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Statistics and Monitoring

Five key principles for a successful monitoring scheme:

  • Have simple and measurable objectives;
  • Do a baseline survey first and consider stratifying the site;
  • Use a good method for data collection (repeatable, practical & powerful);
  • Make sure you have an adequate sample size;
  • Practise good data management all the way through.

Ecological statistics and vegetation monitoring

A good question to begin with is why do we need to do statistical tests anyway? Why can’t we just survey what we are interested in both times and see if the result from one survey is different to the other? Well, we could do that, but if our survey involves sampling (e.g. we do a number of quadrats, rather than surveying every square metre of the habitat) then we are never sure if the samples are truly representative of the conditions in the entire habitat. Our results may appear to be different, but this difference could just be due to random chance associated with the way we sampled. Using standard, well-established statictical tests allows us to quantify how uncertain we are that the results are not just due to chance. This allows us to be more confident about the strength (or lack of strength) of the evidence from the survey results.

How can we help you?

We can help you in designing monitoring schemes/programmes. The best time to think about what type of statistical tests you will use is right at the start, as this more or less determines the types of field methodologies you should use. If you don’t consider statistics at the start there is a risk that the hard work you put in doing the surveying will not end up being useful. You may either get no result or a misleading result.

Some general tips (see the bullet points above also):

  • The simplest schemes are often the best;
  • Avoid methods involving estimating (e.g. DAFOR or DOMIN); instead count or measure what you are interested in;
  • Avoid methods that result in lots of zero values.

We can also help with statistical analysis of data. We are vegetation and plant specialists, but many of the principles apply equally to animal species and communities. These analyses might involve:

  • Describing: i.e. summarising the main points about your species/habitat/site using graphs and key numbers;
  • Differences: e.g. comparing two or more sites, or comparing a site over time;
  • Relationships/Associations: e.g. is species-richness negatively associated with increased fertility on your site?
  • Predictions: e.g. which set of factors are most important in determining how well your plant does at your site?

Below are some interesting results from past work

Stanley Moss is a DWT nature reserve with degraded raised bog. The ‘disturbed’ side had conifers removed two years before the survey. 40 vegetation quadrats were surveyed on each side and Simpson’s Reciprocal Index (a standard measure of species diversity) was calculated for each quadrat. This graph summarises the results. On average, the ‘disturbed’ side was more diverse than the ‘undisturbed’ side.

Accepting this result uncritically could be misleading. Disturbance often results in an increase in diversity as new niches are provided for ‘weedy’ species in addition to the species that were already present. However this tells us nothing about habitat quality.

Using positive and negative indicator species we devised a ‘bog quality index’ and calculated a bog quality score for each quadrat. There was much more overlap between the quality scores than between the diversity scores. Both sides were different, but neither was in optimum condition. The ‘undisturbed’ side was mostly dominated by heather due to lack of recent management.

In fact, plotting diversity against bog quality, as in the graph below, shows that the two measures are more or less completely unrelated.

The five ‘good bog’ quadrats (red dots) are all in the top ten on the ‘bog quality’ (horizontal) scale, but they don’t all score highly on the diversity (vertical scale). On the other hand, the ‘cottongrass bog’ (orange dots) quadrats were mostly uninteresting in terms of quality, yet several of them have high diversity scores.

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