Biology subjects

Mihaljevic, J. R.

Publications and source records attributed to Mihaljevic, J. R..

6 recordsLinked to original sources

bioRxiv2023

The impact of late Pleistocene mammal extinctions on pathogen richness in extant hosts.

Many species of large mammals were driven to extinction during the late Pleistocene and early Holocene (approx. 10,000 - 50,000 years ago), with cascading effects on the physical structure of ecosystems and the dispersal of seeds, nutrients, and microbes. However, it remains uncertain whether the parasites associated with these extinct hosts also disappeared or persisted in surviving (extant) mammals. We hypothesize that if some parasites endured, extant mammals sharing their ranges with phylogenetically similar extinct mammals would have a greater pathogen richness than expected based on current levels of host diversity. We find that the inclusion of variables related to these extinctions account for an additional 5% of deviance when modelling per-host viral and bacterial richness, compared to models run without these variables. Partial dependence plots show a positive correlation between the number of extinct mammals lost and per-host viral and bacterial richness (p < 0.001 and p = 0.03, respectively). Additionally, decreasing phylogenetic distance between the extinct and extant species is associated with an increasing viral richness (p < 0.001). We discuss four mechanisms that may be driving these patterns and highlight future research to distinguish between them. Next, we use the models and IUCN range maps to identify geographic regions where viral and bacterial richness differs due to the inclusion of extinction variables. Notably, the richness of both pathogen types is increased in South America (viruses: +6.8%; bacteria: +3.1%) and decreased in Africa (viruses: -2.6%; bacteria: -13.6%), two continents known to have high and low levels of historical mammal extinctions, respectively. Viral richness is also elevated in North America (+8.6%), Europe (+5.1%), Oceania (+3.3%), and Asia (+2.3%). These results support the inclusion of extinction variables in future models of pathogen richness and may allow for improved targeting of future surveillance efforts.

paleontology↗

bioRxiv2021

Systematic shifts in the variation among host individuals must be considered in climate-disease theory

To make more informed predictions of host-pathogen interactions under climate change, studies have incorporated the thermal performance of host, vector, and pathogen traits into disease models. However, this body of work has ignored the fact that disease spread and long-term patterns of host population dynamics are largely determined by the variation in susceptibility among individuals in the host population. Furthermore, and especially for ectothermic host species, variation in susceptibility is likely to be plastic, influenced by variables such as environmental temperature. For example, as host individuals respond idiosyncratically to temperature, this could affect the population-level variation in susceptibility, such that there may be predictable functional relationships between variation in susceptibility and temperature. Quantifying the relationship between temperature and among-host trait variation will therefore be critical for predicting how climate change and disease will interact to influence host-pathogen population dynamics. Here, we use a model to demonstrate how short-term effects of temperature on the variation of host susceptibility can drive epidemic characteristics, fluctuations in host population sizes, and probabilities of host extinction. Our results emphasize that more research is needed in disease ecology and climate biology to understand the mechanisms that shape trait variation, not just trait averages.

ecology↗

bioRxiv2019

Use of a mechanistic growth model in fish habitat restoration: juvenile Chinook salmon and steelhead as a test case

Individual growth data are useful in assessing relative habitat quality, but this approach is less common when evaluating the efficacy of habitat restoration. Furthermore, available models describing growth are infrequently combined with computational approaches capable of handling large data sets. We apply a mechanistic model to evaluate whether selection of restored habitat can affect individual growth. We used mark-recapture to collect size and growth data on sub-yearling Chinook salmon and steelhead in restored and unrestored habitat in five sampling years (2009, 2010, 2012, 2013, 2016). Modeling strategies differed for the two species: For Chinook, we compared growth patterns of individuals recaptured in restored habitat over 15-60 d with those not recaptured regardless of initial habitat at marking. For steelhead, we had enough recaptured fish in each habitat type to use the model to directly compare habitats. The model generated spatially explicit growth parameters describing size of fish over the growing season in restored vs. unrestored habitat. Model parameters showed benefits of restoration for both species, but that varied by year and time of season, consistent with known patterns of habitat partitioning among them. The model was also supported by direct measurement of growth rates in steelhead and by known patterns of spatio-temporal partitioning of habitat between these two species. Model parameters described not only the rate of growth, but the timing of size increases, and is spatially explicit, accounting for habitat differences, making it widely applicable across taxa. The model usually supported data on density differences among habitat types in Chinook, but only in a couple of cases in steelhead. Modeling growth can thus prevent overconfidence in distributional data, which are commonly used as the metric of restoration success.

ecology↗

bioRxiv2019

Evaluating the within-host dynamics of Ranavirus infection with mechanistic disease models and experimental data

Mechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen population growth and immune system processes) and among-host dynamics (i.e., transmission). Rarely, however, have within-host models been synthesized with data to infer processes, validate hypotheses, or generate new theories. In this study we use mechanistic models and empirical, time-series data of viral titer to better understand the growth of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral growth and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: the viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, that can be validated in future studies.

ecology↗

bioRxiv2018

Untangling the dynamics of persistence and colonization in microbial communities

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here we introduce a dynamic statistical model that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We apply our modeling approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.

ecology↗

bioRxiv2018

Using biological control data to understand host-pathogen dynamics

A key assumption of models of infectious disease is that population-scale spread is driven by transmission between host individuals at small scales. This assumption, however, is rarely tested, likely because observing disease transmission between host individuals is non-trivial in many infectious diseases. Quantifying the transmission of insect baculoviruses at a small scale is in contrast straightforward. We fit a disease model to data from baculovirus epizootics (= epidemics in animals) at the scale of whole forests, while using prior parameter distributions constructed from branch-scale experiments. Our experimentally-constrained model fits the large-scale data very well, supporting the role of small-scale transmission mechanisms in baculovirus epizootics. We further compared our experimentally-based model to an unconstrained model that ignores our experimental data, serving as a proxy for models that include large-scale mechanisms. This analysis supports our hypothesis that small-scale mechanisms are important, especially individual variability in host susceptibility to the virus. Comparison of transmission rates in the two models, however, suggests that large-scale mechanisms increase transmission compared to our experimental estimates. Our study shows that small-scale and large-scale mechanisms drive forest-wide epizootics of baculoviruses, and that synthesizing mathematical models with data collected across scales is key to understanding the spread of infectious disease.

ecology↗