Landscape connectivity, habitat isolation, and tick-borne pathogen ecology
Data files
Sep 23, 2024 version files 541.59 KB
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2021pathogen_data.csv
3.58 KB
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biFrag.csv
488.70 KB
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Full_summary_NoWP100.csv
19.72 KB
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Full_summary.csv
19.73 KB
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README.md
9.86 KB
Abstract
Habitat loss and forest fragmentation are often linked to increased pathogen transmission, but the extent to which habitat isolation and landscape connectivity affect disease dynamics through movement of disease vectors and reservoir hosts has not been well examined. Tick-borne diseases are the most prevalent vector-borne diseases in the US and on the West Coast, Ixodes pacificus is one of the most epidemiologically important vectors. We investigated the impacts of habitat fragmentation on pathogens transmitted by I. pacificus and sought to disentangle the effects of wildlife communities and landscape metrics predictive of pathogen diversity, prevalence, and distribution. We collected pathogen data for four co-occurring bacteria transmitted by I. pacificus and measured wildlife parameters. We also used spatial data and cost-distance analysis integrating expert opinions to assess landscape metrics of habitat fragmentation. We found that landscape metrics were significant predictors of tick density and pathogen prevalence. However, wildlife variables were essential when predicting the prevalence and distribution of pathogens reliant on wildlife reservoir hosts for maintenance. We found that landscape structure was an informative predictor of tick-borne pathogen richness in an urban matrix. Our work highlights the implications of large-scale land management on human disease risk.
https://doi.org/10.5061/dryad.63xsj3v9b
This repository contains code to analyze the data and prepare individual figures. It also contains the full data sets analyzed in the preparation of the associated manuscript.
Description of the data and file structure
The “FragPathDiv_script.R” document contains the R code used to generate the final models and all figures presented in our results section.
“Full_summary.csv”, “Full_summary_NoWP100.csv”, “biFrag.csv”, and ”2021pathogen_data.csv” contain the raw and processed data used for figure generation and model building.
For all spreadsheets, empty cells represent years or sites in which a metric was not collected, or for pathogen prevalence metrics, a blank cell may mean that no relevant ticks were collected from that year/site and therefore no pathogen assays were run. Ticks from years prior to 2021 may not have been tested for all four pathogens of interest, and therefore a blank cell indicates an unknown infection status in the “biFrag.csv” dataset. For the “Rodents_Present” column in the “Full_summary.csv” and “Full_summary_NoWP100.csv” datasets, a blank cell in years other than 2020 (when no wildlife data was collected) indicates that no rodents were collected via live trapping. Wildlife metrics could only be calculated for groups of species that were present at a site, and therefore may also be blank if no relevant species were observed at a site/year. Lagged variables represent data collected during the previous or following year’s nymphal tick season (see variable key below) which means that the 2021 rows of data do not have data in many of the ‘lagged’ variable columns as 2021 was our last year of data collection and we could not associate this year with data in the following season. In 2020, no wildlife metrics were collected due to pandemic related restrictions so wildlife metric columns are blank.
Variable header key (applies to “Full_summary.csv”, “Full_summary_NoWP100.csv”, “biFrag.csv”, and ”2021pathogen_data.csv”):
1. AllMammal_Richness – Number of rodent and large vertebrate species detected at the site via rodent live trapping and wildlife camera trapping
2. Area_ha – Site area in hectares
3. Bbsl_status – Borrelia burgdorferi s.l. infection status (1= infected, 0= uninfected)
4. Bbss_status – Borrelia burgdorferi s.s. infection status (1= infected, 0= uninfected)
5. Bm_status – Borrelia miyamotoi infection status (1= infected, 0= uninfected)
6. CD_comm_forest – Wildlife community cost distance from the site to the nearest in-tact forest (no units)
7. CD_comm_source – Wildlife community cost distance from the site to the nearest in-tact habitat (no units)
8. CD_deer_forest – Deer cost distance from the site to the nearest in-tact forest (no units)
9. CD_deer_source – Deer cost distance from the site to the nearest in-tact habitat (no units)
10. CD_pred_forest – Predator cost distance from the site to the nearest in-tact forest (no units)
11. CD_pred_source – Predator cost distance from the site to the nearest in-tact habitat (no units)
12. Collectio_Date – Date of field collection
13. DIN_Bbsl - Density of Borrelia burgdorferi s.l. infected Ixodes pacificus nymphs
14. DIN_Bbss – Density of Borrelia burgdorferi s.s. infected Ixodes pacificus nymphs
15. DOA – Density of Ixodes pacificus adults collected across 995 square meters
16. DOL – Density of Ixodes pacificus larvae collected across 995 square meters
17. DON – Density of Ixodes pacificus nymphs collected across 995 square meters
18. DOT – Density of Ixodes pacificus collected across 995 square meters (all life stages)
19. Euc_forest_km – Euclidean distance from the site to the nearest in-tact forest in kilometers
20. Euc_forest_m – Euclidean distance from the site to the nearest in-tact forest in meters
21. Euc_source_km – Euclidean distance from the site to the nearest in-tact habitat in kilometers
22. Euc_source_m – Euclidean distance from the site to the nearest in-tact habitat in meters
23. Lagged_AllMammal_Rich – Number of rodent and large vertebrate species detected at the site via rodent live trapping and wildlife camera trapping the previous year
24. Lagged_DIN_Bbsl – Density of Borrelia burgdorferi s.l. infected Ixodes pacificus nymphs the following year
25. Lagged_DIN_Bbss – Density of Borrelia burgdorferi s.s. infected Ixodes pacificus nymphs the following year
26. Lagged_DON – Density of Ixodes pacificus nymphs collected the following year
27. Lagged_Predator_Rich – Number of predator species detected at the site via wildlife camera trapping the previous year
28. Lagged_Predator_Shannon – Shannon diversity index of predator species detected at the site via wildlife camera trapping the previous year, excluding domestic cats
29. Lagged_Qnymph_Bbsl_prev – Prevalence of Borrelia burgdorferi s.l. among questing Ixodes pacificus nymphs the following year
30. Lagged_Qnymph_Bbss_prev – Prevalence of Borrelia burgdorferi s.s. among questing Ixodes pacificus nymphs the following year
31. Lagged_Rodent_Rich – Number of rodent species detected at the site via live trapping the previous year
32. Lagged_wildlife_richness – Number of large vertebrate species detected at the site via wildlife camera trapping the previous year
33. Log_Area – (log of) Site area in hectares
34. Log_CD_comm_forest – (log of) Wildlife community cost distance from the site to the nearest in-tact forest (no units)
35. Log_CD_comm_source – (log of) Wildlife community cost distance from the site to the nearest in-tact habitat (no units)
36. Log_CD_deer_forest – (log of) Deer cost distance from the site to the nearest in-tact forest (no units)
37. Log_CD_deer_source – (log of) Deer cost distance from the site to the nearest in-tact habitat (no units)
38. Log_CD_pred_forest – (log of) Predator cost distance from the site to the nearest in-tact forest (no units)
39. Log_CD_pred_source – (log of) Predator cost distance from the site to the nearest in-tact habitat (no units)
40. Log_Euc_source_m – (log of) Euclidean distance from the site to the nearest in-tact habitat in meters
41. Log_perim – (log of) Site perimeter length in meters
42. Min_OspC_Rich – minimum number of outer surface protein C genotypes of Borrelia burgdorferi detected (metric was not used in any analyses in the associated publication)
43. Natural_1km – Surrounding green space within a one-kilometer radius of our survey plot (measured in square kilometers)
44. Nefu_abund – abundance of dusky-footed woodrats
45. NEFUwPERO_abund – abundance of dusky-footed woodrats and Peromyscus sp.
46. NIP_weight – number of Ixodes pacificus nymphs collected and tested for pathogens (used to weight NIP models)
47. ospC_genotype – Outer surface protein C genotype of Borrelia burgdorferi (metric not used in any analyses in the associated publication)
48. PA_ratio – Site edginess (permitter divided by area)
49. Patch_proximity – (metric not measured for this study)
50. Pathogen_Div – Shannon diversity index of focal pathogens among questing Ixodes pacificus nymphs
51. Pathogen_Rich – Number of focal pathogens present among questing Ixodes pacificus nymphs
52. Perim_m – Site perimeter length in meters
53. Pero_abund – abundance of Peromyscus sp.
54. Predator_Richness – Number of predator species detected at the site via wildlife camera trapping
55. Predator_Shannon_NoCat – Shannon diversity index of predator species detected at the site via wildlife camera trapping, excluding domestic cats
56. Predator_Shannon_wCat – Shannon diversity index of predator species detected at the site via wildlife camera trapping, including domestic cats
57. Predators_Present – Predator species detected at the site via wildlife camera trapping
58. Qnymph_Bbsl_prev – Prevalence of Borrelia burgdorferi s.l. among questing Ixodes pacificus nymphs
59. Qnymph_Bbss_prev – Prevalence of Borrelia burgdorferi s.s. among questing Ixodes pacificus nymphs
60. Qnymph_Bm_prev – Prevalence of Borrelia miyamotoi *among questing *Ixodes pacificus nymphs
61. Qnymph_Rt_prev – Prevalence of Rickettsia tillamookensis *among questing *Ixodes pacificus nymphs
62. Rodent_Rich – Number of rodent species detected at the site via live trapping
63. Rodent_Shannon – Shannon diversity index of rodent species detected at the site via live trapping
64. Rodents_Present – Species of rodents detected at the site via live trapping, listed by 4-letter species code
65. Rt_status – Rickettsia tillamookensis infection status (1= infected, 0= uninfected)
66. Site – Abbreviation of the site name
67. Tick_ID – Individual Ixodes pacificus identification number
68. Wildlife_Present – Large vertebrate species detected at the site via wildlife camera trapping
69. Wildlife_Richness – Number of large vertebrate species detected at the site via wildlife camera trapping
70. Wildlife_Shannon – Shannon diversity index of large vertebrate species detected at the site via wildlife camera trapping
71. Year – Year that field sampling took place
Additional data
Expert opinion results generated from this study can be found at the following link:
https://docs.google.com/spreadsheets/d/13b3A71Xy3pKdnyNAv7H8IidpD8y9Rv4lxnAheaXd_5w/edit?usp=sharing
Code/Software
Models were created in R studio (Version 1.4) using the ‘glmmTMB’ package.
Location: California, U.S.A.
Time Period: 2016-2021
Major taxa studied: Ixodes pacificus, zoonotic bacterial pathogens, large vertebrate and rodent communities
Methods:
We collected pathogen data for four co-occurring bacteria transmitted by I. pacificus; Borrelia burgdorferi sensu stricto, Borrelia bissettiae, Borrelia miyamotoi, and Rickettsia tillamookensis. Additionally we measured wildlife and landscape variables for 19 sites in northern California over six years. Methods involved surveys of host-seeking ticks, wildlife camera trapping, and mark-recapture rodent surveys. We used spatial data and cost-distance analysis integrating expert opinions to assess landscape metrics of habitat fragmentation.
Results:
Landscape metrics, i.e., habitat size, measurements of surrounding green space, and habitat isolation, are significant predictors for tick density and pathogen prevalence. However, wildlife variables, i.e., wildlife, predator, and rodent diversity, also contribute to understanding the prevalence and distribution of pathogens which rely on wildlife reservoir hosts for maintenance. In our study, we found minimal benefit to the use of cost-distance over simpler habitat isolation metrics.
Main Conclusions:
A modification to island biogeography theory can be used to predict tick-borne pathogen richness for habitat patches within an urban matrix. Our work highlights the importance of considering the impacts of large-scale land management on human disease risk.