Barbu Corentin M, Sethuraman Karthik, Billig Erica M W, Levy Michael Z
Department of Biostatistics & Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA.
UMR Agronomie, INRA, AgroParisTech, Université Paris-Saclay, 78850 Thiverval-Grignon, France.
Ecography. 2018 Apr;41(4):661-672. doi: 10.1111/ecog.02575. Epub 2017 May 30.
Biological invasions reshape environments and affect the ecological and economic welfare of states and communities. Such invasions advance on multiple spatial scales, complicating their control. When modeling stochastic dispersal processes, intractable likelihoods and autocorrelated data complicate parameter estimation. As with other approaches, the recent synthetic likelihood framework for stochastic models uses summary statistics to reduce this complexity; however, it additionally provides usable likelihoods, facilitating the use of existing likelihood-based machinery. Here, we extend this framework to parameterize multi-scale spatio-temporal dispersal models and compare existing and newly developed spatial summary statistics to characterize dispersal patterns. We provide general methods to evaluate potential summary statistics and present a fitting procedure that accurately estimates dispersal parameters on simulated data. Finally, we apply our methods to quantify the short and long range dispersal of Chagas disease vectors in urban Arequipa, Peru, and assess the feasibility of a purely reactive strategy to contain the invasion.
生物入侵重塑环境，影响国家和社区的生态与经济福祉。此类入侵在多个空间尺度上推进，使其控制变得复杂。在对随机扩散过程进行建模时，难以处理的似然性和自相关数据使参数估计变得复杂。与其他方法一样，随机模型的最新综合似然框架使用汇总统计量来降低这种复杂性；然而，它还提供了可用的似然性，便于使用现有的基于似然性的方法。在此，我们扩展此框架以参数化多尺度时空扩散模型，并比较现有和新开发的空间汇总统计量以表征扩散模式。我们提供评估潜在汇总统计量的通用方法，并提出一种拟合程序，该程序能准确估计模拟数据上的扩散参数。最后，我们应用我们的方法来量化秘鲁阿雷基帕市恰加斯病媒介的短程和远程扩散，并评估单纯反应性策略控制入侵的可行性。
