## 2 Materials and Methods
### Data Analyses  
Daily correlations between SIF and other measured parameters were examined using linear regression performed in R [28]. Multiple regressions were performed separately for both species, with SIF\({}_{\text{FR}}\), SIF\({}_{\text{red}}\), YSIF\({}_{\text{FR}}\), and YSIF\({}_{\text{red}}\) as response variables, and all other measured parameters as predictors.  
For analysis of seasonal trends multidimensional scaling (MDS) was used. The advantage of MDS analysis is that it reduces standardised daily measurements of many calculated photosynthetic parameters to a reduced set of dimensions, usually two, that enable visualisation of trends while still capturing the majority of the variance. Leaves with higher overall similarity in photosynthetic response will be closer together, and vectors can be placed to indicate which leaves are highest in each parameter. Raw fluorescence measurements of Fm, F'm, Fo and F', which are sensitive to small changes in distance between LIFT and target and incomparable between different leaf types and species, were excluded from analyses. Temporal measurement series collected with LIFT and QE Pro were smoothed using a centred moving average of the two previous and following measurements. Daily measurements were integrated as the AUC of each measured parameter, per leaf, for the period from sunrise until 2 h pre sunset. For leaf pigments, daily measurements were taken as the mean pigment concentrationsin \(\upmu\)g\(\cdot\)cm\({}^{-2}\) averaged for the four daily samples at SHF. Additionally, daily maximum and minimum air temperatures were collected from Bureau of Meteorology, Australia weather stations situated 7.0 km (station number: 68,228) and 9.9 km (station number: 58,214) from UOW and SHF respectively. Standardization of each daily parameter was performed using \(z\) scores.  
MDS analyses were performed in the R programming environment [28] using the packages: vegan [35] for calculation of Euclidean distances and Modern Applied Statistics with S (MASS) [36] for MDS analyses. MDS analyses were performed with both two- and three-dimensions separately for each species and with a combined data set. In each case, MDS were run on the following spectral and photosynthetic parameters: YPSII, ETR, YNO, YNPQ, \(\mathrm{SIF}_{\mathrm{red}}\), \(\mathrm{SIF}_{\mathrm{FR}}\), \(\mathrm{YSIF}_{\mathrm{red}}\), \(\mathrm{YSIF}_{\mathrm{FR}}\) NDVI, PRI and LUE, with vectors calculated for the spectral and photosynthetic parameters listed above, the environmental variables: day length, global PAR, leaf PAR, daily min air temperature and daily max air temperature (avocado and orange jasmine), and the leaf physical properties: leaf thickness, total Chl content, Chl a/b ratio, \(\mathrm{\sum VAZ}\), \(\mathrm{\sum LLx}\) and \(\alpha/\beta\) car ratio (avocado only). Separate MDS analyses of both species data with both two- and three-dimensions produced similar results, with two-dimensional MDS plots for the combined dataset found to adequately explain \(\geq\)84% of the variability in the data. For this reason, only results from two-dimensional MDS are presented here; results of three-dimensional MDS analysis can be found in the supplementary material (Figure S3 and Table S1).