## 3 Results
### Drivers of Macrophyte Density Patterns  
Table 4 lists significant models where the environmental variables increased the xR\({}^{2}\) when added to NPMR. Variables that were included alongside time and season included Chl-a, Secchi disk depth, herbivorous birds, mean lake level, and the winter (DJF) mean  
Figure 4: Comparison of macrophyte cover trends (%) from MIVIS maps (16/09/1997, 27/07/2005, 15/07/2010) and summer seasonal maps derived from S2 images (2015–2020). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year—(Day)**} & \multicolumn{3}{c}{**Area (km\({}^{2}\))**} & \multicolumn{3}{c}{**\(\Delta\) Area (km\({}^{2}\))**} \\ \cline{2-7}  & **S\({}_{\rm RM}\)** & **D\({}_{\rm RM}\)** & **RM** & **\(\Delta\) S\({}_{\rm RM}\)** & **\(\Delta\) D\({}_{\rm RM}\)** & **\(\Delta\) RM** \\ \hline
23/03/2016 & 0.925 & 1.505 & 2.430 & \multirow{2}{*}{\(-\)0.641} & \multirow{2}{*}{\(-\)1.107} & \multirow{2}{*}{\(-\)1.748} \\
19/04/2016 & 0.284 & 0.398 & 0.682 & & & \\ \hline
28/03/2017 & 0.688 & 1.578 & 2.266 & \multirow{2}{*}{\(+\)0.380} & \multirow{2}{*}{\(-\)1.491} & \multirow{2}{*}{\(-\)1.111} \\
17/05/2017 & 1.068 & 0.087 & 1.155 & & & \\ \hline
23/03/2018 & 0.447 & 1.578 & 2.025 & \multirow{2}{*}{\(+\)0.253} & \multirow{2}{*}{\(-\)0.613} & \multirow{2}{*}{\(-\)0.360} \\
22/04/2018 & 0.700 & 0.956 & 1.665 & & & \\ \hline
23/03/2019 & 2.293 & 0.279 & 2.572 & \multirow{2}{*}{\(-\)1.225} & \multirow{2}{*}{\(+\)0.282} & \multirow{2}{*}{\(-\)0.943} \\
17/04/2019 & 1.068 & 0.561 & 1.629 & & & \\ \hline
24/03/2020 & 1.068 & 0.784 & 1.852 & \multirow{2}{*}{\(+\)0.011} & \multirow{2}{*}{\(+\)0.044} & \multirow{2}{*}{\(+\)0.055} \\
23/04/2020 & 1.079 & 0.828 & 1.907 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of area (km\({}^{2}\)) occupied by macrophyte classes (S\({}_{\rm RM}\), D\({}_{\rm RM}\), and RM) for the two spring images (early spring vs. late spring) of each year (2016–2020). In green are positive values (increase of area) and in red are negative values (decrease in area). wave height. All of the models were for total macrophyte area (RM), while only one model was included for dense macrophytes (\(\mathrm{D_{RM}}\)) with Chl-a, albeit with a \(p\) value of 0.06. No significant models were produced for sparse macrophytes (\(\mathrm{S_{RM}}\)). The xR\({}^{2}\) ranged from 0.51 for Chl-a to 0.39 for Secchi disk depth and winter mean wave height. The sensitivity values, indicating the importance of the parameters, ranged from 0.05 for winter mean wave height (for total macrophytes, RM) to 0.23 for Chl-a (for dense macrophytes, \(\mathrm{D_{RM}}\)). Several environmental parameters were not found to increase the xR\({}^{2}\): total phosphorus, nitrate, wind vectors u and v, tourist numbers, surface solar radiation downwards, and the sum of piscivorous birds. Table 5 interprets the changes that have occurred over the period for macrophytes, and the key parameters noted above. Of note is the fact that an increase in herbivorous birds (estimated from a winter (January) census) tends to be followed by a decline in macrophytes, and if the decline is substantial, this in turn leads to a reduction in bird numbers the following winter. For example, in 2016 the lowest total cover (RM) was recorded for all macrophyte growth seasons considered (spring, summer, and autumn) (Figure 3a). This decline followed directly after an increase in herbivorous birds the preceding winter (Figure 3b); in addition, high northeasterly winter wind speeds were recorded (Figure S3). Winter herbivorous birds declined in 2017, and macrophyte recovery followed in 2017 and 2018, after which the macrophytes declined again in 2019, with a notable reduction of the area covered by dense macrophytes--reaching the lowest values recorded for the dataset in summer and being almost absent in autumn (0.025 km\({}^{2}\)) (Figure 3a). This was preceded by the highest numbers of winter herbivorous birds (Figure 3b).