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BriefAvian Host Diversity and Landscape Characteristics as Predictors_Split

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BriefAvian Host Diversity and Landscape Characteristics as Predictors_Split

  1. 1. AVIAN DIVERSITY AND LANDSCAPEAVIAN DIVERSITY AND LANDSCAPE RISK FACTORS FOR WEST NILE VIRUSRISK FACTORS FOR WEST NILE VIRUS INFECTION IN HOUSE SPARROWSINFECTION IN HOUSE SPARROWS ByBy Lara M. JuliussonLara M. Juliusson An Honors ThesisAn Honors Thesis Submitted to the Department of Ecology and Evolutionary BiologySubmitted to the Department of Ecology and Evolutionary Biology in partial fulfillment for departmental honors for the degree ofin partial fulfillment for departmental honors for the degree of BACHELOR OF ARTSBACHELOR OF ARTS University of Colorado, BoulderUniversity of Colorado, Boulder
  2. 2. – WNV Emerging zoonosis in North and South America – Exotic pathogen – Example, Yellow-billed magpie (Pica nuttalli) IntroductionIntroduction www.mcssb.com/photos/birds.htm Koenig et al., 2007 % WNV prevalence Conservation Implications of West Nile Virus for American Birds
  3. 3. Culex tarsalis Breeding habitats: – wetlands – flood-irrigated crops – hoof prints – new water sources with high nutrient content Host seeking: – elevated canopy cover IntroductionIntroduction WNV Transmission Dynamics – Focal Vector www.smcmad.org
  4. 4. – Predominant WNV host in rural CO – resident all-year in study foci – moderately high reservoir competence – sometimes open- cup nester (trees & hedges) IntroductionIntroduction WNV Transmission Dynamics – Focal Host House sparrow (Passer domesticus)
  5. 5. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  6. 6. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  7. 7. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water 2) sod, corn, and vegetable crop production (highly inorganic N fertilized crops) Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  8. 8. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water 2) sod, corn, and vegetable crop production (highly inorganic N fertilized crops) 3) flood irrigated crops Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  9. 9. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water 2) sod, corn, and vegetable crop production (highly inorganic N fertilized crops) 3) flood irrigated crops 4) highly fertilized crops in Interaction with flood irrigated crops Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  10. 10. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water 2) sod, corn, and vegetable crop production (highly inorganic N fertilized crops) 3) flood irrigated crops 4) highly fertilized crops in Interaction with flood irrigated crops 5) high total nitrogen input from all crops, and Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  11. 11. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water 2) sod, corn, and vegetable crop production (highly inorganic N fertilized crops) 3) flood irrigated crops 4) highly fertilized crops in Interaction with flood irrigated crops 5) high total nitrogen input from all crops, and 6) tree canopy Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  12. 12. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water 2) sod, corn, and vegetable crop production (highly inorganic N fertilized crops) 3) flood irrigated crops 4) highly fertilized crops in Interaction with flood irrigated crops 5) high total nitrogen input from all crops, and 6) tree canopy II. WNV infection is predicted to be positively associated with greater numbers of : Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  13. 13. I. WNV infection is predicted to be positively associated with greater percent surrounding area covered by: 1) perennial water, wetlands, and intermittent water 2) sod, corn, and vegetable crop production (highly inorganic N fertilized crops) 3) flood irrigated crops 4) highly fertilized crops in Interaction with flood irrigated crops 5) high total nitrogen input from all crops, and 6) tree canopy II. WNV infection is predicted to be positively associated with greater numbers of : 1) livestock confinement areas Vector feeding preference IntroductionIntroductionHypotheses: LandscapeHypotheses: Landscape
  14. 14. III. WNV infection is predicted to be negatively associated with: Vector feeding preference IntroductionIntroductionHypotheses: Avian DiversityHypotheses: Avian Diversity
  15. 15. III. WNV infection is predicted to be negatively associated with: 1) high relative abundance, species richness, and diversity of a group of low reservoir-competent avian orders: Columbiform, Piciform, and Anseriform, Vector feeding preference IntroductionIntroductionHypotheses: Avian DiversityHypotheses: Avian Diversity Reservoir competence = Low: Diluters, e.g. Mallard Mourning dove Rock dove Northern flicker
  16. 16. III. WNV infection is predicted to be negatively associated with: 1) high relative abundance, species richness, and diversity of a group of low reservoir-competent avian orders: Columbiform, Piciform, and Anseriform, 2) higher density proportions of diluter orders to a group of all Passeriform species. Vector feeding preference IntroductionIntroductionHypotheses: Avian DiversityHypotheses: Avian Diversity Reservoir competence = High: Spreaders, e.g. Low: Diluters, e.g. Mallard Mourning dove Rock dove Northern flicker Blue jay House sparrow
  17. 17. IV. WNV infection is predicted to be: Vector feeding preference IntroductionIntroductionHypotheses: ControlsHypotheses: Controls
  18. 18. IV. WNV infection is predicted to be: 1) Positively associated with high vector densities, Vector feeding preference IntroductionIntroductionHypotheses: ControlsHypotheses: Controls Vector density (+)
  19. 19. IV. WNV infection is predicted to be: 1) Positively associated with high vector densities, 2) Negatively associated with application of adulticide and larvacide control measures, and Vector feeding preference IntroductionIntroductionHypotheses: ControlsHypotheses: Controls Adulticide & Larvacide (-) Vector density (+)
  20. 20. IV. WNV infection is predicted to be: 1) Positively associated with high vector densities, 2) Negatively associated with application of adulticide and larvacide control measures, and 3) Negatively associated with high preepizotic House sparrow immunity. Vector feeding preference IntroductionIntroductionHypotheses: ControlsHypotheses: Controls Adulticide & Larvacide (-) Vector density (+) Immunity (-)
  21. 21. Study Area • 23 small, agricultural town cores, in Weld County, CO • 2-kilometer surrounding regions provide independent land cover / land use context for 22 sites MethodsMethods I-25
  22. 22. • The Centers for Disease Control and Prevention provided raw WNV seroprevalence data • Collected all season • Infection rate from HY sparrow seroprevalence and AHY birds negative in spring, but positive in fall • N=23 for each year MethodsMethods Outcome Variable: 2004 and 2005 house sparrow infection rate at each study site
  23. 23. • 2004 Model Land Cover: – Perennial water – Intermittent water – Wetlands – Tree canopy – From the National Land Cover Dataset, 2001 GIS grid • 2005 Model Land Use: – Crop type – Irrigation type – From the Colorado Department of Water Resources, 2005 GIS layer – Derived flood-irrigated acreage of corn, sod, and vegetable crops – Estimated pounds of inorganic nitrogen applied to corn, sod, and vegetable crops which were flood-irrigated MethodsMethods Predictor Variables: Land Cover / Land Use Estimated N for 2005 Example Gilcrest: 12 acres F-I Sod x 125 lbs / acre = 1,486 lbs
  24. 24. • 2005 Model Land Use: – Livestock Confinement Operations (LCOs) – Uses requiring special permits GIS layer from Weld Co. permitting – Verified and improved with digitized LCOs from 2004 aerials, and online business databases MethodsMethods Predictor Variables: Land Cover / Land Use
  25. 25. • 2005 Model Land Use: – Livestock Confinement Operations (LCOs) – Uses requiring special permits GIS layer from Weld Co. permitting – Verified and improved with digitized LCOs from 2004 aerials, and online business databases MethodsMethods Predictor Variables: Land Cover / Land Use
  26. 26. • 2004 Model: – CDC provided in-town bird survey of all birds seen or heard during four 1-minute observation intervals – Program MARK closed capture models used to estimate population abundance and density for: 1. Group of diluter orders – Species richness – Shannon’s diversity 2. Group of Passeriformes, and 3. Proportion of diluter density / hectare per 100,000 Passeriformes per hectare MethodsMethods Predictor Variables: Avian Diversity
  27. 27. • 2004 Model: – CDC provided in-town bird survey of all birds seen or heard during four 1-minute observation intervals – Program MARK closed capture models used to estimate population abundance and density for: 1. Group of diluter orders – Species richness – Shannon’s diversity 2. Group of Passeriformes, and 3. Proportion of diluter density / hectare per 100,000 Passeriformes per hectare Percent of Each Order in the Diluter Group 98% 1% 1% Columbiform Anseriform Piciform MethodsMethods Predictor Variables: Avian Diversity
  28. 28. • 2004 and 2005 Models: – Cx. tarsalis density data collected by the CDC, 2004 and 2005. – Mosquito control measures for each town (Y/N) provided by CMC, 2004, 2005 – Immunity rate: spring seroprevalence data for AHY sparrows collected by the CDC, 2004 and 2005 MethodsMethods Predictor Variables: Controls http://www.chesapeake.va.us/
  29. 29. • Poisson single variable regression • Akaike Information Criterion (AICc) model ranking – Screened for best predictors from: • Land Cover predictors (2004 model) • Avian diversity metrics (2004 model) • Land Use predictors (2005 model) • Two separate regression models (ranked by AICc): – 2004: Land cover, avian diversity, and control predictors, plus global model multiple regression – 2005: Land use, and control predictors, plus global model multiple regression • Used “R” statistical software MethodsMethods Statistical Analyses
  30. 30. Results: Variable ScreeningResults: Variable Screening Land Cover Candidates Akaike Weight Avian Diversity Candidates Akaike Weight Land Use Candidates Akaike Weight Percent perennial water (-) 0.307 Diluter group density (+) 0.636 Estimated N application for flood irrigated sod crops (-) 0.696 Percent wetland (+) 0.288 Proportion diluters to Passeriformes (+) 0.237 Estimated N application for flood irrigated corn crops (-) 0.159 Percent intermittent water (-) 0.285 Diluter species richness (+) 0.060 Total estimated N application for all flood irrigated crops (-) 0.135 Percent canopy (-) 0.095 Passeriform density (+) 0.034 Number of LCOs within 2 km (+) 0.006 Diluter group Shannon's Diversity Index (+) 0.029 Index of distance and size of LCOs within 2 km (+) 0.002 Estimated N application for flood irrigated vegetable crops (+) 0.001
  31. 31. Results: Variable ScreeningResults: Variable Screening Land Cover Candidates Akaike Weight Avian Diversity Candidates Akaike Weight Land Use Candidates Akaike Weight Percent perennial water (-) 0.307 Diluter group density (+) 0.636 Estimated N application for flood irrigated sod crops (-) 0.696 Percent wetland (+) 0.288 Proportion diluters to Passeriformes (+) 0.237 Estimated N application for flood irrigated corn crops (-) 0.159 Percent intermittent water (-) 0.285 Diluter species richness (+) 0.060 Total estimated N application for all flood irrigated crops (-) 0.135 Percent canopy (-) 0.095 Passeriform density (+) 0.034 Number of LCOs within 2 km (+) 0.006 Diluter group Shannon's Diversity Index (+) 0.029 Index of distance and size of LCOs within 2 km (+) 0.002 Estimated N application for flood irrigated vegetable crops (+) 0.001
  32. 32. Results: Variable ScreeningResults: Variable Screening Land Cover Candidates Akaike Weight Avian Diversity Candidates Akaike Weight Land Use Candidates Akaike Weight Percent perennial water (-) 0.307 Diluter group density (+) 0.636 Estimated N application for flood irrigated sod crops (-) 0.696 Percent wetland (+) 0.288 Proportion diluters to Passeriformes (+) 0.237 Estimated N application for flood irrigated corn crops (-) 0.159 Percent intermittent water (-) 0.285 Diluter species richness (+) 0.060 Total estimated N application for all flood irrigated crops (-) 0.135 Percent canopy (-) 0.095 Passeriform density (+) 0.034 Number of LCOs within 2 km (+) 0.006 Diluter group Shannon's Diversity Index (+) 0.029 Index of distance and size of LCOs within 2 km (+) 0.002 Estimated N application for flood irrigated vegetable crops (+) 0.001
  33. 33. Results: Variable ScreeningResults: Variable Screening Land Cover Candidates Akaike Weight Avian Diversity Candidates Akaike Weight Land Use Candidates Akaike Weight Percent perennial water (-) 0.307 Diluter group density (+) 0.636 Estimated N application for flood irrigated sod crops (-) 0.696 Percent wetland (+) 0.288 Proportion diluters to Passeriformes (+) 0.237 Estimated N application for flood irrigated corn crops (-) 0.159 Percent intermittent water (-) 0.285 Diluter species richness (+) 0.060 Total estimated N application for all flood irrigated crops (-) 0.135 Percent canopy (-) 0.095 Passeriform density (+) 0.034 Number of LCOs within 2 km (+) 0.006 Diluter group Shannon's Diversity Index (+) 0.029 Index of distance and size of LCOs within 2 km (+) 0.002 Estimated N application for flood irrigated vegetable crops (+) 0.001
  34. 34. Results: 2004 ModelResults: 2004 Model Land Cover, Avian Diversity, and Controls Land Cover, best candidate predictor: • Perennial water, 31% likelihood best model • Wetland, 29% • Intermittent water, 29% Avian Diversity, best candidate predictor: • Diluter group density, 64% likelihood best model • Proportion diluters to Passeriformes, 24% Candidate screening:
  35. 35. Results: 2004 ModelResults: 2004 Model Land Cover, Avian Diversity, and Controls Land Cover, best candidate predictor: • Perennial water, 31% likelihood best model • Wetland, 29% • Intermittent water, 29% Avian Diversity, best candidate predictor: • Diluter group density, 64% likelihood best model • Proportion diluters to Passeriformes, 24% Poisson Model of Infection Rate & Direction of Association Hypothesized Direction Akaike Weight Diluter group density (+) - 0.649 Percent perennial water (-) + 0.095 Immunity rate (+) - 0.083 Global model: diluter group density (+), percent perennial water (-), immunity rate (+), mosquito control (-), mosquito density (+) 0.064 Mosquito control (-) - 0.064 Mosquito density (+) + 0.046 Final model ranking:
  36. 36. Results: 2004 ModelResults: 2004 Model Land Cover, Avian Diversity, and Controls Land Cover, best candidate predictor: • Perennial water, 31% likelihood best model • Wetland, 29% • Intermittent water, 29% Avian Diversity, best candidate predictor: • Diluter group density, 64% likelihood best model • Proportion diluters to Passeriformes, 24% Poisson Model of Infection Rate & Direction of Association Hypothesized Direction Akaike Weight Diluter group density (+) - 0.649 Percent perennial water (-) + 0.095 Immunity rate (+) - 0.083 Global model: diluter group density (+), percent perennial water (-), immunity rate (+), mosquito control (-), mosquito density (+) 0.064 Mosquito control (-) - 0.064 Mosquito density (+) + 0.046 Final model ranking:
  37. 37. Results: 2004 ModelResults: 2004 Model Land Cover, Avian Diversity, and Controls
  38. 38. Results: 2004 ModelResults: 2004 Model Land Cover, Avian Diversity, and Controls
  39. 39. Results: 2005 ModelResults: 2005 Model Land Use and Controls Variable screening: Land use, best candidate predictor: • N application from flood-irrigated sod crops, 70% likelihood best model • N application from flood-irrigated corn crops, 16% • N application from all flood-irrigated sod 14%
  40. 40. Results: 2005 ModelResults: 2005 Model Land Use and Controls Land use, best candidate predictor: • N application from flood-irrigated sod crops, 70% likelihood best model • N application from flood-irrigated corn crops, 16% • N application from all flood-irrigated sod 14% Final model ranking: Poisson Model of Infection Rate & Direction of Association Hypothesized Direction Akaike Weight Estimated N application for flood-irrigated sod crops (-) + 0.954 Global Model: N sod (-), LCO count (+), mosquito density (-), immunity rate (+), mosquito control (+) 0.030 Number of LCOs within 2 km (+) + 0.008 Immunity rate (+) - 0.005 Mosquito density (-) + 0.001 Mosquito control (+) - 0.001
  41. 41. Results: 2005 ModelResults: 2005 Model Land Use and Controls
  42. 42. DiscussionDiscussion Avian Diversity 2004 Model: Diluter density a 65% likelihood of being the best model: associated with increasing house sparrow WNV infection.
  43. 43. DiscussionDiscussion Avian Diversity 2004 Model: Diluter density a 65% likelihood of being the best model: associated with increasing house sparrow WNV infection. • Are nestling Columbids competent WNV hosts?
  44. 44. DiscussionDiscussion Avian Diversity 2004 Model: Diluter density a 65% likelihood of being the best model: associated with increasing house sparrow WNV infection. • Are nestling Columbids competent WNV hosts? o Possibly, Mahmood et al. (2004), found nestling mourning doves competent hosts for St. Louis Encephalitis
  45. 45. DiscussionDiscussion Avian Diversity 2004 Model: Diluter density a 65% likelihood of being the best model: associated with increasing house sparrow WNV infection. • Are nestling Columbids competent WNV hosts? o Possibly, Mahmood et al. (2004), found nestling mourning doves competent hosts for St. Louis Encephalitis o Columbids are multiple brooders throughout a long breeding season
  46. 46. DiscussionDiscussion Avian Diversity 2004 Model: Diluter density a 65% likelihood of being the best model: associated with increasing house sparrow WNV infection. • Are nestling Columbids competent WNV hosts? o Possibly, Mahmood et al. (2004), found nestling mourning doves competent hosts for St. Louis Encephalitis o Columbids are multiple brooders throughout a long breeding season o Long breeding season overlaps with WNV transmission season
  47. 47. DiscussionDiscussion Avian Diversity 2004 Model: Diluter density a 65% likelihood of being the best model: associated with increasing house sparrow WNV infection. • Are nestling Columbids competent WNV hosts? o Possibly, Mahmood et al. (2004), found nestling mourning doves competent hosts for St. Louis Encephalitis o Columbids are multiple brooders throughout a long breeding season o Long breeding season overlaps with WNV transmission season • Are eurasian collared-dove adults competent WNV hosts?
  48. 48. DiscussionDiscussion Perennial Water 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection.
  49. 49. DiscussionDiscussion Perennial Water 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Is there a threshold effect with increasing percent water cover?
  50. 50. DiscussionDiscussion Perennial Water 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Is there a threshold effect with increasing percent water cover? o The Lowess curves suggest that this is possible.
  51. 51. DiscussionDiscussion 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. Perennial Water - Continued -
  52. 52. DiscussionDiscussion 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Does water body size, and not just total % area in 2-km matter? Perennial Water
  53. 53. DiscussionDiscussion 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Does water body size, and not just total % area in 2-km matter? Perennial Water Windsor Wellington Severance Berthoud Fort Lupton
  54. 54. DiscussionDiscussion 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Does water body size, and not just total % area in 2-km matter? Perennial Water Windsor Wellington Severance Berthoud Fort Lupton
  55. 55. DiscussionDiscussion 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Does water body size, and not just total % area in 2-km matter? o Visual review suggests that this is possible. Perennial Water Windsor Wellington Severance Berthoud Fort Lupton
  56. 56. DiscussionDiscussion 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Does water body size, and not just total % area in 2-km matter? o Visual review suggests that this is possible. • Indicative of a “dilution effect”? Perennial Water Windsor Wellington Severance Berthoud Fort Lupton
  57. 57. DiscussionDiscussion 2004 Model: % surrounding perennial water had a 10% likelihood of being the best model: associated with decreasing sparrow infection. • Does water body size, and not just total % area in 2-km matter? o Visual review suggests that this is possible. • Indicative of a “dilution effect”? o Additional bird surveys within 2-km region will be conducted. Perennial Water Windsor Wellington Severance Berthoud Fort Lupton
  58. 58. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection.
  59. 59. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship
  60. 60. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship • Are less female cx. tarsalis emerging due to toxins associated with nitrogenous fertilizer?
  61. 61. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship • Are less female cx. tarsalis emerging due to toxins associated with nitrogenous fertilizer? o 2005 data do not support this.
  62. 62. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship • Are less female cx. tarsalis emerging due to toxins associated with nitrogenous fertilizer? o 2005 data do not support this. • Are smaller females emerging due to larval crowding?
  63. 63. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship • Are less female cx. tarsalis emerging due to toxins associated with nitrogenous fertilizer? o 2005 data do not support this. • Are smaller females emerging due to larval crowding? o Reisen (1984)
  64. 64. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship • Are less female cx. tarsalis emerging due to toxins associated with nitrogenous fertilizer? o 2005 data do not support this. • Are smaller females emerging due to larval crowding? o Reisen (1984) • Are there changes in vector competence due to larval crowding?
  65. 65. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship • Are less female cx. tarsalis emerging due to toxins associated with nitrogenous fertilizer? o 2005 data do not support this. • Are smaller females emerging due to larval crowding? o Reisen (1984) • Are there changes in vector competence due to larval crowding? o Alto et al. (2005)
  66. 66. DiscussionDiscussion Nitrogen Application 2005 Model: Inorganic nitrogen application due to flood-irrigated sod crops had a 95% likelihood of being the best model: associated with a decrease in house sparrow WNV infection. • Lowess lines suggest a non-linear relationship • Are less female cx. tarsalis emerging due to toxins associated with nitrogenous fertilizer? o 2005 data do not support this. • Are smaller females emerging due to larval crowding? o Reisen (1984) • Are there changes in vector competence due to larval crowding? o Alto et al. (2005) • Why was sod more important than other highly fertilized crops?
  67. 67. DiscussionDiscussion Controls 2004 and 2005 Models: House sparrow immunity rate in 2004 had an 8% likelihood of being the best model: associated with an increase in house sparrow WNV infection.
  68. 68. DiscussionDiscussion Controls 2004 and 2005 Models: House sparrow immunity rate in 2004 had an 8% likelihood of being the best model: associated with an increase in house sparrow WNV infection. • Are there increased vector feeding rates due to improved sparrow defense mechanisms?
  69. 69. DiscussionDiscussion Controls 2004 and 2005 Models: House sparrow immunity rate in 2004 had an 8% likelihood of being the best model: associated with an increase in house sparrow WNV infection. • Are there increased vector feeding rates due to improved sparrow defense mechanisms? •Edman and Scott (1987), Darbro and Harrington (2007)
  70. 70. DiscussionDiscussion Controls 2004 and 2005 Models: House sparrow immunity rate in 2004 had an 8% likelihood of being the best model: associated with an increase in house sparrow WNV infection. • Are there increased vector feeding rates due to improved sparrow defense mechanisms? •Edman and Scott (1987), Darbro and Harrington (2007) • Effects of other host species not considered.
  71. 71. DiscussionDiscussion Controls 2004 and 2005 Models: House sparrow immunity rate in 2004 had an 8% likelihood of being the best model: associated with an increase in house sparrow WNV infection. • Are there increased vector feeding rates due to improved sparrow defense mechanisms? •Edman and Scott (1987), Darbro and Harrington (2007) • Effects of other host species not considered. • Effects of nestlings not considered.
  72. 72. ConclusionsConclusions
  73. 73. ConclusionsConclusions • Avian diversity and Landscape characteristics have complex interactions and non-linear relationships with WNV infection rate and transmission risk in house sparrows
  74. 74. ConclusionsConclusions • Avian diversity and Landscape characteristics have complex interactions and non-linear relationships with WNV infection rate and transmission risk in house sparrows • Columbid nestlings are likely to be important amplifiers of WNV infection
  75. 75. ConclusionsConclusions • Avian diversity and Landscape characteristics have complex interactions and non-linear relationships with WNV infection rate and transmission risk in house sparrows • Columbid nestlings are likely to be important amplifiers of WNV infection • Size-dependent water body thresholds were suggested for perennial water cover effects
  76. 76. ConclusionsConclusions • Avian diversity and Landscape characteristics have complex interactions and non-linear relationships with WNV infection rate and transmission risk in house sparrows • Columbid nestlings are likely to be important amplifiers of WNV infection • Size-dependent water body thresholds were suggested for perennial water cover effects • Certain flood-irrigated crops with high nitrogen fertilizer application rates showed important associations with infection rate
  77. 77. ConclusionsConclusions • Avian diversity and Landscape characteristics have complex interactions and non-linear relationships with WNV infection rate and transmission risk in house sparrows • Columbid nestlings are likely to be important amplifiers of WNV infection • Size-dependent water body thresholds were suggested for perennial water cover effects • Certain flood-irrigated crops with high nitrogen fertilizer application rates showed important associations with infection rate • Often assumed predictors of WNV transmission showed few important associations with house sparrow infection
  78. 78. AcknowledgementsAcknowledgements I thank Dr. Nicholas Komar of the Centers for Disease Control and Prevention for the wonderful opportunity to work on this project, and the support he provided while undertaking it. I also thank my committee members, Drs. Sharon Collinge, Alexander Cruz, and Barbara Demmig-Adams, who provided advice to me on subject content, relevant disease ecology questions, and writing and revision. I also thank GIS personnel from several agencies who provided data to me for free, or before it was available to the public. I additionally thank the CDC staff who collected the data I used. I give special thanks to the Fort Collins Audubon Society for their generous grant that provided gas money for field studies that will extend this research. Lastly, I thank my friends, family, and pets for putting up with me, and Carmen for her love and support.

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