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Most pathogens infect more than one host species, and given infection, the individual-level impact they have varies among host species. Nevertheless, variation in individual-level impacts of infection remains poorly characterised. Using the impactful and host-generalist ectoparasitic mite Sarcoptes scabiei (causing sarcoptic mange), we assessed individual-level variation in pathogen impacts by (1) compiling all documented individual-level impacts of S. scabiei across free-living host species, (2) quantifying and ranking S. scabiei impacts among host species, and (3) evaluating factors associated with S. scabiei impacts. We compiled individual-level impacts of S. scabiei infection from 77 host species, spanning 31 different impacts, and totalling 683 individual-level impact descriptions. The most common impacts were those affecting the skin, alopecia (130 descriptions), and hyperkeratosis coverage (106). From these impacts, a standardised metric was generated for each species (average impact score (AIS) with a 0-1 range), as a proxy of pathogen virulence allowing quantitative comparison of S. scabiei impacts among host species while accounting for the variation in the number and types of impacts assessed. The Japanese raccoon dog (Nyctereutes viverrinus) was found to be the most impacted host (AIS 0.899). We applied species inclusion criteria for ranking and found more well-studied species tended to be those impacted more by S. scabiei (26/27 species AIS < 0.5). AIS had relatively weak relationships with predictor variables (methodological, phylogenetic, and geographic). There was a tendency for Diprotodontia, Artiodactyla, and Carnivora to be the most impacted taxa and for research to be focussed in developed regions of the world. This study is the first quantitative assessment of individual-level pathogen impacts of a multihost parasite. The proposed methodology can be applied to other multihost pathogens of public health, animal welfare, and conservation concern and enables further research to address likely causes of variation in pathogen virulence among host species.

 

Pellet feces are generated by a number of animals important to science or agriculture, including mice, rats, goats, and wombats. Understanding the factors that lead to fecal shape may provide a better understanding of animal health and diet. In this combined experimental and theoretical study, we test the hypothesis that pellet feces are formed by drying processes in the intestine. Inspirational to our work is the formation of hexagonal columnar jointings in cooling lava beds, in which the width L of the hexagon scales as L ∼ J −1 where J is the heat flux from the bed. Across 22 species of mammals, we report a transition from cylindrical to pellet feces if fecal water content drops below 0.65. Using a mathematical model that accounts for water intake rate and intestinal dimensions, we show pellet feces length L scales as L ∼ J −2.08 where J is the flux of water absorbed by the intestines. We build a mimic of the mammalian intestine using a corn starch cake drying in an open trough, finding that corn starch pellet length scales with water flux −0.46 . The range of exponents does not permit us to conclude that formation of columnar jointings is similar to the formation of pellet feces. Nevertheless, the methods and physical picture shown here may be of use to physicians and veterinarians interested in using feces length as a marker of intestinal health. 

Feline foamy virus (FFV) is a contact-dependent retrovirus forming chronic, largely apathogenic, infections in domestic and wild felid populations worldwide. Given there is no current ‘gold standard’ diagnostic test for FFV, efforts to elucidate the ecology and epidemiology of the virus may be complicated by unknown sensitivity and specificity of diagnostic tests. Using Bayesian Latent Class Analysis, we estimated the sensitivity and specificity of the only two FFV diagnostic tests available—ELISA and qPCR—as well as the prevalence of FFV in a large cohort of pumas from Colorado. We evaluated the diagnostic agreement of ELISA and qPCR, and whether differences in their diagnostic accuracy impacted risk factor analyses for FFV infection. Our results suggest ELISA and qPCR did not have strong diagnostic agreement, despite FFV causing a persistent infection. While both tests had similar sensitivity, ELISA had higher specificity. ELISA, but not qPCR, identified age to be a significant risk factor, whereas neither qPCR nor ELISA identified sex to be a risk factor. This suggests FFV transmission in pumas may primarily be via non-antagonistic, social interactions between adult conspecifics. Our study highlights that combined use of qPCR and ELISA for FFV may enhance estimates of the true prevalence of FFV and epidemiological inferences.

 

Does the structure and connectivity of host populations influence the dynamics and evolution of their pathogens? This topical question is the essence of research investigating the ecology of aPteropusfruit bat and its zoonotic Nipah virus (NiV) published by Olival et al. in this issue ofMolecular Ecology. Questioned less overtly, but nonetheless implicit to the study, is “what are the mechanisms underpinning intraspecific host–pathogen congruence (IHPC) of genetic structure?”. Olival et al. investigated the phylogeographical structure ofPteropus mediusand NiV isolates across Bangladesh, from areas inside and outside of the Nipah belt—an area where most human spillover events occur. A high degree of host panmixia was discovered, with some population differentiation east of the Nipah belt. NiV genetic structure was congruent with the host. The authors attributed the panmixia and structuring, respectively, to (a) the highly vagile nature ofP. medius, and (b) possible differences between bioregions within and outside the Nipah belt. Other potential explanatory mechanisms were acknowledged, including hybridization and transmission mode. This study makes a valuable contribution to a growing body of literature examining IHPC. This has implications not only for pathogen spillover to humans and domestic animals, but more generally for thinking about the mechanisms that underlie patterns of host and pathogen genetic associations.

 

Urban expansion can fundamentally alter wildlife movement and gene flow, but how urbanization alters pathogen spread is poorly understood. Here, we combine high resolution host and viral genomic data with landscape variables to examine the context of viral spread in puma (Puma concolor) from two contrasting regions: one bounded by the wildland urban interface (WUI) and one unbounded with minimal anthropogenic development (UB). We found landscape variables and host gene flow explained significant amounts of variation of feline immunodeficiency virus (FIV) spread in the WUI, but not in the unbounded region. The most important predictors of viral spread also differed; host spatial proximity, host relatedness, and mountain ranges played a role in FIV spread in the WUI, whereas roads might have facilitated viral spread in the unbounded region. Our research demonstrates how anthropogenic landscapes can alter pathogen spread, providing a more nuanced understanding of host-pathogen relationships to inform disease ecology in free-ranging species.

 

We introduce a new R package “MrIML” (“Mister iml”; Multi‐response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to quantify multilocus genomic relationships, to identify loci of interest for future landscape genetics studies, and to gain new insights into adaptation across environmental gradients. Relationships between genetic variation and environment are often nonlinear and interactive; these characteristics have been challenging to address using traditional landscape genetic approaches. Our package helps capture this complexity and offers functions that fit and interpret a wide range of highly flexible models that are routinely used for single‐locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package's broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first, we model genetic variation of North American balsam poplar (Populus balsamifera, Salicaceae) populations across environmental gradients. In the second case study, we recover the landscape and host drivers of feline immunodeficiency virus genetic variation in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to modelling genetic variation; for example, it can quantify the environmental drivers of microbiomes and coinfection dynamics.

 

Predicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data are available on a subset of individuals and nonlinear relationships between variables are common.

Here we provide a guide to the latest advances in statistical machine learning to construct pathogen‐risk models that automatically incorporate complex nonlinear relationships with minimal statistical assumptions from ecological data with missing data. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus.

Our modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualize strong nonlinear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years.

When combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure that are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.

 

Determining parameters that govern pathogen transmission (such as the force of infection, FOI), and pathogen impacts on morbidity and mortality, is exceptionally challenging for wildlife. Vital parameters can vary, for example across host populations, between sexes and within an individual's lifetime.

Feline immunodeficiency virus (FIV) is a lentivirus affecting domestic and wild cat species, forming species‐specific viral–host associations. FIV infection is common in populations of puma (Puma concolor), yet uncertainty remains over transmission parameters and the significance of FIV infection for puma mortality. In this study, the age‐specific FOI of FIV in pumas was estimated from prevalence data, and the evidence for disease‐associated mortality was assessed.

We fitted candidate models to FIV prevalence data and adopted a maximum likelihood method to estimate parameter values in each model. The models with the best fit were determined to infer the most likely FOI curves. We applied this strategy for female and male pumas from California, Colorado, and Florida.

When splitting the data by sex and area, our FOI modeling revealed no evidence of disease‐associated mortality in any population. Both sex and location were found to influence the FOI, which was generally higher for male pumas than for females. For female pumas at all sites, and male pumas from California and Colorado, the FOI did not vary with puma age, implying FIV transmission can happen throughout life; this result supports the idea that transmission can occur from mothers to cubs and also throughout adult life. For Florida males, the FOI was a decreasing function of puma age, indicating an increased risk of infection in the early years, and a decreased risk at older ages.

This research provides critical insight into pathogen transmission and impact in a secretive and solitary carnivore. Our findings shed light on the debate on whether FIV causes mortality in wild felids like puma, and our approach may be adopted for other diseases and species. The methodology we present can be used for identifying likely transmission routes of a pathogen and also estimating any disease‐associated mortality, both of which can be difficult to establish for wildlife diseases in particular.

 

Apex predators are important indicators of intact natural ecosystems. They are also sensitive to urbanization because they require broad home ranges and extensive contiguous habitat to support their prey base. Pumas (Puma concolor) can persist near human developed areas, but urbanization may be detrimental to their movement ecology, population structure, and genetic diversity. To investigate potential effects of urbanization in population connectivity of pumas, we performed a landscape genomics study of 130 pumas on the rural Western Slope and more urbanized Front Range of Colorado, USA. Over 12,000 single nucleotide polymorphisms (SNPs) were genotyped using double‐digest, restriction site‐associated DNA sequencing (ddRADseq). We investigated patterns of gene flow and genetic diversity, and tested for correlations between key landscape variables and genetic distance to assess the effects of urbanization and other landscape factors on gene flow. Levels of genetic diversity were similar for the Western Slope and Front Range, but effective population sizes were smaller, genetic distances were higher, and there was more admixture in the more urbanized Front Range. Forest cover was strongly positively associated with puma gene flow on the Western Slope, while impervious surfaces restricted gene flow and more open, natural habitats enhanced gene flow on the Front Range. Landscape genomic analyses revealed differences in puma movement and gene flow patterns in rural versus urban settings. Our results highlight the utility of dense, genome‐scale markers to document subtle impacts of urbanization on a wide‐ranging carnivore living near a large urban center.