Prediction of forest soil nutrient status using vegetation

Abstract

Abstract. A large number of plots (306) were sampled for floristic data and variables of soil nutrient availability in a wide range of forest conditions in the Vosges mountains (NE France). Half of the data were used to model species presence/absence as a function of nutritional variables using two numerical methods: parametric logistic regression and non‐parametric Kernel estimation. Species responses were established for calcium, aluminium, Ca : Al ratio, base saturation, pH and C:N measured in the upper soil horizon. The other half were used to predict these variables with vegetation with three approaches: maximum likelihood, indicator values and ecological groups. More than 80% of the 122 studied species showed a significant response to one or more nutrition factors. Species often had optima for maxima or minima of the studied values. For each variable, species were more frequent in conditions with good nutrition and/or low toxicity: maximum values were most frequent for Ca, Ca: Al, base saturation, pH and minimum values were most frequent for Al and C:N. For all variables, optima estimated by logistic regression were well correlated with those estimated by kernel estimation: R2 > 0.9. Mean differences between measured values and predictions by vegetation were ca. 2.6 meq for Ca, 1.2 for log Ca, 3.1 meq for Al, 1.8 for log Al, 30 for Ca:Al, 2.1 for log Ca:Al, 0.21 for base saturation, 0.7 for pH and 5.7 for C:N. The quality of prediction as measured by R2 between predicted and measured values varied from 0.60 to 0.14. The quality of prediction decreases from soil base saturation to aluminium according to the next order: base saturation > log Ca : Al > log Ca > Ca > C :N > pH > log Al > Al > Ca :Al. Maximum likelihood and ecological groups methods of prediction were more efficient than the indicator values method.

A new bioclimatic model calibrated with vegetation for Mediterranean forest areas

“…Floristic composition and structure integrate all site conditions, including local conditions as well as global climate variables and their variability and extremes in the middle term, and the many interactions between all of them . Some of the plants are individually considered as good indicators of specific site conditions such as hydromorphy, pH, nutrient status or water availability (Gégout et al, 2003;Rameau et al, 1989). However, Mediterranean vegetation is generally disturbed by fire, grazing or clearing, so that the fast-changing vegetation is not representative of site potential; in such conditions, site assessment must rely mainly on abiotic variables.…”

Can understory vegetation accurately predict site index? A comparative study using floristic and abiotic indices in sessile oak (Quercus petraea Liebl.) stands in northern France

“…The non-parametric kernel estimation method is interesting because it does not require assumptions about the form of the species response curve [25]. We found that 42% of the species had maximum or minimum indicator values for SI 100 .…”

“…Much more important is the value of the bandwidth h. This parameter determines how local or how smooth the response curve will be. There is a trade off between smoothness and localness: as h increases, the smoothness increases, but the localness decreases [25]. The smoothing parameter h = 12 m (half the observed range of site index) was used.…”

“…Among the different methods [10,53,62], we chose a non-parametric kernel estimation method developed by Yee and Mitchell [63] and as used by Gégout et al [23,25]. The method estimates the probability of occurrence of each woodland plant species as a function of a quantitative variable, and does not require assumptions about the form of the species response curve [25]. It works by estimating the probability of a species i for a value x of the variable (e.g.…”

“…It is generally accepted that its precise form is not very important. For the present study, the standard Gaussian kernel was chosen [25]:…”