PLOS | One, Published 09 March 2016

By Guinevere O. U. Wogan

“A primary assumption of environmental niche models (ENMs) is that models are both accurate and transferable across geography or time; however, recent work has shown that models may be accurate but not highly transferable. While some of this is due to modeling technique, individual species ecologies may also underlie this phenomenon. Life history traits certainly influence the accuracy of predictive ENMs, but their impact on model transferability is less understood. This study investigated how life history traits influence the predictive accuracy and transferability of ENMs using historically calibrated models for birds.

“In this study I used historical occurrence and climate data (1950-1990s) to build models for a sample of birds, and then projected them forward to the ‘future’ (1960-1990s). The models were then validated against models generated from occurrence data at that ‘future’ time. Internal and external validation metrics, as well as metrics assessing transferability, and Generalized Linear Models were used to identify life history traits that were significant predictors of accuracy and transferability. This study found that the predictive ability of ENMs differs with regard to life history characteristics such as range, migration, and habitat, and that the rarity versus commonness of a species affects the predicted stability and overlap and hence the transferability of projected models. Projected ENMs with both high accuracy and transferability scores, still sometimes suffered from over- or under- predicted species ranges. Life history traits certainly influenced the accuracy of predictive ENMs for birds, but while aspects of geographic range impact model transferability, the mechanisms underlying this are less understood.”

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Parasites & Vectors, 2016, 9:148, Published 15 March 2016

By Li-ying Wang, Wei-ping Wu, Qing Fu, Ya-yi Guan, Shuai Han, Yan-lin Niu, Su-xiang Tong, Israyil Osman, Song Zhang, and Kaisar Kaisar
“Background: Kashi Prefecture of Xinjiang is one of the most seriously affected areas with anthroponotic visceral leishmaniasis in China. A better understanding of space distribution features in this area was needed to guide strategies to eliminate visceral leishmaniasis from highly endemic areas. We performed a spatial analysis using the data collected in Bosh Klum Township in Xinjiang China.

“Methods: Based on the report of endemic diseases between 1990 and 2005, three villages with a high number of visceral leishmaniasis cases in Bosh Klum Township were selected. We conducted a household survey to collect the baseline data of kala-azar patients using standard case definitions. The geographical information was recorded with GIS equipment. A binomial distribution fitting test, runs test, and Scan statistical analysis were used to assess the space distribution of the study area.

“Results: The result of the binomial distribution fitting test showed that the distribution of visceral leishmaniasis cases in local families was inconsistent (χ2 = 53.23, P < 0.01). The results of runs test showed that the distribution of leishmaniasis infected families along the channel was not random in the group of more than five infected families. The proportion of this kind of group in all infected families was 63.84 % (113 of 177). In the Scan statistical analysis, spatial aggregation was analyzed by poisson model, which found 3 spatial distribution areas 1) Zone A was located in a center point of 76.153447°E, 39.528477°N within its 1.11 mile radius, where the cumulative life-incidence of leishmaniasis was 1.95 times as high as that in surrounding areas (P < 0.05); 2) Zone B was located in a center point of 76.111968°E, 39.531895°N within its 0.54 mile radius, where the cumulative life-incidence of leishmaniasis was 1.82 times as high as that in surrounding areas (P < 0.01); and 3) Zone C was located in a center point of 76.195427°E, 39.563835°N within its 0.68 mile radius, where the cumulative life-incidence of leishmaniasis was 1.31 times as high as that in surrounding areas (P < 0.05).

“Conclusions: The spatial distribution of visceral leishmaniasis-infected families was clustered. Thus, the proper use of this finding would be an improvement in highly endemic areas, which could help identify the types of endemic areas and population at high risk and carry out appropriate measures to prevent and control VL in this area as well.”

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