Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA.
Department of Biology, University of Florida, Gainesville, FL, 32611, USA.
Sci Rep. 2024 Aug 29;14(1):20065. doi: 10.1038/s41598-024-70412-8.
Nitrogen (N)-fixing symbiosis is critical to terrestrial ecosystems, yet possession of this trait is known for few plant species. Broader presence of the symbiosis is often indirectly determined by phylogenetic relatedness to taxa investigated via manipulative experiments. This data gap may ultimately underestimate phylogenetic, spatial, and temporal variation in N-fixing symbiosis. Still needed are simpler field or collections-based approaches for inferring symbiotic status. N-fixing plants differ from non-N-fixing plants in elemental and isotopic composition, but previous investigations have not tested predictive accuracy using such proxies. Here we develop a regional field study and demonstrate a simple classification model for fixer status using nitrogen and carbon content measurements, and stable isotope ratios (δN and δC), from field-collected leaves. We used mixed models and classification approaches to demonstrate that N-fixing phenotypes can be used to predict symbiotic status; the best model required all predictors and was 80-94% accurate. Predictions were robust to environmental context variation, but we identified significant variation due to native vs. non-native (exotic) status and phylogenetic affinity. Surprisingly, N content-not δN-was the strongest predictor, suggesting that future efforts combine elemental and isotopic information. These results are valuable for understudied taxa and ecosystems, potentially allowing higher-throughput field-based N-fixer assessments.
固氮共生关系对陆地生态系统至关重要，但已知只有少数植物物种具有这种特性。共生关系的更广泛存在通常是通过与通过操纵实验进行调查的分类单元的系统发育关系来间接确定的。这种数据差距最终可能低估了固氮共生关系在系统发育、空间和时间上的变化。仍然需要更简单的基于野外或收集的方法来推断共生状态。固氮植物与非固氮植物在元素和同位素组成上存在差异，但以前的研究并未使用这些替代指标测试预测准确性。在这里，我们进行了一项区域野外研究，并使用野外收集的叶片中的氮和碳含量测量值以及稳定同位素比值（δN 和 δC），展示了一种用于确定固氮状态的简单分类模型。我们使用混合模型和分类方法证明固氮表型可用于预测共生状态；最佳模型需要所有预测因子，准确率为 80-94%。预测结果对环境背景变化具有鲁棒性，但我们发现由于本地与非本地（外来）状态和系统发育亲缘关系而存在显著差异。令人惊讶的是，氮含量而不是 δN 是最强的预测因子，这表明未来的研究将结合元素和同位素信息。这些结果对于研究较少的分类群和生态系统很有价值，可能允许更高通量的基于野外的固氮评估。
