Hughes Kristen, Fosgate Geoffrey T, Budke Christine M, Ward Michael P, Kerry Ruth, Ingram Ben
Department of Production Animal Studies, University of Pretoria, Onderstepoort, South Africa.
Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America.
PLoS One. 2017 Sep 13;12(9):e0182903. doi: 10.1371/journal.pone.0182903. eCollection 2017.
The population density of wildlife reservoirs contributes to disease transmission risk for domestic animals. The objective of this study was to model the African buffalo distribution of the Kruger National Park. A secondary objective was to collect field data to evaluate models and determine environmental predictors of buffalo detection. Spatial distribution models were created using buffalo census information and archived data from previous research. Field data were collected during the dry (August 2012) and wet (January 2013) seasons using a random walk design. The fit of the prediction models were assessed descriptively and formally by calculating the root mean square error (rMSE) of deviations from field observations. Logistic regression was used to estimate the effects of environmental variables on the detection of buffalo herds and linear regression was used to identify predictors of larger herd sizes. A zero-inflated Poisson model produced distributions that were most consistent with expected buffalo behavior. Field data confirmed that environmental factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds were more likely to be detected in dense vegetation (P = 0.005) and during the wet season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models for African buffalo can produce biologically reasonable results but environmental factors have significant effects and therefore could be used to improve model performance. Accurate distribution models are critical for the evaluation of disease risk and to model disease transmission.
野生动物宿主的种群密度会增加家畜疾病传播的风险。本研究的目的是对克鲁格国家公园非洲水牛的分布进行建模。第二个目的是收集实地数据以评估模型，并确定水牛被发现的环境预测因素。利用水牛普查信息和先前研究的存档数据创建了空间分布模型。在2012年8月的旱季和2013年1月的雨季，采用随机行走设计收集实地数据。通过计算与实地观测偏差的均方根误差（rMSE），对预测模型的拟合进行了描述性和正式评估。使用逻辑回归估计环境变量对水牛群发现的影响，并使用线性回归确定较大牛群规模的预测因素。零膨胀泊松模型产生的分布与预期的水牛行为最为一致。实地数据证实，包括季节（P = 0.008）、植被类型（P = 0.002）和植被密度（P = 0.010）在内的环境因素是水牛被发现的重要预测因素。与较大的混合性别牛群相比，单身牛群在茂密植被中（P = 0.005）和雨季（P = 0.022）更有可能被发现。非洲水牛的静态分布模型可以产生生物学上合理的结果，但环境因素有显著影响，因此可用于提高模型性能。准确的分布模型对于评估疾病风险和模拟疾病传播至关重要。
