Three Pathogens in Sympatric Populations of Pumas, Bobcats, and Domestic Cats: Implications for Infectious Disease Transmission

Anthropogenic landscape change can lead to increased opportunities for pathogen transmission between domestic and non-domestic animals. Pumas, bobcats, and domestic cats are sympatric in many areas of North America and share many of the same pathogens, some of which are zoonotic. We analyzed bobcat, puma, and feral domestic cat samples collected from targeted geographic areas. We examined exposure to three pathogens that are taxonomically diverse (bacterial, protozoal, viral), that incorporate multiple transmission strategies (vector-borne, environmental exposure/ ingestion, and direct contact), and that vary in species-specificity. Bartonella spp., Feline Immunodeficiency Virus (FIV), and Toxoplasma gondii IgG were detected in all three species with mean respective prevalence as follows: puma 16%, 41% and 75%; bobcat 31%, 22% and 43%; domestic cat 45%, 10% and 1%. Bartonella spp. were highly prevalent among domestic cats in Southern California compared to other cohort groups. Feline Immunodeficiency Virus exposure was primarily associated with species and age, and was not influenced by geographic location. Pumas were more likely to be infected with FIV than bobcats, with domestic cats having the lowest infection rate. Toxoplasma gondii seroprevalence was high in both pumas and bobcats across all sites; in contrast, few domestic cats were seropositive, despite the fact that feral, free ranging domestic cats were targeted in this study. Interestingly, a directly transmitted species-specific disease (FIV) was not associated with geographic location, while exposure to indirectly transmitted diseases – vectorborne for Bartonella spp. and ingestion of oocysts via infected prey or environmental exposure for T. gondii – varied significantly by site. Pathogens transmitted by direct contact may be more dependent upon individual behaviors and intra-specific encounters. Future studies will integrate host density, as well as landscape features, to better understand the mechanisms driving disease exposure and to predict zones of cross-species pathogen transmission among wild and domestic felids

Wild Felids as Hosts for Human Plague, Western United States

Plague seroprevalence was estimated in populations of pumas and bobcats in the western United States. High levels of exposure in plague-endemic regions indicate the need to consider the ecology and pathobiology of plague in nondomestic felid hosts to better understand the role of these species in disease persistence and transmission

Three Pathogens in Sympatric Populations of Pumas, Bobcats, and Domestic Cats: Implications for Infectious Disease Transmission

Anthropogenic landscape change can lead to increased opportunities for pathogen transmission between domestic and non-domestic animals. Pumas, bobcats, and domestic cats are sympatric in many areas of North America and share many of the same pathogens, some of which are zoonotic. We analyzed bobcat, puma, and feral domestic cat samples collected from targeted geographic areas. We examined exposure to three pathogens that are taxonomically diverse (bacterial, protozoal, viral), that incorporate multiple transmission strategies (vector-borne, environmental exposure/ingestion, and direct contact), and that vary in species-specificity. Bartonella spp., Feline Immunodeficiency Virus (FIV), and Toxoplasma gondii IgG were detected in all three species with mean respective prevalence as follows: puma 16%, 41% and 75%; bobcat 31%, 22% and 43%; domestic cat 45%, 10% and 1%. Bartonella spp. were highly prevalent among domestic cats in Southern California compared to other cohort groups. Feline Immunodeficiency Virus exposure was primarily associated with species and age, and was not influenced by geographic location. Pumas were more likely to be infected with FIV than bobcats, with domestic cats having the lowest infection rate. Toxoplasma gondii seroprevalence was high in both pumas and bobcats across all sites; in contrast, few domestic cats were seropositive, despite the fact that feral, free ranging domestic cats were targeted in this study. Interestingly, a directly transmitted species-specific disease (FIV) was not associated with geographic location, while exposure to indirectly transmitted diseases – vector-borne for Bartonella spp. and ingestion of oocysts via infected prey or environmental exposure for T. gondii – varied significantly by site. Pathogens transmitted by direct contact may be more dependent upon individual behaviors and intra-specific encounters. Future studies will integrate host density, as well as landscape features, to better understand the mechanisms driving disease exposure and to predict zones of cross-species pathogen transmission among wild and domestic felids

Improvements on GPS Location Cluster Analysis for the Prediction of Large Carnivore Feeding Activities: Ground-Truth Detection Probability and Inclusion of Activity Sensor Measures

<div><p>Animal space use studies using GPS collar technology are increasingly incorporating behavior based analysis of spatio-temporal data in order to expand inferences of resource use. GPS location cluster analysis is one such technique applied to large carnivores to identify the timing and location of feeding events. For logistical and financial reasons, researchers often implement predictive models for identifying these events. We present two separate improvements for predictive models that future practitioners can implement. Thus far, feeding prediction models have incorporated a small range of covariates, usually limited to spatio-temporal characteristics of the GPS data. Using GPS collared cougar (<i>Puma concolor</i>) we include activity sensor data as an additional covariate to increase prediction performance of feeding presence/absence. Integral to the predictive modeling of feeding events is a ground-truthing component, in which GPS location clusters are visited by human observers to confirm the presence or absence of feeding remains. Failing to account for sources of ground-truthing false-absences can bias the number of predicted feeding events to be low. Thus we account for some ground-truthing error sources directly in the model with covariates and when applying model predictions. Accounting for these errors resulted in a 10% increase in the number of clusters predicted to be feeding events. Using a double-observer design, we show that the ground-truthing false-absence rate is relatively low (4%) using a search delay of 2–60 days. Overall, we provide two separate improvements to the GPS cluster analysis techniques that can be expanded upon and implemented in future studies interested in identifying feeding behaviors of large carnivores.</p></div

Logistic regression models for predicting feeding presence/absence at GPS cluster sites were compared with AIC model selection, feeding event predictive performance, and predicted number of feeding events.

<p>Each component includes:</p><p>*log(POSCOUNT), NIGHTPROP, log(POSCOUNT)*NIGHTPROP, CENTR, CENTR²</p><p>^†ACCX_AVG, ACCX_AVG², ACCXYDIFF_AVG</p><p>^‡SEARCH, FIELDPROP, FIELDPROP²</p><p>^§SUM (binary)</p><p>For simplification, models shown are reduced to components based on: Spatio-temporal (SpTemp), Accelerometer activity sensor (Acvty), ground-truthing errors (GrndTru), and calendar season (Seas) covariates.</p

Histogram of Search Lag Times.

<p>The distribution of time (SEARCH_LAG) between the initiation of a kill by cougar and the visitation by a ground-truth observer for 1171 unique cluster visits. Red dashed line indicates the distribution mean.</p

Feeding Event Probability Response Plots.

<p>Response plots for the predicted probability (y-axis) of a cluster being a feeding event and 95% CI (gray shading) for each individual covariate while holding all other covariates at their mean observed values. Parameter space for each covariate value (x-axis) is given for a realistic range of values. Various combinations with other variables (interaction or additive effect), discretized to factor values are given in plots with both solid and dashed lines.</p

Model selection table and coefficient estimates (non-standardized) of the candidate model set holding a cumulative AICc weight of 0.95.

<p>² refers to a quadratic term</p><p>X refers to interaction between variables.</p><p>Model selection table and coefficient estimates (non-standardized) of the candidate model set holding a cumulative AICc weight of 0.95.</p

Supplement 1. Data used for mark-resight, density, and occupancy analyses.

<h2>File List</h2><div> <p><a href="data.txt">data.txt</a> (MD5: 2763d8f75c752f0e190815df66121618) </p> </div><h2>Description</h2><div> <p>The data.txt is a tab-separated file. It contains the data for the mark-resight, density, and occupancy analyses for bobcats, pumas, and their prey on the Western Slope (exurban grid 1 and wildland grid 2) and Front Range (WUI grid 1 and wildland grid 2) grids.</p> <p>Column headings and definitions</p> <ol> <li>ch = capture history</li> <li>Time spent on grid (TSOG) = time spent on grid for individual animal based on telemetry locations</li> <li>Weight = weight (kg) of animal                </li> <li>Grid = urbanized (0) or wildland (1) sampling grid area</li> <li>HumDev = amount of human influence associated with camera location                                                                                                                                                     </li> </ol> For values of TSOG used in mark resight analysis, if telemetry data was not available for a particular individual (due to collar malfunction), then the mean value of TSOG across all other animals with functioning collars was used for an individual as a covariate value, but the mean value of TSOG for an animal was excluded when estimating density. For pumas that were captured and marked during camera surveys, their photos were counted as unmarked in mark-resight models, but their telemetry data was used to estimate TSOG for density. Please see manuscript for further details and notes about calculations and data used.                                                         </div