A general species distribution model does not necessarily contribute to disease control

Abstract

The spread of PPR is an active area of research and under close scrutiny of the national government. There has not been significant progress on this issue yet in China, among others, because of a lack of a suitable monitoring method and consequently of information.In the published paper, the presented MaxEnt model seems neither a good geographic predictor of PPR risk nor useful for PPR control. In the concluding coloured map, the reader can easily assess that not all occurrence points are covered by the predicted probability of PPR occurrence ('risk') in China. Many PPR presence points are spread over the full width of the northern half of China in the low-risk zone (blue). Further, the presented model fails to predict the historical PPR presence in Tibet from 2007.Next, we tried to assess the input data quality and the application of the MaxEnt algorithm. Several issues were identified. The most important issue could be that the presence point data do not represent reported PPR point locations, as suggested in Methods section. Instead, each presence point seems to represent a county 1 as described in the last text line of the publication. However, the reader is not informed about how the point is located within each county. Secondly, Methods section informs us that the presence points are 'rarefied at 1 km 2 '. If we assume that the PPR presence points are located in the centre of counties, none of the points might be within a 1-km distance of the centre of the neighbouring county.However, the reader is told that the rarefication has eliminated 15 out of 294 presence points. Further, assuming point locations within the centre of counties, the 1-km distance is too small to achieve substantial rarefication and consequently spatial auto-correlation of presence points has not been reduced. This illustrates that it was improper to accept a standard (1 km 2 ) from another related study to reduce spatial auto-correlation. The prediction is therefore doubtful, because the use of uncorrected spatial auto-correlated input data. The use of the Pearson correlation coefficient for elimination of environmental variables needs clarification as well. Are both variables of a pair of correlated variables eliminated? If so, the pairing procedure needs to be made transparent. The Pearson statistic assumes a normal distribution and a linear correlation. However, the presented results suggest that precipitation is not linearly correlated with PPR virus presence. Other approaches, including PCA and stepwise backward elimination, are proven ways to effectively and transparently reduce the number of environmental variables (Farrell et al., 2019; Wubetie, 2019). Finally, the use of goat and sheep density as environmental predictor variables of risk areas seems doubtful. More appropriate would be to 'cross/overlay' the risk areas obtained by the MaxEnt model based on bioclimatic and ultraviolet variables with the goat and sheep density maps.Based on our above assessment, a scientifically sound PPR risk zoning for China needs multiple addi...