Statistics in monitoring - detecting trends and correlations
“It looks like it’s rising” is not enough - in monitoring it is worth checking numerically whether a trend is real and whether the indicators are actually linked. This does not require advanced statistics; a few proven methods and an awareness of common pitfalls suffice.
Is there a trend
- The Mann-Kendall test - a popular, non-parametric trend test, coping well with environmental data (it does not require a normal distribution and handles outliers).
- Sen’s slope - an outlier-robust measure of the rate of change.
Together they answer the questions “is it rising/falling” and “how fast”.
Are the indicators linked
- Pearson correlation - a linear relationship,
- Spearman correlation - a monotonic (rank) relationship, more robust to extreme values.
They help, for example, to confirm that oxygen falls as temperature rises, or that turbidity rises after rainfall (see combining with public data).
The most common pitfalls
- Seasonality posing as a trend - first remove or account for the annual rhythm (see time series).
- Autocorrelation - successive measurements can depend on the previous ones; it inflates the “significance”.
- Correlation ≠ cause - a link between two indicators does not prove that one causes the other.
- Gaps and values below the threshold - they affect the results; they must be treated consciously.
What to compute it with
Simple analyses you can do in Excel, fuller ones in R or STATISTICA - the key is well-prepared data.
In practice
The starting point is an orderly, continuous series. In LimnoLog measurements immediately form charts (with a comparison of seasons), which lets you make a preliminary judgement of the trend and relationships, and for heavier statistics you can export the data ready for processing.
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