Climate warming is impacting vegetation productivity and plant leaf phenology, but the precise climate drivers and windows of key leaf phenological phases, such as emergence and fall, are still not well understood. Recent intensive computational approaches based on pinpointing the optimal climate window of leaf phenophases by maximizing the signal could help to advance in this question. In this study, we assess the climate variables, the climate windows, and the type of relationship (linear or nonlinear) that drive leaf emergence and fall in 21 deciduous and 13 evergreen woody plant species inhabiting two sites in Mediterranean Spain. We used precipitation, solar radiation, and different temperature measures, including forcing and chilling, as climate variables. We found that forcing variables were the best predictors of leaf phenology, but other temperature variables, as well as precipitation and radiation, were also important. However, chilling was not a good predictor. Most selected models showed nonlinear relationships. The best thresholds for calculating forcing were different from those commonly used. In addition, the best climate window for leaf phenology was species-specific and contingent on climatic and phenological conditions. This optimum climate window often covered longer periods than those usually considered in phenology studies. Our approach could be used to assess and better forecast future plant phenological responses to climate warming.
Climate warming is impacting vegetation productivity and plant leaf phenology, but the precise climate drivers and windows of key leaf phenological phases, such as emergence and fall, are still not well understood. Recent intensive computational approaches based on pinpointing the optimal climate window of leaf phenophases by maximizing the signal could help to advance in this question. In this study, we assess the climate variables, the climate windows, and the type of relationship (linear or nonlinear) that drive leaf emergence and fall in 21 deciduous and 13 evergreen woody plant species inhabiting two sites in Mediterranean Spain. We used precipitation, solar radiation, and different temperature measures, including forcing and chilling, as climate variables. We found that forcing variables were the best predictors of leaf phenology, but other temperature variables, as well as precipitation and radiation, were also important. However, chilling was not a good predictor. Most selected models showed nonlinear relationships. The best thresholds for calculating forcing were different from those commonly used. In addition, the best climate window for leaf phenology was species-specific and contingent on climatic and phenological conditions. This optimum climate window often covered longer periods than those usually considered in phenology studies. Our approach could be used to assess and better forecast future plant phenological responses to climate warming. Read More
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