Ecological and evolutionary mechanisms behind the persistence of highly virulent pathogens: plague as a case study, The

2013 Spring.Includes bibliographical references.The persistence of emerging infectious diseases is the result of eco-evolutionary feedbacks between a pathogen and its novel host. Spatial structure both within and between host populations (i.e., a metapopulation) in particular can have a large effect on the establishment and subsequent coevolution of a host and pathogen. Here, my colleagues and I explore how differing metapopulation structures in a host and pathogen affect the coevolutionary maintenance of high virulence and low resistance in an emerging infectious disease. We use the relatively recent emergence of plague, caused by the bacterium Yersinia pestis, in North America as a case study to both understand how spatial structure in the pathogen may differ from that of its host and how these differences may affect coevolutionary trajectories. Host responses to Y. pestis infection are highly variable with some species, like black-tailed prairie dogs (Cynomys ludovicianus), experiencing massive population declines upon introduction of the plague bacterium (i.e., epizootics), while others, like the California ground squirrel (Spermophilus beecheyi), exhibit enzootic maintenance of Y. pestis. These species in particular have markedly different spatial structures, but it is unclear how regional transmission of plague may structure the pathogen population. To understand transmission more fully, we developed a mechanistic model of plague infection in a single population that incorporated multiple routes of transmission and parameterized the model for the two species mentioned above. We found that transmission in the epizootic system is driven largely through on-host cycling of fleas (i.e., a booster-feed infection cycle). In contrast, enzootics are driven by an off-host, questing flea reservoir. The potential for off-host fleas to drive plague dynamics reveals the potential for non-overlapping host and pathogen metapopulation structures. The effect of such a structure on coevolution is not well-understood, particularly for quantitative traits where no theoretical methods exist to study coevolution in a metapopulation. Consequently, we also developed a novel theoretical framework for studying quantitative trait coevolution in a metapopulation. This new framework reveals that coevolutionary outcomes for resistance and virulence depend on the interaction between host and pathogen dispersal strategies with local reproduction and transmission dynamics favoring a diversity of resistance-virulence combinations. Host-pathogen coevolution is also affected by the shape of life-history trade-offs for both the host and the pathogen. We predicted coevolutionary outcomes under different host and pathogen dispersals assuming three different trade-off functions when resistance comes at the cost of reproduction and virulence increases transmission while decreasing the infectious period: accelerating, linear, and decelerating costs. We found that selection on resistance is most sensitive to concave trade-off functions, and selection on virulence was most sensitive to convex functions, although coevolutionarily stable strategies were only predicted when both resistance and virulence had accelerating cost trade-off functions. Predictions from the model also differ from those observed in well-mixed and spatially structured single populations indicating that eco-evolutionary dynamics do not scale directly with space. Implications for future models of plague coevolution are also discussed

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease

Transmission Shifts Underlie Variability in Population Responses to Yersinia pestis Infection

Host populations for the plague bacterium, Yersinia pestis, are highly variable in their response to plague ranging from near deterministic extinction (i.e., epizootic dynamics) to a low probability of extinction despite persistent infection (i.e., enzootic dynamics). Much of the work to understand this variability has focused on specific host characteristics, such as population size and resistance, and their role in determining plague dynamics. Here, however, we advance the idea that the relative importance of alternative transmission routes may vary causing shifts from epizootic to enzootic dynamics. We present a model that incorporates host and flea ecology with multiple transmission hypotheses to study how transmission shifts determine population responses to plague. Our results suggest enzootic persistence relies on infection of an off-host flea reservoir and epizootics rely on transiently maintained flea infection loads through repeated infectious feeds by fleas. In either case, early-phase transmission by fleas (i.e., transmission immediately following an infected blood meal) has been observed in laboratory studies, and we show that it is capable of driving plague dynamics at the population level. Sensitivity analysis of model parameters revealed that host characteristics (e.g., population size and resistance) vary in importance depending on transmission dynamics, suggesting that host ecology may scale differently through different transmission routes enabling prediction of population responses in a more robust way than using either host characteristics or transmission shifts alone

Using community detection on networks to identify migratory bird flyways in North America

2012 Fall.Includes bibliographical references.Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 years. However, recent desires to incorporate uncertainty regarding biological processes into an adaptive harvest management program have underscored the need to re-evaluate the traditional flyway concept and bring uncertainty in flyways themselves into management planning. Here, we use bird band and recovery data to develop a network model of migratory movement for four waterfowl species, mallard (Anas platyrhnchos), northern pintail (A. acuta), American green-winged teal (A. carolinensis), and Canada Goose (Branta Canadensis) in North America. A community detection algorithm is then used to identify migratory flyways. Additionally, we compare flyway structure both across species and through time to determine broad applicability of the previous flyway concept. We also propose a novel metric, the consolidation factor, to describe a node's (i.e., small geographic area) importance in determining flyway structure. The community detection algorithm identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway structure of Canada geese exhibiting higher complexity. For mallards, flyway structure was relatively consistent through time. However, consolidation factors and cross-community mixing patterns revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between flyways. Additionally, interspersed throughout these major flyways were smaller mixing zones that point to added complexity and uncertainty in the four-flyway concept. Not only does the incorporation of this uncertainty due to mixing provide a potential alternative management strategy, but the network approach provides a robust, quantitative approach to flyway identification that fits well with the adaptive harvest management framework currently used in North American waterfowl management

Assessment of paper interstate certificates of veterinary inspection used to support disease tracing in cattle

Objective—To evaluate the differences among each state’s Interstate Certificate of Veterinary Inspection (ICVI) form and the legibility of data on paper ICVIs used to support disease tracing in cattle. Design—Descriptive retrospective cross-sectional study. Sample—Examples of ICVIs from 50 states and 7,630 randomly sampled completed paper ICVIs for cattle from 48 states. Procedures—Differences among paper ICVI forms from all 50 states were determined. Sixteen data elements were selected for further evaluation of their value in tracing cattle. Completed paper ICVIs for interstate cattle exports in 2009 were collected from 48 states. Each of the 16 data elements was recorded as legible, absent, or illegible on forms completed by accredited veterinarians, and results were summarized by state. Mean values for legibility at the state level were used to estimate legibility of data at the national level. Results—ICVIs were inconsistent among states in regard to data elements requested and availability of legible records. A mean ± SD of 70.0 ± 22.1% of ICVIs in each state had legible origin address information. Legible destination address information was less common, with 55.0 ± 21.4% of records complete. Incomplete address information was most often a result of the field having been left blank. Official animal identification was present on 33.1% of ICVIs. Conclusions and Clinical Relevance—The inconsistency among state ICVI forms and quality of information provided on paper ICVIs could lead to delays and the need for additional resources to trace cattle, which could result in continued spread of disease. Standardized ICVIs among states and more thorough recording of information by accredited veterinarians or expanded usage of electronic ICVIs could enhance traceability of cattle during an outbreak

The impact of movements and animal density on continental scale cattle disease outbreaks in the United States

Globalization has increased the potential for the introduction and spread of novel pathogens over large spatial scales necessitating continental-scale disease models to guide emergency preparedness. Livestock disease spread models, such as those for the 2001 foot-and-mouth disease (FMD) epidemic in the United Kingdom, represent some of the best case studies of large-scale disease spread. However, generalization of these models to explore disease outcomes in other systems, such as the United States’s cattle industry, has been hampered by differences in system size and complexity and the absence of suitable livestock movement data. Here, a unique database of US cattle shipments allows estimation of synthetic movement networks that inform a near-continental scale disease model of a potential FMD-like (i.e., rapidly spreading) epidemic in US cattle. The largest epidemics may affect over one-third of the US and 120,000 cattle premises, but cattle movement restrictions from infected counties, as opposed to national movement moratoriums, are found to effectively contain outbreaks. Slow detection or weak compliance may necessitate more severe state-level bans for similar control. Such results highlight the role of large-scale disease models in emergency preparedness, particularly for systems lacking comprehensive movement and outbreak data, and the need to rapidly implement multi-scale contingency plans during a potential US outbreak

Data from: Identification of migratory bird flyways in North America using community detection on biological networks

Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 years. However, previous flyway characterizations are not easily updated with current bird movement data and fail to provide assessments of the importance of specific geographical regions to the identification of flyways. Here, we developed a network model of migratory movement for four waterfowl species —mallard (Anas platyrhnchos), northern pintail (A. acuta), American green-winged teal (A. carolinensis), and Canada goose (Branta canadensis) — in North America using bird band and recovery data. We then identified migratory flyways using a community detection algorithm and characterize the importance of smaller geographic regions in identifying flyways using a novel metric, the consolidation factor. We identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway identification in Canada geese exhibiting higher complexity. For mallards, flyways were relatively consistent through time. However, consolidation factors revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between other flyways. Our results demonstrate that the network approach provides a robust method for flyway identification that is widely applicable given the relatively minimal data requirements and is easily updated with future movement data to reflect changes in flyway definitions and management goals

Band encounter data from hunter recovered mallards (MALL), northern pintails (NOPI), American green-winged teal (AGWT), and Canada geese (CAGO) between 2004 and 2013 in North America.

Included fields are the band number (BAND), species (SPEC), banding date (BMONTH, BDAY, BYEAR), recovery date (RMONTH, RDAY, RYEAR), banding location reported at the node level (BNODELAT, BNODELONG), and recovery location reported at the node level (RNODELAT, RNODELONG). For information on requesting the full band and encounter data, please go to https://www.pwrc.usgs.gov/BBL/homepage/datarequest.cfm