should cluster more closely with angular individuals from other slopes than with individuals on their own slope. Slope and watershed are significant in discriminant analyses of morphological measurements (Ross, in prep). However, the populations' morphological measurements overlap too much to support the definition of a subspecies based on shell characteristics. The population on slope 62 cannot be distinguished morphologically as a separate entity either, as the snails from that slope are in the center of the distribution of measurements. Both color morphs were found with the same haplotypes on the same slopes. Because no genetic difference exists between color morphs, the rsvo morphs must indeed be the same species. The genetic pattern from a single locus does not address the question of whether there is a genetic basis for the difference in color, however. From a conser\'ation perspective, the ability of these populations to generate and 98 maintain high levels of haplotype diversity is a positive sign, whatever the mechanism. Although population size can be more important to maintaining species than genetic diversity (Lande 1988), recent research indicates a link between genetic diversity and fitness traits (Saacheri et al. 1998). Management emphasis should be placed on maintaining the processes that allow for such diversity to exist. Summary How are geological history and geographical separation reflected in the genetic relationships observed today? Clearly, geological history has played an important role in structuring the populations of the Iowa Pleistocene snail. Historically, watersheds have been extremely imponant as avenues for dispersal. Populations on different watersheds have indeed been isolated for a long time, at a minimum since the climate began to warm as the glacier retreated about 16,000 ybp. The separation of populations on Buck Creek appears to be a much more recent event, and some gene flow may still be occurring between a few of the populations at the headwaters. Does the arrangement of the populations influence the amount of genetic variation present? Two downstream populations do have higher levels of haplotype diversity than populations farther upstream. However, with such a small sample size, no conclusions can be drawn that stream position is a significant factor in the amount of variation present. Has the isolation of these populations diminished the amount of genetic diversity? Although most of the populations are isolated now due to the harshness of the surrounding 99 habitat, they do maintain high levels of genetic diversity. The extended period of isolation has not caused fixation of alleles. In addition, populations with fewer haplotypes are not necessarily less important, because they have haplotypes that are absent in other populations. Thus, conservation efforts should be made to preserve as many different populations as possible. Do the data support the legitimacy of subspecies? Despite the abundance of haplotype diversity observed and the high levels of divergence among populations at this locus, no support was found for the existence of subspecies, based either on morphology or color. However, the external morphology of snail species can be misleading, so a comparison of the internal anatomy of specimens from different populations may be warranted. Finally, the amount of haplotype diversity detected within this and other pulmonate species suggests that the 16s region is under fewer constraints than in other organisms, so that more mutations are allowed with little adverse effect. More research into the mechanisms behind the maintenance of such high levels of diversity is warranted. Acknowledgements Scientific collecting permits for Discus macclintocki were obtained from the United States Fish and Wildlife Service (#PRT 801463), the Iowa Department of Natural Resources (#SC62960I), and the Illinois Department of Natural Resources (/r973S). Funding for this project was provided by grants from: Iowa State Preserves Board, Iowa Department of Natural Resources, American Museum of Natural History, Conchologists of 100 American, Western Society of Malacologists, and the Ecology and Evolutionary Biology Program at Iowa State University. Thanks to Ellinor Michel and the Wes Brown lab at the University of Michigan for technical assistance and to Daryl Howell of the Iowa DNR for his assistance. I especially appreciate the comments on this manuscript provided by my committee members Brent Danielson, Diane Debinski, Fred Janzen, Richard Hoffmann, and Rob Wallace. Literature Cited Abrams P (1983) The theory of limiting similarity. Annual Review of Ecology and Systematics, 14, 359-76. Anderson RC (1988) Reconstruction of preglacial drainage and its diversion by earliest glacial forebulge in the upper Mississippi Valley region. Geology, 16, 254-257. Avise JC, Giblin-Davidson C, Laerm J, Patton JC, Lansman RA (1979) Mitochondrial DNA clones and matriarchal phylogeny within and among geographic populations of the pocket gopher, Geomys pinelis. Proceedings of the National Academy of Science, 76, 6694-6698. 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Ross Abstract A comparison of the amount of variation 16s rDNA sequences across several taxonomic groups show that evolutionary rates are not constant across groups. Pulmonate snails appear to have a mutation rate much higher than other groups. Introduction A specific gene is often chosen for a phylogenetic study if the gene is expected to have enough accumulated mutations to distinguish between the species (or taxa) being compared, but not so many mutations that informative changes are undetectable due to many mutations after speciation occurs. Molecular clock theory assumes a constant rate of mutations through time across species being studied (Zuckerkandl and Pauling 1965). A constant molecular clock is critical for obtaining the correct phylogenetic tree with some analytical methods, notably the unweighted pair group method with arithmetic mean method, or UPGMA (Li and Graur 1991). However, the variability among groups is rarely known before such studies are conducted. In this study, I examine variable regions of the 16s rDNA locus and their relative rates of change among groups. Ribosomal DNA regions are often useful for phylogenetic comparisons because they contain variable regions near more conserved regions. The variable regions contain 107 the information used to build the phylogeny, while the conserved regions allow primers to be designed that will work across many taxa. Different ribosomal genes (both nuclear and mitochondrial) are used for comparisons of different levels of taxonomic groups. In molluscs, 28s nuclear rDNA is often used for phylogeny reconstruction of higher taxa (Emberton ct a/. 1990; Rosenberg ei a/. 1997) because the mutation rate is relatively slow. In contrast, mitochondrial ribosomal DNA loci are commonly assumed to be evolving at a faster rate and are used for phylogenetic studies of taxa diverging within the last 65 million years (Hillis and Dixon 1991). Various studies have used mitochondrial ribosomal RNA genes to estimate relationships at many levels, from families within orders (Flook and Rowell 1997; Milinkovitch 1994) to populations within species (Thomaz ei al. 1996; Vogler and DeSalle 1993). However, Mindell and Honeycutt (1990) suggest that the 16s gene is only useful for determining relationships among groups with divergences dating back 150 to 300 million years. Determining whether informative variation exists at the level being studied is imponant. Pulmonate snails show an extremely high rate of sequence divergence, at least in 16s ribosomal DNA (Ross, in prep; Douris et al. 1998b; Thomaz et al. 1996). High mutation rates will result in repealed mutations at particular sites, which will eventually swamp out phylogenetically informative changes. In this paper, I explore the level at which the 16s rDNA is useful for determining phylogenetic relationships in pulmonate snails and compare the levels of variation among taxonomic groups. In any DNA sequence, some sites are variable while others are conserved. Variable regions have sometimes been referred to as "hotspots" (Moritz ei al. 1987). The 108 constraints on the variability at a particular site are often attributed to the secondary structure that forms after transcription into RNA. Orti et al. (1997) showed that the majority of variable sites in ribosomal RNA genes in fish are found in the loop regions. However, Wheeler and Honeycutt (1988) suggest that high levels of compensatory changes occur in stem regions to maintain structure after a change in one of two paired bases. Orti et al. (1996) suggest weighting base-pair changes differently depending on whether the changes occur in stems or loops. Weighting schemes based on sequence position are only useful if the positions and structures are equivalent in all the species being compared. In piranhas, the same regions of the DNA sequence are variable at increasing ta.xonomic levels, although the regions expand with higher ta.xonomic groups (Orti et al. 1996; Orti 1997). The question remains whether the same regions are variable across taxonomic groups for a particular gene. In order to determine whether the same regions are variable in different species, I compare variability profiles of the 16s rDNA region from three species of pulmonate snails within the same genera. In order to investigate how the amount of conservation in the regions of variability, I expanded the comparisons to include profiles of other invertebrates and then vertebrate genera. Understanding the nature of the variation in the 16s rDNA is essential for effective use in phylogeny reconstruction and proper weighting of character changes. 109 Methods Partial 16s rDNA sequences from a variety of species in several taxonomic groups were downloaded from GenBank. Table 1 gives the references for each sequence. In order to keep the opportunity for variation constant, three ta.xa were used for each comparison unless otherwise noted. Flook and Rowell (1997) found that increasing the number of taxa increases the possibility that variation will be detected. Sequences were aligned using CIustal-w-Big'n'Fat. Using the statistics option on MEGA (Kumar et al. 1993). the variable areas were plotted with a non-overlapping window size of 10 base pairs (Figure 1). A partial secondary structure for Discus niacctinlocki was constructed (Figure 2) with RNA Draw version 1.01 (Matzura 1995) with pairing constraints determined using templates from other published secondary structures for 16s DNA (Clar>' and Wolstenholme 1985). The entire secondary structure could not be determined reliably because only part of the 16s region was amplified, so in some cases only one side of a potential stem was sequenced. Results and Discussion In a single species of pulmonate snail. Discus maccliniocki, 47 variable sites (9.6%) were found in seven main regions (Figure la). This comparison uses one sequence from each of ten populations to determine an upper bound for this species. (Other comparisons use only three sequences unless othenvise noted.) When rwo other species in the genus Discus are compared to one D. maccliniocki sequence, the result is 31.96% (163 of 510) variable sites in seven regions (Figure lb). Table 1. Sequences used in this analysis Soecies GenBank Accession it Reference A Ibinaria butoti AF012075 Douris et al. 1998a A Ibinaria comigaia AF031680 Douris et al. 1998b A Ibinaria discolor AF012079 Douris et al. 1998a A Ibinaria discolor haessleini AF012078 Douris et al. 1998a A ustropeplea lessoni U82066 Remegio and Blair 1997 A ustropeplea ollula U82067 Remegio and Blair 1997 Bulimnea megasoma U82069 Remegio and Blair 1997 Balaena mysticetus UI3102 Milinkovitch et al. 1994 Bullasira comingiana U82068 Remegio and Blair 1997 Cepaea nem oralis U23045 Terrett et al. 1996 Clausilia bidentata AF012082 Douris et al. 1998a Delphinus delphinus U13106 Milinkovitch et al. 1994 Discus catskillensis AF063140 Ross, in prep Discus cronkhitei AF063141 Ross, in prep Discus macclinlocki AF064420 Ross, in prep Drosophila funebris M93987 DeSalle 1992 Drosophila pinicola M93987 DeSalle 1992 Drosophila willistoni M93996 DeSalle 1992 Eschrichti robusius U13108 Milinkovitch et al. 1994 Felis chaus AF006393 Johnson and O'Brien 1997 Felis libyca AF006395 Johnson and O'Brien 1997 Felis mai-garita AF006397 Johnson and O'Brien 1997 Felis pardalis AF006415 Johnson and O'Brien 1997 Felis silvestris AF006401 Johnson and O'Brien 1997 Idyla bicristaia AF012083 Douris et al. 1998a Kogia breviceps U13111 Milinkovitch et al. 1994 Lymnaea generic sp. U82070 Remigio and Blair 1997 Lymnaea stagnalis U82072 Remigio and Blair 1997 Lymnaea stagnalis U82071 Remigio and Blair 1997 Lynx canadensis AF006409 Johnson and O'Brien 1997 Lynx lynx AF006415 Johnson and O'Brien 1997 Megaptera novaengliae U13117 Milinkovitch et al. 1994 Mus cervicolor M55070M38500 Fort et al. 1984 Mus musculus V00665 Van Etten et al. 1980 Mus musculus musculus M55046M38491 Fort et al. 1984 Mus spretus M55061M38495 Fort et al. 1984 Radix peregra U82074 Remigio and Blair 1997 Radix quadrasi U82075 Remigio and Blair 1997 Radix rubiginosa U82076 Remigio and Blair 1997 Ratlus norvegicus V00665 Saccone et al. 1981 Stagnicola catascopium U82078 Remigio and Blair 1997 111 (ai Hiii I , I'p..I.I _ , .ill _L._.iLlili _. I .1- • "• "T 'O B c— ^ —T c c "T J X '*ir?nO'n«rT'T*T»rc r< -r X a: w "T • s w.nacw's Pcs.!.c" a^crg Se^ue'^ce (b] llETIinl •• o c o r« T T T '.'incow's Pc5'!;cr a cng Secuence (c) hi-EHiiillllkll A'.rccv^'s Pss.tiC!" A c-5 Sequence (d) lliV liln III 'tVmflcw's Pcsi'jcn Aicrg Secuerce Figure I. Partial 16s rDNA sequence variability profiles for: (a) Discus macclintocki (10 individuals. 1 from each of 10 populations), (b) Genus Discus {D. caiskillensis. D. cronkheti. and D. macclintocki), (c) Genus Albinaria (.-1. butoti, A. comigaia. A. discolor. A. discolor haessleini). (d) Genus Radix (R. peregra. R. quadrasi. R. mbiginosa), (e) Genus Lyninaea (L. generic species. L. stagnalis. L. stagnalis2). (f) Order Stylomatophora {A. buioii. D. macclintocki. R. peregra). (g) Subclass Pulmonata (Bulimnea megasoma. Discus macclintocki. Lymnaea stagnalis). (hi Subclass Pulmonata (20 species). 112 (e) 3 ^ = 1 Q •- cio^iNn*Tknic Wincow'5 PQS'ticn Along Sequence (0 ^ n I» llli;lllj|||l|Ji;i|ld^^ Window's Pcsittcn Along Sequence (g) lljdilllllrllrji. ^(Nfn*ruitcN.®cic v*/indcw's Posiiicn Along Sequence (h) •-tNpl»r«0-^N.oc>o~ Figure 1. Continued. Figure 2. Proposed secondary- structure for two portions of the I6s rRXA gene in Discus macclintocki. The boxes and dots represent connections to areas where secondary foldin was not possible because complimentan,' areas were not sequenced. 114 Comparisons of three species in other pulmonale genera also show high levels of variability. The genus Athinaria has 80 variable sites (20.2%) in seven main regions (Figure Ic), while the genus Radix has 47 variable sites (12.7%) in seven regions (Figure Id). The genus Lymnaea, which is in a separate order of pulmonates than the previous genera, also has seven variable regions with a total of 87 (24,0%) variable sites (Figure le). The variable regions in all four of these genera are located in approximately the same location along the sequence for the 16s gene. When comparing representative species from separate genera within an order of pulmonates, the variable sites increase because more differences are expected to accumulated between species more distantly related. Three taxa (Discus macclintocki, Radix peregra, and Albinaria buloti) within the Order Stylomatophora show variability at 166 of 393 sites (42.2%). This variation is centered in the same regions of the sequence as in the comparisons of species within genera (Figure If). Pulmonates show 30.7% variability (120 of 391 sites) when Bidimnea megasoma, Lymnaea stagnalis, and Discus macclintocki are compared (Figure Ig). These species represent two of the three orders of pulmonates, Stylommatophora and Basomatophora. (No sequences from the third order, Systellomatophora, were available from GenBank) When additional species are added to the comparison of pulmonates, the variable regions expand until distinct regions are no longer discemable. Using 20 pulmonale taxa, 304 of 435 sites (69.9%) were variable (Figure Ih). Much of this variation is due to large insertions and shifts in Idyla bicristata and Cepaea nenioralis, which create alignment problems (see alignment Figure 3). 115 The proposed secondary structure for D. macclin'.ocki is shown in Figure 2. Of the 23 variable sites (not counting gaps) where the structure could be determined, 12 are found on proposed stems, and 11 are unpaired or are in loop regions. In order to determine whether the same patterns held for ta.xa other than snails, 1 compared the extent of variation in several invertebrate and vertebrate groups. Within cats, 3 species of the genus Felis show 4.53% variability (17 of 375 sites) (Figure 4a). In the Family Felidae, 7.18% (27 of 376) sites are variable (Figure 4b). Baleen whales (Suborder Mysticeti) show variability at 24 of 521 (4.6%) sites (Figure 5a). The Order Cetacea shows variability at only 9.23% (48 of 520) of sites (Figure 5b). Twenty-six percent of sites were variable for a very short (111 bp) region in mice (Figure 6a). The complete 16s sequence was available for two species of the suborder Myomorpha. Comparing Mus musculus and Ratius no/vegicus over the entire 16s gene (1620 bp) shows 15.7% variable sites (Figure 6b). Three species in the genus Drosophila show 6.4% variation (51 of 799 sites) (Figure 7). Figure 8 shows the alignment of the regions used for all comparisons. These results illustrate the wide range of variability in evolutionary rates across taxa. Variation in evolutionary rates across species has been discussed in Moritz ei al. (1987). Previous studies have suggested fast evolutionary rates for rodents (D'Erchia el al. 1996), but at least some species of snails appear to be evolving even faster al the 16s rRNA gene. One cautionary note is that similar taxonomic levels in different groups do not always imply a similar level of historical relationship, so comparisons within genera of different groups may not be equivalent. 116 Alr.'cuir:: J.—. ... - - .--.".C J-OU -J'— -.-.o - A. - - .-.lb_rrrr-;^ J ~ - . T T . ~ ^ - - . .A ^ - A:=_ii3rc: .-.3- J - - ATT . A. - AA.CGGC C GC - .AGTAC - ,-.l=_i-3r.a.3 -J c.-. - - - .-.'w . T . - - A.G. A.C - Ausir;_l^s _ - AT - - T - TTA.'JCG^JCC GC -.AGTACC _ 3 • r c _ c 11. _ _• .- ~ - T . A.'-jC GGCCGC -AGTACC H-li.Tr.ea_r. _ _• . . - -TA'jCGGCCGC -AGTACC Bullastra _ _• - TCA.'JCGGCCGC -.-.G«^ Zepa-ra.ner, Jw 3 CAT _ . J*- w J\. -.-.G -'w - riausil.a CA--.T ...... ---.'w J'J'-— TAG. .-,CA :dy:a_cicr J-J .J.--.".. .. sJW -.-.G * rvC.-. l\Tj~.aea_ce _ ^ A - . TA.ov^ GG\- CGC -AGTACC l>T--.aea_s:; - -. — -AGTACC '-.•.~._sia?2 CG" - -- - -CT .. AGCovj'-CGC -.AGTACC ?.adi:-:_pere „ J _ GGCCGC -A.GTACC Stacr.iccla CG" A--A-A - " - A. AKT-.TTAUCSJ-JCCGC -AGTACC Gcai2 AC" ---TTA - . C.--ACA,GCCGC -.AGTA-AT Tcrcr. - - - T.--r*.. A. JCCGC - A.G. A-r-A :~ac9? - — "* JCCGC - AG * A---.-. --ac6 2 .--A - . - - -.- T T .A-----A G C C GC -A.GTAA'-A ..--A. C.-.T. TG.-.GTTTTA--«ATGGA.GCCT .-.J.- J.-----. -. —•_ • -J« Gv-\jG. A.3C.--. rA^ATCA.GTTGA.GTTT. A-AAT. -.-----.GG. .-.GC,-. rA_ATCA.TTTG.ACT. T. A-A.AT GGA.GC Al"-_dishae . AA«CA.• . TGA.CTT - TruA •. LjGA.GCCTA.GA-^.TGA.--AGA-r^A .-.U3trc_les ..---.uGTAGC.A • ••*-A- CA-A.TG-j\- -. TT.-u-.TTGA-AGTCTGGA-ATG.^-r-A3GTT Au3"rc_rll ..""-r. • C.~—«TGo\-»T»«A-r.«»GA-r»G«CTGGA«.ATG-'*-r-**..j-J». B-lirr.ea_" S-llas-ira ^C".'J • T'jvjv—- • .T."—**.. • .C • G"-?.""—~TGr5-"".'ij'j»« Zepaea.-e.T. . r-ATr-ATTTGGCTTATA-ATTGA-AGTCTGGCTCGA-AA.GA-AT :iiU3ilia . A-ATA-AA.TTGGCTTTTA-ATTGGA.GTCTGGA-ATA«--AA.3A.GT :iyla_i:i-r :A.ATA-A.ATTGGCTTTTA-ATTGA-AGTCGGG.A.ATGA---AGA-AA. lvrr.ae2_ge rA-ATC-AA.TTGGCTTTTA-ATTGA-AGTCTGGA-ATGA-A.^.jGT' -.aea_Sw sj. '^uTAAGGTA.GCA C.""—-! * TGvj>- *. « T"* * A.G - CTGGA--*.TGA-A'—oGA. L-..'rr._starZ . w'w > A-AGVJTA.'JC.A ..--A. CAA.TTGGCTTTTAATTGTAGTCTGGA-ATGA-AC 3GGA ?.aGix_pere •J. — •_ «.--AGG. A.'JC.A . -"-A * CA—ATTGGv- TTTTA-ATTGA-AGTCTGGA-ATGA-AA.GGT Siagr.iccla "GTGCT.--^GGTAGCA :A-ATCA.ATTGGCTTTTA-ATTGA-AGTCTGGA-ATGA-A.AGGG J. -jN,. .--r.GGTA.GCA. . A-r'.TA«AA.TTGGCCTTTA-A.TTA.GGGTCTGGA»ATGA-AA.GGA-r*. •^T j^-TAA.GGTA.GCA.- TTG * CC •.T."—«*.-.GGG« CTG'>jA-ATGA-A • GGA-A r.T.ac?? "ZkZ GT\JC -A-AGGTAGCA . -•*-"** - .TG'jwu - T.f--. * T ^GGG»CTTGAA.TGA-n.. GG.-.'J Z.-ac6: G.G-^TA-AGGTA.GCA' :A^.TA---ATTGGCCTTTA-ATTGGGGTCTTGAATGr-ATGGAG igure 3. Alignment of pulmonates used in variability profile Ih. 117 A:b_bu:cc i GA.-.CGT-AGAT.i.ACTTGTCTTAT-- T.-A- T ATA.i-ATTT.-_i_A.ATTG - -CC AAATGAGTGA_\ a:b_cc rrug G.-.^CGT - AGAT.-.-.^iTTGTCTCAT - • TA_i.ACACTATATCT.AAJ^TTA--ATTGATGAGTGA_A Alb_ciscc 1 GA.ACGT-AGACAACTTGTCTCAT--• T.i.A - T.i_AT.ACCTT.i.A.A.ATTA - - CC TCATGAGTG.^.^ Alb_cisr.ae GCACGT-AGACAACTTGTCTTAT- • T.i_A-T.-.-.TACTTTT AAATTG - -CTGC ATGAGTG.i.\ Aus:ro_les A.i_ATA.A.AGA.ATTAGCTGTCTCTT - ••TTA-.i_ATTTATTTGA.ATTTATTT--ATG.i-AGTGA.A Aus: ro_c 11 TGTT.AAAGA^TTTTCTGTCTCTT- ••TTT -.AGCTTATATGAATTTATTT - -AT.V-AGTGA-=i =ul i^r.ea.rr. A.-.ATGG-GGGTTTACTGTCTTTA- ••TTT-A_A.AT7T.ATTGA.ATTT.ATTT - -.ATTA.AGTG.i_i. = uilas:ra T.AAT.A.V.G.AA.TA.AGCTGTCTCTT - • T.i-A -.i_i TTTTTTTG.A.ATTTATTT - -.iui.AA-AGTG.i_i Ce?aea_r.e.T. TAJiTGG-G.-.AGCAGCTGTCTCCA- • GGGGT ACT.AGT ACAAAATT AGT A - - AGT A-AGTG.i-A Clausilia TCATGG-GGGGTACATGTCTCAT- • .i-AT-TTTTTACGGG.i-AATTA--CTA.AGCA-iGTGCA Idylajoicr .ATTTGG-GATTTTACTGTCTTTA--.ATTATTTTT.ATAT.i.iAATTA-ATCTTAG.i.i.AGTGA.ii LyTOaea_ge TATTAJUGAA.TTTTCTGTCTCTT- • "TT - AGTTTTTATG.AATTTATTT - - ATTA.AGTGA.i. Ly:nr.aea_s t TAATAG -GGA.AGA.^CTGTCTCTT - ••TTT-A-ATTTTATTGAAATTTTTT- -ATTAJ>iGTGi_A Lyar._scag: T-A-ATAG-GAGAGAACTCTCTCTA-- TT- -.A-A-ATTT-ATTGAJ^.TTTATTT- -ATT.i-AGTGr-A Kadix_pere A-A-ATAAAA^TTAACTCTCTCTT-- TTTTA.ATTTCTATGAJVTTTATTT - -.ATT AAGTG.i.A. sragr.icola TA^TGG - GAATTTACTGTCTTTA - • TG.A -TA.TTTTTTTG A-ATTTATTT .ATTA-AGTG.- A Dc a 12 TTATAT - ATAGTAGCTGTCTC A.AG:•TGTGCATTTTTCTT.A.ACTT.ACTC - -ATA.AGGTG.i_n Dcror. TCATAT-ATAGAATCTGTCTCA.A-- .i.TT.AC i.TTT.ATTTT.iATTTACTT --.AGC.AGGTG.i.^ r-ac ? 9 TCATAT-GAGG.AAGCTGTCTCA- - • TAGATACTATTTTG.iJVCTTATTTTCATT.i.GGTG.iJl Dr-.a c 6 2 TC A7AT - GTGG.-.^GCTGTCTCA - - • • TGTATACT.i.AATTG.i.ACTT A.ATT - CATTAGGTG.i.A Alb_but3Ci AATGCTCATGCTT-TG.V.T.A.ATAGACGAGA.iG.ACCCTTAGAATTTTAJ!Li_AATAT-.iJ^TA- Alb.corrug AJ^TACTCAT.i-i.A.iJiiTTAA.AAATAGACGAGAJi.GACCCTTAGAATTTTAAAGATGTTACA.A- .Alb_discol A.ATACTCTTGA_iT-TTA_iTi.TTAGACGAGA.AGACCCTT.AGAATTTTAATTA.A.AT-ATA_A- A-b_dishae .i_ATACTCTTGA.AT -T.iJi.ATAATAGACGAG.i-AGACCCTTAGAATTTTA.A.AA.AG.A.A - AT.i-A - A.-us:ro_les A.ATACTTCTTCTAAGAA - - A.A.A.AGACGAGA.AG.ACCCTTAGAATTTTCATT i.ATTAJ\GGT - A.uscro_cil A_AT.iCTTTTT.iTTAG.i.A--.A.iJiAG.ACG.AGA.AG.i.CCCTT.AG.i.ATTTTT.ATTA.iJ^.AGGTTT- Sul i.T".ea_~ A.ATACTTATT.iTT.AG.i.i--.i-i.AAGACG.AG.i.AGACCCTTAGA.ATTTTT.AATi.i.i.i.TATT-- 5-uHastra A.iTACTTTTATTTAGAA.--.A.AAAGACGAGAAGACCCTTAGAATTTTA.ATTA.ATT.AAATTT Cepaea.r.en-, A.ATACTTGCGGGAAGAA.--A-ATAGACGAGAAG.ACCCTAGAAGCTTGTTATTTGTTTTGTT Clausilia A.iTTCTTGCTTTATCATTTA.ATAGACG.AGA.AGACCCTATAAATTTTTGACAAATGGTAT- Idyla_bicr AJi.TACTTTCAG - AAAAT.ATAATAGACGAGA.AGACCCTATAAATTTTAAATAA.AGGTA.AC - Lytnnaea_ge AATACTTTATATTAGAA- - AAAAGACGAGAAGACCCTTAGAATTTTTATTAAAAGCTTT - Lyr'j;aea_s: AATACTT.ATATTTAGAT - -.i_i.AAGACGAG.A.AGACCCTTAGA.ATTTTTATTA.i.A.ATGTA.A - Ly:rr._scag2 A.ATACTTAT.iA.ATTGAT - - A.AAAG ACGAGAAGACCCTAAGA.ATTTTTAT - - AAATGATT - ?.adix_pere AJi:TACTTTTTTAA.AG.iJV--.A.A.i.AGACGAGA_AGACCCTT.AGAATTTTAJiTTTCTTTTACT- Stagr.icala A.ATACTTAC.ACGTAGA.A--.A.AAAGACGAGAAGACCCTT.AGAATTTTTiJi.TAA.A.iJi.ATTA- 2ca:2 AATTCCTTA.i.ATTCTAT-AGATAGACGAGAAGACCCTTAGAATTTTTATTAGCCTAGTA- Dcrc.-i A_ATACCCGCTTTTTTTAATGATAGACGAGAAGACCCTTAGAATTTTTATTATATACTTAT 2-.ac95 A.ATACCTATGATCCTA.i.CAGATAGACGAGA.AGACCCTCAGAJ^TTTTTATAJiGATT.AGCA- Z~.acS2 A.iTACCTATGATCTAATC.AGATAGACG.AGA_i.G.ACCCTCAGAA.TTTTTATAA.i.ATTAGCA- Figure 3. Continued. IIS A:B_=U ; C ;: JTCTG - - TCTGTTA.-.GATTTTTTGTTGGGGC.^I.C.I.ATATTTCAC - - A- -TA A-.B_CCRR"U^ —J..---.GC — TCTTGTA.GGAT. .TT .G .TGJGGCA.-.C.---.TATTTCAT—A--AA A_c_c 13c^ - j.C.---. — .CTTGT.^»AATTTTT. .G.TGGGGCAA.C.-.GTATTTCAT--AGCGA Au s: r 3_: e s - TTA - - - Tr.-.TTTT 7TTATTTTTGTTGGGGCGACA7TAA.-.i CA - - - TTTTA Austirc.oll TT T.i.-.ATCT CTTATTTTTGTTGGGGCGAC.i.-.TGA.-.-.CA TTTA.A Eu_ .TAGTAT- — -TATTTTTTTG- .GG'jGCGACA.TTGA.--,-.CA.~--TTT-A 5ul lis^ra TTA- --T.-Ji.TTTA TTTATTTTTGTTGGGGCGACATTGAAJiiCA TTA-A C-5Cd^s_r.r;rr. GG jo - OACAG.~--.TTGCA i G-j- — .---.CCA--.TTTT.-. - GGA.GTGACG-T.^T.AGC.-.GGA-A.^TT.^ Clc'-Siiic - — - ..i..-. -GTCCTTCGTTTT * G. TGG'jGC.Ar.C.--AGTTG.-.C.^. GTTGA I dy 1 a _b i c r TTTTTTAAT AGTGGTATTACTATTTTTTGTTGGGGC^.nC.AACTTAC CATTAGCTA.=i L>Tr.aea_ge TTA- - -T.AAATCT TTTATTTTTGTTGGGGCGACA--.TGr-i.-.CA ATTTA L'/:r.-.ae a_s; .ACT .AAAACC A T^.AATTTTTGTTGGGGCGAC AGTAA.i.TC.AC ATT A Lyi7-._scac2 ATT---TTAATC TATGTTTTTGTTGGGGCGACAJ1.TA.-.ATCA GTA .= adix_cere TTA---TTGT.A.^A CTTATTTTTGTTGGGGCGAC.ATTG;i_i_AC.iAAATTTTG Scagr.icola TTT- -TTTATTTT TATTTTTTTGTTGGGGCGACATTGA.iJ^.CA TTTTA Dca; 2 GTATCACTA- - TTTACTTC ^cttTTTT - -GTTGGGGCGACA_iGTA.nTCA-GATAA.AA Dcrcr. ATACGAJVTA.^^ATGATTAAGTATTTTATTTTCGTTGGGGCGACGTGTATTC.-ATATTT.AA Dn-.ac 9 9 .-.CCAG.ATTAGGTTTCCTTT.A-ATTTTATTT - -GTTGGGGCGAC.iJ^TTA-iTCA-TTC -TA_A 2-ac = 2 .-.CC.AGGTTAGGTT-CCTTTA-ATTTTATTT--GTTGGGGCGACA.nTTA.ATCA-TA--TA.A .-.1 S_bu ZCZL .ATA.A". - TATTTATAATA TG A. .ATAT.ATCGA A1 b.c 0 r r-j g - T ATATTATTPJ^'. A.A A ATGTGTCGA A:b_di.scol .ATAA-n-TATTTCCTTGG--CAG A TT.AA-TCGA Alb_Gishae AT.A.A.\-TATTTACATTA-NCCGG A TTA.VlTCGA Au s - r 3_ 1 e s A_-.CTTTTA .^T AATTA C.AAGTC AT Au s: r o_c 11 ACTTTT.^iT .AT.i_i_AA.A CATGTCAT Sul i.T.-.ea_~. r.=iCTTTCA TT.i_ATT CATGACAT 3ullastra A.-.CTTTC A .i-ATTAJ^-A C A.AGTC AT Ce?&ea_r.e~ AGCT.-.CTCTAGGGAT.^-ACAGCATA.ATT .i.i.-.CTTGTTTGTGACCTCGA Cla-JSiiia r.-.--A.n-.A.iTTTAA.CTCAATAAA TTTTA-AGGA.ATTCTTCGA :dyla_bicr ATGA_IT.AAT.ATA.ATGTAGGTAAAGTATTATAGTTAC.^TATACTTTTT.I.-.T.I.ITAATCCAA LyT.-.aea_ce A.ACTTTCA AAAAAAA CAAGTCAT Lyrr.aea_st; r.^CATTTA AA.AGTTTA CATGAC;i-A LyT.i_s:ag2 AACATTTA TTTATATA CATAACAJii r.adi:c_=ere A.-.CTTTCAC .A^TTTTTA CGAGACAT Stag.-.icola r-ACTTTCT TTCTCA CATGACAT -ca;2 CATTACTTTGGAjkGATATTATT .ACATGACTA ZcrcT. C A_ATATTTTTA-ATTTTATAACA CATGTCCA C 9 9 CATTA-ATATTATATTA.A.ACATAC ACATGAACA 3~ac6 2 C.ATT.A-.TAT-.ATAT.AA.1CTATGTCTT .\CATGA.^CT Figure 3. Continued. 119 r - - .-.-.T.-_-.TT.-.C.i.Gr.i.A.2uMT.ACCT.-.AGGG.AT.-.-.C.AGC.AT;iJi-.TCTT.=iA T?u\7.\~ r.li:_c;rrug 3 — T.AT.^A. .A.--A- ..--"-AAT..nC- ..•'-AGGG.-...--AC.AGC.ATAATTTTA T.AGTAA .-.lb_ciscol - .i.i.TA.-.TTATAG AG.-.-_i TT AC C T.-_AGGGAT.i.ACAGCATAJiTTTTA.A T.i.i.T.-.-. .-.lc_cisr.se -.-.-.T.iJi.TTAT.nGAG.--i-iTTArCT.A.i.GGG.AT.i.AC.-.GCAT.A.-.TTTT.V. A.ust.ro_ies TTTGTTA- - -G.-.TTA.-.ATT.ACCTT.AGGGATA.ACAGCATA.AT.AAAAT TTTTA s " - _ •TTTGTAT---GA7.A.A.A.^TT.ACCTT.AGGG.AT.i.ACAGCATAATGATTA ATATA 5uli.Tj:ea_r'. i.TTTGA.-.A -G.i.AT.-.-.ATTACCTTAGGGATAJi.CAGCATAJ^.TTAACT .ATTTA Eulias^ra -TT.ATTTGT.AA G.A.-.T.AA-ATT.ACCTT.AGGGA.T.AA.CAGCATA-ATAAAAT TTTT.-. Cepaea_r.e.T. TGT. GG.ACT.AGGT.AGA-A-.AGTCC^T-AG.AAGGG.-.CT.AA.-j\ATGCTCTGTTCGA.GC.AGCTT.A Clausilia -GTTTTACT.i.AGG.AT?.i-AT.nA_ATT.-.CTATAGGGAT?.ACAGCATAATTTTTC - - - ATTT.A.A Tdyia.bicr -GTATT.ACT.ATT.-.ATA.--A.i_A.i.ATT.ACT.ATAGGGATAJiiCAGCATA.2iTTTTAT T.-.i.AAJi. L'..-:rr.aea_gc —CTTTTTGTGT GAT.AAAATT.ACCTTAGGG.ATA.-.CAGCATAATG.ATTT .AT.AT.A I.yT.iaea_sc --TT.AGATTTATAAGAGTAT.AATTACCTTAGGGATA.AC.AGCATAATTTCTT ATA--.-. -•/rTzi_sza^2 --TTTGGTT-AT.iTAG.i.AGA.i.ATT.ACCTTAGGGATAJi.CAGCATAJiiTTTTTT AT.i.i.-. ?.adi;<_pere --TT.ATTTTG.i.AT--GATC.A.i.ATT.ACCTTAGGGATA.ACAGCATr.ATGAJU^T TATTA Scagr.icola - - T.-.AGATTTG.AA.A-GATTA.A.ATTACCTTAGGGATA.ACAGCATA.ATTAATT TTTTA Dca:2 TGTTTTGATTAGTGAGACTAJ^ATTACCTGAGGGATAA.CAGCATA-nTATTA- - -.ATTACAT Dcror. CTT.i.ATAGTTGGTAAGATT.AA.ATT.ACCTGAGGGATA-AC.AGCAT.V.T.ATTAT .i.-.AT.A.A ::r.ac99 -.i.--A.ATGATACGCGTGAGTGA.ATTACCTGAGGGAT.A.AC.AGCAT.-.ATATTATT.-_-Ji>.TTAA.T :52 -.AT.i.ATGAT.ACGCGTGAGTG.i.ATT.ACCTGAGGGATr.i.CAGCAT.i.ATATT.^T - -.--AT.-.i.AT a:b_bu ; 0 :1 GTTTGTGACCTCGATGTTGG.ACT.AGGTACT.ATT.-AGGCT.-.ATC Alb.corrug GTTTATGACCTCGATGTTGG.ACT.AGGTACTATTA.AGGCT.A.ATC Alb.r.iscol GCTTGTGACCTCGATGTTGG.ACTAGGT.ACT.ATTAGGGCTAJi.TC Alo_ci.s;-.ae GCT7GTGACCTCG.=lTGTTGGACT.^GGTACTATTAAGGCT.A.ATC Aus:ro_les GCTTGTGACCTCGATGTTGGACTAGGA-.ACTAG.iJiiG.ACT.A.ACA Aus:ro_c:i GCTTATGACCTCGATGTTGGACT.AGGA-ACT.i.AA.AGATTA.ACC Hu 1 i.T.-.ea_- GTTTGTGACCTCGATGTTGGACTAGGA-.ACT.AGTTGACTA.ACC Hullasrra GCTTGTGACCTCGATGTTGGACT.AGGA - ACTAGAAG.ACTAACA Cepaea.r.er. ACCT.ACATGATCTG.AGTCAGACCGGCGTA.AGCCAGGTCAGTTT Clausilia GATTGTGACCTCGATGTTGGATTAGGGACTTATTAJi.ATA.AACC :dy1a_b1c r GATTGTGACCTCGATGTTGGACTAGGGA.ATATTTAGGGCAGCA Lyn-r.aea_ge GCTT.ATG.ACCTCG.ATGTTGG.ACTAGGA-.ACTAGA.AGATT.A.ACC ly:rr.aea_s t GTTTGTGACCTCG.ATGTTGGACT.AGGA-ACTA.AATAACTA.ACC Lv~.-'._scac2 GTTTGTGACCTCGATGTTGGACT.AGGA-ACTAGATGACTAACC ?.adi:<_pere GCTTGTGACCTCGATGTTGGACT.AGGA-ACTA.A.AA.G.ACTAACC Sragr.icola GTTTGTG.ACCTCGATGTTGGACT.AGGA-.ACTA.i.i.AG.ACTA.ACC -•ca-2 GTTTATGACCTCGATGTTGGACTAGGG.A.i.AT.AAT.AGGCTAGCA Ccror. GTTT.ATGACCTCGATGTTGG.ACTAGGAT.AGAT.AATGGTC.AGA.2i :9 9 GTTTATG.ACCTCG.ATGTTGG.ACT.AGG.ATAGGT.-.AGGGCT.AGCA 5 c 6 2 GTTTATGACCTCG.ATGTTGG.ACT.AGGAT.AGGTA.AGGGCT.AGCA Figure 3. Continued. 120 (a) i c 2 5 5 I 2 B V = 0 • 1 ll III l _ ll If IN n f lO '.VmcQw's Position Aicng Sequence (b) i H J 11J JLI11J .UJ Wm4ow I Po»iuof\ Along S€i,^uence Figure 4. Variability profiles of partial 16s rDNA sequence for species within the following groups (a) Genus Felis (Felis chaus, F. libyca, F. margarita) and (b) Family Felidae {F. chaus, F. pardalis, Lynx canadensis). 121 (a) 11 •I I k_ .1• iL I I WmJuu'i Poiiiion Along Scqueni;c (b) ^ JiJLIuiJ T cx s'*i'rc9csri»rJl c« c WiniJow's Po^iitcn Along Scquencc Figure 5. Variability profiles of partial 16s rDNA sequence for (a) Suborder Mvsticeti (Balaena mysticetus, Eschrichti robusius, Megaptera novaeangliae) and (b) Order Cetacea {Megaptera novaeangliae, Delphinus delphiniis, Kogia breviceps). Wiiiikm'v AIimi^ Sri]iirnrr (b) IUJ L.ii. .. Jj IA Jkki JiL. .Li.. J Jillbi.. h.Li O 0» ri *ri cr, -T »- C* -O t? r i «o — -r t .Aat-tioo O ^ o o o O — — ~ VViiuli>*'* I'imlioii Ali»fiK Sc.iurn.c I'ij^^iirc 6. Varinhility profiles of partial 16s rDNA scqucncc for (a) (icnus A/i/.v (A/h.v duisciiIiis musciiltis, Mus ccivicolor, ami Mas spivtii.s) ami (li) roilents (A/i<.v inusculus, lialtus noivi-i^L-ciis). .S S IE s 3 o 5 "i 2 £ > 1 C CO k ^COCN^JO'^COCNCOO'TOOfNCOO'a-OOCNCD i-i-CNCNOsinnT^TLniiicococor^-r^ Window's Position Along Sequence Figure 7. Variability profiles of partial 16s rDN.-v sequence for Genus Drosophila (D. funebris, D. pinicola, D. willisioni). 124 ?-s" t'js_^r.cr CTA-nCrCTAGCCCTAC.--nCC.---.rCA.-.CA.T.---.CT.~«-_-.CCCCCAC.-.TrJk.~.CT.~.-JvkCATTT.- S'- • c "S ^T'.' IC - — — - — — — - — I-\T.X_C2r.3 Z l>Tr.aea_s: Eal^r.ys" ^ r^v r.i ~ ~ u S __r.O r ACTCAAAA-AGT.ATTGGA.GAA-AGAAATTTA.CTTACCAAGAGCTATAGAGAAAGTACCGCA-- Mus^cervic Feline.cha - -yr.:<_car.ad I.>-maea_st C_rr.acc Ea:_riyst - -r_willisc Kac »i;s_r.or GGGA.AATGA.TGAAAGACTAATTTr-P-.AGTA-AA-AAGA-AGACA-AAGATTA-A.ACCTGTACCTT" y.u3_cer'v*ic reline_cr.a -/"T.A^car.ad Alb_bucoci Z_r.acc .".a" .us^nor TGCA.TAATGAATTAA.CTAGA-AA-ATCCTTA-.^CA^AAA-.-.GAATTTAAGCT.--.AGr-ACCCCGA-Ar Mus_cer\*ic reline_cha -/~>:_car:ad :.>~.aea_s" -.rnacc 5al_rr.ysr 2r_w.llLSZ ".a" -US_r.or -CA-,A.ACGAGCTACCTA_--AAACA-ATTTCA"rGAA.TCA_nCCCGTCTATGTA.GC.AA„--ATA.GTGC :':us_cervic reli::e_cna lVT.A_car.ac Alc_buwcti lyrr.aea^sr — « rr.acc 5a 1 _.T.ys t Figure 8. Alignment of the 16s rDNA regions used for the comparisons. Species aligned are Dwsophila willistoni. Rattiis noiTegicus, Mus ceiyicolor, Felis chaus, Lynx canadensis, Albinaiia buioti. Discus maccliniocki, Lymnaea stagnalis. and Balaena mysticeius. Dashes reflect sequence not available for this species and asterisks reflect sites which are conserved for all species in the alignment. 125 Mus_cer"/ic ?el ir.e_cha ->*r.x_car.ad Alb_bu-oti 1/Trr".aea_st -_r.acc Bal_mys- Dr_v;iilisc - r.ac CUS_r.or AATATAGCC.^AA.^G.AGGGACAGCTCTTTAGGAAACGGAAAAAACCTTAAATAGTG.---.TA.^ Xus_cer'/ic feline_cha :.vT.:<_car.ac Ali:_c-roci l^.-rr.aea.s" D_"acc =Gl_r.ys:: r.a " »us_r'.cr" ACAACTACA.nTCA.CTTAACGATTGTA.GGCTTAAAAGCA-nGCGA.TC.AATA-AAGA-A.nGCGTC -Js csic ir.e_cha ll.T.:-:_car.ad kibjzuzozi -•..Tr.aea^sr _ _rrsc c — — Figure 8. Continued. 126 Zr s~ r.2;;us_r.or . J.-. - >« w - .-.o . o.-un ..-.w . J. . . J - J.^ - Mus_cer-/ic .-el:.r.e_cr.a LyT.;<_car.ac Aib_bu;oci LyTr.aea_st D_:r.acc Bai_r,ys; Rat:rus_r.or CAAGCAC.AAGTGC. A.-.GACAACCGGATAACCATTGTT.---.TTr.TTGA.^.TCATAGGCAT.---.C .••;us_csr-.-ic relir.e_chQ l.yr.x_car.ad Alb_buto:1 Z.virr.ae2_s; D_r,acc 3al_.T:ys: 3r_WLllis- ?.Q;-us_r.or CCA.^C.---.':AG.A.-.7TACCTATCCCT;^J-C?CGrTAGCCC.--nCACAGGCGTGCTTTi.iGGAA-. Mus_cer~.-ic reiir.e_ch3 Lvr.x_car.ac l.v~.-.ae3_s; -_:r.acc =al_r,ys- 'jru". - - - ..-u-. » , C. G. . T.--.-,CA«AAA-.ACA r.ai;us_r.or r.----r_AAAC . j.~«ACTCGGC.-'—"u'^C.-.C G ArtCC C >. GCC ;;us_cer-.-ic rel;.r.e_cr.a ->T.x_car.ad Aib.butoti l.yTnr.aea_sc r_.T.acc .•V. w r'j-u-i-'-r.- 3al_r.ysr 2r_villisr TG. C - TT »TG.--.ATTA.-nA . TTr'-.-'-.AGTCTAA.CCTGCCCACTGA---AATT"r — . .A,^-.TGGC' Rat;us_r.or CTCTAGCATAACAAGTA.TTAGTGGLATCGCCTGCCCAGTGAC • A.-*--.G-* . . CCA.CGG-- X.us_cer-.-ic r'5lir.e_:ha lyr.x_C2r.ad Alb_bu;o:i — - — TTATCTGCCCA.GTGAGAAATT — T^--ACG-GC' l>~-3ea_s; 3_-acc TAGCT • AAT3TATCG .A.A.TP'-AC Hal — w —J - jVw - . JC J ^ —J' Figure 8. Continued. 127 r.a":tu3_r.cr •/us^cer-'ic r el-r.e_cr.a L>T.:<_canaG _ ___ _ ^ ^ .CC»». r-^njnT.n^sjG.nC. . GTATG.-*. Alb_burot:i AGTACCTTGAC - GTGCA,---.GGTAGCAT.-<,-.TCA.TTTGACTTTTA.\ATGGAGCCTAGTATGA Lvirr.aea.sr AGTACCTTGAC - GTGCT.---.GG. AGCA.T.-_-.TC.---.TTGGCTTTTAATTATAGTCTGGA.-.TGA D_r.ac= AGTA.-*-"iCTGAC i G •. GCT.-iAovjTAGC.-.T.AATA-**-r.TTGGCCTTTAATTGGGGTCTTGAATGA Sal_nysr: G>jTA • n.C . G."*.^ C'j - . C.-.C. TGTTCTCTAATT.--r.GG , J .r,. ".J-**. ^r_w1. 1SC ATGGTTGGACGAGATATTAACTGTTTCATTTT.-iATTTAAAATA.GA-n' T..AT»TT•TAu ?.aiius_nor ATGGCT.-_AACGAGGGTCC.---.CCGTCTCTTAC7TGC.--^TCAGTG.A^A M'-S_cs!r'.'ic ATA-AAGAAAGAGGTTATGA-AA.TTTCCTAAA.TTTACAAAACC.AA-ATT - - iJ^.TTATTTTA' reline.cha ACGGCCACACCCGGGCTT7ACTGTCTCTTAG':TCC.---.TCCGTGP-^.' -TGACCTTCCGG' L-/r.A_canad ATGGCCACACGAGGGCTTTACTGTCTCTTACT-CC.-J^.TCCGTG.iJ^" .•'•-b_bu"OC i ATGA.--nGAACGT.-.G-ATAACTTGTCTTATT.--nTAT.Ar-nTTTAA«A".T -TGCC.w.TGAG' Lyrrj^aea /*.CGG."*.» «rLr.TrtVj'j'j-AAGAAC.GTC«CTTTTT.AATTTTATTGA-AA.T D_n:acc ATGGAGTCATATGA-GG.---.GCTGTCTCAT Hal _:nys c ATGGCCACACGAGGG' ~.JAw w.C•CC o rr_vill:.s:: CAA-r-AAGCTAiAA.-.TT - '"TATTTA.-«AnGACGAGAA.GACCCTAT.^A.nTCTTTATATTTTGTT ?.actus_nor GA.-vGo'jGCGGrtC i r-nGACGAGAAGACCCTATGGAGCTTT.--".TTTACTA.-. Mus^cer'/ic CA.AC AC AAAAAT AT.^G GAT :elir.e_cha GAAG AGGCGGGA--.TA - - T AAT AAT AAGA.C GAGA-^GACC CTATGGAGCTTT AA.TT AAC CG A lyr.x_car.ad GAAGAGGCGGGA-ATA" - TAACAATAAGACGAGA-AGACCCTGTGGAGCTTTAATTAACCGG Alb_burcii GAA.AATGCTCAT GCTT - TGA-ATAATA.GA.CGAGAA.GAGCCTTAGAATTTT.AAAA_ATATAA.T ly:rr.aea_st GAA-AATACTTATATT - - TAGATAA.--AGACGAGA-AGACCCTTAGnATTTTTATTAr'-A.-.TGT D_r:acc GAAAA.TACCTATGATCCTAACAGATAGACGAGAA.GACCCTCAGAATTTTTAT.Ar.GATTAG Eai_myst G.-i-^GA-jGCGGGG.-. . A - T'.. A.^--r.TAAGACG.AGA»A.GACCCTA.TGGAGCTTC.AATTAATCAA. rr_wii:ist « ATTTTAATTGAAAAGATTAATTTTATTTTAATAAATTAA-AATATTTTATTGGGGTGATA ?.a-tus_r.cr • * 1 « i ~ /v'-ruT'-rvVrtUC .."-n - — — — — •GGwCGrLAA.**kC.Ar.C.AAr'-rv y.us_cer-'ic reline_cha CCC.^--AGAGA CCCTATTA--.TT.--^CCGACA GG.---.C.^CA^--.TCTCTA -:-T.x_car.ad CCCAAAGAGA CCCCATTATCTA.nCCGACA -GGAA.CAACAAACCTCTG Alc_buto"1 -G• • .--.-^G.-i>» . . . ,GTTG'jvjG\_.-V.'>..'*. lyrr.aea_st r_T.acc - T ~ ~ .~«**.TT > — — — — — — — — — — — — _ ^ GTT^GG'o „ GAC.**. Eal.-ysr — wC. - C.~. . w------jGGA -,»-iAC.--r*-r-.~.TTT . V Figure 8. Continued. 12S -r__1 _ «1s" ._. .- . .TTT.-...-----.C.-.. T.^-.TTTATGn-nTr-ATTCATCC.-.TT.---. ?.ii - - -S ^r.C I* ...... -.C"... G>S3 . . . j.-.w w . C . CC . C C o.-u-. . o.-, Mus_cervic r € - 1r.€_cr.a - .w. ovj'jv •- j.-.Lrt.**.• - i . • .-.uG. TGuvj-j. G.nCs.. CGGr'.'u.---.C.AA.~'-nu.-«.-.CCTCCGA>j. 3A —ynx c 2nsG V. jAl*-u-*. " - . .. .-^Gu. . . G.-.C — . CGGA.Gj---. .-.GA."iC.~-~'.CCTCCG.-.^. j Alb.buroci ATATTTCACATA^T .-JUTAT'rTATA-ATATG.--ATATATCGA. T. - T AA ^ vTTj'.ass^S ti GTAA.nTCACA - - .AA ACATTTAA.—A-AGTTTACATGACAATTAG ATTTA D_.TiflCC ATTAATC.-.. TCTAACA. • -TAATATTATATTA.A.nCATACACATGAACA----.— — —.A-.TGA BaI.n-,ySw -TATGGGCTGACA.^--TTTCGGTTGGGGTGACCTCGGAGCAC.--AAAAnCCCTCCGA3TjA Dir__wi 1 i s w TA.-,TGATT.--i-'-"-"-A-. TT.^nGTTA.CTTTA.G - -GGATAACAGCGTr-ATTTTTTTGG.AG Razz'j.s_r.or TT:;T^-nCCGAGTCGGTA.ACCGTGTCCGACCCAG':CA-^G7.=u-.TACT.^ATATCTTATTGAC: Mus_cerv:.c r elir.e^cr.a TTT.V^.TCTAGAC TA.-.CCAGTCGA- - - A-^GTATTAC - -ATCACTTAT7GATC LyT.x_car.ac; TTT.AAATCA-nG.-.C -T.AriCCAGTCA.-. A-nGTATTAC- -A.TCACTTATTTATC A*C_bu COti 1 TTACAGTAA — — -.AnTTACCTAAG" -GGATAACAGCATAATCTTA.-.T.A-.-- -yr-j-.aea^s" Tr-nGAGTAT AA.TTACCTTAG--GGAT;^--.CAGCA'r.A^T-TTCTTA--- 3_~acc TACGCGTG.-.G"- --- — — —TGA.nTTACCTGA,G-" -GGATAACAGCAT.AATATTATTA.A-.T 5a:_.myst TT.^^.GCCTAGGC CCACTAGCC-A---A.^GCATAAT--ATGACTTATTGATC 1 * «z. s » .-.G i TCA. - r.. Cu.**. • /•«r«n--«-«AGA- TGCGACCTCGATG-TTvjGA,TT.A.~.'jrtTAT A*-.. . . T jG P'G -cus_nc r C.^ATTAT — .G.-.TC.A.-.'^GGACC-^-nGTTA.CCCTA.GGG-TAACA.GCGCGACGT —ATTT.-_-.G Xus_cer/ic r "5- ir.s_cr.a C.AA'--AACCT. ^A.C.--nCGGAACA.^GTTACCCTAGGGATAACAGCGC.--ATCCCTATTT.---.G l:.r.x_car.ad CAA.w.C-TTGATCA.\CGG.--AC.-.2iGTTACCCCAGGGATAACAGCGC.--^TCC-TATTTTAG Alb_bu"OCi TA-TGTTTGTGACCTCGATG-TTGGACTAGGTA CTATT.^-. i-'/TT^.a^a s C — " ~ ..-ui-nLjTTTGTGnCG *CGATG -TTGGrtCTAGG.**. — — ~ ACT."-'V", • D_r.acc TAA.TGTTrATGACCTCGATG-TrGGACrAGGAT — — AGGTAAG sd -._.T;y s z C.A".TC -T - •TGATCA.-.CGGAu^C.AnGTTACCCTAGGGATr'-ACAGCGCA.ATCC -TATTCTA.G —^G.G*.n-j -Co• . .TTTAAo.CTGTTCG.~.»TTTTA.-i./*..TCTT.'\C.'*.-GA.C•GAs^. .C .-.az^us^r.or Au; TCATA.TCGAC.--ATTAGGGTTTACGACCTCGATGTTGGPiTCAGGACATCCCAA.TGG. G Mus_cer'/ic r el i.'ie.cr.a AGTCC.=iTATCGACA.AT-AGGG I-yr.x.canad AGTCCATATCGACA.-.T -GAGG Alb.bu::©::! GGCT.=u-.TCGTTTT.V-. - '^'/rrr.aea^sz A.-.CTA.nCC C_rr.ac c ..-".u ..---.TA^jG ..C » GTTC'>j.--"\^ «»»G - C -CCT.-^uu. G.n •C i G.**.»j... —^^'VSC .".-oTCC.-.. A.CGAC.A.A. -.-.GGGTTTACGACCTCGA.TGTTGG.n. CA.GGACATCCTAA.TGGTG Figure 8. Continued. 129 -r.'.-.'i.. .J.- J r.azz'is^r.zr . J'J . :. .jr. .. _-..j. C _ .AC3T .. J. - J J A-s.cervi; ?el.r.e_cr.s IVTrr.aea^sc 2_r.acc Eal_riys- C.*%GCCGC . TC J • . GGAC - AAA...-.. T.A.r-A.AT.AATTA - ATTTTTAA-AA_AAGA«ATA.TT.ATTA«ATTA-ATA-AGA-A.-.C" ?.ar:us_r.cr • CC."*.vj'j. C-j j. . . C • A.. . ..-.u.n,**.... CTC-C.-.G. T.-^CG."-A»".GG."'.CA-r.G.**.GA»AA.. X'js_cervic relir.e_cr.a Lyr.x_car.ai Alb_buio'i Lyrr.aea^st G_T,acc Hal_r.ysc Cr_willisr ATTTTGGC.-.G.**.. TAGTGCA»ATAA.-.TTTA.GA.ATTTATTTA.TGTA-ATTGTT.ATT.^.CA-AA.T.AC ?.a-zus_r.cr GGAGACC.--ACCA-ATCCTAGGCTTCCAACCA-ATTTAG.A.AAA-^CTTA.ATA 'TTT.ATATAGA-ATTAA.TTTT.AT .- , J • .-.'w . . . Rar::us_r.rr ACA,-.T.-«-»-.. .-ACCTT.AGACCC.AA.jTT.^.* Mus^cervic felir.e^cr.a lyr.x_canad Alb_b'-;toci lyTT.aea^st D_T.acc 3al_~ysr Figure 8. Continued. 130 Comparing extreme dates of divergence may be more useful. For e.xample, the oldest known occurrence of Discus macclintocki in the fossil record of the midwestem United States is 400,000 ybp (Frest 1984), so it is highly unlikely that the D. macclintocki populations represent divergence events older than that time period. The domestic cat lineage diverged from the rest of the cats 8-10 million years ago (Johnson and O'Brien 1997), so the family Felidae represents divergence that is at least this old. Despite the much shorter time for divergence to occur, Discus macclintocki still contains much more variation than the Felidae. Because exact divergence times for many of these species are unknown, tip-level taxonomic designations are the most convenient groups to use. Is there something about the secondary structure of pulmonate DNA that may allow more variability? Nothing is immediately obvious. Although the structure for D. macclintocki is based on structures for Drosophila, Kjer (1995, 1997) states that secondary structure is largely conserved in the ribosomal DNA across taxa. In D. macclintocki a higher percentage of sites in stems were variable than in loops or non-complementary regions of stems (33.3% vs. 11%). Findings by Flook and Rowell (1997) in Orthopterans showed the same pattern where a higher percentage of paired positions varied than did unpaired positions (65.4% vs. 56.9%). Secondary structures for all of the species being considered are not available, so similar information is not available for comparison. Dixon and Hillis (1993) show that both stems and loops contain informative changes in 28s rDNA, which is the case observed in the D. macclintocki sequence. Within pulmonate snails, the 16s rDNA gene is useful at several taxonomic levels from family-level comparisons to intraspecific studies. The range of useful comparisons 131 contrasts with the divergence times for which Wheeler and Honeycutt (1988) proposed 16s to be useful in estimating phylogeny. Alignments begin to become problematic at extremely deep levels (Figure 8), so it is unlikely that this would be a good region to examine for comparisons of sequence from species that underwent very ancient divergence events. The high rate of variability shown here is consistent with the high divergence rates reported in previous studies of land snails (Ross, in prep; Douris et al. 1998b; Thomaz et al. 1996). Therefore, in at least part of the 16s gene, constraints on variability are low in pulmonates. One reason for fewer constraints may be the presence of a transfer RNA editing system as proposed in Euhadra herklotsi (Yokobori and Paabo 1995), Additional investigation into the functional role of this gene in pulmonale snails is warranted to determine whether different molecular processes occur in this group than in other organisms. Literature Cited Clary, D. 0. and D. R. Wolstenholme. 1985. The ribosomal RNA genes of Drosopliila mitochondrial DNA. Nucleic Acids Res. 13; 4029-4045. D'Erchia, A. M., C. Gissi, G. Pesole, C. Saccone, and U. Amason. 1996. The guinea-pig is not a rodent. Nature 381: 597-600. DeSalle, R. 1992. The phylogenetic relationship of flies in the Family Drosophilidae deduced from mtDNA sequences. Mol. Phylo. Evol. 1: 31-40. Dixon, M. T. and D. M. Hillis. 1991. Ribosomal RNA secondary structure: compensatory mutations and implications for phylogenetic analysis. Mol. Biol. Evol. 10: 256- 267. 132 Douris, v., S. Giokas, R. Lecanidou, M. Mylonas, and G. C. Rodakis. 1998a. Phylogenetic analysis of mitochondrial DNA and morphological characters suggests a need for taxonomic re-evaluation within the Alopiinae (Gastropoda: Clausiliidae). J. Moll. Stud. 64; 81-92. Douris, v., R. A. D. Cameron, G. C. Rodakis, and R. Lecanidou. 1998b. Mitochondrial phylogeography of the land snail Albinaria in Crete: long-term geological and short-term vicariance effects. Evolution 52: 116-125. Emberton, K. C., G. S. Kuncio, G. M. Davis, S. M. Phillips, K. M. Monderewicz, and Y. H, Guo. 1990. Comparison of recent classifications of stylommatophoran land- snail families, and evaluation oflarge-ribosomal-RNA sequencing for their phylogenies. Malacologia 31: 327-352. Flook, P. K. and C. H. F. Rowell. 1997. The effectiveness of mitochondrial rRNA gene sequences for the reconstruction of the phylogeny of an insect order (Orthoptera). Mol. Phylo. Evol. 8: 177-192. Fort, P., F. Bonhomme, P. Darlu, M. Piechaczyk, P. Jeanteur, L. Thaler. 1984. Clonal divergence of mitochondrial DNA versus populational evolution of nuclear genome. Evol. Theory 7: 81-90. Frest, T. J. 1984. National Recovery Plan for Iowa Pleistocene snail. United States Fish and Wildlife Service. Hillis. D. M. and M. T. Di.xon. 1991. Ribosomal DNA: molecular evolution and phylogenetic inference. Quart. Rev. Biol. 66: 411-453. Johnson, W. E. and S. J. O'Brien. 1997. Phylogenetic reconstruction of the Felidae using 16s rRNA and NADH-5 mitochondrial genes. J. Mol. Evol. 44(Supplement 1): S98-S116. Kjer, K. M. 1995. Use of rRNA secondary structure in phylogenetic studies to identify homologous positions: an example of alignment and data presentation from the frogs. Mol. Phylo. Evol. 4: 314-33. Kjer, K. M. 1997. Conserved primary and secondary structural motifs of amphibian 12S rRNA, domain III. J. Herpetology 31: 599-604. Kumar, S., K. Tamura, and M. Nei. 1993. MEGA: Molecular Evolutionary Genetics Analysis, version 1.0. Pennsylvania State University, University Park, Pennsylvania. 133 Li, W. H. and D. Graur. 1991. Fundamentals of Molecular Evolution. Sinauer Associates, Inc. Sunderland, Massachusetts. Mazura, 0. 1995. RNA Draw, version 1.01. Milinkovitch, M. C. , A. Meyer, and J. R. Powell. 1994. Phylogeny of all major groups of Cetaceans based on DNA sequences from three mitochondrial genes. Mol. Biol. Evol. 11: 939-948. Mindell, D. P. and R. L. Honeycutt. 1990. Ribosomal RNA in vertebrates: evolution and phylogenetic applications. Annu. Rev. Ecol. Syst. 21: 541-566. Moritz, C., T.E. Dowling, and W.M. Brown. 1987. Evolution of animal mitochondrial DNA: relevance for population biology and systematics. Annu. Rev. Ecol. Syst. 18: 269-92. Orti. G., P. Petry, J. I. R. Porto, M. Jegu, and A. Meyer. 1996. Patterns of nucleotide change in mitochondrial ribosomal RNA genes and the phylogeny of piranhas. J. Mol. Evol. 42: 169-182. Orti, G. 1997. Radiation of characiform fishes: evidence from mitochondrial and nuclear DNA sequences. Pp. 219-243 in R. D. Kocher and C. A. Stepien. eds. Molecular Systematics of Fishes. Academic Press. San Diego. California. Remigio. E. A. and D. Blair. 1997. Molecular systematics of the freshwater snail family Lymnaeidae (Pulmonata: Basommatophora) utilizing mitochondrial ribosomal DNA sequences. J. Moll. Stud. 63: 173-185. Rosenberg, G., S. Tillier, A. Tillier, G. S. Kuncio, R. T. Hanlon. M. Masselot. and C. J. Williams. 1997. Ribosomal RNA phylogeny of selected major clades in the mollusca. J. Moll. Stud. 63: 301-309. Saccone, C., P. Cantatore, G. Gadaleta, R. Gallerani, C. Lanave, G. Pepe, and A. M. Kroon. 1981. The nucleotide sequence of the large ribosomal RNA gene and the adjacent tRN.A genes from rat mitochondria. Nucleic Acids Res. 9: 4139-4148. Terrett, J. .A.., S. Miles, R. H. Thomas. 1996. Complete DNA sequence of the mitochondrial genome of Cepaea nemoralis (Gastropoda: Pulmonata). J. .Mol. Evol. 42: 160-168. Thomaz, D., Guiller, and B. Clarke. 1996. E.xtreme divergence of mitochondrial DNA within species of pulmonale land snails. Proc. R. Soc. Lond. B 263: 363-368. 134 \'an Etten. R.A.. M. W. Walberg. and D. A. Clayton. 1980. Precise localization and nucleotide sequence of the two mouse mitochondnal rRNW genes and three immediately adjacent novel tRX.A genes. Cell 22; 15"-170, \'olger. .A.P. and R. DeSalle. 1993. Phylogeographic patterns in coastal Nonh .American tiger beetles (Cicindela dorsalis Say) inferred from mitochondnal DN.A sequences. Evolution 47: 1192-1202. Wheeler. W.C. and R.L. Honeycun. 1988. Paired sequence difference m ribosomal RNAs evolutionar>- and phylogenetic implications. Mol. Biol. Evol. 5: 90-96. •^'okobori. S. and S. Paabo. 1995. Transfer RNA editing in land snail mitochondna. Proc. Natl. Acad. Sci. 92; 10432-10435. Zuckerkandl. E. and L. Pauling. 1965. Evolutionary divergence and convergence in proteins. Pp. 97-166 in W Brv'son and H. J. \'ogel. eds. Evolving Genes and Proteins. Academic Press. New York. 135 CHAPTER 5. NUCLEAR VERSUS MITOCHONDRIAL VARUTION DISPARITIES AS A FUNCTION OF DISPERSAL STRATEGIES A paper to be submitted to Evolution Tamara Kay Ross Intuitively, genetic structure examined at the nuclear and mitochondrial levels should show congruent patterns in the amount of variation present within populations, but this is not always the case. Many ideas have been proposed to explain the differences between the levels of variation in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA). Several molecular mechanisms that might cause incongruent mtDNA and nDNA trees (lineage sorting, varying rates of evolution, non-neutral loci) have been suggested (Reeb and Avise 1989; Karl and Avise 1992; Moore 1995). Natural selection at particular loci could also be a factor in the amount of variation present (Koehn et al. 1973; Singh and Zouras 1978; Karl and Avise 1992). However, the intention here is not to provide additional support for the use of mtDNA in the examination of genetic structure, for that has been covered extensively elsewhere (Avise 1994), nor to compare rates of variation in different portions of the genome. Rather, the issue is the amount of genetic diversity within a population and whether dispersal strategies can explain conflicts in the pattern of the amount of variation present within populations for mtDNA and nDNA. Because mtDNA is usually maternally inherited, it does not undergo recombination, and, in general, has a relatively fast rate of nucleotide change compared to 136 nuclear DNA (Avise et al. 1987). The differences between mtDNA and nDNA mean that population genetic models constructed for biparental nuclear inheritance are not sufficient to describe diversity in mtDNA as well. In fact, mitochondrial DNA may reach fixation in as little as 4n generations where 'n' is the size of the population (Avise et al. 1984). The maintenance of genetic diversity in a population is dependent on population size, because rare alleles will be lost at a rate relative to the size of the population. The effective population size for nuclear DNA is calculated as: = (1) N.+N, and for mitochondrial DNA: N.N.. (2) -- a-N^ + p-Nj where N^^ is the effective number of mitochondrial genes in the population (in contrast to Nj for nDNA), a is the proportion of the mitochondrial genes in the population contributed by eggs (usually 1), P is the proportion contributed by sperm (usually 0), and N„ and N,- are the number of males and females in the population respectively (Birky ei al. 1983, 1989; Birky 1991). If (3=0, then N^^ is dependent only on Nf and a' will equal I (Birky 1991). These differences between nuclear and mitochondrial inheritance become important when calculating the e.xpected genetic diversity in a population. For mtDNA, = (3) - 2N„,n+l 137 where is the expected equilibrium diversity within a subpopulation (or colony) and n is the mutation rate per generation (Birky 1991). The equivalent calculation for nuclear diversity is: "4N,p +l ^ where H is the heterozygosity within a population and is the effective nuclear population size (Birky 1991). Avise (1995) points out that only a proportion (0 of the total nuclear genome is expected to still exist in populations with uniparental inheritance according to the equation: where G is the number of generations that have gone by since the population was established. Under equilibrium conditions, mtDNA diversity should be lower than nDNA diversity (Avise et al. 1984; Wade et al. 1994). Certain conditions exist where these expectations are not met. Birky et al. (1983, 1989) predict that organelle diversity will be greater than nuclear diversity in panmictic populations with large numbers of females. Nuclear diversity may be greater than organelle diversity when the population is subdivided or where males migrate more than females (Lansman et al. 1981; Birky et al. 1989; Wade et al. 1994). Implicit assumptions include no paternal leakage of mitochondria (a=I, |3=0), similar mutation rates in nuclear and mitochondrial DNA, no selection at any of the loci, and no deviation from an equilibrium condition (i.e. no 138 founder effects or metapopulation dynamics) (Birky et al. 1983, 1989; McCauley 1995). If genetic structure is measured (Fst or Gst), rather than just overall variation, constant mutation rates are not as important (Birky et al. 1989). At equilibrium where migration rates are larger than mutation rates, more structure in subpopulations generally appears with mtDNA because mtDNA will go to fixation faster. The relationship between mitochondrial and nuclear Gst values is given by: 1 mitochondzialGst _ ^ nuclearGst 1 where L is the number of subpopulations (Birky 1989). When there are many subpopulations, equation 6 simplifies to: mitochondrialGst _ nuclearGst ^ecPe (Birky 1989). However, when males and females migrate equally or when the breeding ratio is highly skewed towards females, nuclear Gst may be higher than mitochondrial Gst (Birky 1989). The relationship of Gst from mitochondria to Fst from nuclear genes has been used in plants to explore pollen distance relationships (McCauley 1995). Any natural processes that affect the and N„ differently will result in observed differences in diversity patterns between nuclear and mitochondrial DNA. Processes that have such potential include: skewed sex ratios, differential dispersal/ migration between sexes, kin migration, and population subdivision where subpopulations may differ (Birky et al. 1983, 1989, 1991; Reeb and Avise 1990). Which one or combination of these is 139 most important is impossible to tell without specific information on the population size, dispersal propensities of each sex, history of colonization, etc. Evidence From the Literature The theory is well-developed, but the question remains: are these the patterns that exist when observations from natural populations are examined? Here, I focus only on the influence of dispersal strategy on mitochondrial and nuclear diversity. To determine if dispersal strategy has a detectable effect, I compiled studies that included both mitochondrial and nuclear (usually allozyme) data. The species used for the analyses are listed in Table 1. Each species was assigned to a group based on dispersal strategies and sex-ratio. The dispersal strategy groups are: male-biased dispersal (category 1), sexes disperse equally or not at all (category 2), female-biased dispersal (category 3). Mammalian species are assumed to be male-biased and avian species are assumed to be female-biased unless other information is available (Greenwood 1980). Unless explicit information was available to the contrary (i.e. for sea turtles and butterflies), other species were assumed to fall into category 2. Studies of populations affected by extreme inbreeding or hybridization were not used (i.e. Taylor et al. 1994), nor were studies of species complexes where subspecies versus species status is uncertain (Gotelli et al. 1994; Murphy et al. 1995). Although data exist for certain human populations (Lahermo et al. 1996; Kolman and Bermingham 1997), they were not used due to the uncertainty of sex-biased dispersal. For studies with multiple populations, each population was used as a separate data point. Populations with I ;iblc 1. Sjiccics uscii in lliis analyses. S|H'cie.s Coiiiiuoii iKiiiie INipiilntioii Niiclenr II MiDNA II Calc{ior>' References ( 'Ih'liilliil niYihls green lutllc (JOC ().K7 0.607 1 iMt/Simnions i-l iil. 1997; Norman cl nl l')9-l ('hdonin inyiliLs green liirllc N(iHR 0.9 0,237 1 !• it/Simmons cl til. 1997; Norman cl til 1994 ( lu'lintui ni yi/ii\ green liirllc -SGBR 0.9 0.36 1 I'll/Simmons el ttl. 1997; Norman cl til. 1994 ('IftlirioiiomyM ni/Dfiiniis grey-siileil voles 0.77 0.84 1 Ishibashi cl til. 1997 l>n'iiK iiiliii l)iitlerny AH!-: 0.1506 0.58 1 lluagt'fH/. 1993 l>iyii\ i IIIill l)iillciny Il'll 0.1872 0,72 1 llaagc-/u/. 1993 />/!•iytis iiiliii liiilletny SCI' 0.0651 0,7 1 llaagfJ.W 1993 lhyii\ iiiliii liiilterlly 1 UK 0.0651 0,55 1 llaage/d/ 1993 Ihriis iiiliii butterfly ZOO 0.1317 0,44 1 Haag cl 111. 1993 Myopiis scliislicolor wood lenitning RI.SB 0.076 0,219 1 1-cdcrov cl III. 1995; l-cderov cl til. 1996 My opus M'liislirolor wood Icmmnig HALLKl-OK 0.0605 0,358 1 l-ederov cl til. 1995; i-cdcrov i-; nl 1996 Myii/iiis sfhislicolor wood lemming FINI.AN!) 0.07-18 0.889 1 I'cderov el til. 1995; Fcdcrov cl til. 1996 Oilitiiiili'iis lifiiiiiiiiiis mule deer BMl- 0.369 0,52 I Cronin iil. 1991 ()iliHi>ilvus hfiiiii>nii.\ mule tieer BMW 0.33 1 0,266 1 Cronin cl nl. 1991 1>Ji>itiilfii\ licniioiiiis mule ileer (il.AS 0.21-1 0.555 1 Cronin cl al. 1991 ()ilin oilviis viijiiniiiiiux vvhite-luiled deer ISAB 0.395 0.757 I Cronin ct nl. 1991 (hlortiilciis viifiiniimus wliite-laded deer HLY 0.^19 0.779 1 Cronin ct nl. 1991 ()ili)niilfiis yiif^inimiis wliilc-tuiled deer COOK 0.362 0,789 I Cronin el nl. 1991 <)ili>toilfii\ vii) roek-wallnby HMMi;r 0.633 0,37 1 I'ope Cl til. 1996 l'cliiiy,iilc Minthopiis yellow-footed rock-wallaby USB 0.658 0,644 1 Pope el al. 1996 ciiifiviis cuicrcus masked shrew Seebe 0.093 0.572 1 Stcwcrt el nl. 1993; Slcwcrt & Baker 1997 Suh'x iiiifrciis <'iin'rviis masked shrew Okotoks 0.082 0,593 1 Slcwcrt et nl. 1993; Slcwcrt & Baker 1997 Si>iv\ l iiu'iviis rineri'ux masked shrew Ghost River 0.074 0.741 1 Stcwcrt et nl. 1993; Stcwcrt & Baker 1997 Soivx riiifivm cnwreiis masked shrew Toronto O.OS-4 0.75 1 Stewcrt el nl. 1993; Stcwcrt & Baker 1997 Soivx riiii'ivii\ I'liii'iviix masked shrew (iogama 0.079 0,531 1 Stcwcrt cl al. 1993; Slcwcrt & Baker 1997 .S'oii'v liiiilfiil prairie shrew Okotok.s 0.088 0,642 I Stewcrt cl al 1993; Slcwcrt & Baker 1997 I ;)lilc I, conlimicil Spec it's Coiiiiuoii IIUIIIC Population Niiclcnr li MtDNA 11 CaleKOO' References /iin-irtititniiii lly A l).27.S 0 2 DcSallc ct III 1987 1 >iii\i)i>liilii mi-rrtitonim (ly B 0.297 0.0234 2 DeSalle <•/(//. 1987 1 hd\i>i>liilii infiCiiiDniiii lly C 0.3191 0 2 DeSallc .•/lrf>pi.s iDmtiius minnow MICO 0.049 0.004 2 Dowling & Brown 1989 Niiiiopis com iiiiis minnow NYCO 0.054 0.003 2 Dowlmg & Brown 1989 Noinipis <1)111 iiliis minnow VACO 0.033 0 2 Dowling & Brown 1989 Noiro/>i\ mhclus minnow VAKU 0.02 0 2 Dowling & Brown 1989 Ntiinipis nihcliis minnow NYRU 0.03 0.005 2 Dowling & Brown 1989 Nouopis mUclus minnow MIRU 0.05 0.01 2 Dowling & Brown 1989 rciwiifus monmUm Anslrnlinn prnwns nortli 0.058 0.21 2 Benzie ci ul. 1992, 1993 Solino cmpiiis troiil CAl 0.1148 0.34 2 Bcnatchez & Osinov 1995 Siilinii liihriix Iroiil BLI 0.0697 0.667 2 Bcnalchcz & Osinov 1995 Siiliii o liihrax trout ni.2 0.0532 0.32 2 Bcnatchcz & Osinov 1995 .Sdlmo iniimiix trout AKI 0.0154 0.694 2 Bcnatchez & Osinov 1995 Sdlmo tniiinii.t trout AR2 0.0054 0.779 2 Bcnatchez & Osinov 1995 Siiimo siiliir Aliunlic sulmon RauxSaumons 0.6748 0,138 2 Tcssier f/<;/. 1997 Sillnil) siiltir Atlantic salmon Ashuup 0.6281 0.438 2 Tcssier ct til. 1997 Siiliiio Miliir Atlantic sulmun Metabctch 0.4851 0.1 86 2 Tessier el al. 1997 .Sillnil) siiliir Atlantic salmon Ouasienisca 0.6765 0,327 2 Tcssier ct ul. 1 997 Sillnil) inillii brown trout KARK'J 0.2'J'^ 0.47 2 Hanson & Locschckc I99() Siilnto mil III brown trout WHl 0.0856 0,245 2 Bcnatchez & Osinov 1995 Siitiiiii livltii brown trout KR089 0.5804 0.474 2 Hansen & Locschckc 1996 Sillmo livllii brown trout KR094 0.3297 0.201 2 Hansen & Locschckc 1996 .1 mm minim us cuudiwius sharp-tailcil sparrow 0.1 1 12 0.444 3 Zink & Avise 1990 .1 mmoilriimiis lieiixli>wii llenslow's sparrow 0.04384 0.219 3 Zink & Avise 1990 .1 mmoilriimii.s marUimus seaside sparrow 0.05516 0.493 3 Zink & Avise 1990 r;tble 1. continued Specks Common name Population Nuclear 11 MtDNA II Category References Citliiplfi tiurntus nuratux yellow shafted northern flicker Clare 0.444 3 Fletchcr & Moore 1991; Moore i-l at. 1991 Coliiptfx aurtiliis aurotus yellow shafted northern flicker •Saginaw 0.112 0.18 3 r'letchcr & Moore 1991; Moore cl al I*)')I CoUtptcs (uirntus cnfrr red shafted northern flickcr 0.0485 0.698 3 I-lctcher & Moore 1991; Moore <•/ al 1991 (iiiviii iiiinirr common loon NI- 0.0194 0.0875 3 Dhin ct al. 1997 (htvia immfr common loon Ml 0.4428 0.0589 3 Dhnr rl al 1997 I. im nodrom us i;riscus short-tailed dowitcher 0.02 0.04 3 A vise A Zink 1988 Limnothomiis scolopact'us long-billed dowitcher 0.05 0 3 A vise & Zink 1988 Poms hirnlnr lUiirnstnlus hiack-crestcd titmice 0.049 0.0.^4 3 Aviso & Zink 1988 /'iinis hirolnr hicntnr tufted titmice 0.015 0.023 3 A vise & Zink 1988 Quiscalus major boat-tailed grackle 0.008 0 3 A vise & Zink 1988 Quiscalus mcxicanus great-tailed grackle 0.0086 0.015 3 A vise Zink 1988 R alius eU'f^atis king rail 0.03 0.009 3 A vise & Zink 1988 IIalius lani^iroslris clapper rail 0.04 0 3 Avtsc & Zink 1988 143 mitochondrial to nuclear diversity ratios that differed substantially from those of the rest of the populations in the same study were discarded because they were assumed to be affected by some other factor rather than different dispersal patterns. Within Population Genetic Diversity Of the many measures of genetic diversity that exist, I chose to use expected heterozygosity and haplotype diversity because they both have similar assumptions about neutrality and involve similar calculations. Frequencies of each nuclear allele were used to calculate heterozygosity at each locus, y, according to: (8) where q, is the frequency of allele i (as per Avise 1994). All hj were then averaged to obtain the average expected heterozygosity over all of the loci. Haplotype diversity for mitochondria was figured in a similar fashion; (9) where f, is the frequency of each haplotype i (as per Avise 1994). (A haplotype here is defined as a particular banding pattern resulting from restriction enzyme digests or a particular nucleotide sequence obtained from sequencing reactions.) Many studies report heterozygosity but do not indicate whether it is observed or expected (Avise et al. 1979). To ensure comparable values were used, I only included studies that reported actual frequencies. From these frequencies, I calculated h 144 and H. Examination of the relationship between nuclear and mitochondrial diversity (H and h) exhibits no obvious patterns (Figure 1). The data points from each of the dispersal categories are represented by different symbols. No significant relationship was found between the two diversity measures for any of the three dispersal groups using a linear regression analysis (p>0.05). However, a test of the differences between the means of the ratio of mtDNA to nDNA diversity in the three categories was significant (Figure 2, Van der Waerden test p=.0215 (see SAS 1994), 2 outliers from Salmo oxianus that were above 40 were removed). Fsl comparisons In the few studies from which data were available, the relationship between nuclear and mitochondrial Fst is consistent with theoretical expectations (Figure 3). The species used in this analysis were: green turtle, Chelonia mydas (Norman ei al. 1994; FitzSimmons et al. 1997); mule deer, Odocoileus hemionus (Cronin et al. 1991); white- tailed deer, Odocoileus virginianus (Cronin et al. 1991); rhesus monkey, Macaca mulatta (Melnick and Hoelzer 1992); and a butterfly, Dryas iulia (Haag et al. 1993). The expected relationship under neutrality (from equation 7) is shown for comparison. All of these data points came from species assigned to category 1 (male-biased dispersal). Species with male-biased dispersal should be above this line. Four of the five species are actually above the line as expected. Using the expected slope of 2 to 1, I divided the state space into two unequal areas (25% above the line and 75% below the line). 145 Nuclear vs Mitochondrial Diversity by Dispersal Category 0.9 X A Nuclear Diversity Figure 1. Nuclear versus mitochondrial diversit>-. Nuclear diversity was calculated as the average heterozygosity across all loci examined (according to equation 7). Mitochondrial diversity is the haplotype diversity as shown in equation 8. The symbols represent the three different dispersal categories. Triangles represent species with male-biased dispersal, squares with stars represent species with female-biased dispersal, and solid squares represent equal dispersal. 146 Ratio of Mitochondria to Nuclear Diversity by Dispersal Category 12 ' ' XII'' < 10 " Q 9 ' - 8 ' I 7 ' 6<' I 5 - ' t I 3 I 2 I it i 1 I 0 1 2 Dispersal Category Figure 2. Plot of dispersal category by ratio of mitochondrial diversity to nuclear diversity. Category 1 represents species with male-biased dispersal, category 2 represents species with equal dispersal, and category 3 represents female-biased dispersal. A rank- score test of the category means show a significant difference between categories (p<0.05). 147 Nuclear Fst vs Mitochondrial Fst 1.0 1 0.8 • 0.6 • ifl u. i 0.4 - 0.00 0.02 0.04 0.06 0.08 0.10 NucFst Figure 3. Nuclear Fst versus mitochondrial Fst. The dashed line represents the expectation under neutral conditions (including equal dispersal rates). All data points are from category 1 (male-biased dispersing) species. All but one data point is above the line, which is expected for male-biased dispersing species. A binomial test for points above the line and one point below is significant (p=0.015) 148 A binomial test for four points above this line and one point below the line is significant (p=0.015). Certainly, the power of concluding a non-effect of sex-biased dispersal is ver^' low with so few data points. Implications for Further Study The results were not robust due to large variances and low sample sizes. Many factors contributed to the variance, including those discussed above: population size, sex ratio, mutation rate, etc. The sensitivity of the different genetic methods in assessing the level of genetic variation present, may have also contributed to the large variances. Different rates of change exist between different markers and methods (Karl and Avise 1992; Bossart and Prowell 1998; Hare and Avise 1998). Conducting analyses using the same methods for both nuclear and mitochondrial regions would clarify the relationship. The scale at which variation is being examined (i.e., within or between populations) is another important consideration. With population subdivision, the effects of genetic diversity are slightly different. For example, within a population, the homoplasmic condition of mtDNA inheritance reduces the variation, but in a subdivided population, homoplasmy may increase the diversity between subpopulations and, thus, the total diversity (Birky el al. 1989). Even when studies using different methods were considered together, evidence for a pattern exists in the difference between dispersal categories. With limited data, male- biased dispersers show the expected pattern of higher mitochondrial Fst values than nuclear Fst values (Figure 3). The expected relationships between nuclear and 149 mitochondrial Gst have also been supported in some instances in the literature when strong biases in dispersal pattern are observed. A study of macaques show a mitochondrial Fst of 0.91 and a nuclear Fst of 0.09 (Melnick and Hoelzer 1992). The females in this species are very philopatric. The opposite patterns between nuclear Fst and mitochondrial Fst show exactly what is expected from male-biased dispersal. More rigorous testing are needed to examine the effects of dispersal strategy on DNA variation. Experimental work such as manipulating populations in a laboratory to allow migration or to vary sex ratios may provide such evidence, although such experiments may not reflect natural occurrences. Other corroborating evidence could come from comparative studies of closely related species with different dispersal strategies. The inclusion of other types of markers would also be useful (e.g. Y-linked or X-linked markers). For example, Jones et ai (1995) successfully used Y-linked markers to trace the genetic success of male house mice. Contrasting Y-linked markers with mitochondrial markers in species where males are heterogametic would allow tracing male and female gene flow separately. Male and female markers will be useful to measure the contribution of each sex to the overall genetic structure. The relative contribution of each sex can be important for questions in basic population genetics, as well as in studies that use genetics as a tool to examine other aspects of population and species-level biology such as: overall genetic diversity, historical connections between populations, hybrid zone interactions, etc. Without knowledge of the influence of dispersal on each of these markers, incorrect conclusions may be made about the structure of populations when little or no variation 150 exists within a species due only to the sex bias in the species' dispersal patterns (Melnick and Hoelzer 1992). Although mtDNA studies alone are quite useful, the strength of their interpretations could be improved with additional evidence from nuclear markers. Bass et al. (1996) were able to trace female migration patterns in Caribbean hawksbill turtles and could determine the type of social system in this species. However, the conclusions that female natal homing is occurring would be even more powerful if nuclear loci were available to show contrasting patterns. Another potential use of population genetics is to identify social structure and dispersal patterns for species that are rare or difficult to observe. For example, Garza ei al. (1997) examined autosomal and x-linked microsatellite loci in mound-building mice. They were able to extrapolate from genetic data and determine the previously unknown social structure. However, Bossart and Prowell (1998) point out that many difficulties exist when using genetic data on an ecological time scale. Should sex-biased dispersal show clear trends in species for which information is available about dispersal, then the use of male and female genetic markers in other species will provide at least circumstantial evidence for the existence or lack, thereof, of sex- biased dispersal. Recent studies of avian species showing female-biased dispersal (Clarke et al. 1997) emphasizes that sex-biases in dispersal are not as clear as once thought. Although not a substitute for field ecology studies, this method would be a useful tool for determining natural patterns of dispersal to be implemented in conservation and management plans for species with only a few populations left in the wild. 151 References Avise, J. C. 1995. Mitochondrial DNA polymorphism and a connection between genetics and demography of relevance to conservation. Cons. Biol. 9:686-690. Avise, J. C. 1994. Molecular markers, natural history and evolution. Chapman & Hall, NY. Avise, J. C., J. Arnold, R. M. Ball, E. Bermingham, T. Lamb, J. E. Neigel, C. A. Reeb, and N. C. Saunders. 1987. Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics. Ann. Rev. Ecol. Syst. 18: 489-522. Avise, J. C., C. Giblin-Davidson, J. Laerm, J. C. Patton, and R. A. Lansman. 1979. 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McKnight, and H. B. Shaffer. 1994. The effects of kin-structured colonization on nuclear and cytoplasmic genetic diversity. Evolution 48: 1114- 1120. Williams, S. T. and J.A. H. Benzie. 1998. Evidence of a biogeographic break between populations of a high dispersal starfish: congruent regions within the Indo-West Pacific defined by color morphs, mtDNA, and allozyme data. Evolution 52: 87-99. Zink, R. M. and J. C. Avise. 1990. Patterns of mitochondrial DNA and allozyme evolution in the avian genus Ainmondramus. Syst. Zool. 39: 148-161. 156 CHAPTER 6. GENERAL CONCLUSION The previous chapters investigated the demography and genetic diversity of the Iowa Pleistocene snail, related the level of diversity within snails to the amount of diversity within other groups, and explored the relationship between sex-biased dispersal and genetic diversity in mitochondrial and nuclear markers. Sex-biased dispersal may be a factor in the amount of diversity observed in mitochondrial vs. nuclear markers. However, without more data for both mitochondrial and nuclear Fst, the importance of dispersal strategies cannot be determined. The Discus macclintocki populations studied had sizeable populations, with estimates ranging from 182 to 22,097 individuals. More monitoring of multiple populations is needed in order to understand the seasonal and yearly fluctuations in the snail populations. Knowledge of such fluctuations is essential to understanding changes in populations over the long term. The DNA sequences from populations on the same watersheds clustered together, suggesting watersheds were important avenues for dispersal historically. Despite the strong genetic structuring by watershed, genetic diversity within populations was surprisingly high. Haplotype diversity estimates up to 0.730 were observed. High levels of genetic variability, relative to other taxonomic groups, also occur in other groups of snails. The high variability suggests that snails may have a mechanism that promotes the maintenance of genetic diversity. Although a high level of genetic diversity is no guarantee of fitness, D. macclintocki does not seem to be losing diversity despite the 157 extended period of isolation of the populations. Whether demographic concerns or genetic diversity are more important to the maintenance of species of concern, Discus macclintocki appears healthy from both perspectives. Persistence of the species is likely, provided its habitat is maintained. 158 APPENDIX A. SEQUENCE ALIGNMENT The 16s rDNA sequence alignment of all of the unique haplotypes found in this study is given on the following pages. Haplotypes are labeled by slope number and individual number (i.e. 99.04 corresponds to individual number four from slope 99) as well as the haplotype designation they represent (i.e. A, B, C). This alignment was constructed using ClustalW-big&fat and is printed from MacClade. haplo3maclade 1 1 2 3 4 5 6 7 a 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 e 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 a 2 9 3 0 3 1 3 2 3 3 1 99 (M (A) T G T 1 T A A C A A A A A C A T A G C T T A A T G T A 1 C (i T A ? 99 27 (0) _ _ _ _ a IE) 4 99 20 (F) _ _ 5 99 1'2 (G) 6 99 \9 7 121 03 (A) 6 121 01 (0) 9 \2\ 06 «) 1 0 121 14 (J) 1 1 119 Ifi (A) 1 2 119 19 (B) A 1 a 11902 (K) 1 4 119 30 (L) C 1 5 119 Oa (M) A C 1 1 6 103 OS 91) 1)4 (A) no 27 JD) QO 2r> (D 99 26 (F) f)f) 22 (G)_ 99 19 (H) 121 03 (A) 121 01 (0) 121 06(1) 121 14 (J) I19^1<^(A) 119 19 (0) 119 02 (K) 1^9 30 (L> 119 OB (M) 103 05 (A) I03 2Z_(C) 103 10 (N) )03 0) (0)_^ 103 OB |P) 103 02_{0) 120 01 (A) 120 14(C) ON 12007 (R) O 120 02 (S) 120 13 (T| 2 1 213 01 (U) 2 8 297 01 (V) 297 14 (W) 207 15 (X) 297 04 (Y) 207 17 (?) 297 22_(AA) 297 2fl (AB)_ 232 OMAC) 36 232 05 (AO) 3 7 232 09 (AE) 232 17 (AF) 3 9 232 Of. (AG)_ 232 11 (AH) 232 20 (AI) 33 04 (AJ) 33 11 (AK| _ 33 29 (Al) 4^ 33 nr> (AM) 4 6 1 99 U4 (A) 7 7 A A A A A G C C G c A G 7 A A A C 7 O A C 1 G 7 G C f 2 99 27 (D) _ 3 99 25 4 B D r.ronkhilet - - 7 A 7 7 haplo3mftclade 4 t DO 1 0 1 103 103 1 04 105 1 06 1 07 1 Ofi 1 09 1 10 1 1 1 1 1 7 1 3 1 4 1 5 1 6 1 7 1 8 1 1 9 1 2 0 1 2 1 1 2 2 1 2 3 1 2 4 1 25 1 26 1 2 7 1 2 a 1 2 9 1 3 0 13 11 1 9*J()4 (A) A A G G T A G C A T A A T A A A T T G G C C T T 1 A A I I G G ti 2 99 27 (D) 3 99 2S (E» 4 99 ?f. (F) 5 99 ?? (G) 6 99 10 IH) 7 121 03 {A) a 121 01 (D) 9 121 06 (1) 0 121 14 1 OU 04 (A) T C 7 T G A A T G A A T G G A G T C A T A T G A G G A A G C T G 2 09 27 |D) 3 00 25 (E) 4 00 26 {f\ 5 09 22 e 121 01 (B) .... -- - - — - - ^ 9 121 06 (1) 0 121 14 (J) 1 110 lf> lA) 1 2 119 19 (0) 1 3 119 02 (K| 1 4 119 30 (L) 1 5 119 08 (M) 1 6 103 05 (A) 1 7 103 17 (C) 1 8 103 10 (N) 1 9 103 01 (O) 2 0 103 08 IP) 2 1 103 02 (O) T 2 2 120 01 (A) 2 3 120 14 (C) 2 4 120 07 (R) 2 5 120 02 (S) 2 6 120 13 (T) 2 7 213 01 (U) 2 a 297 01 |V) 2 9 297 14 1 UU 04 (A) C T c A T A G A T A C T A T T T 7 G A A C I A T T T 1 c 7 09 27 (D) _ _ 3 99 2f» IE) 4 99 2fi (F) 5 99 22 (G) 6 99 t9 (^0 7 121 03 (A) a 121 01 (H) 9 121 06 (1) 1 0 12) 14 (J) A 1 1 119 16 (A) 1 2 119 10 (B) 1 3 119 02 (K) \ 4 11'J 30 ID 1 S noon (M) 1 6 103 06 (A) _ _ 1 7 103 17 (C) 1 a 103 in (N) 1 9 103 01 (O) 2 0 103 08 IP) 2 \ 103 02 JO) 2 2 120 01 (A) 2 3 120 14 IC) 2 4 120 07 (R) 2 S 120 02 (SI C 2 6 120 13 IT) _ _ 2 7 21301 |IJ) 2 B 297 01 iV) 2 9 297 14 |W) T A 3 0 297 15 |X) A 3 1 297 04 |Y) A 3 2 297 17 |Z) C 3 3 297 22 (AA) _ _ 3 4 297 2H |AB) _ 3 S 232 01 |AC> A 3 6 232 05 lAP) A 3 7 232 09 |AE) A 3 a 232 17 (AF) A 3 9 232 06 (AG) A 4 0 232 11 (AH) A 4 1 232 20 (Al) A 4 2 33 04 (AJ) C A 4 3 33 11 (AK) _ _ c G A 4 4 33 29 (AL) _ _ c A 4 5 33 on (AM) c G A 4 6 r.:»05 (AN) _ a T A A A 4 f r> i.ilhkillefisib A G 7 G r G C T T C T C C 4 B 0 rtonVhitf^i A - A T T C T T A T T C haplo3macladc 199 200 20120?203204?0&20620720B209210211212213|2 142lS2162172ie21922022122222322422S226227?2 229230231 99 0^(A)_ Q9 21 (D) 90 2S (E)_ 99 26 (F| 99 22 (Gl_ 99 10 t21_03_(A) 121 01_|B) J21 06 (l)_ 121 14 (J) 110 16 (A) U9 ^9 Ifi), 119 02 (K) t19 30 (L) __!19 OB_(MJ 103 OS (A) _ 103 17 JC) 103 10 (N) J03 OMO) _ 103 OB (P| 103 02 (O) 120 01 (A) 12014 rc> ON 120 07 (R) 120 02 (S) 120 13 (T) 213 0! (U) 207 01 |V) 297 14 (W)_ 297 15 (X) 297 04 (Y) _ 297 17 (Z) 297 22^ (AA) 297 2B (AD) 232 01 (AC) 232 05 (AD) 232 09 (AE)_ 232 17 (AF) 232 or. (A0)_ 232 11 (AM) 232 20 (Al)_ 33 04 (AJ) 33 11_(AK)_ 33 20 (AL) 33 06 (AM) _ 62 05 1 i)'J()4 (A) A C G A G A A G A C C C T C A U A A T T T T T A T A A G A 2 09 27 (0) _ 3 tm21. (t) 4 90 2fi |F) S no 22 (G) 6 00 10 (H) 7 121 03 (A) B 121 01 (B) 9 121 06 (1) 1 0 121 14 (J) 1 1 119 in (A) 1 2 119 19 (0) 1 3 11902 (K) 1 4 119 30 (L) 1 5 1 19 OR (M) t 6 103 05 (A| 1 7 103 17 |C) 1 6 103 to (N) 1 9 103 01 (O) 2 0 103 OB (P) 2 1 103 02 (Q) 2 2 120 01 (A) 2 3 120 14 jC) 2 4 120 07 (R) 2 5 120 02 (S) 2 6 120 13 (T) 7 1 213 01 |U) A 2 6 207 01 (V) A 2 9 297 14 (W) A 3 0 297 15 (X) A 3 1 297 04 (Y) A 3 2 297 17 (21 A 3 3 297 22 (AA) A 3 4 207 28 (AD) A 3 5 232 01 (AC) A 3 6 232 05 (AD) A 3 7 232 09 (AE) A 3 a 232 17 (AF) A 3 9 232 06 (AG) A 4 0 232 11 (AH) A 4 1 232 20 (At) A 4 2 33 04 (AJ) A 4 3 33 11 (AK) A 4 4 33 29 (AL) A 4 S 33 06 (AM) c A 4 6 (AN) A 4 7 D calsktllen&is T I C 4 6 D cronV.liitei T T T A I A c haploSmaclade 9 365 266 267 266 269 270 27 1 272 273 274 275 276 277 2 7 eU 7 9 280 281282 283 284 285 286 287 288 389 290 291 393 293 394 295 296 3 1 99 04 (A) T A G C A A C C A G A T T A G a T T T C C T 1 I A A T 7 T T A 2 09 27 3 99 25 (E) 4 09 26 |F) 5 99 22 (G) 6 99 19 (H) _ 7 121 03 (A) fl 121 ni (R) 9 121 06 (1) t 0 121 14 (J) 1 1 U9 16 fA) 1 2 119 19 (R) 1 3 110 02 (K\ 1 4 119 no (I) 1 s noon (M) 1 6 103 05 fA) 1 7 103 17 (C) 1 a 103 10 (N) C 1 9 103 01 (O) 2 0 103 OB 1 99^04 (A) ? 119 27 JO) 3 09 25 (E) ^ 99 26 (F) 5 99 ?2JG) 6 99 19 (M) 7 121 03 fA) a 121 01 1 !)9 04 (A) T A A T A T T A 7 A T T A A A _ _ C A 7 A c A C A T G A A C A 2 99 27 (OJ A _ _ 3 99 25 {El G 4 99 2f» {r\ _ _ . 5 99 22 (G) _ 6 99 19 T 7 121 03 (A) a 121 01 IB) C 9 121 on (1) 0 121 M (J) C 1 119 16 (A) 2 119 19 (Hi c 1 3 11902 (K) G 1 4 119 30 (L) C 1 S llOOfl (M) 1 6 103 OS (A) 1 7 103 17 (C) 1 8 103 to (N) 1 9 103 01 (O) 2 0 103 OB iP) 2 1 tOl 02 (O) 2 2 120 01 (A) 2 3 120 14 (C) 2 4 120 07 (R) 2 S 120 02 (S) 2 6 120 13 JT) 2 7 21301 (U) 7 c 2 B 297 01 (V) 7 C C 2 9 297 14 (W) 7 7 3 0 297 15 (X) 7 7 3 1 297 04 (V) 7 7 7 3 2 297 17 (Z) 7 C C 3 3 297 22 (AA) 7 7 C C 3 4 297 28 (AH) T C c 3 5 232 01 (AC) 7 c 3 6 232 05 (AO) T 7 c 3 7 232 09 (AE) 7 c C 3 a 232 17 (AF) T c 3 9 232 Of> (AG) 7 c 4 0 232 11 (AMI 7 c 4 1 232 20 (Al) 7 c C 4 7 33 04 (AJ) C 7 c 4 3 33 11 (AK) C T c 4 4 33 29 (AL) C T A c 4 5 33 06 (AM) C T c 4 6 62 05 (AN) A C 7 A 7 G C 7 7 7 4 7 0 caiskiilensis C T G G A G A T 7 7 7 C T 4 B [) rriinkhilf!) T T A I T 7 _ A c T i: haplo3m*c)ade 12 364 365 366 367 366 369 37037 1 372 373 374 375 376 377 37B 379 38036 1 3 B 2 3B3 3B4 3B5 3B6 387 368 389 390 391 392 393 394 395 396 1 99 04 (A| A A A A T G A T A C G C G T G A G T G A A T 7 A C C 7 0 A G G G A 7 99 27 (D) 3 99 25 (E) 4 99 26 (F) S 99 22 (G» T 6 09 19 (H) 7 121 03 {A\ B 121 01 (B) 9 121 06 (1) 1 0 121 14 fJ) 1 1 119 16 (A) 1 7 119 19 (B) t 3 119 02 (K) 1 4 iu) no a) 1 5 119 OB (M) T 1 6 103 OS (A) 1 7 103 17 (C) 1 8 103 10 IN) 1 9 103 01 (0) 7 0 103 on (P) T 7 1 103 02 (O) 7 7 120 01 (A) 7 3 120 14 (C) 2 4 120 07 (M) 2 5 120 02 |S> 2 6 120 13 (T) 2 7 21301 (U) G 2 6 297 01 (V) G 2 9 297 14 (W) G 3 0 297 15 (X) G 3 1 297 04 (Y) G 3 2 297 17 {Z\ G 3 3 297 22 (AA) G 3 4 297 26 (AD) G 3 S 232 01 fAC) G 3 6 232 05 (AD) G 3 7 232 09 (AE) G 3 B 232 17 (AF) G 3 9 232 06 (AG) G 4 0 232 11 (AH) G 4 t 232 20 (Al) G 4 2 33 04 (A.)) G ~ 4 3 33 U (AK) G 4 4 33 29 (Al) G 4 5 ;n (»fi (AM) G 4 6 •I 'Of. (AN) 1 4 / () c.itskillrilhih n T 7 T 7 A 7 A C A 4 a () CtOMkhllOl T T A G 7 G 7 A A T A h»plo3mftcUde 13 397 398 399 400 401 402 403 404 405 406 407 4 0 8 409 4 0 4 1 1 4 1 2 4 1 3 4 14 4 1 5 4 1 6 4 17 4 1 8 4 1 9 420 4 2 1 422 423 4 2 4 425 426 427 4 2 8 4 1 1)9 04 (A) T A A C A G C A T A A T A 1 T A T T A A A T I A A T G T 1 T A t 2 99 27 (0) 3 99 2S (El A 4 99 2n |F) S 99 22 (G) 6 99 19 (H) 7 121 01 (A) 8 121 01 (B) 9 121 Of> 0) 1 0 121 14 (J) 1 1 119 16 (A) I 2 119 19 (0) 1 3 119 02 (K) 1 4 119 30 (L) 1 5 119 on (M) 1 6 103 OS (A) 1 7 103 17 (C| C 1 e 103 10 (N) 1 9 103 01 lO) 2 0 103 Ofl (P) C 2 1 103 02 (O) 2 2 U'0 01 (A) 2 3 120 14 (C) C 2 4 120 07 {f\\ 2 5 120 02 (Si 7 6 120 13 |T| a 7 213 01 (U) G 2 a 297 01 (V| G 2 9 297 14 |W) G 3 0 297 IS (X) a 3 1 297 04 (Y) G 3 2 297 17 (71 G 3 3 297 22 (AA| G 3 4 297 2B (AD) A G 3 5 232 01 (AC) G 3 6 232 05 (AD) G 3 7 232 09 (AE) G 3 6 232 17 (AF) G G 3 9 232 OB (AG) G 4 0 232 11 (AH) G G 4 1 232 20 (All G 4 2 33 04 (AJ) T G 4 3 33 11 (AK) T G 4 4 33 29 (AL) T G 4 5 33 on (AM) T a 4 6 62 OS (AN) A — 4 7 D nalsKillensis A A C 0 cronKhitei 4 8 - _ A A T A hapioSmacladfl 14 430 43 1 433 433 434 435 436 437 436 439 440 44 1 442 443 44 4 445 446 447 448 449 450 45 1 452 453 454 455 456 457 458 459 480 4 6 1 462 1 •JDO'I (A) A C C T C G A T G T T G G A C T A G G A T A G G I A A G G (« C 1 A 2 (0) 3 no 2fi (P) 4 99 2ri (F) 5 90 22 (G) 6 99 19 (Mi 7 121 03 (A) 8 121 01 (D) 9 121 06 (1) t 0 121 14 |J) — — -- - —------1 1 119 16 (A) 1 2 119 19 (D) 1 3 119 02 (K) 1 4 119 30 ID 1 S 119 on (M) 1 6 103 05 (A) 1 7 103 17 (C) 1 8 103 10 (N) 1 9 103 01 (O) 2 0 103 08 (P) 2 1 103 02 (0) 2 2 120 01 (A) 2 3 120 14 (C) 2 4 12007 (R) 2 & 120 02 (S^ 2 6 120 13 (T) 2 7 21301 |U) 2 8 297 01 (V) 2 9 297 14 IW) 3 0 297 15 (X) 3 1 297 04 (Y) 3 2 297 17 (Z) 3 3 297 22 (AA) 3 4 297 28 (AO) 3 5 232 01 (AC) A 3 6 232 Of> (AD) A 3 7 232 09 (AE) A 3 a 232 17 (AF) A 3 9 232 06 (AG* A 4 0 232 11 (AH) A 4 1 232 20 (Al) A 4 2 33 04 (AJ) 4 3 33 11 (AK) 4 4 33 20 (AL) 4 5 33 Ofi (AM) 4 6 (AfJ» 4 7 D catskillensis n A A T A T A 4 8 0 nodkhitei A T T C hpplo3macUde 15 463464<6S466467468469470471472473474475476477478479480461482483484465466467466489490491492493494495 111) 04 |A) flO 27 tO) 99 25 fej__ 99 26 (F) 99 22 (G) 99 10 (H) 121 03 (A> 121 01 (D) 121 121 14 (J) 119 I6_(A) no 10 tB) 119 02 (K^ 119 30 (1.) 110 OB (M) 103 05 (A) 103 17 (C) 103 10 (N) 103 0] (O) 103 OB (P» 1(13 02 (O) 120 01 (A) 120 M (C) 120 07 (R) OJ 120 02 |S) 2 6 120 13 (7) 2 7 21301_(U) 297 01 (V) _ 29j^l4 (W) 297 15 (X) 297 04 (V) 297 17 (Z) 297 22 (AAl 3 4 297 2fl (AB) 3 5 232 01 tAC) 3 6 232 05 (ADJ 3 7 232 09 (AE) 3B 232 17 (AF) 3 9 232 06 (AG) 4 0 232 11 (AH) 4 1 232 20 (Al) 4 2 33 04 (AJ) 4 3 33 11 (AK) 4 4 33 29 (AL) 4 S 33 nf» (AM) f»L' 05 (ANl 4 7 n C3l!>killensi:> 4 B haploSmiiclarie Ift 496 497 4 96 4 9 9 500 501 502 503 504 505 506 507 506 509 5 0 5 1 1 5 1 2 5 1 3 5 1 4 5 1 5 5 1 6 5 1 7 5 1 6 5 1 9 5 2 0 5 2 1522 1 UU 04 |A) G T C T C C T A C G T G A T c T G A G T T C A G 2 Of) 27 10) _ 3 00 25 (E) 4 0!) 2fi (P) 5 09 22 (G) 6 09 10 (H) 7 t2l 03 (A) 8 t2l Ot (0) 9 121 nri (t) 1 0 121 14 (J) 1 1 110 in (A) 1 2 119 10 (n) 1 3 119 02 (K) 1 4 119 30 (L) 1 5 110 on (M) 1 6 103 05 (A) 1 7 ion 17 (C) 1 fi 103 10 (N) 1 9 10.101 (O) 7 0 103 OH |P) 2 t 103 02 (O) 2 2 120 01 (A) A 2 3 120 14 (C) 2 4 120 07 (Fl) 2 5 120 03 (S) 2 6 120 in (T) _ 2 7 21301 (U) 2 B 297 01 (V) A 2 9 297 14 JW) A G 3 0 207 15 (X) _ A 3 1 207 04 (Y) A 3 2 297 17 (?) A 3 3 207 22 (AA) A 3 4 297 2B (AR) A 3 5 232 01 (AC) A 3 6 232 05 jAD) A 3 7 2.32 09 (AE) 3 a 212 17 (AF) 3 9 212 Of) (AG) 4 0 2.12 11 (AM) 4 1 2.12 20 (Al) _ 4 2 33 04 (A.I) A 4 3 33 U \AK) A 4 4 1,1 29 (At ) A 4 5 33 Ofi (AM) A 4 6 fi2 0S (AN) 4 / 0 A A c A C 4 » n cfotiKhjl*?! - - 175 APPENDIX B. MORPHOLOGICAL DATA ANALYSES A discriminant function analyses was conducted on morphological measurements of Discus niacclintocki collected from nine populations in northeastern Iowa. The measurements made were: shell diameter, shell height, number of whorls, and diameter of the umbilicus. A canonical discriminant analysis was performed and two canonical variables described a combined 91% per of the variation. The slope was a significant predictor of the variation (p=.0001). The loadings for the canonical variables are given in Table 1. Table I. Loadings for canonical variables for the comparison among slopes. Canonical Variable I Canonical Variable 2 Whorl -0.6169 0.6658 Diameter -0.08184 0.7908 Height -0.4344 0.3973 Umbilicus -0.1488 0.5426 In a separate analyses, watershed was a significant predictor of the variation among morphological measurements (p=0.0076). The two canonical variables account for 100% of the variation. The loadings are shown in Table 2. 176 Table 2. Loadings for canonical variables for the comparison among watersheds. Canonical Variable 1 Canonical Variable 2 Whorl 0.6348 -0.5140 Diameter 0.1645 -0.4052 Height 0.4612 -0.3561 Umbilicus 0.4502 0.1746 The following pages show the results of the discriminant analyses. On the graph of slopes (Figure 1) the numbers correspond to the following slopes: l=slope 99, 2=slope 103, 3=slope 119, 4=slope 120, 5=slope 121, 6=slope 33, 7=slope 62, 8=slope 297, and 9=slope 232. On the watershed graph (Figure 2) the numbers correspond to the following watersheds: l=Buck Creek, 2=Turkey/Volga River, and 3=Maquoketa River. 177 3 5 2 1 51 3 99 : S 1 5 4 6 5 4 54 5 1 5 1 53 59 5 115 S 88 1 1 3 6 9 5 5 1 455 325 16 S814 43 23 5 4 241 312 6 2 1 72 484 3 2296 18 1 1 736 32 4 5 4 1 55 36 69 34 571 2 22S 63 22 8 1 3 4 6 21 2 11 8 6 6 6 6 87 4433 63 6 - 1 + 4 62 4 14 382 26 5 4 3 5 6 42 a 13 43 8 26 8 3 4 2 34 2 9 -2 1- 83 3 2 4 6 8 2 -3 -4 + -8 -6 CANl NOTE: 1 obs had missir.g values. 42 obs hidden. Figure 1. Relationsliip bet\veen canonical variables from morphological discriminant analysis by slope. 178 Plot cf CA^;2•CA^:l. Syrnrol is value c: V.'THSHHr:. 2 • 3 2 1 1 11 1 2 2 I 3 3 1 13 II 11 11 111 1 + 1 2 11 1 11 1 311 11 1 3 23 1 31 311 1 32 32 322 11 111 2131 1 1111 1 1 313 21 2 211 11 12111 1 1 312 21 1 11 2 131 1312 2 2 32 1121 31 1 1 3 22 113 11 1 1 1 1 1 31 ] -1 + 1 3 131 1 1 2 2 1 1 3 21 1 1 1 1 1 1 1 -4 CAMl NOTE: 1 obs had missing values. 43 obs hidder.. Figure 2. Relationship between canonical variables for discriminant analysis by watershed. 179 ACKNOWLEDGEMENTS Funding for this project came from the Iowa Department of Natural Resources, the Iowa State Preserves Board, American Museum of Natural History, American Conchologists Society, the Western Society of Malacologists, and the Ecology and Evolutionary Biology Program at Iowa State University. The cooperation of the U.S. Fish and Wildlife Service, the Iowa Chapter of the Nature Conservancy, the Illinois Department of Conservation, and the Iowa Department of Natural Resources was much appreciated. The Ecology and Evolutionary Biology Program at Iowa State University was very supportive of my work. I would also like to thank all those who helped me along the way, a few of whom deserve special mention. First of all, I would like to thank my family for their support throughout my education. I am also e.xtremely grateful to Greg Anderson for his support and companionship throughout the writing phase, although he was probably correct in his assessment that maybe its not so bad to be 750 miles away when your significant other is writing her dissertation. Tawnya Cary and Katherine Holger were a great help during the 1997 field season. Jessemine Fung, Tamra Danielson, and Adam Remsen also provided occasional field assistance. Elinor Michel, then at the University of Michigan, provided technical assistance. Darj'l Howell at the Iowa Department of Natural Resources was extremely helpful in getting this project off the ground. Wayne Ostlie of The Nature Conservancy confirmed the identification of the Discus specimens. Bill Simpkins helped me understand some of the complicated geological information. Bonnie Bowen and Gavin 180 Naylor provided insights on numerous occasions. Bill Clark provided guidance for the population analyses. Katherine Tuxbury pro\ ided moral support and technical suggestions. The EEB Brown Bag, E.L.V.I.S., and B.O.P. discussion groups provided many ideas. Wendy Reed shared many scientific discussions with me over late-night bowls of ice cream. My committee members, Diane Debinski, Fred Janzen, and Rob Wallace, and my co-advisors, Richard Hoffmann, and Brent Danielson, were very supportive and helpful. Brent deserves a special thanks for helping in the field, reading countless drafts of manuscripts and grant proposals, and for being a great mentor. Last but not least, a special thanks to my four-legged friend Sandy for her loyal companionship and constant tail-wagging, without which I probably would have been lost more often and would definitely have enjoyed the journey much less. IMAGE EVALUATION TEST TARGET (QA-3) nluBBa IlilM 1.0 1^ u; imm |36 ll£ 1. L. k. 11^ I.I u ^ 1.8 1.25 1.4 1.6 1 dOnuti ^ o ^ /APPLIED A IIWIGE . Inc 1653 East Main Street Rochester. 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