TY - JOUR
T1 - An under-appreciated difficulty
T2 - Sampling of plant populations for analysis using molecular markers
AU - Suzuki, Jun Ichirou
AU - Herben, Tomás
AU - Maki, Masayuki
N1 - Funding Information:
We thank Michael J. Hutchings, Hiroshi Kudoh and Zuzana Munzbergova for reading and criticising drafts of this paper at various stages. Two anonymous reviewers also gave helpful comments. This paper was written when the second author was on leave at the Institute of Low Temperature Science, Hokkaido University. Thanks are due to Toshihiko Hara and to the staff of the Institute for support and great help during his stay. Parts of the research reported here were funded by the MSMT grant to T.H. and the Ministry of Education, Culture, Sports, Science and Technology grant to J.S.
PY - 2004/9
Y1 - 2004/9
N2 - Results of studies using molecular markers for determining demographic and genetical population parameters especially in plants or sessile animals under field conditions are strongly dependent on the sampling strategy adopted. There are two critical decisions to make when determining this strategy: (i) what is the unit to be sampled?, (ii) how should units to be sampled in the field be selected? For the first decision, there are two conceptually different approaches: sampling ramets of clonal plants as units (to get information about within-genet parameters, such as genet sizes or numbers) and sampling genets of clonal or non-clonal plants as units (to get information of the genetic structure of the population). For the second decision, it is critically important to make the goal of the study explicit. We argue that in this case fully random sampling is needed only when an estimate of the true value of the population parameter is needed; if a comparison between populations is the goal, however, other sampling schemes may be adopted. The efficiency of different types of sampling strategies to recover relative values in a spatially extended population is studied by means of a spatially explicit simulation model. The results show that a regular pattern of sampling is best for obtaining information on genet sizes or inbreeding coefficients; in contrast, random or hierarchical sampling strategies are better for obtaining information on parameters that are based on comparison of pairs of individuals, such as distribution of genet sizes or autocorrelation in genetic structure. A set of recommendations is provided for designing a good sampling strategy.
AB - Results of studies using molecular markers for determining demographic and genetical population parameters especially in plants or sessile animals under field conditions are strongly dependent on the sampling strategy adopted. There are two critical decisions to make when determining this strategy: (i) what is the unit to be sampled?, (ii) how should units to be sampled in the field be selected? For the first decision, there are two conceptually different approaches: sampling ramets of clonal plants as units (to get information about within-genet parameters, such as genet sizes or numbers) and sampling genets of clonal or non-clonal plants as units (to get information of the genetic structure of the population). For the second decision, it is critically important to make the goal of the study explicit. We argue that in this case fully random sampling is needed only when an estimate of the true value of the population parameter is needed; if a comparison between populations is the goal, however, other sampling schemes may be adopted. The efficiency of different types of sampling strategies to recover relative values in a spatially extended population is studied by means of a spatially explicit simulation model. The results show that a regular pattern of sampling is best for obtaining information on genet sizes or inbreeding coefficients; in contrast, random or hierarchical sampling strategies are better for obtaining information on parameters that are based on comparison of pairs of individuals, such as distribution of genet sizes or autocorrelation in genetic structure. A set of recommendations is provided for designing a good sampling strategy.
KW - Genet number
KW - Genet size
KW - Hierarchical sampling
KW - Inbreeding coefficient
KW - Ramet
KW - Random sampling
KW - Sampling strategy
KW - Spatial autocorrelation
KW - Spatially explicit simulation model
KW - Systematic sampling
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U2 - 10.1007/s10682-004-5147-3
DO - 10.1007/s10682-004-5147-3
M3 - Article
AN - SCOPUS:21044432743
VL - 18
SP - 625
EP - 646
JO - Evolutionary Ecology
JF - Evolutionary Ecology
SN - 0269-7653
IS - 5-6
ER -