Skip to main content
ARS Home » Research » Publications at this Location » Publication #315703
Title: Developing quantitative seed sampling protocols using simulations: A reply to comments from Guja et al. and Guerrant et al.

Author
item HOBAN, SEAN - University Of Tennessee
item STRAND, ALLEN - College Of Charleston
item FRAGA, NAOMI - Rancho Santa Ana Botanic Garden
item Richards, Christopher
item SCHLARBAUM, SCOTT - University Of Tennessee

Submitted to: Biological Conservation
Publication Type: Other
Publication Acceptance Date: 3/18/2015
Publication Date: 4/1/2015
Citation: Hoban, S., Strand, A., Fraga, N., Richards, C.M., Schlarbaum, S. 2015. Developing quantitative seed sampling protocols using simulations: A reply to comments from Guja et al. and Guerrant et al.. Biological Conservation. 184 469-470.

Interpretive Summary: The letter is a reply to comments made on a previous publication: Optimal sampling of seeds from plant populations for ex-situ conservation of genetic biodiversity, considering realistic population structure (Hoban and Schlarbaum, 2014). The intent of the reply is to acknowledge some of the practical limitations of this paper but to emphasize the benefit of quantitative evaluations of sampling success in a field largely devoid of quantitative metrics. In particular, the careful use of spatial simulation can be effective in evaluating alternative sampling approaches, developing testable hypotheses and potentially saving considerable resources in planning sampling missions.

Technical Abstract: The letter is a reply to comments made on a previous publication: Optimal sampling of seeds from plant populations for ex-situ conservation of genetic biodiversity, considering realistic population structure (Hoban and Schlarbaum, 2014). The intent of the reply is to acknowledge some of the practical limitations of this paper but to emphasize the benefit of quantitative evaluations of sampling success in a field largely devoid of quantitative metrics. In particular, the careful use of spatial simulation can be effective in evaluating alternative sampling approaches, developing testable hypotheses and potentially saving considerable resources in planning sampling missions.