DOI: 10.5061/dryad.63xsj3v9b
title: Landscape connectivity, habitat isolation, and tick-borne pathogen ecology

description: 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.
description: <em>Location: </em>California, U.S.A.
 <em>Time Period: </em>2016-2021
 <em>Major taxa studied: </em><em>Ixodes
 pacificus</em>, zoonotic bacterial pathogens, large vertebrate and
 rodent communities
 <em>Methods:</em> We collected
 pathogen data for four co-occurring bacteria transmitted by <em>I.
 pacificus</em>; <em>Borrelia burgdorferi sensu
 stricto</em>, <em>Borrelia
 bissettiae</em>, <em>Borrelia
 miyamotoi</em>, <em>and</em> <em>Rickettsia
 tillamookensis</em>. 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.
 <em>Results:</em> 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.
 <em>Main Conclusions:</em> 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.
description: # Landscape connectivity, habitat isolation, and tick-borne pathogen
 ecology
 [https://doi.org/10.5061/dryad.63xsj3v9b](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](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.