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Nonlinear methods for multivariate statistical calibration and their use in palaeoecology: a comparison of inverse (knearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches
 Chemometrics and Intelligent Laboratory Systems
, 1995
"... and their use in palaeoecology: a comparison of inverse (knearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches. ..."
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and their use in palaeoecology: a comparison of inverse (knearest neighbours, partial least squares and weighted averaging partial least squares) and classical approaches.
Inferring pH from diatoms: a comparison of old and new calibration methods. Hydrobiologia
, 1989
"... Two new methods for inferring pH from diatoms are presented. Both are based on the observation that the relationships between diatom taxa and pH are often unimodal. The first method is maximum likelihood calibration based on Gaussian logit response curves of taxa against pH. The second is weighted a ..."
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Two new methods for inferring pH from diatoms are presented. Both are based on the observation that the relationships between diatom taxa and pH are often unimodal. The first method is maximum likelihood calibration based on Gaussian logit response curves of taxa against pH. The second is weighted averaging. In a lake with a particular pH, taxa with an optimum close to the lake pH will be most abundant, so an intuitively reasonable estimate of the lake pH is to take a weighted average of the pH optima of the species present. Optima and tolerances of diatom taxa were estimated from contemporary pH and proportional diatom counts in littoral zone samples from 97 pristine soft water lakes and pools in Western Europe. The optima showed a strong relation with Hustedt’s pH preference groups. The two new methods were then compared with existing calibration methods on the basis of differences between inferred and observed pH in a test set of 62 additional samples taken between 1918 and 1983. The methods were ranked in order of performance as follows (between brackets the standard error of inferred pH in pH units); maximum likelihood (0.63)> weighted averaging (0.71) = multiple regression using pH groups (0.71) = the Gasse & Tekaia method (0.71)> Renberg & Hellberg’s Index B (0.83) % multiple regression
Canonical community ordination. Part I: Basic theory and linear methods. Ecoscience
 Ecoscience
, 1994
"... 1 Canonical community ordination comprises a collection of methods that relate species assemblages to their environment, in both observational studies and designed experiments. Canonical ordination differs from ordination sensu stricto in that species and environment data are analyzed simultaneously ..."
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1 Canonical community ordination comprises a collection of methods that relate species assemblages to their environment, in both observational studies and designed experiments. Canonical ordination differs from ordination sensu stricto in that species and environment data are analyzed simultaneously. Part I reviews the theory in a nonmathematical way with emphasis on new insights for the interpretation of ordination diagrams. The interpretation depends on the ordination method used to create the diagram. After the basic theory, Part I is focused on the ordination diagrams in linear methods of canonical community ordination, in particular principal components analysis, redundancy analysis and canonical correlation analysis. Special attention is devoted to the display of qualitative environmental variables. Key words: principal components analysis, redundancy analysis, canonical correlation analysis, biplot, ordination diagram, speciesenvironment relations. 2
Weighted averaging partial least squares regression (WAPLS): an improved method for reconstructing environmental variables from species assemblages. Hydrobiologia
, 1993
"... methods for speciesenvironment calibration. Chapter 25 in: Multivariate Environmental ..."
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methods for speciesenvironment calibration. Chapter 25 in: Multivariate Environmental
www.elsevier.com/locate/ecolmodel Predictive habitat distribution models in ecology
, 2000
"... With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities ..."
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With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species ’ response (or realized niche). Conceptual considerations include e.g. the tradeoff between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including thresholdindependent measures (e.g. receiver operating characteristic (ROC)plots) and