Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are easily neglected and can lead to spurious findings in downstream analyses. Here, we argue that technological approaches offer potential solutions, but there is no single silver bullet to resolve uncertainty in plant biodiversity quantification. Instead, we propose the use of artificial intelligence (AI) approaches to build a data-driven framework that integrates several data sources – including spectroscopy, DNA sequences, image recognition, and morphological data. Such a framework would provide a foundation for improving species identification in macroecological analyses while simultaneously improving the taxonomic process of species delimitation.

热带生物群落是地球上最多样化的植物群落，因此在许多空间中量化这种多样性至关重要。随着宏观生态学方法的激增，物种发生数据中的分类学不确定性很容易被忽略，并可能导致下游分析中的虚假发现。在这里，我们认为技术方法可以提供潜在的解决方案，但是没有单一的灵丹妙药解决植物生物多样性量化中的不确定性。相反，我们建议使用人工智能（AI）方法来构建一个数据驱动的框架，该框架集成了多个数据源-包括光谱学，DNA序列，图像识别和形态数据。