Environmetrics Lab, Department of Biosciences and Territory, University of Molise, Pesche, Isernia, Italy.
Department of Research Infrastructures for Marine Biological Resources, Stazione Zoologica Anton Dohrn, Naples, Italy.
Glob Chang Biol. 2023 Oct;29(19):5509-5523. doi: 10.1111/gcb.16901. Epub 2023 Aug 7.
Citizen science initiatives have been increasingly used by researchers as a source of occurrence data to model the distribution of alien species. Since citizen science presence-only data suffer from some fundamental issues, efforts have been made to combine these data with those provided by scientifically structured surveys. Surprisingly, only a few studies proposing data integration evaluated the contribution of this process to the effective sampling of species' environmental niches and, consequently, its effect on model predictions on new time intervals. We relied on niche overlap analyses, machine learning classification algorithms and ecological niche models to compare the ability of data from citizen science and scientific surveys, along with their integration, in capturing the realized niche of 13 invasive alien species in Italy. Moreover, we assessed differences in current and future invasion risk predicted by each data set under multiple global change scenarios. We showed that data from citizen science and scientific surveys captured similar species niches though highlighting exclusive portions associated with clearly identifiable environmental conditions. In terrestrial species, citizen science data granted the highest gain in environmental space to the pooled niches, determining an increased future biological invasion risk. A few aquatic species modelled at the regional scale reported a net loss in the pooled niches compared to their scientific survey niches, suggesting that citizen science data may also lead to contraction in pooled niches. For these species, models predicted a lower future biological invasion risk. These findings indicate that citizen science data may represent a valuable contribution to predicting future spread of invasive alien species, especially within national-scale programmes. At the same time, citizen science data collected on species poorly known to citizen scientists, or in strictly local contexts, may strongly affect the niche quantification of these taxa and the prediction of their future biological invasion risk.
公民科学倡议越来越多地被研究人员用作发生数据的来源，以模拟外来物种的分布。由于公民科学仅存在数据存在一些基本问题，因此已经努力将这些数据与科学结构调查提供的数据相结合。令人惊讶的是，只有少数提出数据集成研究评估了该过程对物种环境生态位有效采样的贡献，以及对新时间间隔模型预测的影响。我们依赖生态位重叠分析、机器学习分类算法和生态位模型，比较公民科学和科学调查数据以及它们的整合，以捕获意大利 13 种入侵外来物种的实现生态位。此外，我们评估了每个数据集在多个全球变化情景下预测的当前和未来入侵风险的差异。我们表明，公民科学和科学调查数据捕获了相似的物种生态位，但突出了与可明确识别的环境条件相关的独特部分。在陆地物种中，公民科学数据为组合生态位提供了对环境空间的最大增益，确定了未来生物入侵风险的增加。在区域尺度上建模的少数水生物种与科学调查生态位相比，在组合生态位中报告了净损失，这表明公民科学数据也可能导致组合生态位收缩。对于这些物种，模型预测未来的生物入侵风险较低。这些发现表明，公民科学数据可能对外来入侵物种未来传播的预测做出有价值的贡献，特别是在国家尺度计划中。同时，公民科学收集的关于公民科学家知之甚少的物种或在严格的地方背景下的数据，可能会强烈影响这些类群的生态位量化和对其未来生物入侵风险的预测。
