Is pathogen exposure spatially autocorrelated? Patterns of pathogens in puma (Puma concolor) and bobcat (Lynx rufus)

Abstract

Abstract. Understanding pathogen spread in wildlife has important implications for conservation and management efforts. This is particularly the case for taxa that are susceptible to disease spillover events resulting in outbreaks and rapid population declines, such as carnivores. However, assessment of the spatial structure of pathogen exposure (pathogen spatial autocorrelation) is relatively rare for these kinds of taxa. Structure in pathogen exposure may reflect a number of important features, including host traits, pathogen traits, and detection methods utilized. The relatively wide-ranging nature of many carnivores may lead to rapid pathogen spread and obfuscate any spatial autocorrelation being detectable, but this has not yet been explicitly evaluated. Here, we tested for evidence of spatial structuring of pathogen exposure and coexposures for puma (Puma concolor) and bobcat (Lynx rufus), both mobile and wide-ranging felid species. The study included 440 puma and 639 bobcat from six study regions (one in Florida, two in Colorado, and three in California), as well as each animal's capture location and exposure status for up to eight pathogens. This allowed a thorough examination of spatial patterns of pathogen exposure across different pathogen transmission types, different habitats, and different host ecology. We tested for spatial autocorrelation for each pathogen in each host species at each site, as well as both host species combined. In addition, we tested for coexposure between all pathogens in the study, and for those pathogens that were correlated, we tested for spatial clusters of coexposure. We detected spatial autocorrelation in exposure status for approximately 2% and 10% of examined cases for puma and bobcats, respectively, and spatial clustering in approximately 17% of cases where pathogen coexposures were detected. These results suggest that wide-ranging species, such as puma and bobcat, may rapidly disseminate pathogens across their populations, precluding substantive detection of autocorrelation in pathogen exposure by traditional serological and infection detection methods. Thus, targeted pathogen surveillance or control might focus on individual host characteristics, and advances in understanding pathogen spread in these secretive felids may necessitate examinations of spatial structure in both pathogen and host genetics.

Fine-scale spatial patterns of wildlife disease are common and understudied

“…The only similar study that we know of (Gilbertson et al 2016) used 48 parasite-locality replicates of cougar (Puma concolor) and bobcat (Lynx rufus) populations and found little evidence of spatial autocorrelation in parasite infection. In contrast to their approach, we used a wide set of different hosts, and our replicates all had between 100 and 10,000 samples (Table 1), whereas only a few of their replicates had >100 samples, and none had >200 (Gilbertson et al 2016). Additionally, they used Mantel tests, which do not account for fixed covariates, while the INLA analyses we employed are more suited to controlling for this variation.…”

“…Because wildlife disease studies often use a limited number of discrete sampling locations rather than distributing their sampling locations continuously or randomly in space (Plowright et al 2019), the lower bound for the range at which spatial effects can act has yet to be established. Identifying the range of spatial dependence in wildlife disease systems is important for many reasons, including designing sampling regimes (Nusser et al 2008;Vidal-Martínez et al 2010;Plowright et al 2019), building mechanistic models of pathogen evolution over space (Best et al 2011;Débarre et al 2014), examining how disease risk responds to anthropogenic activities such as urbanisation (Saito & Sonoda 2017), and directing public health and conservation schemes (Brooker et al 2006;Gilbertson et al 2016). Perhaps most importantly, identifying the range of spatial dependence can help to examine how pathogens spread over landscapes and to determine transmission mechanisms (Reynolds 1988).…”

Inferring the Ecological Niche of Toxoplasma gondii and Bartonella spp. in Wild Felids

“…Of these exposed felids, 328 were positive to T. gondii parasites and 234 to Bartonella spp. ; occurrence records contained each animal’s capture location and exposure status ( 16 ). These data were coupled with landscape information from satellite imagery to develop ENMs and create a risk map for each pathogen.…”

“…if immunofluorescence antibody assay (IFA) tests detecting antibodies against B. henselae and B. clarridgeiae were positive ( 21 – 23 ); this was also confirmed independently by performing PCR on matched blood samples ( 12 ). For each study area and species, samples were generally collected over a 2- to 3-year intensive study period, and cumulatively the majority of samples across all sites were collected between 2001 and 2012 ( 12 , 16 ). Puma and bobcat from Florida were not tested for Bartonella spp.…”

Feline immunodeficiency virus in puma: Estimation of force of infection reveals insights into transmission

“…FIV infection persists throughout the lifetime of the animal (VandeWoude & Apetrei, ), and infected pumas exhibit a decline in lymphocytes (Roelke et al, ), suggesting immune perturbations are induced during infection similar to other lentiviral infections. Published accounts indicate that FIV‐infected puma do not have increased co‐exposure to other pathogens (Biek et al, ; Gilbertson et al, ). A study of pumas from Montana and Wyoming found no evidence for an overall reduction in survival due to infection when accounting for other sources of demographic variation (age, sex and population); however, results from stochastic simulations in this study (Biek et al, ) indicated that only larger reductions in annual survival (>20%) could be excluded with confidence.…”