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Myers, C. R.

Publications and source records attributed to Myers, C. R..

3 recordsLinked to original sources

Increased host diversity limits bacterial generalism but may promote microbe-microbe interactions

Host-associated bacteria vary in their host breadth, which can impact ecological interactions. By colonizing diverse hosts, host generalists can have disproportionate ecological impacts. For bacteria, host generalism may advantageous, particularly when the availability of specific hosts is variable. It is unclear how much the ability to evolve generalism, by inhabiting diverse hosts, is constrained in host-associated bacteria. We hypothesized that constraints on bacterial generalism will differ depending on the availability of specific host species. To test this, we assessed patterns of diversity and specialization in the cloacal microbiomes of reptile communities from the temperate zone to the tropics, where the diversity and abundance of host species varies substantially. Within these communities, generalist taxa tended to be Proteobacteria, whereas specialists tended to be Firmicutes. We found that bacterial generalists were less prevalent in the highest diversity host communities, and in keeping with this, Proteobacteria were less diverse in these communities. Generalist taxa became relatively less widespread across host species only in the two most diverse host communities. We therefore conclude that the constraint on generalism is not driven by absolute incompatibility with some host species, but rather from competition with host adapted specialist lineages. In the high-diversity communities, we found that the successful generalists, typically Proteobacteria, were disproportionately likely to co-occur with one another across evolutionarily disparate hosts within the community. Our data indicate that bacterial lineages can adapt to the evolutionary pressures of high diversity host communities either by specializing on hosts or by forming cohorts of co-occurring bacterial lineages.

ecology↗

A time for every purpose: using time-dependent sensitivity analysis to help understand and manage dynamic ecological systems

Sensitivity analysis is often used to help understand and manage ecological systems, by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time--for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act upon. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life-history events, and endogenously-driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

ecology↗

Changes in capture availability due to infection can lead to correctable biases in population-level infectious disease parameters

Correctly identifying the strength of selection parasites impose on hosts is key to predicting epidemiological and evolutionary outcomes. However, behavioral changes due to infection can alter the capture probability of infected hosts and thereby make selection difficult to estimate by standard sampling techniques. Mark-recapture approaches, which allow researchers to determine if some groups in a population are less likely to be captured than others, can mitigate this concern. We use an individual-based simulation platform to test whether changes in capture rate due to infection can alter estimates of three key outcomes: 1) reduction in offspring numbers of infected parents, 2) the relative risk of infection for susceptible genotypes compared to resistant genotypes, and 3) change in allele frequencies between generations. We find that calculating capture probabilities using mark-recapture statistics can correctly identify biased relative risk calculations. For detecting fitness impact, the bounded nature of the distribution possible offspring numbers results in consistent underestimation of the impact of parasites on reproductive success. Researchers can mitigate many of the potential biases associated with behavioral changes due to infection by using mark-recapture techniques to calculate capture probabilities and by accounting for the shapes of the distributions they are attempting to measure.

ecology↗