Water Availability Is the Main Climate Driver of Neotropical Tree Growth

• Climate models for the coming century predict rainfall reduction in the Amazonian region, including change in water availability for tropical rainforests. Here, we test the extent to which climate variables related to water regime, temperature and irradiance shape the growth trajectories of neotropical trees. • We developed a diameter growth model explicitly designed to work with asynchronous climate and growth data. Growth trajectories of 205 individual trees from 54 neotropical species censused every 2 months over a 4-year period were used to rank 9 climate variables and find the best predictive model. • About 9% of the individual variation in tree growth was imputable to the seasonal variation of climate. Relative extractable water was the main predictor and alone explained more than 60% of the climate effect on tree growth, i.e. 5.4% of the individual variation in tree growth. Furthermore, the global annual tree growth was more dependent on the diameter increment at the onset of the rain season than on the duration of dry season. • The best predictive model included 3 climate variables: relative extractable water, minimum temperature and irradiance. The root mean squared error of prediction (0.035 mm.d–1) was slightly above the mean value of the growth (0.026 mm.d–1). • Amongst climate variables, we highlight the predominant role of water availability in determining seasonal variation in tree growth of neotropical forest trees and the need to include these relationships in forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest

Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

Eddy covariance (EC) datasets have provided insight into climate determinants of net ecosystem productivity (NEP) and evapotranspiration (ET) in natural ecosystems for decades, but most EC studies were published in serial fashion such that one study's result became the following study's hypothesis. This approach reflects the hypothetico-deductive process by focusing on previously derived hypotheses. A synthesis of this type of sequential inference reiterates subjective biases and may amplify past assumptions about the role, and relative importance, of controls over ecosystem metabolism. Long-term EC datasets facilitate an alternative approach to synthesis: the use of inductive data-based analyses to re-examine past deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We extracted relative rankings of climate drivers and driver-response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate-ecosystem interactions at this site than traditional deductive analyses alone