Access the full text.
Sign up today, get DeepDyve free for 14 days.
Christophe Coudun, J. Gégout (2005)
Ecological behaviour of herbaceous forest species along a pH gradient: a comparison between oceanic and semicontinental regions in northern FranceGlobal Ecology and Biogeography, 14
E. Ziegel (2002)
Generalized Linear ModelsTechnometrics, 44
E. Heegaard (1997)
Ecology of Andreaea in western NorwayJournal of Bryology, 19
M. Araújo, Paul Williams (2000)
Selecting areas for species persistence using occurrence dataBiological Conservation, 96
(1999)
S-Plus 2000: programmer's guide
J. Franklin, P. McCullough, Curtis Gray (2000)
Terrain variables used for predictive mapping of vegetation communities in southern California
M. Duc, M. Hill, T. Sparks (1992)
A method for predicting the probability of species occurrence using data from systematic surveys
L. Bragazza, R. Gerdol (1996)
Response surfaces of plant species along water-table depth and pH gradients in a poor mire on the southern Alps (Italy)
S. Rushton, S. Ormerod, G. Kerby (2004)
New paradigms for modelling species distributionsJournal of Applied Ecology, 41
G. Shao, P. Halpin (1995)
Climatic controls of eastern North American coastal tree and shrub distributionsJournal of Biogeography, 22
J. Smart, W. Sutherland, A. Watkinson, J. Gill (2004)
A New Means of Presenting the Results of Logistic RegressionBulletin of The Ecological Society of America, 85
A. Lehmann, J. Overton, J. Leathwick (2002)
GRASP: generalized regression analysis and spatial predictionEcological Modelling, 157
(2001)
Zum Reproduktionssystem des Feldahorns (Acer campestre L.) – Blühphänologie und genetische Untersuchungen. PhD Dissertation, Institut für Forstgenetik und Forstpflanzenzüchtung, Universität Göttingen
P. Bénichou, O. Breton (1987)
Prix Norbert Gerbier 1986: prise en compte de la topographie pour la cartographie des champs pluviométriques statistiques
Benichou Benichou, Le Breton Le Breton (1987)
Prise en compte de la topographie pour la cartographie des champs pluviométriques statistiquesLa Météorologie, 7
A. Cherrill, C. McClean, P. Watson, K. Tucker, S. Rushton, R. Sanderson (1995)
Predicting the distributions of plant species at the regional scale: a hierarchical matrix modelLandscape Ecology, 10
T. Yee, N. Mitchell (1991)
Generalized additive models in plant ecologyJournal of Vegetation Science, 2
L. Brotóns, W. Thuiller, M. Araújo, A. Hirzel (2004)
Presence-absence versus presence-only modelling methods for predicting bird habitat suitabilityEcography, 27
J. Franklin (1995)
Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradientsProgress in Physical Geography, 19
T. Hastie, R. Tibshirani (2014)
Generalized Additive Models
M. Vayssières, R. Plant, B. Allen-Diaz (2000)
Classification trees: An alternative non‐parametric approach for predicting species distributionsJournal of Vegetation Science, 11
M. Austin (2002)
Spatial prediction of species distribution: an interface between ecological theory and statistical modellingEcological Modelling, 157
J. Theurillat, A. Guisan (2001)
Potential Impact of Climate Change on Vegetation in the European Alps: A ReviewClimatic Change, 50
M. Austin, Jacqui Meyers (1996)
Current approaches to modelling the environmental niche of eucalypts: implication for management of forest biodiversityForest Ecology and Management, 85
J. Lenihan (1993)
Ecological response surfaces for North American boreal tree species and their use in forest classificationJournal of Vegetation Science, 4
A. Guisan, N. Zimmermann (2000)
Predictive habitat distribution models in ecologyEcological Modelling, 135
(1943)
Professor in Forest Ecology, was an international expert in botany, plant ecology, succession dynamics, habitat conservation and land management. He was the main author of many reports and books
A. Bio, R. Alkemade, A. Barendregt (1998)
Determining alternative models for vegetation response analysis: a non‐parametric approachJournal of Vegetation Science, 9
M. Hooten, D. Larsen, C. Wikle (2003)
Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian modelLandscape Ecology, 18
W. Thuiller, J. Vayreda, J. Pino, S. Sabaté, S. Lavorel, C. Gracia (2003)
Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain)Global Ecology and Biogeography, 12
A. Guisan, S. Weiss, Andrew Weiss (1999)
GLM versus CCA spatial modeling of plant species distributionPlant Ecology, 143
H. Ellenberg, H. Weber, R. Düll, V. Wirth, W. Werner, D. Paulißen (1992)
Zeigerwerte von Pflanzen in Mitteleuropa
A. Journel (1983)
Nonparametric estimation of spatial distributionsJournal of the International Association for Mathematical Geology, 15
A. Fielding, J. Bell (1997)
A review of methods for the assessment of prediction errors in conservation presence/absence modelsEnvironmental Conservation, 24
A. Peterson (2003)
Predicting the Geography of Species’ Invasions via Ecological Niche ModelingThe Quarterly Review of Biology, 78
(2002)
Comportement écologique des espèces forestières vis-à-vis du climat et du sol en France: application à l’évaluation des charges critiques d’acidité et d’azote
Journel Journel (1983)
Nonparametric estimation of spatial distributionsMathematical Geology, 15
M. Luoto, R. Heikkinen, J. Pöyry, K. Saarinen (2006)
Determinants of the biogeographical distribution of butterflies in boreal regionsJournal of Biogeography, 33
M. Cabeza, M. Araújo, R. Wilson, C. Thomas, M. Cowley, A. Moilanen (2004)
Combining probabilities of occurrence with spatial reserve designJournal of Applied Ecology, 41
T. Dirnböck, S. Dullinger, G. Grabherr (2003)
A regional impact assessment of climate and land‐use change on alpine vegetationJournal of Biogeography, 30
(2004)
Presence–absence versus presence-only habitat suitability models: the role of species ecology and prevalence
J. Lawesson, J. Oksanen (2002)
Niche characteristics of Danish woody species as derived from coenoclines, 13
Christian Piedallu is a GIS engineer whose research interests include derivation of high-resolution ecological variables and indices and their testing in plant species predictive distribution models
J. Lennon (2000)
Red-shifts and red herrings in geographical ecologyEcography, 23
Anthony Lehmann, J. Overton, Mike Austin (2002)
Regression models for spatial prediction: their role for biodiversity and conservationBiodiversity & Conservation, 11
C. Braak, C. Looman (2004)
Weighted averaging, logistic regression and the Gaussian response modelVegetatio, 65
D. McKenzie, D. Peterson, David Peterson, P. Thornton (2003)
Climatic and biophysical controls on conifer species distributions in mountain forests of Washington State, USAJournal of Biogeography, 30
J. Briggs (2003)
Marine centres of origin as evolutionary enginesJournal of Biogeography, 30
M. Araújo, R. Pearson, W. Thuiller, M. Erhard (2005)
Validation of species–climate impact models under climate changeGlobal Change Biology, 11
Ter Braak Ter Braak, Barendregt Barendregt (1986)
Weighted averaging of species indicator values: its efficiency in environmental calibrationMathematical Biosciences, 78
E. Heegaard (2001)
Environmental relationships of perichaetial and sporophyte production in Andreaea spp in western NorwayJournal of Bryology, 23
P. Segurado, M. Araújo (2004)
An evaluation of methods for modelling species distributionsJournal of Biogeography, 31
P. Cheek, P. McCullagh, J. Nelder (1990)
Generalized Linear Models, 2nd Edn.Applied statistics, 39
L. Gignac, D. Vitt, S. Bayley (1991)
Bryophyte response surfaces along ecological and climatic gradientsVegetatio, 93
F. Skov, Jens‐Christian Svenning (2004)
Potential impact of climatic change on the distribution of forest herbs in EuropeEcography, 27
(1989)
Flore forestière française
S. Fienberg (2006)
When did Bayesian inference become "Bayesian"?Bayesian Analysis, 1
M. Bakkenes, J. Alkemade, F. Ihle, R. Leemans, J. Latour (2002)
Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050Global Change Biology, 8
A. Guisan, T. Edwards, T. Hastie (2002)
Generalized linear and generalized additive models in studies of species distributions: setting the sceneEcological Modelling, 157
J. Elith (2000)
Quantitative Methods for Modeling Species Habitat: Comparative Performance and an Application to Australian Plants
M. Schwartz, L. Iverson, A. Prasad (2001)
Predicting the Potential Future Distribution of Four Tree Species in Ohio Using Current Habitat Availability and Climatic ForcingEcosystems, 4
J. Franklin (1998)
Predicting the distribution of shrub species in southern California from climate and terrain‐derived variablesJournal of Vegetation Science, 9
J. Leathwick (1998)
Are New Zealand's Nothofagus species in equilibrium with their environment?Journal of Vegetation Science, 9
O. Vetaas (2002)
Realized and potential climate niches: a comparison of four Rhododendron tree speciesJournal of Biogeography, 29
M. Araújo, R. Pearson (2005)
Equilibrium of species’ distributions with climateEcography, 28
G. Carpenter, A. Gillison, J. Winter (1993)
DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animalsBiodiversity & Conservation, 2
M. Araújo, W. Thuiller, R. Pearson (2006)
Climate warming and the decline of amphibians and reptiles in EuropeJournal of Biogeography, 33
E. Cawsey, Mike Austin, B. Baker (2002)
Regional vegetation mapping in Australia: a case study in the practical use of statistical modellingBiodiversity & Conservation, 11
Cajo Ter, Braak And, Leo Barendregt (1986)
Weighted averaging of species indicator values: Its efficiency in environmental calibrationBellman Prize in Mathematical Biosciences, 78
O. Marinoni (2003)
Improving geological models using a combined ordinary–indicator kriging approachEngineering Geology, 69
Jean-Claude Gégout (2001)
Création d'une base de données phytoécologiques pour déterminer l'autécologie des espèces de la flore forestière de France, 53
J. Franklin (2002)
Enhancing a regional vegetation map with predictive models of dominant plant species in chaparral, 5
S. Polasky, J. Camm, A. Solow, B. Csuti, D. White, Rugang Ding (2000)
Choosing reserve networks with incomplete species informationBiological Conservation, 94
M. E, L. H (2004)
Would climate change drive species out of reserves ? An assessment of existing reserve-selection methods
E. Box, D. Crumpacker, E. Hardin (1993)
A climatic model for location of plant species in Florida, U.S.A.Journal of Biogeography, 20
J. Elith, M. Burgman (2002)
Predictions and their validation: Rare plants in the Central Highlands, Victoria, Australia
J. Bayliss, V. Simonite, Stewart Thompson (2005)
The use of probabilistic habitat suitability models for biodiversity action planningAgriculture, Ecosystems & Environment, 108
L. Iverson, A. Prasad, M. Schwartz (1999)
Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginianaEcological Modelling, 115
A. Gelfand, J. Silander, Shanshan Wu, A. Latimer, P. Lewis, A. Rebelo, M. Holder (2006)
Explaining Species Distribution Patterns through Hierarchical ModelingBayesian Analysis, 1
P. Legendre (1993)
Spatial Autocorrelation: Trouble or New Paradigm?Ecology, 74
M. Fortin, T. Keitt, B. Maurer, M. Taper, Dawn Kaufman, T. Blackburn (2005)
Species' geographic ranges and distributional limits: pattern analysis and statistical issuesOikos, 108
C. Thornthwaite, J. Mather (1955)
Instructions and tables for computing potential evapotranspiration and the water balance
Anthony Lehmann, J. Leathwick, J. Overton (2002)
Assessing New Zealand fern diversity from spatial predictions of species assemblagesBiodiversity & Conservation, 11
J. Elith, M. Burgman, H. Regan (2002)
Mapping epistemic uncertainties and vague concepts in predictions of species distributionEcological Modelling, 157
A. Odland, H. Birks, J. Line (1995)
Ecological optima and tolerances of Thelypteris limbosperma, Athyrium distentifolium, and Matteuccia struthiopteris along environmental gradients in Western NorwayVegetatio, 120
G. Lipsett-Moore, D. McKenney, Scott Jones (2003)
Multi-scale species modelling in Ontario: A workshop on needs and opportunitiesForestry Chronicle, 79
(2002)
Predicting presence / absence of plant species for range mapping : a case study from Wyoming
J. Huisman, H. Olff, L. Fresco (1993)
A hierarchical set of models for species response analysisJournal of Vegetation Science, 4
N. Gotelli (2003)
Predicting Species Occurrences: Issues of Accuracy and Scale, 120
C. Margules, J. Stein (1989)
Patterns in the distributions of species and the selection of nature reserves: An example from Eucalyptus forests in South-eastern New South WalesBiological Conservation, 50
R. Pearson, T. Dawson, P. Berry, P. Harrison (2002)
SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of SpeciesEcological Modelling, 154
Jesús Muñoz, Á. Felicísimo (2004)
Comparison of statistical methods commonly used in predictive modelling, 15
J. McPherson, W. Jetz, D. Rogers (2004)
The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?Journal of Applied Ecology, 41
(1971)
Role of regression analysis in plant ecology
B. Huntley, R. Green, Yvonne Collingham, J. Hill, S. Willis, P. Bartlein, W. Cramer, W. Hagemeijer, C. Thomas (2004)
The performance of models relating species geographical distributions to climate is independent of trophic levelEcology Letters, 7
Gégout Gégout, Coudun Coudun, Bailly Bailly, Jabiol Jabiol (2005)
EcoPlant: a forest sites database to link floristic data with soil resources and climatic conditionsJournal of Vegetation Science, 16
D. McKenzie, D. Peterson, D. Peterson (2003)
Modelling conifer species distributions in mountain forests of Washington State, USAForestry Chronicle, 79
P. Schwarz, T. Fahey, C. McCulloch (2003)
FACTORS CONTROLLING SPATIAL VARIATION OF TREE SPECIES ABUNDANCE IN A FORESTED LANDSCAPEEcology, 84
A. Fitter (1977)
Flora Europaea Vol. 4, edited by T. G. Tutin, V. H. Heywood, N. A. Burges, D. M. Moore, D. H. Valentine, S. M. Walters, and D. A. Webb. Cambridge University Press, £25.Oryx, 14
M. Diekmann (2003)
Species indicator values as an important tool in applied plant ecology – a reviewBasic and Applied Ecology, 4
(1961)
Evaluation des besoins en eau d’irrigation et évaporation potentielle
J. Diniz‐Filho, L. Bini, B. Hawkins (2003)
Spatial autocorrelation and red herrings in geographical ecologyGlobal Ecology and Biogeography, 12
E. Mills (1996)
AN APPRECIATION AND NATURAL HISTORY OF THE ENGLISH FIELD MAPLE (ACER CAMPESTRE L.)Arboricultural Journal, 20
(2002)
ArcGIS 8.2. Environmental Systems Research Institutes
A. Bio (2000)
Does vegetation suit our models? : data and model assumptions and the assessment of species distribution in space
J. Swets (1988)
Measuring the accuracy of diagnostic systems.Science, 240 4857
J. Leathwick, D. Whitehead (2001)
Soil and atmospheric water deficits and the distribution of New Zealand's indigenous tree speciesFunctional Ecology, 15
A. Guisan, J. Theurillat, F. Kienast (1998)
Predicting the potential distribution of plant species in an alpine environmentJournal of Vegetation Science, 9
Yvonne Collingham, R. Wadsworth, B. Huntley, P. Hulme (2000)
Predicting the spatial distribution of non‐indigenous riparian weeds: issues of spatial scale and extentJournal of Applied Ecology, 37
Paulina Pinto, J. Gégout (2005)
Assessing the nutritional and climatic response of temperate tree species in the Vosges MountainsAnnals of Forest Science, 62
S. Ferrier, M. Drielsma, G. Manion, G. Watson (2002)
Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modellingBiodiversity & Conservation, 11
D. Knight, D. Mueller‐Dombois, H. Ellenberg (1974)
Aims and Methods of Vegetation EcologyBioScience
W. Thuiller, M. Araújo, S. Lavorel (2004)
Do we need land‐cover data to model species distributions in Europe?Journal of Biogeography, 31
W. Thuiller (2003)
BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global changeGlobal Change Biology, 9
R. Maggini, A. Lehmann, N. Zimmermann, A. Guisan (2006)
Improving generalized regression analysis for the spatial prediction of forest communitiesJournal of Biogeography, 33
N. Zimmermann, F. Kienast (1999)
Predictive mapping of alpine grasslands in Switzerland: Species versus community approachJournal of Vegetation Science, 10
J. Gégout, J. Hervé, F. Houllier, J. Pierrat (2003)
Prediction of forest soil nutrient status using vegetation, 14
J. Leathwick, G. Rogers (1996)
MODELLING RELATIONSHIPS BETWEEN ENVIRONMENT AND CANOPY COMPOSITION IN SECONDARY VEGETATION IN CENTRAL NORTH ISLAND, NEW ZEALAND
E. Heegaard (2002)
A model of alpine species distribution in relation to snowmelt time and altitude, 13
S. Ferrier, G. Watson, J. Pearce, M. Drielsma (2004)
Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modellingBiodiversity & Conservation, 11
A. Peterson, M. Papeş, D. Kluza (2003)
Predicting the potential invasive distributions of four alien plant species in North America, 51
W. Thuiller, M. Araújo, S. Lavorel (2003)
Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales, 14
P. McCullagh, J. Nelder (1993)
Generalized linear models. 2nd ed.Journal of the American Statistical Association, 88
Firbank, Ellis, Hill, Lockwood, Swetnam (1998)
Mapping the distribution of weeds in Great Britain in relation to national survey data and to soil typeWeed Research, 38
D. McKenzie, Charles Halpern (1999)
Modeling the distributions of shrub species in Pacific northwest forestsForest Ecology and Management, 114
R. Guries, E. Nordheim (1984)
Notes: Flight Characteristics and Dispersal Potential of Maple SamarasForest Science, 30
A. Hampe (2004)
Bioclimate envelope models: what they detect and what they hideGlobal Ecology and Biogeography, 13
M. Hohn (1991)
An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US)Computers & Geosciences, 17
Gé gout, PhD, is an ecologist whose research interests include forest plant ecology and sociology as well as geographical information systems (GIS)
Christophe Coudun (2005)
Approche quantitative de la réponse écologique des espèces végétales forestières à l'échelle de la France
(2001)
Flora Europaea, Volumes 1–5
L. Gignac (1992)
Niche Structure, Resource Partitioning, and Species Interactions of Mire Bryophytes Relative to Climatic and Ecological Gradients in Western CanadaThe Bryologist, 95
G. Philip, D. Watson (1982)
A PRECISE METHOD FOR DETERMINING CONTOURED SURFACESThe APPEA Journal, 22
(1972)
Models and analysis of descriptive vegetation data
M. Robertson, M. Villet, A. Palmer (2004)
A fuzzy classification technique for predicting species’ distributions: applications using invasive alien plants and indigenous insectsDiversity and Distributions, 10
Jennifer Miller, J. Franklin (2002)
Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependenceEcological Modelling, 157
C. Randin, T. Dirnböck, S. Dullinger, N. Zimmermann, M. Zappa, A. Guisan (2006)
Are niche‐based species distribution models transferable in space?Journal of Biogeography, 33
J. Gégout, Christophe Coudun, Gilles Bailly, B. Jabiol (2005)
EcoPlant: A forest site database linking floristic data with soil and climate variables, 16
(1995)
L’érable. Actes Sud, Paris
Philip Philip, Watson Watson (1982)
A precise method for determining contoured surfacesAustralian Petroleum Exploration Association Journal, 22
R. Bilonick (1989)
An Introduction to Applied Geostatistics
J. Clark, Eric Macklin, L. Wood (1998)
STAGES AND SPATIAL SCALES OF RECRUITMENT LIMITATION IN SOUTHERN APPALACHIAN FORESTSEcological Monographs, 68
Paul Williams, L. Hannah, S. Andelman, G. Midgley, M. Araújo, G. Hughes, L. Manne, E. Martínez‐Meyer, R. Pearson (2005)
Planning for Climate Change: Identifying Minimum‐Dispersal Corridors for the Cape ProteaceaeConservation Biology, 19
R. Pearson, T. Dawson, Canran Liu (2004)
Modelling species distributions in Britain: a hierarchical integration of climate and land-cover dataEcography, 27
Aim To estimate the relative importance of climate and soil nutritional variables for predicting the distribution of Acer campestre (L.) in French forests. Location France. Methods We used presence/absence information for A. campestre in 3286 forest plots scattered all over France, coupled with climatic and edaphic data. More than 150 climatic variables (temperature, precipitation, solar radiation, evapotranspiration, water balance) were obtained using a digital elevation model (DEM) and a geographical information system (GIS). Six direct soil variables (pH, C/N ratio, base saturation rate, concentrations of calcium, magnesium and potassium) were available from EcoPlant, a phytoecological data base for French forests. Using a forward stepwise logistic regression technique, we derived two distinct predictive models for A. campestre; the first with climatic variables alone and the second with both climatic and edaphic variables. Results The distribution of A. campestre was poorly modelled when including only climatic variables. The inclusion of edaphic variables significantly improved the quality of predictions for this species, allowing prediction of patches of presence/absence within the study region. Main conclusion Soil nutritional variables may improve the performance of fine‐scale (grain) plant species distribution models.
Journal of Biogeography – Wiley
Published: Oct 1, 2006
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.