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. 2017 Oct 11;12(10):e0183583.
doi: 10.1371/journal.pone.0183583. eCollection 2017.

How will climate change pathways and mitigation options alter incidence of vector-borne diseases? A framework for leishmaniasis in South and Meso-America

Affiliations

How will climate change pathways and mitigation options alter incidence of vector-borne diseases? A framework for leishmaniasis in South and Meso-America

Bethan V Purse et al. PLoS One. .

Abstract

The enormous global burden of vector-borne diseases disproportionately affects poor people in tropical, developing countries. Changes in vector-borne disease impacts are often linked to human modification of ecosystems as well as climate change. For tropical ecosystems, the health impacts of future environmental and developmental policy depend on how vector-borne disease risks trade off against other ecosystem services across heterogeneous landscapes. By linking future socio-economic and climate change pathways to dynamic land use models, this study is amongst the first to analyse and project impacts of both land use and climate change on continental-scale patterns in vector-borne diseases. Models were developed for cutaneous and visceral leishmaniasis in the Americas-ecologically complex sand fly borne infections linked to tropical forests and diverse wild and domestic mammal hosts. Both diseases were hypothesised to increase with available interface habitat between forest and agricultural or domestic habitats and with mammal biodiversity. However, landscape edge metrics were not important as predictors of leishmaniasis. Models including mammal richness were similar in accuracy and predicted disease extent to models containing only climate and land use predictors. Overall, climatic factors explained 80% and land use factors only 20% of the variance in past disease patterns. Both diseases, but especially cutaneous leishmaniasis, were associated with low seasonality in temperature and precipitation. Since such seasonality increases under future climate change, particularly under strong climate forcing, both diseases were predicted to contract in geographical extent to 2050, with cutaneous leishmaniasis contracting by between 35% and 50%. Whilst visceral leishmaniasis contracted slightly more under strong than weak management for carbon, biodiversity and ecosystem services, future cutaneous leishmaniasis extent was relatively insensitive to future alternative socio-economic pathways. Models parameterised at narrower geographical scales may be more sensitive to land use pattern and project more substantial changes in disease extent under future alternative socio-economic pathways.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Map of study area indicating the distributional data for the leishmaniases (black circles) used to parameterise environmental models (a) cutaneous leishmaniasis (n = 803 squares) (b) visceral leishmaniasis (n = 201 squares).
Grey squares indicate the locations of pseudo-absence data points for one iteration of the model. Tick marks on the x and y axes indicate degrees latitude and longitude respectively and the study grain is 5 arc minute squares.
Fig 2
Fig 2. The trajectory of changes in climate and land use under alternative future socio-economic storylines, climate change pathways and policies.
A matrix of six alternative scenarios is depicted, 3 SSPs in columns x 2 climate change pathways in rows. Each scenario was realised in terms of mapped values of cover of CLUE land use categories and Worldclim climate variables in 2005 (to reflect the recent past) and 2050.
Fig 3
Fig 3. Predicted (a) mean and (b) standard deviation of the relative probability of presence of CL and (c) the sum of times CL is predicted to be present, across 20 runs of models built with abiotic variables only.
Fig 4
Fig 4. Geographical variation in selected (a) climate and (b) landscape predictors of leishmaniasis distribution in the recent past period (2005).
Fig 5
Fig 5. Predicted (a) mean and (b) standard deviation of the relative probability of presence of VL and (c) the sum of times VL is predicted to be present across 20 runs of models built with abiotic variables only.
Fig 6
Fig 6. The predicted extent of (a) cutaneous and (b) visceral leishmaniasis under recent past conditions and under alternative climate change pathways and socio-economic storylines.
The heavy blacklines across the middle of the box indicate median predicted extent across the 20 model runs; the box indicates the interquartile range of the data whilst the whiskers indicates the extremes. Note that the threshold that best predicted disease presence in cross-validation across model runs was used to assign pixels to presence or absence classes (values in Table 3).
Fig 7
Fig 7. Change in the predicted presence of cutaneous leishmaniasis between the recent past and future (2050) conditions under alternative climate change and socio-economic scenarios.
This was calculated by subtracting the number of times a pixel was predicted as present in the recent past (across 20 model runs) from the number of times a pixel was predicted as present in the future. Areas in pink show areas that are likely more favourable for disease and areas in blue show areas that are less favourable for the disease in the future.
Fig 8
Fig 8. Change in the predicted relative probability of presence of visceral leishmaniasis between the recent past and future (2050) conditions under alternative climate change and socio-economic scenarios.
This was found by subtracting the number of times a pixel was predicted as present in the recent past (across 20 model runs) from the number of times a pixel was predicted as present in the future. Areas in pink show areas that are likely more favourable for disease and areas in blue show areas that are less favourable for the disease in the future.

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