Aim To improve our understanding of species range limits by studying how height growth, a trait related to plant survival, varies throughout the geographic range of Fagus sylvatica L. in France.Location The geographic range of beech in France, representing the western area of its European distribution, within which this species exhibits range distribution limits in both plains and mountainous areas.Methods A generalized linear regression model was used to link beech growth performance to environmental variables using data from 819 plots of the French National Forest Inventory (IFN) database. This model was applied to predict potential growth on 97,281 IFN plots covering the geographic range of beech in France. A kriging technique was used to interpolate estimated growth potential. Finally, the performance of plot-based predictions of potential growth from the map (i.e. map quality) was evaluated against an independent data set. ResultsThe beech growth performance model highlighted the major impact of climate on potential tree growth at a broad spatial scale. The relevant climatic factors were related mainly to spring cold, summer heat, and winter temperatures and rainfall. The study also revealed the predictive power of soil parameters, which explained a large proportion of the variation in potential beech growth (c. 30%). Analyses of height growth patterns near the boundary of the species range in France showed that the limit only partly coincides with the growth decline caused by climatic and soil factors. Along parts of the range limit, the predicted potential for growth was high, suggesting that in these areas the limit of the range could be explained by other factors, such as competition or constraints on reproduction.Main conclusions The spatial variation in the potential height growth of Fagus sylvatica can be explained by environmental factors and is partly correlated with its regional range limits. By identifying areas where growth potential constrains the geographic range of species, environmental growth models can help to improve our knowledge of the spatial drivers of species geographic range limits and shed light on their response to future environmental changes.
Relationships between site index, environmental variables, and understorey vegetation were examined for Norway spruce (Picea abies (L.) Karst.) in the eastern part of France. The study area concerns all the native range of Norway spruce in France and the northeastern plains. The analysis is based on 2087 plots from the French National Forest Inventory database. The data measured on each plot cover topography, soil, geology, and vegetation. Additional environmental variables were estimated using two methods: climatic data estimated from a climatic model developed by Météo-France (AURELHY), and nutritional variables predicted from vegetation data and species indicator values. General linear model regression was used to predict site index as a function of environmental variables. The best model explains 64% of the site index variance and involves eight variables (elevation, mountain zone, topographic concavity, proportion of plot area occupied by rock outcrop, rock type, soil depth, pH, and C/N ratio). The two main results of this study are (i) the combination of large databases allowed the study of soilsite relationships and construction of a pertinent model, which covers a wide range of ecological conditions, and (ii) vegetation was found to be relevant to separate the effect of acidity from those of nitrogen nutrition on Norway spruce productivity.
Ingrid Seynave. Mapping soil water holding capacity over large areas to predict the potential production of forest stands. Geoderma, Elsevier, 2011, 160, pp.355-366. 10 AbstractEcological studies need environmental descriptors to establish the response of species or communities to ecological conditions. Soil water resource is an important factor but is poorly used by plant ecologists because of the lack of accessible data. We explore whether a large number of plots with basic soil information collected within the framework of forest inventories allows the soil water holding capacity (SWHC) to be mapped with enough accuracy to predict tree species growth over large areas. We first compared the performance of available pedotransfer functions (PTFs) and showed significant differences in the prediction quality of SWHC between the PTFs selected.We also showed that the most efficient class PTFs and continuous PTFs compared had similar performance, but there was a significant reduction in efficiency when they were applied to soils different from those used to calibrate them. With a root mean squared error (RMSE) of 0.046 cm 3 cm -3 (n = 227 horizons), we selected the Al Majou class PTFs to predict the SWHC in the soil horizons described in every plot, thus allowing 84% of SWHC variance to be explained in soils free of stone (n = 63 plots). Then, we estimated the soil water holding capacity by integrating the stone content collected at the soil pit scale (SWHC') and both the stone content at the soil pit scale and rock outcrop at the plot scale (SWHC") for the 100.307 forest plots recorded in France within the framework of forest inventories. The SWHC" values were interpolated by kriging to produce a map with 1 km² cell size, with a wider resolution leading to a decrease in map accuracy. The SWHC" given by the map ranged from 0 to 148 mm for a soil down to 1 m depth. The RMSE between map values and plot estimates was 33.9 mm, the best predictions being recorded for soils developed on marl, clay, and hollow silicate rocks, and in flat areas. Finally, the ability of SWHC' and SWHC" to predict 3 height growth for Fagus sylvatica, Picea abies and Quercus petraea is discussed. We show a much better predictive ability for SWHC" compared to SWHC'. The values of SWHC" extracted from the map were significantly related to tree height growth. They explained 10.7% of the height growth index variance for Fagus sylvatica (n = 866), 14.1% for Quercus petraea (n = 877) and 10.3% for Picea abies (n = 2067). The proportions of variance accounted by SWHC" were close to those recorded with SWHC" values estimated from the plots (11.5, 11.7, and 18.6% for Fagus sylvatica, Quercus petraea and Picea abies, respectively). We conclude that SWHC" can be mapped using basic soil parameters collected from plots, the predictive ability of the map and of data derived from the plot being close. Thus, the map could be used just as well for small areas as for large areas, directly or indirectly through water balance indices, to predict forest growth an...
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