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Braak Braak, Looman Looman (1986)
Weighted averaging, logistic regression and the Gaussian response modelVegetatio, 65
M. Stone (1976)
Cross‐Validatory Choice and Assessment of Statistical PredictionsJournal of the royal statistical society series b-methodological, 36
N. Breslow (1984)
Extra‐Poisson Variation in Log‐Linear ModelsApplied statistics, 33
Braak Braak, Dam Dam (1989)
Inferring pH from diatoms: a comparison of old and new calibration methodsHydrobiologia, 178
I. Renberg (1982)
The pH history of lakes in Southwestern Sweden, as calculated from the subfossil diatom flora of the sedimentsAMBIO: A Journal of the Human Environment, 11
Austin (1987)
Models forthe analysis of species' response to environmental gradientsVegetatio, 69
C. Braak, N. Gremmen (1987)
Ecological amplitudes of plant species and the internal consistency of Ellenberg’s indicator values for moisturePlant Ecology, 69
Braak Braak, Barendregt Barendregt (1986)
Weighted averaging of species indicator values: its efficiency in environmental predictionMath. Biosci., 78
J. Oksanen, E. Läärä, P. Huttunen, J. Meriläinen (1988)
Estimation of pH optima and tolerances of diatoms in lake sediments by the methods of weighted averaging, least squares and maximum likelihood, and their use for the prediction of lake acidityJournal of Paleolimnology, 1
G. Oehlert (1988)
Interval estimates for diatom-inferred lake pH historiesCanadian Journal of Statistics-revue Canadienne De Statistique, 16
R. Green (1979)
Sampling design and statistical methods for environmental biologists.
Austin (1976)
On non-linear species response models in ordinationVegetatio, 33
Degórski (1982)
Usefulness of Ellenberg bio indicators in characterizing plant communities and forest habitats on the basis of data from the range' Grabowy' inKampinos forestEkol. Pol., 30
Stone Stone (1974)
Cross‐validatory choice and assessment of statistical predictions (with discussion)J. R. Statist. Soc. B., 36
D. Altman, J. Bland (1983)
Measurement in Medicine: The Analysis of Method Comparison StudiesThe Statistician, 32
McDonald McDonald (1980)
On the Poisson approximation to the multinomial modelCan. J. Statist., 8
Austin (1980)
Searching for a model for use in vegetation analysisVegetatio, 42
G. Tyler (1987)
Probable Effects of Soil Acidification and Nitrogen Deposition on the Floristic Composition of Oak (Quercus robur L.) ForestFlora, 179
Minchin Minchin (1987)
Simulation of multidimensional community patterns: towards a comprehensive modelVegetatio, 71
M. Austin (1986)
The theoretical basis of vegetation science.Trends in ecology & evolution, 1 6
M. Hill, R. Jongman, C. Braak, O. Tongeren (1987)
Data analysis in community and landscape ecologyJournal of Animal Science
J. Elner, S. Ray (1987)
pH profiles from diatom stratigraphies in sediment cores of selected lakes of new brunswick and Nova Scotia, CanadaWater, Air, and Soil Pollution, 32
T. Herben, J. Liška (1986)
A Simulation Study on the Effect of Flora Composition, Study Design and Index Choice on the Predictive Power of Lichen BioindicationThe Lichenologist, 18
Cajo Ter, Braak And, Leo Barendregt (1986)
Weighted averaging of species indicator values: Its efficiency in environmental calibrationBellman Prize in Mathematical Biosciences, 78
Meriläinen Meriläinen (1967)
The diatom flora and hydrogen ion concentration of waterAnn. Bot. Fenn., 4
S. Persson (1981)
Ecological Indicator Values as an Aid in the Interpretation of Ordination DiagramsJournal of Ecology, 69
M. Austin (1987)
Models for the analysis of species’ response to environmental gradientsPlant Ecology, 69
C. Braak (1988)
CANOCO - a FORTRAN program for canonical community ordination by [partial] [etrended] [canonical] correspondence analysis, principal components analysis and redundancy analysis (version 2.1)
R. Flower, R. Battarbee, P. Appleby (1987)
The recent palaeolimnology of acid lakes in Galloway, south-west Scotland: diatom analysis, pH trends, and the rôle of afforestationJournal of Ecology, 75
D. McDonald (1980)
On the poisson approximation to the multinomial distributionCanadian Journal of Statistics-revue Canadienne De Statistique, 8
W. Dixon, Morton Brown (1983)
BMDP statistical software
Oksanen Oksanen, Huttunen Huttunen (1989)
Finding a common ordination for several data sets by individual differences scalingVegetatio, 83
H. Gauch (1984)
Multivariate analysis in community ecology
Abstract. As an example of ecological gradient analysis, Gaussian response functions, with Poisson or quasi‐Poisson error distribution, were fitted for diatom taxa on a pH gradient. It is possible to predict or infer the pH of lake water from the fitted curves using the method of maximum likelihood, which is easily implemented in standard non‐linear regressionprograms. Due to overdis‐persion with respect to the Poisson distribution, moment estimates forthe negative binomial distribution were also applied, both in estimating the species response curves and in prediction. Simulations indicated that the theoretical maximum precision (measuredby standard deviation of prediction errors) in our data set was 0.17 pH units. The observed errors were much greater (SD 0.35 to 0.43). It seems that roughly equal proportions of the excess error were caused (1) by systematic differences between the training (estimation) data and the validation (prediction) data, and (2) from a misspecified model. It is suggested that the error due to model misspecification consists of inadequacy of the presumed error distribution and of inadequacy of the simple Gaussian response function.
Journal of Vegetation Science – Wiley
Published: Feb 1, 1990
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