Biology subjects

Abbott, A. J.

Publications and source records attributed to Abbott, A. J..

4 recordsLinked to original sources

bioRxiv2026

Repeated and widespread introduction of a mosquito-borne virus, but wildlife impact remains localised in urban area

Mosquito-borne viruses are emerging in new regions with increasing regularity. Shifting climatic envelopes, especially in temperate regions, and altered enzootic cycles are changing likelihoods of persistence, meaning new approaches are required to predict and monitor establishment. Usutu virus (USUV) is a mosquito-borne viral zoonosis, which was first recorded in the United Kingdom in wild birds and mosquitoes in 2020. By developing a multidisciplinary approach, incorporating enhanced passive and active surveillance of wild birds, we discovered previously unrecorded introductions and identified USUV persistence and geographic expansion. By combining molecular and serological testing with citizen science derived datasets, we show an associated marked decline in a common, widespread, and highly susceptible host species (Eurasian Blackbird, Turdus merula). However, the clearest continuous signal of host population decline remains localised to a large urban area, despite the much wider virus distribution. This may be a consequence of an urban heat island effect and associated increase in length of the mosquito active season. Our results indicate that the establishment and impact of future mosquito-borne pathogens in temperate zones, anticipated under climate change, may first be most apparent in urban areas. Consequently, enhanced surveillance efforts in urban areas could provide an efficient mechanism for detecting novel pathogen emergence. The epidemiology of emerging vector-borne diseases, especially in temperate areas, depends on the interplay between climate, land-use and available hosts; therefore, considering each in isolation will not be sufficient to reliably inform surveillance or response planning.

ecology↗

bioRxiv2024

Transovarial transmission in field caught mosquitoes identifies a mechanism for the establishment of Usutu virus in a temperate country

Usutu virus (USUV) is an emerging zoonotic flavivirus in Europe, and the first zoonotic mosquito-borne virus to be confirmed in animal hosts in the United Kingdom (UK). Phylogenetic analysis of USUV in the three years following its initial detection in 2020 indicated that the virus is overwintering in the UK. In 2023, USUV was identified outside Greater London for the first time. Therefore, USUV should now be considered endemic within southeast England. Surveillance of avian hosts and mosquito vectors has been insufficient to elucidate the mechanism by which USUV has persisted through temperate winters. It is likely that mosquitoes play a significant role in facilitating the establishment of USUV in temperate areas, as is the case for related mosquito-borne viruses. Here we undertake enhanced targeted vector surveillance at the index site to investigate the role of mosquitoes in facilitating USUV establishment in the UK. Between 2021 and 2024 inclusive, we detected USUV in host-seeking adult female Culex pipiens s.l. (n = 8/554 pools), a key vector of the virus in Europe. During 2023, enhanced surveillance detected transovarial transmission of USUV in wild Cx. pipiens s.l. (n = 1/202 pools), by screening adults following captive rearing of mosquito larvae collected from the field. This is, to our knowledge, the first description of vertical transmission of USUV in an arthropod vector. Consequently, transovarial transmission should be considered a viable mechanism for the persistence of USUV in temperate areas. Our results highlight the importance of undertaking detailed vector surveillance, across life stages, to inform the epidemiology of vector-borne viruses.

microbiology↗

bioRxiv2023

Insecticidal roof barriers mounted on untreated bednets can be as effective against Anopheles gambiae s.l. as insecticidal bednets

Barrier bednets (BBnets), regular bednets with a vertical insecticidal panel to target mosquitoes above the bednet roof, where activity is highest, have the potential to improve existing Insecticidal Treated Bednets (ITNs), by reducing quantity of insecticide required per net, reducing the toxic risks to those using the net, thus increasing the range of insecticides to choose from. We evaluated performance of different BBnet variants based on the PermaNet 3 (i.e., P3 BBnets with pyrethroid and piperonyl butoxide (PBO) on the roof or barrier; pyrethroid alone on the side walls) in room-scale bioassays, simultaneously video-recorded to track mosquitoes. Experimental results showed the longitudinal P3 barrier (P3L) to be highly effective: P3+P3L were consistently though not significantly more effective than the reference P3 bednet while performance of Ut+P3L was comparable to the reference P3. Comparing contact duration at the treated sections of each variant, the Ut+P3L accumulated 1273 contacts with 1374 seconds duration, all on the barrier, greatly exceeding the 792 seconds duration, from 8049 contacts, accumulated across the entire surface of the PermaNet 3 reference bednet. The BBnets potential to augment existing bednets and enhance their performance is considered.

animal behavior and cognition↗

bioRxiv2021

Flying In-formation: A computational method for the classification of host seeking mosquito flight patterns using path segmentation and unsupervised machine learning

The rational design of effective vector control tools requires detailed knowledge of vector behaviour. Yet, behavioural observations, interpretations, evaluations and definitions by even the most experienced researcher are constrained by subjectivity and perceptual limits. Seeking an objective alternative to expertise, we developed and tested an unsupervised method for the automatic identification of video-tracked mosquito flight behaviour. This method unites path-segmentation and unsupervised machine learning in an innovative workflow and is implemented using a combination of R and python. The workflow (1) records movement trajectories; (2) applies path-segmentation; (3) clusters path segments using unsupervised learning; and (4) interprets results. Analysis of the flight patterns of An. gambiae s.s., responding to human-baited insecticide-treated bednets (ITNs), by the new method identified four distinct behaviour modes: with swooping and approaching modes predominant at ITNs; increased walking behaviours at untreated nets; similar rates of reacting at both nets; and higher overall activity at treated nets. The methods validity was tested by comparing these findings with those from a similar setting using an expertise-based method. The level of correspondence found between the studies validated the accuracy of the new method. While researcher-defined behaviours are inherently subjective, and prone to corollary shortcomings, the new approachs mathematical method is objective, automatic, repeatable and a validated alternative for analysing complex vector behaviour. This method provides a novel and adaptable analytical tool and is freely available to vector biologists, ethologists and behavioural ecologists. Author summaryVector control targets the insects and arachnids that transmit 1 in every 6 communicable diseases worldwide. Since the effectiveness of many vector control tools depends on exploiting or changing vector behaviour, a firm understanding of this behaviour is required to maximise the impact of existing tools and design new interventions. However, current methods for identifying such behaviours are based primarily on expert knowledge, which can be inefficient, difficult to scale and limited by perceptual abilities. To overcome this, we present, detail and validate a new method for categorising vector behaviour. This method combines existing path segmentation and unsupervised machine learning algorithms to identify changes in vector movement trajectories and classify behaviours. The accuracy of the new method is demonstrated by replicating existing, expert-derived, findings covering the behaviour of host-seeking mosquitos around insecticide treated bednets, compared to nets without insecticide. As the method found the same changes in mosquito activity as previous research, it is said to be validated. The new method is significant, as it improves the analytical capabilities of biologists working to reduce the burden of vector-borne diseases, such as malaria, through an understanding of behaviour.

animal behavior and cognition↗