In 2023, more than half of olive harvests ( Olea europaea ) across Spain, Greece, and Türkiye were lost to drought. The same year late freeze destroyed 90% of the peach crop ( Prunus persica ) on the Georgia Piedmont and the apple crop ( Malus domestica ) in central New York, Vermont, and southern Quebec. Climate extremes now rank with the costliest threats to agriculture, but their role in forest recovery from diebacks that are happening globally is unknown for lack of tree fecundity estimates in forests. Tolerance of climate extremes could depend on past exposure but constrained by phylogenetic conservatism. We report a continental scale analysis of climate extremes and forest fecundity across North America and Europe showing that responses to late freeze and drought are happening now. Species differences are not explained by the traits typically included in ecological studies and they are weakly associated with phylogeny. Late freeze, that is, freezing temperatures that follow the onset of flower development in spring, is shown to be “normal” in North America, but not Europe, potentially explaining failed seed production due to delayed onset and the resultant shorter growing period by North American transplants dating back at least to the 18th century. Drought has thus far had the greatest impacts in dry forested regions, but here too, species differences are not explained by traditional trait values. If responses have been buffered from drought and late freeze by past exposure, acclimation and local adaptation prove inadequate as extremes intensify.
Soil amendments are widely applied to improve maize (Zea mays L.) production in acid soils, yet the comparative efficacy of different amendment types on soil-plant systems remains incompletely understood. Through a meta-analysis of 509 paired observations from global studies, this study systematically evaluated the impacts of inorganic (lime, industrial by-products), organic (biochar, manure, straw), and combined amendments on soil chemical properties, maize growth, nutrient uptake, and grain yield. All amendments significantly improved key soil parameters including pH, available phosphorus, exchangeable cations (K+, Ca & sup2;(+), Mg & sup2;(+)), base saturation, and organic matter, while substantially reducing toxic Al & sup3; (+) concentrations. Among individual amendments, lime consistently enhanced soil pH (13.5%), root biomass by (60.2%) and grain yield (30.1%). However, the most pronounced effects were observed with combined applications of lime and biochar, which increased grain yield by 61.5%, grain N by 147.9%, and grain P by 191.9%. Correlation analysis revealed that grain yield response was positively associated with changes in soil K+ (r = 0.463, P < 0.001) and base saturation (r = 0.434, P < 0.001), but negatively correlated with Al & sup3; (+) (r = -0.332, P < 0.001). Heterogeneity analysis of lime studies (n = 93) identified application frequency, rate and geographic location explained 13.0%, 7.9% and 5.4% of grain yield variability, respectively. These results highlight the value of integrated amendment strategies and call for future research on root system responses and long-term soil health dynamics.
Forest edges are widespread transition zones that strongly influence forest microclimates and land-atmosphere exchanges. They modify the turbulent transfer of momentum, heat, and trace gases, with potential feedbacks on regional climate. Yet, their flow dynamics under non-neutral thermal stratification remain poorly understood. Using large-eddy simulations that couple canopy-scale biophysical processes with atmospheric boundary-layer (ABL) dynamics, we examine how thermal stratification, from neutral to free convection, modifies the micrometeorology at the transition between a crop and a dense forest over a flat terrain. Under windy but unstable conditions, the internal boundary layer (IBL) developing at the transition keeps the main turbulent edge-flow features identified under neutral conditions. Scalars adjust to the forest more slowly than momentum, due to their accumulation within the stable lower forest layer and their partial upward entrainment by intermittent intrusions of ABL-scale motions. Under free convection, the contrast in buoyancy forces between the crop and the forest generates thermally-driven, ABL-scale circulations, producing a weak near-surface breeze towards the forest and a persistent rising motion above the forest center. Although too weak to form a classical shear-driven IBL, this breeze still induces a sub-canopy jet similar to that in windy conditions. At the top of the forest canopy, thermally-driven turbulence is less effective at coupling with the forest interior than shear-driven turbulence under windy conditions, strengthening the stable layer in the understorey where scalar accumulate towards the forest center, where the breeze converges. Overall, the micrometeorological fields never fully adjust to the forest. These findings, albeit specific to the chosen forest and soil moisture conditions, offers valuable insights for environmental and ecological applications.
Climate extremes and persistent deforestation pose significant threats to Africa's vegetation carbon stocks. However, the patterns of aboveground carbon (AGC) loss, recovery, and their driving factors in Africa remain poorly understood. Here, we utilize low-frequency microwave satellite data to analyze AGC dynamics across Africa during 2010-2020. Results indicate a small net AGC increase of +0.16 +/- 0.03 PgC yr-1 during the study period, composed of gross losses of -1.56 +/- 0.26 PgC yr- 1 offset by gross gains of +1.72 +/- 0.29 PgC yr- 1. The total loss in forested areas amount to -0.50 +/- 0.07 PgC yr-1, of which degradation accounting for twice as much loss as deforestation. In non-forested areas, the total AGC loss was -1. 06 +/- 0.21 PgC yr-1, primarily driven by wildfires (-0.78 PgC yr-1). Following the 2015-2016 El Nino event, 66 % of affected regions exhibited AGC recovery ratios exceeding 100 % during 2015-2020, predominantly in non-forest vegetation, suggesting a higher recover ratio for non-forest vegetation. In contrast, the remaining 34 % of regions did not fully recover, with an average recovery rate of 58 %, predominantly concentrated in forested areas. A machine learning analysis based on random forest suggests that recovery ratios are primarily influenced by vapor pressure deficit (VPD), followed by precipitation and human footprint. Our study provides a comprehensive understanding of the dynamics of African AGC by distinguishing the loss into forest and non-forest vegetation, and also highlights the key drivers of AGC recovery after disturbances. These findings offer valuable insights for ecological conservation, climate adaptation, and global carbon budget assessments.
Rainfall pulses generate rapid increases and subsequent declines in soil moisture (SM), yet global ecosystem responses during SM dry-downs remain poorly quantified. Using 6502 soil dry-down events identified from global eddy-covariance observations, we compared carbon fluxes during dry-downs (treatment) to fluxes during the same periods without dry-downs in other years (control). During early dry-downs days, gross primary production (GPP) and respiration both exceeded controls, with stronger GPP gains enhancing net carbon uptake. This enhancement persisted for several days before diminishing as SM decreased and atmospheric dryness intensified. Latent and sensible heat fluxes also rose initially, but latent heat enhancement weakened over time, accompanied by enhanced sensible heat. Machine-learning analyses show that photosynthetic capacity and radiation drive positive GPP anomalies, while water limitations induce negative ones. Satellite data supported these patterns, whereas Earth system models underestimate their magnitude. These findings highlight transient pulse responses and support the broader applicability of the pulse-reserve paradigm. Ecosystems accumulate carbon for several days after moisture pulses, but this benefit fades as soil dries and heat stress intensifies, according to an analysis that uses carbon flux-tower observations, Earth system models, machine learning, and satellite images.
