TY - JOUR
T1 - Evidence-based advice on timing and location of tsetse control measures in Shimba Hills National reserve, Kenya
AU - Gachoki, S.
AU - Groen, T.
AU - Vrieling, A.
AU - Skidmore, A.
AU - Masiga, Daniel
N1 - Funding Information:
Funding:ThisworkwassupportedbytheGerman FederalMinistryforEconomicCooperationand Development(BMZ)commissionedand administeredthroughtheDeutscheGesellschaft fu ¨r InternationaleZusammenarbeit (GIZ) Fund for InternationalAgriculturalResearch(FIA;grant number81235250toDKM),theBio-Vision Programme(BV;grantnumberBVDPA-005/2018– 2019toDKM)andtheEuropeanUnion–Integrated BiologicalControlAppliedResearchProgramme (EU-IBCARP;grantnumberDCI-FOOD/2014/346– 739toDKM).Wewouldalsoliketoacknowledge theicipecorefundingfromUK’sForeign, Commonwealth&DevelopmentOffice(FCDO);the SwedishInternationalDevelopmentCooperation Agency(SIDA);theSwissAgencyforDevelopment andCooperation(SDC);theFederalDemocratic RepublicofEthiopia;andtheGovernmentofthe RepublicofKenya.Thefundershadnorolein studydesign,datacollectionandanalysis,decision topublish,orpreparationofthemanuscript.
Funding Information:
This work was supported by the German Federal Ministry for Economic Cooperation and Development (BMZ) commissioned and administered through the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) Fund for International Agricultural Research (FIA; grant number 81235250 to DKM), the Bio-Vision Programme (BV; grant number BVDPA-005/2018– 2019 to DKM) and the European Union–Integrated Biological Control Applied Research Programme (EU-IBCARP; grant number DCI-FOOD/2014/346– 739 to DKM). We would also like to acknowledge the icipe core funding from UK’s Foreign, Commonwealth & Development Office (FCDO); the Swedish International Development Cooperation Agency (SIDA); the Swiss Agency for Development and Cooperation (SDC); the Federal Democratic Republic of Ethiopia; and the Government of the Republic of Kenya. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2023 Gachoki et al.
PY - 2023/6/5
Y1 - 2023/6/5
N2 - 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) 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.
AB - 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) 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.
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-GOLD
U2 - 10.1371/journal.pntd.0011398
DO - 10.1371/journal.pntd.0011398
M3 - Article
SN - 1935-2735
VL - 17
SP - 1
EP - 18
JO - PLoS neglected tropical diseases
JF - PLoS neglected tropical diseases
IS - 6
M1 - e0011398
ER -