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What is CLAMP?

Climate Leaf Analysis Multivariate Programe (CLAMP) (Wolfe, 1990, 1993; Kovach and Spicer, 1995; Wolfe and Spicer, 1999; Spicer, 1999; 2000; 2007; 2008; Spicer et al., 2004, Spicer et al., 2009, Yang et al., 2011; 2015) is a multivariate statistical technique that decodes the climatic signal inherent in the physiognomy of leaves of woody dicotyledonous plants.

It was developed as an evolutionarily robust, accurate, and precise tool for direct atmospheric palaeoclimate determinations over land, and as such complements marine-based climate proxies such as oxygen isotopes. CLAMP calibrates the numerical relations between leaf physiognomy of the woody dicotyledons and meteorological parameters in modern terrestrial environments. Using this calibration, past climatic data are potentially determinable from leaf fossil assemblages provided that the calibration is robust over time and that the sampling of the fossil assemblage represents well the characteristics of the living source vegetation. CLAMP has been applied effectively to fossil floras up to 100 million years old (Herman and Spicer, 1996), but is an even more powerful and reliable tool for late Tertiary (Wolfe, 1995; Spicer, 1999) and Quaternary assemblages.

The statistical engine used in CLAMP is Canonical Correspondence Analysis (CANOCO) (Ter Braak, 1986) because this is robust for data that do not necessarily conform to normality and does not assume independence of variables (essential when leaf characters are the product of functional compromise, constructional efficiency and a finite genome). CANOCO is a direct ordination method, used widely in plant ecology, that orders samples, in this case vegetation sites, based on a set of attributes. In CLAMP the attributes are the scores of the 31 different leaf character states taken from more than 20 species of woody dicots in each vegetation site.

The largest calibration data set now available for analyses consists of foliar physiognomic measurements and climate observations from 378 sites worldwide. This data set is experimental, however, and should not be used for routine analyses using the conventional version of CLAMP. For more details of this data set go to the Calibration Data Sets page. Earlier calibrations used fewer modern sites (e.g. 103 in Herman and Spicer, 1996) and different datasets can be used for different purposes. For example if initial results indicate a warm climate then a subset of the full data set that excludes the so-called "alpine nest", such as the Physg3brcAZ data set, offers greater precision.

Because climate variables for these sites are known by observation (either >30 year averages are used from climate stations proximal to the sites or globally gridded climate data are utilized) vectors for each of the measured climate variables can be positioned in physiognomic space and calibrated. Palaeoclimate variables can be quantified by scoring a fossil assemblage in the same manner as for the modern vegetation and positioning the fossil site in physiognomic space in a passive manner so it does not distort the calibration by its presence. By projecting the position of the fossil site on to the calibrated climate vector the ancient palaeoclimate can be determined.

 

  CLAMP global CCA plot
   
  A canonical correspndence analysis plot in Axis 1 and 2 (X,Y) space of modern global vegetation samples (represented by cloured balls) and climate vectors (coloured arrows). The vegetation samples form a cloud known as physiognomic space. Samples that plot close together have a similar leaf physiognomic spectrum, while those that plot far apart are physiognomically dissimilar. In this plot the samples are colour coded for the mean annual temperature (MAT) that they experience. There is a distinct trend from cool climates in the bottom right to warm climates in the top left. This trend is represented by the MAT vector (red), here positioned behind the green cold month mean vector. This is a two dimensional plot but CLAMP routinely operates in four dimensional space, and can used in even more dimensions.

 

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