Abstract Biological invasions are one of the major threats to biodiversity, especially in oceanic islands. In the Canary Islands, the relationships between plant Alien Species Richness (ASR) and their environmental and anthropogenic determinants were thoroughly investigated using ecological models. However, previous predictive models rarely accounted for spatial autocorrelation (SAC) and uncertainty of predictions, thus missing crucial information related to model accuracy and predictions reliability. In this study, we propose a Generalized Linear Spatial Model (GLSM) for ASR under a Bayesian framework on Tenerife Island. Our aim is to test whether the inclusion of SAC into the modelling framework could improve model performance resulting in more reliable predictions. Results demonstrated as accounting for SAC dramatically reduced the model's AIC (ΔAIC = 4423) and error magnitudes, showing also better performances in terms of goodness of fit. Calculation of uncertainty related to predicted values pointed out those areas where either the number of observations (e.g. under-sampled areas) or the reliability of the environmental predictors was lower (e.g. low spatial resolution in highly heterogeneous environments). Although our results confirmed what was already observed in other ecological studies, such as the important role of roads in ASR spread, methodological considerations on the applied modelling approach point out the importance of considering spatial autocorrelation and researcher's prior knowledge to increase the predictive power of statistical models as well as the correctness in terms of coefficients estimates. The proposed approach may serve as an essential management tools highlighting those portions of territory that will be more prone to biological invasions and where monitoring efforts should be addressed.

中文翻译：

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特内里费岛（加那利群岛）外来植物丰富度的空间显式模型

摘要 生物入侵是生物多样性的主要威胁之一，尤其是在海洋岛屿。在加那利群岛，使用生态模型彻底调查了植物外来物种丰富度 (ASR) 与其环境和人为决定因素之间的关系。然而，以前的预测模型很少考虑空间自相关 (SAC) 和预测的不确定性，因此缺少与模型准确性和预测可靠性相关的关键信息。在这项研究中，我们在特内里费岛的贝叶斯框架下为 ASR 提出了一个广义线性空间模型 (GLSM)。我们的目标是测试将 SAC 纳入建模框架是否可以提高模型性能，从而产生更可靠的预测。结果表明，考虑到 SAC 显着降低了模型 s AIC (ΔAIC = 4423) 和误差幅度，在拟合优度方面也显示出更好的性能。与预测值相关的不确定性计算指出了那些观测次数（例如欠采样区域）或环境预测因子的可靠性较低（例如高度异质环境中的低空间分辨率）的区域。尽管我们的结果证实了在其他生态研究中已经观察到的内容，例如道路在 ASR 传播中的重要作用，但对应用建模方法的方法论考虑指出了考虑空间自相关和研究人员的先验知识以提高统计预测能力的重要性模型以及系数估计的正确性。