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Spatial Analysis of Anthropogenic Landscape Disturbance and Buruli Ulcer Disease in Benin.

Campbell LP, Finley AO, Benbow ME, Gronseth J, Small P, Johnson RC, Sopoh GE, Merritt RM, Williamson H, Qi J - PLoS Negl Trop Dis (2015)

Bottom Line: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence.Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales.A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America; Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America.

ABSTRACT

Background: Land use and land cover (LULC) change is one anthropogenic disturbance linked to infectious disease emergence. Current research has focused largely on wildlife and vector-borne zoonotic diseases, neglecting to investigate landscape disturbance and environmental bacterial infections. One example is Buruli ulcer (BU) disease, a necrotizing skin disease caused by the environmental pathogen Mycobacterium ulcerans (MU). Empirical and anecdotal observations have linked BU incidence to landscape disturbance, but potential relationships have not been quantified as they relate to land cover configurations.

Methodology/principal findings: A landscape ecological approach utilizing Bayesian hierarchical models with spatial random effects was used to test study hypotheses that land cover configurations indicative of anthropogenic disturbance were related to Buruli ulcer (BU) disease in southern Benin, and that a spatial structure existed for drivers of BU case distribution in the region. A final objective was to generate a continuous, risk map across the study region. Results suggested that villages surrounded by naturally shaped, or undisturbed rather than disturbed, wetland patches at a distance within 1200 m were at a higher risk for BU, and study outcomes supported the hypothesis that a spatial structure exists for the drivers behind BU risk in the region. The risk surface corresponded to known BU endemicity in Benin and identified moderate risk areas within the boundary of Togo.

Conclusions/significance: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence. Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

No MeSH data available.


Related in: MedlinePlus

Examples of land cover configurations resulting in high and low values for the three landscape metric calculations.
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pntd.0004123.g003: Examples of land cover configurations resulting in high and low values for the three landscape metric calculations.

Mentions: Collinearity among landscape metrics is common (46). In a regression context, collinearity, or correlation, among predictor variables can cause problems with inference. Hence, applied regression texts suggest avoiding correlation among predictors beyond 0.7, see, e.g., [47]. Here, we took a conservative cut-off of 0.6 to reduce issues arising from collinearity. Potential predictor pairs were assessed using Pearson's correlation coefficient and Spearman's rank order correlation. Both correlation metrics yielded comparable results. Using this criterion and exploratory analysis using the models detailed in subsequent sections, the predictor variables we consider in the subsequent analyses are: 1) shape index mean, 2) percent land cover adjacency, and 3) landscape shape index. Fig 3 provides an illustration of land cover configurations which give rise to high and low values of each metric. The following metric calculation descriptions were derived from the FragStats documentation directly. Shape index mean (SHAPE_MN) characterized patch shape complexity and was calculated asSHAPE=pijminpijwhere pij = perimeter of patch ij in terms of number of cell surfaces and min pij = minimum perimeter of patch ij in terms of number of cell surfaces [46]. Values closer to 1.0 indicate more uniformly-shaped land cover patches with complexity increasing as values increase.


Spatial Analysis of Anthropogenic Landscape Disturbance and Buruli Ulcer Disease in Benin.

Campbell LP, Finley AO, Benbow ME, Gronseth J, Small P, Johnson RC, Sopoh GE, Merritt RM, Williamson H, Qi J - PLoS Negl Trop Dis (2015)

Examples of land cover configurations resulting in high and low values for the three landscape metric calculations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608567&req=5

pntd.0004123.g003: Examples of land cover configurations resulting in high and low values for the three landscape metric calculations.
Mentions: Collinearity among landscape metrics is common (46). In a regression context, collinearity, or correlation, among predictor variables can cause problems with inference. Hence, applied regression texts suggest avoiding correlation among predictors beyond 0.7, see, e.g., [47]. Here, we took a conservative cut-off of 0.6 to reduce issues arising from collinearity. Potential predictor pairs were assessed using Pearson's correlation coefficient and Spearman's rank order correlation. Both correlation metrics yielded comparable results. Using this criterion and exploratory analysis using the models detailed in subsequent sections, the predictor variables we consider in the subsequent analyses are: 1) shape index mean, 2) percent land cover adjacency, and 3) landscape shape index. Fig 3 provides an illustration of land cover configurations which give rise to high and low values of each metric. The following metric calculation descriptions were derived from the FragStats documentation directly. Shape index mean (SHAPE_MN) characterized patch shape complexity and was calculated asSHAPE=pijminpijwhere pij = perimeter of patch ij in terms of number of cell surfaces and min pij = minimum perimeter of patch ij in terms of number of cell surfaces [46]. Values closer to 1.0 indicate more uniformly-shaped land cover patches with complexity increasing as values increase.

Bottom Line: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence.Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales.A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America; Biodiversity Institute, University of Kansas, Lawrence, Kansas, United States of America.

ABSTRACT

Background: Land use and land cover (LULC) change is one anthropogenic disturbance linked to infectious disease emergence. Current research has focused largely on wildlife and vector-borne zoonotic diseases, neglecting to investigate landscape disturbance and environmental bacterial infections. One example is Buruli ulcer (BU) disease, a necrotizing skin disease caused by the environmental pathogen Mycobacterium ulcerans (MU). Empirical and anecdotal observations have linked BU incidence to landscape disturbance, but potential relationships have not been quantified as they relate to land cover configurations.

Methodology/principal findings: A landscape ecological approach utilizing Bayesian hierarchical models with spatial random effects was used to test study hypotheses that land cover configurations indicative of anthropogenic disturbance were related to Buruli ulcer (BU) disease in southern Benin, and that a spatial structure existed for drivers of BU case distribution in the region. A final objective was to generate a continuous, risk map across the study region. Results suggested that villages surrounded by naturally shaped, or undisturbed rather than disturbed, wetland patches at a distance within 1200 m were at a higher risk for BU, and study outcomes supported the hypothesis that a spatial structure exists for the drivers behind BU risk in the region. The risk surface corresponded to known BU endemicity in Benin and identified moderate risk areas within the boundary of Togo.

Conclusions/significance: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence. Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

No MeSH data available.


Related in: MedlinePlus