Undersampling bias: the null hypothesis for singleton species in tropical arthropod surveys

被引:268
作者
Coddington, Jonathan A. [1 ]
Agnarsson, Ingi [1 ,2 ]
Miller, Jeremy A. [4 ]
Kuntner, Matjaz [1 ,3 ]
Hormiga, Gustavo [5 ]
机构
[1] Smithsonian Inst, Dept Entomol, Natl Museum Nat Hist, Washington, DC 20013 USA
[2] Univ Puerto Rico, Dept Biol, San Juan, PR 00931 USA
[3] Slovenian Acad Sci & Arts, Ctr Sci Res, Inst Biol, SI-1001 Ljubljana, Slovenia
[4] Natl Natuurhist Museum Nat, NL-2333 CR Leiden, Netherlands
[5] George Washington Univ, Dept Biol Sci, Washington, DC 20052 USA
基金
美国国家科学基金会;
关键词
Araneae; Guyana; neutral theory; sampling intensity; species richness estimation; spiders; RAIN-FOREST; COMMUNITY STRUCTURE; BEETLE ASSEMBLAGES; TEMPORAL DIMENSIONS; RICHNESS ESTIMATION; RELATIVE ABUNDANCE; INSECT HERBIVORES; NEUTRAL THEORY; SEX-RATIOS; DIVERSITY;
D O I
10.1111/j.1365-2656.2009.01525.x
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Frequency of singletons - species represented by single individuals - is anomalously high in most large tropical arthropod surveys (average, 32%). We sampled 5965 adult spiders of 352 species (29% singletons) from 1 ha of lowland tropical moist forest in Guyana. Four common hypotheses (small body size, male-biased sex ratio, cryptic habits, clumped distributions) failed to explain singleton frequency. Singletons are larger than other species, not gender-biased, share no particular lifestyle, and are not clumped at 0.25-1 ha scales. Monte Carlo simulation of the best-fit lognormal community shows that the observed data fit a random sample from a community of similar to 700 species and 1-2 million individuals, implying approximately 4% true singleton frequency. Undersampling causes systematic negative bias of species richness, and should be the default null hypothesis for singleton frequencies. Drastically greater sampling intensity in tropical arthropod inventory studies is required to yield realistic species richness estimates. The lognormal distribution deserves greater consideration as a richness estimator when undersampling bias is severe.
引用
收藏
页码:573 / 584
页数:12
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