Climate/growth relationships in a Pinus cembra high-elevation network in the Southern French Alps

Abstract

Introduction In the context of climate change, assessing climate-growth relationships is of high importance in order to understand how forest ecosystems evolve and to test climate models at regional scale.•Objectives This study aims to identify the climate variables that explain most of the variation in Pinus cembra radial growth at different spatial-temporal scales, response functions and moving response functions were processed on chronologies from a dense tree ring network. This original network is centred on the French Alps influenced by the Atlantic, Continental, and Mediterranean synoptic systems.•Results A spatial clustering pattern matching the latitudinal climatic gradient was observed in tree ring indexed chronologies and climate responses. The stationary response functions computed for each cluster-averaged population evidenced a thermo-dependent northern cluster (Cl1), a rainfall-dependent southern cluster (Cl3), and an alternative response for the intermediate cluster in between (Cl2). Since the late nineteenth century, the moving response functions indicate an increasing influence of winter precipitation for the northern and the intermediate clusters.•Conclusion Considering the increase in temperature and reduction both of summer precipitation predicted by general circulation models and of the snow cover and duration in the French Alps, stone pine is likely to be exposed to stressful conditions during the twenty-first century.

Climate-growth relationships in a Larix decidua Mill. network in the French Alps

“…from May-June to September-October, depending on the elevation) has a dominant positive influence on radial growth. Similar positive and significant correlations with spring precipitation sums have been reported for several conifer species at dry sites in Valais (Switzerland, Rigling et al, 2002), Tyrol (Austria, Oberhuber and Kofler, 2002) and the Southern French Alps (Saulnier et al, 2011). Whereas L. decidua has been shown to develop a specific drought avoidance strategy by osmotic adjustment resulting from the accumulation of solutes (Rolland et al, 1998;Urbinati, 2004, 2006), these profiles suggest a sensitivity of low-elevation sites to water stress, especially during the summer months.…”

“…SEASCORR correlates tree-ring series with a primary climate variable and uses partial correlations to investigate a secondary variable controlling for the influence of the primary variable. We selected temperature as the primary and precipitation as the secondary variable because previous work on climate controls in trees at high-elevation sites has identified temperature as the key driver of growth in the Alps (Büntgen et al, 2007;Corona et al, 2011;Saulnier et al, 2011). We then used individual monthly values and considered a 16-month window, assessing a period extending from June of the year preceding growth-ring formation (n − 1) to September of the year of actual ring formation (n); the approach thus analyzes 32 climatic variables.…”

Spatiotemporal heterogeneity of larch budmoth outbreaks in the French Alps over the last 500 years

“…Therefore, we used regional chronologies of stone pine as reference chronologies, each corresponding to several local stone pine chronologies grouped according to their specific latitudinal climatic response (Saulnier et al 2011). The use of regional chronologies can minimize the effect of non-climatic factors and enhance the common climatic signal (Fritts D r a f t 1976; Carrer 2011).…”

Retrospective analysis of tree architecture in silver fir (Abies alba Mill.): ontogenetic trends and responses to environmental variability

“…diameter increment) and interpret climatic trends and local or particular climatic events (Schweingruber 1996), forest history (gap, fire (Everett 2008), competition (Linares et al 2010)) or recurrent biological phenomena (natural trauma, (Stoffel and Bollschweiler 2008), masting (Drobyshev et al 2010)). Dendrochronology is also used to draw data from permanent plots for purpose of precise inter-annual variability analyses (Metsaranta and Lieffers 2009;Dulamsuren et al 2011;Saulnier et al 2011).…”