A species that, coming from a source population, appears in a new environment where it was not present before is named alien. Due to the harm it poses to biodiversity and the expenses associated with its control, the phenomenon of alien species invasions is currently under careful examination. Although the presence of a considerable literature on the subject, the formulation of a dedicated statistical model has been deemed essential. The objective is to overcome current computational constraints while also correctly accounting for the dynamics behind the spread of alien species. A first record can be seen as a relational event, where the species (the sender) reaches a region (the receiver) for the first time in a certain year. As a result, whenever an alien species is introduced, the relational event graph adds a time-stamped edge. Besides potentially time-varying exogenous and endogenous covariates, our smooth relational event model (REM) also incorporates time-varying and random effects to explain the invasion rate. Particularly, we aim to track temporal variations in impacts' direction and magnitude of the ecological, socioeconomic, historical, and cultural forces at work. Network structures of particular interest (such as species' co-invasion affinity) are inspected as well. Our inference procedure relies on case-control sampling, yielding the same likelihood as that of a logistic regression. Due to the smooth nature of the incorporated effects, we may fit a generalised additive model where random effects are also estimated as 0-dimensional splines. The consequent computational advantage makes it possible to simultaneously examine many taxonomies. We explore how vascular plants and insects behave together. The goodness of fit of the smooth REM may be evaluated by means of test statistics computed as region-specific sums of martingale-residuals.
翻译:来自源群体并出现在以前不存在的新环境中的物种被称为异种物种。由于其对生物多样性造成的伤害以及其控制所需的费用,异种物种入侵现象目前正在得到仔细研究。尽管已经有相当多的文献资料,但制定专门的统计模型被认为是必要的。目标是克服当前的计算约束,同时正确考虑异种物种传播背后的动态。首次记录可以看作是一种关系事件,物种(发送方)在某个年份第一次到达一个地区(接收方)。因此,每当引入异种物种时,关系事件图会添加一个时间戳的边缘。除了潜在的时变外生和内生协变量外,我们的平滑关系事件模型(REM)还包括时变和随机效应,以解释入侵率。特别是,我们旨在跟踪生态、社会经济、历史和文化力量的方向和数量的时间变化。还检查了特定的网络结构(如物种共同入侵的亲和力)。我们的推断过程依赖于病例对照抽样,产生与逻辑回归相同的似然函数。由于纳入的效应具有平滑性质,因此我们可以拟合一个广义加性模型,其中随机效应也被估计为零维样条。由此带来的计算优势使我们能够同时研究许多分类。我们探索了维管植物和昆虫如何一起行动。平滑REM的适合性可以通过计算马丁格尔残差的区域特定求和来评估。