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

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.

Epidemiologic Factors Supporting Triage of Infected Dog Patients Admitted to a Veterinary Hospital Biological Isolation and Containment Unit

“…In our study, the median age was 4 months old and young age (<2 years old), and this age group was significantly associated with an increased risk of being hospitalized at the BICU with parvovirosis. These results are consistent with the known epidemiological factors of parvovirosis which mainly affects puppies aged between 6 weeks and 6 months [ 8 , 24 , 30 , 35 , 36 ]. Some previous studies also found more cases and increased mortality among male patients [ 36 , 37 ], especially in intact males older than 6 months [ 8 , 24 ].…”

“…This might be explained by a temperate climate of the Lisbon area, which records an average temperature of 8 °C–15 °C in winter and 18 °C–28 °C in the summer [ 34 ]. A peak incidence of parvovirosis during summer months described in some studies is associated with an increasing rate of outdoor activities by dogs, increasing the chance of viral exposure [ 8 , 24 , 35 , 36 , 37 ].…”

Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China

“…The downloaded layer format of environment variables also needed to be converted to the ASCII format required by the Maxent software. As too many environmental variables would increase the complexity of the model and the random error and reduce the accuracy of the prediction results, the SPSS22 (Statistical Product and Service Solutions) software was used to perform principal component analysis (PCA) and correlation analysis on the attribute values of 68 environmental variables of the three species (Jiang, 2018). Among the 67 climatic variables we selected, 19 bioclimatic and 48 other monthly related climatic variables have great similarities.…”

Potential distribution of Biscogniauxia mediterranea and Obolarina persica causal agents of oak charcoal disease in Iran’s Zagros forests

“…Initially, 29 bioclimatic/environmental variables that may affect the species distribution were considered. They include growing season-length TREELIM (Gsl, number of days), The mean temperature of the growing season TREELIM (Gst, °C), Near-surface relative humidity (Hurs, %), Potential evapotranspiration (Pet, kg m −2 month −1 ), Near-surface wind speed (Sfcwind, m s −1 ), Accumulated precipitation amount on (Gsp, kg m −2 gsl −1 ), Climate moisture index (CMI, kg m −2 month −1 ), Annual mean temperature (Bio1, °C), Mean Diurnal Range (Mean of monthly (max temp–min temp) (Bio2, °C), Isothermality (BIO2/BIO7) (× 100) (Bio3, °C), Temperature Seasonality (standard deviation × 100) (Bio4, °C), Max Temperature of Warmest Month (Bio5, °C), Min Temperature of Coldest Month (Bio6, °C), Temperature Annual Range (BIO5-BIO6) (Bio7, °C), Mean Temperature of Wettest Quarter (Bio8, °C ), Mean Temperature of Driest Quarter (Bio9, °C), Mean Temperature of Warmest Quarter (Bio10, °C), Mean Temperature of Coldest Quarter (Bio11, °C), Max Temperature of Warmest Month (Bio12, °C), Precipitation of Wettest Month (Bio13, mm), Precipitation of Driest Month (Bio14, mm), Precipitation Seasonality (Coefficient of Variation) (Bio15, mm), Precipitation of Wettest Quarter (Bio16, mm), Precipitation of Driest Quarter (Bio17, mm), Precipitation of Warmest Quarter (Bio18, mm), Precipitation of Coldest Quarter (Bio19, mm) 29 – 31 following with Slope (%) and Solar-radiation Aspect Index, derived from The SRTM (Shuttle Radar Topography Mission data) aimed to prepare the digital elevation model (DEM) 27 , 32 , 33 .…”