The impact of climate change on the potential distribution of agricultural pests: the case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe.

The production of agricultural commodities faces increased risk of pests, diseases and other stresses due to climate change and variability. This study assesses the potential distribution of agricultural pests under projected climatic scenarios using evidence from the African coffee white stem borer (CWB), Monochamus leuconotus (Pascoe) (Coleoptera: Cerambycidae), an important pest of coffee in Zimbabwe. A species distribution modeling approach utilising Boosted Regression Trees (BRT) and Generalized Linear Models (GLM) was applied on current and projected climate data obtained from the WorldClim database and occurrence data (presence and absence) collected through on-farm biological surveys in Chipinge, Chimanimani, Mutare and Mutasa districts in Zimbabwe. Results from both the BRT and GLM indicate that precipitation-related variables are more important in determining species range for the CWB than temperature related variables. The CWB has extensive potential habitats in all coffee areas with Mutasa district having the largest model average area suitable for CWB under current and projected climatic conditions. Habitat ranges for CWB will increase under future climate scenarios for Chipinge, Chimanimani and Mutare districts while it will decrease in Mutasa district. The highest percentage change in area suitable for the CWB was for Chimanimani district with a model average of 49.1% (3 906 ha) increase in CWB range by 2080. The BRT and GLM predictions gave similar predicted ranges for Chipinge, Chimanimani and Mutasa districts compared to the high variation in current and projected habitat area for CWB in Mutare district. The study concludes that suitable area for CWB will increase significantly in Zimbabwe due to climate change and there is need to develop adaptation mechanisms

Data from: The impact of climate change on the potential distribution of agricultural pests: the case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe

The production of agricultural commodities faces increased risk of pests, diseases and other stresses due to climate change and variability. This study assesses the potential distribution of agricultural pests under projected climatic scenarios using evidence from the African coffee white stem borer (CWB), Monochamus leuconotus (Pascoe) (Coleoptera: Cerambycidae), an important pest of coffee in Zimbabwe. A species distribution modeling approach utilising Boosted Regression Trees (BRT) and Generalized Linear Models (GLM) was applied on current and projected climate data obtained from the WorldClim database and occurrence data (presence and absence) collected through on-farm biological surveys in Chipinge, Chimanimani, Mutare and Mutasa districts in Zimbabwe. Results from both the BRT and GLM indicate that precipitation-related variables are more important in determining species range for the CWB than temperature related variables. The CWB has extensive potential habitats in all coffee areas with Mutasa district having the largest model average area suitable for CWB under current and projected climatic conditions. Habitat ranges for CWB will increase under future climate scenarios for Chipinge, Chimanimani and Mutare districts while it will decrease in Mutasa district. The highest percentage change in area suitable for the CWB was for Chimanimani district with a model average of 49.1% (3 906 ha) increase in CWB range by 2080. The BRT and GLM predictions gave similar predicted ranges for Chipinge, Chimanimani and Mutasa districts compared to the high variation in current and projected habitat area for CWB in Mutare district. The study concludes that suitable area for CWB will increase significantly in Zimbabwe due to climate change and there is need to develop adaptation mechanisms

Occurrence data for the coffee white stem borer in Zimbabwe obtained from biological surveys

Data collected from biological field surveys done in 2003. The data shows the XY cordinates of coffee farms surveyed and the occurrence of CWB (1 present, 0 absent) The data is in decimal degrees format (WGS84) and unprojected

Map of the study areas showing the districts and coffee zones in Zimbabwe.

<p>The closed and open circles indicate locations where CWB was sampled as present and absent respectively. A context map is given to provide location of the study area in Zimbabwe.</p

Probability of occurrence of CWB that determines its distribution surface in the four Districts.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073432#pone-0073432-g002" target="_blank">Figure 2(a)</a> shows the probability of CWB occurrence for each area obtained from BRT model. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073432#pone-0073432-g002" target="_blank">Figure 2(b)</a> shows the probability of CWB occurrence for each area obtained from GLM.</p

Environmental layers used for current and predicted climate in the study.

<p>Environmental layers used for current and predicted climate in the study.</p

Distribution of CWB in coffee zones from GLM.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073432#pone-0073432-g004" target="_blank">Figure 4(a)</a> shows the current distribution of CWB from the GLM. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073432#pone-0073432-g004" target="_blank">Figure 4(b)</a> shows the projected distribution of CWB from the GLM. The red zones are areas with a probability of occurrence above 0.5 indicating CWB is likely to occur and the yellow zones are areas that have a probability below 0.5 indicating CWB is less likely to occur.</p

Evaluation of model performance.

<p>Evaluation of model performance.</p

Comparison of area suitable for CWB for each district as determined by BRT and GLM under current (•) and projected (♦) climatic conditions.

<p>Comparison of area suitable for CWB for each district as determined by BRT and GLM under current (•) and projected (♦) climatic conditions.</p

Relative importance of factors in each model.

<p>Relative importance of factors in each model.</p