Goel Nikunj, Liebhold Andrew M, Bertelsmeier Cleo, Hooten Mevin B, Korolev Kirill S, Keitt Timothy H
Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, USA,78712.
USDA Forest Service Northern Research Station, Morgantown, West Virginia, USA, 15349.
Ecol Monogr. 2025 Feb;95(1). doi: 10.1002/ecm.70003. Epub 2025 Mar 4.
The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine spatiotemporal patterns of range expansion. This knowledge gap may stem from two reasons. First, current analytical models fail to integrate the invader's life-history dynamics with heterogeneity in human-mediated dispersal patterns. Second, classical statistical methods often fail to provide reliable estimates of model parameters, such as time and place of species introduction and life-history characteristics, due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads radially along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a hierarchical Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel () expansion in North America through the inland commercial shipping network. Our analysis suggests that zebra mussels might have been introduced before 1981, indicating a lag of five years between time of introduction and first detection in late 1986. Furthermore, using our statistical model we estimated a one in three chance that they were introduced near Kingsville (Ontario, Canada), where they were first reported. We also find survival, fecundity, and dispersal during early life (1-2 years) play a critical role in determining the expansion success of these mollusks. These results underscore the importance of fusing prior scientific knowledge with observation and demographic processes in a Bayesian framework for conceptual and practical understanding of how invasive species spread by human agency.
贸易网络中入侵物种引入的增加威胁着生物多样性和生态系统服务。然而，我们对于运输网络如何决定入侵范围扩张的时空模式了解有限。这种知识差距可能源于两个原因。首先，当前的分析模型未能将入侵者的生活史动态与人类介导的扩散模式中的异质性整合起来。其次，由于仅存在记录中的空间偏差以及缺乏信息丰富的人口统计数据，经典统计方法往往无法提供模型参数的可靠估计，例如物种引入的时间和地点以及生活史特征。为了填补这些差距，我们首先构建了一个年龄结构的集合种群模型，该模型使用概率矩阵来模拟人类介导的扩散模式。该模型表明，入侵者沿着最短的网络路径呈放射状扩散，使得斑块间的网络距离随着交通流量和搭便车者的繁殖价值增加而减小。接下来，我们提出一种分层贝叶斯统计方法，使用仅存在数据和先前的人口统计知识来估计模型参数。为了展示这种统计方法的效用，我们通过内陆商业航运网络分析了北美斑马贻贝（）的扩张情况。我们的分析表明，斑马贻贝可能在1981年之前就已被引入，这表明引入时间与1986年末首次发现之间存在五年的滞后。此外，使用我们的统计模型，我们估计它们有三分之一的可能性是在首次报告它们的加拿大安大略省金斯维尔附近被引入的。我们还发现，早期生活（1 - 2年）中的存活率、繁殖力和扩散在决定这些软体动物的扩张成功方面起着关键作用。这些结果强调了在贝叶斯框架中将先前的科学知识与观察和人口统计过程相结合对于从概念和实际层面理解入侵物种如何通过人类活动传播的重要性。
