Tree mortality is a key factor influencing forest functions and dynamics, but our understanding of the mechanisms leading to mortality and the associated changes in tree growth rates are still limited. We compiled a new pan-continental tree-ring width database from sites where both dead and living trees were sampled (2970 dead and 4224 living trees from 190 sites, including 36 species), and compared early and recent growth rates between trees that died and those that survived a given mortality event. We observed a decrease in radial growth before death in ca. 84% of the mortality events. The extent and duration of these reductions were highly variable (1-100 years in 96% of events) due to the complex interactions among study species and the source(s) of mortality. Strong and long-lasting declines were found for gymnosperms, shade- and drought-tolerant species, and trees that died from competition. Angiosperms and trees that died due to biotic attacks (especially bark-beetles) typically showed relatively small and short-term growth reductions. Our analysis did not highlight any universal trade-off between early growth and tree longevity within a species, although this result may also reflect high variability in sampling design among sites. The intersite and interspecific variability in growth patterns before mortality provides valuable information on the nature of the mortality process, which is consistent with our understanding of the physiological mechanisms leading to mortality. Abrupt changes in growth immediately before death can be associated with generalized hydraulic failure and/or bark-beetle attack, while long-term decrease in growth may be associated with a gradual decline in hydraulic performance coupled with depletion in carbon reserves. Our results imply that growth-based mortality algorithms may be a powerful tool for predicting gymnosperm mortality induced by chronic stress, but not necessarily so for angiosperms and in case of intense drought or bark-beetle outbreaks.

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30–210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.

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We investigated forest responses to global warming by observing: (1) planted Pinus halepensis forests, (2) an aridity gradient-with annual precipitation (P) ranging from ~300 to ~700 mm, and (3) periods of wet and dry climate that included the driest period during at least the last 110 years. We examined: (1) how the length of climatic integration periods to which trees are most responsive varies in space and time, (2) the extent to which competition modulates growth decline during drought (2011) and subsequent recovery (2012) years. The temporal scale of rainfall that was most influential on growth shortened in progressing southward, and in the drier than in the wetter period. Long-term underground water storage, as reflected in the relationship of growth to multiple-year rainfall, remained significant up to the point where P ≈ 500 mm. Under drier conditions (P < 500 mm) in both space and time, influential rainfall scales shortened, probably reflecting a diminishing role of water storage. These drier locations are the first from which the species would be likely to retreat if global warming intensified. Competition appeared to set an upper limit to growth, while growth variation among individual trees increased as competition-intensity decreased. That upper limit increased in 2012 compared with 2011. The observed insensitivity of slow-growing trees to competition implies that mortality risk may be density independent, when even any potential for higher soil moisture availability in open stands is lost to evapotranspiration before it can benefit tree growth.

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