Department of Ecology and Evolutionary Biology & Biodiversity Institute, University of Kansas, Lawrence, KS, United States of America.
Zoology Department/Faculty of Science, University of Tripoli, Tripoli, Libya.
PeerJ. 2024 Aug 23;12:e17944. doi: 10.7717/peerj.17944. eCollection 2024.
Environmental dimensions, such as temperature, precipitation, humidity, and vegetation type, influence the activity, survival, and geographic distribution of tick species. Ticks are vectors of various pathogens that cause disease in humans, and and are among the tick species that transmit pathogens to humans across the central and eastern United States. Although their potential geographic distributions have been assessed broadly ecological niche modeling, no comprehensive study has compared ecological niche signals between ticks and tick-borne pathogens. We took advantage of National Ecological Observatory Network (NEON) data for these two tick species and associated bacteria pathogens across North America. We used two novel statistical tests that consider sampling and absence data explicitly to perform these explorations: a univariate analysis based on randomization and resampling, and a permutational multivariate analysis of variance. Based on univariate analyses, in , three pathogens , , and ) were tested; pathogens showed nonrandom distribution in at least one environmental dimension. Based on the PERMANOVA test, the null hypothesis that the environmental position and variation of pathogen-positive samples are equivalent to those of could not be rejected for any of the pathogens, except for the pathogen in maximum and minimum vapor pressure and minimum temperature. For six pathogens (, , sensu lato, , , and -like) were tested; only was not distinct from null expectations in all environmental dimensions, based on univariate tests. In the PERMANOVA analyses, the pathogens departed from null expectations for and sensu lato, with smaller niches in , and larger niches in sensu lato, than the vector. More generally, this study shows the value of large-scale data resources with consistent sampling methods, and known absences of key pathogens in particular samples, for answering public health questions, such as the relationship of presence and absence of pathogens in their hosts respect to environmental conditions.
环境维度，如温度、降水、湿度和植被类型，影响着蜱种的活动、生存和地理分布。蜱是多种病原体的载体，这些病原体可导致人类患病，而 和 是在美国中东部传播病原体给人类的蜱种之一。尽管已经广泛地评估了它们的潜在地理分布，但没有综合研究比较过蜱和蜱传病原体之间的生态位信号。我们利用了 across North America 国家生态观测网络（NEON）的数据来研究这两种蜱种以及与其相关的细菌病原体。我们使用了两种新颖的统计测试方法，这些方法明确考虑了采样和缺失数据：一种基于随机化和重采样的单变量分析，以及一种基于置换的多变量方差分析。基于单变量分析，在 中，测试了三种病原体 、 和 ；病原体在至少一个环境维度中表现出非随机分布。基于 PERMANOVA 测试，除了最大和最小蒸气压以及最小温度外，对于除了病原体 之外的所有病原体，都不能拒绝病原体阳性样本的环境位置和变化与 相同的零假设。基于 PERMANOVA 测试，对于 种病原体（ 、 、 、 、 和 ），除了 之外，都不能拒绝所有环境维度中病原体阳性样本的环境位置和变化与 相同的零假设。基于单变量测试，只有 没有在所有环境维度中与零假设明显不同。在 PERMANOVA 分析中，对于 和 ，病原体与零假设的偏离程度较大，与载体相比，它们的生态位较小，而 sensu lato 的生态位较大。更一般地说，这项研究表明了具有一致采样方法和特定样本中关键病原体已知缺失的大规模数据资源的价值，这些数据资源可用于回答公共卫生问题，例如宿主中病原体的存在和缺失与环境条件的关系。
