GENETIC CHARACTERIZATION OF AVIAN MALARIA PARASITES ACROSS THE BREEDING RANGE OF THE MIGRATORY LARK SPARROW (CHONDESTES GRAMMACUS) Bethany L Swanson

Transcription

1 GENETIC CHARACTERIZATION OF AVIAN MALARIA PARASITES ACROSS THE BREEDING RANGE OF THE MIGRATORY LARK SPARROW (CHONDESTES GRAMMACUS) Bethany L Swanson A Thesis Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December 2012 Committee: Juan L Bouzat, Advisor Jeffrey Miner Moira van Staaden

2 ii ABSTRACT Juan L Bouzat, Advisor The lark sparrow (Chondestes grammacus) is a ground-nesting passerine that breeds across much of the central North American steppe and sand barrens. Through genotyping and sequencing of avian malaria parasites we examined levels of malaria prevalence and determined the distribution of Haemoproteus and Plasmodium lineages across the breeding range of the lark sparrow. Analysis of 459 birds collected from five breeding locations revealed relatively high levels of malaria prevalence in adults (80.3%) and juveniles (45.9%), with infections being primarily of Haemoproteus (88.3% of sequenced samples). Levels of genetic diversity and genetic structure of malaria parasites with respect to the avian host populations revealed distinct patterns for Haemoproteus and Plasmodium, most likely as a result of their distinct life histories, host specificity, and transmission vectors. With the exception of one common Haemoproteus haplotype detected in all populations, all other haplotypes were population-specific. A hierarchical analysis of molecular variance of Haemoproteus sequences revealed that 46-51% of the genetic variation can be explained by differences among host populations (p<0.001). In contrast to the regional patterns of genetic differentiation detected for the lark sparrow populations, Haemoproteus parasites showed high levels of population-specific variation and no significant differences among regions, which suggest that the population dynamics of the parasites may be driven by evolutionary processes operating at small spatial scales (e.g., at the level of host populations). These results highlight the potential effects of host population structure on the demographic and evolutionary dynamics of parasites.

3 iii ACKNOWLEDGMENTS On the technical side I would like to thank Jeremy D. Ross for coordinating collection of blood samples of lark sparrows, and Verner P. Bingman, Maria Herman, Tim Herman, Elizabeth Keller, Jennifer Noland, Elizabeth A. Ross, Joseph M. Swanson, Todd Brininger, and Luca Brininger for field assistance. I would also like to thank the following institutions/individuals for allowing field access to Lark Sparrow populations: Oak Openings Preserve, Metroparks of the Toledo Area; Kitty Todd Nature Preserve, The Nature Conservancy; Lost Mound Unit of Upper Mississippi National Wildlife Refuge, US Fish and Wildlife Service; Cedar Point Biological Station, University of Nebraska at Lincoln; Daniel Brown and Skipper Duncan, Tom Green County, Texas; Hopland Research and Extension Center, University of California at Davis; and Gray Davis/Dye Creek Preserve, The Nature Conservancy; Ross C. Dawkins, Angelo State University; Daniel G. Wenny, Illinois Natural History Survey; and Mark C. Shieldcastle, Ohio Department of Natural Resources. This project was funded through State Wildlife Grants from the Ohio Department of Natural Resources Division of Wildlife, in-kind contributions from The Nature Conservancy, and the Department of Biological Sciences at Bowling Green State University. I also owe many thanks to my advisor, Juan L. Bouzat, for sticking with me on this long and winding road and not giving up on me. Thank you also to my committee members, Jeff Miner and Moira van Staaden for continuing to support me and provide guidance throughout this process. Knowing that I had people believing in me has kept me going forward. On a personal note I would like to thank my husband, Todd Brininger, for coming along on this journey with me and providing constant support, encouragement, and patience. While neither the path nor the end point were what we expected, I would not change a thing. Thank you to my parents, brother, grandparents, and the rest of my extended family who have kept up their support over the years. Thank you to my sons, Luca and Leo, for providing me inspiration and always keeping my spirits up. You may have made the road more difficult but you made it much more enjoyable. Lastly, I would like to again thank Jeremy Ross who not only provided so much in both

4 the field and the lab, but also has been a true friend who is always willing to lend an ear and his iv support.

5 v TABLE OF CONTENTS Page INTRODUCTION... 1 MATERIALS AND METHODS Field Sampling... 7 Molecular Analysis... 8 Statistical and Phylogenetic Analysis... 9 RESULTS DISCUSSION REFERENCES TABLES FIGURES APPENDIX... 38

6 vi LIST OF TABLES Table Page 1 Avian malaria prevalence in the lark sparrow estimated by population and by age class Lark sparrow malaria haplotypes with BLAST matches found in GenBank Genetic diversity estimates for malaria parasites (Haemoproteus and Plasmodium) by breeding population of lark sparrow Hierarchical analysis of molecular variance (AMOVA) based on Haemoproteus parasite haplotype sequences... 31

7 vii LIST OF FIGURES Figure Page 1 Breeding range of the lark sparrow as determined by the Breeding Bird Survey Summer Distribution Map, PCR products from malaria screening on a 1% agarose gel stained with ethidium bromide and visualized under UV light Avian malaria prevalence in adult and juvenile lark sparrows Haplotype networks for Haemoproteus and Plasmodium avian malaria parasites Evolutionary relationships of avian malaria parasites from the lark sparrow and other bird species inferred using Bayesian analysis Evolutionary relationships of avian malaria parasites from the lark sparrow inferred using Bayesian analysis... 37

8 1 INTRODUCTION Avian malaria parasites are globally distributed among birds, infecting a wide range of host species (Valkiunas 2005). To date, many studies of avian malaria have not shown direct evidence for a significant negative effect of the disease on the fitness of its avian hosts (e.g. Weatherhead 1990; Dale et al. 1996; Kilpatrick et al. 2006). However, experimental infections have shown that avian malaria parasites can have significant consequences on their hosts, including enlargement of the liver and spleen, declines in body weight, and even death (Atkinson et al. 1995, 2000). Furthermore, avian malaria has been implicated in the severe decline of some bird species endemic to islands. For example, several Hawaiian endemic species have had widespread declines and have even been driven to possible extinction as a result of avian malaria (van Riper et al. 1986; Atkinson et al. 1995, 2000; Massey et al. 1996). In the Galapagos Islands the first documentation of the presence of Plasmodium in the endangered Galapagos penguins (Spheniscus mendiculus) has recently been reported (Levin et al. 2009). Evidence of high mortality associated with malaria infections in several species of captive penguins (Fix et al. 1988; Cranfield et al. 1990, 1991) coupled with the documentation of a competent malaria vector (Whiteman et al. 2005) may lead to similar detrimental effects in these penguins and other endemic birds to the islands. As avian malaria represents a potential threat to bird populations, it is important to address its impact on birds when examining species of conservation concern. With increasing human mediated impacts on habitats, there has been a worldwide decline in natural populations of vertebrates, leading to increasingly smaller and more fragmented populations. Threatened populations, which may already be subject to small-population processes such as inbreeding and genetic drift, may be highly affected by disease. Understanding the population dynamics of

9 2 avian malaria and other wildlife diseases in nature is therefore important for the long-term persistence of natural populations. The evolutionary history of parasites and their hosts plays an important role in resistance and susceptibility to infection with malaria. Avian malaria itself is caused by related parasites in two genera, Plasmodium and Haemoproteus, in the phylum Apicomplexa, class Haemosporida (Atkinson and Van Riper 1991). The two genera differ in both their primary vectors and in certain stages of their life cycles. The infective stages of both parasites in their vertebrate hosts are differentiated sex cells, which reside inside red blood cells in the peripheral circulation. Haemoproteus undergoes asexual reproduction in various tissues of the vertebrate host prior to entering the blood circulation while Plasmodium undergoes additional cycles of asexual reproduction in the peripheral blood. Both parasite gametocytes undergo sexual fusion and develop sporozoite-containing oocytes inside dipteran primary hosts, which for Plasmodium are mostly Culex or other culicid mosquitoes (Culicidae) and for Haemoproteus mostly Culicoides midges (Ceratopogonidae) (Atkinson and Van Riper 1991). The relationships between parasites and their hosts are complex with many interacting factors. Because malaria is a vector-born infection, there is a strong environmental component associated with this disease. In order for a bird to become infected, it must be infected through a vector (e.g., mosquito or midge) containing parasites able to infect that particular host bird species. Thus, the area inhabited by the bird must have suitable vectors as well as an already circulating population of parasites specific to that host species. It should be added though that host specificity of avian malaria parasites can range from a single host to a wide range of host species that may only be distantly related (Bensch et al. 2000; Ricklefs and Fallon 2002; Waldenstrom et al. 2002; Fallon et al. 2003; Ricklefs et al. 2004). In fact, earlier studies have

10 3 suggested that Haemoproteus are more likely to be host specialists, having species-specific parasites, than Plasmodium (Atkinson and Van Riper 1991). The detrimental effects of malaria on birds may be difficult to determine. Malaria may cause severe anemia, weight loss, or even death in susceptible birds (Atkinson 1999), or may have little to no effect on the host. Birds captured via mist-nets are unlikely to be suffering severe effects of malaria as severely infected birds tend to stay still in order to recover from the infection or to avoid predation (Valkiunas 2005; Moller & Nielsen 2007). Instead, birds sampled that test positive for malaria are usually assumed to be newly infected, and therefore not yet showing any major detrimental effects, or have passed the dangerous stage of infection and are now in either a chronic phase or in the process of clearing the parasites entirely from their system. In avian malaria, similar to human malaria (Hill et al. 1991), the first exposure to the infection is what may cause the most detrimental effects on fitness. After this initial infection, parasites can be cleared from the host or may enter a latent stage, remaining in host tissues for extended time periods, and potentially for life (Manwell 1934; Jarvi et al. 2002, 2003). When the parasites persist long-term, there is a chronic stage of infection that is characterized by mild fitness effects, or even no effects (Atkinson & van Riper 1991; Hill et al. 1991). Several studies have estimated levels of malaria parasite prevalence in birds (defined simply as the proportion of infected individuals) and have focused on prevalence either in individual species (e.g., Bensch et al. 2007; Wood et al. 2007) or at the community level (e.g., Fallon et al. 2003; Ricklefs et al. 2005). Published infection rates are highly variable for different species. For example, in a four-year study in southern Missouri, individual species infection rates ranged from 11% in the blue-winged warbler, Vermivora pinus, to 58% in the yellow-breasted chat, Icteria virens (Ricklefs et al. 2005). Prevalence has also been shown to vary year-to-year at a

11 4 specific locale (e.g., Bensch et al. 2007; Ricklefs et al. 2005) and geographically, across independent populations of the same species (e.g., Fallon et al. 2005; Gibb et al. 2005). Variation in malaria parasite prevalence may be due to a variety of factors including the host species exposure to suitable vectors, the density of vectors present in a given habitat, whether the parasite community is composed of host generalists or specialists, and the host s immunological response to the parasites. Despite the large number of studies examining avian malaria, it is not clear which of these factors may play the most important roles in determining malaria prevalence. While a few recent studies (e.g., Fallon et al. 2006; Durrant et al. 2008; Pagenkopp et al. 2008) have examined avian malaria prevalence across the breeding range of single species, there is still a paucity of studies examining breeding populations of migratory species across a wide-ranging geographic area. The present study focuses on the lark sparrow (Chondestes grammacus), a ground-nesting passerine with both sedentary and migratory populations that breeds across much of the central North American steppe and sand barrens (Martin and Parrish 2000). Some sedentary populations are found in the milder climates of Texas and California whereas the migratory populations are spread over a large geographic area, ranging from the Canadian prairies to northern Texas, and from Oregon to Ohio (Figure 1). Eastern populations of the lark sparrow have been described as a separate subspecies (C.g. grammacus, Say, 1823), distinct from populations that breed west of the Mississippi River (C.g. strigatus, Swainson, 1827; Baepler 1968). Furthermore, both morphological and genetic studies suggest that California populations represent a demographically independent unit, consistent with Oberholser s (1932) original description of the Oregon lark sparrow as a distinct subspecies (C.g. actitus, Oberholser 1932; Miller 1941; Ross 2011). The eastern populations have become increasingly fragmented over the past century

12 5 as population sizes have declined. In the state of Ohio the species is currently listed as stateendangered (Ohio Division of Wildlife), as only a small population persists in northwest Ohio, isolated from any other breeding population by more than 300 km. The lark sparrow represents a good model species to study the potential role of host population structure on parasites population dynamics. The species has demographically independent breeding populations distributed across three major migratory corridors bounded by the Mississippi River valley and the Rocky Mountains (Ross 2011). In addition, there are small, isolated populations of conservation concern as well as large, contiguous breeding populations. The population dynamics of the lark sparrow can therefore have direct consequences on the distribution and prevalence of avian malaria across breeding populations. In this study, we used a genotyping technique and sequencing of an avian malaria-specific DNA marker to assess levels of infection and potential genetic structuring of avian malaria in the lark sparrow. Specifically, we addressed three research questions. First, is there variation in avian malaria prevalence across independent breeding populations of the lark sparrow? Secondly, is there any genetic structuring of avian malaria parasites with respect to the lark sparrow breeding populations? Thirdly, are there independent parasite lineages and, if so, what is the distribution of malaria lineages in geographically distinct breeding populations of the lark sparrow? Looking at differences in parasite prevalence and the composition of the parasite community in these distinct breeding populations will allow us to assess the potential role of host dispersal and population isolation on the dynamics of avian malaria. Furthermore, the characterization of malaria parasite lineages in the lark sparrow and their ancestral relationships with malaria lineages from other avian species may provide insight into differences in host specificity between Haemoproteus and Plasmodium parasites. This study of a wildlife disease system

13 6 across the breeding range of a migratory bird species will expand our knowledge of how vectorborn parasites may affect populations in the wild, and how host distribution may affect parasites population dynamics.

14 7 MATERIALS AND METHODS Field Sampling Breeding populations of the lark sparrow included in this study were selected based on migratory behavior and geographic distribution throughout the species breeding range (Figure 1). Populations were sampled over three breeding seasons (May-July ) and include populations in California (Tehama County, and Mendocino County, 2006), Texas (Tom Green County, and Kimble County, 2005), Ohio (Lucas and Wood Counties, ), Illinois (Jo Daviess and Caroll Counties, ), and Nebraska (Keith County, 2007). Due to the proximity of the sampling locations within each state, samples are further classified only by state. In the field, birds were captured by mist-net. Captured birds were assigned to three age classes. Adults were classified as after-hatch-year (AHY) and juveniles, i.e., birds born that year which had fledged and were captured by mist net, were classified as hatch-year (HY). The third age category was that of nestlings, i.e., birds that were sampled directly from the nest. Adults were sexed by examination of the cloacal protuberance, measured (wing chord, tail, tarsus, and bill length, bill depth and width), weighed, and fitted with a US Fish and Wildlife aluminum leg band and plastic color bands arranged in individual specific patterns. The same was done for juveniles with the exception of sexing, which is not possible through cloacal examination in that age class. Less than 100 µl of blood was sampled by puncture of the brachial wing vein and stored in Longmire s lysis buffer (Longmire et al. 1986) for further genetic analysis. Nestlings were sampled directly at the nest between five and nine days after hatching. In order to minimize handling time of nestlings, only wing chord was measured, birds were weighed, leg bands fitted, and blood drawn. All fieldwork was carried out under appropriate collecting

15 8 permits and animal care and use protocols approved by the Institutional Animal Care and Use Committee at Bowling Green State University (IACUC protocol number and ). Molecular Analysis In the laboratory, DNA was isolated using a standard salt-precipitation extraction (Sambrook et al. 1989), and extracted DNA was suspended in 1X TE buffer. Quantity and quality of DNA extractions were assessed through electrophoresis on a 1% agarose gel. A total of 459 samples were screened for the presence of malaria parasites in the peripheral blood using the polymerase chain reaction (PCR). These included 50 samples from California, 33 from Nebraska, 144 from Texas, 38 from Illinois, and 194 from Ohio. PCR was performed with primers designed to amplify an approximately 380 base pair region of the mitochondrial cytochrome b (cyt b) gene of avian malaria parasites (primers 543F: 5 -AAA AAT ACC CTT CTA TCC AAA TCT-3 and 926R: 5 -CAT CCA ATC CAT AAT AAA GCA T-3 ). The primers were designed from published sequences of the cytochrome b gene for Plasmodium (Escalante et al. 1998) as described in Fallon et al. (2003). These primers have been shown to amplify DNA from both Haemoproteus and Plasmodium species from a wide range of avian hosts worldwide (Ricklefs et al. 2005). PCRs were run in 15 µl volumes that contained the following components in their final concentrations: 1X Promega GoTaq Flexi Buffer (Promega Corporation, Madison, WI), 2.5 mm MgCl 2, 0.2 mm of each dntp, 0.5 µm of each primer, units of Promega GoTaq DNA polymerase and approximately 50 ng DNA as template. PCR amplification profiles included an initial denaturation for 1 min at 94 C, followed by 35 cycles with denaturation for 50 sec at 94 C, annealing for 50 sec at 49 C, and extension for 70 sec at 72 C, followed by a final extension step of 3 min at 72 C. In order to enhance DNA typing accuracy, at least two negative

16 9 controls and a positive control were included with each PCR. To attempt to eliminate false negatives, all samples initially screened as negative were subjected to a second PCR amplification. PCR products were checked on a 1% agarose gel and infection status was determined by the presence of a band at approximately 380 bp (Figure 2). To characterize parasite lineages, PCR products were subjected to an ethanol precipitation and resuspended in sterile deionized water for direct sequencing. A subset of all individuals that tested positive was chosen for sequencing. Samples were chosen so that individuals from each population, age class, and sampling year were included. This included a total of 25 individuals from California, 11 from Nebraska, 42 from Texas, 14 from Illinois, and 45 from Ohio. Overall, 54% of individuals found to be infected were subjected to DNA sequencing of parasite mtdna (137 out of 255 samples). PCR products were then sequenced via direct sequencing on an ABI377 automated gene sequencer (Applied Biosystems, Foster City, CA) according to the manufacturer s protocol using Big Dye version 3.1 dye-terminator sequencing chemistry (Applied Biosystems, Foster City, CA). Sequencing reactions were carried out in 5 µl volumes with ng of purified PCR product DNA, 1 µl Big Dye terminator, 1 µl 2.5X Big Dye buffer, and 0.1 µm of the 543F forward primer. If multiple ambiguities were observed in the chromatograms, samples were either re-sequenced or discarded from analysis. Statistical and Phylogenetic Analysis Samples were grouped into different arrangements (i.e., by sampling location, by region, by age of birds, and by sex of adults) and prevalence was estimated as the number of infected birds (as determined by positive PCR amplification) divided by the total number of individuals

17 10 sampled. Chi-square tests were performed to test for heterogeneity in malaria prevalence among defined groups. Mitochondrial cytochrome b sequences were manually edited by checking and correcting for miscalls and ambiguities. Sequences were aligned using Clustal X multiple sequence alignment software (Thompson et al. 1997; Larkin et al. 2007) and ambiguous ends were trimmed. This resulted in a 295 bp fragment for use in phylogenetic analyses. Sequences differing by as little as one nucleotide were considered independent haplotypes. Individual sequences were identified to the genus level, as either Haemoproteus or Plasmodium, by comparison with parasites sequences that had been previously classified. Each haplotype was also subjected to a BLAST search to determine if it had previously been found in avian species other than the lark sparrow. Levels of genetic variation were computed in MEGA, version 4 (Tamura et al. 2007). First, genetic variation was computed for Haemoproteus and Plasmodium haplotypes separately based on the number of base pair differences and p-distance among DNA sequences and included estimates of standard errors based on 500 bootstrap replicates. Average values were computed for both within and between the genera. Genetic variation was also computed by region, both within east, central, and west breeding regions, and between paired regions (east-central, eastwest, central-west). To visualize mutational pathways, a haplotype network based on statistical parsimony was generated using TCS software (Clement et al. 2000). The confidence limit for creating connections between haplotypes was set to 95%. Estimates of haplotype (h) and nucleotide diversity (π) for Haemoproteus and Plasmodium and their standard deviations were obtained for each host population and all samples in general using Arlequin version 3.0 (Excoffier et al. 2005). Arlequin was also used to perform a hierarchical analysis of molecular variance (AMOVA) and assess potential genetic structure of

18 11 avian malaria parasites across the lark sparrow breeding range. Genotyped host populations were grouped by breeding region (i.e., west, central and east). Two independent AMOVA were performed based on pairwise differences between haplotypes and haplotype frequencies, respectively. Fixation indices (F CT, F SC, and F ST ) were used to assess levels of genetic variation among regions, between populations within regions, and among individuals within populations, respectively. Significant values for each index were calculated using 1000 random permutations of haplotypes between populations. Given the paucity of samples that tested positive for Plasmodium and the fact that this genus was not detected in Nebraska or Illinois, the AMOVA was restricted to the analysis of Haemoproteus samples. Pair-wise exact tests of population differentiation were also performed through permutation procedures implemented in Arlequin (10000 permutations). To evaluate ancestral relationships between parasite lineages, two phylogenies were constructed using both maximum parsimony and Bayesian inference methods (Felsenstein 2004). The first included all of the avian malaria haplotypes found in the lark sparrow and a selected sample of Haemoproteus and Plasmodium parasites (selected from BLAST searches in GenBank) found in other avian species. This phylogeny was aimed at determining if malaria parasites from the lark sparrow represented species-specific lineages that have evolved as independent monophyletic groups within the species. The second phylogenetic analysis was based only on the lark sparrow haplotypes characterized in this study, and focused on determining potential phylogeographic patterns in relation to the presence/absence of individual parasite lineages in distinct lark sparrow host populations/regions. The cytochrome b sequence from a mammalian malaria parasite, Plasmodium falciparum (GenBank accession number AF069605), was used as an outgroup in reconstructing phylogenies.

19 12 Phylogenies were constructed using both maximum parsimony and Bayesian analysis. Maximum parsimony trees were constructed using MEGA, version 4 (Tamura et al. 2007). Confidence of nodes was evaluated through 500 bootstrap replicates. A branch-and-bound search was used, which guarantees finding all trees without an exhaustive search. Consensus trees were computed as a composite of all equally parsimonious trees generated by the analyses. The program jmodeltest (Posada 2008; Guindon and Gascuel 2003) was used in order to determine the best-fit model of nucleotide substitution for the data. Seven substitution schemes were utilized for a total of 56 possible models. After computation of likelihood scores for each model, the Akaike Information Criterion (AIC) was used to select the best model for the given sequence data (GTR+G for both analyses). Phylogenetic trees of parasite lineages were also constructed using Bayesian Markov chain Monte Carlo (MCMC) analysis in the program BEAST (Drummond and Rambaut 2007). Input files were generated using BEAUTi (included with the BEAST software package). For the MCMC analysis the length of the chain was set to 10,000,000. TreeAnnotator (also part of the BEAST software package) was used to summarize the trees produced in BEAST.

20 13 RESULTS Across its breeding range, the lark sparrow populations show relatively high levels of infection with malaria parasites. Overall, 255 out of 459 samples (55.5%) including nestlings, juveniles, and adults screened positive by PCR. Overall prevalence in adults (AHY birds) was 80.3% (204 out of 254 samples) and in the HY juveniles (in this study meaning hatch-year birds that had fledged and were captured by mist-net) prevalence was 45.9% (51 out of 111 samples) (Table 1). The remainder of the samples (n=94) consisted of nestlings and no infections were found in these individuals. Infection rates between adult and juvenile birds differed significantly (χ 2 =43.34, df=1, p<0.001); therefore they will be further considered separately. Considering adult lark sparrows, infection rates in each studied population were as follows: 80.0% in California (20 of 25 samples), 87.5% in Nebraska (21 of 24 samples), 80.8% in Texas (84 of 104 samples), 73.9% in Illinois (17 of 23 samples), and 79.5% in Ohio (62 of 78 samples) (see Table 1). There were no significant differences in the rate of infection among populations (χ 2 =1.42, df=3, p>0.5). Furthermore, in populations that were sampled in all three years from (i.e., Illinois, Texas, and Ohio), there were no significant differences in infection rates among sampling years (χ 2 =5.51, df=2, p>0.05). There was, however, a significant difference in malaria prevalence when the sex of the birds was considered (χ 2 =7.13, df=1, p<0.01), with females having a significantly higher infection rate (88.5%) than males (74.6%). In populations where a substantial number of juvenile birds were sampled, infection rates of juveniles were consistently lower than those detected in adults of the same population (Figure 3). Malaria prevalence in juveniles was 28.0% (7 of 25 samples) in California, 67.5% (27 of 40 samples) in Texas, and 37.0% (17 of 46 samples) in Ohio. In contrast to that observed in adults,

21 14 differences in the rate of infection of juveniles among populations were significant (χ 2 =12.22, df=2, p<0.005). Furthermore, differences between adult and juvenile infection rates were found to be significant in California (χ 2 =13.61, df=1, p<0.005) and Ohio (χ 2 =22.64, df=1, p<0.005), but not in Texas (χ 2 =2.66, df=1, p>0.1). Malaria prevalence was also evaluated across eastern, central and western breeding regions of the lark sparrow. There were no significant differences in the rate of infection by region (χ 2 =0.52, df=2, p>0.5). Each region was also evaluated for differences in prevalence by both age and sex of the birds. In the eastern and western regions, adults were found to have a significantly higher infection rates than juvenile birds (eastern, χ 2 =23.75, df=1, p<0.001 and western, χ 2 =13.61, df=1, p<0.001). In the central region, however, there was no significant difference by age class (χ 2 =3.72, df=1, p>0.05). Differences in adult prevalence by sex also showed variation among regions. In the central region, adult females were found to have a significantly higher rate of infection than males (χ 2 =4.99, df=1, p<0.05) while in the eastern and western regions, no significant differences were found (east, χ 2 =1.45, df=1, p>0.10 and west, χ 2 =1.04, df=1, p>0.25). A total of 137 samples that tested positive for malaria infections were sequenced to obtain the parasite cyt b mtdna sequences. Of those, 88.3% (121 of 137 samples) were identified as Haemoproteus sequences while only 11.7% (16 of 137) were identified as Plasmodium (Table 1). Haemoproteus sequences consisted of two common haplotypes (with haplotype frequencies higher than 20%) and seven unique haplotypes found in single individual lark sparrows (Figure 1 and Table 1). One of the common haplotypes (H1) was found in all sampled breeding populations (n=84) while the other high frequency haplotype (H2) was unique to the Texas population (n=30). Other than H1, all Haemoproteus haplotypes were restricted to single populations. Unique Haemoproteus haplotypes were detected in the Ohio, California, and Texas

22 15 populations. The presence of Plasmodium was rare and found only in California, Texas, and Ohio populations (Table 1). A total of six Plasmodium haplotypes were detected with two being found only in single individual lark sparrows. Furthermore, none of the Plasmodium haplotypes were found in more than one population. There were also differences in the haplotypes found in the different age classes of lark sparrows. In Texas, only one haplotype, H2, was found in the hatch-year birds. In Ohio, Haemoproteus was restricted to the after-hatch-year birds while Plasmodium was restricted to the hatch-year individuals. All malaria haplotypes were subjected to BLAST searches to determine if they were restricted to the lark sparrow or found in other bird hosts (Table 2). The two most prevalent Haemoproteus haplotypes (H1 and H2) did not have any complete matches in the GenBank database (out of 1187 Haemoproteus cytochrome b sequences reported to GenBank as of October 2012). Haplotypes HU2 through HU7 also failed to have any exact matches. Haplotype HU1 was the only Haemoproteus haplotype with an exact match to a sequence detected in the chestnut-sided warbler, Dendroica pensylvanica. While most of the Haemoproteus lineages did not have any exact matches, the opposite was true for Plasmodium haplotypes. Only two haplotypes (PU1 and PU2) did not have any exact matches but the remaining Plasmodium haplotypes (P1 through P4) had matches with sequences from a variety of host species (out of 2742 Plasmodium cytochrome b sequences reported to GenBank as of October 2012; Table 2). Genetic variation was found to be higher in the Plasmodium (p-distance = 0.048; SE = 0.008) compared to the Haemoproteus sequences (p-distance = 0.013; SE = 0.004) and, as expected, was highest between the two genera of malaria parasites (p-distance = 0.086; SE= 0.014). When considering regional differences, average levels of diversity within regions (p-distance = 0.049; SE = 0.008) were not significantly different than that of the entire population (p-distance =

23 ; SE = 0.008), which resulted in relatively low levels of inter-region diversity (p-distance = 0.004; SE = 0.001). The connection of haplotypes into mutational networks using a 95% confidence interval resulted in the characterization of one Haemoproteus network while the Plasmodium haplotypes were divided into three independent networks separated by more than 10 mutational differences (Figure 4). The Haemoproteus network revealed a star shape with the most common lineage (H1) in the center. The network is fairly tight with no more than three intermediate haplotypes being inferred between observed haplotypes. In contrast, the Plasmodium networks were linear networks with a larger number of mutational steps connecting haplotypes. Given the limited number of Plasmodium haplotypes detected across populations, the analysis of malaria genetic structure was restricted to the Haemoproteus genus. Overall, host populations had significant differences in their levels of avian malaria genetic diversity as measured by haplotype and nucleotide diversities (Table 3); however, some of these differences may be due to differences in sample sizes. AMOVAs based on haplotype distances and frequencies both indicated significant levels of genetic diversity among populations, with 46.1% to 51.7% of the total variation being explained by differences among populations within regions (Table 4). Although 9.8% to 14.8% of the variation was assigned to differences among regions, these values were not statistically significant. Pair-wise estimates of population differentiation revealed that the Texas population was significantly different from each of the other four populations studied. The phylogenies for the lark sparrow malaria parasites produced by Maximum Parsimony and Bayesian methods revealed evolutionary relationships among parasite lineages (Figures 5 and 6). Both methods resulted in congruent phylogenetic patterns. The general phylogeny including malaria parasites from multiple avian species showed a clear separation between

24 17 Haemoproteus and Plasmodium sequences, which formed reciprocally monophyletic groups (Figure 5). The Plasmodium haplotypes found in the lark sparrow did not cluster as an independent monophyletic group. Instead sequences were dispersed throughout the tree grouping independently with parasite lineages from other avian species. In contrast, Haemoproteus haplotypes from the lark sparrow tended to cluster into a few independent monophyletic lineages within the general phylogeny. The phylogeny of avian malaria parasites from the lark sparrow allowed for assessment of the phylogeographic distribution of malaria haplotypes across populations (Figure 6). As expected, Plasmodium and Haemoproteus sequences formed clearly distinct monophyletic groups. Interestingly, two major lineages (i.e., major monophyletic groups with high bootstrap support) were identified within each of the Plasmodium and Haemoproteus genera. Only one malaria haplotype from Haemoproteus (haplotype H1) was detected in all populations studied. Although CA, TX, and OH showed all population-specific haplotypes, no major phylogeographic patterns associating major malaria lineages to specific host populations were evident (Figure 6). Furthermore, none of the populations had representative haplotypes from all four major lineages. CA, TX, and OH, however, were the most variable populations, with haplotype representatives from three of the four major malaria lineages. Both IL and NE populations revealed malaria infections with a single haplotype corresponding to the Haemoproteus haplotype H1, which was common to all populations (Figure 4).

25 18 DISCUSSION Results from this study showed that levels of avian malaria prevalence were consistently high across breeding populations of the lark sparrow. The overall prevalence of avian malaria in adult lark sparrows was 80.3% (n=254). When compared to published infection rates for other species, the prevalence across the lark sparrow s range is notably high. For example, in a study of the black-throated blue warbler (Dendroica caerulescens) across its North American breeding range, overall malaria prevalence was 21.8% with infection rate varying from 14.5% to 43.1% by region (Fallon et al. 2006). Similarly, the American redstart (Setophaga ruticilla) was shown to have overall malaria prevalence of 33% on its breeding grounds with prevalence by region varying from 22% to 54% (Durrant et al. 2008). One breeding region of the common yellowthroat (Geothlypis trichas) had malaria prevalence of 78.5%, but overall prevalence was 52.7% and prevalence in the other four breeding regions varied from 19.8% to 64.0% (Pagenkopp et al. 2008). There were no significant differences in prevalence in adult lark sparrows by population or by region. Temporal differences in populations sampled over three sampling years were also not apparent. There was, however, a significant difference in malaria prevalence between adult males and females, with females having higher infection rates. This trend was consistent across regions, though statistical significance was detected only in the central populations. A possible explanation for this general pattern may be related to the fact that in the lark sparrow females do the majority of incubation, potentially leaving them more exposed to vectors and infections. Overall, significant differences in malaria prevalence were found between age classes of host birds, with adults having higher rates of infection than hatch-year juveniles. Population-specific significant differences in prevalence by age class were found in the California and Ohio

26 19 populations. Several factors can potentially contribute to this difference. For example, differences in the rate of infection between adult and juvenile birds could be related to exposure time. The juveniles sampled were at most a few weeks from fledging, so there may not have been much opportunity for exposure to suitable vectors or enough time for the infection to reach the peripheral blood. The location where infections are occurring could also be a major factor. The fact that juveniles were infected indicates that some infections were effectively occurring on the breeding grounds, but it is thus far unknown what proportion of infections occurs on breeding versus wintering grounds. In the Ohio population it appears that only Plasmodium parasites are being transmitted on the breeding grounds as no Haemoproteus parasites were found in hatchyear individuals. This may imply that the common Haemoproteus haplotype that is found across all populations is being transmitted on the lark sparrows common wintering grounds or that infected birds are effectively migrating from other regions/populations. The genetic characterization of avian malaria parasites revealed that the majority of infections occurring in the lark sparrow were found to be with Haemoproteus parasites. The most common haplotype (H1) was found in all five of the breeding populations. The second most prevalent haplotype (H2) was found only in Texas and, in fact, made up the majority of Texas infections (71%). Most Haemoproteus haplotypes appear to be restricted to the lark sparrow, as inferred by BLAST searches, which revealed no exact matches in the GenBank database for the most common haplotypes detected in this study. In fact, only one (HU1) of the nine Haemoproteus haplotypes found in the lark sparrow has also been found in other avian species. Furthermore, many of the Haemoproteus haplotypes found in the lark sparrow were found to group together in a general phylogeny including malaria parasites from multiple avian species (Figure 5). This suggests that at least some Haemoproteus lineages may be host-specific.

27 20 In contrast, Plasmodium infections were rare and found only in Ohio, Texas, and California. The two most prevalent Plasmodium haplotypes were found in only four individuals each. When Plasmodium sequences were subjected to BLAST searches, four of the six haplotypes were found to have exact matches with sequences from other host species. In fact, the general phylogeny of avian malaria parasites revealed that Plasmodium haplotypes found in the lark sparrow were dispersed throughout the phylogenetic tree, clustering with malaria parasites/lineages detected in other avian hosts. These results suggest that, compared to Heaemoproteus, Plasmodium lineages are more generalist, able to infect a wide range of host bird species. This is consistent with previous studies, which have suggested that Haemoproteus infections are more host-specific than Plasmodium (Beadell et al. 2004). Differences in host specificity are likely related to differences in resistance mechanisms and to the specific vectors involved in transmitting Plasmodium versus Haemoproteus parasites. Despite smaller sample size and distribution, Plasmodium haplotypes had more genetic variation than Haemoproteus haplotypes. Overall, Plasmodium had higher levels of diversity in terms of mean number of pairwise differences, haplotype diversity, and nucleotide diversity (Table 3). This is also consistent with the information from the BLAST searches, indicating that Haemoproteus parasites are more host-specific (and thus show less variation) while Plasmodium parasites tend to be more generalists. These major genetic differences between the two parasite genera, consistent with previous studies, illustrate the importance of treating Haemoproteus and Plasmodium independently to have a proper understanding of the population dynamics of avian malaria.

28 21 Significant levels of genetic diversity in Haemoproteus parasites were found among populations, as evidenced by the results of the AMOVA, which showed that 46-52% of the total genetic variation was explained by differences among populations. However, there were not significant differences between lark sparrows breeding regions. This result is likely related to the high variation in haplotype frequencies detected among populations and is also supported by the distribution of population-specific haplotypes as reported in the phylogenetic analysis (Figure 6). The significant genetic structuring of the Haemoproteus parasites across host populations may be driven by the specific dynamics of the host population and/or the vector distribution and abundance in each geographically distinct population. The high proportion of host populationspecific variation detected in this study may therefore prevent or mask potential regional effects (Fallon et al. 2006). Significant differentiation in both morphological and genetic characteristics has been previously reported among western, central, and eastern breeding populations of the lark sparrow (Ross 2011), suggesting that potential geographic isolation between migratory corridors may play an important role in promoting demographic independence among lark sparrow populations. The lack of concurrent differentiation of malaria parasites by region indicates that the population dynamics of the parasites may be decoupled from the regional population structure of lark sparrows. The demographic dynamics of avian malaria may instead be driven at smaller spatial scales; e.g., at the population level of avian hosts. This is evidenced by the relatively large number of population-specific haplotypes of both Haemoproteus and Plasmodium parasites. The presence of a common Haemoproteus haplotype (H1) distributed across all sampled breeding populations suggests, however, a distinct demographic history from that revealed by the Plasmodium haplotype distribution, likely driven by the specificity of Haemoproteus lineages

29 22 associated with the lark sparrow. This is also reflected by the star shape of the Haemoproteus haplotype network, characteristic of rapid demographic expansions, in contrast to the linear networks separating haplotypes with multiple mutational steps detected in Plasmodium. This study shows that avian malaria prevalence across the breeding range of the lark sparrow is considerably high compared to previously reported rates on other avian species (with up to 81% of the samples tested being positive for avian malaria). The detection of malaria parasites in both adult and juveniles of the lark sparrow and the differences in their prevalence revealed that malaria infections are effectively occurring on the lark sparrow breeding grounds, but suggest that the dynamics of infection vary among populations and, potentially, between breeding and overwintering grounds. Furthermore, levels of genetic diversity and genetic structure of malaria parasites with respect to the host populations revealed distinct patterns for Haemoproteus and Plasmodium, likely as a result of their distinct life histories and transmission vectors. In contrast to the regional patterns of genetic differentiation detected for lark sparrow populations, Haemoproteus showed high levels of population-specific variation with no regional differentiation, which suggest that the population dynamics of the parasites may be driven by evolutionary processes operating at small spatial scales, at the level of host populations. These results highlight the potential effects of host population structure on the demographic and evolutionary dynamics of parasites, an essential issue in understanding the potential effects of wildlife diseases on avian host species.

30 23 REFERENCES Atkinson CT, van Riper C, III (1991) Pathogenicity and epizootiology of avian hematozoa: Plasmodium, Leucocytozoon, and Haemoproteus. In: Loye JL, Zuk M (eds) Bird-Parasite Interactions. Ecology, Evolution, and Behavior. Oxford University Press, New York, pp Atkinson CT, Woods KL, Dusek RJ, Sileo LS, Iko WM (1995) Wildlife diseases and conservation in Hawaii: pathogenicity of avian malaria (Plasmodium relictum) in experimentally infected Iiwi (Vestiaria coccinea). Parasitology 111: S59-S69 Atkinson CT (1999) Hemosporidiosis. In: Ciganovich EA (ed) Field Manual of Wildlife Diseases: General Field Procedures and Diseases of Birds. U.S. Department of the Interior, U.S. Geological Survey, pp Atkinson CT, Dusek RJ, Woods KL, Iko WM (2000) Pathogenicity of avian malaria in experimentally-infected Hawaii Amakihi. J. Wildl. Dis. 36: Baepler DH (1968) In: Bent, AC (ed) Life histories of North American cardinals, grosbeaks, buntings, towhees, finches, sparrows, and allies. U. S. National Museum Bulletin 237: Beadell JS, Gering E, Austin J, Dumbacher JP, Peirce MA, Pratt TK, Atkinson CT, Fleischer RC (2004) Prevalence and differential host-specificity of two avian blood parasite genera in the Australo-Papuan region. Mol. Ecol. 13: Beadell JS, Fleischer RC (2005) A restriction enzyme-based assay to distinguish between avian hemosporidians. J. Parasitol. 91: Beadell JS, Ishtiaq F, Covas R, Melo M, Warren BH, Atkinson CT, Bensch S, Graves GR, Jhala YV, Peirce MA, Rahmani AR, Fonseca DM, and Fleischer RC (2006) Global phylogeographic limits of Hawaii s avian malaria. Proc. R. Soc. London, Ser. B 273: Bensch S, Stjernman M, Hasselquist D, Ostman O, Hansson B, Westerdahl H, Pinheiro RT (2000) Host specificity in avian blood parasites: a study of Plasmodium and Haemoproteus mitochondrial DNA amplified from birds. Proc. R. Soc. London, Ser. B 267: Bensch S, Waldenstrom J, Jonzen N, Westerdahl H, Hansson B, Sejberg D, Hasselquist D (2007) Temporal dynamics and diversity of avian malaria parasites in a single host species. J. Anim. Ecol. 76: Clement M, Posada D, Crandall K (2000) TCS: a computer program to estimate gene genealogies. Mol. Ecol. 9:

31 24 Cranfield MR, Shaw ML, Beall FB, Skjoldager ML (1990) Review and update of avian malaria in African penguins (Spheniscus demersus). Proceedings of the American Association of Zoo Veterinarians 21: Cranfield MR, Beall FB, Skjoldager MT, Ialeggio DM (1991) Avian malaria. Spheniscus Penguin Newsletter 4: 5-7 Dale S, Kruszewicz A, Slagsvold T (1996) Effects of blood parasites on sexual and natural selection in the pied flycatcher. J. Zool. 238: Drummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7: 214 Durrant KL, Marra PP, Fallon SM, Colbeck GJ, Gibbs HL, Hobson KA, Norris DR, Bernik B, Lloyd VL, Fleischer RC (2008) Parasite assemblages distinguish populations of a migratory passerine on its breeding grounds. J. Zool. 274: Escalante AA, Freeland DE, Collins WE, Lal AA (1998) The evolution of primate malaria parasites based on the gene encoding cytochrome b from the linear mitochondrial genome. Proc. Natl. Acad. Sci. U.S.A. 95: Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol. Bioinf. Online 1: Fallon SM, Bermingham E, Ricklefs RE (2003) Island and taxon effects in parasitism revisited: avian malaria in the Lesser Antilles. Evolution 57: Fallon SM, Bermingham E, Ricklefs RE (2005) Host specialization and geographic localization of avian malaria parasites: a regional analysis in the Lesser Antilles. Am. Nat. 165: Fallon SM, Fleischer RC, Graves GR (2006) Malarial parasites as geographical markers in migratory birds? Biol. Lett. 2: Felsenstein J (2004) Inferring Phylogenies. Sinauer Associates, Inc., Sunderland, Massachusetts Fix AS, Waterhouse C, Greiner EC, Stoskopf MK (1988) Plasmodium relictum as a cause of avian malaria in wild-caught Magellanic penguins (Spheniscus magellanicus). J. Wildl. Dis. 24: Gibb CE, Jones J, Girvan MK, Barg JJ, Robertson RJ (2005) Geographic variation in prevalence and parasitemia of Haemoproteus paruli in the cerulean warbler (Dendroica cerulea). Can. J. Zool. 83: Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:

32 25 Harpending HC (1994) Signature of ancient population growth in a low-resolution mitochondrial DNA mismatch distribution. Hum. Biol. 66: Hill AVS, Allsopp CEM, Kwiatkowski D, Anstey NM, Twumasi P, Rowe PA, Bennett S, Brewster D, McMichael AJ, Greenwood BM (1991) Common West African HLA antigens are associated with protection from severe malaria. Nature 352: Ishak HD, Dumbacher JP, Anderson NL, Keane JJ, Valkiunas G, Haig SM, Tell LA, Sehgal RNM (2008) Blood parasites in owls with conservation implications for the spotted owl (Strix occidentalis). PloS ONE 3: e2304 Jarvi SI, Schultz JJ, Atkinson CT (2002) PCR diagnostics underestimate the prevalence of avian malaria (Plasmodium relictum) in experimentally-infected passerines. J. Parasitol. 88: Jarvi SI, Farias MEM, Baker H, Freifeld HB, Baker PE, Van Gelder E, Massey JG, Atkinson CT (2003) Detection of avian malaria (Plasmodium spp.) in native land birds of American Samoa. Conserv. Genet. 4: Kilpatrick AM, LaPointe DA, Atkinson CT, Woodworth BL, Lease JK, Reiter ME, Gross K (2006) Effects of chronic avian malaria (Plasmodium relictum) infection on reproductive success of Hawaii amakihi (Hemignathus virens). Auk 123: Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23: Levin II, Outlaw DC, Vargas FH, Parker PG (2009) Plasmodium blood parasite found in endangered Galapagos penguins (Spheniscus mendiculus). Biol. Conserv. 142: Longmire JL, Lewis A, Meincke LJ, Hildebrand CE (1986) A new and rapid procedure for the isolation of ultra-high molecular weight eukaryotic DNA. Fed. Proc. 45: 1849 Manwell RD (1934) The duration of malaria infection in birds. Am. J. Hyg. 19: Martin JW, Parrish JR (2000) Lark Sparrow (Chondestes grammacus). In: Poole A, Gill F (eds) The Birds of North America, no Academy of Natural Sciences, Philadelphia, and American Ornithologists Union, Washington, D.C. Massey JG, Graczyk TK, Cranfield MR (1996) Characteristics of naturally acquired Plasmodium relictum capistranoae infections in naïve Hawaiian crows (Corvus hawaiiensis) in Hawaii. J. Parasitol. 82: Miller AH (1941) A review of centers of differentiation for birds in the western great basin region. Condor 43:

33 26 Moller AP, Nielsen JT (2007) Malaria and risk of predation: a comparative study of birds. Ecology 88: Oberholser HC (1932) Descriptions of new birds from Oregon, chiefly from the Warner Valley region. Sci. Pub. Cleveland Mus. Nat. Hist. 4: 1-12 Outlaw DC, Ricklefs RE (2009) On the phylogenetic relationships of haemosporidian parasites from raptorial birds (Falconiformes and Strigiformes). J. Parasitol. 95: Pagenkopp KM, Klicka J, Durrant KL, Garvin JC, Fleischer RC (2008) Geographic variation in malarial parasite lineages in the common yellowthroat (Geothlypis trichas). Conserv. Genet. 9: Posada D (2008) jmodeltest: Phylogenetic Model Averaging. Mol. Biol. Evol. 25: Ricklefs RE, Fallon SM (2002) Diversification and host switching in avian malaria parasites. Proc. R. Soc. London, Ser. B 269: Ricklefs RE, Fallon SM, Bermingham E (2004) Evolutionary relationships, cospeciation, and host switching in avian malaria parasites. Syst. Biol. 53: Ricklefs RE, Swanson BL, Fallon SM, Martinez-Abrain A, Scheuerlein A, Gray J, Latta SC (2005) Community relationships of avian malaria parasites in southern Missouri. Ecol. Monogr. 75: Ricklefs RE, Outlaw DC (2010) A molecular clock for malaria parasites. Science 329: Rogers AR, Harpending H (1992) Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9: Ross, JD (2011) The evolutionary history, demographic independence and conservation status of two North American prairie bird species: the greater prairie chicken and the lark sparrow. Ph.D. dissertation, Bowling Green (OH): Bowling Green State University Sambrook J, Fritsch EF, Maniatis T (1989) Molecular Cloning: A Laboratory Manual, 2 nd edition. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York Sauer JR, Hines JE, Fallon J (2008) The North American Breeding Bird Survey, Results and Analysis Version USGS Patuxent Wildlife Research Center, Laurel, Maryland Schneider S, Roessli D, Excoffier L (2000) Arlequin, Version 2.000: a software for population genetics data analysis. Genetics and Biometry Laboratory, University of Geneva, Switzerland Slatkin M, Hudson RR (1991) Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations. Genetics 129:

34 27 Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24: Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG (1997) The ClustalX windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 24: Valkiunas G (2005) Avian malaria parasites and other haemosporidia. CRC Press, Boca Raton, Florida van Riper C, III, van Riper SG, Goff ML, Laird M (1986) The epizootiology and ecological significance of malaria in Hawaiian land birds. Ecol. Monogr. 56: Waldenstrom J, Bensch S, Kiboi S, Hasselquist D, Ottosson U (2002) Cross-species infection of blood parasites between resident and migratory songbirds in Africa. Mol. Ecol. 11: Weatherhead PJ (1990) Secondary sexual traits, parasites, and polygyny in red-winged blackbirds, Agelaius pheoniceus. Behav. Ecol. 1: Whiteman NK, Goodman SJ, Sinclair BJ, Walsh T, Cunningham AA, Kramer LD, Parker PG (2005) Establishment of the avian disease vector Culex quinquefasciatus Say, 1823 (Diptera: Culicidae) on the Galapagos Islands, Ecuador. Ibis 147: Wood MJ, Cosgrove CL, Wilkin TA, Knowles SCL, Day KP, Sheldon BC (2007) Withinpopulation variation in prevalence and lineage distribution of avian malaria in blue tits, Cyanistes caeruleus. Mol. Ecol. 16:

35 28 Table 1. Avian malaria prevalence in the lark sparrow estimated by population (identified by state abbreviations: CA, NE, TX, IL, OH) and by age class (after hatch year, AHY; hatch year, HY). N refers to sample size, Ninf refers to number of infected samples, Nseq identifies the number of individuals sequenced in each population, and Nhap indicates the number of haplotypes found in each population. Sequences and haplotypes are reported independently for Haemoproteus (Haem) and Plasmodium (Plas). Population Age Class N Ninf Prevalence Nseq Nhap Total Haem Plas Total Haem Plas CA AHY HY NE AHY HY TX AHY HY IL AHY HY OH AHY HY Overall AHY HY Total

36 29 Table 2. Lark sparrow malaria haplotypes with BLAST matches found in GenBank. GenBank accession number (GenBank #), host species in which the matching parasite sequence was found, host family, and reference study (source) are reported. Lineage GenBank # Host species Host family Source H1 N/A Chondestes grammacus Emberizidae This study H2 N/A Chondestes grammacus Emberizidae This study HU1 AF Dendroica pensylvanica Parulidae Ricklefs and Fallon 2002 HU2 N/A Chondestes grammacus Emberizidae This study HU3 N/A Chondestes grammacus Emberizidae This study HU4 N/A Chondestes grammacus Emberizidae This study HU5 N/A Chondestes grammacus Emberizidae This study HU6 N/A Chondestes grammacus Emberizidae This study HU7 N/A Chondestes grammacus Emberizidae This study P1 DQ Carpodacus mexicanus Fringillidae Beadell et al DQ Molothrus ater Icteridae Beadell et al P2 AF Passerina cyanea Cardinalidae Ricklefs and Fallon 2002 AY Spheniscus demersus Spheniscidae Beadell and Fleischer 2005 AY Coereba flaveloa Coerebidae Fallon et al EU Strix varia Strigidae Ishak et al JN Sylvia atricapilla Sylviidae Perez-Rodriguez and Perez-Tris GQ Turdus fumigatus Turdidae Outlaw and Ricklefs 2009 GQ Geotrygon montana Columbidae Outlaw and Ricklefs 2009 GQ Mniotilta varia Parulidae Outlaw and Ricklefs 2009 P3 AY Dendroica coronata Parulidae Ricklefs et al HM Catharus ustulatus Turdidae Ricklefs and Outlaw 2010 P4 AY Vireo solitarius Vireonidae Ricklefs et al PU1 N/A Chondestes grammacus Emberizidae This study PU2 N/A Chondestes grammacus Emberizidae This study NOTE: N/A indicates haplotypes with no identical match in the GenBank database as of October 2012.

37 30 Table 3. Genetic diversity estimates for malaria parasites (Haemoproteus and Plasmodium) by breeding population of lark sparrow. Populations are listed by state abbreviation codes. Sample sizes refer to the number of sequences of each specific parasite detected in each population (Table 1). Estimates include mean values ± standard deviations. Haemoproteus Plasmodium Population Sample size Number of haplotypes Mean number pairwise differences Haplotype diversity Nucleotide diversity CA ± ± ± NE TX ± ± ± IL OH ± ± ± Overall ± ± ± CA NE TX IL OH ± ± ± 0.02 Overall ± ± ± 0.02

38 31 Table 4. Hierarchical analysis of molecular variance (AMOVA) based on Haemoproteus parasite haplotype sequences. AMOVAs were performed independently using genetic distances based on pairwise differences and haplotype frequencies. Source of variation Pairwise difference Variance component Percent variation Haplotype frequency Variance component Percent variation Among regions Among populations within regions Among individuals within populations * P < * * * *

39 32 Figure 1. Breeding range of the lark sparrow as determined by the Breeding Bird Survey Summer Distribution Map, (Sauer et al. 2008). Color shading on map indicates number of birds counted (scale represented in box). Sampling sites (CA 1, CA 2, NE, TX 1, TX 2, IL, and OH) are located within three breeding regions (west, central, east) of the lark sparrow associated with major migratory flyways (dashed lines indicate estimated migratory divides). Pie charts indicate haplotype frequencies for malaria parasites found in each population.

40 33 Figure 2. PCR products from malaria screening on a 1% agarose gel stained with ethidium bromide and visualized under UV light. Lane 1 represents a sample negative for malaria. Lane 2 represents a sample positive for malaria infection determined by the presence of a band at approximately 380 base pairs (bp). Haemoproteus and Plasmodium characterizations were based on DNA sequence analyses of amplified products. Lane 3 is a standard of size (Promega 1kb DNA ladder) with relevant band sizes indicated.

41 34 Figure 3. Avian malaria prevalence in adult and juvenile lark sparrows. Significant differences between juvenile and adult malaria prevalence were found in the California and Ohio populations with adults having a significantly higher rate of infection (*** p<0.05).