Christina A. Cobbold

Measuring diversity: the importance of species similarity

Realistic measures of biodiversity should reflect not only the relative abundances of species, but also the differences between them. We present a natural family of diversity measures taking both factors into account. This is not just another addition to the already long list of diversity indices. Instead, a single formula subsumes many of the most popular indices, including Shannons, Simpsons, species richness, and Raos quadratic entropy. These popular indices can then be used and understood in a unified way, and the relationships between them are made plain. The new measures are, moreover, effective numbers, so that percentage changes and ratio comparisons of diversity value are meaningful. We advocate the use of diversity profiles, which provide a faithful graphical representation of the shape of a community; they show how the perceived diversity changes as the emphasis shifts from rare to common species. Communities can usefully be compared by comparing their diversity profiles. We show by example that this is a far more subtle method than any relying on a single statistic. Some ecologists view diversity indices with suspicion, questioning whether they are biologically meaningful. By dropping the naive assumption that distinct species have nothing in common, working with effective numbers, and using diversity profiles, we arrive at a system of diversity measurement that should lay much of this suspicion to rest.

Critical Interplay between Parasite Differentiation, Host Immunity, and Antigenic Variation in Trypanosome Infections

Increasing availability of pathogen genomic data offers new opportunities to understand the fundamental mechanisms of immune evasion and pathogen population dynamics during chronic infection. Motivated by the growing knowledge on the antigenic variation system of the sleeping sickness parasite, the African trypanosome, we introduce a mechanistic framework for modeling within‐host infection dynamics. Our analysis focuses first on a single parasitemia peak and then on the dynamics of multiple peaks that rely on stochastic switching between groups of parasite variants. A major feature of trypanosome infections is the interaction between variant‐specific host immunity and density‐dependent parasite differentiation to transmission life stages. In this study, we investigate how the interplay between these two types of control depends on the modular structure of the parasite antigenic archive. Our model shows that the degree of synchronization in stochastic variant emergence determines the relative dominance of general over specific control within a single peak. A requirement for multiple‐peak dynamics is a critical switch rate between blocks of antigenic variants, which implies constraints on variant surface glycoprotein (VSG) archive genetic diversification. Our study illustrates the importance of quantifying the links between parasite genetics and within‐host dynamics and provides insights into the evolution of trypanosomes.

Modelling the effect of temperature on the seasonal population dynamics of temperate mosquitoes

Mosquito-borne diseases cause substantial mortality and morbidity worldwide. These impacts are widely predicted to increase as temperatures warm and extreme precipitation events become more frequent, since mosquito biology and disease ecology are strongly linked to environmental conditions. However, direct evidence linking environmental change to changes in mosquito-borne disease is rare, and the ecological mechanisms that may underpin such changes are poorly understood. Environmental drivers, such as temperature, can have non-linear, opposing impacts on the demographic rates of different mosquito life cycle stages. As such, model frameworks that can deal with fluctuations in temperature explicitly are required to predict seasonal mosquito abundance, on which the intensity and persistence of disease transmission under different environmental scenarios depends. We present a novel, temperature-dependent, delay-differential equation model, which incorporates diapause and the differential effects of temperature on the duration and mortality of each life stage and demonstrates the sensitivity of seasonal abundance patterns to inter- and intra-annual changes in temperature. Likely changes in seasonal abundance and exposure to mosquitoes under projected changes in UK temperatures are presented, showing an increase in peak vector abundance with warming that potentially increases the risk of disease outbreaks.

High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations

Several human genetic diseases are associated with inheriting an abnormally large unstable DNA simple sequence repeat. These sequences mutate, by changing the number of repeats, many times during the lifetime of those affected, with a bias towards expansion. These somatic changes lead not only to the presence of cells with different numbers of repeats in the same tissue, but also produce increasingly longer repeats, contributing towards the progressive nature of the symptoms. Modelling the progression of repeat length throughout the lifetime of individuals has potential for improving prognostic information as well as providing a deeper understanding of the underlying biological process. A large data set comprising blood DNA samples from individuals with one such disease, myotonic dystrophy type 1, provides an opportunity to parameterize a mathematical model for repeat length evolution that we can use to infer biological parameters of interest. We developed new mathematical models by modifying a proposed stochastic birth process to incorporate possible contraction. A hierarchical Bayesian approach was used as the basis for inference, and we estimated the distribution of mutation rates in the population. We used model comparison analysis to reveal, for the first time, that the expansion bias observed in the distributions of repeat lengths is likely to be the cumulative effect of many expansion and contraction events. We predict that mutation events can occur as frequently as every other day, which matches the timing of regular cell activities such as DNA repair and transcription but not DNA replication.

Coexistence of multiple parasitoids on a single host due to differences in parasitoid phenology

There are many well-documented cases in which multiple parasitoids can coexist on a single host species. We examine a theoretical framework to assess whether parasitoid coexistence can be explained through differences in timing of parasitoid oviposition and parasitoid emergence. This study explicitly includes the phenology of host and parasitoid development and explores how this mechanism affects the population dynamics. Coexistence of the host with two parasitoids requires a balance between parasitoid fecundity and survival and occurs most readily if one parasitoid attacks earlier but emerges later than the other parasitoid. The host density can either be decreased or increased when a second coexisting parasitoid is introduced into the system. However, there always exists a single parasitoid type that is most effective at depressing the host density, although this type may not be successful due to parasitoid competition. The coexistence of multiple parasitoids also affects the population dynamics. For instance, population oscillations can be removed by the introduction of a second parasitoid. In general, subtle differences in parasitoid phenology can give rise to different outcomes in a host–multi-parasitoid system, and this may offer some insight into why establishing criteria for the ‘ideal’ biological control agent has been so challenging.

Connecting Earth observation to high-throughput biodiversity data

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services.

Mean occupancy time: linking mechanistic movement models, population dynamics and landscape ecology to population persistence

Reaction–diffusion models for the dynamics of a biological population in a fragmented landscape can incorporate detailed descriptions of movement and behavior, but are difficult to analyze and hard to parameterize. Patch models, on the other hand, are fairly easy to analyze and can be parameterized reasonably well, but miss many details of the movement process within and between patches. We develop a framework to scale up from a reaction–diffusion process to a patch model and, in particular, to determine movement rates between patches based on behavioral rules for individuals. Our approach is based on the mean occupancy time, the mean time that an individuals spends in a certain area of the landscape before it exits that area or dies. We illustrate our approach using several different landscape configurations. We demonstrate that the resulting patch model most closely captures persistence conditions and steady state densities as compared with the reaction–diffusion model.

The Impact of Mutation and Gene Conversion on the Local Diversification of Antigen Genes in African Trypanosomes

Patterns of genetic diversity in parasite antigen gene families hold important information about their potential to generate antigenic variation within and between hosts. The evolution of such gene families is typically driven by gene duplication, followed by point mutation and gene conversion. There is great interest in estimating the rates of these processes from molecular sequences for understanding the evolution of the pathogen and its significance for infection processes. In this study, a series of models are constructed to investigate hypotheses about the nucleotide diversity patterns between closely related gene sequences from the antigen gene archive of the African trypanosome, the protozoan parasite causative of human sleeping sickness in Equatorial Africa. We use a hidden Markov model approach to identify two scales of diversification: clustering of sequence mismatches, a putative indicator of gene conversion events with other lower-identity donor genes in the archive, and at a sparser scale, isolated mismatches, likely arising from independent point mutations. In addition to quantifying the respective probabilities of occurrence of these two processes, our approach yields estimates for the gene conversion tract length distribution and the average diversity contributed locally by conversion events. Model fitting is conducted using a Bayesian framework. We find that diversifying gene conversion events with lower-identity partners occur at least five times less frequently than point mutations on variant surface glycoprotein (VSG) pairs, and the average imported conversion tract is between 14 and 25 nucleotides long. However, because of the high diversity introduced by gene conversion, the two processes have almost equal impact on the per-nucleotide rate of sequence diversification between VSG subfamily members. We are able to disentangle the most likely locations of point mutations and conversions on each aligned gene pair.

Moving forward in circles: challenges and opportunities in modelling population cycles

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer-resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research.

Effects of Forest Spatial Structure on Insect Outbreaks: Insights from a Host-Parasitoid Model

Understanding how cycles of forest-defoliating insects are affected by forest destruction is of major importance for forest management. Achieving such an understanding with data alone is difficult, however, because population cycles are typically driven by species interactions that are highly nonlinear. We therefore constructed a mathematical model to investigate the effects of forest destruction on defoliator cycles, focusing on defoliator cycles driven by parasitoids. Our model shows that forest destruction can increase defoliator density when parasitoids disperse much farther than defoliators because the benefits of reduced defoliator mortality due to increased parasitoid dispersal mortality exceed the costs of increased defoliator dispersal mortality. This novel result can explain observations of increased outbreak duration with increasing forest fragmentation in forest tent caterpillar populations. Our model also shows that larger habitat patches can mitigate habitat loss, with clear implications for forest management. To better understand our results, we developed an approximate model that shows that defoliator spatial dynamics can be predicted from the proportion of dispersing animals that land in suitable habitat. This approximate model is practically useful because its parameters can be estimated from widely available data. Our model thus suggests that forest destruction may exacerbate defoliator outbreaks but that management practices could mitigate such effects.