## 4 Discussion  
So far, most approaches to diatom analysis have been based on the calculation of relative frequencies [26]). However, using diatom concentrations instead of relative abundances can improve the accuracy, comparability, and interpretability of diatom data, which can enhance their performance as a biological indicator for environmental reconstructions and water quality assessments [27]). Diatom concentrations provide more quantitative information on the abundance of diatoms in a sample, which can increase the sensitivity of the analysis [28]. This is particularly important for detecting subtle changes in diatom populations, such as those associated with environmental stressors or changes in water quality. Diatom concentrations allow for more direct comparisons between samples and across different studies. Relative abundances can be influenced by changes in the total diatom population, which can make it difficult to compare samples with different overall diatom abundances. Concentrations provide a standardized measure of diatom abundance that is not influenced by changes in the total population [29]. Diatom concentrations can be analyzed using a wider range of statistical techniques, which can provide more robust and informative results [30]. Relative abundances may be less suitable for some statistical techniques because they are constrained to a limited range (0-100%). Diatom concentrations can provide insights into the productivity of a lake or other water body, which is important for understanding its ecological health and functioning. Concentrations can also be used to estimate other environmental variables, such as nutrient concentrations, pH, and temperature, which can provide additional information for environmental reconstructions. There are several sources of error when counting diatom valve concentrations under a microscope [31]. It is important to select a representative sample size. If the sample size is too small, the results may not be accurate. Similarly, if the sample size is too large, it can be difficult to count all the cells or particles accurately. Additionally, diatom cells vary greatly in size and shape, which can make it difficult to count them accurately. Some cells or particles may be missed or counted multiple times if they are similar in size to others. Some samples can have debris or artifacts that can interfere with accurate counting. Differences in preparation techniques can lead to counting bias and errors in concentration calculations [32]. The person counting can introduce bias if there are preconceived ideas \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Interclass Correlation Coefficients**} \\ \hline  & **FOV** & **Microspheres** & **Combined** \\ \hline ICC2 & 0.95 & 0.98 & 0.40 \\ Lower bound & 0.93 & 0.95 & 0.17 \\ Upper bound & 0.98 & 0.99 & 0.71 \\ \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Intraclass correlation coefficients for the analyzed samples using the microspheres and FOV method and the coefficient for both methods combined. Upper and lower limits for the confidence classes and \(p\)-values are also shown. about what is expected to find or if the analyst is influenced by the results of previous counts [33]. Moreover, different counting techniques can produce different results. Overall, it is important to minimize these sources of error through careful sample preparation, use of appropriate counting techniques, quality control measures, and standardization of laboratory procedures. Errors related to the sample itself include variability in sample size, variability in cell or particle size, and interference from debris or artifacts [34]. These errors are related to the quality and nature of the sample being analyzed and can be minimized through careful sample preparation and quality control measures. On the other hand, errors external to the sample include observer bias, counting technique, and variability in preparation techniques.