1. To understand how ecological communities will respond to global change we need new tools and datasets on species across large spatial and temporal scales. Hyperspectral reflectance ‘spectra’ capture a promising set of traits that show potential to be scaled up in time and space via remote sensing. Thus far, spectra have been shown to distinguish the taxa and trait responses of a substantial number of species within a plethora of vascular plant communities, but not yet for biological soil crust communities (biocrusts).
2. Here, we assess if spectra can be applied to identify biocrust species and their trait variation. We collected biocrust specimens across an aridity gradient spanning 650 km within drylands of Eastern Australia and acquired their spectra, over 12,700 spectral readings, with a high-resolution radiospectrometer. A machine learning method (random forests) was used to assess how well the spectra of biocrust specimens could distinguish their species and broader structural and chemical traits.
3. Spectra were able to differentiate a substantial number of biocrust species (35) with considerable accuracy (~78.5%). Furthermore, spectral features related to chemical traits were found to primarily drive species spectral differences.
4. Synthesis. Our findings establish that biocrust species hold unique and detectable spectral responses, providing an important basis for remote sensing applications on biocrust species and their trait responses across dryland systems.

中文翻译：

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测量微小生物体的反射率：物种级生物地壳遥感的前景

1. 为了了解生态群落将如何应对全球变化，我们需要关于跨大空间和时间尺度的物种的新工具和数据集。高光谱反射“光谱”捕获了一组有希望的特征，这些特征显示出通过遥感在时间和空间上扩大的潜力。迄今为止，光谱已被证明可以区分大量维管植物群落中大量物种的分类群和性状响应，但尚未用于生物土壤结皮群落（生物结皮）。
2. 在这里，我们评估是否可以应用光谱来识别生物结皮物种及其性状变异。我们在澳大利亚东部旱地内跨越 650 公里的干旱梯度收集了生物地壳标本，并使用高分辨率辐射光谱仪获取了超过 12,700 个光谱读数的光谱。机器学习方法（随机森林）用于评估生物外壳标本的光谱如何区分它们的物种以及更广泛的结构和化学特征。
3. Spectra 能够以相当高的准确度（~78.5%）区分大量的生物结皮物种 (35)。此外，发现与化学特征相关的光谱特征主要驱动物种光谱差异。
4. 合成。我们的研究结果表明，生物结皮物种具有独特且可检测的光谱响应，为生物结皮物种及其跨旱地系统性状响应的遥感应用提供了重要基础。