%0 Research article
%T On the hidden significance of differing micro-sites on tree-ring based climate reconstructions
%A Düthorn, Elisabeth
%A Schneider, Lea
%A Konter, Oliver
%A Schön, Philipp
%A Timonen, Mauri
%A Esper, Jan
%D 2015
%J Silva Fennica
%V 49
%N 1
%R doi:10.14214/sf.1220
%U https://silvafennica.fi/article/1220
%X Tree-ring chronologies are commonly extended back in time by combining samples from living trees with relict material preserved in man-made structures or natural archives (e.g. lakes). Although spatially close, these natural archives and living-tree-sites often comprise different micro-climates. Inhomogeneous growth conditions among these habitats, which may yield offsets in growth-rates, require caution in data processing. Here we assess species-specific growth dynamics in two micro-habitats and their potential effects on long chronologies by combining tree-ring data from different living-tree-sites with an “artificial” subfossil dataset. Well replicated (n > 80) Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) chronologies from northern Fennoscandia, sampled directly at the lakeshore (wet) and several meters beyond the lakeshore (dry) reveal high coherence of the variance between micro-sites (rspruce = 0.59, rpine = 0.68). Significant differences of the Regional Curves (RC) indicate faster growth of both species at the drier site though. Growth differences are more pronounced between the spruce micro-sites. The combination of recent dry and wet spruce data with artificial relict data results in two long chronologies covering the last 800 years with substantially different trends, although they consist of the same relict material and the micro-site chronologies correlate significantly over the past two centuries. The combination of spruce samples from dry inland micro-sites with subfossil samples originating from the wet lake shore can result in an underestimation of past temperatures prior to the 19th century. Such effects, hidden in the composition of long chronologies (living trees + subfossil samples) can bias long-term trends in climate reconstructions.