Innate mean relative growth rate (mean RGR) of seedlings is a key attribute for the performance of species in their natural habitats. This study aimed firstly at identifying easily measurable correlates of mean RGR of temperate zone woody species. Secondly it tested the hypothesis that functional groups of woody plants could be characterized by their mean RGR and associated allocation and leaf attributes. 2 In a standardized experiment 80 woody species from the British Isles and North Spain ranging widely in leaf habit and life-form were screened for seed weight and potential seedling mean relative growth rate (RGR) biomass allocation and leaf attributes. 3 Mean RGR when based on plant weights excluding any attached thick cotyledons was linearly and closely correlated with leaf area ratio (LAR total leaf area/plant dry weight) and one of the two components of LAR specific leaf area (SLA leaf area/leaf dry weight). The other component leaf weight fraction (leaf weight/plant dry weight) was only correlated with mean RGR when based upon true leaves disregarding leafy cotyledons. These relationships were also demonstrated when taxonomic relatedness was accounted for. 4 The data supported the hypothesis that differentiation as seen among functional groups of species in terms of leaf habit and life-form corresponded with differentiation in mean RGR and other seedling attributes. For instance deciduous species grew consistently faster than evergreens. 5 When SLA was split into its two components specific saturated leaf area (SSLA total leaf area/total saturated leaf weight) and leaf saturated weight/dry weight ratio (SW/DW) it was found that SSLA was consistently smaller in evergreens than in deciduous species both for sclerophyllous and succulent leaves. Among species of the same leaf habit variation in SLA among life-forms could be explained by variation in leaf SW/DW. 6 SSLA and leaf SW/DW both easy to measure together could help to categorize growth rate within the evergreen or deciduous species. This may be useful in vegetation monitoring. 7 The data may provide useful predictive tools to infer potential growth rates and nutrient conservation strategies of real vegetation from the functional attributes and composition of its functional species groups.