Spatial Selection and Inheritance: Applying Evolutionary Concepts to Population Dynamics in Heterogeneous Space

Schauber, Eric M.; Goodwin, Brett J.; Jones, Clive G.; Ostfeld, Richard S.

doi:10.1890/06-1578

Cited by 16 publications

(16 citation statements)

References 47 publications

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“…When mice show much greater preference for foraging in covered trays, they are highly sensitive to risks; when they show a smaller difference, they show lower risk sensitivity. Mice with lower risk sensitivity are more likely to forage across a wide range of microhabitats, decreasing “mouse‐free space” (Schauber, Goodwin, Jones, & Ostfeld, ; Schmidt, ). To measure the GUD of individual mice that varied in infection status, we deployed foraging arenas on nontrapping nights in June and July 2015, after an average of ~70% of mice had been either true‐vaccinated or sham‐vaccinated (Figure b).…”

Section: Methodsmentioning

confidence: 99%

Effects of a zoonotic pathogen, Borrelia burgdorferi, on the behavior of a key reservoir host

Ostfeld

Brisson

Oggenfuss

et al. 2018

Ecology and Evolution

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Most emerging infectious diseases of humans are transmitted to humans from other animals. The transmission of these “zoonotic” pathogens is affected by the abundance and behavior of their wildlife hosts. However, the effects of infection with zoonotic pathogens on behavior of wildlife hosts, particularly those that might propagate through ecological communities, are not well understood. Borrelia burgdorferi is a bacterium that causes Lyme disease, the most common vector‐borne disease in the USA and Europe. In its North American range, the pathogen is most frequently transmitted among hosts through the bite of infected blacklegged ticks (Ixodes scapularis). Using sham and true vaccines, we experimentally manipulated infection load with this zoonotic pathogen in its most competent wildlife reservoir host, the white‐footed mouse, Peromyscus leucopus, and quantified the effects of infection on mouse foraging behavior, as well as levels of mouse infestation with ticks. Mice treated with the true vaccine had 20% fewer larval blacklegged ticks infesting them compared to mice treated with the sham vaccine, a significant difference. We observed a nonsignificant trend for mice treated with the true vaccine to be more likely to visit experimental foraging trays (20%–30% effect size) and to prey on gypsy moth pupae (5%–20% effect size) compared to mice treated with the sham vaccine. We observed no difference between mice on true‐ versus sham‐vaccinated grids in risk‐averse foraging. Infection with this zoonotic pathogen appears to elicit behavioral changes that might reduce self‐grooming, but other behaviors were affected subtly or not at all. High titers of B. burgdorferi in mice could elicit a self‐reinforcing feedback loop in which reduced grooming increases tick burdens and hence exposure to tick‐borne pathogens.

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Section: Methodsmentioning

confidence: 99%

Effects of a zoonotic pathogen, Borrelia burgdorferi, on the behavior of a key reservoir host

Ostfeld

Brisson

Oggenfuss

et al. 2018

Ecology and Evolution

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“…It is important to distinguish fixed heterogeneity as it is used here-that is, as the repeatability of individual performance-from other sources of variation that are not due to the properties of individuals (e.g., climatic variations among years). Indeed, only fixed differences among individuals can be the target of selection and allow for adaptation, provided that these fixed differences are passed on to the next generation-be it through genes (Keller and Waller 2002), philopatry (Schauber et al 2007), or other processes (Bonduriansky 2012).…”

Section: Introductionmentioning

confidence: 99%

Successful by Chance? The Power of Mixed Models and Neutral Simulations for the Detection of Individual Fixed Heterogeneity in Fitness Components

Bonnet

Postma

2016

The American Naturalist

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Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g., genetic variation) and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods and apply them to empirical data from a wild rodent population. While MM show high false-positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false-positive rates but always have low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis. Online enhancements: appendixes. Dryad data: http://dx.doi.org/10.5061/dryad.3cb61.abstract: Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g., genetic variation) and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods and apply them to empirical data from a wild rodent population. While MM show high false-positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false-positive rates but always have low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.

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“…Our model produces different results because short-range dispersal enables a disease to take advantage of the aggregated structure of a host population, a process that has previously been described by ecologists in the context of population growth in landscapes with spatial covariance in habitat quality ( habitat association , Bolker, 2003; growth-density covariance , Snyder and Chesson, 2003; spatial inheritance , Schauber et al, 2007). This factor is responsible for the advantage to intermediate over long-range dispersal in the models of Brown and Bolker (2004) and Schreiber and Lloyd-Smith (2009).…”

Section: Discussionmentioning

confidence: 99%

The dynamics of disease in a metapopulation: The role of dispersal range

North

Godfray

2017

Journal of Theoretical Biology

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The establishment and spread of a disease within a metapopulation is influenced both by dynamics within each population and by the host and pathogen spatial processes through which they are connected. We develop a spatially explicit metapopulation model to investigate how the form of host and disease dispersal jointly influence the probability of disease establishment and invasion. We show that diseases are more likely to establish if both the host and the disease tend to disperse locally, since the former leads to the spatial aggregation of host populations in the environment while the latter facilitates the pathogen's exploitation of this spatial pattern. In contrast, local pathogen dispersal is likely to reduce the probability of subsequent disease spread because it increases the spatial segregation of infected and uninfected populations. The effects of local dispersal on disease dynamics are less pronounced when the pathogen spreads through the movement of infected hosts and more pronounced when pathogen dispersal is independent (for example through airborne viruses) though the details of host and pathogen biology can be important. These spatial effects tend to be more pronounced if the sites available for host occupation are themselves spatially aggregated.

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Spatial Selection and Inheritance: Applying Evolutionary Concepts to Population Dynamics in Heterogeneous Space

Cited by 16 publications

References 47 publications

Effects of a zoonotic pathogen, Borrelia burgdorferi, on the behavior of a key reservoir host

Effects of a zoonotic pathogen, Borrelia burgdorferi, on the behavior of a key reservoir host

Successful by Chance? The Power of Mixed Models and Neutral Simulations for the Detection of Individual Fixed Heterogeneity in Fitness Components

The dynamics of disease in a metapopulation: The role of dispersal range

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