SUMMARY OF TRANSFER FUNCTION RECONSTRUCTION

This chapter has presented the methods used in developing transfer function models for three training sets comprising site specific assemblages (JDT and BLT) and a total combined dataset (TCD). The results from unscreened analyses were variable and largely reflected the short environmental gradients of each training set. Detrended canonical correspondence analysis was used to assess the relationship between the foraminiferal assemblages in the training sets and altitude. This showed a strongly linear response along the environmental gradient with gradient lengths varying between 0.775 and 1.536 SD units. As a result linear regression models (PLS) were developed to assess the individual training sets reconstructive ability. In addition, unimodal regression (WA-PLS) was also investigated as a comparative tool for the BLT and TCD training sets which displayed longer gradient lengths in comparison to JDT.

The performance of each regression model was assessed using cross-validation results produced by jack-knifing the data. These statistical measures provided an evaluation of strength of relationship between observed and predicted values (r2jack) and associated errors of prediction (RMSEP jack). Again results were variable, with small r2jack values reflecting the short environmental gradients, especially for JDT (r2 jack = 0.11), despite prediction errors remaining small (RMSEP jack = 0.07). The low strengths of relationship at this site perhaps reflect the influence of other environmental variables effecting the distribution of foraminiferal assemblages. Indeed inter-correlations between the variables were high for the JDT training set (69.7%), as shown in chapter 4. In comparison, inter-correlation between the variables for BLT training set was significantly lower (33.5%) which is reflected in a higher strength of relationship for this training set (r2 jack = 0.71). Prediction errors remained relatively low for the BLT training set (RMSEP jack = 0.09). When combining both training sets to create a total combined dataset (comprising 56 samples), the strength of relationship deteriorated and prediction errors increased (r2jack = 0.32; RMSEP jack = 0.11).

The TCD training set was further investigated to remove sample outliers with a goal of improving model performance. This was achieved by removing all surface samples with an absolute residual greater than the standard deviation of altitude (0.141 SD units). Statistical parameters showed this procedure was useful in improving model performance with an increased strength of relationship (r2 jack = 0.54) and lower prediction errors (RMSEP jack = 0.08) for component three using linear regression (table 6.11). Unimodal regression (WA- PLS) showed similar performance. Component two was chosen to calibrate fossil samples as it performed better than components one and two but degraded thereafter. Similarly PLS

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The MAT was used to assess similarities and dissimilarities of fossil samples in cores JD1, JD2 and BL with assemblages from the modern environment. Percentiles produced by dissimilarity measures in the TCD training set were used to define thresholds allowing to distinguish between good, close and poor modern analogues. The number of fossil samples with poor modern analogues varied between the cores and largely reflected the limited number of modern analogues in the contemporary training set which was biased towards the upper part of the environmental gradient. Clearly the range of environments observed in the fossil sequences was greater than that sampled in the modern environment with many lower core fossil samples lacking modern equivalents.

Changes in palaeo-marsh altitude were investigated highlighting those levels where reconstructed values were considered unreliable. Overall the cores showed a variable, but increasing trend in palaeo-marsh altitude towards the surface. To determine if the selection of transfer function model had a significant impact on the reconstruction, an additional transfer function using the ML approach was developed. Results were comparable where reconstructed values fell within the error margins of each technique. However it also highlighted the uncertainty of reconstruction for fossil samples which contained an almost exclusive calcareous component towards the bottom of each core.

Reconstructed palaeo-marsh altitudes were converted to produce estimates of MSL, first plotted against depth and then age. A high-resolution record is observed for core JD1 where age-depth modelling suggests a record dating back to AD 1751. The record here suggests relatively stable MSL observations up until the early 20th century where fossil samples with good modern analogues suggest a sharp increase in MSL around AD 1940. Mean sea-level continues to rise up to AD 1968 and then a fall to AD 1987. A fluctuating record is then observed to the present day. In contrast, core JD2 demonstrates a relatively poor resolution record when plotted against age due to the limited number of samples included in the reconstruction which dates back to AD 1955. Nonetheless the record is based on fossil samples which display a good relationship with modern assemblages and suggests an increase in MSL up until AD 1971 with stable observations of MSL recorded thereafter. Reconstructed MSL for core BL allows sea-level inferences to be made dating back to AD 1888. The record indicates increasing trends in MSL to AD 1911 after which MSL stabilises up until AD 1962. An increasing trend in MSL is observed thereafter before faster rates of MSL rise are observed from 4 cm depth (AD 1986).

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CHAPTER 7