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Table of contents
INTRODUCTION
1. INTRODUCTION TO GLOBAL CHANGE AND VEGETATION DISTRIBUTION
1.1. Main issues of Global Change
1.2. Facts and figures on the French forest
2. MATERIALS AND METHODS
2.1. Presentation of the region
2.2. Presentation of the database
2.2.1. The concept of forest sites
2.2.2. Sources of data
2.2.2.1. The catalogue of forest sites
2.2.2.2. Other sources of data
2.2.3. Aims and extent of the database
2.2.4. Structure of the database
2.2.5. Quality of the data entered in the database
2.3. Presentation of the variables
2.3.1. Dependent variables
2.3.2. Independent variables
2.3.2.1. Climatic variables
2.3.2.2. Pedologic variables
2.3.2.3. Summary statistics
2.4. Presentation of the computation methods
2.4.1. Principal component analyses
2.4.2. Correspondence analyses
2.4.3. Non-parametric Kernel estimation regression
2.4.4. Logistic regression
2.4.4.1. Logistic regression analysis
2.4.4.2. Fitting a logistic regression model to data
2.4.4.3. Test statistics
2.4.4.4. Comparison of different logistic regression models
2.4.4.5. Interpretation of the fitted logistic regression model
3. RESULTS
3.1. Results from the principal components analyses
3.1.1. Principal components analyses on soil variables
3.1.1.1. Characterisation of correlation between soil variables
3.1.1.2. Characterisation of the links between soil variables
3.1.2. Principal component analyses on climate variables
3.1.2.1. Characterisation of correlation between climatic variables
3.1.2.2. Characterisation of the links between climatic variables
3.2. Results from the correspondence analyses
3.2.1. First correspondence analysis
3.2.1.1. Representation of the forest species factorial planes
3.2.1.2. Ecological meaning of the first axis
3.2.1.3. Ecological meaning of the second axis
3.2.1.4. Ecological meaning of the third axis
3.2.2. Second correspondence analysis
3.2.2.1. Preparation of the second correspondence analysis
3.2.2.2. Correlation of forest sites factorial co-ordinates from the two analyses
3.2.2.3. Ecological meaning of the second axis
3.2.3. Conclusion
3.2.3.1. Selection of soil variables for further computations
3.2.3.2. Selection of climatic variables for further computations
3.2.3.3. Summary of the main explicative variables
3.3. Results from the Kernel estimation regression
3.4. Results from logistic regression
3.4.1. Choosing a “best” model for each species
3.4.2. Presentation of logistic regression results
3.4.3. Interpretation of logistic regression results
3.4.3.1. Five types of sub-models
3.4.3.2. Sensitivity of species towards variables
3.4.3.3. Significance of the regression coefficients
3.4.4. Discussion of logistic regression results
3.4.4.1. Goodness-of fit (pseudo-R2)
3.4.4.2. Presence/absence data versus probability of presence
3.4.4.3. Other link functions
3.5. Comparison of ecological indicator values
3.5.1. Interests of ecological indicator values
3.5.2. Summary of the different methods
3.5.3. Correlation between the different methods
4. DISCUSSION ON THE SENSITIVITY OF VEGETAL SPECIES TO GLOBAL CHANGE
4.1. Scenarios of Global Change
4.1.1. Change in temperature: global warming
4.1.2. Change in soil pH: acidification
4.1.3. Change in the C/N ratio: eutrophication
4.1.4. Combination of the changes
4.2. Choice of species
4.2.1. Choice of non-tree plants
4.2.2. Sensitive species
4.3. Results from the different scenarios
4.3.1. Spatial representation of probabilities of presence
4.3.1.1. Lamium maculatum
4.3.1.2. Oxalis acetosella
4.3.2. Effect of Global Change scenarios on the numbers of occurrences
4.3.2.1. Disappearance of species
4.3.2.2. Decrease/increase in the probability of presence
4.3.3. Discussion on the methodology
4.3.3.1. Long-term predictions
4.3.3.2. Factors of uncertainty
5. CONCLUSIONS
5.1. Difficulty of ecological modelling
5.1.1. Heterogeneity of the data
5.1.2. Simple models
5.2. Autecology vs. synecology
5.3. Further research
6. REFERENCES
7. ACRONYMS
