Cargando…

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

1. Monitoring abundance is essential for vector management, but it is often only possible in a fraction of managed areas. For vector control programmes, sampling to estimate abundance is usually carried out at a local‐scale (10s km(2)), while interventions often extend across 100s km(2). Geostatisti...

Descripción completa

Detalles Bibliográficos
Autores principales: Lord, Jennifer S., Torr, Stephen J., Auty, Harriet K., Brock, Paddy M., Byamungu, Mechtilda, Hargrove, John W., Morrison, Liam J., Mramba, Furaha, Vale, Glyn A., Stanton, Michelle C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032868/
https://www.ncbi.nlm.nih.gov/pubmed/30008483
http://dx.doi.org/10.1111/1365-2664.13091
_version_ 1783337589180203008
author Lord, Jennifer S.
Torr, Stephen J.
Auty, Harriet K.
Brock, Paddy M.
Byamungu, Mechtilda
Hargrove, John W.
Morrison, Liam J.
Mramba, Furaha
Vale, Glyn A.
Stanton, Michelle C.
author_facet Lord, Jennifer S.
Torr, Stephen J.
Auty, Harriet K.
Brock, Paddy M.
Byamungu, Mechtilda
Hargrove, John W.
Morrison, Liam J.
Mramba, Furaha
Vale, Glyn A.
Stanton, Michelle C.
author_sort Lord, Jennifer S.
collection PubMed
description 1. Monitoring abundance is essential for vector management, but it is often only possible in a fraction of managed areas. For vector control programmes, sampling to estimate abundance is usually carried out at a local‐scale (10s km(2)), while interventions often extend across 100s km(2). 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 Glossina 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. Glossina 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.
format Online
Article
Text
id pubmed-6032868
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-60328682018-07-12 Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania Lord, Jennifer S. Torr, Stephen J. Auty, Harriet K. Brock, Paddy M. Byamungu, Mechtilda Hargrove, John W. Morrison, Liam J. Mramba, Furaha Vale, Glyn A. Stanton, Michelle C. J Appl Ecol Parasites and Pathogens 1. Monitoring abundance is essential for vector management, but it is often only possible in a fraction of managed areas. For vector control programmes, sampling to estimate abundance is usually carried out at a local‐scale (10s km(2)), while interventions often extend across 100s km(2). 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 Glossina 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. Glossina 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. John Wiley and Sons Inc. 2018-02-13 2018-07 /pmc/articles/PMC6032868/ /pubmed/30008483 http://dx.doi.org/10.1111/1365-2664.13091 Text en © 2018 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Parasites and Pathogens
Lord, Jennifer S.
Torr, Stephen J.
Auty, Harriet K.
Brock, Paddy M.
Byamungu, Mechtilda
Hargrove, John W.
Morrison, Liam J.
Mramba, Furaha
Vale, Glyn A.
Stanton, Michelle C.
Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania
title Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania
title_full Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania
title_fullStr Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania
title_full_unstemmed Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania
title_short Geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in Serengeti, Tanzania
title_sort geostatistical models using remotely‐sensed data predict savanna tsetse decline across the interface between protected and unprotected areas in serengeti, tanzania
topic Parasites and Pathogens
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032868/
https://www.ncbi.nlm.nih.gov/pubmed/30008483
http://dx.doi.org/10.1111/1365-2664.13091
work_keys_str_mv AT lordjennifers geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT torrstephenj geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT autyharrietk geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT brockpaddym geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT byamungumechtilda geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT hargrovejohnw geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT morrisonliamj geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT mrambafuraha geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT valeglyna geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania
AT stantonmichellec geostatisticalmodelsusingremotelysenseddatapredictsavannatsetsedeclineacrosstheinterfacebetweenprotectedandunprotectedareasinserengetitanzania