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

Abstract

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 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.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.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. 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.

Keywords: Glossina; disease; geostatistical models; pathogens; protected areas; remote‐sensing; surveillance; trypanosomiasis; tsetse; vector control.

Figure 1

Study area and Nzi trap locations. Triangles: 2010 Nzi trap sampling sites >10 km inside the Serengeti National Park. Circles: 2015 Nzi trap sampling sites across the interface between protected and unprotected areas. WMA, wildlife management area [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 2

Number of tsetse caught per trap per day by habitat type inside the Serengeti National Park, February 2010. Y‐axes on a log scale. (a) Glossina pallidipes was significantly lower in grassland and savanna than in open and dense woodland and was lower in grassland compared with savanna (ANOVA F 38.46, p < .001, Tukey HSD p < .001 for each pairing with either grassland or savanna, p = .02 for grassland‐savanna); (b) Glossina swynnertoni—no significant difference between habitat types (p > .05)

Figure 3

Predictive map of Glossina pallidipes abundance based on geostatistical models fitted to 2010 data >10 km inside the Serengeti National Park (triangles). (a) Bayesian posterior mean predicted values, circles—abundance observed during 2015 across the protected area boundary and (b) Bayesian credible interval width (log₁₀)—larger values indicating greater model uncertainty in predicted values. See Figure 1 for details on locations of protected areas [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 4

Predictive map of Glossina swynnertoni abundance based on geostatistical models fitted to 2010 data >10 km inside the Serengeti National Park (triangles). (a) Bayesian posterior mean predicted values; and (b) Bayesian credible interval width (log₁₀)—higher values indicating greater model uncertainty in predicted values. See Figure 1 for details on locations of protected areas [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 5

Observed (a, b) and predicted (c, d) tsetse abundance and model residuals (observed—predicted) (e, f) at the interface between protected and unprotected areas. (a) Glossina pallidipes observed; (b) Glossina swynnertoni observed; (c) G. pallidipes predicted; (d) G. swynnertoni predicted; (e) G. pallidipes residuals; and (f) G. swynnertoni residuals. Geostatistical models fitted to 2010 data from >10 km inside the Serengeti National Park. Grey lines in (a) and (b)—SE, grey lines in (c) and (d)—95% credible intervals. Negative distance values on the x‐axis indicate locations inside the protected area. GGR, Grumeti Game Reserve; IGRS, Ikorongo Game Reserve South; IGRN, Ikorongo Game Reserve North; SNP, Serengeti National Park. For map of trap locations see Figure1 [Colour figure can be viewed at wileyonlinelibrary.com]