Skip to main content
Journal cover image

A comparison of diversity estimators applied to a database of host–parasite associations

Publication ,  Journal Article
Teitelbaum, CS; Amoroso, CR; Huang, S; Davies, TJ; Rushmore, J; Drake, JM; Stephens, PR; Byers, JE; Majewska, AA; Nunn, CL
Published in: Ecography
September 1, 2020

Understanding the drivers of biodiversity is important for forecasting changes in the distribution of life on earth. However, most studies of biodiversity are limited by uneven sampling effort, with some regions or taxa better sampled than others. Numerous methods have been developed to account for differences in sampling effort, but most methods were developed for systematic surveys in which all study units are sampled using the same design and assemblages are sampled randomly. Databases compiled from multiple sources, such as from the literature, often violate these assumptions because they are composed of studies that vary widely in their goals and methods. Here, we compared the performance of several popular methods for estimating parasite diversity based on a large and widely used parasite database, the Global Mammal Parasite Database (GMPD). We created artificial datasets of host–parasite interactions based on the structure of the GMPD, then used these datasets to evaluate which methods best control for differential sampling effort. We evaluated the precision and bias of seven methods, including species accumulation and nonparametric diversity estimators, compared to analyzing the raw data without controlling for sampling variation. We find that nonparametric estimators, and particularly the Chao2 and second-order jackknife estimators, perform better than other methods. However, these estimators still perform poorly relative to systematic sampling, and effect sizes should be interpreted with caution because they tend to be lower than actual effect sizes. Overall, these estimators are more effective in comparative studies than for producing true estimates of diversity. We make recommendations for future sampling strategies and statistical methods that would improve estimates of global parasite diversity.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Ecography

DOI

EISSN

1600-0587

ISSN

0906-7590

Publication Date

September 1, 2020

Volume

43

Issue

9

Start / End Page

1316 / 1328

Related Subject Headings

  • Ecology
  • 4104 Environmental management
  • 4102 Ecological applications
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management
  • 0501 Ecological Applications
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Teitelbaum, C. S., Amoroso, C. R., Huang, S., Davies, T. J., Rushmore, J., Drake, J. M., … Nunn, C. L. (2020). A comparison of diversity estimators applied to a database of host–parasite associations. Ecography, 43(9), 1316–1328. https://doi.org/10.1111/ecog.05143
Teitelbaum, C. S., C. R. Amoroso, S. Huang, T. J. Davies, J. Rushmore, J. M. Drake, P. R. Stephens, J. E. Byers, A. A. Majewska, and C. L. Nunn. “A comparison of diversity estimators applied to a database of host–parasite associations.” Ecography 43, no. 9 (September 1, 2020): 1316–28. https://doi.org/10.1111/ecog.05143.
Teitelbaum CS, Amoroso CR, Huang S, Davies TJ, Rushmore J, Drake JM, et al. A comparison of diversity estimators applied to a database of host–parasite associations. Ecography. 2020 Sep 1;43(9):1316–28.
Teitelbaum, C. S., et al. “A comparison of diversity estimators applied to a database of host–parasite associations.” Ecography, vol. 43, no. 9, Sept. 2020, pp. 1316–28. Scopus, doi:10.1111/ecog.05143.
Teitelbaum CS, Amoroso CR, Huang S, Davies TJ, Rushmore J, Drake JM, Stephens PR, Byers JE, Majewska AA, Nunn CL. A comparison of diversity estimators applied to a database of host–parasite associations. Ecography. 2020 Sep 1;43(9):1316–1328.
Journal cover image

Published In

Ecography

DOI

EISSN

1600-0587

ISSN

0906-7590

Publication Date

September 1, 2020

Volume

43

Issue

9

Start / End Page

1316 / 1328

Related Subject Headings

  • Ecology
  • 4104 Environmental management
  • 4102 Ecological applications
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management
  • 0501 Ecological Applications