About me
From field naturalist to integrative evolutionary ecologist of disease vectors
Keywords: evolutionary ecologist · mosquito biologist · quantitative naturalist
As an evolutionary ecologist, I’m fascinated by how organisms adapt to diverse and changing environments. I’m also interested in the wider consequences of adaptation for populations, species boundaries, and species interactions. Mosquitoes—especially African malaria vectors—have become my primary focus; above all, I am a mosquito biologist. They are a powerful study system because they reveal how ecology, evolution, behaviour, physiology, genetics, and the transmission of mosquito-borne pathogens are interconnected. My research addresses questions such as: how do populations adapt to novel environments? How is adaptive variation maintained despite gene flow? How does ecological variation translate into physiological, behavioural, and genomic differences? At what point does divergence lead to speciation? For disease vectors, how do these evolutionary processes ultimately influence pathogen transmission? My scientific journey began as a field naturalist and ornithologist. My curiosity for natural history hasn’t changed, despite the shift from binoculars and mist-nets to mosquito traps and genome sequencing. I still want to observe organisms in their habitats and understand the forces that create the patterns of diversity we observe.
Scientific profile
Evolutionary ecologist by question. My research focuses on how insect vectors adapt and evolve, how new species form, how genes move among populations, and how traits affecting pathogen transmission arise and change.
Mosquito biologist by system. I am obsessed with African malaria mosquitoes such as Anopheles funestus and Anopheles gambiae, but my interests also cover mosquito ecology, behaviour, and evolution more broadly.
Field biologist by instinct. Natural populations matter. Much of my work starts with mosquitoes in real landscapes rather than idealised laboratory populations.
Naturalist by origin. I entered science through ornithology and field observation. That background still shapes how I think about biological variation and ecological context.
Quantitative biologist by necessity. Population processes are noisy, observations are incomplete, and biological systems are hierarchical. Biostatistics, statistical modelling, Bayesian inference, and causal reasoning are essential rather than optional technical details.
Genomicist by opportunity. Whole-genome sequencing has transformed how I investigate questions that I once approached through chromosomes, phenotypes, and geography. Population genomics now allows precise examination of barriers to gene flow, introgression, ancestry, and the genomic architecture of adaptation and speciation.
Computational biologist by practice. Modern biological research relies extensively on computation to manage genetic and genomic data, model populations, integrate environmental information, and build reproducible analytical workflows. I see scientific computing, data architecture, provenance, and reproducibility as parts of biological inference, not merely technical tools.
Environmental data scientist by scale. Mosquitoes experience landscapes, not spreadsheets. GIS, remote sensing, climate products, and other environmental datasets allow individual organisms and populations to be placed back into their ecological context.
Methodological pluralist by conviction. No particular technology defines my research. Field experiments, genomics, behavioural assays, environmental data, statistical modelling, and computation are useful only where they help discriminate among biological explanations.
Organism × Environment
A useful shorthand for how I think about biology is:
organism × environment
An organism can be described at many interconnected levels:
genome · epigenome · transcriptome · metabolome · physiology · morphology · behaviour
The environment is equally multidimensional:
climate · hydrology · landscape · conspecifics · communities · hosts · pathogens · human activities
The interesting biology lies in the interactions between them. This is why my research combines evolutionary biology, ecology, population genetics and genomics, behavioural science, environmental science, biostatistics, and computational biology. I do not regard these as separate research identities. They are different viewpoints and tools for understanding the same biological systems.
Why mosquitoes?
Mosquitoes matter because some species transmit pathogens that cause devastating diseases. Yet their role as vectors is not the only reason they are biologically fascinating. They inhabit an astonishing range of environments, evolve rapidly, exhibit complex behavioural and physiological adaptations, exchange genes across incompletely isolated groups, harbour remarkable chromosomal variation, and continually adapt to environmental change. Their importance in disease transmission simply makes the consequences of these evolutionary processes exceptionally explicit. When a mosquito population shifts its host preference, colonises brackish habitats, changes its biting schedule, adapts to urban environments, or acquires insecticide-resistance alleles from another population, it both represents an evolutionary experiment and alters the conditions for pathogen transmission. My research focuses broadly on this intersection between fundamental evolutionary ecology and its consequences for human health.
How I approach science
I’m wary of rigid disciplinary boundaries and of allowing methods to become ends rather than means. A genome does not explain a phenomenon by itself, just as a statistical association does not reveal a mechanism. A complex model cannot compensate for a flawed sampling design, and a massive dataset is not inherently insightful. I prefer to begin with the biological question, state assumptions clearly, identify observations that could distinguish among competing explanations, and only then choose the most appropriate methods. In practice, this may mean sampling mosquito larvae in muddy rice paddies, analysing large genomic datasets, reconstructing past environmental change from satellite imagery, or modelling possible future change. It may even mean painstakingly troubleshooting a bioinformatics pipeline that produces an unexpected result. These activities are different facets of the same endeavour: drawing biological inferences from observations by testing explicit hypotheses.
Research
This page is intentionally the short version. For more information on my biological research and the methods I use, see: → Research Themes
In one sentence: I study how mosquitoes thrive and evolve in changing environments, using whatever combination of field biology, experiments, genomics, environmental data, and quantitative inference is necessary to understand why.