Genetic Variation of the American Crow (Corvus Brachyrhynchos) and the Emergence of West Nile Virus

MISSING IMAGE

Material Information

Title:
Genetic Variation of the American Crow (Corvus Brachyrhynchos) and the Emergence of West Nile Virus
Physical Description:
1 online resource (181 p.)
Language:
english
Creator:
Verdugo Reyes, Claudio Marcelo
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Veterinary Medical Sciences, Veterinary Medicine
Committee Chair:
Long, Maureen T
Committee Members:
Hernandez, Jorge A
Kimball, Rebecca T
Heard, Darryl J
Wayne, Marta L

Subjects

Subjects / Keywords:
corvus -- crow -- diseases -- microsatellites -- wnv
Veterinary Medicine -- Dissertations, Academic -- UF
Genre:
Veterinary Medical Sciences thesis, Ph.D.
Electronic Thesis or Dissertation
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )

Notes

Abstract:
The objective of this study was to assess the impact ofthe emergence of West Nile virus (WNV) on the genetic diversity of the Americancrow (Corvus brachyrhynchos). Thisavian species has been the most negatively affected host species resulting inestimated population decline up to 60% since the arrival of the virus. Thus, itis hypothesized that the genetic diversityand genetic structure of the crow has been modulated by WNV in a shorttimescale. Changes on mortality rate and seroprevalence to WNV inavian species were extensively reviewed as an indication of heterogeneity of hostsusceptibility across time, geographic areas, and host species. A consistentincrement of the seroprevalence was detected in eight species, including C. brachyrhynchos, although changes ontime were at different magnitudes. C.brachyrhynchos showed also a consistent decline in the mortality rate.  Thirty two polymorphic microsatellite markers distributedin six multiplex panels were developed and characterized for genetic analysis.The temporal analysis detected a significantly lower allelic richness andheterozygosity after four years of WNV arrival. Reduction of allelic diversity wasfaster than heterozygosity, a measure of recent population bottleneck. Thegenetic diversity was rapidly recovered by the year 2002 showing evidence of a populationexpansion by immigrants. Finally, a consistent pattern of higher heterozygositylevels towards resistance to WNV was detected. However, these differences ininfection status were not significant, neither by a local effect nor by overallgenetic diversity. This study provides molecular evidence for a stronggenetic impact induced by severe mortality events attributable to WNV since1999 in the American crow. These changes occurred in a short time after the introductionof the virus. Relatively slow changes in the seroprevalence and mortality ratesby WNV may indicate the slow process of adaptation of the American crow to thisvirulent pathogen. Thus, WNV may be exerting considerable selection force todrive genetic evolution of the crow population but in an ecologically relevanttime scale. These results suggest a lack of specific genetic adaptation of the Americancrow towards resistance to WNV may be due to a constant immigration ofsusceptible individuals.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2012.
Local:
Adviser: Long, Maureen T.
Statement of Responsibility:
by Claudio Marcelo Verdugo Reyes.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2012
System ID:
UFE0044881:00001


This item has the following downloads:


Full Text

PAGE 1

1 GENETIC VARIATION OF THE AMERICAN CROW ( CORVUS BRACHYRHYNCHOS ) AND THE EMERGENCE OF WEST NILE VIRUS By CLAUDIO MARCELO VERDUGO REYES A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012

PAGE 2

2 2012 Claudio Marcelo Verdugo Reyes

PAGE 3

3 For Catherina and Olivia

PAGE 4

4 ACKNOWLEDGMENTS I want to thank my advisor, Maureen Long for allowing me to develop this idea with her unconditional support throughout this entire project. I also thank the members of my committee Marta Wayne, Rebecca Kimball, Jorge Hernandez, and Darryl Heard for their invaluable sup port, enthusiasm, and excellent advices. I want to thank Darryl Heard for the personal support to conceive the idea on my graduate studies. T he members of the Long lab, including Melisa Burgeois, Katie Maldonado, Junjie Jet Liu, Dhani Prakoso, Gretchen Henry, Nanny Wenzlow, Carmen Glofelty, Nikea Aytes, a nd, especially, Sally Beachboar d for their endless disposition for help, talk, and discuss. Thanks to Laura Kramer from Wadsworth Center, New York State Department of Health, Paul Sweet from American Mus eum of Natural History, Irby Lovette and Charles Dardia from Cornell University Museum of Vertebrates Jeremy Kirchman from New York State Museum for providing all the biological samples. I also thank Ginger Clark and Angela Gomez from the UF Interdiscipli nary Center for Biotechnology and Research, and Claudio Fuentes from Oregon State University for their technical support during the development of this project. I thank the financial support for this work provided by Comision Nacional de Ciencia and Tecnol ogia (CONICYT Chile), Bernice Barbour Foundation, the University of Florida College of Veterinary Medicine, and Universidad Austral de Chile. Finally, I thank my family and my friends for have encourag ed me fulfilling this project.

PAGE 5

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 8 LIST OF FIGU RES ........................................................................................................ 10 ABSTRACT ................................................................................................................... 12 CHAPTER 1 EVOLUTIONARY GENETICS OF INFECTIOUS DISEASES IN NATURAL HOST POPULATIONS ........................................................................................... 14 Introduction ............................................................................................................. 14 The Role of Genetic Diversity in Disease Susceptibility .......................................... 15 Neutral Genetic Variation and Infectious Diseases ................................................. 17 Population Bottlenecks and Infectious Diseases .............................................. 20 Inbreeding and Infectious Diseases ................................................................. 24 Adaptive Genetic Variation and Infectious Diseases .............................................. 25 Evidence for Toler ance on Natural Populations ............................................... 26 Genetic Variation at the Major Histocompatibility Complex (MHC) ................... 27 Genetic Variation at Other ImmuneRelated Genes ......................................... 33 Conclusions ............................................................................................................ 36 2 DISSIMILAR CHA NGES ON SEROPREVLANCE AND MORTALITY RATES IN AVIAN SPECIES AFFECTED BY WEST NILE VIRUS IN UNITED STATES ......... 45 Introduction ............................................................................................................. 45 Material and Methods ............................................................................................. 47 Database Search and Dataset Construction .................................................... 47 Data Analysis ................................................................................................... 49 Results .................................................................................................................... 51 Literature Search Analysis ................................................................................ 51 Overall Analysis ................................................................................................ 53 Trend Analysis by Species ............................................................................... 54 Discussion .............................................................................................................. 56 3 MULTIPLEXED MICROSATELLITE LOCI IN AMERICAN CROW (CORVUS BRACHYRHYNCHOS): A SEVERELY AFFECTED NATURAL HOST OF WEST NILE VIRUS ............................................................................................................ 73 Introduction ............................................................................................................. 73 Materials and Methods ............................................................................................ 75 SpeciesSpecific Markers ................................................................................. 75

PAGE 6

6 Cross Species Amplification ............................................................................. 76 Microsatellite Genotyping ................................................................................. 76 Da ta Analysis ................................................................................................... 77 Species differentiation ...................................................................................... 77 Results .................................................................................................................... 80 Microsatellites development ............................................................................. 80 Marker Genotyping ........................................................................................... 80 Species Di fferentiation ..................................................................................... 81 Discussion .............................................................................................................. 82 4 EFFECT OF WEST NILE VIRUS ON THE GENETIC VARIATION IN THE AMERICAN CROW (CORVUS BRACHYRHYNCHOS) .......................................... 98 Introduction ............................................................................................................. 98 Material and Methods ........................................................................................... 101 Avian Samples ............................................................................................... 101 DNA Extraction and Microsatellite Genotyping ............................................... 101 Data Analysis ................................................................................................. 102 General statistics ..................................................................................... 102 Temporal changes on the genetic diversity .............................................. 102 Genetic bottleneck test ............................................................................ 104 Population structure analysis ................................................................... 104 Immigration test ....................................................................................... 105 Results .................................................................................................................. 105 Summary Statistics ......................................................................................... 105 Temporal Effect on Genetic Diversity ............................................................. 106 Genetic bottleneck analysis ............................................................................ 107 Population Structure Analysis ......................................................................... 107 Immigration test .............................................................................................. 108 Discussion ............................................................................................................ 108 Conclusions .......................................................................................................... 113 5 RESISTANCE TO WEST NILE VIRUS INFECTION AND INDIVIDUAL GENETIC VARIABILITY IN THE AMERICAN CROW .......................................... 125 Introduction ........................................................................................................... 125 Material and Methods ........................................................................................... 126 Avian Samples ............................................................................................... 126 DNA Extraction and Microsatellite Genotyping ............................................... 127 Estimates of Genetic Diversity ........................................................................ 127 Statistical Analysis .......................................................................................... 128 Results .................................................................................................................. 128 Discussion ............................................................................................................ 129 6 CONCLUSIONS ................................................................................................... 141 APPENDIX

PAGE 7

7 A SUPPLEMENTARY DATA FOR CHAPTER 2 ...................................................... 144 B SUPPLEMENTARY DATA FOR CHAPTER 3 ...................................................... 155 REFERENCES ............................................................................................................ 164 BIOGRAPHICAL SKETCH .......................................................................................... 181

PAGE 8

8 L IST OF TABLES Table page 1 1 Summary of diseasemediated population decline in wildlife. ............................. 38 1 2 Summary of studies on genetic variation at neutral markers in natural populations. ........................................................................................................ 39 1 3 Summary of association studies on genetic variation at MHC and infectious diseases in wildlife. ............................................................................................. 40 1 4 Species that have evidenced reduced MHC variability with no association studies available. ................................................................................................ 42 2 1 Summarize of literature search for seroprevalencebased and mortality ratebased articles. .................................................................................................... 60 2 2 Description of the taxonomic data extracted from the selected references articles. ............................................................................................................... 61 2 3 Odds ratios and confidence interval for descriptive analysis of data collected from seroprevalence studies. .............................................................................. 62 2 4 Odds ratios and confidence interval of avian orders from data collected of mortality rate studies.. ........................................................................................ 63 2 5 Odds ratios and confidence interval of avian families within the respective order from the data collected of seroprevalence studies.. .................................. 64 2 6 Odds ratios and confidence interval of avian families within the respective order from the data collected of mortality rate studies.. ...................................... 65 2 7 Linear regression and logistic regression estimates and P values for species selected from seroprevalence articles. ............................................................... 66 2 8 Linear regression and logistic regression estimates and P values for species selected from mortality rate articles. ................................................................... 67 3 1 Sequences of Corvus brachyrhynchos and Corvus ossifragus used for the development of primers and probes. .................................................................. 86 3 2 Descriptive summary of the microsatellite loci identified in 143,477 reads from the 454 shotgun library of Corvus brachyrhynchos ................................... 87 3 3 Species specific polymorphic microsatellite markers developed for Corvus brachyrhynchos. ................................................................................................. 88

PAGE 9

9 3 4 Cross species polymorphic microsatellite markers used for Corvus brachyrhynchos. ................................................................................................. 89 3 5 Multiplex panels of 32 microsatellite loci developed from the DNA of 30 individual Corvus brachyrhynchos ...................................................................... 90 3 6 Polymorphism characteristics of 32 microsatellite loci developed from the DNA of 41 individual Corvus brachyrhynchos from New York State. .................. 92 3 7 Polymorphism characteristics of 32 microsatellite loci developed from the DNA of nine individual Corvus brachyrhynchos from Florida. ............................. 93 3 8 Polymorphism characteristics of 32 microsatellite loci developed from the DNA of 50 individual Corvus brachyrhynchos .................................................... 94 3 9 Mean Ct values obtained from the real time PCR on 100 ng of Corvus brachyrhynchos and Corvus ossifragus DNA samples. ...................................... 95 4 1 Comparison of genetic diversity statistics between preand post outbreak year cohorts samples of the State of New York. ............................................... 114 4 2 Comparison of individual heterozygosity estimates for preWNV and post WNV American crow samples. ......................................................................... 115 4 3 P values of population bottleneck tests for heterozygosity excess after correction for multiple comparisons. ................................................................. 116 4 4 PT values and P values estimated among samples of each year cohort. ....... 117 5 1 Correlation coefficients (r) for estimates of individual heterozygosity asse ssed in American crows (n=143). ............................................................. 134 5 2 Test for effects of individual heterozygosity and WNV infection ....................... 135

PAGE 10

10 LIST OF FIGURES Figure page 1 1 Allele frequency changes driven by coevolution. ............................................... 44 2 1 Distribution of articles selected by year of publication and by y ear of data collection. .. .......................................................................................................... 68 2 2 Distribution of data collected from selected articles by order of avian species sampled. ............................................................................................................. 69 2 3 Distribution of species reported in articles selected for analysis. ........................ 70 2 4 Odds ratio and confidence intervals for seroprevalence and mortality rate by avian order. ......................................................................................................... 71 2 5 Odds ratio and confidence intervals for seroprevalence and mortality rate by family within the Passeriformes order. ................................................................ 72 3 1 Alignment of nucleotide sequences of the COI segment in Corvus brachyrhynchos and Corvus ossifragus .............................................................. 96 3 2 Distribution of repeat motif sequences of identified microsatellite loci and the subset of loci with flanking region for priming ..................................................... 97 4 1 Map of the sample locations at county level of the American crows collected from New York State, United States. ................................................................ 118 4 2 Temporal estimates of the mean private allele richness (PAR) by year in American crows. Bars denote standard error. .................................................. 119 4 3 Temporal comparison of estimates of inbreeding coefficient (FIS). Bars denote standard error. ...................................................................................... 120 4 4 Temporal comparison of estimates of internal relatedness (IR). Bars denote standard error. .................................................................................................. 121 4 5 Principal coordinates analysis (PCA) f or clustering of American crows. Each diamond represents the population medians of each geographic location. ...... 122 4 6 Pairwise popPT) plotted against geographical distance in the American crow. ......................................................................... 123 4 7 Frequency of significant (P< 0.05) first generation migrants of American crows distributed by year cohort. ...................................................................... 124 5 1 Individual heterozygosity estimate (SHE) in American crows grouped by WNV infection status. ................................................................................................. 136

PAGE 11

11 5 2 Internal relatedness (IR) in American crows grouped by WNV infection statu s. ............................................................................................................... 137 5 3 The distribution of standardized heterozygosity and individual internal relatedness in American crow typed at 36 microsatellite loci. ........................... 138 5 4 Individual internal relatedness of American crows used in this study. .............. 139 5 5 Aligned sequences of 757 bases of the exon 1 to 3 of 5 3 OAS from 3 Fish crows ( Corvus ossifragus ) and 21 American crows ( C. brachyrhynchos ) ......... 140

PAGE 12

12 Abstract of Dissertation Presented to the Graduate School of T he University o f Florida in Partial Fulfillment of the Requirements for the Degree of Doctor o f Philosophy GENETIC VARIATION OF THE AMERICAN CROW ( CORVUS BRACHYRHYNCHOS ) AND THE EMERGENCE OF WEST NILE VIRUS By Claudio Marcelo Verdugo Reyes December 2012 Chair: Maureen T. Long Major: Veterinary Medical Sciences The objective of this study was to assess the impact of the emergence of West Nile virus (WNV) on the genetic diversity of the American crow ( Corvus brachyrhynchos ) This avian species has been the most negatively affected host species resulting in estimated population decline up to 60% since the arrival of the virus. Thus, i t is hypothesized that the genetic diversity and genetic structure of the crow has been modulated by WNV in a short timescale. Changes on mortality rate and seroprevalence to WNV in avian species were extensively rev iewed as an indicat ion of heterogeneity of host susceptibility across time, geographic areas and host species A consistent increment of the seroprevalence was detected in eight species, including C. brachyrhynchos although changes on time were at different magnitudes. C. brachyrhynchos showed also a consistent decline in the mortality rate. T hirty two polymorphic microsatellite markers distributed in six multiplex panels were developed and characterized for genetic analysis The temporal anal ysis detected a significantly lower allelic richness and heterozygosity after four years of WNV arrival. R eduction of allelic diversity was faster than heterozygosity, a measure o f recent

PAGE 13

13 population bottleneck. The genetic diversity was rapidly recovered b y the year 2002 showing evidence of a population expansion by im migrants Finally, a consistent pattern of higher heterozygosity levels towards resistance to WNV was detected. However, the se differences in infection status were not significant, neither by a local effect nor by overall genetic diversity This study provides molecular evidence for a strong genetic impact induced by severe mortality events attributable to WNV since 1999 in the A merican crow These changes occurred in a short time after the int roduction of the virus. Relatively slow changes in the seroprevalence and mortality rates by WNV may indicate the slow process of adaptation of the American crow to this virulent pathogen. Thus, WNV may be exert ing considerable selection force to drive genetic evolution of the crow population but in an ecologically relevant time scale. Th ese result s suggest a lack of specific genetic adaptation of the A merican crow towards resistance to WNV may be due to a constant immigration of susceptible individuals

PAGE 14

14 CHAPTER 1 EVOLUTIONARY GENETIC S OF INFECTIOUS DISE ASES IN NATURAL HOST POPULATIONS Introduction Pathogens exert profound effects on natural host population dynamics. For instance, infectious diseases have been implicated, directly or indirectly, in drastic and rapid declines of several wildlife species during the last two decades (Ladeau et al. 2007; Robinson et al. 2010) including the extinction of Partula turgida, a Polynesian tree snail by the microsporidian Steinhausia sp. (Cunningham et al. 1998), and the sharpsnouted day frog ( Taudactylus acutirostris ) by the fungi Batrachochytrium dendrobatidi s (Schloegel et al. 2006). The consequences of diseases on natural host populations are not limited to changes on the demographics of the affected species, but also may be evident on shaping the genetic composition of the population redefining the defense mechanisms of the host. However, the strength of the relationship between disease and genetic effects may be variable having bidirectional consequences beneficial or detrimental, on host diversity. Neutral loci, such as noncoding DNA regions, are under w eak selection and may be affected as a consequence of demographic changes, decreasing in processes of population reduction (e.g. bottlenecks) or increasing in population expansions. Adaptive loci such as immunerelated genes, are subject to different intensities of natural selection and genetic variation may not be only negatively affected (decrease genetic variation) but also may be maintained or enhanced as raw material for coevolutionary dynamics between the pathogen and the host population (Jeffer y et al. 2000). The aim of this review is to assess empirical studies testing for theoretical predictions of different host pathogen evolutionary dynamics affecting the genetic

PAGE 15

15 variation on natural host population. First, we briefly reviewed the theoretical mechanism by which genetic variation is generated and how pathogen mediated selection act s on host popul ation Then, mechanisms of genetic variation in neutral loci are reviewed extensively showing evidence of empirical studies on wildlife population. Fi nally, the concept of adaptive genetic variation is discussed defining tolerance and resistance as the two mechanisms of defense acting on animal populations. Different m echanisms of evolution and effects of genetic variation at the major histocompatibilit y complex (MHC) and other relevant immune genes on disease susceptibility are show n with examples on wildl ife populations. The Role of Genetic Diversity in Disease Susceptibility Genetic variation is generated by mutations or by migration of new individual s and is lost by genetic drift incorporating in and removing alleles from the population. Natural selection may decrease or increase genetic variation leading to fixation of alleles by directional and stabilizing selection or promoting polymorphisms as balancing or diversifying selection (Nei and Li 1979). There is substantial evidence that host genetic s is an influential component of resistance to infectious diseases for animals. If an infectious disease causes significant host morbidity and mortality, th is may exert strong selective genetic pressure on host allel ic frequencies (Segal and Hill 2003). Thus, the interaction of the pathogen with the host through adaptive changes in virulent traits and defense mechanisms respectively, create a continuum of int eractive dynamics. These antagonistic coevolutionary interactions may follow dissimilar dynamics from the arms race dynamics to negative frequency dependent selection (Figure 1 1), impacting differently on the genetic diversity of both the host and the p athogen (Woolhouse et al. 2002). Arms race dynamics accumulate an adaptive allele

PAGE 16

16 through directional selection by selective sweeps (Figure 11a). Thus, a favorable mutation for a resistant trait or a new favorable allele from a migrant will be positively selected follow ed by the fixation of the allele on the population (i.e. every individual in the population has only one specific allele) and, consequently, reducing the genetic diversity. At the other extreme, pathogens may track the most common host genotypes present in the population. Also known as the Red Queen hypothesis, rare advantaged host genotypes conferring disease resistance may be selected increasing its frequency through negative frequency dependent selection, becoming a common genotype on the population again (Figure 11b). Since the pathogen genotype tracks the host genotype, dynamic polymorphisms are maintained on the population to offer new chances for evolution of rare advantage genotypes (Gandon et al. 2008), maintaining the genetic diversity on the population. The evolutionary potential thus is limited by the amount of genetic variation available on resistance and virulence traits for the interacting species. Therefore, the effect of genetic variation may contribute to the long term survival of individuals and as a result, affec t the population dynamics. Studies have primarily screened neutral molecular markers to assess genetic diversity. The study of neutral (nonadaptive) markers, despite the limited ability to reveal patterns of adaptive selection, is extremely relevant since this helps to understand hidden demographic and social events that otherwise are minimally rec ognized. Levels of genetic variation are positively cor related to population size (Frankham 1996). Populations that have experienced a recent large reduction in size may lose significant levels of genetic diversity through a decrease in the gene flow, by t he major effects of genetic drift or with mating among relatives. Negative effects such

PAGE 17

17 as increased rates of allelic loss, fixation of deleterious alleles, and decreased average individual heterozygosity can lead to the expression of deleterious or rare phenotypes resulting in reduc ed phenotypic values and, consequently, the overall fitness (Hansson & Westerberg 2008 b ; Keller et al. 2002). The expression of deleterious alleles can affect the immune system and the capacity to respond against pathogenic infections. Thus, g enetically uniform host individuals or populations would be more susceptible to diseases than genetically diverse hosts. On the other hand, t he major mechanism underlying genetic variation appears to be based on the difference among individ uals in immune response genes Thus, studying functional genetic markers, such as those within the major histocompatibility complex (MHC), provides an opportunity to assess genetic variation directly associated with adaptive selection. Two of the major sources of variation in immune responses are differences in antigen recognition and the different type s of cytokines produced. Genes that influence both these sources of variation are associated with differences in resistance to pathogen infection. Neutral Ge netic Variation and Infectious Diseases Neutral loci, such as noncoding DNA regions, are under weak selection and, therefore, variation is primarily driven by nonselective evolutionary factors such as Thus, neutral loci are affected as a consequence of demographic changes, either decreasing in population reduction (e.g. bottlenecks) or increas ing in population expansion. The loss of neutral genetic variation is expressed as a decay of the heterozygosi ty, defined as the proportion of heterozygous individuals at a specific locus. O ther common measures of genetic diversity are allelic diversity (number of alleles per locus) and the proportion of polymorphic loci. N eutral markers such as

PAGE 18

18 m icrosatellites ar e commonly used for genetic diversity studies in natural populations (Nei 1987). Assuming that microsatellite and genomewide heterozygosity are correlated, heterozygosity estimated from one set of microsatellites should be positively correlated with heter ozygosity from an independent set of microsatellites from the same individual (heterozygosity heterozygosity correlations) (Balloux et al 2004). Analysis of empirical data shows that parasite load or parasite prevalence in experimental systems (Spielm an et al. 2004), wild individuals (AcevedoWhitehouse et al. 2003), and wild populations (Meagher 1999; Whiteman et al. 2006) has been correlated negatively with neutral heterozygosity The effect of increased homozygosity and lack of genetic variability on pathogen susceptibility is thought to happen by two means: by a genome wide effect, which suggest s that inbreeding is occurring on the affected population, or by an improper immune response against pathogens because of the linkage of the neutral loci under study to functional loci in heterozygosity advantage (i.e. a ssociative overdominance). For instance, the loss of microsatellite diversity may reflect loss of resistant alleles at the MHC or at another important immune gene (Potts et al. 1990; Spielman et al 2004). A state of l ow genetic variability may continue caus ing a positive feedback loop, where pathogens more easily invade this small genetically homogenous population which, in turn, decreases their population size and genetic variability even mor e (De Castro & Bolker 2005). Whiteman et al. (2006) linked loss of genetic variability by i nbreeding, innate immunity and parasite load in an in situ wildlife population. However, s tudies on natural populations commonly associate directly the genetic var iation with a disease outcome

PAGE 19

19 (e.g. infection clinical signs, disease prevalence, mortality rate). For instance the endangered Florida panther has very low levels of genetic diversity and show s a high prevalence of infectious diseases (Roelke et al. 1993). Although some other studies on natural populations have used experimental immune challenges as an indirect measure of immune responses (Reid et al. 2007), more studies are needed to understand the direct genetic mechanisms responsible for this particular relationship. Also, i f individuals with reduced heterozygosity are less able to resist parasite infection, the opposite direction should also be applied for outbred populations. MacDougall Schackleton et al (2005) studied the predicted association between risk and intensity of Haemoproteus infection and microsatellite heterozygosity and diversity in whitecrowned sparrows ( Zonotrichia leucophrys oriantha) from an outbred population. In the same way, Gompper et al. (2011) found in a large outcrossing popula tion of raccoons ( Procyon lotor ) that individuals with higher heterozygosity and lower measures of inbreeding exposed to canine distemper virus and feline parvovirus were more likely to mount a successful immune response, measured by ser o conversion and sur vived infection. This type of study, rarely represented in literature, leads to the conclusion that parasitemediated selection may also favor genetic variation through heterozygosity advantage in neutral markers. Many correlations between parasite resistance and genetic diversity in natural populations are either weak or absent (Muller Graf et al. 1999; Cote et al. 2005; Hale et al. 2007; Ortego et al. 2007). The lack of association on heterozygosity fitness studies may probably be because of a low sample size or the power of the markers to detect correlations, which is often weak when using less than 15 neutral markers, a common

PAGE 20

20 number of markers used on studies (DeWoody et al 2005). Thus, a wider range of individual heterozygosity has been proposed to provide higher power to detect heterozygosity to pathogen associations (Luikart et al 2008 ; Coulon 2009). Another explanation may be a l ow pathogen prevalence result ing in small or no impact on host fitness reducing thus the power to detect associations between genetic diversity and pathogen infection (Ortego et al 2007). Pathogens can be effective in removing inbred individuals from the population (Rijks et a l. 2008). For instance, homozygosity was higher for pups of California sea lions that died due to hookworm infection (AcevedoWhitehouse et al. 2006). Thus, association may also be rejected if only adult (heterozygous) are sampl ed after pathogen susceptibl e juvenile (homozygous) individuals have been removed from the population under study. Population Bottlenecks and Infectious Diseases Large randomly mating population exposed to a temporal (i.e. for at least one generation) size constriction is predicted to experience a population bottleneck ( Allendorf 2005). Genetic drift is expected to act more rapidly on small size populations and the barriers for gene flow (i.e. migration from outer populations) to increase, los ing significant levels of both allelic diversity and h eterozygosity (Frankham 2005). R are alleles (i.e. alleles at low frequency) modestly contribute to heterozygosity and are likely to be lost in bottlenecks. Thus, the allelic diversity is predicted to be affected first during populati on size reduction (i.e. minimum bottleneck size of a population) whereas the decline of average heterozygosity is associated with the rate of population increase after the bottleneck (Luikart et al. 2008). Either way, this genetic erosion has a considerable impact on the evolutionary potential of a population, such as disease susceptibility, depending on the intensity of the perturbation, the length of time before

PAGE 21

21 recovery, and the rate of recovery to original population numbers (England et al 2003). Some examples of increased disease susceptibility following genetic bottlenecks are the black footed ferret ( Mustela nigripes) affected by canine distemper (Thorne & Williams 1988) and the Florida panther ( Puma concolor coryi ) affected by a number of infectious diseases (Johnson et al. 2010). I nfectious diseases have also been the main force in populat ion size reduction (Table 11), pathogens thus plays a direct role in the popul ation adaptation and evolution o f disease resistance. Pathogenrelated mortality may reduce population size depending on the virulence of the pathogen and the defense mechanism s within the host population. This diseasemediated population decline may be followed by changes on genetic structure of the population, increased divergence with the uninfected populations, and a reduced overall genetic variability through genetic drift. Also, epizootics may select for individuals genetically resistant to the pathogens exerting a directional select ion for rare advantageous genotypes with increased resistance or a balancing selection for those with genetic polymorphisms. The Hawaii amakihi ( Hemignathus virens ) has been severely affected by avian malaria Plasmodium relictum at low elevation habitats. The disease pressure rapidly selected survivors with resistance to malaria at that specific elevation, and allow ed the spread to remnant populations (Foster et al 2007). Populations of the black tailed prairie dog ( Cynomys ludovicianus ) experienced repeat ed bottlenecks from sylvatic plague ( Yersinia pestis ). Colonies affected showed lower number of alleles and heterozygosity than did those from colonies without plague. However, gene flow has reintroduced variability to genetically depauperate post plague c olonies (Trudeau et al. 2004). Although

PAGE 22

22 population decline by diseases arise as a common threat to wildlife populations worldwide (Dobson et al. 2001), there are still only moderate number of studies investigating the genetic consequences of epizootics in host populations (Table 12). The genetic and demographic changes in the host population due to diseases may occur over a few generations and in short time scales. Lachish et al (2011) measured the genetic impact of an infectious cancer, the devil facial tumo r disease (DFTD), on a natural population of Tasmanian devil ( Sarcophilus harrisii ) using temporally replicated genetic data that include pre outbreak population data. After a few generations (two to three) of DFTD pressure, up to 80% of the population had declined (Hawkins et al. 2006) The consequences were an increas ed inbreeding, a genetic differentiation from other populations, chang es on selection regimes, and reduc ed dispersal of females (Lachish et al 2011). This represents one of the few studi es comparing samples before and after disease outbreaks. Several other studies of disease mediated population decline, however, have not found associations predicted by the theoretical expect ations. Affected populations have been hypothesized to retain div ersity due to several factors which includes the effect of gene flow among subpopulations (Le Gouar et al. 2009), the short time elapsed after the decline and the sampling, a large remaining population size enough to retain the genetic diversity (Queney e t al. 2000), or behavioral changes such as assortative mating where infected individuals are unable to compete for healthy mates (Teacher et al 2009). D espite the high mortality induced by Ebola virus in Western lowland gorillas, Gorilla gorilla gorilla the lack of finding a loss in genetic diversity (i.e. loss of rare alleles) in post epidemic populations was attributed to immigration ( LeGouar et al

PAGE 23

23 2009) Queney et al (2000) found no loss of genetic diversity in wild European rabbits ( Oryctolagus cuniculus ) in populations that had declined up to 90% due to rabbit viral h emorrhaghic disease. Teacher et al (2009) proposed a behavioral effect on mate choice when Ranavirus i nfected populations of the common frog ( Rana temporaria) showed significantly homozygote excess, a significantly reduced relatedness, and no detectable effect on allelic richness despite an evidence of 83% population decline over non infect ed populations. Founder effect is another example of population bottleneck, where genetic vari ation is decreased because the individuals of the population are coming from a small number of parents. Two predicted genetic consequences of founder events are loss of genetic diversity and a rapid divergence between the source population and the founder population increasing the frequencies of specific genetic traits inherited from the few common ancestors. Although sometimes successful (Carson 1990; Tsutsui et al. 2000; Wattier et al. 2007), founders are associated with detrimental effects on fitness su ch as increased susceptibility to local prevalent pathogens or invasion of pathogens from the source following susceptible founders An example is when a novel strain of Mycoplasma gallisepticum spread through the eastern introduced population of wild hous e finches ( Carpodacus mexicanus ). Hawley et al. (2006) showed that the introduced populations of eastern susceptible finches (i.e. susceptible population) had signatures of population bottlenecks compared to the western resistant finches contributing to the emergence of the Mycoplasma epidemic. The recent translocation and reintroduction of individuals of Western barred bandicoots ( Perameles bougainville) from a susceptible and genetically invariable population have influenced the rapid

PAGE 24

24 spread of bandicoot papillomatosis carcinomatosis virus type 1 (BPCV1) in the relocated individuals (Woolford et al. 2009). Inbreeding and Infectious Diseases Inbreeding or mating between relatives, reduces genetic variation within offspring and may result in a dec lin e of fi tness (i.e. inbreeding depression). Inbreeding is an expected consequence after population bottlenecks, increasing the chance for genomewide effects of homozygosity on fitness. Reduced fitness resulting from inbreeding depression is thought to be caused m ostly by two mechanisms: the exposure of deleterious recessive alleles named the dominance hypothesis or by an excess of homozygous alleles that have reduced fitness than heterozygous ( i.e. heterozygosity advantage) which is termed the overdominance hy pothesis (Charlesworth & Charlesworth 1999) Cassinello et al (2001) c ompared three species of gazelles in captivity concluding that at both species and individual level s, the coefficient of inbreeding was positively correlated to levels of gastrointesti nal parasitism. However, studies in population in captiv ity might underestimate the evolutionary consequences of the fitness costs relative to wild populations such as population extinctions and inbreeding avoidance (Crnokrak et al. 1999). Examples of the effect of inbreeding on susceptibility to infectious diseases in natural animal populations are very limited in the literature. AcevedoWhitehouse et al. (2003) found that inbreeding in California sea lions ( Zalophus californianus ) increases disease susceptibility but at different infection intensity depending on the class of the pathogen. Individuals with a higher relatedness index were more affected by carcinomas associated to herpesvirus infection, followed by helminthes infection s and, then, by bacterial infections. A wild population of naked

PAGE 25

25 mole rat (Heterocephalus glaber ), considered one of the most inbre d mammal species, was affected by a virulent enteric coronavirus outbreak. Using a multivariate model, Ross Gillespie et al (2007) predicted that inbred molerats were 300% more likely to die than outbred individuals. Similar results were found during a morbillivirus outbreak in Mediterranean striped dolphins ( Stenella coeruleoalba) where the disease spread first among inbred i ndividuals (Valsecchi et al. 2004). In the same way, a s imilar effect on disease susceptibility has been also observed at population level. The Italian agile frog ( Rana latastei ) populations with reduced genetic diversity assumed from inbreeding experienced higher mortality rates when exposed to Ranavirus (Pearman & Garner 2005) Common in the literature is the correlation of heterozygosity levels with the outcome of disease. Since l ow heterozygosity is assumed to cause inbreeding any negative association of heterozygosity and disease have lead to the assum ption of inbreeding depression without providing further evidence of mating between relatives or pedigreebased inbreeding coefficients Although paternities are hard to define on natural populations, rec ent molecular markers techniques and statistical methods may help to develop better strategies to analyze pedigree s tructure or relatedness indexes (Nielsen et al. 2012; Chaves et al. 2011; Garcia et al. 2012). Adaptive Genetic Variation and Infectious Dis eases Host d efe nse mechanisms including behavioral avoidance of pathogens, barriers to infection and immune system are highly variable among individuals and populations (Combes 2001). This h ost heterogeneity results in differences i n susceptibility and, if the pathogen pressure is constant, in the selection of specific heritable host defense trait s.

PAGE 26

26 Adaptive loci, such as immunerelated genes, are subject to different intensities of natural selection. Thus, depending on the co evolutionary dynamic s existing between the pathogen and the host (Woolhouse et al. 2002) the genetic variation may be affected differently. Hosts are exposed to a diverse array of pathogens and have developed a wide range of adaptive mechanisms in order to minimize the agent s impact on their health and on population structure. D efense against pathogens by hosts can be divided into two broad classes: resistance and tolerance. Resistance mechanisms actively reduce the pathogen load, whereas tolerance mechanisms limit the impac t of disease caused by a particular load. Both can improve host fitness but may have different evolutionary trajectories (Best et al. 2008). Variation in resistance genes may lower parasite fitness and prevalence since they inhibit infection and/or reduce parasite growth rate, maintaining polymorphisms of the population through balancing selection and, thus, the genetic diversity. In contrast, tolerance defense mechanisms allow the presence of the parasite even diminishing the damage caused and having a neutral or positive effect on prevalence, leading to fixation of tolerant alleles through positive selection, and affecting genetic diversity on the population level (Roy et al. 2000; Raberg et al. 2007; Best et al 2008). Evidence for Tolerance on Natural Po pulations Since most studies of the toleranceresistance axis have focused on plant parasiteherbivore systems, there is not much data on w hether or not animals show thalassaemia trait (a heritable blood disorder) se ems to be a tolerance factor in humans. While not affecting parasite load there is a reduced incidence of severe/fatal malaria in individuals carrying the trait (Williams 2006). Using rodent malaria ( Plasmodium chabaudi ) in laboratory mice as a model

PAGE 27

27 syst em, Raberg et al. (2007) demonstrated genetic variation for tolerance as measured in terms of anemia or weight loss, yet the parasitic burden did not affect the parasite fitness L ittle is known about tolerance mechanisms relative to resistance mechanisms. Most of studies have been conducted focusing on the overall health status as the main outcome, which may imply both resistance and tolerance mechanisms. Hence, it is not well known how tolerance per se act on animal health and it is necessary to decompose the outcome into these two factors. Molecular pathways and the genetic component for the tolerance mechanistic s are likely to include an increase in the investment of the susceptible tissue preand post infection, as physical barriers or replac ement of affected cells, and immunological responses directed to secondary parasite products, as toxins (Read et al 2008). Mechanisms that allow reservoirs the persistence and spread of pathogens to susceptible individuals may also involve tolerance. As a n example, the Hawaii amakihi ( Hemignathus virens ) densely use low elevation habitats where malaria is extremely prevalent allowing the local transmission and the persistence of malaria in those areas (Woodworth et al. 2005). Knowing the genetic basis resp onsible for tolerance will allow us to identify the evolutionary process involved in this yet unknown defense mechanism and its relationship with resistance defenses. Genetic Variation at the Major Histocompatibility Complex (MHC) In contrast to tolerance, there is more empirical evidence demonstrating t hat resistance is an important mechanism used by animals against pathogens. In vertebrates, growing evidence suggests that genetic diversity is particularly important at the level of the major histocompatibi lity complex (MHC). The MHC, the most polymorphic class of functional genes described in vertebrates, plays a key role in the acquired immune system. The MHC encodes cell surface glycoproteins that bind

PAGE 28

28 antigens derived from diverse pathogens and present t hese to T lymphocytes which trigger the appropriate immune response (Piertney et al. 2006). The variability of the MHC molecules is correlated with the diversity of the T lymphocyte receptors which in turn determine the disease and pathogen resistance of an organism thus influenc ing the long term survival probability of populations (Hedrick 2002). Three mechanisms of pathogenmediated selection may operate to maintain MHC diversity in wild populations: heterozygosity advantage, rarealleles advantage, and fluctuating selection (Spurgin and Richardson 2010). Stochastic simulations that includ e mutation, genetic drift in a finite population size, reveal that heterozygote advantage on its own is insufficient to explain the high population diversity of the MHC (De Boer et al. 2004). Thus, the evolution of polymorphism at the MHC should be under the influence of more than one selection mechanism at the same time. Heterozygote advantage through overdominance models of selection predict that heterozygote genotypes will be fitter than homozygote genotypes, because two variant alleles will potentially identify a broader range of pathogens (Doherty & Zinkernagel 1975). In consequence, balancing selection has been suggested as the main selection mechanism for retain ing high levels of genetic diversity at the MHC in vertebrates (Piertney & Oliver 2006). S tudies testing for associations between MHC heterozygosity and pathog en load or richness suggest a role of heterozygosity advantage. A rare allele advantage is proposed to result from a cyclic coevolutionary arms race by a negative frequency dependent selection (NFDS). Common host alleles are the primary targets of pathogen evolution, giving an advantage to rare host alleles and have been positively selected for that determined pathogen pressure (Takahata et al. 1990). Thus,

PAGE 29

29 associations between infection from a specific pathogen and specific MHC haplotype suggest rarealle les advantage. Finally, t he spatial and temporal heterogeneity of pathogens diversity and intensity is proposed to maintain diversity at the MHC by fluctuating selection (Hill et al. 1991). Spatiotemporal variation on pathogen pressure will lead to differe nt intensity of the directional selection at MHC, generating multiple subsets of MHC haplotypes. Thus, process es such as local adaptations to pathogen infection and MHC multilocus alleles are expected to be maintained by fluctuating selection. Populations exposed to a more diverse pathogen regimen are predicted to exhibit higher MHC diversity than those exposed to fewer pathogens. Alcaide et al. (2010) compared MHC diversity between two species of falcons in continental and island populations. Continental populations were exposed to a more diverse spectrum of pathogen and consequently, had higher MHC diversity than island populations. However, MHC heterozygosity and resistance to pathogens may not be a linear relationship. A t oo high diversity at the MHC can limit T cell diversity because of the greater number of T cells eliminated during ontogeny of the immune system when negative selection occurs. This in effect will limit the efficiency of pathogen recognition (i.e. optimal hypotheses). Wegner et al. ( 2003) provided experimental evidence that intermediate level of individual MHC diversity (optimal rather than maximal) display s the strongest level of resistance against multiple nematode parasites and, in consequence, the highest level of fitness. Some studies, also, have shown that animals with a low overall genetic diversity can maintain high diversity at MHC by a strong positive balancing selection. The Iiwi

PAGE 30

30 ( Vestiaria coccinea), the most susceptible Hawaiian honeycreeper to malaria, demonstrated extr emely low overall mtDNA diversity relative to other sympatric honeycreepers as the amakihi ( Hemignathus virens ). The differences in degree of genetic diversity may indicate that past population bottlenecks have reduced their ability to resist disease by reducing variability at the MHC. However, the diversity of iiwi at MHC loci is similar to amakihi, indicating balancing selection (perhaps for disease resistance) in the species under study (Foster et al 2007). An extensive review of published association s tudies on natural wild populations is shown on Table 13. Association studies compare simultaneously two variables which include the MHC polymorphism and the disease/pathogen outcome in a natural population under study. Although the number of studies avai lable is small, the mammalian species are overrepresented when compared to avian, reptile and amphibian species. No fish studies were found from wild populations. Most of the pathogens under study are association between gastrointestinal helminthes indirec tly deter mined by count of eggs on feces and MHC class II DRB exon 2. Results are not conclusive whether overdominance or NFDS is the main mechanism of selection on the MHC. F luctuating selection was evident on a few studies comparing populations at differ ent geographic locations. However, differences in MHC variation between populations could be due to genetic drift, rather than to differences in pressure from parasites at different times and locations. Recent studies includ e parasite communities or a mix of known pathogens of interest to a particular species in different populations. In general, these studies have shown heterozygosity advantage to multiple pathogens across populations and local adaptation to specific pathogens through specific rare

PAGE 31

31 MHC alleles. Thus, these three mechanisms of selection are likely to operate together in order to find a better protection strategy against local prevalent pathogens. This was also evident in experimentally infected lowland leopard frogs ( Lithobates yavapaiensis ) with Batrachochytrium dendrobatidis from populations that naturally differ in disease susceptibility (Savage et al. 2011). In that study, MHC heterozygosity was a significant predictor of survival suggesting heterozygosity advantage across populations. In addition, individuals bearing a locally adapted allele had a significantly reduced risk of death suggesting a recent immunogenetic adaptation in response to disease pressure. If a particular MHC array is specific for a determined parasite community affect ing the population, sympatric rather than allopatric species infected by similar parasite diversity may have similar MHC array or other immunerelated genes composition. It would be expected in those cases that rarealleles specific for a prevalent pathogen exerting strong selective force on the host species would be the main mechanism of MHC diversity. Schwensow et al. (2010) found a direct connection between certain antigen binding sites of MHC molecules with a particular Ascaris sp. in two sympatric wild lemur populations. However, recent population declines or population structure (e.g. metapopulations) may cause the los s of resistant alleles to prevalent pathogens. Thus, heterozygosity advantage would prevail as mechanism of MHC diversity. Two sympatric mouse opossum species showing similar fauna of gastrointestinal helminthes have impaired association on MHC diversity and parasite load leading to an unbalanced scenario (Meyer Lucht et al. 2010). A ssociation studies are not available for many interesti ng host pathogen systems, but strong inference had helped to assume these associations. In the facial

PAGE 32

32 tumor dis ease (DFTD) and Tasmanian devil system, a low genetic diversity at MHC class I and II due to a past genetic bottleneck followed by intense inbreeding has been linked to disease susceptibility (Siddle et al. 2010). These findings suggest ed a selective sweep and the fixation of selectivel y favored alleles to the disease (Cheng et al. 2012) as occur in arms race dynamics. Several other studies have shown low MHC diversity in wild populations but with no association to pathogen burden (Table 14). Low MHC diversity may be indicative of a los s of genetic variation, possibly as a result of population bottlenecks, isolated populations with limited gene flow, recent founder event, or directional selection favoring specific allelic variants. Przewalski's horse ( Equus ferus przewalskii ), which at o ne time was extinct in the wild and of which all the living individuals descend from only 13 founders, shows a low variation in this important adaptive region compared to domestic horses at homologous loci (Hedrick et al. 1999). Below a certain population size, balancing selection may not be strong enough to maintain genetic diversity in functionally important genes and increase pathogen susceptibility. A ssociation studies are mostl y based on polymorphisms of a small segment of the MHC complex, the peptide binding region (PBR) of the class II transmembrane recognition molecules. This specific segment is widely used as a genetic marker to show signatures of the effects of balancing selection on the genome. However, there is no reason to assume that this part icular region is the responsible for primary immune response against all pathogen infection (Acevedo Whitehouse & Cunningham 2006) Hence, t he use of other loci such as pathogen specific candidate genes that can result

PAGE 33

33 in differences in the resistance to disease would help to understand the different evolutionary mechanisms involved in specific host pathogen interactions. Genetic V ariatio n at Other I mmune Related G enes Host heterogeneity in susceptibility to infectious diseases can be also be determined by the genetic diversity of immune related genes other than MHC, especially those at the host pathogen interface. The innate immune system, for instance, constitutes the first line of defe nse against invading pathogens, and provides nonspecific protection against a wide variety of organisms. Variation of genes encoding pathogen recognition receptors such as Toll like receptors (TLR), C type lectin receptors (CLR), and mannose receptors, or immune regulation molecules such as cytokines and defensins, can lead to changes in gene expression and been associated with susceptibility to infectious diseases Products of these loci are likely to affect the interaction with pathogens determining diff erences i n disease susceptibility and therefore the survival success in natural populations. For instance, the involvement of viral receptor alleles in disease resistance has been widely reported through candidate gene approaches in humans and other model organisms ( Mosbruger et al. 2010; Amos et al. 2011; Lee et al 2011) However, the study of natural populations may better explain the m echanisms driving genetic diversity in pathogen resistance, although studies investigating the impact of genetic variati on of immune loci in pathogen resistance are rarely represented in the literature (Table 15 ). These association studies have the function to identify first mutations on the gene, characterize the position of the polymorphism according to the relevance to the gene, and then compare the frequencies of selected mutations or haplotypes in susceptibility and resistance individuals at population lev el.

PAGE 34

34 Genes of the adaptive immune system are hypothesized to be under either balancing or positive selection. However, the innate immune system is predecessor to the adaptive system, an evolution of the genes, although exhibit high levels of polymorphism driven by rapid rates of pathogen evolution, might be already highly optimized by natural selection having therefore low tolerance to changes (purifying selection) (Mukherjee et al. 2009) or may be plastic enough to continue evolving with low selection pres sure (balancing selection) (Ferrer Admetlla et al. 2008). However, different selective forces at different strengths are likely to act across the variety of gene families of the innate immune system. For example, Alcaide & Edwards (2011) found that avian T LRs are subjected to stabilizing selection (likely acting through purifying selection) but also to positive selection on specific amino acid sites linked to species specific differences in pathogenassociated molecular pattern recognition (PAMPs). Thus, ge ographic distribution of haplotypes frequencies in specific genes may correlate with the infectious diseases prevalent to the specific area. Neutral evolutionary forces, such as migration and drift, may also be important in driving immune gene variation patterns. In natural populations, Bollmer et al. (2011) found that not only balancing selection but genetic drift shaped variation of five immune genes in the greater prairie chicken ( Tympanuchus cupido). Therefore, previous population demographic history su ch as bottleneck or fragmentation can make difficult the detection of current selection forces on immune genes. Identification of candidate genes may come from a previously mapped quantitative trait locus (QTL) for resistance to the region such as the microsatellite found at the interferon gamma ( IFN (Coltman et al 2001) or extrapolation data from other

PAGE 35

35 host species (cite). Thus, the identification of candidate genes may be highly biased to other species with different disease outcome and consequent ly, different evolutionary dynamics. The limited genomic resources of relevant regions such as promoter and protein coding domains available for nonmodel species has the disadvantage to also biased the search for candidate genes and be less successful o n the finding of associations. Another disadvantage of the use of a sole candidate gene is that i ndividuals in natural populations are subject to concurrent or sequential infection by multiple pathogen species The use of unique candidate gene may fall into the same argument on the use of one MHC locus to investigate infections from multiple pathogens. Thus, Turner et al. (2011) demonstrated that genetic variation at multiple cytokine genes provides a source for phenotypic variation in both immunological p arameters and in multiple pathogen resistance in the natural environment. Instead of a unique candidate gene, more of these types of studies are n eeded to understand a broader picture of the relationship of variation of immune related genes and disease susceptibility. Genetic association studies offer a powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. The recent advance of geno mic tools such as microarray analysis and whole genome transcriptomes has offered new insights into host pathogen systems. The construction of speciesspecific arrays to investigate genomic response to host disease has allowed rapid identification and char acterization of candidate genes in organisms w h ere previous information was lack ing The use of transcriptomes to identify differently expressed genes has been used in experimental conditions to

PAGE 36

36 explore associations occurring in natural populations ( Murchi nson et al 2010; Bonneaud et al. 2012; Shaw et al. 2012) In a short time, costs associated with th e se new technologies will be accessible for the use on studies with greater sample size and in natural conditions in order to fully understand the genetic consequences of infectious dis eases in wildlife populations. Conclusions The genetic consequences of infectious diseases on natural populations have several consequences affecting the host susceptibility. Wildlife researches tend to screen for the prevalence of diseases in surviving animals. It is extremely rare to detect wild animals wit h a current infection showing clinical signs. This might explain why a limited number of studies have found fitness effects caused by diseases such as avian malaria infections under natural conditions. The construction of sensitive noninvasive sampling techniques and the implementation of highly sensitive molecular procedures may help to increase the chance of detection not only disease prevalence but pathogen load at the same time with host gene expression. Only well designed epidemiological studies may help to estimate the hosts genetic contribution to disease susceptibility (progression, manifestation, etc) independently from other environmental effects. Whole genome transcriptome s may help to elucidate not only the immune response to pathogens but eval uate a more complete picture of the physiological effects of diseases by gene expression changes in multiple tissues at multiple timepoints. Since pathogens are part (and necessary) of the natural functioning of ecosystems, wildlife diseases management should also consider the evolutionary natural processes such as the host pathogen interactions in addition to measures of prevention and control. For instance, the interruption of the rate of evolution and genetic

PAGE 37

37 variation on host and pathogen populations a t different times through mass culling of infected birds by avian influenza (Shim et al. 2009) may have long term detriments including the increment of more genetically susceptible host population, a greater mortality, and elevated pathogen virulence. Because the maintenance of polymorphism within populations is highly dependent on the product of selection intensity, mutation rate, and effective population size, small population size under inbreeding or genetic drift might lose allelic diversity and overall heterozygosity at these particular loci and thus can be severely affected by introduction of new pathogens. Infectious diseases often have large fitness effects on their hosts, so parasites can strongly shape host population sizes and genetic structur e. The identification of specific resistance genes of the host might then help elucidate normal host mechanisms of defense, and, thus, disease impacts on host abundance and distribution. Some of these traits are more likely to persist than others. This wil l be according to the genetic variability, the prevalence of the disease, and the cost of resistance associated. Empirical evidence available collectively indicates that either genomewide variation or locus specific variation may be attributable to resist ance traits and, moreover, can be of significant magnitude and have significant implications in natural populations.

PAGE 38

38 Table 11 Summary of diseasemediated population decline in wildlife Host Pathogen Year Location Pop ulation decline Reference Yellow legged frog ( Rana muscosa ) Batrachochytrium dendrobatidis since 1950 California, United States 80% Rachowicz et al. 2006 Common frog ( Rana temporaria ) Ranavirus (Iridoviridae) since 1996 South Eastern England 81% Teacher et al. 2010 Hawaiian honeycreepers Plasmodium relictum ND Hawaii, United States Extinction, decline Van Riper et al. 1986 American crow ( Corvus brachyrhynchos ) West Nile virus (Flaviviridae) 1999 United States 40% LaDeau et al. 2007 Yellow billed magpie ( Pica nuttalli ) West Nile virus (Flaviviridae) 20042006 California, United States 49% Crosbie et al. 2008 Greenfinch ( Carduelis chloris ) Trichomonas gallinae 2005 England 35% Robinson et al. 2010 Chaffinch ( Fringilla coelebs ) Trichomonas gallinae 2005 England 21% Robinson et al. 2010 Cape buffalo ( Syncerus caffer caffer ) Rinderpest virus (Paramyxoviridae) 1889 South Eastern Africa 8090% Mack 1970 European rabbit ( Oryctolagus cuniculus ) Myxoma virus (Poxviridae) 1952 Europe 90% Fenner & Ross 1994 European rabbit ( Oryctolagus cuniculus ) Rabbit hemorrhagic disease virus (Caliciviridae) 19951998 Spain, France, Australia 5590% Mutze et al. 1998; Villafuerte et al. 1995 Arctic fox ( Vulpes lagopus semenovi ) Otodectes cynotis 19701980 Mednyi Island, Russia 8590% Goltsman et al. 1996 Island fox ( Urocyon littoralis ) Canine distemper virus (Paramyxoviridae) 1999 California, United States 95% Timm et al. 2009 Black tailed prairie dog ( Cynomys ludovicianus ) Yersinia pestis 1990 Montana, United States ND Tradeu et al. 2004 Tasmanian devil ( Sarcophilus harrisii ) Tasmanian devil facial tumor disease 1996 Northeastern Tasmania 6090% McCallum et al. 2007 ND: not determined

PAGE 39

39 Table 12 Summary of studies on genetic variation at neutral markers in natural populations Host Pathogen Mechanism of genetic variation Reference Common frog ( Rana temporaria ) Ranavirus (Iridoviridae) Assortive mating Teacher et al 2009 White crowned sparrows ( Zonotrichia leucophrys ) Haemoproteus sp. Heterozygote advantage MacDougall Shackleton et al 2005 Galpagos hawk ( Buteo galapagoensis ) Colpocephalum turbinatum, Degeeriella regalis Island inbreeding Whiteman et al 2006 House finch ( Carpodacus mexicanus ) Mycoplasma gallisepticum Founder effect Hawley et al 2006 New Zealand robin ( Petroica australis ) Ectoparasites, endoparasite nematodes, and protozoan Population bottleneck Hale & Briskie 2007 American crow ( Corvus brachyrhynchos ) West Nile virus Inbreeding Townsend et al 2009 Galapagos mockingbird species ( Mimus spp.) Brueelia galapagensis, Myrsidea nesomimi Inbreeding Hoeck & Keller 2012 African lion ( Panthera leo ) Spirometra spp. No Association Muller Graf et al 1999 Deer mice ( Peromyscus maniculatus gracilis ) Capillaria hepatica Island inbreeding Meagher 1999 Soay sheep ( Ovis aries ) Gastrointestinal strongyle eggs Inbreeding Coltman et al 1999 European rabbit ( Oryctolagus cuniculus ) Rabbit hemorrhagic disease virus (Caliciviridae) No association Queney et al 2000 Black tailed prairie dog ( Cynomys ludovicianus ) Yersinia pestis Population bottleneck Trudeau et al 2004 Mediterranean striped dolphin ( Stenella coeruleoalba ) Morbillivirus (Paramyxoviridae) Inbreeding Valsecchi et al 2004 Svalbard reindeer ( Rangifer tarandus p.) Ostertagia gruehneri, Marshallagia marshalli Heterozygosity fitness correlation Cote et al 2005 California sea lion ( Zalophus californianus ) Uncinaria spp. Inbreeding Acevedo Whitehouse et al 2006 Bighorn sheep ( Ovis canadensis ) Protostrongylus spp. Population bottleneck Luikart et al 2008 Harbour seal ( Phoca vitulina ) Lungworms Inbreeding Rijks et al 2010 New Zealand sea lion ( Phocarctos hookeri ) Uncinaria spp. Inbreeding Acevedo Whitehouse et al 2009 Tasmanian devil ( Sarcophilus harrisii ) Devil facial tumor disease Inbreeding Lachish et al 2011

PAGE 40

40 Table 13 Summary of association studies on genetic variation at MHC and infectious diseases in wildlife. Host Pathogen M echanism of selection Reference House finch ( Carpodacus mexicanus ) Mycoplasma gallisepticum H A Hawley & Fleischer 2012 Water voles ( Arvicola terrestris ) Gamasid mites, Megabothris walkeri Ixodes ricinus H A (Overdominance) Oliver et al 2009 Soay sheep ( Ovis aries L .) Five nematode spp. F DS Paterson et al. 1998 European bison ( Bison bonasus ) Ashworthius sidemi Neutrality Radwan et al 2010 Long tailed giant rat ( Leopoldamys sabanus ) Helminth endoparasites H A, FS Lenz et al 2009 Mouse lemurs ( Microcebus murinus ) Thirteen nematode spp. F DS Schad et al 2005 Yellow necked mice ( Apodemus flavicollis ) Eight nematode, four cestode, one trematode spp. F DS Meyer Lucht & Sommer 2005 Brazilian gracile mouse opossum ( Gracilinanus microtarsus ) Eleven helminthes morphotypes HA Meyer Lucht et al 2010 Fat tailed dwarf lemur ( Cheirogaleus medius ) Five nematode, one trematode, one cestode spp. F DS FS Schwensow et al 2010 Gray mouse lemur ( Microcebus murinus ) Five nematodes, one trematode, one cestode spp. F DS FS Schwensow et al 2010 Three spined sticklebacks ( Gasterosteus aculeatus ) Four nematode, five trematode, two cestode, one crustacean spp. H A (O ptimality hypothesis) Wegner et al. 2003 Striped mouse ( Rhabdomys pumilio ) Gastrointestinal nematodes spp. HA FDS Froeschke & Sommer 2005 Bank vole ( Myodes glareolus ) Six nematode, one cestode spp. HA (O ptimality hypothesis), FDS FS Kloch et al 2010 Fat tailed dwarf lemur ( Cheirogaleus medius ) Seven nematode, one cestode, one trematode spp. F DS Schwensow et al 2007 Gray mouse lemur ( Microcebus murinus ) Thirteen helminthes morphotypes H A Meyer Lucht et al. 2010 Hairy footed gerbils ( Gerbillurus paeba ) Six nematode, two cestode spp. HA FDS Harf & Sommer 2005

PAGE 41

41 Table 13 Continued Host Pathogen Mechanism of selection Reference Lesser kestrel ( Falco naumanni ) and Eurasian kestrel ( Falco tinnunculus ) 35 avian protozoa, bacterial, viral, fungal, hematozoan, helminthes spp. FDS, FS Alcaide et al. 2010. Water vole ( Arvicola scherman) Eleven helminthes, two coccidia, antibodies against three viruses FS, directional selection Tollenaere et al. 2008 Iberian red deer ( Cervus elaphus hispanicus ) One helminth, one nematode, four tick spp., Mycobacterium tuberculosis HA, FDS Fernandez de Mera et al. 2009 White tailed deer ( Odocoileus virginianus ) Six abomasal nematodes, two tick spp. HA (co dominance), FDS Ditchkoff et al. 2005 Bank vole ( Myodes glareolus ) Two viruses, one coccidia, two larval of cestodes, three cestode, three nematode, one mite spp. FDS, FS Deter et al. 2008 Water python ( Liasis fuscus) Hepatozoon sp. FDS, HA (Optimal hypothesis), Madsen & Ujavari 2006 House sparrow ( Passer domesticus ) Plasmodium relictum FS (diversifying selection) Loiseau et al. 2011 Great reed warblers ( Acrocephalus arundinaceus ) Haemoproteus payevskyi Plasmodium sp FDS, HA Westerdahl et al. 2005 House sparrow ( Passer domesticus ) Plasmodium sp., Haemoproteus sp. FDS, FS Bonneaud et al. 2006 House sparrow ( Passer domesticus ) Plasmodium sp., Haemoproteus sp. FDS Loiseau et al. 2008 Mexican wolf ( Canis lupus baileyi ) Canine Parvovirus 2 ( Parvoviridae ), Canine Distemper virus ( Paramyxoviridae ) HA Hedrick et al. 2003 Bank vole ( Myodes glareolus ) Puumala virus ( Bunyaviridae ) FDS, FS Guivier et al. 2010 Raccoon ( Procyon lotor ) Raccoon Rabies virus ( Rhabdoviridae ) FDS, FS Srithayakumar et al. 2011 Common frog ( Rana temporaria ) Ranavirus ( Iridoviridae ) HA, FDS Teacher et al. 2009 California sea lion ( Zalophus californianus ) Virus associated urogenital cancer ( Herpesvirus 1) None Bowen et al. 2005 HA: Heterozygosity advantage; FDS: Frequency dependent selection; FS: fluctuating selection.

PAGE 42

42 Table 14 Species that have evidenced reduced MHC variability with no association studies available. Host Re ference North Brother Island tuatara ( Sphenodon guntheri ) Miller et al. 2008 Crested ibis ( Nipponia nippon ) Zhang et a l 2006 Galpagos penguin ( Spheniscus mendiculus ) Bollmer et al. 2007 Southern elephant seal ( Mirounga leonina ) Slade 1992 Koala ( Phascolarctos cinereus ) Houlden et al. 1996 Przewalski's horse ( Equus ferus przewalskii ) Hedrick et al. 1999 Arabian oryx ( Oryx leucoryx ) Hedrick et al. 2000 Northern elephant seal ( Mirounga angustirostris ) Weber et al. 2004 Spanish ibex ( Capra pyrenaica ) Amills et al. 2004 European bison ( Bison bonasus ) Luenser et al. 2005 Chamois ( Rupicapra pyrenaica parva ) Alvarez Busto et al. 2007 Vaquita ( Phocoena sinus ) Mungia Vega et al. 2007 Gulf of California Fin Whale ( Balaenoptera physalus ) NigendaMorales et al. 2008 African wild dog ( Lycaon pictus ) Marsden et al. 2009 Western barred bandicoot ( Perameles bougainville ) Smith et al. 2010 Tasmanian devil ( Sarcophilus harrisii ) Siddle et al. 2010

PAGE 43

43 Table 15. Examples of variation at immunerelated genes associated to infectious diseases in natural populations. Host Pathogen Candidate Genes Reference Black rat ( Rattus rattus ) Yersinia pestis CCR5 Tollenaere et al 2008 European harbour seal ( Phoca vitulina) Morbillivirus phocine distemper virus (PDV) CD46,IFN ,IL4,IL8,IL10,RARa, SLAM TLR2 McCarthy et al 2011 Soay sheep ( Ovis aries ) Teladorsagia circumcincta BL4 microsatellite locus at IFN Coltman et al 2001 Field vole ( Microtus agrestis ) M ultipathogen infections Turner et al. 2011 Bank vole ( Myodes glareolus ) Puumala hantavirus (PUUV) TNF Guivier et al. 2010 Five ducks species ( Anas spp.) Influenza A virus Mx gene Dillon and Runstadler 2010 African buffalo ( Syncerus caffer ) Gastrointestinal nematodes BL4 microsatellite locus at Ezenwa et al. 2010

PAGE 44

44 Figure 11. Allele frequency changes driven by coevolution. The frequency of host genotype is in blue and the frequency of pathogen genotype is in red (from Woolhouse et al. 2002). 1a (above) A series of selective sweeps by host and pathogen alleles derived by mutation or migration leads to the fixation of the advantage genotype, followed by the fixation of the pathogen genotype. 1b (below) Red Queen cycles leading to dynamic polymorphisms in both the host and pathogen

PAGE 45

45 CHAPTER 2 DISSIMILAR CHANGES ON SEROPREVL ANCE AND MORTALITY RATES IN AVIAN SPECIES AFFECTED BY WEST NILE VIRUS IN UNITED STATES Introduction Emerging pathogens may cause d isease outbreaks of large magnitude and unusual virulence after a successful introduction infecting multiple hosts. The introduction of these pathogens into new host populations is of special concern because of the absenc e of a shared evolutionary history constraining susceptibility and pathogenicity ( Woolhouse et al. 2005). Multihost pathogens (i.e. pathogens capable of infecting greater than one type of host) establish a more complex dynamic compared to single host pathogens since the machinery required f or infection, exploitation and transmission are highly variable among hosts (Gandon 2004). For instance, multihost pathogens may exploit the heterogeneity of host to spread more rapidly as a result of the disproportional contribution of some hosts with hig her susceptibilities (Dobson & Foufopoulos 2001; Yates et al. 2006). West Nile virus (WNV) has rapidly dispersed throughout the United States (US) and the Americas since its introduction in 1999 in New York, affecting humans, domestic animals and wildlife (Lanciotti et al. 1999). Birds are the natural reservoir for WNV. However, the transmission dynamics and susceptibility to disease is dominated by an extreme heterogeneity in the community of avian host species (LaDeau et al. 2007) E ven though 284 avian species have been reported with seroprevalence to WNV ( Hayes 2001), W est N ile virus has severely affected populations of several North American bird s pecies (Kilpatrick et al. 2007). Analysis of bird abundance data based on the Breeding Bird Survey (BBS) has indicated that some species have declined

PAGE 46

46 significantly since the arrival of WNV, whereas others have remained unaffected (LaDeau et al. 2007, Koening et al. 2007, Wheeler et al. 2009). In terms of susceptibility, c orvids including crows and jays, have been shown to be highly susceptible to WNV whereas sparrows and pigeons differ in the response to the virus showing lower mortality rates after both experimental inoculation and natural infections ( Mclean et al. 2002 ; Koma r et al. 2003, Wheeler et al. 2009). Experimental infections have also shown a broad spectrum of pathology and disease, from asymptomatic infections to severe neurological or sudden death, which in some cases is correlated to the viral load (Komar et al 2 003). Although birds are the natural reservoirs for WNV, t he natures of these host heterogeneities in susceptibility are poorly understood. Two highl y similar WNV strains from the l ineage I were responsible for the recent epizootics in North America (NY99) and in Israel (Isr98 ) causing mortality in birds (Lanciotti et al. 1999; Malkinson et al. 2002). Mutational changes i n the helicase gene of the WNV NY99 strain was implicated in an increased virulence especially for the American crow (Brault et al. 2007). Irrespective of the virus properties, the differences noted in host susceptibility, although many have as yet to be identified, may be contributed by previous exposure to endemic flaviviruses developing cross protective immunologic response (Brault et al. 2004) avoidance of seasonal transmission cycles by migration, and differences o f the host preference by the vector. These wide ranges of heterogeneity among host species highlight the potential divergent response to the emergence of an infectious disease in the long term.

PAGE 47

47 Species adaptation to WNV emergence in the U nited States (US) can be represented by temporal and geographic changes in seroprevalence and mortality rates in wild bird populations. Avian s eroprevalence of WNV in the U S h as been one of the most intensively recorded wildlife diseases in the last decade. Serological data determines which species were exposed naturally to WNV, and survived. Thus, i t i s a useful indicator of host exposure and host susceptibility. On the other hand, m ortality r ates indicate direct susceptibility to the pathogen, which may be complemented with the information from experimental inoculations. We hypothesized that analyzing published literature on seroprevalence and mortality rates is possible to assess the adaptati on of host species to WNV, such as the increasing of specific antibodies prevalence in time accompanied by a decreas e in the diseasemediated mortality. This chapter first reviews the literature available on mortality and seroprevalence to WNV in avian species from 1999 to 2009 Then, we analyzed these two variables to assess the importance of host taxonomic classes, temporal trend and whether these trends are consistent across macro geographic zones. We considered the fact that the measurements under study were obtained from different sources. Thus, the heterogeneity of sources was also analyzed in order to draw statistical inferences from combined data. Material and Methods Database Search and Dataset Construction We conducted an intensive search of peer reviewed publications in the ISI Web of Science and PubMed, selecting any article containing words (West Nile virus [MeSHTerms] OR WNV [All Fields]) AND ("birds"[MeSHTerms OR All Fields] OR "avian"[All Fields]). In total, 421 references were found after the search Relevant

PAGE 48

48 articles were selected screening titles (first step), abstracts (second step), and entire article (third step). Eighty seven references were preselected containing data collected from U nited States from 1999 to 2010. From those, we adopted the inclusion criteria of references that have available the response counts of the number of individuals positive to WNV for covariates of seroprevalence and mortality rate, and sample sizes (number of animals per specie sampled). Thus, 63 ref erences consisting of 39 seroprevalenceand 24 mortality based articles met the final inclusion criteria for our review and were used for further analysis ( Appendix A 1 and A 2 ). From each selected study, we recorded the total number of individuals per sp ecies sampled, the total number of individuals seropositive to WNV per species or the total number of carcasses positive to WNV RNA or virus cultured antigen per species, the year of sampling, and the sampling location at county level when available. An ec ologically relevant sample size greater than 15 individuals was included avoiding oversaturation of the dataset with rare, accidental or nonoccasional bird species. In addition, many articles failed to report sample sizes lower than 1015 individuals. The dataset was constructed with the recorded data and the following variables : Taxonomic classes which included Order and Family. Susceptibility to WNV mortality is extremely variable among birds and is not well established whether this variation is a trait at species level or at higher taxonomic levels. Thus, based on the species affected, we included in the dataset these variables to analyze the effect of clustering at higher taxonomic level. Sampling years which included a 10 year frame from 1999 to 2009. Temporal data was grouped in three categories : i nitial (19992001), mid ( 2002 2004) and r ecent ( 2005200 9 ). Geographic location which was analyzed at state level and included 23 states S tates were clustered in two geographic zones based on macroclimatic characteristics designated as Eastern (CT, FL, GA, IL, KY, LA, MA, MN, MS, NJ, NY, OH, PA, VA, WI) and Western (CA CO, KS, MT, ND, OK, TX, WY)

PAGE 49

49 Reference source was included for each article in the dataset using a numerical code. Data Analysis First, w e compare d proportions of prevalence and mortality rate at different host taxonom ic scales in contingency t ables by using adjusted Mantel Haenszel 2 and odds ratios (OR). As response variable, we used the count of i ndividual positives to antibodies WNV per species per study for the seroprevalencebased studies, and count of individual that virus was detected in carcasses per species per st udy for mortality based studies. As predictors, we used each taxonomic scale. S eroprevalence (SP) and m ortality rates (MR) were log transformed and preliminarily plotted to visualized general patterns. For given species, if data was available, we partitioned by temporal and geographic location using the previous analysis to study the patterns of SP and MR. We first determined across all individual overall patterns by descriptive plots. Then, these associations were further explored using three statistical approaches to assess changes between point estimates of parameters of interest: 1) linear regression, 2) logistic regression with random effects, and 3) logistic regression for interactions. We first used descriptive statistics in determining patterns for SP and MR throughout time by using box plots for the values of SP and MR of e very species at each timeperiod. This general approach is useful to determine the data distribution for each species/time period combination, to detect overall patterns, data dispersion, and data anomalies. For instance any outliers found w ould correspond to specific research articles requiring speci fic attention.

PAGE 50

50 Then, the values of SP and MR for each species at each timeperiod were estimated from pooled data. The corresponding confidence intervals were constructed using Eq uation 21, ( ) (2 1) where is the SP or MR estimated and ( /2 ) i s the z score for a (1 level. This procedure helps to observe overall patterns and changes of the SP and MR, controlling by time and measures the precision of the estimates. Once the overall patterns were determined; a series of regression models were constructed to assess each trend. As a first approach, we fit a linear regression model to determine whether SP and MR are a positive (i.e. increase) or negative (i.e. decrease) function of a year period, controlling for geographic locations when possible. We fitted the model as Eq uation 22, = + + (2 2) where correspond to SP or MR, is the year period and the normal error of the model. Then, a logistic regression model was constructed considering time period as both a categorical variable and continuous regressor. We considered the latter more appropriate since the main objective of this analysis was to retrieve the underlying trend of the data rather than a comparison of differences between categories. The reference article from where the data was obtained was included as a random effect. Thus, any

PAGE 51

51 possible correlation between observations coming from same articles is incorporated and considered into the model. The models were fitted on the form of Eq uation 23, ( ) = log = + + + (2 3 ) where is the SP or MR, is the year period, is the random effect to account for differences by j articles ,and the norm al error of the model. Finally, a logistic regression incorporating the interaction term of geographic location and year period into the model was performed using the Eq uation 24 ( ) = log = + + + + (2 4) where correspond to SP or MR, is the year period, indicates the geographic area (East or West), is the interaction term, and the normal error of the model In this model, the focus of interest is the term for We included only those species with enough data available for comparison. Slopes were inspected first from the linear regression and, then, the interaction effect was incorporated into the model to assess the significance of the term. All statistical anal yses were conducted using R (version 2.15, The R Foundation for Statistical Computing 2012) and SAS 9.3 ( SAS I nstitute, Cary, North Carolina). Results Literature Search Analysis From the 421 article references initially found (244 SP articles and 137 MR articles), 87 were preselected as potentially eligible articles (Table 2 1). From these, 63 articles were finally included for analysis based on the information criteria which

PAGE 52

52 consisted of 39 articles for the seroprevalence based analysis and 24 articles f or mortality rate based analysis. According to the number of articles reporting seroprevalence and mortality rates in bird species, the year with higher sampling and data collection was 2004 ( Figure 21). To summarize specific findings of the literature r eview regarding host heterogeneity, the dataset included 14 3 species exposed to WNV which mount an antibody response against specific WNV antigens detected by ELISA and/or PRNT ( Table A 2). Notably, only three species ( Junco hyemalis Vermivora celata, and Melospiza georgiana) were consistently reported as WNV antibody negative. Eighty eight species were positive for WNV detected by real time reverse transcription PCR, cell culture, or antigenbased commercial kits (RAMP Response Biomedical Corporation, B urnaby, BC ; VecTest Microgenics Corporation, Fremont, CA) ( Table A 3). In total, 174 species that had more than 1 5 samples collected were either positive for specific antibodies against WNV or for WNV antigen (Table 22). Passeriformes represented 77% o f the data, followed by Columbiformes and Galliformes (Figure 2 2 ). The species with the most data collected was Corvus brachyrhynchos followed by the house sparrow ( Passer domesticus ) In the seroprevalence studies, the species most collected consisted of Passer domesticus and the whitecrowned sparrow ( Zonotrichia leucophrys ), followed by the house finch ( Carpodacus mexicanus ), mourning dove ( Zenaida macroura) and Gambel's quail ( Callipepla gambelii ) (Figure 2 3). In the mortality rate studies, the most represented species was Corvus brachyrhynchos followed by Aphelocoma coerulescens and Cyanossita cristata all members of the Corvidae family (Figure 23).

PAGE 53

53 Overall Analysis Using all dat asets, we determined general trends of SP and MR by taxonomic class using contingency tables and odds ratios (OR). Tables and OR showed extreme heterogeneity of responses to WNV across order, family and species in both SP and MR (Figure 24 ). By order, usi ng Passeriformes as a reference, Ciconiiformes, Galliformes, and Columbiformes were more likely to be seropositive than Passeriformes (odds ratio of 1.56, 2.47, and 2.61) respectively, all P<0.0001; Table 23). However, Passeriformes were more likely to have WNV RNA or viral antigen than any other order ( Table 24) Exploring family heterogeneity within order, only three orders had two or more families to compare ( Figure 25 ). Within the Passeriformes, the Corvidae and Cardinalidae families were more likel y to have seropositive species than any other family ( Table 25). In the mortality based dataset, Corvidae and Paridae were more likely to be positive for WNV antigen compared with other families (Table 26). Our dataset contained species from 32 families. We found inconsistency across families when species were compared. Some families showed no significant differences of SP or MR rates among species, such as Anatidae, Emberizidae, Parulidae, and Columbi dae (P>0.5 for all). In families with evidence of heterogeneity response to WNV among species, the variation was disproportionally given by a few (one or two) species. For instance, within Cardinalidae family, the seroprevalence of Cardinalis cardinalis was significantly higher than the other six species. Within Icteridae family, Quiscalus quiscal a was more likely to be seropositive than any of the other 11 species. Carpodacus mexicanus Turdus migratorius and Cyanositta cristatta were more likely to

PAGE 54

54 be seropositive within the Fringillid ae, Turdidae, and Corvidae families, respectively (P<0.0001 for all). Trend Analysis by Species We first analyzed the data by species using a set of descriptive box plots to determine the distribution of each species by year period that may be useful for r egression models. As a result of this exploratory analysis, we selected 19 species for SP analysis and 9 species for MR analysis We then analyzed using confidence intervals and linear regressions to determine whether the SP or MR are increasing or decreasing as a function of time. Based on that analysis, we obtained that nine species ( Callipepla californica, Calllipepla gambelii, Cardinalis cardinalis, Carduelis tristis, Carpodacus mexicanus, Corvus brachyrhynchos, Corvus ossifragus, Cyanocitta cristata, and Sturnus vulgaris ) showed a positive trend (i.e. increasing seroprevalence in time), whereas the results of the other ten species were inconclusive (Table 27) (Figure 26 ). For mortality rate, the linear regression demonstrated a positive trend in time for only Cyanocitta cristata whereas the results for the other species were inconclusive (Table 28). Incorporating the reference article as a random effect, we built a logistic regression which is more appropriate for the nature of the dataset in terms of fitting and predictability. Using this model, most of the species showed a positive trend of Quiscalus quiscala and Mimus polyglottos that showed a negative pattern, although not significant. Eight species ( Callipepla californica, Calllipepla gambelii, Cardinalis cardinalis, Carpodacus mexicanus, Columba livia, Corvus brachyrhynchos, Corvus ossifragus, and

PAGE 55

55 Melospiza melodia) showed significant increment in seroprevalence over time (Table 27 ). The other nine species showed no temporal changes in the seroprevalence. According to the model, the odds of being seropositive across temporal periods increase. The change in the odds o f seroprevalence by increasing one unit of time was highest in Calllipepla gambelii ( e = 80.7) followed by Cardinalis cardinalis ( e = 15.4), and Columba livia ( e = 10.62). The increment of seroprevalence was in lower proportion during the first to second year period (1999 to 2004), increasing mainly during the second to third period (20052009) across all species. However, the change of increment of seroprevalence in time was different among species. The changes of SP between year period 1 and year period 2 were higher for the species Cardinalis cardinalis Columba livia and Corvus ossifragus ( SPperiod2 SPperiod1 = 0.149, 0.173, and 0.122, respectively) whereas C arpodacus mexicanus and Corvus brachyrhynchos the changes on the increment of the seropr evalence were smaller ( SPperiod2 SPperiod1 = 0 .03, and 0.04, respectively) Callipepla gambelii Cardinalis cardinalis and Columba livia had the higher changes on the SP from the year period 2 to year period 3 ( SPperiod3 SPperiod2 = 0.65, 0.58, 0.52, respectively). Corvus brachyrhynchos was the only species that showed significant negative trend in mortality rates across the years ( e = 0.65) (Table 28). The odds of dying by WNV across time showed a slower decrease during the first to second year period ( 0.098), increasing the rate by the second to third year period ( 0.105). When the interaction between year period and geographic location was included in the model, the seroprevalence of Columba livia showed evidence of an incremental

PAGE 56

56 increase in the seroprevalence in the East whereas for the West, the slope was close to zero. Discussion WNV is currently the most widely distributed arbovirus in the world ( Kramer et al. 2008). Birds are the primary hosts for the virus throughout its geographical distribution. The regular exchange of virus between mosquitoes and birds is the regular transmission cycle. However, not all bird species produce a high and long enough viremia to infect susceptible mosquitoes and complete the transmission cycle (Komar et al. 2003). In addition, birds differ in the response to the vir al infection. The dissimilar mortality rates and a broad spect rum of pathology and disease suggest differences in susceptibility to WNV ( Mclean et al. 2002; Komar et al 2003). Using the data retrieved from 63 articles, we were able to determine that these differences in seroprevalence and mortality rates are consistent throughout taxonomic classes. However, these results must be carefully interpreted due to the nature of the dat a sources Thus, these analyses only complement or reveal general trends from local or specific studies already described. According to the results, susceptibility to WNV infection may not be a trait specific of either an order or a family of species. Within family, some species showed similar responses to WNV (i.e. Anatidae, Emberizidae, Parulidae, and Columbi dae). However, other families had one or two species that disproportionally contributed to the prevalence of the pathogen. Those species such as the Carpodacus mexicanus Cardinalis cardinalis, Quiscalus quiscal a Turdus migratorius and Cyanositta cristatta are relatively common, abundant species in urban and periurban areas Thus, susceptibility is most likely to be associated to chances of encounter, which is given by the occurrence of the mosquito preference and the bird

PAGE 57

57 abundance (Kilpatrick et al. 2006), linking WNV disease to the urban and periurban environments (Brown et al 2008). Although seroprevalence means that the individual have survived t o the infection mounting an effective immune response, this also means that the virus pressure is relatively high in the population. Thus, the cost of maintaining an immune response is highly beneficial to the individual. The maintenance of elevated seropr evalence in common avian species may help to prevent the virus amplification and the transmission intensity to other species including humans. A level of herd immunity in peridomestic passerine populations has been recently proposed as mechanism to protect and delay WNV outbreaks in California (Kwan et al. 2012). In order to assess temporal trajectories of SP and MR in different avian species, we fitted a logistic regression model incorporating a random effect which accounted for the references articles. We first used a series of descriptive and exploratory approaches to define and select the species with appropriate and valid data. From these initial analyses, 19 species were selected for the SP dataset and nine spec ies for MR. The initial lineal regression model showed significant estimates in three other different species. However, this analysis was used to explore the data and, based on the nature of the data the logistic regression model with random effect is more appropriate and robust allowing the detection of more subtle difference in the dataset (Agresti 2007) We found the seroprevalence for Corvus brachyrhynchos is increasing in time, although at a slower magnitude compared to other passerines. However, at th e same time, it is the only species that the mortality rate is decreasing in time. C. brachyrhynchos has been impacted by WNV particularly severely (Ladeau et al. 2007,

PAGE 58

58 Wheeler et al. 2009, Foppa et al 2011). T he displacement of the NY99 genotype by the W N02 genotype may be implicated in these differences ( Davis et al. 2005). However, the new genotype conserved the mutational changes at the NS3 helicase associated with the increased morbidity and viral load in the American crow (Moudy et al 2007). These t emporal changes may evidence the host adaptation to the virus such as the selection for resistant individuals or the development of an adaptive immune response. The data also showed the increment of the seroprevalence in time for the other seven species. O verall, during the first three years of WNV introduction, the immune response to WNV was slower than later years. These changes may however be confounded by the persistence of immune individuals from previous years that retained PRNT titers accumulating, t hus, the proportion of seropositives These patterns in host seroprevalence and mortality rates obtained from previously published articles revealed how the virus has been circulating throughout all these years until becoming endemic in the United States. More importantly the persistence of the virus that firstly relied on a highly virulent strategy causing high mortality of amplifying hosts seems now to have changed to a moderate virulence in hosts with acquired herd immunity (Kwan et al. 2012). Since t he emergence of WNV in North America in 1999, the number of articles has reached an average of 323 per year, a larger number than the average of 13 articles per year since the virus first description in 1943 until 1999. However, a small fraction of the literature is focused on the epidemiology of the natural avian host. Despite the intrinsic limitations of these analytic approaches, we were able to determine temporal changes of two basic epidemiological measurements in av ian species that

PAGE 59

59 may determine the adaptation of the host to WNV and the strategies of viral persistence in a highly heterogeneity matrix of hosts.

PAGE 60

60 Table 21. Summarize of literature search for seroprevalencebased and mortality ratebased articles. Article types References found Potentially eligible References Included Number of journals Seroprevalence 244 55 39 11 Mortality rate 137 32 24 9

PAGE 61

61 Table 22 Description of the taxonomic data extracted from the selected references articles. Cases Species Family Order Seroprevalence 91,919 143 32 10 Mortality rate 54,763 88 30 11

PAGE 62

62 Table 23. Odds ratios and confidence interval for descriptive analysis of data collected from seroprevalence studies. Passeriformes was used as a reference. Order Odds ratio Confidence Interval P value Passeriformes 1 NA Piciformes 0.19 0.06 0.46 0.001 Anseriformes 0.54 0.36 0.77 0.001 Strigiformes 0.60 0.33 1.01 0.080 Gruiformes 0.82 0.20 2.23 0.740 Ciconiiformes 1.56 1.26 1.92 0.0001 Falconiformes 2.81 2.31 3.39 0.0001 Columbiformes 2.61 2.45 2.78 0.0001 Galliformes 2.47 2.32 2.63 0.0001 NA, not applicable.

PAGE 63

63 Table 24. Odds ratios and confidence interval of avian orders from data collected of mortality rate studies. Passeriformes was used as a reference. Order Odds ratio Confidence Interval P value Passeriformes 1 NA Gruiformes 0.055 0.02 0.15 0.0001 Columbiformes 0.085 0.07 0.09 0.0001 Piciformes 0.0114 0.07 0.17 0.0001 Anseriformes 0.167 0.11 0.25 0.0001 Ciconiiformes 0.22 0.15 0.32 0.0001 Strigiforme 0.233 0.19 0.28 0.0001 Galliformes 0.229 0.17 0.29 0.0001 Falconiformes 0.331 0.29 0.37 0.0001 NA, not applicable.

PAGE 64

64 Table 25. Odds ratios and confidence interval of avian families within the respective order from the data collected of seroprevalence studies. In Passeriformes, Corvidae was used as reference. In Galliformes, Tetraonidae was used as a reference. Order Family Odds ratio Confidence Interval P value Passeriformes Corvidae 1 NA Cardinalidae 0.99 0.89 1.11 0.955 Emberizidae 0.06 0.05 0.07 0.0001 Fringillidae 0.63 0.56 0.71 0.0001 Hirundinidae 0.07 0.03 0.17 0.0001 Icteridae 0.27 0.23 0.32 0.0001 Lanidae 0.43 0.21 0.89 0.024 Mimidae 0.29 0.25 0.33 0.0001 Paridae 0.05 0.02 0.09 0.0001 Parulidae 0.04 0.03 0.05 0.0001 Passeridae 0.53 0.47 0.58 0.0001 Sturnidae 0.273 0.15 0.49 0.0001 Turdidae 0.467 0.39 0.557 0.0001 Tyrannidae 0.066 0.03 0.14 0.0001 Galliformes Tetraonidae 1 NA Odontophoridae 6.28 3.11 12.7 0.0001 Phasianidae 31.22 14.72 66.22 0.0001 NA, not applicable.

PAGE 65

65 Table 26. Odds ratios and confidence interval of avian families within the respective order from the data collected of mortality rate studies. In Passeriformes, Corvidae was used as reference. In Galliformes, Tetraonidae was used as a reference. Order Family Odds ratio Confidence Interval P value Passeriformes Corvidae 1 NA Icteridae 0.063 0.05 0.08 0.0001 Sturnidae 0.068 0.05 0.089 0.0001 Emberizidae 0.07 0.05 0.08 0.0001 Tyrannidae 0.07 0.03 0.138 0.02 Mimidae 0.1 0.08 0.13 0.008 Parulidae 0.104 0.07 0.148 0.04 Passeridae 0.11 0.09 0.127 0.0004 Turdidae 0.115 0.09 0.135 0.006 Hirundinidae 0.125 0.064 0.24 0.59 Cardinalidae 0.129 0.08 0.2 0.52 Fringillidae 0.176 0.15 0.19 0.026 Lanidae 0.45 0.28 0.71 0.0001 Paridae 0.629 0.29 1.32 0.0001 Galliformes Tetraonidae 1 NA NA Odontophoridae 0.06 0.02 0.14 0.0001 NA, not applicable.

PAGE 66

66 Table 27 Linear regression and logistic regression estimates and P values for species selected from seroprevalence articles. Species Linear Regression Logistic Regression with Random effects Estimate P value Estimate P value Agelaius phoeniceus 0.0551 0.452 1.0144 0.091 Callipepla californica 0.1482 0.008 0.9472 0.003 Callipepla gambelii 0.0516 0.054 4.3908 0.018 Cardinalis cardinalis 0.2390 0.000 2.7375 0.000 Carduelis tristis 0.0167 0.008 1.7764 0.181 Carpodacus mexicanus 0.0995 0.000 1.4309 0.000 Columba livia 0.0232 0.710 2.3624 0.000 Corvus brachyrhynchos 0.0950 0.020 1.1418 0.002 Corvus ossifragus 0.1055 0.041 1.3817 0.000 Cyanocitta cristata 0.2398 0.061 2.3681 0.116 Dumetella carolinensis 0.0023 0.956 0.8083 0.132 Geothlypis trichas 0.0040 0.635 0.1406 NA Melospiza melodia 0.0023 0.881 1.3801 0.047 Mimus polyglottos 0.0043 0.894 0.04756 0.961 Molothrus ater 0.0328 0.236 1.0071 0.104 Passer domesticus 0.0418 0.248 0.5017 0.236 Quiscalus quiscala 0.0455 0.702 0.2327 0.697 Sturnus vulgaris 0.0576 0.061 NA NA Turdus migratorius 0.0321 0.659 0.7509 0.184

PAGE 67

67 Table 28. Linear regression and logistic regression estimates and P values for species selected from mortality rate articles. Species Linear Regresion Logistic Regression with Random effects Estimate P value Estimate P value Bubo virginianus 0.0863 0.519 0.8367 NA Columba livia 0.018 0.581 0.2807 NA Corvus brachyrhynchos 0.0668 0.183 0.4262 0.042 Corvus ossifragus 0.015 NA 0.04879 NA Cyanocitta cristata 0.293 0.023 1.0632 0.160 Passer domesticus 0.0272 0.415 0.4381 0.161 Quiscalus quiscula 0.0161 0.653 0.6584 0.386 Sturnus vulgaris 0.0013 0.968 0.0592 0.927 Turdus migratorius 0.0481 0.490 0.1708 NA

PAGE 68

68 Figure 21. Distribution of articles selected by year of publication (above) and by year of data collection (below). Dark grey bars are articles on seroprevalence and in light grey are articles on mortality rate.

PAGE 69

69 Figure 22. Distribution of data collected from selected articles by order of avian species sampled.

PAGE 70

70 Figure 23. Distribution of the number of indivi duals (count) per species reported in the articles selected for analysis. Dark grey bars are articles on seroprevalence and in light grey are articles on mortality rate. Only sample size > 1,000 is shown.

PAGE 71

71 Figure 24. Odds ratio and confidence interv als for seroprevalence (above) and mortality rate (below) by avian order. Passeriformes was used as a reference.

PAGE 72

72 Figure 25. Odds ratio and confidence intervals for seroprevalence (above) and mor t a lity rate (below) by family within the Passerifor mes order. Corvidae family was used as reference.

PAGE 73

73 CHAPTER 3 MULTIPLEXED MICROSAT ELLITE LOCI IN AMERI C AN CROW (CORVUS BRACHYRHYNCHOS): A SEVERELY AFFECTED NATURAL HOST OF WEST NILE VIRUS1 Introduction Pathogens play a significant role in the evolution of the host genome by influencing the maintenance of much genetic variation in natural populations (Antonovics et al. 1994). Novel or newly introduced pathogens can exert a strong selective pressure on nave host populations (Dobson & Foufopoulos, 2001) modul ating the genetic composition in relatively short timescales. However, the identification of these impacts is challenged by the limited information available to distinguish the effect of disease from other environmental forces. West Nile virus (WNV) is sti ll the leading cause of arboviral encephalitis in the United States 13 years after its North American arrival. From 1999 to 2010, 30,625 confirmed human cases of clinical disease have been reported with 1,202 deaths (Center for Disease Control and Preventi on, CDC, 2012). Similarly, from 2001 to 2010, 61,845 wild birds have died of WNV ( CDC, 2012 ) although these data may underestimate the real impact of this virus on wild populations. The American crow (AC, Corvus brachyrhynchos ) has been by far the most neg atively affected host species by the emergence of WNV resulting in estimated population decline up to 60% since the arrival of the virus (Caffrey et al. 2005; LaDeau et al. 2007). The genetic consequences of population decline on the AC have not been explored thus far. Therefore, study of the potential population constraints, in particular, is required 1 This chapter has been previously published as: Verdugo, C., et al. Multiplexed microsatellite loci in American crow ( Corvus brachyrhynchos ): A severely affected natural host of West Nile virus. Infect. Genet. Evol (2012), http://dx.doi.org/10.1016/j.meegid.2012.08.020

PAGE 74

74 to understand the resulting adaptation of a natural host to a highly pathogenic emergent disease within a decade of viral activity. Microsatellites are segm ents of the nuclear genome composed of short tandem repeats of DNA sequences. Although discovery can be laborious, these markers are well established and accepted methodology to study genetic variation in animal populations. Microsatellite libraries using repeat motifs enriched libraries approach have been previously developed for corvids (Tarr & Fleischer 1998; Stenzler & Fitzpatrick 2002; Ernest et al 2008 ; Busch et al 2009) including the AC (Schoenle et al. 2007). However, the detection of sequence dat a using this approach is time consuming and expensive. Recently, 454 shotgun genome random pyrosequencing has demonstrated high efficiency and effectiveness for the development of microsatellite libraries in model and nonmodel organisms (Bai et al. 2010; Castoe et al. 2010). In the study described here, this strategy is further developed for obtaining microsatellite loci for the AC, allowing for rapid identification and genotypic characterization of a large set of species specific markers. Additionally, we analyzed a set of heterologous microsatellite markers isolated from other corvid species to develop a polymorphic cross species library for further genetic analysis. The AC is morphologically similar to the sympatric Fish crow (FC, Corvus ossifragus ). Al though both species are susceptible to WNV, a higher WNV antibody prevalence in FCs and lower mortality rates of experimentally infected FCs (Komar et al. 2003; Yaremych et al. 2004; Wilcox et al. 2007) supports the conclusion that the C. ossifragus is l ess affected by WNV than the C. brachyrhynchos. These susceptibility differences may define divergent evolutionary trajectories in response to disease over

PAGE 75

75 time, particularly, for the evolution of resistance and tolerance defense traits (Roy & Kirchner 2000) among species. Thus, it is relevant to distinguish samples at a species level in order to elucidate whether changes on neutral or adaptive markers have occurred over time as a consequence of this viral disease. We have tested a set of primers and probes we developed allowing rapid differentiation between these two natural host species with different responses to WNV. Materials and Methods SpeciesSpecific Markers Ten micrograms of high quality DNA was extracted from muscle by standard phenol/chloroform/ isoamy l alcohol (PCI) method (Strauss 2001) from a single C. brachyrhynchos tested WNV negative and collected from Florida in 2010. DNA was used to generate a shotgun sequence library performed on 1/8th Titanium PicoTiter plate (454 GS FLX Titanium, 454 Life Sciences, Roche, Branford, CT) (Margulies et al. 2005). Sample preparation and analytical processing were performed at the Interdisciplinary Center for Biotechnology Research (ICBR, University of Florida) according to manufacturers protocol. The fast a file including the total reads obtained were screened for di tri and tetranucleotides with a minimum of repeat motifs of 6, 4, and 4, respectively, using a Perl script developed at the University of Florida (William Farmerie, ICBR, Gainesville, FL). This dataset was then analyzed for loci containing a minimum suitable space of > 25 bp in the flanking 5 and 3 region for priming. BatchPrimer3 v2.0 (You et al. 2008) was used to design primer pairs in flanking regions with a product size of 100400, a 4 0 60 % of GC content, and a 5565C melting temperature. All loci containing reads with more than one microsatellite were discarded to avoid redundancy. Amplification test and gradient optimization (55C to 65C) were

PAGE 76

76 performed in 10 L reaction volumes co ntaining 1 L of 30 50ng of DNA, 0.5 units of Taq polymerase (JumpStart, SigmaAldrich), 1X PCR Buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.5 M of each primer. The PCR cycle conditions were 92C for 5 min, followed by 35 cycles at 92C, 55C, and 72C o f 40 s each, and a final extension for 5 min at 72C. Amplicons were then visualized in 3% agarose gel. Length polymorphism was tested in 12 samples using the same protocol. Cross Species Amplification Fifty nine microsatellite primer pairs developed for other corvid species (Tarr & Fleischer 1998; Martinez et al. 1999 ; Stenzler & Fitzpatrick 2002; Ernest et al 2008; Busch et al 2009) were screened against C. brachyrhynchos DNA (Table B 1). All primers were tested for amplification on a single sample, an d then for polymorphisms on 12 individuals using the same PCR protocol specified for species specific markers. Microsatellite Genotyping Genomic DNA from frozen muscle and brain tissues of 50 AC was extracted by standard PCI method (Strauss 2001). These specimens were collected from Florida (n=9) and New York (n=41) between 2000 and 2010 and test ed posit ive and negative for WNV (Table B 2). Different geographic locations and a broad time span were chosen to increase the chance of detection of more polymor phisms from the molecular markers under study. Species specific and crossspecies forward primers were labeled with one fluorescent dye consisting of either 6 FAM, VIC, NED or PET, and tested on a panel of 50 individuals. Loci were sorted by allele size into multiplex sets to maximize simultaneous analysis while avoiding overlap between markers of the same label dye in the same reaction. Multiplex PCR reactions were conducted on 15 L reactions containing 1X Typeit Microsatellite PCR Master Mix (Qiagen), 0 .2 M of each primer

PAGE 77

77 and 5 ng of DNA template. The cycling conditions consisted in a denaturing step of 95C for 5 min, followed by 30 cycles at 95C for 30 s, 57C for 30 s and72C for 30 s and a final extension step of 30 min at 60C. PCR fragments were run on a ABI 3730 fluorescent DNA analyzer (Applied Biosystems, Foster City, CA) using a size standard GeneScan600 LIZ. Electropherograms were examined using Genemarker (Softgenetics, State College PA) for allele size scoring. Data Analysis The number of alleles (NA), expected (HE) and observed heterozygosity (HO), exact Hardy Weinberg tests (HW) and linkage disequilibrium between loci were calculated with Arlequin 3.5.1.3 (Excoffier et al. 2010). Significance values were corrected by sequential Bonferroni procedure. Polymorphic information content (PIC) was determined for each locus using Cervus 3.0.3 (Kalinowski et al. 2007). Scoring errors, large allele dropouts, and null allele frequencies were assessed in MicroChecker 2.2.3 (van Oosterhout et al. 200 4). Input data files were reformatted in GeneAlEx 6.41 (Peakall et al. 2006). In order to identify potential genomic location of each species specific locus, read sequences containing a polymorphic locus were compared with available orthologous sequences on the zebra finch ( Taeniopygia guttata) genome assembly (Build 1.1) using BLASTN. Repeat motifs were masked using the filter for low complexity segments whereas the default values for the other parameters were maintained. A potential genomic location was assigned if it provided a unique hit at E value of 110 or less. Species differentiation In order to develop a molecular method that could be used as alternative the negligible morphological differentiation of the AC from the sympatric FC, mtDNA from

PAGE 78

78 five individuals of each species was amplified and Sanger sequenced targeting 570 bases of the mitochondrial cytochrome oxidase 1 (COI) gene using cbCOI 2F (5 CTCCTCATCCGAGCAGAACT 3) and cbCOI 1R (5 GCTGGATCGAAGAATGTGGT 3) primers. The COI gene is used as a standardized barcode sequence because is highly conserved intraspecies even among individuals from different geographic locations and has deep interspecific divergences (Hebert et al. 2004). Species were previously identified by standard body measurement s (Pyle 1997) and later confirmed based on sequence similarities using BOLD (Ratnasingham et al. 2007). Sequences were aligned using MAFFT (Katoh et al. 2008) and tested for suitability of restriction enzymes. One NheI site was found to digest in the FC am plicon only. After amplification following same PCR conditions as described above, AC and FC purified PCR products were digested with NheI enzyme according to manufacturers recommendation (New England Biolabs, Ipswich, MA) at 37C overnight and visualized by electrophoresis in 2% agarose gel (Figure A1). We explore d further for the feasibility of a high throughput and rapid species determination by a real time PCR protocol using information from the previous experiment. Primer Express v 3.0 (ABI, Applied B iosystems, Foster City, CA) was used to design the primers CrowCOI F (5 ATTGGTGCCCCAGACATAGC3) and CrowCOI R (5 TTCATCCTGTTCCTGCTCCTG 3) and the MGB TaqMan labeled probes COI CB (5 FAM CTTCTCCTTCTAGCCTCMGB 3) for C. brachyrhynchos and COICO (5 NEDCCTTCTACTGCTAGCCT MGB 3) for C. ossifragus based on 16 sequences available from BOLD (Ratnasingham & Hebert 2007) and this study (Table 31). Primers and probes target a 120 bp region of the COI gene, including two base differences

PAGE 79

79 between the AC and the FC, and the NheI enzyme digestion site (Figure 31). The real time PCR assays were performed in triplicates using a 7500 Fast real time PCR system (ABI). Primer dilutions were first optimized using SYBR Green method according to manufacturers recommendations (ABI). Then, labeled probes were tested in singleplex and multiplex assays. The 20 L optimized reaction mix contained 10 L of TaqMan Fast Universal Master mix (ABI), 0.4 L of each primer (100 nM each), 0.5 L of each probe (125 nM each) for si ngleplex and multiplex experiments, 2 L of template (20 ng total), and nuclease free water. Cycling conditions consisted in an initial step of 20 s at 95C followed for second step of 40 cycles of 3 s at 95C and 30 s at 60C. The limit of detection, PCR efficiency, and test precision were also assessed. Real Time PCR assays were conducted on three replicates repeated three times using AC and FC DNA for PCR assessment and limit of detection. A standard curve was created using 10fold dilution (100 ng, 10ng, 1ng, 100pg, 10pg, and 1pg) of each DNA species. Test precision was evaluated by intra assay repeatability, assessed on three replicates of 100 ng of each DNA species, and by inter assay reproducibility, evaluated on three replicates in three repeated as says using also 100 ng of each DNA samples. Finally, we conducted a blind test using 40 individuals total, 30 AC and 10 FC, to determine the specificity of the assay using duplicates samples in the multiplex protocol. A species determination was considered successful when the amplification signal was detected for one only fluorescence (FAM for reactions containing AC DNA template and NED for reactions containing FC DNA).

PAGE 80

80 Results Microsatellites development A total of 143,477 reads were obtained from the s hotgun library of AC with an average length of 324 bases. Thus, a total of 46,526,067 bases were sequenced with an average quality of 31.4 (base call accuracy of 99.9%). The approximate AC genome size is 1222.51242.06 Mb (Gregory 2012 ). Thus, the sequenci ng data represented a genome coverage of approximately 3.73.8%. There were 4,800 microsatellites identified on the dataset (3% of SSR abundance) and 3,431 had a minimum suitable flanking region for priming (Table 32; Figure 32). Primers were designed for 323 microsatellite loci. Fifty nine loci (44 tetranucleotides and 15 trinucleotides) were selected for primer synthesis. Fifty four amplified successfully, 12 showed no specific amplification or unexpected electrophoretic pattern and these were not consi dered further. Of the remaining 42, 22 were monomorphic and 20 polymorphic. Fifty nine crossspecies loci were selected to test on C. brachyrhynchos DNA. Of these, 55 (93%) amplified successfully and 12 (22%) were subsequently polymorphic. Marker Genotypi ng In total, 32 polymorphic loci, 20 species specific (Table 33) and 12 cross species (Table 34), were selected for further genotyping of 50 individuals distributed in 6 multiplex panels (Table 35). Multiplex panels ranged from three to seven markers wi th sizes ranging between 65 and 450 bp. Results of microsatellite characteristics are shown by geographic location (Table 36 and 37) and for all 50 individuals (Table 38). Overall, the number of alleles per locus ranged from 2 to 16 (mean = 6.69), obser ved and expected heterozygosity ranged from 0.16 to 0.84 (mean = 0.57) and 0.18 to 0.88 (mean = 0.59), respectively, and polymorphic information content (PIC) ranged from

PAGE 81

81 0.18 to 0.87 (mean =0.55). Two loci (Cb09, and Cb12) departed from HW equilibrium at P < 0.05, although one of them, Cb09, remained significant after the Bonferroni correction. The locus Cb09 from the New York samples remained also significant. The same Cb09 locus showed significant homozygosity excess with null allele frequencies of 0.137 There was no evidence for scoring error due to stuttering or large allele dropout at any locus. Twenty eight of 496 test pairs showed departure from linkage disequilibrium but none remained significant after Bonferroni correction. From the 20 species spe 10) and these were distributed over nine different chromosomes. The lower distance between two loci was 4.5Mbp between Cb01 and Cb14 on chromosome 1. Species Differentiation The COI gene segment was s uccessfully amplified and the restriction enzyme reactions verified the species identification in all crows tested. The multiplex real time PCR successfully identified the COI gene segment in 40 crow samples and probes correctly assigned each species in AC and FC samples. Ct (threshold cycle) values were undetermined for cross species reactions, reflecting a 100% specificity of the assay (Figure A2). For the same amount of DNA template (100 ng), the mean Ct values of the multiplex assay were 15.9 for COI CB in AC samples and 15.1 for COI CO in FC samples. We obtained correct species assignment using even DNA concentration as low as 1 pg for both probes, although Ct values were 33.6 and 32.6 for AC and FC, respectively. The PCR efficiency was estimated throug h the linear regression of the dilution curve (Figure A3). The AC and the FC average slope were 3.56 and 3.48, respectively. Mean Ct values showed no significant differences between replicate assays, although Ct values from AC were slightly lower than FC (Table 39).

PAGE 82

82 Discussion We developed and characterized 32 polymorphic microsatellite markers distributed in 6 multiplex panels useful for population genetic studies in Corvus brachyrhynchos and other species of the Corvus genera. In particular, the loci reported here are part of an ongoing research which will study the genetic diversity of the AC population after the emergence of WNV. Although previously reported loci for American crow (Schoenle et al 2007) were also tested (data not shown), only 6 were polymorphic, less than that the needed number of microsatellite loci to achieve high power detecting population changes of genetic variation (Luikart et al. 1998). Therefore, new markers were obtained using both global and target technologies, including next generation sequencing (NGS) and cross species amplification of previously published markers. The use of NGS as a source of sequence data has been a successful technique for nonmodel species, resulting in enhanced discovery while saving resources in laboratory time and expense (Guichoux et al. 2011). Using whole genome shotgun sequencing on 1/8th of a plate was enough to cover roughly 3.73.8% of the genome. Bird genomes have documented low frequency of short tandem repeats (Primmer et al. 1997) and, consequently, the present study resulted in a lower SSRs abundance than organisms from a different class (Castoe et al 2010; (Saarinen et al. 2010) using the same search parameters. Even so, this approach allowed us to obtain a sufficient quantity of repeat motifs (4,800) to develop a large number of polymorphic microsatellite loci. We selected for further investigation only perfect trinucleotides and tetranucleotides motifs, which are more informative loci to infer demographic events using coalescent methods and also making score process less difficult (Guichoux et al 2011).

PAGE 83

83 Although we have a successful rate of cross species amplification (93%), most of the loci gave monomorphic product in C. brachyrhynchos. This low rate of cross species polymorphism was also evident in a previous study using cross amplification in the corvid Siberian jay ( Perisoreus infaustus ) (Lillandt et al. 2002). The frequency of successful amplification and polymorphism increased as pairwise cytochrome b (cytb) genetic distances decr eased between AC and the species that the marker was developed for (data not shown). The 32 loci evaluated here were also tested for cross amplification in two FC ( Table A 3). In this current data, 93% amplified successfully whereas 38% were polymorphic in these two individuals. Thus, these markers may have a potential for application on this closely related species using a larger number of samples. The six loci used here for cross species amplification were previously tested for polymorphism also in AC (Tarr & Fleischer 1998; Ernest et al 2008). Although we were not able to replicate the same results as in the previous studies, we provide not only further measurements on genetic diversity but also information of compatible loci for multiplex panels. Differ ent sample sizes used and geographic areas of individuals sampled may have influenced in the different cross amplification and polymorphism levels. Overall polymorphism was considered moderate to high (PIC > 0.5) with 78% of the loci having all 4 or more alleles. Since number of alleles depends heavily on sample size, we may have missed alleles from Florida samples. In fact, Cb20 showed monomorphism in those individuals sampled. However, although sample sizes differ, NA, HE, HO and PIC were strongly correl ated (r = 0.87, 0.74, 0.75, and 0.81, respectively) among both sampling locations. One locus significantly deviated from

PAGE 84

84 Hardy Weinberg (HW) equilibrium. According to the expected distribution of homozygote size classes of this locus, the deviation from HW probably resulted more from the existence of null alleles than a Walhund effect by pooling samples from different regions. The real time PCR resulted in a highly specific assay, generating only one product intended for the target sequence. Linear regress ion of the Ct values and the quantity of DNA revealed a negative linearity. A slope of 3.32 0.332 reflects a PCR efficiency of 100%. Thus, the average slope for both species indicates a high efficiency of the assay developing more products in fewer cycl es. In addition, the real time PCR protocol allowed a faster method of species determination (i.e. two hours) than a traditional restriction enzyme PCR (i.e. 824 hours), or gene sequencing (24 days). Both species have slight differences in specific body measurements (i.e. tarsal length) making it difficult for researchers to determine species based only on that parameter. Thus, this real time assay may be used during virus outbreaks and concomitant mortality of crows as a fast, accurate, sensitive, and hi gh throughput method of avian survey. Additionally, this method can be further explored and applied for other avian species that are difficult to differentiate morphologically. This large set of polymorphic microsatellite loci will allow further studies t o characterize, compare and monitor historic and contemporary genetic structure of the C. brachyrhynchos affected by WNV emergence. Given the level of cross species amplification and polymorphism, this set of molecular markers could potentially be used to analyze other corvid species. Using rapid and highthroughput genetic techniques now available provides a unique opportunity to improve our understanding of

PAGE 85

85 evolutionary history of emerging infectious diseases in naturally occurring species for which there is limited genetic information.

PAGE 86

86 Table 31 Sequences of Corvus brachyrhynchos and Corvus ossifragus used for the development of primers and probes. Sample Sequence ID / Accession number Species Year collected Location Institution Cbrachy*1 UFAC1M / NA C. brachyrhynchos 2009 Florida, U S FMNH Cbrachy*2 UFAC2M / NA C. brachyrhynchos 2009 Florida, US UFCVM Cbrachy*3 BOTW110 04 / DQ432866 C. brachyrhynchos NA Florida, US NMNH Cbrachy*4 BROMB332 06 / NA C. brachyrhynchos 1989 Ontario, Canada ROM Cbrachy*5 BROMB352 06 / NA C. brachyrhynchos 1988 Ontario, Canada ROM Cbrachy*6 GBIR0122 06 / AY527241 C. brachyrhynchos NA Maryland, US GenBank, NCBI Cbrachy*7 GBIR1357 09 / EU834854 C. brachyrhynchos NA NA GenBank, NCBI Cbrachy*8 TZBNA303 03 / AY666408 C. brachyrhynchos NA Ontario, Canada ROM Cbrachy*9 TZBNA304 03 / AY666482 C. brachyrhynchos NA Ontario, Canada ROM Cbrachy*10 USNML165 11/ JQ174546 C. brachyrhynchos 2001 Virginia, US NMNH Cossif*1 BOTW107 04 / DQ432874 C. ossifragus NA Florida, US NMNH Cossif*2 BOTW317 05 / DQ432873 C. ossifragus NA Florida, US NMNH Cossif*3 UFFC193 / NA C. ossifragus 2011 Florida, US UFCVM Cossif*4 UFFC187/ NA C. ossifragus 2011 Florida, US UFCVM Cossif*5 UFFCNB 144 / NA C. ossifragus 2011 Florida, US UFCVM Cossif*6 UFFC1381 / NA C. ossifragus 2011 Florida, US UFCVM Data extracted from BOLD (Ratnasingham and Hebert, 2007) and from this study. US: United States; NA: Not applicable; FMNH: Florida Museum of Natural History; UFCVM: University of Florida College of Veterinary Medicine; NMNH: National Museum of Natural History Smithsonian Institution; ROM: Royal Ontario Museum.

PAGE 87

87 Table 32 Descriptive summary of the microsatellite loci identified in 143,477 reads from the 454 shotgun library of Corvus brachyrhynchos Type of Repeat (number of repeats per loci) Number of loci identified Number of loci for priming (%) Primer pairs tested Loci amplified Polymorphic Loci 1405 831 (59.1) 0 N/A N/A 2672 2072 (75.9) 15 11 9 723 528 (73.0) 44 34 16 Total 4800 3431 (71.4) 59 46 25

PAGE 88

88 Table 33 Species specific polymorphic microsatellite markers developed for Corvus brachyrhynchos. Locus Forward primer sequence Reverse primer sequence Cb01 CCAGCTTGGGCTACATGC TGCCGCAGCAAGTTGAAAG Cb02 AAATCCGTTGTCACCCAGC TCCAGCTCCTGCATTTCTG Cb03 TCGTGCATAACTGAGGAATTCTG GAGTGGCTGAAGTGAACAGG Cb04 ACACTGGAGCCAGGCAAAG GGCGCTACTGAAATAGGCAG Cb05 TAGAAGCCAGAGAGTGAAGC GAGAAGGAGCTGTGAGCTG Cb06 AGACACAGCTCAGCTCTCAT ACAGAGGGGAAGTACAGACTC Cb07 CACACTGGAGTGAGCAAAG TCATCGAAGGAGATGCAC Cb08 GCTGGTGTATGGGTTCAA GTAAGTGCACAGACATGTGG Cb09 TCCGTGTTTCTAGGTAGG CTGGGTTACCATGTGTGC Cb10 CCTACGTCTAGGAAGGGAAT GCTCCAGCACTTGTATCTTC Cb11 CTCTGGAAAACTGCCTGAGT CCCTGCACTTCCATAGGTA Cb12 CCCTCAGAGCATAATCTCC CCGAGGAGATGAAAGACTTC Cb13 CTGCCACTAACAGACCATCT GAACCACATTTCCAGCAG Cb14 GGGTACCCAAACATGTCA GAGGCCCTGAAATAGGTG Cb16 TGTGTTAGGGTCTCACTTCC CCTGAAAGTCAGCTTCTCC Cb17 ACAAAGTCAGGCCCTGTT ATGATCTCTGAGGTCCCTTC Cb20 TCTCGATACAGAGAGGAAGC AGTTGCAAGCCTCCAGTT Cb21 GCTGATAAGCTGCATTCC GCGGGCTACTTTCTTGTAGT Cb22 CCATTTGGGGATTCTGTG GCTGCTGACAGAGGAAAGT Cb23 TCAGTGCATCCCTGAAGT ACAAAGCAAGTGGGAGGT

PAGE 89

89 Table 34 Cross species polymorphic microsatellite markers used for Corvus brachyrhynchos. Locus Primer sequences References ApCo18 F: CTAGTGCCTTGGCTTTCAGT Stenzler & Fitzpatrick 2002. R: ACTACCTTACAAATCCCTTATCTG ApCo30 F: GCCCTGATGCTGTTGATGGT Stenzler & Fitzpatrick 2002. R: CTGGAGCCTGGTTTAGAGTTATGC ApCo31 F: AGCTGAAGGTGGTATTATGACAGG Stenzler & Fitzpatrick, 2002. R: GTCAGCAGGACTAGTGTTCCATCA ApCo40 F: CTTCTGACAAGACACAGGAGCC Stenzler & Fitzpatrick 2002. R: GCACAGATCTCAGTTGCATCACTC ApCo95 F: GGCCACAGCAAAGCCCTCAT Stenzler & Fitzpatrick 2002. R: GAACTTCTCTTGTTGCCCTGAAACAG Ck.5A4D F: CACAAACATGGGTTGAGTCT Tarr & Fleischer 1998. R: TGAAAGTTTGGTAGAGGCTC Ck1B5D F: ACTGCGTGTCCTGAATACGC Tarr & Fleischer 1998. R: ACAAGACCAGAGCTTCAGCT Ck4A3G F: GAGATGTCATCTATGGAACC Tarr & Fleischer 1998. R: TTCCAGTAGCTGGTGCAGAC Ck4B6D F: TTGCATCCCTGATTTATGGC Tarr & Fleischer 1998. R: CTAGGAAGCAATCCAGAGTC PJAAAG1 F: GAACGGTAATTTAAGAATCACTGC Busch et al 2009. R: GACACCATCAACCTGAACATCAC PnuA3w F: GACAGGAGCCCAACTTTCTG Ernest et al 2008. R:GGCGTTCCAAAGGTAGTCTTC PnuC222w F: CCTGACACTTCACAGTTCCAAA Ernest et al 2008. R: CATGCCAATTCTTGGTAAGACA F: Forward; R: Reverse

PAGE 90

90 Table 35 Multiplex panels of 32 microsatellite loci developed from the DNA of 30 individual Corvus brachyrhynchos Panel Loci Accession Number Repeat Motif Dye Allele range(bp) 1 ApCo95 AF520872 (CA) 15 6 FAM 131 147 1 Cb13 JQ241347 (AAT) 14 NED 164 194 1 Cb12 JQ241346 (TTTA) 6 PET 201 213 1 Cb01 JQ241335 (ATTG) 5 6 FAM 230 242 1 Cb11 JQ241345 (TTGT) 7 PET 259 263 2 Ck4A3G AF026335.1 (GT) 11 6 FAM 64 84 2 Cb07 JQ241341 (TTCC) 5 PET 98 106 2 Cb08 JQ241342 (TCCA) 6 6 FAM 107 143 2 Ck4B6D AF026340.1 (TA) 10 (GT) 12 VIC 142 148 2 ApCo40 AF520887 (ATGAG) 13 VIC 163 178 2 PJAAAG1 EU346855 (AAAG) 24 PET 252 260 3 Cb22 JQ241356 (ATA) 14 6 FAM 77 95 3 Cb21 JQ241355 (CAAT) 6 NED 102 110 3 Cb10 JQ241344 (ATTT) 6 VIC 123 135 3 Cb09 JQ241343 (TATC) 7 PET 134 154 3 PnuC222w EF580128 (TAAA) 8 VIC 162 166 3 Cb16 JQ241350 (TAGA) 6 NED 161 177 3 Cb14 JQ241348 (ACT) 15 PET 169 206 4 Ck1B5D AF026332.1 (GT) 15 6 FAM 84 102 4 Cb23 JQ241357 (GGAT) 12 PET 99 127 4 Cb17 JQ241351 (AGC) 14 NED 160 181 4 Cb02 JQ241336 (TGT) 4 PET 331 337 5 Cb05 JQ241339 (TAC) 21 6 FAM 96 156 5 Cb04 JQ241338 (AGG) 8 NED 157 187 5 ApCo31 AF520891 (GAT) 5 (GGT) 1 (GAT) 5 (GGT) 1 (GAT) 8 PET 111 144 5 ApCo18 AF520898 (TG) 12 VIC 148 162 5 ApCo30 AF520890 (GAT) 16 NED 193 229 5 PnuA3w EF580122 (TTTG) 12 PET 255 269 5 Cb03 JQ241337 (ATA) 4 6 FAM 428 467

PAGE 91

91 Table 35 Continued Panel Loci Accession Number Repeat Motif Dye Allele range(bp) 6 Ck.5A4D AF026337.1 (AT) 2 GTTT(GT) 13 6 FAM 85 111 6 Cb20 JQ241354 (GAGG) 5 PET 101 113 6 Cb06 JQ241340 (TAAA) 6 VIC 120 160

PAGE 92

92 Table 36 Polymorphism characteristics of 32 microsatellite loci developed from the DNA of 41 individual Corvus brachyrhynchos from New York State. Locus NA a HO b HE c PICd HWe NullB1 f Cb01 4 0.634 0.571 0.470 0.741 0.045 Cb02 3 0.317 0.316 0.285 1.000 0.004 Cb03 4 0.366 0.375 0.325 0.038 0.004 Cb04 8 0.707 0.727 0.675 0.621 0.007 Cb05 14 0.854 0.788 0.763 0.899 0.042 Cb06 8 0.829 0.812 0.775 0.990 0.015 Cb07 3 0.220 0.238 0.213 0.538 0.013 Cb08 6 0.659 0.624 0.571 0.676 0.026 Cb09 7 0.474 0.787 0.744 0.000 0.171 Cb10 4 0.659 0.706 0.638 0.373 0.023 Cb11 2 0.488 0.419 0.328 0.449 0.052 Cb12 4 0.244 0.304 0.283 0.048 0.043 Cb13 11 0.829 0.832 0.798 0.543 0.004 Cb14 8 0.756 0.818 0.782 0.632 0.029 Cb16 5 0.707 0.690 0.629 0.617 0.015 Cb17 8 0.829 0.787 0.744 0.931 0.030 Cb20 3 0.268 0.332 0.297 0.246 0.045 Cb21 3 0.220 0.203 0.190 1.000 0.016 Cb22 8 0.700 0.695 0.647 0.280 0.008 Cb23 9 0.780 0.812 0.774 0.163 0.012 Ck.1B5D 7 0.707 0.739 0.684 0.371 0.013 Ck.4A3G 9 0.707 0.769 0.728 0.424 0.030 Ck.4B6D 5 0.171 0.163 0.157 1.000 0.008 Ck.5A4D 9 0.707 0.622 0.585 0.949 0.057 ApCo18 7 0.683 0.770 0.723 0.345 0.044 ApCo30 11 0.756 0.718 0.682 0.749 0.028 ApCo31 12 0.800 0.879 0.855 0.465 0.037 ApCo40 4 0.317 0.322 0.300 0.637 0.001 ApCo95 10 0.805 0.855 0.825 0.229 0.021 PJAAAG1 2 0.293 0.253 0.219 0.571 0.034 PnuA3w 5 0.250 0.318 0.297 0.363 0.046 PnuC222w 3 0.098 0.139 0.130 0.180 0.035 Mean 6.438 0.557 0.575 0.535 S.D. 3.162 0.243 0.244 0.238 Significant P values and frequencies before Bonferroni correction are in bold. S.D: standard deviation; aNA: number of alleles; bHO: observed heterozygosity; cHE: expected heterozygosity; dPIC: polymorphism information content; eHW: Hardy Weinberg equilibrium exact test P value; fNullB1: null allele frequencies using Brookfields estimator 1 (Brookfield 1996).

PAGE 93

93 Table 37 Polymorphism characteristics of 32 microsatellite loci developed from the DNA of nine individual Corvus brac hyrhynchos from Florida. Locus NA a HO b HE c PICd HWe NullB1 f Cb01 3 0.889 0.627 0.505 0.002 0.186 Cb02 3 0.667 0.503 0.404 0.639 0.130 Cb03 3 0.222 0.216 0.194 1.000 0.015 Cb04 3 0.444 0.627 0.505 0.311 0.093 Cb05 8 0.778 0.869 0.798 0.329 0.024 Cb06 6 0.889 0.830 0.753 0.670 0.059 Cb07 2 0.111 0.111 0.099 1.000 0.006 Cb08 4 0.556 0.739 0.649 0.317 0.084 Cb09 5 0.778 0.804 0.721 0.164 0.011 Cb10 3 0.444 0.542 0.426 0.637 0.045 Cb11 2 0.667 0.471 0.346 0.456 0.154 Cb12 4 0.556 0.660 0.579 0.273 0.042 Cb13 10 0.778 0.843 0.779 0.362 0.010 Cb14 7 0.889 0.843 0.768 0.920 0.052 Cb16 3 0.333 0.307 0.269 1.000 0.034 Cb17 6 0.889 0.810 0.730 0.294 0.070 Cb20 1 0 0 0 NA 0 Cb21 3 0.556 0.627 0.505 1.000 0.023 Cb22 6 0.778 0.810 0.730 0.838 0.007 Cb23 5 0.889 0.784 0.700 1.000 0.085 Ck.1B5D 5 0.889 0.791 0.709 0.938 0.011 Ck.4A3G 6 0.667 0.830 0.751 0.123 0.066 Ck.4B6D 2 0.111 0.294 0.239 0.175 0.130 Ck.5A4D 6 0.667 0.680 0.610 0.868 0.015 ApCo18 6 0.778 0.686 0.620 0.788 0.079 ApCo30 5 0.875 0.800 0.708 0.768 0.071 ApCo31 7 0.889 0.843 0.768 0.919 0.052 ApCo40 2 0.333 0.425 0.321 1.000 0.049 ApCo95 7 0.889 0.824 0.747 0.671 0.063 PJAAAG1 2 0.444 0.471 0.346 1.000 0.000 PnuA3w 3 0.222 0.386 0.327 0.342 0.104 PnuC222w 2 0.444 0.471 0.346 1.000 0.000 Mean 4.375 0.604 0.610 0.530 S.D. 2.152 0.272 0.237 0.226 Significant P values and frequencies before Bonferroni correction are in bold. S.D: standard deviation; aNA: number of alleles; bHO: observed heterozygosity; cHE: expected heterozygosity; dPIC: polymorphism information content; eHW: Hardy Weinberg equilibrium exact test P value; fNullB1: null allele frequencies using Brookfields estimator 1 (Brookfield 1996).

PAGE 94

94 Table 38 Polymorphism characteristics of 32 microsatellite loci developed from the DNA of 50 individual Corvus brachyrhynchos Locus NA a HO b HE c PICd HWe NullB1 f Cb01 4 0.680 0.576 0.477 0.087 0.070 Cb02 3 0.380 0.351 0.313 0.844 0.024 Cb03 5 0.340 0.350 0.310 0.067 0.005 Cb04 8 0.660 0.709 0.653 0.429 0.025 Cb05 16 0.840 0.817 0.796 0.429 0.017 Cb06 8 0.840 0.809 0.773 0.921 0.022 Cb07 3 0.200 0.216 0.195 0.525 0.011 Cb08 7 0.640 0.651 0.606 0.217 0.003 Cb09 7 0.532 0.783 0.741 0.000 0.137 Cb10 4 0.620 0.692 0.624 0.390 0.038 Cb11 2 0.520 0.465 0.355 0.539 0.041 Cb12 4 0.300 0.377 0.351 0.045 0.053 Cb13 11 0.820 0.835 0.805 0.747 0.004 Cb14 10 0.780 0.835 0.804 0.701 0.025 Cb16 5 0.640 0.651 0.595 0.629 0.003 Cb17 8 0.840 0.785 0.745 0.842 0.035 Cb20 3 0.220 0.281 0.256 0.136 0.046 Cb21 3 0.280 0.325 0.293 0.335 0.032 Cb22 8 0.714 0.715 0.670 0.580 0.004 Cb23 9 0.800 0.806 0.769 0.252 0.001 Ck.1B5D 8 0.740 0.745 0.695 0.351 0.001 Ck.4A3G 9 0.700 0.789 0.755 0.246 0.046 Ck.4B6D 5 0.160 0.188 0.179 0.396 0.022 Ck.5A4D 10 0.700 0.632 0.599 0.898 0.046 ApCo18 7 0.700 0.773 0.727 0.560 0.037 ApCo30 11 0.776 0.739 0.705 0.817 0.026 ApCo31 12 0.816 0.888 0.867 0.564 0.033 ApCo40 4 0.320 0.347 0.322 0.457 0.017 ApCo95 10 0.820 0.858 0.831 0.105 0.016 PJAAAG1 2 0.320 0.298 0.252 1.000 0.019 PnuA3w 5 0.241 0.338 0.318 0.127 0.068 PnuC222w 3 0.160 0.216 0.195 0.193 0.044 Mean 6.688 0.566 0.589 0.549 S.D. 3.393 0.238 0.232 0.229 Significant P values and frequencies before Bonferroni correction are in bold. S.D: standard deviation; aNA: number of alleles; bHO: observed heterozygosity; cHE: expected heterozygosity; dPIC: polymorphism information content; eHW: Hardy Weinberg equilibrium exact test P value; fNullB1: null allele frequencies using Brookfields estimator 1 (Brookfield 1996).

PAGE 95

95 Table 39 Mean Ct values obtained from the real time PCR on 100 ng of Corvus brachyrhynchos and Corvus ossifragus DNA samples N C. brachyrhynchos C. ossifragus Mean S.D. Mean S.D. Intra Assay 3 15.897 0.048 15.096 0.017 Inter Assay 9 15.991 0.123 15.128 0.064 N: number of replicates; S.D.: standard deviation.

PAGE 96

96 Figure 31 Alignment of nucleotide sequences of the COI segment in Corvus brachyrhynchos (Cbrachy*1 10) and Corvus ossifragus (Cossif*1 5) which span the primers and MGB TaqMan labeled probes. Note: Sequence information can be obtained from Table A 3. Asterisks highlight nucleotide differences. Grey line and yellow line indicates position of forward primer (CrowCOI F) and reverse primers (CrowCOI R), respectively. The COI CB probe and COI CO probe are highlighted in blue and red, respectively

PAGE 97

97 Figure 32 Distribution of repeat motif sequences of identified microsatellite loci (grey) and the subset of loci with flanking region for priming (dark grey) from (A) dinucleotide repeats, (B) trinucleotide, and (C) tetranucleotide.

PAGE 98

98 CHAPTER 4 EFFECT OF WEST NILE VIRUS ON THE GENETIC VARIA TION IN THE AMERICAN CROW (CORVUS BRACHYRHYNCHOS) Introduction Emerging infectious diseases exert strong effects on the dynamics of natural host populations For instance, emerging pathogens have been implicat ed, directly or indirectly, in drastic and rapid declines of several wildlife species during the last decades (McCallum et al. 2007; Robinson et al. 2010) including global and local extinction of host species (Cunningham et al. 1998; DeCastro & Bolker, 2005 ; Sc hloegel et al. 2006). West Nile virus (WNV) (Flavivirus: Flaviviridae ) is one of the most recent and dramatic emerge nt diseases in the western hemisphere becoming the predominant vector borne disease in the United States within three years (Kramer et al. 2008). W est Nile virus r apidly dispersed throughout the United States and then the rest of America since its introduction in 1999 in New York City, affecting humans, and a wide range of domestic animals and wildlife (Komar 2003). Although birds are res ervoir hosts of the virus, the susceptibility to WNV is highly variable among avian species. There is an inter and intraspecific variability in mortality rates under both natural and experimental conditions suggesting the potential for genetic variability necessary for different evolutionary dynamics. The American crow ( AC, Corvus brachyrhynchos ) has been the most negatively affected avian species resulting in estimated population decline up to 60% since the arrival of the virus (Caffrey et al 2005; LaDe au et al. 2007). Previous studies on the AC have suggested a disease mediated inbreeding in a high proportion of crows (Townsend et al. 2009). Furthermore, those inbred crows were more likely to

PAGE 99

99 die with disease symptoms (including WNV), had poor condition, and weaker innate immune response (Townsend et al 2010). Levels of genetic variation are positively cor related to population size (Frankham 1996). Populations that have experienced a recent dramatic reduction in size may have increased the barriers fo r gene flow and lose significant levels of genetic diversity due to genetic drift or mating among relatives. The n egative consequences, including increased rates of allelic loss, fixation of deleterious alleles, and decreased average individual heterozygos ity can lead to the expression of deleterious or rare phenotypes reducing phenotypic values and, consequently, the overall fitness (Hansson & Westerberg 2008b ; Keller et al. 2002). When infectious diseases are the main force of population size reduction ( Rachowicz et al. 2006; Robinson et al. 2010; McCallum et al. 2007), pathogens have a significant role in population adaptation and evolution for disease resistance. A diseasemediated population decli ne may be followed by changes in the genetic structure o f the population, increased divergence from the uninfected populations, and a reduced overall genetic var iability through genetic drift (Foster et al 2007; Lachish et al. 2011). O n the other hand, gene flow among unaffected populations may reintroduce gen etic variability to genetically distressed populations (Trudeau et al. 2007; LeGouar et al. 2009). Although population decline by disease is a common threat to wildlife populations worldwide (Daszak et al 2000; Dobson et al 2001), studies evaluating the impact of infectious diseases in a natural population and comparing the post disease genetic diversity with a predisease cohort are uncommon in the literature. Model systems such as the Tasmanian devil ( Sarcophilus harrisii ) and devil facial tumor diseas e (Lachish et

PAGE 100

100 al. 2011), the European rabbit ( Oryctolagus cuniculus ) and rabbit hemorrhagic disease virus (Queney et al. 2000), and the Western lowland gorilla ( Gorilla gorilla ) and Ebola virus (Le Gouar et al. 2009) are a few recent examples that compare temporal genetic stability with pre disease samples. Temporal genetic analysis is essential for determination of the impact of an emergent disease on a host population. More broadly, these analyses contribute to our understanding of the adaptive changes necessary for the evolutionary dynamics between a host and a pathogen which may ultimately result in the selection for resistance (Woolhouse et al. 2002). The objective of the present study was to investigate the effect of W NV outbreaks on the genetic divers ity and structure of the AC population in United States Archived samples available from museums were used to characterize and compare the genetic diversity of the American crow before WNV emergence in the United States in 1999 ( P re WNV samples). Samples c ollected by the surveillance system of New York State Department of Health from 1999 to 2010 (Post WNV samples) were used to determine changes on the genetic diversity of the affected population. Using a large set of polymorphic microsatellite loci previously developed (Verdugo et al. 2012) this study hypothesized that the genetic diversity and population structure of the corvid host has been modulated by the dynamic interaction between the virus and the natural reservoir host. In particular p re WNV populations should have higher genetic diversity and structure divergence than contemporary post WNV crow populations. Understanding the genetic consequences of the virus selection pressures on the American crow is essential to understanding fully the impact of an emergent virulent pathogen in nave

PAGE 101

101 populations, and improving also the understanding of evolutionary history of emerging infectious diseases in naturally occurring species. Material and Methods Avian Samples Pre WNV outbreak samples consisted o f 30 museum specimens collected in New York State from 1975 to 1999. These samples consisted of toe pads, and alcohol and formalin preserved tissues. For post WNV outbreak samples consisted of frozen tissues of 343 crows collected by the New York State Department of Health in New York State from 2000 to 2010 as part of the surviellnce system. All samples were tested for WNV by virus isolation and/or real time reverse transcription PCR and stored then at 80C until laboratory analysis DNA Extraction and M icrosatellite Genotyping All DNA extraction and amplification of museum samples were performed under a double HEPA filter cabinet in a clean room separate from other PCR products to avoid crosscontamination. M useum samples were treated with 1X PBS buffer previous DNA extraction. Genomic DNA from was extracted using a commercial kit following manufacturers instructions (QIAamp DNA Mini Kit, Qiagen, California, USA). For toe pad samples 50 ng of d ithiothreitol (DTT) was added to the lysis buffer. Genomic D NA was extracted from frozen muscle and brain tissues using standard phenol chloroform isoamyl alcohol method (Strauss 2001). Species for all samples were confirmed as C. brachyrhynchos using a real time PCR protocol previously described (Verdugo et al. 2012). In order to discard any sampling bias towards the sex ratio, the sex of all individuals was determined by the amplification of the chromohelicase DNA binding (CHD) W gene using a multiplex protocol (Han et al. 2009).

PAGE 102

102 Thirty six polymorphic microsatellite markers previously characterized for AC (Schoenle et al. 2007; Verdugo et al. 2012) were used to genotype the samples, including, 25 species specific and 1 1 crossspecies loci. Genotyping was performed as 15 L multiplex reactions using premixed enzymes and buffer ( 1X Typeit Microsatellite PCR Master Mix, Qiagen), 0.2 M of each primer and 5 ng of DNA template. The cycling conditions consisted of a denaturing step of 95C for 5 min, followed by 30 cycles at 95C for 30 s, 57C for 30 s and72C f or 30 s and a final extension step of 30 min at 60C. PCR fragments were run on a fluorescent DNA automated analyzer ( ABI 3730, Applied Biosystems, Foster City, CA) using GeneScan600 LIZ as a size standard. Electropherograms were examined for allele size scoring using Genemarker (Softgenetics, State College PA). Data Analysis General s tatistics Scoring errors, large allele dropouts, and null allele frequencies were assessed in Micro Checker 2.2.3 (van Oosterhout et al 2004). Arlequin 3.5.1.3 (Excoffier e t al. 2010) was used to detect departures from the Hardy Weinberg equilibrium by exact tests at each locus. The l inkage disequilibrium between pairs of loci was calculated in GENEPOP v4.1 (Raymond & Rousset 1995). Significance values were corrected by sequ ential Bonferroni procedure. Temporal changes on the genetic diversity Samples from 1975 to 1999 were grouped and analyzed together as pre WNV samples. Samples collected from 2000 to 2010 were analyzed both as yearly cohorts and by grouping all as post WNV samples. In order to compare each year cohort and detect changes i n preand post WNV populations, the number of alleles (NA), expected

PAGE 103

103 (HE) and observed heterozygosity (HO), were calculated using Arlequin (Excoffier et al. 2010) for each locus and t he mean of each cohort year. Allelic diversity is largely affected by the size of the sample wherein larger samples are expected to have more alleles. Because sample sizes differed between year ly cohorts, overall allelic richness (AR) which is the number of alleles expected for a determined sample size taken from a population, and private allelic richness (PAR) which estimate the alleles unique to a particular year were corrected for the year cohort with the smallest sample size using a rarefaction method implemented in HP rare software (Kalinowski 2005). Individual genetic diversity based on all microsatellite loci were estimated using four estimators implemented in the R function, GenHet v2.3 (Coulon 2010). The first estimate determined the prop ortion of heterozygous loci (PHT) in an individual (i.e. number of heterozygous loci / number of genotyped loci). The second estimate was the standardized heterozygosity based on the mean expected heterozygosity (SHE) (i.e. PHT / mean expected heterozygosi ty of typed loci, Coltman et al. 1999). The third estimate was the standardized heterozygosity based on the mean observed heterozygosity (SHO) (i.e. PHT / mean observed heterozygosity of typed loci). The forth estimate, the homozygosity by locus (HL) (Apar icio 2006), weights more informative loci in proportion to their allelic variability. Reduction in population size is expected to lead to increase of inbreeding. We estimated two indices of inbreeding: the inbreeding coefficient, FIS, calculated at each lo cus for each year cohort (FSTAT v.2.9.3.2, Goudet 2001) and the internal relatedness (IR) (Amos et al. 2001) calculated in GenHet v2.3 (Coulon 2010) Internal relatedness weights homozygotes for rare alleles more heavily than homozygotes for common

PAGE 104

104 alleles since the former are more likely derived from related parents giving a relative measure of the extent of inbreeding. Genetic diversity measures ( heterozygosity, inbreeding indic es and allelic richness estimates) between preWNV group and each post WNV year cohort were compared using the Wilcoxons signed rank test, Kruskall Wallis test, and Chi square test All analyses were performed in SAS 9.2 ( SAS I nstitute, Cary, North Carolina) and Genetic bottleneck test We t ested for evidence of recent genetic bottleneck in the preWNV group and in each of the following year cohort s using the program BOTTLENECK 1.2.02 (Pyri et al 1990). Because rare alleles are lost rapidly after a recent population bottleneck while heterozy gosity is less affected, this software test s the expectation of a transient heterozygosity excess given the available total number of alleles ( Cornuet & Luikart 1996). We tested for three microsatellite mutational models : the infinite alleles model (IAM), the stepwise mutation model (SMM) and the twophased model of mutation (TPM). The TPM was programmed at 70% SMM and 30% IAM with a variance of 30. Analyses were performed in 1000 replicates and significance of deviations from mutationdrift equilibrium was based on the Wilcoxon signedrank test after the Bonferroni correction for multiple comparisons. Population structure analysis Changes i n the allele frequencies may be due to spatial patterns of genetic diversity. Thus, we tested whether a geographic pop ulation structure was detectable in New York State t h rough three methods (Figure 41). First, we conducted a hierarchical analysis of molecular variance (AMOVA) to perform an analysis of genetic

PAGE 105

105 differentiation. Then, w e explored for multivariate patterns of molecular diversity relative to individual location by principle coordinates analysis (PCA). Third, using ST, an analog to FST value) and geographical distances, we tested for isolation by distance using the Mantel test. These th ree tests were performed in GenAlEx 6.4 (Peakall & Smouse 2006). In addition, we PT. Pop PT and significance values matrices were calculated for each year cohort with 9999 permutations using GenAlEx. The significant P values were corrected for multiple comparisons. Immigration test The presence of recent (first generation) migration into each post WNV year cohort was evaluated using assignment tests (GeneClass2.0, Piry et al. 2004). A B ayesian method ( Rannala & Mountain 1997) was performed to estimate the likelihood of the individual genotype within the population where the individual s was sampled, Lhome ( Piry et al. 2004). Significant probabilities were produced after 10,000 Monte Carlo simulations (Paetkau et al. 2004). Results Summary S tatistics Samples did not 2= 9.403, P =0.58, df = 11) and thus the data was pooled for analysis After multiple test correction s, three loci from pre and post WNV year cohorts showed deviation from HWE. Two of these loci (Cb09 and Cobr03) also showed significant homozygosity excess suggesting the presence o f null alleles in samples from post outbreak years. For those two loci, adjusted genotypes and allele frequencies using the Brookfield 1 estimate (Brookfield

PAGE 106

106 1996) were used for further analyses. The remaining locus (Cb06 in 1999) was discarded from the analyses. Temporal E ffect on G enetic D iversity We compared the genetic diversity of samples from 1975 to1999 as the preWNV group with samples from 2000 to 2010 as the post WNV group. The post WNV group was partitioned in to year ly cohort s and each year cohort was compared with the preWNV group ( Table 41 ). The expected heterozygosity (HE) and the number of alleles (NA) were significantly higher in the preWNV group than the post WNV group (Wilcoxon: P<0.001 in both). When the post WNV group was analyzed by year, preWNV group had higher HE and NA than any other post WNV year (Wilcoxon: P<0.01) except for NA in 2010. The rarefaction method limited the allelic diversity estimates to 20 genes and provided adjusted estimates for each year cohort (Table 41 ) The preWNV group had significantly higher estimates of allelic richness (AR= 5.60) and private allelic richness (PAR =0.57) than the post WNV group (AR = 4.96 PAR=0.084, Wilcoxon: P<0.001 in both) (Figure 4 2) Each post WNV year cohort had significantly lower AR and PAR than pre WNV group (P<0.001). The 2001 year cohort had the lowest AR estimate (4.83) although 2002 and 2008 had the lowest PAR values (0.05 and 0.039). Overall, it is evident that post WNV years have lost genetic diversity when compared with the preWNV group. Crows from the post WNV outbreak group lost 8, 14, 11, and 85% of the HE, NA, AR and PAR, respectively The i ndividual heterozygosity estimates of PHT, SHO, SHE, and HL were not signi ficantly different among preand post outbreak groups but were significantly different between year ly cohorts (Kruskal Wallis: P<0.05 for all estimates) ( Table 42 ).

PAGE 107

107 The year 2001 had the lowest heterozygosity estimates ( PHT, SHO, and SHE), whereas in the following year, 2002 and 2005 had the highest values of individual heterozygosity. The within population estimates of inbreeding, FIS, were not significantly different among preand post WNV groups, although the pre WNV group had a higher estimate than t he post WNV group. However, when compared by year, the inbreeding coefficient, FIS, was significantly lower in 20022003 and 20052007 than the preWNV year (Wilcoxon: P<0.001 in all comparisons) ( Figure 43 ) The highest FIS estimate was in 2001 (0.213).This year estimate was significantly higher than any other post WNV year (Wilcoxon: P<0.001). Internal relatedness (IR) was significantly different across year cohorts (Kruskal Wallis: P<0.05) ( Table 42 ). The IR estimate decre ase d right after the emergence of WNV in the year 2000. However, the IR reached the highest mean value the following year in 2001 (0.11). By the year 2002, the IR significantly decreased until the year 2005. Since then, the IR value increase d slowly until the last year of sampling attaining to pre WNV values ( Figure 44 ). Genetic bottleneck analysis After correction f or multiple comparisons, the years of 2002, 2003 and 2010 showed significant evidence for heterozygosity excess under the IAM model only (Wi lcoxon: P<0.004). There was no significant heterozygosity excess in the preoutbreak group (19751999) as defined by all three mutation models (Table 43 ) Population Structure Analysis N o significant genetic differentiation was detected across the geographic location (AMOVA, PT=0.01 P =0 .08 ). The AMOVA analysis revealed that only a 1% of the genetic variance was found between populations. By PCA analysis, 72.54% of the variance was accounted for by the first three principal components ( Figure 45 ).

PAGE 108

108 Howeve r, there was no clear pattern of hierarchical genetic structure among the spatial locations. This result was consistent with the Mantel tests in which no significant evidence for isolationby distance in the genetic data (Mantel, Rxy=0.012, P=0.43) was found (Figure 46 ). The pairwise PT values between all combinations of years are shown in Table 4 4. After P value correction, the comparisons between year cohorts showed significant temporal changes in the AC. The genetic differentiation was mostly evident between the pre WNV group against post WNV years and the year 2001 against the other post WNV years. Immigration test Assignment tests demonstrated an increase in the frequency of recent immigrants in the year 2002 and 2003 compared to the other post W NV years (Figure 4 7 ). After 2003, either t here was no first generation migrants or decreased migrants detected. Discussion Our results provide evidence for a strong genetic impact induced by severe mortality events attributable to West Nile virus since 1 999 in the AC Although heterozygosity was also significantly affected by the diseaseinduced population decline, the genetic changes were characterized for a significant loss of the allelic diversity in the AC Post WNV individuals lost 8, 11, 14, and 85% of the HE, AR, NA, and PAR respectively, when compared to preoutbreak individuals. These major genetic changes were evident during the first four years after the introduction of the virus. Furthermore, during those years, a genetic subdivision among preand post WNV was evident. After that, genetic changes were less evident even though virus pressure on

PAGE 109

109 the population was still present. This demonstrat es the rapid recovery and stabilization of the AC to th is virulent emergent pathogen. One of the most striking differences is evidenced by the temporal instability of private allelic richness ( PAR). Private alle les are those found in a single group of samples only. Thus, preWNV group had significantly higher unique alleles compared to the following years, indicating higher genetic diversity previous to the emergence of WNV. Private alleles decreased suddenly after 1999. This may be because of a major effect of genetic drift on the decrease of the alleles present on the population (i.e. mortality events) and/or an increase in the gene flow (i.e. migration rate) ( Slatkin & Takahata 1985). The data demonstrates no evidence for geographic barrier for gene flow and immigration was relatively frequent during the year 2002 and 2003. Inbreeding can be inferred by FIS values, which were greater than zero for all years, except for year 2002. The year 2001 was significantly higher (FIS = 0.213) tha n the other years, indicating a deficiency of heterozygotes in the population due to higher levels of nonrandom mating a nd inbreeding within that particular year. Surprisingly, the year 2002 showed a significantly lower FIS value than the other years (FIS=0.004), indicating increased randomization on the mating system producing heterozygote excess in the progeny. Variation on FIS estimates may be potentially confounded by a population subdivision (i.e. Wahlund effect). We did not find evidence for geographic structure on the individuals sampled. However, we found population subdivision for that particular year compared with the other year cohorts. Hence, changes on the allele frequencies were more likely to have changed by a temporal effect rather than geographic sampling or geographic subdivision. In addition, w e have estimated internal

PAGE 110

110 relatedness us ing multilocus heterozyg osity These estimates have been proved to be positively correlated with parental relatedness in AC (Townsend et al. 2009). Internal relatedness (IR) estimates changed similarly to FIS. The year 2001 showed the higher mean IR value which is significantly d ifferent than the other years. Twenty per cent of the individuals showed IR larger than 0.25 (the expected value for offspring of half sibling mating) and another 20% showed IR values larger than 0.125, indicating a high level of inbreeding during this par ticular year cohort. Townsend et al. (2009) found a similar proportion of inbred American crows in Ithaca, New York, during 2004 and 2007. Townsend et al. (2009) determined that the survival probability is lower for inbred birds and, in part, was mediated by infectious diseases. Thus, our results complement that previous study on a larger, regional scale reaffirming that a severe WNV mediated inbreeding depression occurred in the American crow. This diseasemediated inbreeding may lead to positive feedback loops affecting overall heterozygosity but also diversity at immunerelated genes (DeCastro & Bolker 2005). There is evidence that inbred crows have poor body condition and mount a weaker innate immune response (Townsend et al. 2010). Innate immunity is a key component responsible for recognition and blocking WNV replication (Mashimo et al 2002; Fredericksen et al 2008). Thus, inbred and less genetically diverse crows are likely to have increased susceptibility to WNV if there has not been a positive selection for resistant genotypes. There is also evidence that crows from the state of New York sampled between 2006 and 2008 have been affected by disease other than WNV infections such as poxvirus associated lesions and fungal pneumonia attributable to Asper gillus sp. (Miller et al 2010). Thus,

PAGE 111

111 the reduced immune competence may lead to a higher susceptibility to infectious diseases, affecting even more the remnant population. Large randomly mating populations exposed to a temporal size constriction are predicted to experience genetic bottleneck (Allendorf 2005). Smaller sizes populations are predicted to be exposed to genetic drift and gene flow, losing allelic diversity and heterozygosity (Frankham 2005). Since rare alleles contribute modestly to heterozygosity, allelic diversity is predicted to be lost before heterozygosity during a population size reduction (Luikart et al 2008). Our results show that allelic diversity was mo re severely reduced than heterozygosity right after the year of WNV emergence, consistent with theoretical predictions of recent population bottleneck. We detected signatures of heterozygosity excess (i.e. recent bottlenecks) on the population of the year 2002, 2003 and 2010 even though a short time elapsed since the emergence of WNV However, these results should be taken with precaution since they are significant only under the infinite allele mutation model (IAM). The method used by P i r y et al. (1999) is highly sensitive to the underlying microsatellite mutational model selected. The IAM assumes that microsatellite mutations may create an infinite number of repeat units and allelic states (Kimura & Crow 1964), an assumption that better fits to the evoluti on of dinucleotides and imperfect microsatellites. Even though our dataset was constructed using eight dinucleotides and five imperfect loci out of 36 markers, previous studies suggest that avian microsatellites tend to reach an equilibrium allele frequenc y distribution and stepwise mutations are more likely to occur ( Beck et al 2003). The geographic population structure analysis showed no significant genetic differentiation between individuals from different locations. Based on the lack of

PAGE 112

112 evidence for g eographic structure of the populations sampled, we may assume small restriction of gene flow in the State of New York. However, we did identify evident genetic differentiation among samples comparing by year cohorts. Again, and consistent with the other results, we found strong evidence of moderate differentiation in the genetic variation of preWNV individuals compared with the other post WNV years. We found also that the year 2001, the most affected year, had moderate genetic differentiation with other post WNV years. This suggests a fragmentation of the population on that particular year. Together with the increased inbreeding levels and decreased individual heterozygosity indicates a temporal change on the population structure probably due to a reduction on the population size. Changes on the genetic structure induced by disease are predicted to increase the divergence with unaffected populations or preexposed populations (Lachish et al. 2011). We showed here that the post WNV population significantly di verged from the preWNV. Since we found no geographic barriers for gene flow, a massive immigration during the year 2002 and 2003 is suggested to have occurred and consequently have rise the estimates of genetic variation. Thus, we tested for first gene ration migrants in each year cohort. The year 2002 and 2003 showed the highest frequency of immigrants compared to the other year cohorts. A large migration from unaffected populations may reintroduce enough genetic variability to populations severely affected by disease outbreaks (Trudeau et al 2004). Thus, a population expansion by immigration followed the population decline after 199 9 may have resulted in the post epizootic recovery of genetic diversity in the American crow. There is no evidence for geo graphic variation in susceptibility among AC populations under experimental conditions (Komar et al. 2003)

PAGE 113

113 and seroprevalence studies. In consequence, migrants are likely to be susceptible again to the disease. However, t he presence of immigrants may increase the occurrence of polygamous mating and loss of parentage as suggested for other crow species ( Corvus corone) (Baglione et al 2002), increasing the chances of genetic recovery. Conclusions Disease induced mortality events may result in rapid reduction of population size. The emergence of WNV in United States severely affected the population of the AC, the most susceptible natural host. We provided evidence that after the virus emergence, the population genetic diversity was severely reduced altering the temporal genetic structure and increasing the levels of inbreeding. Overall, genetic diversity estimates indicated that the year 2001 had the strongest impact on the AC. Individual heterozygosity estimates indicated that individuals from the year 2001 had significantly lower heterozygosity than other years and FIS and IR indicated higher levels of inbreeding during that particular year. However, the genetic diversity was rapidly recovered by the year 2002, even using a rarefaction method that corrected di fferences on the sample size. This rapid recovery and stabilization of the population on the genetic diversity and decrease on the inbreeding estimates was due to immigration of individuals from unaffected areas. Nevertheless, the contemporary crow population is divergent from the original preoutbreak population. T he genetic monitoring of the American crow provided molecular evidence for the short term genetic impact of the e mergence of West Nile virus in United States, and demonstrates how emergent infectious diseases impact the host genetic diversity in natural populations.

PAGE 114

114 Table 41. Comparison of genetic diversity statistics between preand post outbreak year cohorts samples of the State of New York. Year N H O H E N A A R PA R F IS Pre WNV 1975 1999 29 0.585 0.668 7.583 5.596 0.572 0.155 Post WNV 2000 2010 343 0.590 0.609* 6.455* 4.964* 0.084* 0.083 2000 25 0.602 0.609* 6.278* 4.863* 0.152* 0.077 2001 25 0.566 0.595* 5.972* 4.832* 0.096* 0.213 2002 25 0.616 0.607* 6.167* 4.871* 0.050* 0.004* 2003 25 0.597 0.626* 6.472* 5.032* 0.089* 0.063* 2004 25 0.604 0.619* 6.417* 5.076* 0.084* 0.093 2005 25 0.620 0.606* 6.500* 5.150* 0.080* 0.045* 2006 25 0.593 0.613* 6.250* 5.037* 0.057* 0.066* 2007 25 0.590 0.616* 6.333* 5.001* 0.103* 0.054* 2008 26 0.576 0.599* 6.222* 4.832* 0.039* 0.076 2009 24 0.566 0.608* 6.472* 5.034* 0.096* 0.101 2010 93 0.558 0.601* 7.917 4.881* 0.076* 0.116 N, sample size; Ho, observed heterozygosity; HE, expected heterozygosity; NA, number of alleles; AR, Allelic richness; PAR, private allelic richness; FIS, inbreeding coefficient. *significant P value of Wilcoxons sign rank test.

PAGE 115

115 Table 42 Comparison of individual heterozygosity estimates for preWNV and post WNV American crow samples. Year PH T SH O SH E HL IR Pre WNV 1975 1999 0.58 1.00 0.95 0.35 0.09 Post WNV 2000 2010 0.57 1.00 0.94 0.33 0.04 2000 0.59 1.02 0.97 0.31 0.02 2001 0.53 0.92 0.87 0.38 0.11 2002 0.60 1.05 1.00 0.30 0.01 2003 0.59 1.02 0.97 0.32 0.04 2004 0.58 1.00 0.95 0.32 0.04 2005 0.60 1.04 0.99 0.30 0.01 2006 0.57 1.00 0.95 0.33 0.05 2007 0.58 1.00 0.95 0.33 0.05 2008 0.56 0.98 0.93 0.34 0.05 2009 0.55 0.96 0.91 0.35 0.09 2010 0.55 0.96 0.91 0.35 0.08 PHT, Proportion of heterozygous loci; SHO, Standardized heterozygosity based on the mean observed heterozygosity; SHE, Standardized heterozygosity based on the mean expected heterozygosity; HL, Homozygosity by locus. *significant P value of Kruskall Wallis test.

PAGE 116

116 Table 43. P values of population bottleneck tests for heterozygosity excess after correction for multiple comparisons. Year IAM TPM SMM 1975 1999 NS NS NS 2000 NS NS NS 2001 NS NS NS 2002 0.0010 NS NS 2003 0.0037 NS NS 2004 NS NS NS 2005 NS NS NS 2006 NS NS NS 2007 NS NS NS 2008 NS NS NS 2009 NS NS NS 2010 0.0001 NS NS IAM: infinite allele model; TPM: twophased mutation model; SMM: stepwise mutation model.

PAGE 117

117 Table 44 PT values (below diagonal) and P values (above diagonal) estimated among samples of each year cohort. 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1999 0.006 0.000* 0.005 0.008 0.013 0.000* 0.000* 0.001* 0.002* 0.000* 0.000* 2000 0.014 0.010 0.064 0.472 0.474 0.053 0.023 0.406 0.471 0.374 0.013 2001 0.040 0.019 0.003* 0.001* 0.016 0.004 0.008 0.001 0.001 0.003 0.000 2002 0.015 0.008 0.026 0.168 0.473 0.474 0.293 0.471 0.474 0.325 0.425 2003 0.014 0.000 0.028 0.005 0.482 0.181 0.285 0.476 0.471 0.476 0.094 2004 0.012 0.000 0.018 0.000 0.000 0.479 0.343 0.483 0.475 0.479 0.047 2005 0.020 0.009 0.026 0.000 0.005 0.000 0.476 0.462 0.031 0.100 0.059 2006 0.022 0.011 0.021 0.003 0.003 0.002 0.000 0.480 0.215 0.335 0.074 2007 0.018 0.001 0.028 0.000 0.000 0.000 0.000 0.000 0.307 0.473 0.057 2008 0.016 0.000 0.027 0.000 0.000 0.000 0.010 0.004 0.002 0.483 0.061 2009 0.022 0.001 0.026 0.002 0.000 0.000 0.007 0.002 0.000 0.000 0.108 2010 0.020 0.008 0.036 0.000 0.005 0.006 0.006 0.005 0.006 0.005 0.004 *significant value after Bonferroni correction

PAGE 118

118 Figure 4 1 Map of the s ample locations at county level of the American crows collected from New York State, United States.

PAGE 119

119 Figure 42. Temporal estimates of the mean private allele richness (PAR) by year in American crows. Bars denote standard error.

PAGE 120

120 Figure 43. Temporal comparison of estimates of inbreeding coefficient ( FIS). Bars denote standard error.

PAGE 121

121 Figure 44. Temporal comparison of estimates of i nternal relatedness (IR). Bars denote standard error.

PAGE 122

122 Figure 45. Principal coordinates analysis (PCA) for clustering of American crows. Each diamond represents the population medians of each geographic location.

PAGE 123

123 Figure 46. Pairwise population genetic distance PT) plotted again st geographical distance in the American crow.

PAGE 124

124 Figure 47 Frequency of significant (P<0.05) fi rst generation migrants of American crows distributed by year cohort.

PAGE 125

125 CHAPTER 5 RESISTANCE TO WEST N ILE VIRUS INFECTION AND INDIVIDUAL GENET IC VARIABILITY IN THE AMERICAN CROW Introduction W est Nile virus (WNV) (Flavivirus: Flaviviridae ) is one of the most recent and dramatically emergent diseases in the western hemisphere becoming the predominant vector borne disease in the United States (Kramer et al 2008). W est Nile virus r apidly dispersed throughout the United States and then the rest of the America s since its introduction in 1999 in New York City, affecting humans, and a wide range of domestic animals and wildlife (Komar 2003). Although birds are reservoir hosts of the virus, the susceptibility to WNV is highly variable among avian species. There is an inter and intraspecific variability in mortality rates and viremia loads under both natural and experimental conditions The higher susceptibility and infectivity of particular species and some individuals may contribute disproportionally to the spread of the disease. This host heterogeneity suggests the potential for genetic variation in susceptibility to WNV. The American crow ( AC, Corvus brachyrhynchos ) has been the most negatively affected avian species resulting in an estimated population decline up to 60% since the arrival of the virus (Caffrey et al. 2005; LaDeau et al 2007). Previous studies on the AC have suggested a diseasemediated inbreeding in a high proportion of crows (Townsend et al. 2009). The effect of g enetic variability on pathogen susceptibility is thought to occur by an apparent heterozygote advantage as a result of effects of homozygosity at a genomewide level, suggesting inbreeding, or at the local level by the link of the marker ( under study ) to a functional locus, suggesting associative overdominance (Hansson & Westenberg 200 8 a ). Inbred crows were more likely to die with disease symptoms, had poor condition, and weaker innate immune response

PAGE 126

126 (Townsend et al 2010) suggesting a local effect by associative overdominance of the markers with immune genes in heterosis. The objective of the present study was to evaluate whether genetic diversity influences the likelihood to be infected to WNV in the AC. The AC has shown temporal changes on the genetic diversity due to WNV mediated population decline, reducing the genetic variability and increasing the inbreeding estimates during the year 2001 to 2003. In order to avoid a potential confounding for temporal changes, we have used individuals from the year 20082010, that have no genetic differences by year. U sing a large set of polymorphic microsatellite loci previously developed (Verdugo et al 2012), we hypothesize that a change in the genetic variation has occurred after large WNV epizootics which trended towards the resistance to WNV infection. We also wanted to investigate if a local effect rather than genome wide effect is the expected mechanism for heterozygosity fitness association in this recently bottlenecked and expanded population. Material and Methods Avian Samples Samples were obtained from frozen tissues of 143 crows collected in New York State by the New York State Department of Health during 2008 to 2010. All collections were done in sites and time of the year (July to September) of occurr ing virus activity. I ndividual samples were tested for WNV by virus isolation and/or real time reverse transcription PCR using standard procedures (Lanciotti et al 2000). Samples were then stored at 80C until further analysis

PAGE 127

127 DNA Extraction and Microsatellite Genotyping Genomic DNA was extracted from frozen muscle and brain tissues using standard phenol chloroform isoamyl alcohol method (Strauss 2001). Species for all samples were confirmed as C. brachyrhynchos using a real time PCR protocol previously developed (Verdugo et al. 2012). T he sex of each individual was determined by the amplification of the chromohelicaseDNA binding (CHD) W gene using a multiplex protocol (Han et al. 2009). Thirty six polymorphic microsatellite markers previously characterized for the AC (Schoenle et al 2007; Verdugo et al 2012) were used to genotype the samples, including, 25 species specific and 11 crossspecies loci. Genotyping was performed on 15 L multiplex reactions containing 1X Typeit Microsatellite PCR Master M ix (Qiagen), 0.2 M of each primer and 5 ng of DNA template. The cycling conditions consisted of a denaturing step of 95C for 5 min, followed by 30 cycles at 95C for 30 s, 57C for 30 s and72C for 30 s and a final extension step of 30 min at 60C. PCR f ragments were run on a ABI 3730 fluorescent DNA analyzer (Applied Biosystems, Foster City, CA) using a size standard GeneScan600 LIZ. Electropherograms were examined for allele size scoring using Genemarker (Softgenetics, State College PA). Linkage diseq uilibrium between pairs of loci and departures from Hardy Weinberg equilibrium w ere calculated in GENEPOP v4.1 (Raymond & Rousset, 1995) by 1,000 batches of 10,000 Markov chain iterations per batch. Significance values were corrected by sequential Bonferroni procedure. Estimates of Genetic Diversity Five estimates of i ndividual level heterozygosity were calculated: the proportion of heterozygous loci (PHT) in an individual (i.e. number of heterozygous loci / number of

PAGE 128

128 genotyped loci); the s tandardized heterozygosity based on the mean expected heterozygosity (SHE) (i.e. PHT / mean expected heterozygosity of typed loci, Coltman et al 1999); the standardized heterozygosity based on the mean observed heterozygosity (SHO) (i.e. PHT / mean observed heterozyg osity of typed loci); the homozygosity by locus (HL) (Aparicio 2006) that weights more informative loci in proportion to their allelic variability; a nd the i nternal relatedness (IR) (Amos et al 2001) that weights homozygotes for rare alleles more heavily than homozygotes for common alleles. These five estimates were calculated in the R function, GenHet v2.3 (Coulon 2010): Statistical Analysis Crows were categorized as infected or uninfected depending on the presence or absence of viral nucleic acids i n samples tested by RT PCR or the virus in samples tested by virus isolation. The association between genetic diversity estimates and WNV infection were analyzed by generalized linear models with a binomial error and logit link function. Since the individual estimates of heterozygosity were highly correlated, estimates were individually included into the analysis avoiding multicollinearity. In addition, the evidence for the contribution of single locus to the association to WNV infection was assessed. Singl e locus effect might have resulted from the linkage of a particular microsatellite marker with a gene experiencing balancing selection. For this analysis, SHE, HL and IR estimates were recalculated with each of the 36 loci removed. All statistical analys es were performed using SAS 9.2. Results From the 143 individuals collected, 66 (46%) tested positive to WNV and 77 (54%) tested negative. Samples did not differ in the sex ratio among WNV infection 2= 3.12, P=0.07, df = 1). After multiple test c orrections, no evidence for linkage

PAGE 129

129 disequilibrium was found at any pair of loci. However, three loci (Cb09, Cb22, and CoBr03) showed deviation from HWE and were discarded for further analysis. Values of all five individual estimates of heterozygosity (PHT, SHO, SHE, IR and HL) were significantly correlated (Table 51). Independent test s of individual heterozygosity did not reveal significant differences between WNV infected and WNV uninfected crows ( P > 0.1 for all tests), although the difference in mean values did go in the direction if heterozygosity decreases susceptibility to WNV infection (Figures 51 and 5 2), uninfected crows being on average slightly more heterozygous and with lower inbreeding estimates than WNV infected crows (Table 52). We also test ed whether the heterozygosity (or homozygosity) values are disproportionally contributed by a particular locus on WNV infection (i.e. local effect) or are influenced by inbred individuals present on the study population (genomewide effect). After remo ving each single locus from the calculation of SHE, IR, and HL, the relationship between WNV infection and heterozygosity estimates remained nonsignificant Discussion We analyzed 143 individuals from different counties of New York State using 36 polymorphic microsatellite markers. Although we found a consistent pattern of higher heterozygosity levels towards resistance to WNV, we did not find significant evidence of genetic variation between crow s that differ in infection status, neither by linkage of markers to genes experiencing balancing selection or heterosis (i.e. local effect) nor by overall genetic diversity (i.e. genomewide). West Nile virus has decimated the AC population in eastern Uni ted States, decreasing populations up to 60% (LaDeau et al. 2007). Furthermore, WNV had a

PAGE 130

130 strong genetic effect on crows from the year 2000 to the year 2003 (see Chapter 4). However, after a few years, the estimates of the genetic diversity have shown to be recovered and stabilized in the population. Thus, we used individuals from later year ly cohorts in order to avoid genetic heterogeneity data confounded by temporal variability. Nonetheless, the data does not provide significant evidence for the hypothesi s that changes i n the genetic variation of the AC after a large WNV epizootic trended towards the resistance to WNV infection. Many associations between pathogen resistance and genetic diversity in nat ural populations have resulted i n either weak or absent correlation (MullerGraf et al. 1999; Cote et al 2005; Hale et al 2007; AcevedoWhitehouse et al 2009). The lack of association in heterozygosity fitness studies may be attributable for several different reasons A common reason of lack of association is the low sample size used, the small variation in levels of heterozygosity or the low power of the neutral markers used to detect correlations Associations are often weak when 15 or less neutral markers are used. This number actually is a common number of markers used on several association studies (DeWoody et al. 2005). O ur genetic data is extensive considering 36 polymorphic loci genotyped in 143 individuals, an uncommonly high number of markers used for heterozygosity fitness correlat ion studies (Sl ate et al. 2004). This design has an increas ed likelihood of demonstrating a locus linked to a candidate gene or the ability to estimate better genomewide heterozygosity (DeWoody & DeWody 2005). In addition, the genetic diversity measures used here (IR, H L, SHE) have increased genetic resolution compared to those based solely on heterozygosity (Aparicio et al

PAGE 131

131 2006). Our estimates of individual heterozygosity showed great variability (Figure 53) which would increase the chances for heterozygosity fitness correlations. In addition, a previous study has shown that multilocus heterozygosity is highly correlated to inbreeding coefficient in the AC (Townsend et al 2009) using a smaller set of markers that were partially included in this study. Thus, the lack of significant evidence of association in our study was not due to the quality of the genetic data, the small variation on the dataset or the low power of the genetic markers used. On the other hand, a possible explanation of the lack of association in this particular study is the fact that crows from the uninfected group may actually be resistant animals but may also include animals that have not been exposed yet to the virus, increasing thus the false negatives in the uninfected group. The inclusion of potential susceptible but unexposed animals may raise the levels of IR and HL and homogenize the genetic diversity in the uninfected group, having less contrast with the infected group. P revious studies have shown consistently low seroprevalence of the AC to WNV. In the same way, the > 90% mortality rate suggests that most of the exposed crows may die from WNV. Thus, in order to have a better chance of detection of exposed uninfected, it ma y be necessary to increase the sample size of individuals, something that is difficult to accomplish in wildlife species. Although not significant, we detected a general pattern of higher genetic diversity in the 77 uninfected crows. Thus, increasing the s ample size may detect stronger and significant differences. Finally, another explanation is that WNV resistance may not be associated with individual heterozygosity in the AC We found that 23% of the individuals had an IR estimate of 0.125 or higher (Fig ure 54), reflecting mating between closely related

PAGE 132

132 parents. Townsend et al (2009) detected a larger proportion of inbred crows in a particular population in Ithaca, New York suggesting that inbreeding and incest may not be uncommon in ACs Furthermore, T ownsend et al. (2009) reported that inbred crows had higher probability of disease mortality. However, WNV may not have been a disease factor since the low number of WNV cases (n=3) included in that study. Using a larger number of individuals sampled, a larger number of markers, and strictly comparing cases of WNV infection, we did not detect association between genetic diversity and WNV infection. Thus, genetic diversity and disease resistance may be associated for other pathogens, such as Pox virus, a prevalent infection in the AC (Miller et al 2010). Overall, our results suggests that WNV did not contribute to maintain overall genetic diversity nor to variability at specific loci involved in pathogen resistance in the AC The virus may not be impacted by the host immune system and other immune defense mechanism s may be responsible for modulat ing the crow WNV interaction. This is consistent with the lack of intrahost diversity in the AC at one of the candidate genes associated with susceptibility to WNV, the 5 3 Oligoadenylate synthetase (OAS) (Figure 5 5). Several genetic studies in mice, humans, and horses suggest that RNA degradation of RNaseL mediated by the OAS pathway is fundamental to control WNV replication (Perelygin et al 2002; Lim et al 2009; Rios et al 2010). In resistant strains of mice, when infected with a flavivirus, have significantly lower virus titers and dissemination than susceptible mice (Scherbik et al 2006). Thus, a quantitative (i.e. viral load) rather than a qualitative response variable may be associated with host genetic diversity.

PAGE 133

133 WNV ha s exerted a strong demographic effect on the crow population evidenced by temporal changes on the genetic diversity. Even though WNV is considered endemic to the United States, the AC h a s a low seroprevalence to WNV compared to other sympatric species (See Chapter 2). Relatively slow changes in the seroprevalence and mortality rates for WNV may indicate the slow process of adaptation of the AC to this virulent pathogen even though the se lective pressure is still present. Thus, WNV may be exert ing considerable selection force to drive genetic evolution of the crow population but in an ecologically relevant time scale. Th ese result s suggest a lack of specific genetic adaptation of the AC in response to a highly virulent strain of WNV may be due to a constant population expansion by immigration of susceptible individuals

PAGE 134

134 Table 51. Correlation coefficients (r) for estimates of individual heterozygosity assessed in American crows (n=143). All coefficients with a P <0.0001. HL SH E SH O IR PH T HL 0.940 0.940 0.954 0.943 SH E 1 0.961 0.997 SH O 0.960 0.997 IR 0.961 PH T PHT, Proportion of heterozygous loci; SHO, Standardized heterozygosity based on the mean observed heterozygosity; SHE, Standardized heterozygosity based on the mean expected heterozygosity; IR, internal relatedness; HL, Homozygosity by locus.

PAGE 135

135 Table 52. Test for effects of individual heterozygosity and WNV infection PH T SH O SH E IR HL Infected 0.557 0.99 0.95 0.053 0.344 Uninfected 0.575 1.017 0.976 0.024 0.3243 Estimate 2.604 1.327 1.367 2.022 2.779 F 1.27 1.07 1.04 1.95 1.88 P value 0.263 0.305 0.311 0.163 0.17 PHT, Proportion of heterozygous loci; SHO, Standardized heterozygosity based on the mean observed heterozygosity; SHE, Standardized heterozygosity based on the mean expected heterozygosity; IR, internal relatedness; HL, Homozygosity by locus.

PAGE 136

136 Figure 51. Individual heterozygosity estimate (SHE) in America n crows grouped by WNV infection status. Bars denote standard error.

PAGE 137

137 Figure 52. Internal relatedness (IR) in American crows grouped by WNV infection status. Means and standard errors are shown.

PAGE 138

138 Figure 53. The distribution of standardized heterozygosity (above) and internal relatedness (below) in American crow typed at 36 microsatellite loci.

PAGE 139

139 Figure 54. Individual internal relatedness of American crows used in this study. The red line demarks the mean of IR across all individuals (0.45).

PAGE 140

140 Figure 5 5. Aligned sequences of 757 bases of the exon 1 to 3 of 5 3 OAS from 3 Fish crows ( Corvus ossifragus ) (numbers 13) and 21 American crows ( C. brachyrhynchos ) (numbers 4 24) Highlighted in colors is the consensus sequence and polymorphisms occurring in individual samples.

PAGE 141

141 CHAPTER 6 CONCLUSIONS The objective of this study was to assess the impact of the emergence of West Nile virus (WNV) on the genetic diversity of the American crow ( Corvus brachyrhynchos ) This avian species has been the most negatively affected host species resulting in estimated population decline up to 60% since the arrival of the virus. Thus, i t is hypothesized that the genetic diversity and genetic structure of the crow has been modulated by WNV in a short timescale. In the first portion of this project, published literature on seroprevalence and mortality rates was analyzed with the objective to assess the adaptation of host species to WNV, especially for the American crow. The increased specific antibodies prevalence in time accompanied by a decreas e in the diseasemediated mortality in this species revealed the potential adaptation of this hosts to the virus. In addition to the America n crow, other common avian species evidenced an elevated seroprevalence. This species may help to prevent the virus amplification and the transmission intensity to other species including humans Thus, the persistence of the virus that firstly relied in a highly virulent strategy causing high mortality of amplifying hosts, seems now have changed to a moderate virulence in hosts with acquired herd immunity The second portion of this project was to develop a large library of microsatellite markers to be used for the molecular analysis of genetic variation in the C. brachyrhynchos. Thus, w e developed and characterized 32 polymorphic microsatellite markers distributed in six multiplex panels using Next Generation sequencing and previously published markers. In addition, we developed a highly specific real time PCR

PAGE 142

142 protocol that allow ed a rapid differentiation between two sympatric crow species (Fish crow and American crow); both natural host species but with different response to WNV. Then, almost 400 crow samples from New York State were analyzed using the molecular markers developed. The third chapter consisted to investigate the effect of W NV outbreaks on the genetic diversity and structure of the A merican crow population. Comparing samples from e ach year cohort with preWNV outbreak samples obtained from museums, o ur results provided evidence for a strong genetic impact induced by severe mortality events attributable to West Nile virus since 1999.Th e se changes were characterized by a significantly lower a llelic richness and heterozygosity The genetic diversity was rapidly recovered by the year 2002 showing evidence of a population expansion by immigrants Finally, the last part of this project was to evaluate whether genetic diversity influences the likelihood to be infected by WNV in the American crow. We analyzed 143 individuals from different counties of New York State. Although we found a consistent pattern of higher heterozygosity levels towards r esistance to WNV, we did not find significant evidence of genetic variation between crows that differ in infection status, neither by linkage of markers to genes experiencing balancing selection or heterosis (i.e. local effect) nor by overall genetic diver sity (i.e. genomewide). This study provides molecular evidence for a strong genetic impact induced by severe mortality events attributable to WNV since 1999 in the AC. These changes occurred in a short time after the introduction of the virus. Relatively slow changes in the seroprevalence and mortality rates by WNV may indicate the slow process of adaptation of the AC to this virulent pathogen. Thus, WNV may be exert ing considerable selection

PAGE 143

143 force to drive genetic evolution of the crow population but in an ecologically relevant time scale. Th ese result s suggest a lack of specific genetic adaptation of the AC towards resistance to WNV may be due to a constant immigration of susceptible individuals F uture directions of this project would be to test the host genetic diversity hypothesis and pathogen isolates differences in contemporary crows using experimental inoculation with NY99 and WN02. This may also impl y the use spatial replicates of not yet exposed populations to determine if the change in time experienced in New York was not evidenced in a population in absence of the virus pressure. Another immediate direction is to determine whether genetic diversity at other immune related genes such as MHC are associated to resistance and susceptibility. Also, t he role of genetic variation in hosts that develop high viremia but have no evidence of disease such as House sparrows, American robins and Cardinals to understand what the costs are associated of some host species acting as reservoirs of an infectious dis ease.

PAGE 144

144 APPENDIX A SUPPLEMENTARY DATA F OR CHAPTER 2 Appendix A 1. List of seroprevalencebased reference articles selected for analysis. ALLISON AB, MEAD DG, GIBBS SE, HOFFMAN DM, STALLKNECHT DE (2004). West Nile virus viremia in wild rock pigeons. Emerg. Infect. Dis. 10: 2252 2255. BELL JA, BREWER CM, MICKELSON NJ, GARMAN GW, VAUGHAN JA (2006). West Nile virus epizootiology, central Red R iver Valley, North Dakota and Minnesota, 20022005. Emerg. Infect. Dis., 12: 12451247. BEVEROTH TA, WARD MP, LAMPMAN RL, RINGIA AM, NOVAK RJ (2006). Changes in seroprevalence of West Nile virus across Illinois in freeranging birds from 2001 through 2004. Am J Trop. Med. Hyg. 74: 174179. BRADLEY CA, GIBBS SEJ, ALTIZER S (2008). Urban land use predicts West Nile Virus exposure in songbirds. Ecological Applications 18: 10831092. DUSEK RJ, MCLEAN RG, KRAMER LD, UBICO SR, DUPUIS AP, EBEL GD, et al. (2009). Prevalence of West Nile virus in migratory birds during spring and fall migration. Am J Trop. Med. Hyg. 81: 11511158. DUSEK RJ, RICHARDSON D, EGSTAD KF, HEISEY DM (2006). Evaluating red cockaded woodpeckers for exposure to West Nile virus and blood parasites. Southeastern Naturalist 5: 561 565. GIBBS SEJ, ALLISON AB, YABSLEY MJ, MEAD DG, WILCOX BR, STALLKNECHT DE (2006). West Nile virus antibodies in avian species of Georgia, USA: 20002004. Vector Borne and Zoonotic Diseases 6: 5772. GODSEY MS, JR., BLACKMORE MS, PANELLA NA, BURKHALTER K, GOTTFRIED K, HALSEY LA, et al. (2005). West Nile virus epizootiology in the southeastern United States, 2001. Vector. Borne. Zoonotic. Dis. 5: 8289. HULL J, HULL A, REISEN W, FANG Y, ERNEST H (2006). Variation of West Nile virus antibody prevalence in migrating and wintering hawks in central California. Condor 108: 435439. HULL JM, KEANE JJ, TELL L, ERNEST HB (2010). West Nile Virus Antibody Surveill ance in Three Sierra Nevada Raptors of Conservation Concern. Condor 112: 168172. JOZAN M, EVANS R, MCLEAN R, HALL R, TANGREDI B, REED L, et al. (2003). Detection of West Nile Virus Infection in birds in the United States by blocking ELISA and immunohistochemistry. Vector Borne and Zoonotic Diseases 3: 99 110. KOMAR N, BURNS J, DEAN C, PANELLA NA, DUSZA S, CHERRY B (2001a). Serologic evidence for West Nile virus infection in birds in Staten Island, New York, after an outbreak in 2000. Vector. Borne. Zoonotic. Dis. 1: 191196. KOMAR N, PANELLA NA, BURNS JE, DUSZA SW, MASCARENHAS TM, TALBOT TO (2001b). Serologic evidence for West Nile virus infection in birds in the New York City vicinity during an outbreak in 1999. Emerg. Infect. Dis. 7: 621625. KOMAR N, PANELLA NA, LANGEVIN SA, BRAULT AC, AMADOR M, EDWARDS E, et al. (2005). Avian hosts for West Nile virus in St. Tammany Parish, Louisiana, 2002. Am J Trop. Med. Hyg. 73: 10311037.

PAGE 145

145 LILLIBRIDGE KM, PARSONS R, RANDLE Y, DA ROSA APAT, GUZMAN H, SIIRIN M, et al. (2004). The 2002 introduction of West Nile Virus into Harris County, Texas, an area historically endemic for St. Louis encephalitis. American Journal of Tropical Medicine and Hygiene, 70: 676681. LOSS SR, HAMER GL, GOLDBERG TL, RUIZ MO, KITRON UD, WAL KER ED, et al. (2009a). Nestling Passerines Are Not Important Hosts for Amplification of West Nile Virus in Chicago, Illinois. Vector Borne and Zoonotic Diseases 9: 13 17. LOSS SR, HAMER GL, WALKER ED, RUIZ MO, GOLDBERG TL, KITRON UD, et al. (2009b). Avia n host community structure and prevalence of West Nile virus in Chicago, Illinois. Oecologia. 159: 415424. MEDICA DL, CLAUSER R, BILDSTEIN K (2007). Prevalence of west Nile virus antibodies in a breeding population of American Kestrels (Falco sparverius) in Pennsylvania. J Wildl. Dis. 43: 538541. NASCI RS, KOMAR N, MARFIN AA, LUDWIG GV, KRAMER LD, DANIELS TJ, et al. (2002). Detection of West Nile virus infected mosquitoes and seropositive juvenile birds in the vicinity of virus positive dead birds. Am J Trop. Med. Hyg. 67: 492496. NEMETH N, KRATZ G, EDWARDS E, SCHERPELZ J, BOWEN R, KOMAR N (2007). Surveillance for West Nile virus in clinic admitted raptors, Colorado. Emerg. Infect. Dis. 13: 305307. NEMETH NM, DWYER JF, MORRISON JL, FRASER JD (2009). Prevalence of Antibodies to West Nile Virus and Other Arboviruses among Crested Caracaras (Caracara cheriway) in Florida. Journal of Wildlife Diseases 45: 817822. NEWMAN SH, PADULA VM, CRAY C, KRAMER LD (2007). Health assessment of Black crowned Night herons (Nycticorax nycticorax) of the New York Harbor estuary. Comp Biochem. Physiol B Biochem. Mol. Biol. 148: 363374. REED LM, JOHANSSON MA, PANELLA N, MCLEAN R, CREEKMORE T, PUELLE R, et al. (2009). Declining mortality in American crow (Corvus brachyrhy nchos) following natural West Nile virus infection. Avian Dis. 53: 458461. REISEN WK, BARKER CM, CARNEY R, LOTHROP HD, WHEELER SS, WILSON JL, et al. (2006a). Role of corvids in epidemiology of west Nile virus in southern California. J Med. Entomol. 43: 356367. REISEN WK, CARROLL BD, TAKAHASHI R, FANG Y, GARCIA S, MARTINEZ VM, et al. (2009a). Repeated West Nile Virus Epidemic Transmission in Kern County, California, 20042007. Journal of Medical Entomology 46: 139157. REISEN WK, MARTINEZ VM, FANG Y, GARCIA S, ASHTARI S, WHEELER SS, et al. (2006b). Role of California (Callipepla californica) and Gambel's (Callipepla gambelii) quail in the ecology of mosquitoborne encephalitis viruses in California, USA. Vector. Borne. Zoonotic. Dis. 6: 248 260. REISEN WK, WHEELER S, ARMIJOS MV, FANG Y, GARCIA S, KELLEY K, et al. (2009b). Role of Communally Nesting Ardeid Birds in the Epidemiology of West Nile Virus Revisited. Vector Borne and Zoonotic Diseases 9: 275280. REISEN WK, WHEELER SS, YAMAMOTO S, FANG Y, GARC IA S (2005). Nesting ardeid colonies are not a focus of elevated West Nile virus activity in southern California. Vector Borne and Zoonotic Diseases 5: 258 266. RINGIA AM, BLITVICH BJ, KOO HY, VAN DE WYNGAERDE M, BRAWN JD, NOVAK RJ (2004). Antibody prevalence of West Nile Virus in birds, Illinois, 2002. Emerging Infectious Diseases 10: 11201124.

PAGE 146

146 SEBASTIAN MM, STEWART I, WILLIAMS NM, POONACHA KB, SELLS SF, VICKER S ML, et al. (2008). Pathological, entomological, avian and meteorological investigation of a West Nile virus epidemic in a horse farm. Transboundary and Emerging Diseases 55: 134139. SHELITE TR, ROGERS CM, LITZNER BR, JOHNSON RR, SCHNEEGURT MA (2008). W est Nile virus antibodies in permanent resident and overwintering migrant birds in southcentral Kansas. Vector Borne and Zoonotic Diseases 8: 321329. STOUT WE, CASSINI AG, MEECE JK, PAPP JM, ROSENFIELD RN, REED KD (2005). Serologic evidence of West Nile virus infection in three wild raptor populations. Avian Dis. 49: 371375. SULLIVAN H, LINZ G, CLARK L, SALMAN M (2006). West Nile virus antibody prevalence in redwinged blackbirds (Agelaius phoeniceus) from North Dakota, USA (20032004). Vector. Borne. Zoonotic. Dis. 6: 305309. WALKER BL, NAUGLE DE, DOHERTY KE, CORNISH TE (2007). West Nile virus and greater sagegrouse: estimating infection rate in a wild bird population. Avian Dis. 51: 691696. WHEELER SS, BARKER CM, FANG Y, ARMIJOS MV, CARROLL BD, H USTED S, et al. (2009). Differential Impact of West Nile Virus on California Birds. Condor 111: 120. WILCOX BR, YABSLEY MJ, ELLIS AE, STALLKNECHT DE, GIBBS SEJ (2007). West Nile virus antibody prevalence in American crows (Corvus brachyrhynchos) and fish crows (Corvus ossifragus) in Georgia, USA. Avian Diseases 51: 125128. YAREMYCH SA, WARNER RE, MANKIN PC, BRAWN JD, RAIM A, NOVAK R (2004). West Nile virus and high death rate in American crows. Emerging Infectious Diseases 10: 709711.

PAGE 147

147 Appendix A 2. List of mortality based reference articles selected for analysis BECKWITH WH, SIRPENSKI S, FRENCH RA, NELSON R, MAYO D (2002). Isolation of eastern equine encephalitis virus and West Nile virus from crows during increased arbovirus surveillance in Connect icut, 2000. American Journal of Tropical Medicine and Hygiene, 66: 422426. BERNARD KA, MAFFEI JG, JONES SA, KAUFFMAN EB, EBEL G, DUPUIS AP, et al. (2001). West Nile virus infection in birds and mosquitoes, New York State, 2000. Emerg. Infect. Dis. 7: 679685. BLACKMORE CGM, STARK LM, JETER WC, OLIVERI RL, BROOKS RG, CONTI LA, et al. (2003). Surveillance results from the first West Nile virus transmission season in Florida, 2001. American Journal of Tropical Medicine and Hygiene, 69: 141150. CAFFREY C, SM ITH SCR, WESTON TJ (2005). West Nile virus devastates an American crow population. Condor 107: 128132. CROSBIE SP, KOENIG WD, REISEM WK, KRAMER VL, MARCUS L, CARNEY R, et al. (2008). Early impact of West Nile virus on the Yellow Billed Magpie (Pica nutta lli). Auk 125: 542550. DAWSON JR, STONE WB, EBEL GD, YOUNG DS, GALINSKI DS, PENSABENE JP, et al. (2007). Crow deaths caused by West Nile virus during winter. Emerg. Infect. Dis. 13: 19121914. ELLIS AE, MEAD DG, ALLISON AB, STALLKNECHT DE, HOWERTH EW (2 007). Pathology and epidemiology of natural West Nile viral infection of raptors in Georgia. J Wildl. Dis. 43: 214223. GARVIN MC, TARVIN KA, SMITH J, OHAJURUKA OA, GRIMES S (2004). Patterns of West Nile virus infection in Ohio blue jays: Implications for initiation of the annual cycle. American Journal of Tropical Medicine and Hygiene, 70: 566570. GERHOLD RW, TATE CM, GIBBS SE, MEAD DG, ALLISON AB, FISCHER JR (2007). Necropsy findings and arbovirus surveillance in mourning doves from the southeastern Uni ted States. J Wildl. Dis. 43: 129135. GIBBS SEJ, HOFFMAN DM, STARK LM, MARLENEE NL, BLITVICH BJ, BEATY BJ, et al. (2005). Persistence of antibodies to West Nile virus in naturally infected rock pigeons (Columba livia). Clinical and Diagnostic Laboratory Immunology 12: 665667. HADLER J, NELSON R, MCCARTHY T, ANDREADIS T, LIS MJ, FRENCH R, et al. (2001). West Nile virus surveillance in Connecticut in 2000: An intense epizootic without high risk for severe human disease. Emerging Infectious Diseases 7: 63 6 642. KRAMER LD, BERNARD KA (2001). West Nile virus infection in birds and mammals. Ann. N. Y. Acad. Sci. 951: 8493. NEMETH NM, BECKETT S, EDWARDS E, KLENK K, KOMAR N (2007). Avian mortality surveillance for West Nile virus in Colorado. Am J Trop. Med. Hyg. 76: 431437. REISEN WK, BARKER CM, CARNEY R, LOTHROP HD, WHEELER SS, WILSON JL, et al. (2006). Role of corvids in epidemiology of west Nile virus in southern California. J Med. Entomol. 43: 356367. REISEN WK, CARROLL BD, TAKAHASHI R, FANG Y, GARCIA S, MARTINEZ VM, et al. (2009a). Repeated West Nile Virus Epidemic Transmission in Kern County, California, 20042007. Journal of Medical Entomology 46: 139157.

PAGE 148

148 REISEN WK, WHEELER S, ARMIJOS MV, FANG Y, GARCIA S, KELLEY K, et al. (2009b). Role of Communally Nesting Ardeid Birds in the Epidemiology of West Nile Virus Revisited. Vector Borne and Zoonotic Diseases 9: 275280. SAITO EK, SILEO L, GREEN DE, METEYER CU, MCLAUGHLIN GS, CONVERSE KA, et al. (2007). Raptor mortality due to West Nile virus in the Un ited States, 2002. J Wildl. Dis. 43: 206213. SIIRIN M, SARGENT C, LANGER RC, PARSONS R, VANLANDINGHAM DL, HIGGS S, et al. (2004). Comparative sensitivity of the VecTest antigencapture assay, reverse transcriptasePCR, and cell culture for detection of West Nile virus in dead birds. Vector Borne & Zoonotic Diseases 4: 204209. SIRIGIREDDY KR, KENNEDY GA, BROCE A, ZUREK L, GANTA RR (2006). High prevalence of West Nile Virus: A continuing risk in acquiring infection from a mosquito bite. Vector Borne and Zoonotic Diseases 6: 351 360. STONE WB, OKONIEW SKI JC, THERRIEN JE, KRAMER LD, KAUFFMAN EB, EIDSON M (2004). VecTest as diagnostic and surveillance tool for West Nile virus in dead birds. Emerg. Infect. Dis., 10: 21752181. TESH RB, PARSONS R, SIIRIN M, RANDLE Y, SARGENT C, GUZMAN H, et al. (2004). Year round West Nile virus activity, Gulf Coast region, Texas and Louisiana. Emerging Infectious Diseases 10: 1649. WALKER BL, NAUGLE DE, DOHERTY KE, CORNISH TE (2007). West Nile virus and greater sagegrouse: estimating infection rate in a wild bird population. Avian Dis. 51: 691696. WHEELER SS, BARKER CM, FANG Y, ARMIJOS MV, CARROLL BD, HUSTED S, et al. (2009). Differential Impact of West Nile Virus on California Birds. Condor 111: 120. YAREMYCH SA, WARNER RE, MANKIN PC, BRAWN JD, RAIM A, NOVAK R (2004). West Nile virus and high death rate in American crows. Emerging Infectious Diseases 10: 709711.

PAGE 149

149 Table A 2. Species found in the seroprevalencebased articles. Order Fam ily S pecie s Name Common Name Anseriformes Anatidae Aix sponsa Wood Duck Anseriformes Anatidae Anas platyrhynchos Mallard duck Anseriformes Anatidae Aythya collaris Ring necked duck Anseriformes Anatidae Branta canadensis Canada Goose Ciconiiformes Ardeidae Ardea alba Great egret Ciconiiformes Ardeidae Bubulcus ibis Cattle egret Ciconiiformes Ardeidae Egretta thula Snowy egret Ciconiiformes Ardeidae Ixobrychus exilis Least Bittern Ciconiiformes Ardeidae Nycticorax nycticorax Black crowned night heron Columbiformes Columbidae Columba livia Rock pigeon Columbiformes Columbidae Columbina passerina Common ground dove Columbiformes Columbidae Zenaida asiatica White winged dove Columbiformes Columbidae Zenaida macroura Mourning dove Cuculiformes Cuculidae Coccyzus americanus Yellow billed cuckoo Falconiformes Accipitridae Accipiter cooperii Coopers Hawk Falconiformes Accipitridae Accipiter gentilis Northern Goshawk Falconiformes Accipitridae Buteo lineatus Red shouldered Hawk Falconiformes Accipitridae Buteo jamaicensis Red tailed Hawk Falconiformes Accipitridae Buteo swainsoni Swainson's hawk Galliformes Odontophoridae Callipepla californica California quail Galliformes Odontophoridae Callipepla gambelii Gambel's quail Galliformes Tetraonidae Centrocercus urophasianus Greater sage grouse Galliformes Phasianidae Gallus gallus Chicken Gruiformes Rallidae Fulica americana American coot Passeriformes Icteridae Agelaius phoeniceus Red winged blackbird Passeriformes Icteridae Agelaius tricolor Tricolored blackbird Passeriformes Corvidae Aphelocoma californica Western scrub jay Passeriformes Remizidae Auriparus flaviceps Verdin Passeriformes Paridae Baeolophus bicolor Tufted titmouse Passeriformes Cardinalidae Cardinalis cardinalis Norhtern Cardinal Passeriformes Fringillidae Carduelis tristis American goldfinch Passeriformes Fringillidae Carpodacus mexicanus House Finch Passeriformes Turdidae Catharus fuscescens Veery Passeriformes Turdidae Catharus guttatus Hermit thrush Passeriformes Turdidae Catharus minimus Gray cheeked thrush Passeriformes Turdidae Catharus ustulatus Swainsons thrush Passeriformes Emberizidae Chondestes grammacus Lark sparrow Passeriformes Troglodytidae Cistothorus palustris Marsh wren Passeriformes Tyrannidae Contopus virens Eastern wood pewee Passeriformes Corvidae Corvus brachyrhynchos American crow

PAGE 150

150 Passeriformes Corvidae Corvus ossifragus Fish crow Passeriformes Corvidae Cyanocitta cristata Blue Jay Passeriformes Parulidae Dendroica caerulescens Black throated blue warbler Passeriformes Parulidae Dendroica coronata Yellow rumped Warbler Passeriformes Parulidae Dendroica discolor Prairie warbler Passeriformes Parulidae Dendroica magnolia Magnolia warbler Passeriformes Parulidae Dendroica palmarum Palm warbler Passeriformes Parulidae Dendroica pensylvanica Chestnut sided warbler Passeriformes Parulidae Dendroica petechia Yellow warbler Passeriformes Parulidae Dendroica pinus Pine warbler Passeriformes Parulidae Dendroica striata Blackpoll warbler Passeriformes Icteridae Dolichonyx oryzivorus Bobolink Passeriformes Mimidae Dumetella carolinensis Gray catbird Passeriformes Tyrannidae Empidonax flaviventris Yellow bellied flycatcher Passeriformes Tyrannidae Empidonax traillii/alnorum Traills flycatcher Passeriformes Tyrannidae Empidonax virescens Acadian flycatcher Passeriformes Icteridae Euphagus cyanocephalus Brewer's blackbird Passeriformes Parulidae Geothlypis trichas Common yellowthroat Passeriformes Cardinalidae Guiraca caerulea Blue grosbeak Passeriformes Parulidae Helmitheros vermivorus Worm eating warbler Passeriformes Turdidae Hylocichla mustelina Wood thrush Passeriformes Parulidae Icteria virens Yellow breasted chat Passeriformes Icteridae Icterus bullockii Bullock's oriole Passeriformes Icteridae Icterus galbula Baltimore oriole Passeriformes Icteridae Icterus spurious Orchard oriole Passeriformes Emberizidae Junco hyemalis Dark eyed junco Passeriformes Lanidae Lanius ludovicianus Loggerhead shrike Passeriformes Emberizidae Melospiza georgiana Swamp sparrow Passeriformes Emberizidae Melospiza lincolnii Lincolns sparrow Passeriformes Emberizidae Melospiza melodia Song sparrow Passeriformes Mimidae Mimus polyglottos Northern mockingbird Passeriformes Parulidae Mniotilta varia Black and white warbler Passeriformes Icteridae Molothrus ater Brown headed cowbird Passeriformes Tyrannidae Myiarchus cinerascens Ash throated Flycatcher Passeriformes Tyrannidae Myiarchus crinitus Great crested flycatcher Passeriformes Parulidae Oporornis formosus Kentucky warbler Passeriformes Parulidae Oporornis tolmiei MacGillivrays warbler Passeriformes Parulidae Parula americana Northern parula Passeriformes Passeridae Passer domesticus House sparrow Passeriformes Emberizidae Passerculus sandwichensis Savannah Sparrow Passeriformes Emberizidae Passerella iliaca Fox sparrow Passeriformes Cardinalidae Passerina ciris Painted bunting

PAGE 151

151 Passeriformes Cardinalidae Passerina cyanea Indigo bunting Passeriformes Cardinalidae Pheucticus ludovicianus Rose breasted grosbeak Passeriformes Cardinalidae Pheucticus melanocephalus Black headed grosbeak Passeriformes Corvidae Pica nuttalli Yellow billed Magpie Passeriformes Emberizidae Pipilo aberti Aberts towhee Passeriformes Emberizidae Pipilo erythrophthalmus Eastern towhee Passeriformes Thraupidae Piranga ludoviciana Western tanager Passeriformes Cardinalidae Piranga olivacea Scarlet tanager Passeriformes Cardinalidae Piranga rubra Summer tanager Passeriformes Paridae Poecile atricapillus Black capped chickadee Passeriformes Paridae Poecile carolinensis Carolina chickadee Passeriformes Parulidae Protonotaria citrea Prothonotary warbler Passeriformes Icteridae Quiscalus major Boat tailed grackle Passeriformes Icteridae Quiscalus mexicanus Great tailed grackle Passeriformes Icteridae Quiscalus quiscala Common grackle Passeriformes Regulidae Regulus calendula Ruby crowned kinglet Passeriformes Tyrannidae Sayornis nigricans Black phoebe Passeriformes Tyrannidae Sayornis phoebe Eastern phoebe Passeriformes Parulidae Seirus noveboracensis Northern waterthrush Passeriformes Parulidae Seiurus aurocapillus Ovenbird Passeriformes Parulidae Setophaga ruticilla American redstart Passeriformes Turdidae Sialia sialis Eastern bluebird Passeriformes Sittidae Sitta carolinensis White breasted nuthatch Passeriformes Emberizidae Spizella arborea American tree sparrow Passeriformes Emberizidae Spizella passerina Chipping Sparrow Passeriformes Emberizidae Spizella pusilla Field sparrow Passeriformes Hirundinidae Stelgidopteryx serripennis Northern rough winged swallow Passeriformes Sturnidae Sturnus vulgaris European starling Passeriformes Hirundinidae Tachycineta bicolor Tree swallow Passeriformes Troglodytidae Thryomanes bewickii Bewick's wren Passeriformes Troglodytidae Thryothorus ludovicianus Carolina wren Passeriformes Mimidae Toxostoma redivivum California thrasher Passeriformes Mimidae Toxostoma rufum Brown thrasher Passeriformes Troglodytidae Troglodytes aedon House wren Passeriformes Turdidae Turdus migratorius American robin Passeriformes Tyrannidae Tyrannus verticalis Western Kingbird Passeriformes Parulidae Vermivora celata Orange crowned warbler Passeriformes Parulidae Vermivora peregrina Tennessee warbler Passeriformes Parulidae Vermivora pinus Blue winged warbler Passeriformes Parulidae Vermivora ruficapilla Nashville warbler Passeriformes Vireonidae Vireo gilvus Warbling vireo

PAGE 152

152 Passeriformes Vireonidae Vireo griseus White eyed vireo Passeriformes Vireonidae Vireo olivaceous Red eyed vireo Passeriformes Parulidae Wilsonia canadensis Canada warbler Passeriformes Parulidae Wilsonia citrina Hooded warbler Passeriformes Parulidae Wilsonia pusilla Wilsons warbler Passeriformes Emberizidae Zonotrichia albicollis White throated Sparrow Passeriformes Emberizidae Zonotrichia atricapilla Golden crowned sparrow Passeriformes Emberizidae Zonotrichia leucophrys White crowned sparrow Passeriformes Emberizidae Zonotrichia querula Harris sparrow Falconiformes Falconidae Caracara cheriway Crested caracara Falconiformes Falconidae Falco sparverius American kestrel Piciformes Picidae Melanerpes carolinus Red bellied woodpecker Piciformes Picidae Picoides borealis Red cockaded Woodpecker Piciformes Picidae Picoides pubescens Downy woodpecker Piciformes Picidae Picoides villosus Hairy woodpecker Strigiforme Strigidae Bubo virginianus Great Horned Owl Strigiforme Strigidae Strix nebulosa Great gray owl Strigiforme Strigidae Strix occidentalis Spotted owl Strigiforme Strigidae Tyto alba Barn owl

PAGE 153

153 Table A 3. Species identified in mortality ratebased studies. Order Family S pecie s Name Common N ame Anseriformes Anatidae Anas platyrhynchos Mallard duck Anseriformes Anatidae Branta canadensis Canada goose Charadriiformes Laridae Larus delawarensis Ring billed gull Ciconiiformes Ardeidae Ardea herodias Great blue heron Ciconiiformes Ardeidae Butorides virescens Green Heron Ciconiiformes Ardeidae Egretta thula Snowy egret Ciconiiformes Ardeidae Nycticorax nycticorax Black crowned night heron Columbiformes Columbidae Columba livia Rock pigeon Columbiformes Columbidae Columbina inca Inca Dove Columbiformes Columbidae Patagioenas fasciata Band tailed pigeon Columbiformes Columbidae Zenaida macroura Mourning dove Falconiformes Accipitridae Accipiter cooperii Coopers Hawk Falconiformes Accipitridae Accipiter striatus Sharp shinned Hawk Falconiformes Accipitridae Buteo jamaicensis Red tailed hawk Falconiformes Accipitridae Buteo lineatus Red shouldered hawk Galliformes Phasianidae Bonasa umbellus Ruffed grouse Galliformes Odontophoridae Callipepla californica California quail Galliformes Tetraonidae Centrocercus urophasianus Greater sage grouse Galliformes Phasianidae Meleagris gallopavo Wild Turkey Gruiformes Rallidae Fulica americana American coot Passeriformes Icteridae Agelaius phoeniceus Red winged blackbird Passeriformes Corvidae Aphelocoma coerulescens Western scrub jay Passeriformes Paridae Baeolophus inornatus Oak Titmouse Passeriformes Bombycillidae Bombycilla cedrorum Cedar Waxwing Passeriformes Cardinalidae Cardinalis cardinalis Norhtern Cardinal Passeriformes Fringillidae Carduelis pinus Pine Siskin Passeriformes Fringillidae Carduelis psaltria Lesser Goldfinch Passeriformes Fringillidae Carduelis tristis American goldfinch Passeriformes Fringillidae Carpodacus mexicanus House Finch Passeriformes Fringillidae Carpodacus purpureus Purple Finch Passeriformes Turdidae Catharus guttatus Hermit thrush Passeriformes Turdidae Catharus ustulatus Swainsons thrush Passeriformes Corvidae Corvus brachyrhynchos American crow Passeriformes Corvidae Corvus corax Common raven Passeriformes Corvidae Corvus ossifragus Fish crow Passeriformes Corvidae Cyanocitta cristata Blue jay Passeriformes Corvidae Cyanocitta stelleri Stellers Jay Passeriformes Parulidae Dendroica coronata Yellow rumped Warbler Passeriformes Parulidae Dendroica petechia Yellow warbler Passeriformes Mimidae Dumetella carolinensis Gray catbird Passeriformes Icteridae Euphagus cyanocephalus Brewer's blackbird

PAGE 154

154 Passeriformes Hirundinidae Hirundo rustica Barn Swallow Passeriformes Icteridae Icterus bullockii Bullock's oriole Passeriformes Emberizidae Junco hyemalis Dark eyed junco Passeriformes Lanidae Lanius ludovicianus Loggerhead shrike Passeriformes Emberizidae Melospiza melodia Song sparrow Passeriformes Mimidae Mimus polyglottos Northern mockingbird Passeriformes Icteridae Molothrus ater Brown headed cowbird Passeriformes Passeridae Passer domesticus House sparrow Passeriformes Emberizidae Passerella iliaca Fox sparrow Passeriformes Hirundinidae Petrochelidon pyrrhonota Cliff Swallow Passeriformes Cardinalidae Pheucticus melanocephalus Black headed grosbeak Passeriformes Corvidae Pica hudsonia Black billed magpie Passeriformes Corvidae Pica nuttali Yellow billed magpie Passeriformes Emberizidae Pipilo crissalis California Towhee Passeriformes Emberizidae Pipilo maculatus Spotted Towhee Passeriformes Thraupidae Piranga ludoviciana Western tanager Passeriformes Icteridae Quiscalus quiscula Common grackle Passeriformes Tyrannidae Sayornis nigricans Black phoebe Passeriformes Parulidae Seiurus aurocapillus Ovenbird Passeriformes Turdidae Sialia mexicana Western Bluebird Passeriformes Sturnidae Sturnus vulgaris European starling Passeriformes Turdidae Turdus migratorius American robin Passeriformes Tyrannidae Tyrannus verticalis Western Kingbird Passeriformes Parulidae Vermivora celata Orange crowned warbler Passeriformes Parulidae Wilsonia pusilla Wilsons warbler Passeriformes Emberizidae Zonotrichia atricapilla Golden crowned sparrow Passeriformes Emberizidae Zonotrichia leucophrys White crowned sparrow Falconiformes Falconidae Falco sparverius American kestrel Piciformes Picidae Colaptes auratus Northern flicker Piciformes Picidae Melanerpes formicivorus Acorn Woodpecker Piciformes Picidae Picoides nuttallii Nuttalls Woodpecker Strigiforme Strigidae Bubo virginianus Great Horned Owl Strigiforme Strigidae Megascops asio Eastern streech owl Strigiforme Strigidae Megascops kennicottii Western Screech owl Strigiforme Strigidae Strix varia Barred owl Strigiforme Strigidae Tyto alba Barn owl Trochiliformes Trochilidae Calypte anna Annas Hummingbird Cathartidae Cathartes aura Turkey Vulture

PAGE 155

155 APPENDIX B SUPPLEMENTARY DATA FOR CHAPTER 3 Table B 1. List of microsatellite primers used for cross species amplification on Corvus brachyrhynchos Locus Repeat Motif Primer sequences N A Reference Florida Scrub Jay Stenzler et al. 2002 Aphelocoma coerulescens ApCo2 (CAT) 21 F: TGCCTGTGCAGAATGTAGCAT 1 R: CAGGAATGGAGCAAGGCTTCTC ApCo9 (CCA) 7 F: GTAGGTTCTGTAGGCTGATGGA NSA R: CTGTAGTGCCTTCCAAGCTC ApCo15 (GAT) 11 (GAA) 1 (GAT) 5 F: TGTGCCTCAGTATACCATCAGT 1 R: TTTTCAGAGTCTCTGTCTTAGCTG ApCo18 (TG) 12 F: CTAGTGCCTTGGCTTTCAGT 7 R: ACTACCTTACAAATCCCTTATCTG ApCo19 (TTG) 8 F: CAGACTGCAGTCTTGCTATAGC 1 R: GCCTTGGATGCTTTTACG ApCo22 (AG) 14 F: AGAGCTGCCCAGGTCATAGCAT 1 R: CACATGCTCATTCTCTTCTTTGTA ApCo23 (GA) 18 F: TGCCAGTGACAATAGTAAGAGT 1 R: GGGACCCACAGAGCAGT ApCo28 (GTT) 8 F: CCACAACGCTCCCTATATGGTA NA R: AAGCCTTTGCAATCAGCAAGAT ApCo29 (GAT) 12 (GAC) 1 (GAT) 2 F: GGGTTCCAAGACAAAATCATAGTG 1 R: CATCTGGAACTGGCATTTGCTTA ApCo30 (GAT) 16 (GAT) 5 (GGT) 1 (GAT) 5 F: GCCCTGATGCTGTTGATGGT 9 R: CTGGAGCCTGGTTTAGAGTTATGC ApCo31 (GGT) 1 (GAT) 8 F: AGCTGAAGGTGGTATTATGACAGG 11 R: GTCAGCAGGACTAGTGTTCCATCA ApCo36 (GT) 6 (GTT) 7 F: TTGTTTCAGAGGCTGTAACAGTAGC 1 R: CCTCCCTCAGCAACCAAGAAA ApCo37 (GTT) 13 F: TGCCAAATGCAACCAAATCTT 1 R: CATCACTTGCAGAGAGGGCA ApCo40 (ATGAG) 13 F: CTTCTGACAAGACACAGGAGCC 4 R: GCACAGATCTCAGTTGCATCACTC ApCo41 (GAT) 14 F: CCTACTCTGGGCACTGTTATTATC 1 R: CCCATTATCAGCATGTCGTACA ApCo46 (AG) 11 F: GGGAGCCTAGTATGTTAAGATGCT 1 R: TTCCAGGTGAGGTGATTCAGC ApCo48 (CT) 11 F: TAAAGAGCCATATCGTCACCT 1 R: AACTTGCCTTTTGCTCCTAA ApCo55 (GTT) 9 F: CTAGAGCAGCCCTGGAGATTTTTG 1 R: GCAGAAGGCCATGCTTTGTGTT ApCo59 (GA) 20 F: CATATAAACTCATTTGAATATTTGC 1 R: CTTGTGGTGTGAGAAAGATAGATA ApCo68 (ACT) 12 (GCT) 10 F: TCTGCAAACATTCACTCAAACTTC Polymorphic but need optimization R: TCCTAGGAAAGCTCTGAATCACAT ApCo69 (ATT) 6 (GTT) 8 F: TGGACCTTTAAAACACTGACCTGA 1 R: GCTTGATCATCCCCTTAAATCTGA ApCo80 (CAT) 5 (CTT) 1 (CAT) 2 F: TTCAGCCTGGCTTAAGGAAGAT 1 R: TGCCTATAGGAGAAATGGGTAA ApCo81 (CAT) 7 F: TGTGTTTAAGTGCTTTGTTCTA 1 R: ACTGGCCTCCTGGTGTC ApCo82 (CAA) 8 F: CTGGGGCTTTGTCACTAA 1

PAGE 156

156 R: AGGGCTTTTTCGTTCTG ApCo86 (GAT) 6 F: CATGATCTTCCCTGAGTGCT 1 R: GTCCAACATTAATCCCAAAGA ApCo88 (TTG) 9 F: GAAAGCCTTTCTGATGGGATTATT 1 R: TGCCCTCTAGCCTGACCTGTAT ApCo91 (CAA) 9 F: GTAGTCCCAATGGTTTCTCTGTC 1 R: GATGAAGTAATGTGAAACCTGG ApCo95 (CA) 15 F: GGCCACAGCAAAGCCCTCAT 9 R: GAACTTCTCTTGTTGCCCTGAAACAG ApCo97 (GAT) 10 F: AGGTCTGTAACATGGCATACTC 1 R: CTTATTCTACTGGAGATCTTTTG ApCo103 (GTA) 8 F: TGGGCATCAGTCAGTTGGTG NA R: CAAATGGGAAACCTCTTGCG ApCo104 (CAA) 9 F: TTGCCTCTGCTGACGACTTTAT 1 R: TTTTTCCCTCTCGTAACACTGC Mariana crow Tarr et al. 1998 Corvus kubaryi Ck.1B5D (GT) 15 F: ACTGCGTGTCCTGAATACGC 6 R: ACAAGACCAGAGCTTCAGCT Ck.1B6G (CT) 2 (GT) 11 F: ATGGAGTGGAGGGAGAGATG 1 R: AGTACTACCAGTTCACCTGC Ck.2A5A (GT) 11 F: TGCTAAGCACAGTTAGAGAC 1 R: GAAGACAGGCAGGAGAGTTG Ck.4A3G (GT) 11 F: GAGATGTCATCTATGGAACC 7 R: TTCCAGTAGCTGGTGCAGAC Ck.4B6D (TA) 10 (GT) 12 F: TTGCATCCCTGATTTATGGC 4 R: CTAGGAAGCAATCCAGAGTC Ck.5A4B (GT) 2 (CT) 2 (GT) 11 TT(GT) 2 F: TTATTCAAGATGCTGAGTGC 1 R: ACTTAAGAAACAGTCTGCTG Ck.5A4D (AT) 2 GTTT(GT) 13 F: CACAAACATGGGTTGAGTCT 9 R: TGAAAGTTTGGTAGAGGCTC Ck.5A5F (AT) 4 (GT) 14 F: GTGGTTATACCAGAGGTCCT 1 R: TTTTGTTCTCTCAAGACACC Ck.5A5G (GT) 13 GCAT(GT) 3 F: TTCAGACTGCCTTCTGCACT 1 R: GCTTCTATCCTCAGTGAGCT Pinyon jay Busch et al. 2009 Gymnorhinus cyanocephalus PJAAAG1 (AAAG) 24 F: GAACGGTAATTTAAGAATCACTGC 2 R: GACACCATCAACCTGAACATCAC PJAAAG5 (AG) 5 (AAAG) 5 F: GGCCAGAGTTGCTTCAGC NSA R: GTGTCCCTGTCACTCTTTCTGTC PJAAAG9 (AAAG) 18 F: AAAGGTGTTGGCTTGAGTAAAC 1 R: GGATGTTAAACTCAACCCTGAC PJGATA1 (CA) 8 (GATA) 10 F: CCATCAAGTCTCTTGCTGGC NSA R: GAATCCAGGCTTAACTCTTCTGG PJGATA2 (GATA) 12 F: CCAGTGCTCCATGAATTTCC NA R: GCAGTGCAGGGCTGATGCA PJGATA3 (GATA) 18 (CT) 6 F: CAGTTTCAACTGAAGTTGC NA R: GCTCCTCTGCCTGGATT PJGATA4 (GATA) 12 F: GAACCAGTGTCTGGATTTGTCAC 1 R: GTTGCTGTTAAACAATGTTTATCTGG Yellow billed magpie Ernest et al. 2007 Pica nuttalli PnuA106w (TGTT) 11 F: GTATTTTGGGATGTCTTAGGGTTG 1 R: CACACTCGAGCTACAATAAACCTG PnuA120w (AAAC) 9 F: AAATACTGAAATAGCCACCAGGTC 1 R: GGCCTCAGATACCTATTGAATATGAC

PAGE 157

157 PnuA2w (AAAC) 11 F: TGCCAGTGCACTCATTTACTT NA R: TGTCTCTTATTTCAGGCTTTGC PnuA3w (TTTG) 12 F: GACAGGAGCCCAACTTTCTG 5 F: GGCGTTCCAAAGGTAGTCTTC PnuA408w (AAAC) 6 (AACC) 3 (CAAA) 4 R: ATAAACCTGTCAGACTGTGCAAGA 1 (CAAACCAAA) 2 F: TTGTCACCAGTGAGGAGAATGTAT PnuB213w (AAGA) 8 R: GCAGCTTTGTAGAGGACTTCGT 1 F: TTTGGAAATGTCCTCCAGTTTC PnuC107w (AAAT) 8 R: CCAAGCCTACACTACTCCAATTTC NSA F: ACAGTCTGCTAGGTTTCATCAGC PnuC222w (TAAA) 8 F: CCTGACACTTCACAGTTCCAAA 2 R: CATGCCAATTCTTGGTAAGACA PnuC405w (TATT) 9 F: TTCGGTTCATCAGGGAAGG 1 R: ATTGGAGTTTGCCTGTCATCA PnuC424 (AAAT) 8 F: CCCTGGCTCCTGTGACTAAGTA 1 R: GCCCATTATTTGTATGGAGAA Magpie Martinez et al. 1999 Pica pica Ppi1 Not available F: TTATCCACATCCACGCAGTC 1 R: GAAAGGGCTGCAATGATTTC Ppi2 Not available F: CACAGACCATTCGAAGCAGA 1 R: GCTCCGATGGTGAATGAAGT NA, number of alleles NA, no target amplification. NSA, nonspecific amplification.

PAGE 158

158 Table B 2. Samples of Corvus brachyrhynchos used for microsatellite characterization. NK, not known. Sample Year of collection Location WNV test Sex 1 2000 Rockland, NY Positive Male 2 2000 Nassau, NY Positive Female 3 2000 Kings, NY Positive Female 4 2000 Rockland, NY Positive Female 5 2000 Rockland, NY Positive Male 6 2001 Suffolk, NY Positive Female 7 2001 Richmond, NY Positive Male 8 2001 New York, NY Positive Male 9 2001 Bronx, NY Positive Male 10 2002 Ontario, NY Positive Male 11 2002 Westchester, NY Positive Male 12 2002 Albany, NY Negative Female 13 2002 Suffolk, NY Negative Male 14 2002 Albany, NY Negative Male 15 2002 Suffolk, NY Negative Female 16 2003 Albany, NY Positive Male 17 2003 Dutchess, NY Positive Female 18 2003 Jefferson, NY Positive Female 19 2003 Cayuga, NY Negative Male 20 2003 Jefferson, NY Negative Male 21 2004 St. Lawrence, NY Positive Male 22 2004 Richmond, NY Positive Female 23 2004 Chautauqua, NY Positive Female 24 2004 Rockland, NY Positive Female 25 2005 Albany, NY Positive Male 26 2005 Broome, NY Positive Female 27 2005 Orleans, NY Positive Female 28 2006 Onondaga, NY Positive Male 29 2006 Wayne, NY Positive Female 30 2006 Livingston, NY Positive Male 31 2006 Nassau, NY Positive Male 32 2007 Erie, NY Positive Female 33 2007 Suffolk, NY Positive Male 34 2007 Niagara, NY Positive Male 35 2008 Albany, NY Positive Male 36 2008 NK, FL Negative Female 37 2008 NK, FL Negative Male 38 2009 Columbia, NY Positive Male 39 2009 Ocala, FL Negative Not determined 40 2009 Alachua, FL Negative Female

PAGE 159

159 41 2009 Alachua, FL Negative Male 42 2009 Alachua, FL Negative Female 43 2009 NK, FL Negative Female 44 2010 NK, NY Positive Male 45 2010 NK, NY Negative Female 46 2010 NK, NY Negative Female 47 2010 NK, NY Negative Female 48 2010 NK, NY Negative Female 49 2010 High Spring, FL Negative Male 50 2010 NK, FL Negative Male

PAGE 160

160 Table A 5 Cross amplification of 32 microsatellites in the Corvus ossifragus (n = 2) Locus Allele range (bp) N A Cb01 230 234 2 Cb02 334 1 Cb03 446 1 Cb04 160 163 2 Cb05 129 2 Cb06 120 1 Cb07 106 1 Cb08 127 135 2 Cb09 136 144 2 Cb10 127 1 Cb11 247 1 Cb12 212 1 Cb13 163 169 2 Cb14 181 1 Cb16 157 1 Cb17 160 1 Cb20 109 1 Cb21 106 1 Cb22 74 1 Cb23 91 1 Ck.1B5D 84 90 3 Ck.4A3G 84 96 2 Ck.4B6D 0 0 Ck.5A4D 109 115 3 ApCo18 0 0 ApCo30 190 1 ApCo31 123 126 2 ApCo40 178 188 3 ApCo95 137 139 2 PJAAAG1 252 1 PnuA3w 263 1 PnuC222w 166 1 NA, Number of alleles.

PAGE 161

161 Figure A 1. Nhe restriction enzyme digested cytochrome oxidase I (COI) gene amplification products of two corvid species electrophoresed in a 2% agarose gel. AC, American crow ( Corvus brachyrhynchos ). FC, Fish crow ( Corvus ossifragus ).

PAGE 162

162 Figure A 2. Amplification pl ots of multiplex reactions using Corvus brachyrhynchos (n=30) and C. ossifragus (n=10) DNA samples in duplicates. Only probe specific for each species emits fluorescence signals. Blue lines correspond to fluorescence signals form probe specific for C. brac hyrhynchos (COI CB) and red lines correspond to fluorescence signals form probe specific for C. ossifragus (COI CO).

PAGE 163

163 Figure A 3. Linear regression of the standard curves showing amplification using five 10fold dilutions of Corvus brachyrhynchos (top panel) and C. ossifragus (bottom panel) DNA samples. Three replicates were tested for each dilution. Each assay was done three times.

PAGE 164

164 REFERENCES AcevedoWhitehouse K, Cunningham AA (2006) Is MHC enough for understanding wildlife immunogenetics?. Trends in Ecology & Evolution, 21 433 438. AcevedoWhitehouse K, Gulland F, Greig D, Amos W (2003) Disease susceptibility in California sea lions. Nature, 422, 35. AcevedoWhitehouse K, Petetti L, Duignan P, Castinel A (2009) Hookworm infection, anaemia and genetic variability of the New Zealand sea lion. Proceedings of the Royal Society B: Biological Sciences 276, 35233529. AcevedoWhitehouse K, Spraker T, Lyons E et al. (2006) Contrasting ef fects of heterozygosity on survival and hookworm resistance in California sea lion pups. Molecular Ecology 15, 19731982. Agresti A (2007) An introduction to categorical data analysis 423 edn. Wiley Interscience. Alcaide M, Edwards SV (2011) Molecular evolution of the toll like receptor multigene family in birds. Molecular Biology and Evolution, 28 1703 1715. Alcaide M, Lemus J, Blanco G et al. (2010) MHC diversity and differential exposure to pathogens in kestrels (Aves: Falconidae). Molec ular Ecology 19, 691705. Alvarez Busto J, Garcia Etxebarria K, Herrero J, Garin I, Jugo BM (2007) Diversity and evolution of the Mhc DRB1 gene in the two endemic Iberian subspecies of Pyrenean chamois, Rupicapra pyrenaica. Heredity 99, 406413. Amills M, Jimenez N, Jordana J et al. (2004) Low diversity in the major histocompatibility complex class II DRB1 gene of the Spanish ibex, Capra pyrenaica. Heredity 93, 266272. Amos W, Driscoll E, Hoffman JI (2011) Candidate genes versus genomewide associations: which are better for detecting genetic susceptibility to infectious disease? Proceedings of the Royal Society B: Biological Sciences 278 1183 1188. Amos W, Wilmer JW, Fullard K, Burg TM, Croxall JP, Bloc h D, et al. (2001) The influence of parental relatedness on reproductive success. Proceedings of the Royal Society of London. Series B: Biological Sciences 268 20212027. Antonovics J, Thrall PH (1994) C ost of resistance and the maintenance of genetic p olymorphism in host pathogen systems Proceedings of the Royal Society of London Series B Biological Sciences 257, 105110.

PAGE 165

165 Aparicio JM, Ortego J, Cordero PJ (2006) What should we weigh to estimate heterozygosity, alleles or loci? Mol ecular Ecol ogy 15 46594665. Bai X, Zhang W, Orantes L et al. (2010) Combining next generation sequencing strategies for rapid molecular resource development from an invasive aphid species, Aphis glycines. PLoS One. 5, e11370. Baglione V, Marcos JM, Canestrari D, Murphy M (2002) Cooperatively breeding groups of carrion crow (Corvus corone corone) in northern Spain. The Auk 119 790 799. Balloux F, Amos W, Coulson T (2004) Does heterozygosity estimate inbreeding in real populat ions? Molecular Ecology 13, 30213031. Beck NR, Double MC, Cockburn A (2003) Microsatellite evolution at two hypervariable loci revealed by extensive avian pedigrees. Molecular Biology and Evolution 20 54 61. Best A, White A, Boots M (2008) Maintenanc e of host variation in tolerance to pathogens and parasites. Proceedings of the National Academy of Sciences of the United States of America, 105, 2078620791. Bollmer JL, Ruder EA, Johnson JA, Eimes JA, Dunn PO (2011) Drift and selection influence geographic variation at immune loci of prairiechickens. Molecular Ecology 20, 46954706. Bollmer J, Vargas F, Parker P (2007) Low MHC variation in the endangered Galapagos penguin (Spheniscus mendiculus ) Immunogenetics 59, 593 602. Bonneaud C, Balenger SL, Hill GE, Russell AF (2012 ) Experimental evidence for distinct costs of pathogenesis and immunity against a natural pathogen in a wild bird. Molecular Ecology 21, 47874796. Bonneaud C, Perez Tris J, Federici P, Chastel O, Sorci G (2006) Major histocompa tibility alleles associated with local resistance to malaria in a passerine. Evolution 60, 383389. Bowen L, Aldridge BM, DeLong R et al. (2005) An immunogenetic basis for the high prevalence of urogenital cancer in a freeranging population of California sea lions (Zalophus californianus). Immunogenetics 56, 846 848. Brault AC, Huang CY, Langevin SA et al. (2007) A single positively selected West Nile viral mutation confers increased virogenesis in American crows. Nature Genetics 39, 11621166. Brault AC, Langevin SA, Bowen RA, Panella NA, Biggerstaff BJ, Miller BR, et al. (2004) Differential virulence of West Nile strains for American crows. Emerging Infectious Diseases 10 2161.

PAGE 166

166 Brookfield JF (1996) A simple new method for estimating null allele frequency from heterozygote deficiency. Molecular Ecology 5 453 455. Brown HE, Childs JE, UK Wasser MA, Fish D (2008) Ecologic factors as sociated with West Nile virus transmission, northeastern United States. Emerging Infectious Diseases 14 1539. Busch JD, Benford R, Pearson T et al. (2009) Development of polymorphic tetranucleotide microsat ellites for pinyon jays (Gymnorhinus cyanocephalus). Conservation Genetics 10, 689691. Caffrey C, Smith SCR, Weston TJ (2005) West Nile virus devastates an American crow population. Condor 107, 128132. Carson HL (1990) Increased genetic variance after a population bottleneck. Trends in Ecology & Evolution 5, 228 230. Cassinello J, Gomendio M, Roldan ER (2001) Relationship between Coefficient of Inbreeding and Parasite Burden in Endangered Gazelles. Conservation Biology 15, 11711174. Castoe TA, Poole AW, Gu WJ et al. (2010) Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence. Molecular Ecology Resources 10, 341 347. Center for Disease Control and Prevention, CDC, ( 2012) Available at: http://www.cdc.gov/ncidod/dvbid/westnile/USGS_frame.html?a_gotolink=http://disease maps.usgs.gov accessed 22.02.12. Charlesworth B, Charlesworth D (1999) The g enetic basis of inbreeding depression. Genetics Research, 74, 329 340. Cheng YY, Sanderson C, Jones M, Belov K (2012) Low MHC class II diversity in the Tasmanian devil (Sarcophilus harrisii). Immunogenetics 64, 525 533. Coltman DW, Pilkington JG, Smith JA, Pemberton JM (1999) ParasiteMediated Selection against Inbred Soay Sheep in a FreeLiving, Island Population. Evolution 53, 12591267. Coltman DW, Wilson K, Pilkington JG, Stear MJ, Pemberton JM (2001) A microsatellite polymorphism in the gamma interferon gene is associated with resistance to gastrointestinal nematodes in a naturally parasitized population of Soay sheep. Parasitology 122 571582.

PAGE 167

167 Combes C (2001) Parasitism: the ecology and evolution of intimate interactions University of Chicago Press. Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 20012014. Cote SD, Stien A, Irvine RJ et al. (2005) Resistance to abomasal nematodes and individual genetic variability in reindeer. Molecular Ecology 14, 4159 4168. Coulon A (2010) G enhet: an easy to use R function to estimate individual heterozygosity. Molecular Ecology Resour ces 10 167 169. Crnokrak P, Roff DA (1999) Inbreeding depression in the wild. Heredity 83, 260 270. Crosbie SP, Koenig WD, Reisen WK et al. (2008) Early Impact of West Nile Virus on the Yellow Billed Magpie (Pica Nuttalli). The Auk 125, 542550. Cunningham AA, Daszak P (1998) Extinction of a species of land snail due to infection with a microsporidian parasite. Conservation Biology 12, 11391141. Daszak P, Cunningham AA, Hyatt AD (2000) Emerging infectious diseases of wildlife-Threats to biodiversity and human heal th. Science 287, 443449. Davis CT, Ebel GD, Lanciotti RS, Brault AC, Guzman H, Siirin M, et al. (2005) Phylogenetic analysis of North American West Nile virus isolates, 20012004: evidence for the emergence of a dominant genotype. Virology 342 252 265 De Boer RJ, Borghans JA, van BM, Kesmir C, Weissing FJ (2004) Heterozygote advantage fails to explain the high degree of polymorphism of the MHC. Immunogenetics 55, 725 731. De Castro F, Bolker B (2005) Mechanisms of diseaseinduced extinction. Ecolo gy Letters 8, 117126. Deter J, Bryja J, Chaval Y et al. (2008) Association between the DQA MHC class II gene and Puumala virus infection in Myodes glareolus, the bank vole. Infection, Genetics and Evolution, 8, 450458. DeWoody YD, DeWoody JA (2005) On the estimation of genomewide heterozygosity using molecular markers. Journal of Heredity 96, 8588. Ditchkoff SS, Hoofer SR, Lochmiller RL et al. (2005) MHC DRB evolution provides insight into parasite resistance in whitetailed deer. The Southwestern Naturalist 50, 5764.

PAGE 168

168 Dobson A, Foufopoulos J (2001) Emerging infectious pathogens of wildlife. Philosophical Transactions of the Royal Society of London.Series B: Biological Sciences 356, 10011012. Doherty PC, Zinkernagel RM (1975) Enhanced Immunolog ical Surveillance in Mice Heterozygous at H 2 Gene Complex. Nature, 256, 5052. England PR, Osler GHR, Woodworth LM et al. (2003) Effects of intense versus diffuse population bottlenecks on microsatellite genetic diversity and evolutionary potential. Cons ervation Genetics 4, 595604. Ernest HB, Well JA, Kurushima JD (2008) Development of 10 microsatellite loci for Yellow billed Magpies (Pica nuttalli) and corvid ecology and West Nile virus studies. Molecular Ecology Resources 8, 196 198. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10, 564567. Fernandez deMera IG, Vicente J, Naranjo V et al. (2009) Impact of major histocompatibility complex class II polymorphisms on Iberian red deer parasitism and life history traits. Infection, Genetics and Evolution, 9, 12321239. FerrerAdmetlla A, Bosch E, Sikora M, Marques Bonet T, Ramirez Soriano A, Mun tasell A, et al. (2008) Balancing selection is the main force shaping the evolution of innate immunity genes. Journal of Immunology 181, 13151322. Foster Jt, Woodworth Bl, Eggert Le et al. (2007) Genetic structure and evolved malaria resistance in Hawai ian honeycreepers. Molecular Ecology 16, 4738 4746. Frankham R (1996) Relationship of genetic variation to population size in wildlife. Conservation Biology 10, 15001508. Frankham R (2005) Genetics and extinction. Biological Conservation, 126, 131 140. Froeschke G, Sommer S (2005) MHC class II DRB variability and parasite load in the striped mouse (Rhabdomys pumilio) in the southern Kalahari. Molecular Biology and Evolution 22, 12541259. Gandon S, Buckling A, Decaestecker E, Day T (2008) Hos t parasite coevolution and patterns of adaptation across time and space. Journal of Evolutionary Biology 21, 18611866. Goltsman M, Kruchenkova EP, Macdonald DW (1996) The Mednyi Arctic foxes: treating a population imperilled by disease. Oryx 30, 25125 8.

PAGE 169

169 Gompper ME, Monello RJ, Eggert LS (2011) Genetic variability and viral seroconversion in an outcrossing vertebrate population. Proceedings of the Royal Society of London Series B: Biological Sciences 278, 204 210 Gregory TR (2012) Animal Genome Size Database. Available at: http://www.genomesize.com Guichoux E, Lagache L, Wagner S et al. (2011) Current trends in microsatellite genotyping. Molecular Ecology Resources 11, 591 611. Guivier E, Galan M, Male PJ et al. (2010) Associations between MHC genes and Puumala virus infection in Myodes glareolus are detected in wild populations, but not from experimental infection data. Journal of General Virology 91, 25072512. Hale KA, Briskie JV (2007) Decreased immu nocompetence in a severely bottlenecked population of an endemic New Zealand bird. Animal Conservation, 10, 210. Han JI, Kim JH, Kim S, Park SR, Na KJ (2009 ) A simple and improved DNA test for avian sex determination. The Auk, 126, 779 783. Hansson B, W esterberg L (2008 a ) Heterozygosity fitness correlations within inbreeding classes: local or genomewide effects? Conservation Genetics 9, 73 83. Hansson B, Westerberg L (2008 b ) On the correlation between heterozygosity and fitness in natural populations. Mol ecular Ecol ogy 11 24672474. Harf R, Sommer S (2005) Association between major histocompatibility complex class II DRB alleles and parasite load in the hairy footed gerbil, Gerbillurus paeba, in the southern Kalahari. Molecular Ecology 14, 8591. Hawkins CE, Baars C, Hesterman H et al. (2006) Emerging disease and population decline of an island endemic, the Tasmanian devil Sarcophilus harrisii. Biological Conservation, 131, 307 324. Hawley DM, Fleischer RC (2012) Contrasting Epidemic Histories Rev eal PathogenMediated Balancing Selection on Class II MHC Diversity in a Wild Songbird. PLoS ONE 7, e30222. Hawley DM, Hanley D, Dhondt AA, Lovette LJ (2006) Molecular evidence for a founder effect in invasive house finch (Carpodacus mexicanus) populatio ns experiencing an emergent disease epidemic. Molecular Ecology 15, 263 275. Hayes CG (2001) West Nile Virus: Uganda, 1937, to New York City, 1999. Annals of the New York Academy of Sciences 951 2537.

PAGE 170

170 Hebert PD Stoeckle MY, Zemlak TS, Francis CM ( 2004) Identification of birds through DNA barcodes. PLoS Biol 2, e312. Hedrick PW (2002) Pathogen resistance and genetic variation at MHC loci. Evolution, 56, 19021908. Hedrick PW, Lee RN, Buchanan C (2003) Canine parvovirus enteritis, canine distemper and major histocompatibility complex genetic variation in Mexican wolves. Journal of Wildlife Diseases 39, 909913. Hedrick PW, Parker KM, Gutierrez Espeleta GA, Rattink A, Lievers K (2000) Major histocompatibility complex variation in the Arabian Oryx. Evolution 54, 21452151. Hedrick PW, Parker KM, Miller EL, Miller PS (1999) Major histocompatibility complex variation in the endangered Przewalski's horse. Genetics 152, 1701 1710. Hill AVS, Allsopp CEM, Kwiatkowski D et al. (1991) Common west African HLA antigens are associate d with protection from severe malaria. Nature, 352, 595600. Hoeck PEA, Keller LF (2012) Inbreeding, immune defence and ectoparasite load in different mockingbird populations and species in the Galapagos Islands. Journal of Avian Biology 43, 423 434. Ho ulden BA, England PR, Taylor AC, Greville WD, Sherwin WB (1996) Low genetic variability of the koala Phascolarctos cinereus in southeastern Australia following a severe population bottleneck. Molecular Ecology 5, 269 281. Jeffery KJM, Bangham CRM (2000) Do infectious diseases drive MHC diversity? Microbes and Infection 2, 13351341. Johnson WE, Onorato DP, Roelke ME et al. (2010) Genetic Restoration of the Florida Panther. Science 329, 16411645. Kalinowski ST (2005) Hp rare 1.0: a computer program f or performing rarefaction on measures of allelic richness. Molecular Ecology Notes 5 187 189. Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology 16, 10991106. Katoh K, Toh H (2008) Recent developments in the MAFFT multiple sequence alignment program. Brief Bioinformatics 9, 286 298. Keller LF, Waller DM (2002) Inbreeding effects in wild populations. Trends in Ecology & Evolu tion 17, 230241.

PAGE 171

171 Kilpatrick AM, Kramer LD, Jones MJ, Marra PP, Daszak P (2006) West Nile Virus Epidemics in North America Are Driven by Shifts in Mosquito Feeding Behavior. PLoS Biol, 4 e82. Kimura M, Crow JF (1964) The number of alleles that can be m aintained in a finite population. Genetics 49 725. Kloch A, Babik W, Bajer A, Saski E, Radwan J (2010) Effects of an MHC DRB genotype and allele number on the load of gut parasites in the bank vole Myodes glareolus. Molecular Ecology 19, 255265. Komar N, Langevin S, Hinten S et al. (2003) Experimental infection of North American birds with the New York 1999 strain of West Nile virus. Emerging Infectious Diseases 9, 311322. Kramer LD, Styer LM, Ebel GD (2008) A global perspective on the epidemi ology of West Nile virus. Annu. Rev. Entomol. 53 6181. Kwan JL, Kluh S, Reisen WK (2012) Antecedent Avian Immunity Limits Tangential Transmission of West Nile Virus to Humans. PLoS ONE 7: e34127. Lachish S, Miller KJ, Storfer A, Goldizen AW, Jones ME (2011) Evidence that diseaseinduced population decline changes genetic structure and alters dispersal patterns in the Tasmanian devil. Heredity 106, 172182. Ladeau SL, Kilpatrick AM, Marra PP (2007) West Nile virus emergence and largescale declines of North American bird populations. Nature, 447, 710U13. Lanciotti RS, Kerst AJ, Nasci RS, Godsey MS, Mitchell CJ, Savage HM, et al. (2000) Rapid detection of West Nile virus from human clinical specimens, fieldcollected mosquitoes, and avian samples by a TaqMan reverse transcriptasePCR assay. Journal of Clinical Microbiology 38, 40664071. Lanciotti RS, Roehrig JT, Deubel V et al. (1999) Origin of the West Nile virus responsible for an outbreak of encephalitis in the northeastern United States. S cience 286, 23332337 Le Gouar PJ, Vallet D, David L et al. (2009) How Ebola impacts genetics of western lowland gorilla populations. PLoS ONE 4, e8375. Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM (2011) Prioritizing candidate disease genes by network based boosting of genome wide association data. Genome Research, 21 11091121. Lenz T, Wells K, Pfeiffer M, Sommer S (2009) Diverse MHC IIB allele repertoire increases parasite resistance and body conditi on in the Long tailed giant rat

PAGE 172

172 (Leopoldamys sabanus ). BMC Evolutionary Biology 9, 269. Lillandt BG, Bensch S, Hansson B, Wennerberg L, Von Schantz T (2002) Brief report Isolation and cross species amplification of microsatellite loci in the Siberian j ay (Perisoreus infaustus). Hereditas 137, 157160. Lim JK, Lisco A, Mcdermott DH, Huynh L, Ward JM, Johnson B, et al. (2009) Genetic variation in OAS1 is a risk factor for initial infection with West Nile virus in man. PLoS P athogens 5 e1000321. Loiseau C, Zoorob R, Garnier SP et al. (2008) Antagonistic effects of a MHC class I allele on malariainfected house sparrows. Ecology Letters 11, 258 265. Loiseau C, Zoorob R, Robert A et al. (2011) Plasmodium relictum infection and MHC diversity in the house sparrow (Passer domesticus). Proceedings of the Royal Society B: Biological Sciences 278, 12641272. Luenser K, Fickel J, Lehnen A, Speck S, Ludwig A (2005) Low level of genetic variabi lity in European bisons (Bison bonasus) from the Bialowieza National Park in Poland. European Journal of Wildlife Research 51, 8487. Luikart G, Pilgrim K, Visty J, Ezenwa VO, Schwartz MK (2008) Candidate gene microsatellite variation is associated with parasitism in wild bighorn sheep. Biology Letters 4, 228231. Luikart G, Sherwin WB, Steele BM, Allendorf FW (1998) Usefulness of molecular markers for detecting population bottlenecks via monitoring genetic change. Molecular Ecology 7, 963974. MacDoug allShackleton EA, Derryberry EP, Foufopoulos J, Dobson AP, Hahn TP (2005) Parasitemediated heterozygote advantage in an outbred songbird population. Biology Letters 1, 105 107. Mack R (1970) The great African cattle plague epidemic of the 1890's. Tropi cal Animal Health and Production 2, 210219. Madsen T, Ujvari B (2006) MHC class I variation associates with parasite resistance and longevity in tropical pythons. Journal of Evolutionary Biology 19, 1973 1978. Malkinson M, Banet C, Weisman Y, Pokamuns ki S, King R (2002 ) Introduction of West Nile virus in the Middle East by migrating white storks. Emerging Infectious Diseases 8 392. Margulies M, Egholm M, Altman WE et al. (2005) Genome sequencing in microfabricated high density picolitre reactors. N ature, 437, 376380.

PAGE 173

173 Marsden CD, Mable BK, Woodroffe R et al. (2009) Highly endangered African wild dogs (Lycaon pictus) lack variation at the major histocompatibility complex. Journal of Heredity 100, S54 S65. Martinez JG, Soler JJ, Soler M, Moller AP, Burke T (1999) Comparative population structure and gene flow of a brood parasite, the great spotted cuckoo (Clamator glandarius), and its primary host, the magpie (Pica pica). Evolution 53, 269278. Mashimo T, Lucas M, SimonChazottes D, Frenkiel MP, M ontagutelli X, Ceccaldi PE, et al. (2002) A nonsense mutation in the gene encoding 25 oligoadenylate synthetase/L1 isoform is associated with West Nile virus susceptibility in laboratory mice. Proceedings of the National Academy of Sciences 99 11311113 16. McCallum H, Tompkins DM, Jones M et al. (2007) Distribution and impacts of Tasmanian devil facial tumor disease. EcoHealth 4, 318 325. Mclean RG, Ubico SR, Bourne D, Komar N (2002) West Nile virus in livestock and wildlife. Japanese Encephalitis and West Nile Viruses 267, 271308. Meagher S (1999) Genetic diversity and Capillaria hepatica (nematoda) prevalence in Michigan deer mouse populations. Evolution 53, 13181324. Meyer Lucht Y, Otten C, Pottker T et al. (2010) Variety matters: adaptive genetic diversity and parasite load in two mouse opossums from the Brazilian Atlantic forest. Conservation Genetics 11, 20012013. Meyer Lucht Y, Sommer S (2005) MHC diversity and the association to nematode parasitism in the yellow necked mouse (Apodemus flavicollis). Molecular Ecology 14, 22332243. Miller HC, Miller KA, Daugherty CH (2008) Reduced MHC variation in a threatened tuatara species. Animal Conservation, 11, 206214. Miller AD, Townsend AK, Mcgowan KJ, Clark AB, Glaser AL, Patrican LA, et al. (2010) Non West Nile virus associated mortality in a population of American crows (Corvus brachyrhynchos): a gross and histopathologic study. J. Vet. Diagn. Invest, 22 289295. Mosbruger TL, Duggal P, Goedert JJ, Kirk GD, Hoots WK, Tobler LH, et al. (2010) LargeScale Candidate Gene Analysis of Spontaneous Clearance of Hepatitis C Virus. Journal of Infectious Diseases 201 13711380. Moudy RM, Meola MA, Morin LLL, Ebel GD, Kramer LD (2007) A newly emergent genotype of West Nile virus is transmitted earlier and more efficiently by Culex mosquitoes. The American journal of tropical medicine and hygiene, 77 365 370.

PAGE 174

174 Mukherjee S, Sarkar Roy N, Wagener DK, Majumder PP (2009) Signatures of natural selection are not uniform across genes of innate immune system, but purifying selection is the dominant signature. Proc. Natl. Acad. Sci. U. S. A 106 7073 7078. Muller Graf CDM, Woolhouse MEJ, Packer C (1999) Epidemiology of an intestinal parasite (Spirometra spp.) in two populations of African lions (Panthera leo) Parasitology 118, 407 415. Murchison EP, Tovar C, HS A, Bender HS, Kheradpour P, Rebbeck CA, et al. (2010) The tasmanian devil transcriptome reveals Schwann cell origins of a clonally t ransmissible cancer. Science 327: 8487. Mutze G, Cooke B, Alexander P (1998) The initial impact of rabbit hemorrhagic disease on European rabbit populations in South Australia. Journal of Wildlife Diseases 34, 221227. Nei M (1987) Molecular evolutionary genetics. New York, Columbia University Press. Nei M, Li WH (1979) Mathematical model for studying genetic variation in terms of restriction endonucleases. Proceedings of the National Academy of Sciences 76, 5269 5273. NigendaMorales S, Flores Ram irez S, Urban R, Vazquez Juarez R (2008) MHC DQB 1 polymorphism in the Gulf of California Fin whale (Balaenoptera physalus) population. Journal of Heredity 99, 14 21. Oliver MK, Telfer S, Piertney SB (2009) Major histocompatibility complex (MHC) heterozy gote superiority to natural multi parasite infections in the water vole (Arvicola terrestris). Proceedings of the Royal Society B: Biological Sciences 276, 11191128. Ortego J, Cordero PJ, Aparicio JM, Calabuig G (2007) No relationship between individual genetic diversity and prevalence of avian malaria in a migratory kestrel. Mol ecular Ecology 16 48584866. Paetkau D, Slade R, Burden M, Estoup A (2004) Genetic assignment methods for the direct, real time estimation of migration rate: a simulationbas ed exploration of accuracy and power. Mo lecuar Eco logy 13 55 65. Paterson S, Wilson K, Pemberton JM (1998) Major histocompatibility complex variation associated with juvenile survival and parasite resistance in a large unmanaged ungulate population (Ov is aries L.). Proceedings of the National Academy of Sciences 95, 37143719. Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6, 288295.

PAGE 175

175 Pearman PB, Garner TWJ (2005) Susceptibility of Italian agile frog populations to an emerging strain of Ranavirus parallels population genetic diversity. Ecology Letters 8, 401408. Perelygin AA, Scherbik SV, Zhulin IB, Stockman BM, LI Y, Brinton MA (200 2 ) Positional cloning of the murine flavivirus resistance gene. Proceedings of the National Academy of Sciences 99 93229327. Piertney SB, Oliver MK (2006) The evolutionary ecology of the major histocompatibility complex. Heredity 96, 7 21. Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L, Estoup A (2004) GENECLASS2: A Software for Genetic Assignment and First Generation Migrant Detection. Journal of Heredity 95 536 539. Potts WK, Wakeland EK (1990) Evolution of diversity at the major histoco mpatibility complex. Trends in Ecology & Evolution, 5, 181187. Primmer CR, Raudsepp T, Chowdhary BP, Moller AR, Ellegren H (1997) Low frequency of microsatellites in the avian genome. Genome Research, 7, 471482. Pyle P (1997) Identification g uide to North American b irds Slate Creek, California. Queney G, Ferrand N, Marchandeau S et al. (2000) Absence of a genetic bottleneck in a wild rabbit (Oryctolagus cuniculus) population exposed to a severe viral epizootic. Molecular Ecology 9, 1253126 4. Raberg L, Sim D, Read AF (2007) Disentangling genetic variation for resistance and tolerance to infectious diseases in animals. Science 318, 812814. Rachowicz LJ, Knapp RA, Morgan JAT et al. (2006) Emerging infectious disease as a proximate cause of amphibian mass mortality. Ecology 87, 1671 1683. Radwan J, Demiaszkiewicz AW, Kowalczyk R et al. (2010) An evaluation of two potential risk factors, MHC diversity and host density, for infection by an invasive nematode Ashworthius sidemi in endangered E uropean bison (Bison bonasus). Biological Conservation, 143, 20492053. Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences 94, 91979201. Ratnasingham S, Hebert PDN (2007) BOLD: The Barcode of Life Data System (www.barcodinglife.org). Molecular Ecology Notes 7, 355364. Raymond M, Rousset F (1995) GENEPOP (Version 1.2): Population Genetics Software for Exact Tests and Ecumenicism. Journal of Heredity 86 248249.

PAGE 176

176 Reid JM, Arcese P, Keller LF et al. (2007) Inbreeding effects on immune response in free living song sparrows (Melospiza melodia). Proceedings of the Royal Society B: Biological Sciences 274, 697706. Rijks JM, Hoffman JI, Kuik en T, Osterhaus ADME, AMOS W (2008) Heterozygosity and lungworm burden in harbour seals (Phoca vitulina). Heredity 100, 587 593. Rios JJ, Fleming JA Bryant UK, Carter CN, Huber JC, Long MT, et al. (2010) OAS1 polymorphisms are associated with susceptibi lity to West Nile encephalitis in horses. PLoS ONE 5 e10537. Robinson RA, Lawson B, Toms MP et al. (2010) Emerging infectious disease leads to rapid population declines of common British birds. PLoS ONE, 5, e12215. Roelke ME, Martenson JS, O'Brien SJ ( 1993) The consequences of demographic reduction and genetic depletion in the endangered Florida panther. Current Biology 3, 340350. Ross Gillespie A, O'Riain MJ, Keller LF (2007) Viral Epizootic Reveals Inbreeding Depression in a Habitually Inbreeding M ammal. Evolution 61, 22682273. Roy BA, Kirchner JW (2000) Evolutionary dynamics of pathogen resistance and tolerance. Evolution 54, 51 63. Saarinen EV, Austin JD (2010) When technology meets conservation: Increased microsatellite marker production usi ng 454 genome sequencing on the endangered Okaloosa darter (Etheostoma okaloosae). Journal of Heredity 101, 784788. Savage AE, Zamudio KR (2011) MHC genotypes associate with resistance to a frog killing fungus. Proceedings of the National Academy of Sci ences 108, 1670516710. Schad J, Ganzhorn JU, Sommer S, Yoder A (2005) Parasite burden and constitution of major histocompatibility complex in the Malagasy mouse lemur, Microcebus murinus. Evolution 59, 439450. Shaw TI, Srivastava A, Chou WC, Liu L, H awkinson A, Glenn TC, et al. (2012) T ranscriptome sequencing and annotation for the Jamaican fruit bat ( Artibeus jamaicensis ). PLoS ONE, 7 e48472. Scherbik SV, Paranjape JM, Stockman BM, Silverman RH, Brinton MA (2006) RNase L plays a role in the antiviral response to West Nile virus. Journal of virology 80 29872999. Schloegel LM, Hero JM, Berger L et al. (2006) The decline of the sharpsnouted day frog (Taudactylus acutirostris): The first documented case of extinction by infection in a

PAGE 177

177 free ranging wildlife species? EcoHealth, 3, 35 40. Schoenle LA, Townsend AK, Lovette IJ (2007) Isolation and characterization of microsatellite loci in a cooperatively breeding corvid, the American crow (Corvus brachyrhynchos). Mo lecular Ecology Notes 7, 46 48. Schwensow N, Dausmann K, Eberle M, Fietz J, Sommer S (2010) Functional associations of similar MHC alleles and shared parasite species in two sympatric lemurs. Infection, Genetics and Evolution, 10, 662 668. Schwensow N, Fietz J, Dausmann KH, SOMMER S (2007) Neutral versus adaptive genetic variation in parasite resistance: importance of major histocompatibility complex supertypes in a freeranging primate. Heredity 99, 265277. Segal S, Hill AVS (2003) Genetic susceptibi lity to infectious disease. Trends in Microbiology 11, 445448. Shim E, Galvani AP (2009) Evolutionary repercussions of avian culling on host resistance and influenza virulence. PLoS ONE, 4. 5, e5503. Siddle HV, Marzec J, Cheng Y, Jones M, Belov K (2010) MHC gene copy number variation in Tasmanian devils: implications for the spread of a contagious cancer. Proceedings of the Royal Society B: Biological Sciences 277, 2001 2006. Slade RW (1992) Limited MHC polymorphism in the southern elephant seal: implications for MHC evolution and marine mammal population biology. Proceedings of the Royal Society of London.Series B: Biological Sciences 249, 163171. Slatkin M (1985) Rare alleles as indicators of gene flow. Evolution, 53 65. Smith S, Belov K, Hughes J (2010) MHC screening for marsupial conservation: extremely low levels of class II diversity indicate population vulnerability for an endangered Australian marsupial. Conservation Genetics 11, 269 278. Spielman D, Brook BW, Briscoe DA, Frankham R (2004) Does inbreeding and loss of genetic diversity decrease disease resistance? Conservation Genetics 5, 439448. Spurgin LG, Richardson DS (2010) How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. Proceedings of the Royal Society B: Biological Sciences 277, 979988. Srithayakumar V, Castillo S, Rosatte R, Kyle C (2011) MHC class II DRB diversity in raccoons (Procyon lotor) reveals associations with raccoon rabies virus (Lyssavirus ) Immunogenetics 63, 103 113.

PAGE 178

178 Stenzler LM, Fitzpatrick JW (2002) Isolation of microsatellite loci in the Florida ScrubJay Aphelocoma coerulescens. Molecular Ecology Notes 2, 547550. Takahata N, Nei M (1990) Allelic genealogy under overdominant and frequency dependent selection and polymorphism of major histocompatibility complex loci. Genetics 124, 967978. Tarr CL, Fleischer RC (1998) Primers for polymorphic GT microsatellites isolated from the Mariana crow, Corvus kubaryi. Molecular Ecology 7, 253255. Teacher AG, Cunningham AA, Garner TW (2010) Assessing the long term impact of Ranavirus infection in wild common frog populations. Animal Conservation, 13, 514522. Teacher AG, Garner TW, Nichols RA (2009) Population genetic patterns suggest a behavioural change in wild common frogs (Rana temporaria) following disease outbreaks (Ranavirus). Molecular Ecology 18, 31633172. Thorne ET, Williams ES (1988) Disease and endangered species: the black footed ferret as a recent example. Conservation Biology 2, 66 74. Timm SF, Munson L, Summers BA et al (2009) A suspected canine distemper epidemic as the cause of a catastrophic decline in Santa Catalina island foxes (Urocyon Littoralis Catalinae). Journal of Wildlife Diseases 45, 333 343. Tollenaere C, Bryja J, Galan M et al. (2008) Multiple parasites mediate balancing selection at two MHC class II genes in the fossorial water vole: insights from multivariate analyses and population genetics. Journal of Evolutionary Biology 21, 13071320. Townsend AK, Clark AB, McGowan KJ et al. (2009) Diseasemediat ed inbreeding depression in a large, open population of cooperative crows. Proceedings of the Royal Society B: Biological Sciences 276, 20572064. Townsend AK, Clark AB, Mcgowan KJ, Miller AD, Buckles EL (2010) Condition, innate immunity and disease mort ality of inbred crows. Proceedings of the Royal Society B: Biological Sciences 277 28752883. Trudeau KM, Britten HB, Restani M (2004) Sylvatic plague reduces genetic variability in black tailed prairie dogs. Journal of Wildlife Diseases 40, 205 211. Tsutsui ND, Suarez AV, Holway DA, Case TJ (2000) Reduced genetic variation and the success of an invasive species. Proceedings of the National Academy of Sciences 97, 59485953.

PAGE 179

179 Turner AK, Begon M, Jackson JA, Bradley JE, Paterson S (2011) Genetic divers ity in cytokines associated with immune variation and resistance to multiple pathogens in a natural rodent population. PLoS Genet ics 7 e1002343. Valsecchi E, Amos W, Raga JA, Podesti M, Sherwin W (2004) The effects of inbreeding on mortality during a morbillivirus outbreak in the Mediterranean striped dolphin (Stenella coeruleoalba ) Animal Conservation, 7, 139146. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO CHECKER: software for identifying and correcting genotyping errors in m icrosatellite data. Molecular Ecology Notes 4, 535538. Van Riper III C, Riper SG, Goff ML, Laird M (1986) The epizootiology and ecological significance of malaria in Hawaiian land birds. Ecological Monographs 56, 327344. Verdugo C, Clark AM, Prakoso D, Kramer LD, Long MT (2012) Multiplexed microsatellite loci in American crow (Corvus brachyrhynchos): A severely affected natural host of West Nile virus. Infection, Genet ics and Evol ution 12, 19681974. Villafuerte R, Calvete C, Blanco JC, Lucientes J (1995) Incidence of viral hemorrhagic disease in wild rabbit populations in Spain. Mammalia 59, 651659. Wattier RM, Haine ER, Beguet J et al. (2007) No genetic bottleneck or associated microparasite loss in invasive populations of a freshwater amphipod. Oikos 116, 19411953. Weber DS, Stewart BS, Schienman J, Lehman N (2004) Major histocompatibility complex variation at three class II loci in the northern elephant seal. Molecular Ecology 13, 711718. Wegner KM, Kalbe M, Kurtz J, Reusch TBH, Milinski M (2003) Parasite selection for immunogenetic optimality. Science 301, 1343. Westerdahl H, Waldenstrom J, Hansson B et al. (2005) Associations between malaria and MHC genes in a migratory songbird. Proceedings of the Royal Society B: Biological Sciences 272, 15111518. Whiteman NK, Matson KD, Bollmer JL, Parker PG (2006) Disease ecology in the Galapagos Hawk (Buteo galapagoensis): host genetic diversity, parasite load and natural antibodies. Proceedings of the Royal Society B: Biological Sciences 273, 797 804. Wilcox BR, Yabsley MJ, Ellis AE, Stallknecht DE, Gibbs SEJ (2007) West Nile virus antibody prevalence in American crows (Corvus brachyrhynchos) and fish crows (Corvus ossifragus) in Georgia, USA. Avian Diseases 51, 125128.

PAGE 180

180 Williams TN (2006) Human red blood cell polymorphisms and malaria. Current Opinion in Microbiology 9, 388 394. Woodworth BL, Atkinson CT, LaPointe DA et al. (2005) Host population persistence in the face of introduced vector borne diseases: Hawaii amakihi and avian malaria. Proceedings of the National Academy of Sciences of the United States of America, 102, 15311536. Woolford L, Bennett M, Sims C et al. (2009) Prevalence, emergence, and factors associated with a viral papillomatosis and carcinomatosis syndrome in wild, r eintroduced, and captive western barred bandicoots ( Perameles bougainville). EcoHealth, 6, 414 425. Woolhouse ME (2002) Population biology of emerging and reemerging pathogens. Trends Microbiology 10, S3 S7. Woolhouse ME, Haydon DT, Antia R (2005) Emerging pathogens: the epidemiology and evolution of species jumps. Trends Ecology & Evolution, 20, 238 244. Wo olhouse ME Webster JP, Domingo E, Charlesworth B, Levin BR (2002) Biological and biomedical implications of the co evolution of pathogens and their hosts. Nature Genetics 32, 569 577. Yaremych SA, Warner RE, Mankin PC et al. (2004) West Nile virus and high death rate in American crows. Emerging Infectious Diseases 10, 709 711. Yates A, Antia R, Regoes RR (2006 ) How do pathogen evolution and host heterogeneity interact in disease emergence? Proceedings of the Royal Society B: Biological Sciences 273 30753083. You FM, Huo NX, Gu YQ et al. (2008) BatchPrimer3: A high throughput web application for PCR and sequencing primer design. BMC Bioinformatics 9. Zhang B, Fang SG, Xi YM (2006) Major histocompatibility complex variation in the endangered crested ibis Nipponia nippon and implications for reintroduction. Biochemical Genetics 44, 110120

PAGE 181

181 BIOGRAPHICAL SKETCH Claudio Verdugo was born in 1979 in the city of Jundiai, Sao Paulo, Brazil. After living his childhood in Belo Horizonte, and Via del Mar, and Santiago, Chile, he ended u p in Valdivia studying Veterinary Medicine and graduating from the Universidad Austral de Chile. His passion for climbing and hiking make him to be in the middle of beautiful, natural, and remotes places. Thus, his interest for wildlife was almost logical and natural. He received a scholarship from the Chilean government for an externship in a wildlife rehabilitation and conservation center in Costa Rica. Then, he spent most of his time during school the hard way of the self learning wildlife medicine. Once graduated in 2004, he was granted with a position on the Universidad to develop a Wildlife Rehabilitation Center from the ground. After three years working a great group of volunteer students the Center was positioned as a regional and national reference wildlife center. During that period, he collaborated in different research project s mainly in wildlife conservation, ecophysiology, and reproduction physiology. He also was granted for an externship to the Virology Lab at the Universidad de Cordoba, Argentina, where he discovered the ecology and epidemiology of arbovirus. After that experience, he applied for and later granted with a scholarship for graduate studies by the Chilean government. Thus, Claudio began his graduate studies at University of Flor ida in 2008, focusing his studies and interest in molecular epidemiology, host population genetics and in the evolution of infectious diseases in natural populations. He received his Ph.D. in fall of 2012.