|Appears in Collections:||Biological and Environmental Sciences Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Wide variation in spatial genetic structure between natural populations of the European beech (Fagus sylvatica) and its implications for SGS comparability|
|Citation:||Jump A, Rico L, Coll M & Penuelas J (2012) Wide variation in spatial genetic structure between natural populations of the European beech (Fagus sylvatica) and its implications for SGS comparability, Heredity, 108 (6), pp. 633-639.|
|Abstract:||Identification and quantification of spatial genetic structure (SGS) within populations remains a central element of understanding population structure at the local scale. Understanding such structure can inform on aspects of the species' biology, such as establishment patterns and gene dispersal distance, in addition to sampling design for genetic resource management and conservation. However, recent work has identified that variation in factors such as sampling methodology, population characteristics and marker system can all lead to significant variation in SGS estimates. Consequently, the extent to which estimates of SGS can be relied on to inform on the biology of a species or differentiate between experimental treatments is open to doubt. Following on from a recent report of unusually extensive SGS when assessed using amplified fragment length polymorphisms in the tree Fagus sylvatica, we explored whether this marker system led to similarly high estimates of SGS extent in other apparently similar populations of this species. In the three populations assessed, SGS extent was even stronger than this previously reported maximum, extending up to 360 m, an increase in up to 800% in comparison with the generally accepted maximum of 30-40 m based on the literature. Within this species, wide variation in SGS estimates exists, whether quantified as SGS intensity, extent or the Sp parameter. Consequently, we argue that greater standardization should be applied in sample design and SGS estimation and highlight five steps that can be taken to maximize the comparability between SGS estimates.|
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