Geostatistical models using remotely-sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania

JS Lord, SJ Torr, HK Auty, PM Brock, M Byamunga, JW Hargrove, LJ Morrison, F Mramba, GA Vale, MC Stanton

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    Abstract

    1.Monitoring abundance is essential for vector management, but it is often only possible in a fraction of managed areas. For vector control programs, sampling to estimate abundance is usually carried out at a local-scale (10s km2), while interventions often extend across 100s km2. Geostatistical models have been used to interpolate between points where data are available, but this still requires costly sampling across the entire area of interest. Instead, we used geostatistical models to predict local-scale spatial variation in the abundance of tsetse – vectors of human and animal African trypanosomes - beyond the spatial extent of data to which models were fitted, in Serengeti, Tanzania. 2.We sampled Glossina swynnertoni and G. pallidipes >10 km inside the Serengeti National Park (SNP) and along four transects extending into areas where humans and livestock live. We fitted geostatistical models to data >10 km inside the SNP to produce maps of abundance for the entire region, including unprotected areas. 3.Inside the SNP, the mean number of G. pallidipes caught per trap per day in dense woodland was 166 (± 24 SE), compared to 3 (± 1) in grassland. G. swynnertoni was more homogenous with respective means of 15 (± 3) and 15 (± 8). In general, models predicted a decline in abundance from protected to unprotected areas, related to anthropogenic changes to vegetation, which was confirmed during field survey. 4.Synthesis and applications. Our approach allows vector control managers to identify sites predicted to have relatively high tsetse abundance, and therefore to design and implement improved surveillance strategies. In East and Southern Africa, trypanosomiasis is associated with wilderness areas. Our study identified pockets of vegetation which could sustain tsetse populations in farming areas outside the Serengeti National Park. Our method will assist countries in identifying, monitoring and, if necessary, controlling tsetse in trypanosomiasis foci. This has specific application to tsetse, but the approach could also be developed for vectors of other pathogens.
    Original languageEnglish
    Pages (from-to)1997 - 2007
    Number of pages11
    JournalJournal of Applied Ecology
    Volume55
    Issue number4
    Early online date15 Jan 2018
    DOIs
    Publication statusFirst published - 15 Jan 2018

    Fingerprint

    savanna
    national park
    trypanosomiasis
    wilderness area
    vegetation
    sampling
    monitoring
    livestock
    woodland
    spatial variation
    transect
    pathogen
    grassland
    animal

    Bibliographical note

    1026552

    Keywords

    • Geostatistical model
    • Glossina
    • Remote sensing
    • Vector control

    Cite this

    Lord, JS ; Torr, SJ ; Auty, HK ; Brock, PM ; Byamunga, M ; Hargrove, JW ; Morrison, LJ ; Mramba, F ; Vale, GA ; Stanton, MC. / Geostatistical models using remotely-sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania. In: Journal of Applied Ecology. 2018 ; Vol. 55, No. 4. pp. 1997 - 2007.
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    Geostatistical models using remotely-sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania. / Lord, JS; Torr, SJ; Auty, HK; Brock, PM; Byamunga, M; Hargrove, JW; Morrison, LJ; Mramba, F; Vale, GA; Stanton, MC.

    In: Journal of Applied Ecology, Vol. 55, No. 4, 15.01.2018, p. 1997 - 2007.

    Research output: Contribution to journalArticle

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    AU - Torr, SJ

    AU - Auty, HK

    AU - Brock, PM

    AU - Byamunga, M

    AU - Hargrove, JW

    AU - Morrison, LJ

    AU - Mramba, F

    AU - Vale, GA

    AU - Stanton, MC

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