Allison K. Shaw

It's all relative: ranking the diversity of aquatic bacterial communities

The study of microbial diversity patterns is hampered by the enormous diversity of microbial communities and the lack of resources to sample them exhaustively. For many questions about richness and evenness, however, one only needs to know the relative order of diversity among samples rather than total diversity. We used 16S libraries from the Global Ocean Survey to investigate the ability of 10 diversity statistics (including rarefaction, non-parametric, parametric, curve extrapolation and diversity indices) to assess the relative diversity of six aquatic bacterial communities. Overall, we found that the statistics yielded remarkably similar rankings of the samples for a given sequence similarity cut-off. This correspondence, despite the different underlying assumptions of the statistics, suggests that diversity statistics are a useful tool for ranking samples of microbial diversity. In addition, sequence similarity cut-off influenced the diversity ranking of the samples, demonstrating that diversity statistics can also be used to detect differences in phylogenetic structure among microbial communities. Finally, a subsampling analysis suggests that further sequencing from these particular clone libraries would not have substantially changed the richness rankings of the samples.

Sex-Biased Dispersal and the Speed of Two-Sex Invasions

Population models that combine demography and dispersal are important tools for forecasting the spatial spread of biological invasions. Current models describe the dynamics of only one sex (typically females). Such models cannot account for the sex-related biases in dispersal and mating behavior that are typical of many animal species. In this article, we construct a two-sex integrodifference equation model that overcomes these limitations. We derive an explicit formula for the invasion speed from the model and use it to show that sex-biased dispersal may significantly increase or decrease the invasion speed by skewing the operational sex ratio at the invasion’s low-density leading edge. Which of these possible outcomes occurs depends sensitively on complex interactions among the direction of dispersal bias, the magnitude of bias, and the relative contributions of females and males to local population growth.

Leaks in the pipeline: separating demographic inertia from ongoing gender differences in academia

Identification of the causes underlying the under-representation of women and minorities in academia is a source of ongoing concern and controversy. This is a critical issue in ensuring the openness and diversity of academia; yet differences in personal experiences and interpretations have mired it in controversy. We construct a simple model of the academic career that can be used to identify general trends, and separate the demographic effects of historical differences from ongoing biological or cultural gender differences. We apply the model to data on academics collected by the National Science Foundation (USA) over the past three decades, across all of science and engineering, and within six disciplines (agricultural and biological sciences, engineering, mathematics and computer sciences, physical sciences, psychology, and social sciences). We show that the hiring and retention of women in academia have been affected by both demographic inertia and gender differences, but that the relative influence of gender differences appears to be dwindling for most disciplines and career transitions. Our model enables us to identify the two key non-structural bottlenecks restricting female participation in academia: choice of undergraduate major and application to faculty positions. These transitions are those in greatest need of detailed study and policy development.

Mate finding, Allee effects and selection for sex-biased dispersal

Although dispersal requires context-dependent decision-making in three distinct stages (emigration, transit, immigration), these decisions are commonly ignored in simple models of dispersal. For sexually reproducing organisms, mate availability is an important factor in dispersal decisions. Difficulty finding mates can lead to an Allee effect where population growth decreases at low densities. Surprisingly, theoretical studies on mate finding and on sex-biased dispersal produce opposing predictions: in the former, one sex is predicted to move less if the other sex evolves to search more, whereas in the latter, mate-finding difficulties can select for less sex bias in dispersal when mate finding occurs after dispersal. Here, we develop a pair of models to examine the joint evolution of dispersal and settlement behaviour. Our first model resolves the apparent contradiction from the mate search and dispersal literatures. Our second model demonstrates that the relationship between mating system and sex-biased dispersal is more complex than a simple contrast between resource defence monogamy and female defence polygyny. Our results highlight that a key factor is the timing of mating relative to dispersal (before, during, or after). We also show that although movement has the potential to alleviate a mate-finding Allee effect, in some cases, it can actually exacerbate the effect.

The evolution of intermittent breeding

A central issue in life history theory is how organisms trade off current and future reproduction. A variety of organisms exhibit intermittent breeding, meaning sexually mature adults will skip breeding opportunities between reproduction attempts. It’s thought that intermittent breeding occurs when reproduction incurs an extra cost in terms of survival, energy, or recovery time. We have developed a matrix population model for intermittent breeding, and use adaptive dynamics to determine under what conditions individuals should breed at every opportunity, and under what conditions they should skip some breeding opportunities (and if so, how many). We also examine the effect of environmental stochasticity on breeding behavior. We find that the evolutionarily stable strategy (ESS) for breeding behavior depends on an individual’s expected growth and mortality, and that the conditions for skipped breeding depend on the type of reproductive cost incurred (survival, energy, recovery time). In constant environments there is always a pure ESS, however environmental stochasticity and deterministic population fluctuations can both select for a mixed ESS. Finally, we compare our model results to patterns of intermittent breeding in species from a range of taxonomic groups.

Dispersal evolution in the presence of Allee effects can speed up or slow down invasions

Successful invasions by sexually reproducing species depend on the ability of individuals to mate. Finding mates can be particularly challenging at low densities (a mate-finding Allee effect), a factor that is only implicitly accounted for by most invasion models, which typically assume asexual populations. Existing theory on single-sex populations suggests that dispersal evolution in the presence of a mate-finding Allee effect slows invasions. Here we develop a two-sex model to determine how mating system, strength of an Allee effect, and dispersal evolution influence invasion speed. We show that mating system differences can dramatically alter the spread rate. We also find a broader spectrum of outcomes than earlier work suggests. Allowing dispersal to evolve in a spreading context can sometimes alleviate the mate-finding Allee effect and slow the rate of spread. However, we demonstrate the opposite when resource competition among females remains high: evolution then acts to speed up the spread rate, despite simultaneously exacerbating the Allee effect. Our results highlight the importance of the timing of mating relative to dispersal and the strength of resource competition for consideration in future empirical studies.

Migratory Recovery from Infection as a Selective Pressure for the Evolution of Migration

Migration, a widespread animal behavior, can influence how individuals acquire and transmit pathogens. Past work has demonstrated that migration can reduce the costs of pathogen or parasite infection through two processes: migratory escape from infected areas or individuals and migratory culling of infected individuals. Here, we propose a third process: migratory recovery, where infected individuals lose their parasites and recover from infection during migration. Recovery can occur when parasites and/or their intermediate hosts cannot support changes in the migratory host’s internal or external environment during migration. Thus, parasite mortality increases with migration. Although migratory recovery is likely widespread across species, it remains challenging to empirically test it as a selective force promoting migration. We develop a model and determine the conditions under which migratory recovery theoretically favors the evolution of migration. We show that incorporating migratory recovery into a model of migratory escape increases the range of biologically realistic conditions favoring migration and leads to scenarios where partial migration can evolve. Motivated by empirical estimates of infection costs, our model shows how recovery from infection could drive the evolution of migration. We suggest a number of future directions for both theoretical and empirical research in this area.

Theoretical insight into three disease-related benefits of migration

Migration (seasonal round-trip movement across relatively large distances) is common within the animal kingdom. This behaviour often incurs extreme costs in terms of time, energy, and/or survival. Climate, food, predation, and breeding are typically suggested as factors favouring the evolution of migration. Although disease regulation has also been considered, few studies consider it as the primary selective pressure for migration. Our aim was to determine, theoretically, under what conditions migration could reduce the long-term disease prevalence within a population, assuming the only benefits of migration are infection-related. We created two mathematical models, one where the population migrates annually and one where the entire population remains on the breeding ground year-round. In each we simulated disease transmission (frequency-dependent and density-dependent) and quantified eventual disease prevalence. In the migration model we varied the time spent migrating, disease-related migration mortality, and the overall migration mortality. When we compared results from the two models, we found that migration generally lowered disease prevalence. We found a population was healthier if it: (1) spent more time migrating (assuming no disease transmission during migration), (2) had higher disease-induced migration mortality, and (3) had an overall higher mortality when migrating (compared to not migrating). These results provide support for two previously proposed mechanisms by which migration can reduce disease prevalence (migratory escape and migratory cull), and also demonstrate that non-selective mortality during migration is a third mechanism. Our findings indicate that migration may be evolutionarily advantageous even if the only migratory benefit is disease control.

Population dynamics of the vicuña (Vicugna vicugna): density-dependence, rainfall, and spatial distribution

Abstract The vicuña (Vicugna vicugna) is a South American camelid that has been hunted to near extinction. Following the establishment of conservation programs, vicuñas have successfully recovered to their current “Least Concern” International Union for Conservation of Nature and Natural Resources status. We analyze a 31-year vicuña census data set from northern Chile in order to investigate the influence of various factors on vicuña population dynamics. During the first 15 years, population dynamics are driven by strong growth as the population recovers from overhunting, and during the last 15 years dynamics are dominated by fluctuations around carrying capacity. We find that the best fit of the census data is a logistic growth model that takes into account how changes in rainfall and primary productivity lead to fluctuations in carrying capacity, suggesting that the resources limiting vicuña population size are not constant but change over time. We also find that the spatial distribution of vicuñas changes over time with respect to the nutrient-rich bofedales (Andean peatlands). Our study demonstrates the importance of collecting and analyzing long-term census data, and suggests that further insight could be gained if vicuña location with respect to habitat type was recorded during the census.

Vector population growth and condition‐dependent movement drive the spread of plant pathogens

Plant viruses, often spread by arthropod vectors, impact natural and agricultural ecosystems worldwide. Intuitively, the movement behavior and life history of vectors influence pathogen spread, but the relative contribution of each factor has not been examined. Recent research has highlighted the influence of host infection status on vector behavior and life history. Here, we developed a model to explore how vector traits influence the spread of vector-borne plant viruses. We allowed vector life history (growth rate, carrying capacity) and movement behavior (departure and settlement rates) parameters to be conditional on whether the plant host is infected or healthy and whether the vector is viruliferous (carrying the virus) or not. We ran simulations under a wide range of parameter combinations and quantified the fraction of hosts infected over time. We also ran case studies of the model for Barley yellow dwarf virus, a persistently transmitted virus, and for Potato virus Y, a non-persistently transmitted virus. We quantified the relative importance of each parameter on pathogen spread using Latin hypercube sampling with the statistical partial rank correlation coefficient technique. We found two general types of mechanisms in our model that increased the rate of pathogen spread. First, increasing factors such as vector intrinsic growth rate, carrying capacity, and departure rate from hosts (independent of whether these factors were condition-dependent) led to more vectors moving between hosts, which increased pathogen spread. Second, changing condition-dependent factors such as a vectors preference for settling on a host with a different infection status than itself, and vector tendency to leave a host of the same infection status, led to increased contact between hosts and vectors with different infection statuses, which also increased pathogen spread. Overall, our findings suggest that vector population growth rates had the greatest influence on rates of virus spread, but rates of vector dispersal from infected hosts and from hosts of the same infection status were also very important. Our model highlights the importance of simultaneously considering vector life history and behavior to better understand pathogen spread. Although developed for plant viruses, our model could readily be utilized with other vector-borne pathogen systems.