Joseph R. Mihaljevic

Linking metacommunity theory and symbiont evolutionary ecology

Processes that occur both within and between hosts can influence the ecological and evolutionary dynamics of symbionts, a broad term that includes parasitic and disease-causing organisms. Metacommunity theory can integrate these local- and regional-scale dynamics to explore symbiont community composition patterns across space. In this article I emphasize that symbionts should be incorporated into the metacommunity concept. I highlight the utility of metacommunity theory by discussing practical and general benefits that emerge from considering symbionts in a metacommunity framework. Specifically, investigating the local and regional drivers of symbiont community and metacommunity structure will lead to a more holistic understanding of symbiont ecology and evolution and could reveal novel insights into the roles of symbiont communities in mediating host health.

Taming wildlife disease: bridging the gap between science and management

Summary Parasites and pathogens of wildlife can threaten biodiversity, infect humans and domestic animals, and cause significant economic losses, providing incentives to manage wildlife diseases. Recent insights from disease ecology have helped transform our understanding of infectious disease dynamics and yielded new strategies to better manage wildlife diseases. Simultaneously, wildlife disease management (WDM) presents opportunities for large‐scale empirical tests of disease ecology theory in diverse natural systems. To assess whether the potential complementarity between WDM and disease ecology theory has been realized, we evaluate the extent to which specific concepts in disease ecology theory have been explicitly applied in peer‐reviewed WDM literature. While only half of WDM articles published in the past decade incorporated disease ecology theory, theory has been incorporated with increasing frequency over the past 40 years. Contrary to expectations, articles authored by academics were no more likely to apply disease ecology theory, but articles that explain unsuccessful management often do so in terms of theory. Some theoretical concepts such as density‐dependent transmission have been commonly applied, whereas emerging concepts such as pathogen evolutionary responses to management, biodiversity–disease relationships and within‐host parasite interactions have not yet been fully integrated as management considerations. Synthesis and applications. Theory‐based disease management can meet the needs of both academics and managers by testing disease ecology theory and improving disease interventions. Theoretical concepts that have received limited attention to date in wildlife disease management could provide a basis for improving management and advancing disease ecology in the future.

Does life history mediate changing disease risk when communities disassemble

Biodiversity loss sometimes increases disease risk or parasite transmission in humans, wildlife and plants. Some have suggested that this pattern can emerge when host species that persist throughout community disassembly show high host competence - the ability to acquire and transmit infections. Here, we briefly assess the current empirical evidence for covariance between host competence and extirpation risk, and evaluate the consequences for disease dynamics in host communities undergoing disassembly. We find evidence for such covariance, but the mechanisms for and variability around this relationship have received limited consideration. This deficit could lead to spurious assumptions about how and why disease dynamics respond to community disassembly. Using a stochastic simulation model, we demonstrate that weak covariance between competence and extirpation risk may account for inconsistent effects of host diversity on disease risk that have been observed empirically. This model highlights the predictive utility of understanding the degree to which host competence relates to extirpation risk, and the need for a better understanding of the mechanisms underlying such relationships.

Widespread Co-occurrence of Virulent Pathogens Within California Amphibian Communities

The chytrid fungus Batrachochytrium dendrobatidis, ranaviruses, and trematodes (Ribeiroia ondatrae and echinostomes) are highly virulent pathogens known to infect amphibians, yet the extent to which they co-occur within amphibian communities remains poorly understood. Using field surveillance of 85 wetlands in the East Bay region of California, USA, we found that 68% of wetlands had ≥2 pathogens and 36% had ≥3 pathogens. Wetlands with high pathogen species richness also tended to cluster spatially. Our results underscore the need for greater integration of multiple pathogens and their interactions into amphibian disease research and conservation efforts.

Using multispecies occupancy models to improve the characterization and understanding of metacommunity structure

Two of the most prominent frameworks to develop in ecology over the past decade are metacommunity ecology, which seeks to characterize multispecies distributions across space, and occupancy modeling, which corrects for imperfect detection in an effort to better understand species occurrence patterns. Although their goals are complementary, metacommunity theory and statistical occupancy modeling methods have developed independently. For instance, the elements of metacommunity structure (EMS) framework uses species occurrence data to classify metacommunity structure and link it to underlying environmental gradients. While the efficacy of this approach relies on the quality of the data, few studies have considered how imperfect detection, which is widespread in ecological surveys and the major focus of occupancy modeling, affects the outcome. We introduce a framework that integrates multispecies occupancy models with the current EMS framework, detection error-corrected EMS (DECEMS). This method offers two distinct advantages. First, DECEMS reduces bias in characterizing metacommunity structure by using repeated surveys and occupancy models to disentangle species-specific occupancy and detection probabilities, ultimately bringing metacommunity structure classification into a more probabilistic framework. Second, occupancy modeling allows estimation of species-specific responses to environmental covariates, which will increase our ability to link species-level effects to metacommunity-wide patterns. After reviewing the EMS framework, we introduce a simple multispecies occupancy model and show how DECEMS can work in practice, highlighting that detection error often causes EMS to assign incorrect structures. To emphasize the broader applicability of this approach, we further illustrate that DECEMS can reduce the rate of structure misclassification by more than 20% in some cases, even proving useful when detection error rates are quite low (-10%). Integrating occupancy models and the EMS framework will lead to more accurate descriptions of metacommunity structure and to a greater understanding of the mechanisms by which different structures arise.

The Scaling of Host Density with Richness Affects the Direction, Shape, and Detectability of Diversity-Disease Relationships

Pathogen transmission responds differently to host richness and abundance, two unique components of host diversity. However, the heated debate around whether biodiversity generally increases or decreases disease has not considered the relationships between host richness and abundance that may exist in natural systems. Here we use a multi-species model to study how the scaling of total host community abundance with species richness mediates diversity-disease relationships. For pathogens with density-dependent transmission, non-monotonic trends emerge between pathogen transmission and host richness when host community abundance saturates with richness. Further, host species identity drives high variability in pathogen transmission in depauperate communities, but this effect diminishes as host richness accumulates. Using simulation we show that high variability in low richness communities and the non-monotonic relationship observed with host community saturation may reduce the detectability of trends in empirical data. Our study emphasizes that understanding the patterns and predictability of host community composition and pathogen transmission mode will be crucial for predicting where and when specific diversity-disease relationships should occur in natural systems.

Using biological control data to understand host-pathogen dynamics

A key assumption of epidemiological models is that population-scale disease spread is driven by close contact between hosts and pathogens. At larger scales, however, mechanisms such as spatial structure in host and pathogen populations and environmental heterogeneity could alter disease spread. The assumption that small-scale transmission mechanisms are sufficient to explain large-scale infection rates, however, is rarely tested. Here we provide a rigorous test using an insect-baculovirus system. We fit a mathematical model to data from forest-wide epizootics, while constraining the model parameters with data from branch-scale experiments, a difference in spatial scale of four orders of magnitude. This experimentally-constrained model fits the epizootic data well, supporting the role of small-scale transmission, but variability is high. We then compare this model’s performance to an unconstrained model that ignores the experimental data, which serves as a proxy for models with additional mechanisms. The unconstrained model has a superior fit, revealing a higher transmission rate across forests compared to branch-scale estimates. Our study suggests that small-scale transmission is insufficient to explain baculovirus epizootics. Further research is needed to identify the mechanisms that contribute to disease spread across large spatial scales, and synthesizing models and multi-scale data is key to understanding these dynamics.

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.

Co-exposure to multiple Ranavirus types enhances viral infectivity and replication in a larval amphibian system

Multiple pathogens commonly co-occur in animal populations, yet few studies demonstrate how co-exposure of individual hosts scales up to affect transmission. Although viruses in the genus Ranavirus are globally widespread, and multiple virus species or strains likely co-occur in nature, no studies have examined how co-exposure affects infection dynamics in larval amphibians. We exposed individual northern red-legged frog Rana aurora larvae to 2 species of ranavirus, namely Ambystoma tigrinum virus (ATV), frog virus 3 (FV3), or an FV3-like strain isolated from a frog-culturing facility in Georgia, USA (RCV-Z2). We compared single-virus to pairwise co-exposures while experimentally accounting for dosage. Co-exposure to ATV and FV3-like strains resulted in almost twice as many infected individuals compared to single-virus exposures, suggesting an effect of co-exposure on viral infectivity. The viral load in infected individuals exposed to ATV and FV3 was also higher than the single-dose FV3 treatment, suggesting an effect of co-exposure on viral replication. In a follow-up experiment, we examined how the co-occurrence of ATV and FV3 affected epizootics in mesocosm populations of larval western chorus frogs Pseudacris triseriata. Although ATV did not generally establish within host populations (<4% prevalence), when ATV and FV3 were both present, this co-exposure resulted in a larger epizootic of FV3. Our results emphasize the importance of multi-pathogen interactions in epizootic dynamics and have management implications for natural and commercial amphibian populations.