Summary Tree mortality rates appear to be increasing in moist tropical forests (MTFs) with significant carbon cycle consequences. Here, we review the state of knowledge regarding MTF tree mortality, create a conceptual framework with testable hypotheses regarding the drivers, mechanisms and interactions that may underlie increasing MTF mortality rates, and identify the next steps for improved understanding and reduced prediction. Increasing mortality rates are associated with rising temperature and vapor pressure deficit, liana abundance, drought, wind events, fire and, possibly, CO2 fertilization‐induced increases in stand thinning or acceleration of trees reaching larger, more vulnerable heights. The majority of these mortality drivers may kill trees in part through carbon starvation and hydraulic failure. The relative importance of each driver is unknown. High species diversity may buffer MTFs against large‐scale mortality events, but recent and expected trends in mortality drivers give reason for concern regarding increasing mortality within MTFs. Models of tropical tree mortality are advancing the representation of hydraulics, carbon and demography, but require more empirical knowledge regarding the most common drivers and their subsequent mechanisms. We outline critical datasets and model developments required to test hypotheses regarding the underlying causes of increasing MTF mortality rates, and improve prediction of future mortality under climate change.

Deep-water access is arguably the most effective, but under-studied mechanism that plants employ to survive during drought. Vulnerability to embolism and hydraulic safety margins can predict mortality risk at given levels of dehydration, but deep-water access may delay plant dehydration. Here, we tested the role of deep-water access in enabling survival within a diverse tropical forest community in Panama using a novel data-model approach. •We inversely estimated the effective rooting depth (ERD, as the average depth of water extraction), for 29 canopy species by linking diameter growth dynamics to vapor pressure deficit, water potentials in the whole-soil column, and leaf hydraulic vulnerability curves. We validated ERD estimates against existing isotopic data of potential water-access depths.• Across species, deeper ERD was associated with higher maximum stem hydraulic conductivity, greater vulnerability to xylem embolism, narrower safety margins, and lower mortality rates during extreme droughts over 35 years , especially in evergreen species. Species exposure to water-stress declined with deeper ERD indicating that trees compensate for waterstress related mortality risk through deep-water access.• The role of deep-water access in mitigating mortality of hydraulically vulnerable trees has important implications for our predictive understanding of forest dynamics under current and future climates.

Article type : Primary Research Article Stability of tropical forest tree carbon-water relations in a rainfall exclusion treatment through shifts in effective water uptake depth

Abstract. Uncertainties surrounding vegetation response to increased disturbance rates associated with climate change remains a major global change issue for Amazon forests. Additionally, turnover rates computed as the average of mortality and recruitment rates in the Western Amazon basin are doubled when compared to the Central Amazon, and notable gradients currently exist in specific wood density and aboveground biomass (AGB) between these two regions. This study investigates the extent to which the variation in disturbance regimes contributes to these regional gradients. To address these issues, we evaluated disturbance-recovery processes under two scenarios of increased disturbance rates in a complex Central Amazon forest using first ZELIG-TROP, a dynamic vegetation gap model which we calibrated using long-term inventory data, and second using the Community Land Model (CLM), a global land surface model that is part of the Community Earth System Model (CESM). Upon doubling the mortality rate in the Central Amazon to mirror the natural disturbance regime in the Western Amazon of ∼2% mortality, at steady-state, AGB significantly decreased by 41.9% and there was no significant difference between the modeled AGB of 104 Mg C ha−1 and empirical AGB from the western Amazon datasets of 107 Mg C ha−1. We confirm that increases in natural disturbance rates in the Central Amazon will result in terrestrial carbon loss associated with higher turnover. However, different processes were responsible for the reductions in AGB between the models and empirical datasets. We observed that with increased turnover, the subsequent decrease in wood density drives the reduction in AGB in empirical datasets. However, decrease in stand basal area was the driver of the drop in AGB in ZELIG-TROP, and decreased leaf area index (LAI) was the driver in CLM. Further comparisons found that stem density, specific wood density, and basal area growth rates differed between the two Amazonian regions. This suggests that: (1) the variability between regions cannot be entirely explained by the variability in disturbance regime, but rather potentially sensitive to intrinsic environmental factors; or (2) the models are not accurately simulating all forest characteristics in response to increased disturbances. Last, to help quantify the impacts of increased disturbances on climate and the earth system, we evaluated the fidelity of tree mortality and disturbance in a global land surface model: CLM. For a 100% increase in annual mortality rate, both ZELIG-TROP and CLM were in close agreement with each other and predicted a net carbon loss of 41.9 and 49.9%, respectively, with an insignificant effect on aboveground net primary productivity (ANPP). Likewise, a 20% increase in mortality every 50 years (i.e. periodic disturbance treatment) resulted in a reciprocal biomass loss of 18.3 and 18.7% in ZELIG-TROP and CLM, respectively.

<p><strong>Abstract.</strong> Tropical trees are known to be large emitters of biogenic volatile organic compounds (BVOC), accounting for up to 75% of the global isoprene budget. Once in the atmosphere, these compounds influence multiple processes associated with air quality and climate. However, uncertainty in biogenic emissions is two-fold, (1) the environmental controls over isoprene emissions from tropical forests remain highly uncertain; and (2) our ability to accurately represent these environmental controls within models is lacking. This study evaluated the biophysical parameters that drive the global Model of Emissions of Gases and Aerosols from Nature (MEGAN) embedded in a biogeochemistry land surface model, the Community Land Model (CLM), with a focus on isoprene emissions from an Amazonian forest. Upon evaluating the sensitivity of 19 parameters in CLM that currently influence isoprene emissions by using a Monte Carlo analysis, up to 61% of the uncertainty in mean isoprene emissions was caused by the uncertainty in the parameters related to leaf temperature. The eight parameters associated with photosynthetic active radiation (PAR) contributed in total to only 15% of the uncertainty in mean isoprene emissions. Leaf temperature was strongly correlated with isoprene emission activity (<i>R</i>² = 0.89). However, when compared to field measurements in the Central Amazon, CLM failed to capture the upper 10–14 °C of leaf temperatures throughout the year (i.e., failed to represent ~32 to 46 °C), and the spread observed in field measurements was not representative in CLM. This is an important parameter to accurately simulate due to the non-linear response of emissions to temperature. MEGAN-CLM 4.0 overestimated isoprene emissions by 60% for a Central Amazon forest (5.7 mg m⁻² h⁻¹ vs. 3.6 mg m⁻² h⁻¹), but due to reductions in leaf area index (LAI) by 28% in MEGAN-CLM 4.5 isoprene emissions were within 7% of observed data (3.8 mg m⁻² h⁻¹). When a slight adjustment to leaf temperature was made to match observations, isoprene emissions increased 24%, up to 4.8 mg m⁻² h⁻¹. Air temperatures are very likely to increase in tropical regions as a result of human induced climate change. Reducing the uncertainty of leaf temperature in BVOC algorithms, as well as improving the accuracy of replicating leaf temperature output in land surface models is warranted in order to improve estimations of tropical BVOC emissions.</p>