Lux Slawomir A
inSilico-IPM, Konstancin-Jeziorna, Poland.
Formely: Department of Applied Entomology, Warsaw University of Life Sciences, Warsaw, Poland.
Front Physiol. 2018 Jan 9;8:1121. doi: 10.3389/fphys.2017.01121. eCollection 2017.
The objective of the presented study was to demonstrate the potential of a bottom-up "ethological" approach and individual-based model of Markov-like stochastic processes, employed to gain insights into the factors driving behavior and fate of the invasive propagule, which determine the initial stages of pest invasion and "cryptic" existence of the localized, ultra-low density incipient pest populations. The applied model, PESTonFARM, is driven by the parameters derived directly from the behavior and biology of the target insect species, and spatiotemporal traits of the local terrain and climate. The model projections are actively generated by behavior of the primary causative actors of the invasion processes-individual "virtual" insects-members of the initial propagules or incipient populations. Algorithms of the model were adjusted to reflect behavior and ecology of the Mediterranean fruit fly, , used as a case-example in the presented study. The model was parametrized based on compiled published experimental information about behavior and development, and validated using published data from dispersion and trapping studies. The model reliably simulated behavior, development and dispersion of individual members of an invasive cohort, and allowed to quantify pest establishment and detection chances in landscapes of varying spatiotemporal complexity, host availability and climates. The results support the common view that, under optimal conditions (farmland with continuous fruit availability and suitable climate), even a single propagule of medium size (100 females) usually results in pest establishment and detection within the first year post-invasion. The results demonstrate, however, that under specific sub-optimal conditions determined by the local climate, weather fluctuations and landscape topography (e.g., sub-urban), the incipient cryptic populations may occasionally continue for several generations, and remain undetected by typical pest surveillance grids for the periods extending beyond 2-years post-invasion.
本研究的目的是展示一种自下而上的“行为学”方法以及基于个体的类马尔可夫随机过程模型的潜力，该模型用于深入了解驱动入侵繁殖体行为和命运的因素，这些因素决定了害虫入侵的初始阶段以及局部超低密度初期害虫种群的“隐匿”存在。所应用的PESTonFARM模型由直接从目标昆虫物种的行为和生物学以及当地地形和气候的时空特征得出的参数驱动。模型预测是由入侵过程的主要 causative actors——个体“虚拟”昆虫（初始繁殖体或初期种群的成员）的行为积极生成的。对模型算法进行了调整，以反映地中海实蝇的行为和生态，在地中海实蝇在本研究中用作案例。该模型基于汇编的已发表的关于行为和发育的实验信息进行参数化，并使用来自扩散和诱捕研究的已发表数据进行验证。该模型可靠地模拟了入侵群体个体成员的行为、发育和扩散，并能够量化在不同时空复杂性、寄主可用性和气候的景观中害虫定殖和被发现的机会。结果支持了这样一种普遍观点，即在最佳条件下（有持续水果供应和适宜气候的农田），即使是单个中等大小的繁殖体（100只雌虫）通常也会在入侵后的第一年内导致害虫定殖并被发现。然而，结果表明，在由当地气候、天气波动和景观地形（例如城郊）决定的特定次优条件下，初期隐匿种群可能偶尔会持续几代，并且在入侵后超过2年的时间内仍未被典型的害虫监测网格发现。
