Making the most out of synoptic sampling campaigns and large datasets: development of new statistical tools to optimize evaluation of environmental change

Environmental trends can be caused by influences of large-scale drivers, e.g. climate or airborne deposition, local-scale drivers, such as changes in land use, or a complex combination of both.

To reliably connect any potential drivers to observed trends, monitoring program with high spatial resolution are needed. Many of such programs are, however, monitored with low temporal frequency to keep costs down.

As a result, traditional statistical methodology for the evaluation of trends cannot be applied and much of the collected data is severely underused.Brownification and oligotrophication of waters are two urgent environmental topics, for which both the presence and magnitude of ongoing trends, as well as their causes are still unclear.

Browner and more nutrient poor waters can have negative impact on aquatic plant and animal communities. Brownification can also increase the mobilization of pollutants and have negative impacts on drinking water treatment.

Unique but so far unused information can be gathered from the Swedish Lake Survey (SLS), a monitoring program covering almost 4000 lakes.

In this project, we will develop new statistical methodology to analyze trends in the SLS to identify ongoing changes in brownification and oligotrophication and connect them to an extensive list of potential drivers.

The results of this study and the tools developed can be applied to other monitoring programs in order to utilize similar types of data to the fullest extent possible.