The experiment is a two factorial block design; factorial meaning that each treatment is cross-matched with all other possible treatment combinations giving eight stressor combinations (heated, unheated, heated + nutrients, unheated + nutrients, heated + flushing, unheated + flushing, heated + nutrients + flushing and unheated + nutrients + flushing), block meaning that each of these eight treatments are randomly assigned to one tank in each block (or row) of eight tanks. We have four blocks (see below) which makes four replicates of each stressor combination and 32 tanks in total.
Heated + Nutrients (average): 295 μg L-1
Unheated (average): 60 μg L-1
Here comes the time to make some sweeping generalisations from one sampling date based on some colour charts (and some data too): nutrients support higher algal growth (the 16 tanks to the left) than tanks without nutrient enrichment. It is already generally accepted that nutrients are a strong driver for increased algal growth, especially cyanobacteria; this makes sense, as nutrients are essential for growth. It is also thought that warming also increases algal growth again especially cyanobacteria, the reasons for this are far more complex but it can be seen here that heated tanks seem to have more growth than unheated tanks (without nutrients). But do these single treatments and multiple treatments favour cyanobacteria over other algae? Time at the microscope and some data crunching will soon tell.
The real crux of the matter (of the experiment) is to understand how cyanobacteria respond to combinations of these stressors (treatments) because real lakes can be subjected to nutrient enrichment, warming or changes in flushing rates all at the same time. In the past research has focused on the response of cyanobacteria to individual stressors, and so this experiment along with other the other factorial mesocosm experiments, time series analysis and European scale analysis of the MARS project will contribute to our limited knowledge of how aquatic systems will respond to multiple stressors.