Bioclimatic and altitudinal variables influence the potential distribution of canine parvovirus type 2 worldwide.

Feng Jiang

doi:10.1002/ece3.3994

Abstract

Canine parvovirus type 2 (CPV‐2) is extremely contagious and causes high rate of morbidity to many wild carnivores. It has three variants (CPV‐2a, CPV‐2b, and CPV‐2c) that are distributed worldwide with different frequencies and levels of genetic and antigenic variability. The disease poses a threat to the healthy survival and reproduction of wildlife. The research on the relationship between CPV‐2 epidemic and environmental variables is lacking. To fill this research gap, we used maximum entropy (MaxEnt) approach with principal component analysis (PCA) to evaluate the relation between CPV‐2 and environmental variables and to create a world risk map for this disease. According to the PCA results, 18 environmental variables were selected from 68 variables for subsequent analyses. MaxEnt showed that annual mean temperature, isothermality, altitude, November precipitation, maximum temperature of warmest month, and precipitation of warmest quarter were the six most important variables associated with CPV‐2 distribution, with a total of 77.7% percent contribution. The risk of this disease between 18°N and 47°N was high, especially in the east of China and the United States. These results support further prediction of risk factors for this virus to help secure the health and sustainable survival of wild carnivores.

Highlights

Canine parvovirus type 2 (CPV-2) is extremely contagious and causes high rate of morbidity to many wild carnivores
The relative contribution of environmental variables in predictive species distribution models is evaluated utilizing the jackknife test in maximum entropy (MaxEnt), which indicates that annual mean temperature (Bio 1), isothermality (Bio3), altitude (Alt), November precipitation (Prec11), maximum temperature of warmest month (Bio5), and precipitation of warmest quarter (Bio18) were the most important environmental variables associated with CPV-2 distribution, with a total of 77.7% contribution
The MaxEnt model reveals that high CPV-2 incidence risk occurs in the eastern part of Asia, the southern, Europe, southern North America, and South America

Summary

| MATERIALS AND METHODS

I used MaxEnt to model the association between CPV-2 distributing and environmental variables. I used a regular grid with 1 km × 1 km cells analyzed by ArcGis10.2 software, in order to make a maximum of one distribution point in each grid cell, thereby eliminating duplicate or very close record points This meant that 228 CPV-2 geographical coordinates were excluded, leaving 606 as inputs for MaxEnt. All data were entered into a single spreadsheet file and saved as “.csv” format. The relative contribution of environmental variables in predictive species distribution models is evaluated utilizing the jackknife test in MaxEnt, which indicates that annual mean temperature (Bio 1), isothermality (Bio3), altitude (Alt), November precipitation (Prec11), maximum temperature of warmest month (Bio5), and precipitation of warmest quarter (Bio18) were the most important environmental variables associated with CPV-2 distribution, with a total of 77.7% contribution. The MaxEnt model reveals that high CPV-2 incidence risk occurs in the eastern part of Asia (including the central and eastern coastal areas of China, Japan, Korea, Korea), the southern (including most of India, Bangladesh, Myanmar, southern Thailand, northeastern Vietnam, central Cambodia; including central and eastern parts of Central America and eastern), Europe (central and northern Portugal, central France and Italy), southern North America (including central and eastern United States, northern Mexico), and South America (including eastern Argentina, Uruguay, southern Brazil And coastal, southern Chile; Figure 5)

| DISCUSSION

Findings

Percent contribution