Applied Environment Decision Analysis CERF, School of Botany, University of Melbourne, Parkville 3010, Victoria, Australia.
Ecol Appl. 2011 Mar;21(2):601-7. doi: 10.1890/10-0590.1.
Recent studies suggest that plant detection is not perfect, even for large, highly visible plants. However, this is often not taken into account during plant surveys where failing to detect a plant when present can result in poor management and biodiversity outcomes. Including knowledge of imperfect detectability into survey design and evaluation is hampered by the paucity of empirical data, and in particular, how detectability will change with search effort, plant size and abundance, the surrounding vegetation, or observer experience. We carried out a search experiment to measure the detection-effort curve for the invasive species orange hawkweed (Hieracium aurantiacum) in Victoria, Australia. The probability that hawkweed was detected increased with increasing search effort and the number of plants at the location. While detection probability varied between observers, experience appeared to have little effect. Accounting for imperfect detectability in plant surveys holds much promise for improved survey design and biodiversity outcomes, and we encourage other researchers to undertake similar experiments to further our understanding of plant detectability.
最近的研究表明，即使是对于大型、高度可见的植物，植物的检测也不是完美的。然而，在植物调查中，往往没有考虑到这一点，因为在植物存在的情况下未能检测到植物可能会导致管理不善和生物多样性结果不佳。将不完全可检测性纳入调查设计和评估受到经验数据的匮乏的阻碍，特别是可检测性如何随搜索努力、植物大小和丰度、周围植被或观察者经验的变化而变化。我们进行了一项搜索实验，以测量澳大利亚维多利亚州入侵物种橙色鹰草（Hieracium aurantiacum）的检测努力曲线。随着搜索努力的增加和地点上植物数量的增加，鹰草被检测到的概率增加。虽然观察者之间的检测概率有所不同，但经验似乎影响很小。在植物调查中考虑不完全可检测性，对于改进调查设计和生物多样性结果有很大的希望，我们鼓励其他研究人员进行类似的实验，以进一步了解植物的可检测性。
