Evidence-based advice on timing and location of tsetse control measures in Shimba Hills National reserve, Kenya

Abstract

Controlling tsetse flies is critical for effective management of African trypanosomiasis in Sub-Saharan Africa. To enhance timely and targeted deployment of tsetse control strategies a better understanding of their temporal dynamics is paramount. A few empirical studies have explained and predicted tsetse numbers across space and time, but the resulting models may not easily scale to other areas. We used tsetse catches from 160 traps monitored between 2017 and 2019 around Shimba Hills National Reserve in Kenya, a known tsetse and trypanosomiasis hotspot. Traps were divided into two groups: proximal (<1.0 km)) to and distant (> 1.0 km) from the outer edge of the reserve boundary. We fitted zero-inflated Poisson and generalized linear regression models for each group using as temporal predictors rainfall, NDVI (Normalized Difference Vegetation Index), and LST (land surface temperature). For each predictor, we assessed their relationship with tsetse abundance using time lags from 10 days up to 60 days before the last tsetse collection date of each trap. Tsetse numbers decreased as distance from the outside of reserve increased. Proximity to croplands, grasslands, woodlands, and the reserve boundary were the key predictors for proximal traps. Tsetse numbers rose after a month of increased rainfall and the following increase in NDVI values but started to decline if the rains persisted beyond a month for distant traps. Specifically, tsetse flies were more abundant in areas with NDVI values greater than 0.7 for the distant group. The study suggests that tsetse control efforts beyond 1.0 km of the reserve boundary should be implemented after a month of increased rains in areas having NDVI values greater than 0.7. To manage tsetse flies effectively within a 1.0 km radius of the reserve boundary, continuous measures such as establishing an insecticide-treated trap or target barrier around the reserve boundary are needed.

Modelling the spatial and temporal distribution of tsetse flies for targeted control

“…This finding is consistent with prior research that examined temporal patterns of abundance, such as by Gachoki et al (2023a), who found that tsetse numbers rose with increased rainfall but then declined when rainfall increased for more than a month. Intense rainfall can lead to excessive water accumulation, increasing soil moisture, which, in turn, can lead to the submerging or dislodging of buried pupae, ultimately causing a decrease in tsetse populations (Omoogun et al, 1989;Lukaw et al, 2014;Ngonyoka, et al, 2017b;Signaboubo et al, 2021).…”

“…In all regions, the traps were emptied every two days, with two or four repetitions per month (see Table 5.1). Although in previous studies (Gachoki et al, 2021(Gachoki et al, , 2023b(Gachoki et al, , 2023a data from traps at the same, or similar, locations were used, those studies did not use the 2021 data used here. The G. pallidipes tsetse species was the sole species that occurred in all sampling sites and accounted for the majority of trapped tsetse species (Table 1).…”

“…To minimize model complexity, we utilized predictor variables that have been demonstrated to correlate with tsetse occurrence and population dynamics in previous studies (Lord et al, 2018a;Gachoki et al, 2021Gachoki et al, , 2023aMugenyi et al, 2021). These included land cover fractions for multiple classes, elevation, average daily annual rainfall, soil moisture, land surface temperature, human population density, sand, and silt content (Table 5.2).…”

“…We employed freely and readily available satellite-based estimates of human population, land cover, soil properties, elevation, precipitation, and land surface temperature. In previous studies, these environmental variables have been effectively associated with tsetse relative abundance (Ngonyoka, et al, 2017a;Lord, et al, 2018a;Mugenyi et al, 2021;Gachoki et al, 2023a) and distribution (Gachoki et al, 2021). Our main aim was to assess how well a random forest model, utilizing different sets of predictor variables, could predict the relative spatial abundance of Glossina pallidipes tsetse species across a broader region of Kenya, while utilizing geographically constrained tsetse data.…”

“…Our main aim was to assess how well a random forest model, utilizing different sets of predictor variables, could predict the relative spatial abundance of Glossina pallidipes tsetse species across a broader region of Kenya, while utilizing geographically constrained tsetse data. The tsetse trap locations used in this study have previously been employed to predict tsetse habitats at different life stages (Gachoki et al, 2021), evaluate the accuracy of transferring these habitats between regions (Gachoki et al, 2023b), and comprehend the environmental factors driving the observed temporal dynamics in tsetse numbers (Gachoki et al, 2023a). Here, our specific objectives were to: a) leverage readily available satellite-based estimates of environmental factors and train a random forest model for predicting the relative spatial abundance of G. pallidipes, b) apply the trained model beyond the spatial domain of the data used for training and evaluate the reliability of the spatial predictions, and c) identify limitations and opportunities for accurately mapping the relative spatial densities of tsetse flies beyond the monitored localities.…”