Understanding and predicting macrophyte occurrence: Mission Impossible?

The distribution of aquatic macrophytes is affected by various chemical and hydromorphological variables. Currently, the scientific literature on macrophytes is rather descriptive and is rarely predicting and quantitatively estimating environmental conditions under which species occur. In this thesis, multiple datasets are used to examine the most determinative environmental variables for macrophyte occurrences and to estimate the conditions, under which taxa or taxa groups can be observed.

In lakes, functional macrophyte groups shift along the turbidity gradient. Short meadow forming macrophytes disappear earlier along the gradient compared to longer canopy forming species. In rivers and lakes, the ratio between carbon dioxide and bicarbonate is most relevant for macrophyte distribution patterns. Species only capable of using carbon dioxide disappear earlier along the bicarbonate gradient, which is also serving as proxy of trophic conditions. Higher trophic conditions are associated with high pH values and a high quotient of bicarbonate and carbon dioxide, while lower trophic conditions go along with low pH values, low bicarbonate, and high carbon dioxide concentrations, thus favoring species only able to utilize carbon dioxide. Species able to utilize both carbon dioxide and bicarbonate do not show a particular preference along the bicarbonate gradient. In all types of water bodies also salinity plays a role and species tolerant to eutrophication are often more tolerant to higher salinities. Compared to inorganic carbon and salinity, dissolved nutrients (nitrogen and phosphorus) were less relevant for macrophyte occurrences. While water chemistry best explains macrophyte occurrences over larger spatial scales, nuanced patterns of macrophyte assemblages in lakes and especially rivers are more conditional to hydromorphological variables.

The predictive performance of the models created for this thesis is less optimistic than descriptive results from literature. Often descriptive results are a dichotomous “yes” or “no” statement. The models used in this thesis show an average accuracy of ~75% in predicting present/absence of species. Additionally, Cohen’s kappa, which ranges from 0-1, indicating how far model is from “random guessing” was ~0.25. This highlights the need to use additional predictor variables, or other models need to be developed to improve predictive performance. However, the model results show strong resemblance with descriptive results published by macrophyte experts. Nutrients, which are widely considered as a main driver of macrophyte occurrences, are often inferior to hydromorphological variables in predicting occurrences. Hence, it would be beneficial to develop a concept that incorporates both chemical and hydromorphological variables over different scales to improve our predictive understanding of macrophyte distribution patterns and to guide restoration efforts.


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