# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 3 Results
### Drivers of Change  
Most detected breaks had a wide time of break 95% CI range (negative: min = 1 month, max = 82 months or 6.8 years, average = 11 months; positive breaks: min = 1 month, max = 74 months or 6.2 years, average = 8 months). Hence, we subset the data to keep records that were more likely to show a stronger signal of abrupt change. We ran XGBoost on a subset of negative breaks with narrow ranges (<1.5 months) and with a magnitude of change > \(|-500\ |\ (n\) = 116). For positive breaks, we focused on all breaks with a magnitude of change > 500 (\(n\) = 3263), regardless of the time of break 95% CI range. For positive breaks, we also removed breaks that overlapped with GFC disturbance data. The improvement of our fine-tuning was reflected in the larger \(R^{2}\) adjusted values for the models that used the data subsets compared to those that used all the records. (Table A4 in Appendix A.3 provides the results for the different models that were run). XGBoost models explained 64% of the variance among negative breaks (\(R^{2}\)-adj = 0.639, RMSE = 292.5) and 35% among positive breaks (\(R^{2}\)-adj = 0.352, RMSE = 310.4). Forest age, winter DD5 with a 1-year lag, and summer CMI with 1 to 3-year lags were among the five most important variables for negative and positive breaks but with slightly different rankings (Figure 5). For negative breaks, forest age was the most important predictor while it was the second most important for positive breaks. Winter DD5 with a 1-year lag was the most important predictor for positive breaks and the second most important for negative breaks. The effect of forest age was about twice that of winter DD5 (1-year lag) among negative breaks but about the same among positive ones (Figure A3a). Summer CMI ranking was slightly different, whereas, for negative breaks, 1-year lag summer CMI ranked third, it ranked fifth for positive breaks, and vice versa. Summer CMI with a 3-year lag ranked fourth for both types of breaks. While the percentage of confer composition ranked seventh among negative breaks, it was last (ninth) among positive ones. The predictive strength of forest protective status was relatively very small for positive breaks (ranking second to last) and not at all important for negative breaks (being absent in Figure A3a). BRT partial dependence plots for the magnitude of breaks showed interesting patterns (Figures A4 and A5 Appendix A.3). The magnitude of change of negative breaks was high (\(\sim\)2100) and remained so for younger forests until about 90 years when it dropped sharply (Figure A4a). The percentage of confer composition showed a similar curve for negative breaks, starting at a higher EVI value (\(\sim\)2300) and showing a more gradual decrease beginning at around 40% (Figure A5g). In contrast, positive breaks showed decreasing non-monotonic curves (starting at \(\sim\)850) with forest age but with two small bumps, one at about 60 years and a second one at about 90 years of age (Figure A5b). With respect to the percentage of conifers, positive breaks showed a non-monotonic curve with no clear pattern (Figure A5i). Winter DD5 with a 1-year lag showed a high negative change curve (\(\sim\)2000) throughout its range (Figure A4b). For positive breaks, this predictor increased non-linearly with warmer winters and peaked at 850 around 7-degree days (Figure A5a). Furthermore, the 3-year lag version of this predictor showed a similar pattern for negative breaks with a constant high value around 2100 (Figure A4h) but for positive breaks, it showed a relatively flat non-monotonic curve that fluctuated around 800 (Figure A5f). This means that the lagged effects of warmer winters on abrupt brownness (negative breaks) were more than twice its effect on abrupt greenness (positive breaks). Summer CMI predictors had similar curves for negative breaks; that is, they all showed high constant negative change values (between 2000 and 2200) throughout much of their range with sharp drops at one or both extremes (Figure A4c-f).