How we do it: modeling of potential habitats for Fourleaf mare’s tail

One of the aims of the project is combining the data from Sweden and Finland and seeing what we can glean from the full data. One thing you notice very quickly from a full dataset is an absence of a species from one country, when it is present in the other. Especially if it’s a directive species, such as Hippuris tetraphylla, or Fourleaf mare’s tail.

There are quite a lot of observations of Hippuris tetraphylla on the Finnish side of the project area, but they are relatively close together, so to maybe find the species in Sweden and possibly get some new observations in Finland, we did a quick model of the species in anticipation of the 2019 field season.

We started off by plotting the known observations on a map. A lot of the known positions are in the Krunnit area in Finland, so that’s where we’re going to look.

Hippuris tetraphylla observations in Krunnit.

The next phase is to look at some environmental variables and check if there are any that correspond with the occurrence of the species. Hippuris usually grows really close to the shoreline, so a simple ‘distance from shore’ variable should work well.

After plotting, the histogram gives us an easy way to classify the values in three classes: possible, suitable and highly suitable. X-axis is distance from shoreline, Y is count of observations.

After the classes are made into a raster file, we can repeat this process for several suitable variables. In this case we chose to only use distance to shore, bottom exposure and bottom light. The latter two are derived from different variable layers.

After you combine these, you get a layer giving different levels of occurrence probability. The higher the value, the better. The old observations are well within the red areas signaling high probability.


There’re high probabilities especially in the estuary of Torne river, but these should be eliminated from the final models by introducing an estuary effect model.

For the final models we would add more variables to increase accuracy, but for now this will do. The model still has some flaws, like high probabilities inside estuaries, but for planning purposes we can just ignore them.

The model predicts that there at least should be some Hippuris tetraphylla in Sweden too, so maybe we can find some next summer.  

Written by Jaakko Haapamäki, GIS-specialist

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