占用率
推论
丰度(生态学)
人口
稀有物种
统计
统计推断
可靠性(半导体)
物种丰富度
生态学
相对物种丰度
不完美的
计量经济学
人口规模
样本量测定
计算机科学
数学
生物
栖息地
人工智能
人口学
物理
社会学
哲学
功率(物理)
量子力学
语言学
作者
Darryl I. MacKenzie,James D. Nichols,Nicole Sutton,Kae Kawanishi,Larissa L. Bailey
出处
期刊:Ecology
[Wiley]
日期:2005-05-01
卷期号:86 (5): 1101-1113
被引量:469
摘要
For the vast majority of cases, it is highly unlikely that all the individuals of a population will be encountered during a study. Furthermore, it is unlikely that a constant fraction of the population is encountered over times, locations, or species to be compared. Hence, simple counts usually will not be good indices of population size. We recommend that detection probabilities (the probability of including an individual in a count) be estimated and incorporated into inference procedures. However, most techniques for estimating detection probability require moderate sample sizes, which may not be achievable when studying rare species. In order to improve the reliability of inferences from studies of rare species, we suggest two general approaches that researchers may wish to consider that incorporate the concept of imperfect detectability: (1) borrowing information about detectability or the other quantities of interest from other times, places, or species; and (2) using state variables other than abundance (e.g., species richness and occupancy). We illustrate these suggestions with examples and discuss the relative benefits and drawbacks of each approach.
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