Fire Intensity Research Articles

Laboratory study on the characteristics of fresh and aged PM1 emitted from typical forest vegetation combustion in Southwest China

The frequency and intensity of forest fires are amplified by climate change. Substantial quantities of PM1 emitted from forest fires can undergo gradual atmospheric dispersion and long-range transport, thus impacting air quality far from the source. However, the chemical composition and physical properties of PM emitted from forest fires and its changes during atmospheric transport remain uncertain. In this study, the evolution of organic carbon (OC), elemental carbon (EC), water-soluble ions, and water-soluble metals in the particulate phase of smoke emitted from the typical forest vegetation combustion in Southwest China before and after photo-oxidation was investigated in the laboratory. Two aging periods of 5 and 9 days were selected. The OC and TC mass concentrations tended to decrease after 9-days aged compared to fresh emissions. OP, OC2, and OC3 in PM1 are expected to be potential indicators of fresh smoke, while OC3 and OC4 may serve as suitable markers for identifying aged carbon sources from the typical forest vegetation combustion in Southwest China. K+ exhibited the highest abundant water-soluble ion in fresh PM1, whereas NO3− became the most abundant water-soluble ion in aged PM1. NH4NO3 emerged as the primary secondary inorganic aerosol emitted from typical forest vegetation combustion in Southwest China. Notably, a 5-day aging period proved insufficient for the complete formation of the secondary inorganic aerosols NH4NO3 and (NH4)2SO4. After aging, the mass concentration of the water-soluble metal Ni in PM1 from typical forest vegetation combustion in Southwest China decreased, while the mean mass concentrations of all other water-soluble metals increased in varying degrees. These findings provide valuable data support and theoretical guidance for studying the atmospheric evolution of forest fire aerosols, as well as contribute to policy formulation and management of atmospheric environment safety and human health.

Stimulated photosynthesis of regrowth after fire in coastal scrub vegetation: increased water or nutrient availability?

Fire-prone landscapes experience frequent fires, disrupting above-ground biomass and altering below-ground soil nutrient availability. Augmentation of leaf nutrients or leaf water balance can both reduce limitations to photosynthesis and facilitate post-fire recovery in plants. These modes of fire responses are often studied separately and hence are rarely compared. We hypothesized that under severe burning, woody plants of a coastal scrub ecosystem would have higher rates of photosynthesis (Anet) than in unburned areas due to a transient release from leaf nutrient and water limitations, facilitating biomass recovery post-burn. To compare these fire recovery mechanisms in regrowing plants, we measured leaf gas exchange, leaf and soil N and P concentrations, and plant stomatal limitations in Australian native coastal scrub species across a burn sequence of sites at 1 year after severe fire, 7 years following a light controlled fire, and decades after any fire at North Head, Sydney, Australia. Recent burning stimulated increases in Anet by 20% over unburned trees and across three tree species. These species showed increases in total leaf N and P as a result of burning of 28% and 50% for these macronutrients, respectively, across the three species. The boost in leaf nutrients and stimulated leaf biochemical capacity for photosynthesis, alongside species-specific stomatal conductance (gs) increases, together contributed to increased photosynthetic rates after burning compared with the long-unburned area. Photosynthetic stimulation after burning occurred due to increases in nutrient concentrations in leaves, particularly N, as well as stomatal opening for some species. The findings suggest that changes in species photosynthesis and growth with increased future fire intensity or frequency may be facilitated by changes in leaf physiology after burning. On this basis, species dominance during regrowth depends on nutrient and water availability during post-fire recovery.

Thermo-mechanical performance assessment of geopolymer synthesized with steel slag and glass powder at elevated temperatures

This study utilizes glass powder (GP) and steel slag (SS) as a solid precursor material along with Fly Ash (FA) as a key ingredient to produce geopolymer composite (GPC). The developed geopolymer materials were subjected to elevated temperatures up to 800 °C to simulate intense fire conditions. Thermo-mechanical testing was conducted to evaluate the performance and microstructural characteristics of the exposed geopolymers. Results demonstrated that the GPC incorporating GP exhibited exceptional resistance to high temperatures, significantly retaining compressive strength. Conversely, the SS-based geopolymer showed a 52–54% reduction in strength, indicating improved thermal resistance. Both SS and GP-based GPC demonstrated resilience to high-temperature exposure, with SS addition providing the benefit of preserving structural integrity. However, the noted reduction in compressive strength highlights potential challenges for the application of geopolymer materials in areas exposed to high temperatures or fire.

Pine trees structure plant biodiversity patterns in savannas.

Overstory trees serve multiple functions in grassy savannas. Past research has shown that understory species can vary along gradients of canopy cover and basal area in savannas. This variation is frequently associated with light availability but could also be related to other mechanisms, such as heterogeneity in soil and litter depth and fire intensity. Several savanna studies have found differences in understory plant functional groups within the local environment near trees versus away from them in canopy openings. Although small-scale variation is known to be high in southeastern U.S. pine savannas, patterns in understory species diversity have not been examined at the scale of individual overstory pine trees in this system. We conducted an observational study of the relationship between understory plant communities and proximity to individual pine trees in xeric and mesic pine savannas in frequently burned sites (1-3 year intervals). We recorded the plant community composition in plots adjacent to tree boles (basal) or outside crown driplines (open). Within each environment, raw species richness was significantly greater in open locations, where light transmittance was greater. In contrast, rarified species richness did not differ. Multivariate analyses showed that community composition differed significantly between basal and open plots. One native, woody species in each environment, Serenoa repens (W. Bartram) Small in mesic and Diospyros virginiana L. in xeric, was more abundant in basal plots. In mesic environments, eight species had greater occurrence in open plots. In xeric environments, four understory forbs were more abundant in open plots. Our results support previous research indicating that individual pine trees are associated with significant variation in understory vegetation in pine savannas.

A Framework for Conducting and Communicating Probabilistic Wildland Fire Forecasts

Fire models predict fire behavior and effects. However, there is a need to know how confident users can be in forecasts. This work developed a probabilistic methodology based on ensemble simulations that incorporated uncertainty in weather, fuel loading, and model physics parameters. It provided information on the most likely forecast scenario, confidence levels, and potential outliers. It also introduced novel ways to communicate uncertainty in calculation and graphical representation and applied this to diverse wildfires using ensemble simulations of the CAWFE coupled weather–fire model ranging from 12 to 26 members. The ensembles captured many features but spread was narrower than expected, especially with varying weather and fuel inputs, suggesting errors may not be easily mitigated by improving input data. Varying physics parameters created a wider spread, including identifying an outlier, underscoring modeling knowledge gaps. Uncertainty was communicated using burn probability, spread rate, and heat flux, a fire intensity metric related to burn severity. Despite limited ensemble spread, maps of mean and standard deviation exposed event times and locations where fire behavior was more uncertain, requiring more management or observations. Interpretability was enhanced by replacing traditional hot–cold color palettes with ones that accommodate the vision-impaired and adhere to web accessibility standards.

Imaging sensor band comparison for situational awareness in wildfires

In recent decades, wildfires have become increasingly widespread and hazardous. Dryer, hotter weather combined with more frequent heat waves leave forest areas susceptible to sudden, intense, and fast-growing forest fires. To protect private property and mitigate the damage, Hotshot firefighters are deployed into these dangerous situations. Extensive satellite and aerial platforms possess optical techniques for monitoring wildfire risks and boundary tracking. Small unmanned aerial system (sUAS)-based EO/IR systems provide a solution for real-time, high resolution, targeted response to acquire information critical to the safety and efficacy of wildfire mitigation. Real-time imagery from a sUAS of the position of Hotshots and the progression of the fire boundary would be easily obtained and offer a method of ensuring safe deployment. An ideal sensor system for situational awareness in this environment would be able to image the ambient terrain and firefighters with good contrast while also detecting fire signatures and imaging through the smoke. Longer wavelength infrared bands have demonstrated imaging through the smoke of forest fires. However, near the wildfire where the Hotshots work, they also receive strong radiometric signals from the temperature of the smoke. The emitted signal of the smoke can obscure the line of sight similarly to the scattering effect of wildfire smoke in the visible spectrum. The reflective and emissive components of a wildfire scene are studied and compared in the visible (VIS, 0.4–0.7 µm), shortwave infrared (SWIR, 1.0–1.7 µm), extended SWIR (eSWIR, 2.0–2.5 µm), and longwave infrared (LWIR, 8–14 µm). Both a radiometric model and calibrated field measurements find a band that has the highest, to our knowledge, probability for a continuous line of sight for terrain, firefighters, and fire signatures in a wildfire scene.

Air Pollution and Blood Pressure: Evidence From Indonesia.

Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM2.5 concentrations at 0.5°×0.625° resolution across the IFLS domain. We assessed the association between PM2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM2.5 and blood pressure in Indonesia.

Using AutoML and generative AI to predict the type of wildfire propagation in Canadian conifer forests

Accurate prediction of wildfire behaviour is critical for safe and effective fire management and suppression. This study focused on developing and evaluating machine learning (ML) models based on a dataset collected of outdoor experimental fires in Canadian conifer forests. Binary classification models (surface fire vs. crown fire) and multi-class fire type models (surface, passive crown, active crown) were created using ensemble tree methods (Random Forest, XGBoost) and automated ML (AutoGluon, TabPFN). Generative adversarial networks (GAN) were used to generate synthetic data to overcome imbalances in the type of fire distribution. The results show automated ML methods applied to the binary problem to perform best of all tested methods, with an overall accuracy of 91%. TabPFN had the highest accuracy for the multi-class problem, with the use of GAN improving model fit. Results show overfitting issues with some of the ML models, highlighting the need of independent evaluation when ML models are developed. The TabPFN models have potential for application in supporting fire management, namely in fuel management and identifying situations with high fire spread and intensity potential that can impact firefighter safety.

Evaluating the Combustion Performance of the Usual Timbers in Furniture Using a Grey Correlation Method Based on Thermolysis, Ignition, and Flame Spread

Timber is the most widely used material for furniture in view of its characteristics of light mass, high strength, easy processing, coloring, and decorative appearance. However, the flammability of wood has been frequently associated with increased fire intensity and the rapid spread of fire in buildings. In this paper, the combustion performance of six kinds of common furniture timber was investigated based on thermogravimetric analysis at 25–500 °C, cone calorimetry with 50 kW/m2 thermal radiation intensity, and flame spread experiments with 3 kW/m2 thermal radiation intensity. The ignition, weight loss, thermogenesis, smoke, and flame spread characteristics of these timbers were obtained. Subsequently, a comprehensive index system including thermal stability, heat release ability, smoke production capacity, and flame spreading speed was constructed to evaluate the combustion performance of the selected timbers. In addition, a grey correlation method relying on the game theory to assign weight was proposed for the quantitative analysis of the relevant evaluation indexes. As a result, the combustion performance of the six kinds of timber, which was defined as a specific value from poor to good, was as follows: pine (0.8696) > Chinese fir (0.8568) > Oriented Strandboard (OSB) (0.8425) > density board (0.8122) > plywood (0.8087) > elm (0.7909). Timber with poor combustion performance contributes to the reduction in fire risk in buildings. Our suggestions are of great significance for selecting furniture timber from the perspective of the prevention and control of building fires.

Hybrid kitchen safety guarding with stove fire recognition based on the Internet of Things

In this paper, we design a hybrid kitchen safety guarding framework using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). According to the relevant literature we studied, this is the first framework for kitchen safety guarding that provides the following features: (1) the deep learning based model integrating densely connected convolutional networks with neural architecture search networks is developed to accurately recognize abnormal stove fire, (2) the acceleration correction method is designed to correct the sensed accelerometer values for estimating the actual earthquake level, and (3) the proper gas leakage threshold is defined to precisely detect the gas leakage for automatic gas blocking, and remote surveillance and control are provided to monitor the kitchen environment and control the gas source anytime and anywhere. In particular, an Android-based prototype consisting of the IoT device, diverse sensors, dedicated server, and smartphones is implemented to verify the feasibility and superiority of our framework. Experimental results show that our framework outperforms existing methods and can precisely recognize stove fire intensity and detect earthquake levels for kitchen safety guarding.

Fire resistance behavior and failure analysis of transmission tower exposed to wildfires

This paper deals with the fire resistance behavior and failure mode of transmission towers exposed to wildfires. A nonlinear thermo-mechanical coupling model of a latticed transmission tower was established by the finite element (FE) program ANSYS, which was validated from steel members’ perspectives. Wildfire analysis was conducted to determine the key parameters, including the number of legs in fire, fire intensity, and residence time, and the temperature–time curve was further proposed for parametric analysis. It reveals that the transmission tower may collapse under serviceability state loads in the wildfire, and the final failure mode was determined by the number of tower legs in the wildfire. A higher wildfire intensity will significantly reduce the wildfire resistance time of the transmission tower. Therefore, combustible materials around transmission towers need to be regularly cleaned to reduce the intensity and residence time of wildfires, which is an effective way to prevent wildfires. Moreover, a wildfire protection layer design process was proposed based on the environment surrounding the transmission tower, which proved reliable to provide valuable insights for the fire protection design of transmission towers in areas prone to wildfires.

The impacts of inter- and intra-seasonal burns on the terrestrial orchid Pterostylis curta

Prescribed burning is a management tool used for both management of fuel loads and for ecological purposes across fire prone areas. While in temperate areas wildfires usually occur during the hottest summer months, prescribed burns are generally conducted in autumn and spring, when conditions are more suitable for controlling fire. Orchids maintain avoidance mechanisms, such as persisting as dormant tubers during the predominant fire season, and therefore may be at risk from prescribed burns occurring during their active life cycle period. Using a glasshouse experiment, we investigated the impacts of fire season on the Australian orchid species Pterostylis curta. This approach allowed us to i) implement seasonal burns and relate impacts to quantifiable above and belowground life cycle stages of the study species, ii) isolate and assess the role of smoke, and iii) control for fire intensity and life stage of the study species at each of the treatment levels to enable robust comparison focused on fire season effects. We found that late autumn burns caused complete failure of a cohort in our glasshouse study. Heat alone was not the driver of tuber mortality, because soil heating was similar across all burn seasons, and plants burnt in the three other seasons were able to re-emerge strongly in the growing season after fire. Furthermore, a lack of post-fire emergence was due to tuber mortality, not dormancy. Our results highlight that there is likely an interaction between fire-related heat and the life cycle stage at which burning occurs, especially replacement tuber initiation, that drives post-fire demography. We show that orchids like P. curta had the lowest risk of negative impacts when burnt in the later stages of their growing season, and that an understanding of finer-scale phenological cycles can inform more robust fire management of orchid species.

Tentacle Microelectrode Arrays Uncover Soft Boundary Neurons in Hippocampal CA1.

Hippocampal CA1 neurons show intense firing at specific spatial locations, modulated by isolated landmarks. However, the impact of real-world scene transitions on neuronal activity remains unclear. Moreover, long-term neural recording during movement challenges device stability. Conventional rigid-based electrodes cause inflammatory responses, restricting recording durations. Inspired by the jellyfish tentacles, the multi-conductive layer ultra-flexible microelectrode arrays (MEAs) are developed. The tentacle MEAs ensure stable recordings during movement, thereby enabling the discovery of soft boundary neurons. The soft boundary neurons demonstrate high-frequency firing that aligns with the boundaries of scene transitions. Furthermore, the localization ability of soft boundary neurons improves with more scene transition boundaries, and their activity decreases when these boundaries are removed. The innovation of ultra-flexible, high-biocompatible tentacle MEAs improves the understanding of neural encoding in spatial cognition. They offer the potential for long-term in vivo recording of neural information, facilitating breakthroughs in the understanding and application of brain spatial navigation mehanisms.

Post-fire dynamics of recolonization by Cistus plants in the Aleppo pine and Cork oak forests in Bejaia region, central north Algeria

Abstract In August 2021, very intense fires swept through various forested areas in northern Algeria, particularly in Kabylia. The population dynamics of Cistus salviifolius and Cistus monspeliensis were studied along a post-fire successional gradient during the first nine months (September 2021 – May 2022), in two forest communities: the Cork oak (Quercus suber) and the Aleppo pine (Pinus halepensis) forests in the coastal zone of Bejaia province. The Cistus spp. germinations were counted monthly in each forest on 10 plots of 1 m2, evenly distributed along 2 transects of 45 m each. The results showed that the majority of germinations occur in December with 207.5 ± 24.2 seedlings in the Cork oak forest and 171.3 ± 18.0 seedlings in the Aleppo pine forest. Student's t-test does not reveal significant differences for December between the two communities. During the first months following the fire (September and October 2021), germinations were almost absent. The monthly rainfall in September 2021 was estimated at 16.25 mm, an insufficient amount for effective germination. Late germination results in an extension of its period, showed a high rate in January 2022 with 42.2 ± 2.1 and 11.5 ± 0.8 seedlings, respectively, in the Cork oak forest and the pine forest. Beyond January, germination was practically nonexistent in both communities. Therefore, the majority of the seed germination was concentrated in November, December, and January with a unimodal germination pattern.

A review of Coelopoetinae (Lepidoptera, Gelechioidea, Pterolonchidae), a moth subfamily confined to western North America, with descriptions of seven new species

Systematics and taxonomy of the gelechioid subfamily Coelopoetinae are reviewed. Following the current classification, this group is considered to form its own monotypic subfamily in Pterolonchidae with one recognized genus, Coelopoeta, after a convoluted and, in part, arguably conjectural, historical systematic treatment. On morphological basis (appearance, male genitalia) and with support from DNA barcodes, the genus is divided into two discrete units probably meriting recognition as separate genera. The species groups are informally treated as the nominate C. glutinosi species group, and the C. fissurina species group. In the absence of knowledge of females or the biologies of any of the species of the C. fissurina group, species of both groups are here provisionally included in Coelopoeta. In total, 10 species are recognized, seven of which are here described as new: C. glutinosi species group: C. alboflava Kaila, sp. nov., C. aprica Kaila, sp. nov., C. aurora Kaila, sp. nov., C. fulminea Kaila, sp. nov. and C. sariae Kaila, sp. nov.; C. fissurina species group: C. fissurina Kaila, sp. nov. and C. valalbui Kaila, sp. nov. The three previously known species, C. glutinosi Walsingham, 1907, C. maiadella Kaila, 1995 and C. phaceliae Kaila, 1995 are redescribed. All three of these species belong to the glutinosi species group. A lectotype is designated for C. glutinosi Walsingham, 1907. Some southwestern Coelopoeta species are potentially under threat of decline or even extinction due to the apparently increasingly intense and frequent forest fires. This threat is significant as the species with known life histories spend their entire life cycles above ground in low vegetation.

A Review of Leaf-Level Flammability Traits in Eucalypt Trees

With more frequent and intense fires expected under future climate conditions, it is important to understand the mechanisms that control flammability in Australian forests. We followed a systematic review approach to determine which physical traits make eucalypts leaves more or less flammable. Specifically, we reviewed 20 studies that covered 35 eucalypt species across five countries and found that leaf water content, leaf area (LA), and specific leaf area (SLA) are the main drivers of leaf flammability. These traits are easy and straightforward to measure, while more laborious traits (e.g., volatile organic compounds and structural carbohydrates) are seldom measured and reported. Leaf flammability also varies with species, and, while the biochemistry plays a role in how leaves burn, it plays a minor role in fire behaviour at landscape scales. This review highlights the range of different protocols used to measure flammability and leaf water content, warranting caution when comparing traits and results between studies. As a result, we propose a standardised protocol to measure leaf water content and advocate for long-term measurements of leaf traits and flammability. This study not only contributes to the understanding of how and why eucalypt leaves burn but also encourages research into the relative importance of traits in influencing flammability and provides a guide for selecting traits that can be monitored using satellite images to inform fire management policies and strategies.

Estimating Fire Radiative Energy Density with Repeat-Pass Aerial Thermal-Infrared Imaging of Actively Progressing Wildfires

Studies on estimating cumulative fire intensity from spreading wildland fires based on fire radiative energy density (FRED) have primarily been conducted through controlled experiments. The objective of this study was to assess the potential for estimating FRED for freely-burning wildfires at landscape scales. Airborne thermal infrared image sequences collected 8 and 9 December 2017 during the Thomas Fire were used for surface temperature derivation and FRED estimation. Sensitivity of varying ambient temperatures, and a newly developed method that adjusts for ash radiances on fire radiative flux density (FRFD) and FRED estimates were tested. Pixel-level image classification was run to identify FRFD time sequences that were complete or incomplete because of cloud obscuration and provided the basis for an obscuration gap filling technique. Variations in estimated ambient temperature used to estimate FRFD had little impact on FRED estimates, while our ash adjustment led to notable differences. An exponential decay model characterized FRFD time sequences well, providing a basis for gap filling irregular sequences caused by atmospheric obscuration. FRED estimates were regressed on rate of spread (ROS) magnitudes and found to be positively and significantly correlated. FRED magnitudes were higher on 9 December when the Thomas Fire burned under higher wind speeds and lower relative humidity levels (Santa Ana weather conditions) than on 8 December.

Multitemporal Dynamics of Fuels in Forest Systems Present in the Colombian Orinoco River Basin Forests

In Colombia’s Orinoco, wildfires have a profound impact on ecosystem dynamics, particularly affecting savannas and forest–savanna transitions. Human activities have disrupted the natural fire regime, leading to increased wildfire frequency due to changes in land use, deforestation, and climate change. Despite extensive research on fire monitoring and prediction, the quantification of fuel accumulation, a critical factor in fire incidence, remains inadequately explored. This study addresses this gap by quantifying dead organic material (detritus) accumulation and identifying influencing factors. Using Brown transects across forests with varying fire intensities, we assessed fuel loads and characterized variables related to detritus accumulation over time. Employing factor analysis, principal components analysis, and a generalized linear mixed model, we determined the effects of various factors. Our findings reveal significant variations in biomass accumulation patterns influenced by factors such as thickness, wet and dry mass, density, gravity, porosity, and moisture content. Additionally, a decrease in fuel load over time was attributed to increased precipitation from three La Niña events. These insights enable more accurate fire predictions and inform targeted forest management strategies for fire prevention and mitigation, thereby enhancing our understanding of fire ecology in the Orinoco basin and guiding effective conservation practices.

The Dynamics of Fire Activity in the Brazilian Pantanal: A Log-Gaussian Cox Process-Based Structural Decomposition

We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021.

Evolution of Glutamate Metabolism via GLUD2 Enhances Lactate-Dependent Synaptic Plasticity and Complex Cognition.

Human evolution is characterized by rapid brain enlargement and the emergence of unique cognitive abilities. Besides its distinctive cytoarchitectural organization and extensive inter-neuronal connectivity, the human brain is also defined by high rates of synaptic, mainly glutamatergic, transmission, and energy utilization. While these adaptations' origins remain elusive, evolutionary changes occurred in synaptic glutamate metabolism in the common ancestor of humans and apes via the emergence of GLUD2, a gene encoding the human glutamate dehydrogenase 2 (hGDH2) isoenzyme. Driven by positive selection, hGDH2 became adapted to function upon intense excitatory firing, a process central to the long-term strengthening of synaptic connections. It also gained expression in brain astrocytes and cortical pyramidal neurons, including the CA1-CA3 hippocampal cells, neurons crucial to cognition. In mice transgenic for GLUD2, theta-burst-evoked long-term potentiation (LTP) is markedly enhanced in hippocampal CA3-CA1 synapses, with patch-clamp recordings from CA1 pyramidal neurons revealing increased sNMDA receptor currents. D-lactate blocked LTP enhancement, implying that glutamate metabolism via hGDH2 potentiates L-lactate-dependent glia-neuron interaction, a process essential to memory consolidation. The transgenic (Tg) mice exhibited increased dendritic spine density/synaptogenesis in the hippocampus and improved complex cognitive functions. Hence, enhancement of neuron-glia communication, via GLUD2 evolution, likely contributed to human cognitive advancement by potentiating synaptic plasticity and inter-neuronal connectivity.