Ecological and epidemiological insights from blood mealsUsing interaction networks to explore the community structure and network ecology of biting insects and their hosts
Knowledge of variations in insect biting behaviour and feeding patterns of insect disease vectors is important in understanding disease epidemiology and establishing effective control methods. Blood feeding insects are embedded within complex ecological communities and interact with other species acting as predator, prey, or competitor. These interactions are likely to impact the population dynamics of those co-occurring species1.
Biting insects exist within a variety of landscapes, where landscape change is likely to influence host-insect interactions2,3. For example, deforestation within the Peruvian Amazon resulted in 278 times more human interactions with the malaria vector Anopheles darlingi than in undisturbed habitat, leading to a rise in malaria transmission4. Consequently, creating vector controls requires an understanding of how the landscape modifies biting insect community structure and biting interaction frequencies. Similarly, community structure, host availability, and the frequency of interactions may all be affected by time, again with consequences for disease transmission. For example, the Leishmaniasis vector, sand flies, are most active at dawn and dusk, with a resultant increase in transmission risk to nocturnal hosts5.
Biting insect research has primarily focused on the analysis of single biting insect species or small sets of interacting species to examine host usage and to screen vectors for pathogens of interest (such as Zika virus and malaria). As many biting insects are medically important, research has also focused on the effects of specific control initiatives, such as examining Anopheles mosquito abundances following the implementation of long-lasting insecticidal nets. Whilst limited work has examined the effect of habitat modification and temporal variations on feeding patterns and species abundances, this work has broadly overlooked the community aspect6. Therefore, there is currently an incomplete understanding of the degree to which these variations and distance from human habitation7, impact interaction frequencies, and the structural properties of the wider biting insect-host community. Indeed, as insect communities themselves are rarely studied, the full ecological and epidemiological implications of their interactions is currently unknown, so that the design of control strategies is potentially compromised.
Understanding such interactions is possible by constructing interaction networks, which provide a visual and mathematical representation of a community of species, connected through their feeding interactions8,9. I will create biting insect-host interaction networks using data from insect bloodmeals, focusing on the communities of biting flies in Ghana. I will explore the impact of landscape, comparing distinct habitat categories (such as scrubland and fallow land), and temporal variations (day or night), and the effect of proximity to human habitation on biting insect-host community composition, structural properties, such as how specialised the network is, and relative species’ abundances. I will use metabarcoding to create data on species interactions as it allows for the identification of animal DNA in a mixed sample collected from biting insect bloodmeals10.
I have augmented this fieldwork with a literature-based analysis of published blood meal interaction data. This provides an in-depth opinion on the value of interaction networks in understanding host-insect-disease interactions and highlights their future applications in identifying and monitoring emerging diseases and unrecognized vectors11. Further, using a global data set extracted from the literature, I aim to examine structural variations of biting insect-host communities between distinct habitat types and latitudes. The results generated by my work will provide valuable insight into biting insect community ecology, evaluate the potential impact of interventions on community structure and interactions.
Department of Zoology, University of Oxford, Oxford, UK
1. Ferguson HM, Dornhaus A, Beeche A, Borgemeister C, Gottlieb M, Mulla MS, Gimnig J, Fish D, Kileen G (2010) Ecology: A prerequisite for malaria elimination and eradication. PLOS Medicine 7. doi:10.1371/journal.pmed.1000303
2. Wolinska J, King KC (2009) Environment can alter selection in host-parasite interactions. Trends in Parasitology 25: 236–244. doi:10.1016/j.pt.2009.02.004
3. Lachish S, Knowles S, Alves R, Sepil I, Davies A, Lee S, Wood M, Sheldon B (2013) Spatial determinants of infection risk in a multi-species avian malaria system. Ecography 36:587–598. doi:10.1111/j.1600-0587.2012.07801.x
4. Vittor AY, Gilman RH, Tielsch J, Glass G, Shields T, Lozano WS, Pinedo-Cancino V, Patz J (2006) The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of falciparum malaria in the Peruvian Amazon. American Journal of Tropical Medicine and Hygiene 74:3–11. doi:10.4269/ajtmh.2006.74.3
5. Aklilu E, Gebresilassie A, Yared S, Kindu M, Tekie H, Balkew M, Warburg A, Hailu A, Gebre-Michael T (2017) Comparative study on the nocturnal activity of phlebotomine sand flies in a highland and lowland foci of visceral leishmaniasis in north-western Ethiopia with special reference to Phlebotomus orientalis. Parasites & Vectors 10:393. doi:0.1186/s13071-017-2339-6
6. Braack L, Almeida P, Cornel A, Swanepoel R, de Jager C (2018) Mosquito-borne arboviruses of African origin: review of key viruses and vectors. Parasites & Vectors 11:29. doi:10.1186/s13071-017-2559-9
7. Orsborne, J, Furuya-Kanamori L, Jeffries C, Kristan M, Mohammed A, Afrane Y, O’Reilly K, Massad E, Drakeley C, Walker T, Yakob L (2019) Investigating the blood-host plasticity and dispersal of Anopheles coluzzii using a novel field-based methodology. Parasites & Vectors 12:143. doi:10.1186/s13071-019-3401-3
8. Kaiser-Bunbury C.N, Blüthgen N (2015) Integrating network ecology with applied conservation: a synthesis and guide to implementation. AoB Plants 7. doi:10.1093/aobpla/plv076
9. Proulx S.R, Promislow D, Phillips P (2005) Network thinking in ecology and evolution, Trends in Ecology & Evolution 20:345–353. https://doi.org/10.1016/j.tree.2005.04.004
10. Drinkwater R, Schnell I, Bohmann K, Bernard H, Veron G, Clare E, Gilbert T, Rossiter S (2019) Using metabarcoding to compare the suitability of two blood‐feeding leech species for sampling mammalian diversity in North Borneo, Molecular Ecology Resources 19:105–117. doi:10.1111/1755-0998.12943
11. Bellekom B, Hackett TD, Lewis OT (2021) A network perspective on the vectoring of human disease. Trends in Parasitology. doi: 10.1016/j.pt.2020.12.001
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