Yang Yuzhan, Zhan Aibin, Cao Lei, Meng Fanjuan, Xu Wenbin
School of Life Sciences, University of Science and Technology of China , Hefei , Anhui , China.
Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences , Beijing , China.
PeerJ. 2016 Aug 17;4:e2345. doi: 10.7717/peerj.2345. eCollection 2016.
Food availability and diet selection are important factors influencing the abundance and distribution of wild waterbirds. In order to better understand changes in waterbird population, it is essential to figure out what they feed on. However, analyzing their diet could be difficult and inefficient using traditional methods such as microhistologic observation. Here, we addressed this gap of knowledge by investigating the diet of greater white-fronted goose Anser albifrons and bean goose Anser fabalis, which are obligate herbivores wintering in China, mostly in the Middle and Lower Yangtze River floodplain. First, we selected a suitable and high-resolution marker gene for wetland plants that these geese would consume during the wintering period. Eight candidate genes were included: rbcL, rpoC1, rpoB, matK, trnH-psbA, trnL (UAA), atpF-atpH, and psbK-psbI. The selection was performed via analysis of representative sequences from NCBI and comparison of amplification efficiency and resolution power of plant samples collected from the wintering area. The trnL gene was chosen at last with c/h primers, and a local plant reference library was constructed with this gene. Then, utilizing DNA metabarcoding, we discovered 15 food items in total from the feces of these birds. Of the 15 unique dietary sequences, 10 could be identified at specie level. As for greater white-fronted goose, 73% of sequences belonged to Poaceae spp., and 26% belonged to Carex spp. In contrast, almost all sequences of bean goose belonged to Carex spp. (99%). Using the same samples, microhistology provided consistent food composition with metabarcoding results for greater white-fronted goose, while 13% of Poaceae was recovered for bean goose. In addition, two other taxa were discovered only through microhistologic analysis. Although most of the identified taxa matched relatively well between the two methods, DNA metabarcoding gave taxonomically more detailed information. Discrepancies were likely due to biased PCR amplification in metabarcoding, low discriminating power of current marker genes for monocots, and biases in microhistologic analysis. The diet differences between two geese species might indicate deeper ecological significance beyond the scope of this study. We concluded that DNA metabarcoding provides new perspectives for studies of herbivorous waterbird diets and inter-specific interactions, as well as new possibilities to investigate interactions between herbivores and plants. In addition, microhistologic analysis should be used together with metabarcoding methods to integrate this information.
食物可获得性和饮食选择是影响野生水鸟数量和分布的重要因素。为了更好地了解水鸟种群的变化，弄清楚它们的食物来源至关重要。然而，使用传统方法如微观组织学观察来分析它们的饮食可能既困难又低效。在此，我们通过调查白额雁和豆雁的饮食来填补这一知识空白，这两种鹅是在中国越冬的专性食草动物，主要分布在长江中下游平原。首先，我们为这些鹅在越冬期间可能食用的湿地植物选择了一个合适的高分辨率标记基因。共包括八个候选基因：rbcL、rpoC1、rpoB、matK、trnH - psbA、trnL（UAA）、atpF - atpH和psbK - psbI。通过分析来自NCBI的代表性序列以及比较从越冬地区采集的植物样本的扩增效率和分辨率来进行选择。最终选择了trnL基因及其c/h引物，并以此构建了一个本地植物参考文库。然后，利用DNA宏条形码技术，我们从这些鸟类的粪便中总共发现了15种食物。在这15个独特的饮食序列中，有10个可以在物种水平上鉴定出来。对于白额雁来说，73%的序列属于禾本科植物，26%属于苔草属植物。相比之下，豆雁的几乎所有序列都属于苔草属植物（99%）。使用相同的样本，微观组织学对白额雁的食物组成分析结果与宏条形码技术一致，而豆雁的禾本科植物占比为13%。此外，仅通过微观组织学分析还发现了另外两个分类单元。虽然两种方法鉴定出的大多数分类单元匹配度相对较高，但DNA宏条形码技术提供了更详细的分类信息。差异可能是由于宏条形码技术中PCR扩增存在偏差、当前标记基因对单子叶植物的鉴别能力较低以及微观组织学分析存在偏差。两种鹅类之间的饮食差异可能具有超出本研究范围的更深层次的生态意义。我们得出结论，DNA宏条形码技术为食草水鸟饮食和种间相互作用的研究提供了新的视角，也为研究食草动物与植物之间的相互作用提供了新的可能性。此外，微观组织学分析应与宏条形码技术方法结合使用以整合这些信息。
