Abstract: This paper examines the Lambert conformal conic (LCC) projection. Although its equations are well established, they are rederived here because a new notation, V, defined as the reciprocal of the commonly used U, is introduced to simplify the expressions. Using the resulting distortion formulas, the conditions determining whether the projection has two, one, or no standard parallels are obtained. To identify an optimal LCC configuration, we adopt a criterion requiring that the local linear scale factors at the two boundary parallels be equal, and that the maximum scale factor exceed 1 by the same amount that the minimum falls below 1. Applying this criterion to the territory of Bulgaria, we compute a new, optimized pair of standard parallels, which constitutes the main contribution of this study.

Abstract: Zoogeomorphology, which is the mutual effect of biological activity and landforms, provides a significant yet underused framework for evidence-based wildlife conservation and management. This paper seeks to review international literature on the importance of zoogeomorphological processes toward biodiversity conservation in savanna ecosystems with a focused case study at Maasai Mara National Reserve (MMNR), Kenya. The Maasai Mara happens to be one among many other species-rich savanna landscapes in the world under increasing pressures from climate variability, land-use change, and human activities that create challenges for effective conservation planning. A structured search protocol was used to carry out this review which revealed 86 studies as relevant documentation on how fauna create landforms through processes like trampling, grazing, digging, burrowing, dunging, and wallowing among others influencing soils and hydrology vegetation structure habitat availability as well as species interactions. Evidence has been presented here regarding large mammals playing the role of ecosystem engineers creating heterogeneity in habitats resource distribution as well as population dynamics over different scales. The case study from Maasai Mara brings out these interactions practically by showing how activities of wildlife and livestock around water points floodplains migration corridors significantly demarcate landscape structure ecological viability. Results indicated extensive documentation on zoogeomorphological effects yet confirmed that such events were almost entirely absent from formal integration into conservation planning monitoring frameworks or any regulatory instruments. The study also suggested that management strategies based on insights from zoogeomorphology could enhance ecosystem resilience improve habitat connectivity and foster adaptive conservation under new environmental conditions. It highlighted the imperative need for incorporating landform–biota interactions into wildlife management practices to achieve greater long-term sustainability of savanna protected areas within Kenya and beyond.

Abstract: Recently, several studies from developing economies have reported the presence of per- and polyfluoroalkyl substances (PFAS) in water bodies, with a dominance of Perfluorooctanoic acid (PFOA), a potential endocrine disruptor. In this study, an engineered sugarcane bagasse biochar–chitosan composite (SBCT) was designed, synthesized, and evaluated as an adsorption medium for the removal of PFOA from aqueous systems at concentrations up to 500 ppb in water. Batch adsorption experiments were conducted to investigate the effects of initial PFOA concentration, contact time, pH, adsorbent dosage, and temperature. Scanning electron microscopy (SEM) showed that SBCT has a significant porous structure. The composite showed over 90% of PFOA removal from water. Further, the presence of peaks corresponding to C-F bonds after adsorption by Fourier transform infrared (FTIR) Spectroscopy analysis confirms the adsorption of PFOA on SBCT. The protonated amine groups (NH₃⁺) in chitosan enhanced the adsorption of anionic PFOA through electrostatic attraction with carboxyl groups (COO⁻). The Kinetic study revealed that Pseudo-first order best described the adsorption process, with equilibrium adsorption capacity (qeq) of 2.78 mg/g, suggesting that physisorption is the predominant mechanism. The Langmuir Isotherm model gave the best fit, establishing a maximum adsorption capacity (qmax) of 9.08 mg/g. Thermodynamic analysis revealed that the adsorption process was spontaneous and exothermic, consistent with physisorption. The regeneration capacity of the SBCT composite demonstrated exceptional reusability across five adsorption-desorption cycles with methanol. The adsorption kinetics, equilibrium behavior, and regeneration efficiency suggest that SBCT is a viable low-cost adsorbent for batch adsorption-based treatment systems targeting PFOA removal, particularly in decentralized and resource-constrained water treatment applications.

Abstract: Estimating surface soil moisture in semi-arid regions is challenging due to its high spatial and temporal variability, the scarcity of in-situ measurements, and the limitations of optical sensors in the presence of cloud cover and vegetation cover. Synthetic Aperture Radar (SAR) sensors, such as Sentinel-1, overcome these constraints by operating in the microwave domain and providing high-resolution data regardless of atmospheric conditions or daylight availability. This enables the application of inverse semi-empirical models, notably the Hallikainen model for the soil dielectric constant and the Dubois model for backscattering. This study proposes an integrated methodology applied to the municipality of Villaconejos (Madrid, Spain) over the period 2015–2025. The approach was initially calibrated on a pilot plot near Balcón del Tajo using field measurements of soil moisture and soil texture data (sand and clay content) obtained from the SoilGrids platform. Starting from Sentinel-1 VV and VH backscatter coefficients, the combined Hallikainen–Dubois model is inverted through an iterative search over a range of volumetric soil moisture values (0.02–0.45 m* m*) and surface roughness values (0.85–2 cm), selecting the parameter pair that minimises the difference between modelled and observed backscatter. The calibrated methodology is then extrapolated across the entire municipality of Villaconejos using Empirical Bayesian Kriging Regression Prediction (EBK-RP), incorporating topographic covariates (digital elevation model, slope, aspect), hydrological covariates (Topographic Wetness Index, TWI), and vegetation covariates (NDVI). The results include annual and seasonal maps of near-surface volumetric soil moisture (0–5 cm depth) at 10 m resolution and, after a geostatistical downscaling procedure, at 2 m resolution. Additional outputs comprise analyses of temporal variations between wet and dry periods and spatial patterns related to land use and topography. The developed methodology provides continuous, high-resolution, operational, and low-cost soil moisture estimates, representing a valuable tool for water resource management and agro-environmental monitoring in semi-arid regions.

Abstract: Global deforestation is accelerating at an unprecedented scale, driven by interconnected economic, political, and environmental forces that threaten biodiversity, climate stability, and human well-being. This article synthesizes global datasets and recent evidence to assess the magnitude, spatial distribution, and structural drivers of contemporary forest loss, with particular emphasis on tropical regions. It addresses three core research questions: (i) What is the current scale and geographic concentration of global deforestation and permanent tree-cover loss? (ii) How do agricultural expansion, mining, climate-driven wildfires, and armed conflict interact to intensify forest degradation? (iii) How do global consumption patterns, financial systems, and governance failures—including the symbolic contradictions of U.N. climate summits hosted in major fossil-fuel-exporting and high-emission countries such as the United Arab Emirates, Azerbaijan, and Egypt—externalize deforestation pressures onto vulnerable regions? The analysis shows that permanent land-use change, extractive industries, and conflict-related governance breakdowns dominate forest loss dynamics, while climate change amplifies fire-driven destruction, exposing a widening credibility gap in global climate governance and the urgent need for enforceable, equity-centered forest protection strategies.

Abstract: This study focuses on the ecological vulnerability and its driving mechanism of the Ebinur Lake Basin. Integrating natural factors such as annual average temperature, annual precipitation and elevation, as well as social factors including GDP and population distribution, it systematically evaluates the ecological vulnerability of the basin from 1994 to 2024 by adopting methods like the SRP model, Analytic Hierarchy Process (AHP) and Geodetector. The results show that the overall scale of ecologically vulnerable areas in the basin has presented a shrinking trend over the past 30 years: the area of severe vulnerability reached a peak of 14,270.31 square kilometers in 2004 and then decreased to 13,242.39 square kilometers; the area of slight vulnerability increased by 60.8%; and the proportion of moderate vulnerability has slightly risen since 2014. Spatially, the vulnerability exhibits significant agglomeration characteristics: severe vulnerable areas are concentrated in the mountainous areas of the basin boundary and the eastern region of Ebinur Lake, while slight vulnerable areas are distributed in woodlands and farmlands of alluvial fans in low mountains and hills. Geodetector analysis shows that, fractional vegetation cover, normalized difference vegetation index and land use type are the dominant factors, natural factors and social factors interact significantly.This study provides a scientific basis for ecological protection and sustainable development of the basin.

Abstract:

With accelerating climate change and urbanization, river catchments continue to experience structural modifications through dam construction and concrete-lining of natural channels as adaptation measures. These interventions can alter the natural hydrology. This necessitates assessment of their influence on hydrology at a catchment scale. However, such evaluations are particularly challenging in data-scarce regions such as the Chongwe River Catchment, where hydrometric records capturing conditions before and after structural modifications are limited. Therefore, we applied a 2D rain-on-grid approach in HEC-RAS to evaluate changes in high-flow characteristics in the Chongwe River Catchment in Zambia, where structural interventions have been implemented. The terrain was modified in HEC-RAS to represent 21 km of concrete drains and ten dams. Sensitivity analysis was conducted on five model parameters and showed that Manning’s roughness coefficient had by far the largest impact on peak flows. Model calibration and validation showed strong performance with R² = 0.99, NSE = 0.75 and PBIAS = – 0.68 % during calibration and R² = 0.95, NSE = 0.75, PBIAS = – 2.49 % during validation. Four scenarios were simulated to determine the hydrological effects of channel concrete-lining and dams. The results showed that concrete-lining of natural channels in the urban area increased high flows at the main outlet by approximately 4.6%, generated very high channel velocities of up to 20 m/s, increased flood depths by up to 11%, and expanded flood extents by up to 15%. The existing dams reduced peak flows by about 28%, increased lag times, reduced flood depths by about 11%, and reduced flood extents by up to 8% across the catchment. The findings demonstrate that enhancing stormwater conveyance through concrete-lining must be complemented by storage to manage high flows, while future work should explore nature-based solutions to reduce channel velocities and improve sustainable flood mitigation.

Abstract:

Baird’s tapir (Tapirus bairdii) plays an important ecological role in Mesoamerican forests as a browser and seed disperser, earning it the nickname of “gardener of the forest”. However, knowledge of its diet composition remains scattered. We reviewed and analyzed the available literature of diet composition of Baird’s tapir throughout its geographic distribution. We compiled evidence from 25 studies related to these topics. Baird’s tapir was found to consume 511 plant taxa belonging to 407 genera and 122 families. Five types of dietary components have been identified: fibre (stems), leaf, fruit, bark and flowers. The influence of seasonality on the tapir’s diet is unclear due to the underestimation of some components (fruit). We identified limitations in the techniques used to determine diet components and study designs. Future research should focus on develop novel techniques to improve the quantification of dietary components. Additionally, the direct and indirect effects of Baird’s tapir’s diet and plant consumption on ecosystem dynamics should be investigated to clearly understand the functional role of this species.

Abstract: Climate fluctuations are expected to drive a decline in the growth of many conifer and broadleaf species, especially in the Mediterranean region, where these species grow at or very near the southern limits of their distribution. Such trends have important im-plications not only for forest productivity but also for plant diversity, as shifts in spe-cies performance may alter competitive interactions and long-term community com-position. Using tree-ring data sourced from two Abies cephalonica stands with different elevation in Mount Parnassus in Central Greece, we evaluate the growth responses of the species to climatic variability employing a dendroecological approach. We hy-pothesize that radial growth at higher elevations is more strongly influenced by cli-mate variability than at lower elevations. Despite the moderate to relatively good common signal indicated by the expressed population signal (EPS: 0.645 for the high-altitude stand and 0.782 for the low-altitude stand), the chronologies for both sites preserve crucial stand-level growth patterns, providing an important basis for ecological insights. The calculation of the Average Tree-Ring Width Index (ARWI) for both sites revealed that fir in both altitudes exhibited a decline in growth rates from the late 1980s to the early 1990s, followed by a general recovery and increase throughout the late 1990s. They also both experienced a significant decline in growth between approximately 2018 and 2022. The best-fit model for annual ring-width vari-ation at lower elevations was a simple autoregressive model of order one (AR1), where growth was driven exclusively by the previous year’s growth (p < 0.001). At the higher elevation, a more complex model emerged: while previous year’s growth remained significant (p < 0.001), other variables such as maximum growing season temperature (p = 0.041), annual temperature (inverse effect, p = 0.039), annual precipitation (p = 0.017), and evapotranspiration (p = 0.039) also had a statistically significant impact on tree growth. Our results emphasize the prominent role of carry-over effects in shaping their annual growth patterns.

Abstract: We present an improved algorithm based on the POlarization LIdar PHOtometer Networking (POLIPHON) method to retrieve cloud condensation nuclei (CCN) concentration profiles from spaceborne lidar observations. Our previous paper, which was the first study to demonstrate the feasibility of using measurements from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) to retrieve CCN, is revisited. Our results focus on the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign that took place over Thessaloniki, Greece, in September 2011. We compare our results with our earlier retrievals, discussing the critical changes that have been made and the importance of using the proper conversions factors. We also demonstrate the use of conversion factors acquired based on CALIPSO aerosol typing for CCN retrievals. The analysis highlights the strong influence of smoke on CCN concentrations and shows that the assumed aging state of the smoke can significantly alter the retrieval outcome.

Abstract: River discharge is a pivotal metric in hydrological and water resources management. To address limitations in traditional hydrological monitoring stations, such as sparse distribution and high data acquisition costs, this study focuses on the Fuyang River LHK hydrological station in Handan City, Hebei Province, China, and proposes a synergistic estimation method for river discharge using multi-source remote sensing data. The approach first extracts river water bodies from Sentinel-1 SAR imagery and Sentinel-2 optical imagery via EN-OTSU and MNDWI-OTSU algorithms, respectively. Subsequently, river width is calculated using the water area-to-length ratio method to reduce errors caused by edge effects. Finally, a power-law discharge estimation model is developed by fitting river width to discharge data. For water body extraction, the Sentinel-2 MNDWI-OTSU method achieves the highest accuracy (overall accuracy: 95.31%, Kappa coefficient: 0.90), followed by the Sentinel-1 EN-OTSU method (overall accuracy: 92.55%, Kappa coefficient: 0.89). For discharge estimation, both data sources exhibit robust inversion performance, with the Sentinel-1-based model showing superior error stability (NSE=0.83, R²=0.83, RRMSE=0.24) and the Sentinel-2-based model marginally better theoretical fit (NSE=0.84, R²=0.84, RRMSE=0.26). Compared with traditional in situ measurements and single-sensor approaches, this method enables a shift from point-based to basin-wide dynamic monitoring, resolving data scarcity in ungauged regions; it integrates the high boundary delineation precision of optical remote sensing with the all-weather penetration of radar, effectively countering interruptions from cloudy and rainy conditions; and it reduces reliance on ground infrastructure, providing a cost-effective, reliable framework for river monitoring and informed water resource allocation.

Abstract: This study investigates how a Long Short-Term Memory (LSTM) model inter-nally represents baseflow contributions in snowmelt-driven, semi-arid mountain basins with heterogeneous geologic characteristics. Five basins in the Sangre de Cristo Mountains of northern New Mexico, spanning fractured Precambrian bedrock and sedimen-tary-volcanic terrain, were used to evaluate both model performance and interpretability. Baseflow dynamics were inferred post hoc using the Baseflow Index (BFI) and a two-reservoir HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. Although baseflow was not explicitly included in model training, internal cell state activations showed strong correlations with both shallow and deep baseflow com-ponents derived from the HEC-HMS model. To better understand how these relationships may change under climatic stress, BFI-based baseflow patterns were further analyzed un-der pre-drought and drought conditions. Results indicate that the LSTM learned to inter-nally distinguish between short- and long-residence flowpaths, encoding physically meaningful hydrologic behavior. This work demonstrates the potential for LSTM models to offer valuable insights into baseflow generation and groundwater–surface water inter-actions, particularly critical in water-scarce regions facing increasing drought frequency.

Abstract:

Accurate snow monitoring is critical for understanding hydrological processes and managing water resources. However, traditional snow sensing networks in the United States, such as the United States Department of Agriculture’s (USDA) SNOwpack TELemetry (SNOTEL) system, are costly and limited in spatial coverage. This study presents the design and deployment of a lower-cost, open-source snow sensing station aimed at improving the accessibility and affordability of snow hydrology monitoring. The system integrates research-grade environmental sensors with an Arduino-based Mayfly datalogger, providing high temporal resolution measurements of snow depth, radiation fluxes, air and soil temperatures, and soil moisture. Designed for adaptability, the station supports multiple sensor types, various power configurations—including solar and battery-only setups—multiple telemetry options, and capability for diverse deployment environments, including forested and open terrain. A multi-site case study at Tony Grove Ranger Station in northern Utah, USA demonstrated the station’s performance across different physiographic conditions. Results show that the system significantly reduces costs while increasing the spatial resolution of data, offering a scalable solution for enhancing snow monitoring networks. This study contributes an open-source hardware and software design that facilitates replication and adaptation by other researchers, supporting advancements in snow hydrology research.

Abstract: Hazard risk monitoring of groundwater depletion and land subsidence due to excessive groundwater extraction is crucial for groundwater resource development, especially in densely populated, small-island developing sites. The island of Bali, Indonesia, represents such an urban environment at risk of land subsidence arising from groundwater depletion. The total percentage of groundwater depletion was calculated and interpolated spatially using measurements of groundwater level from 2008 to 2017 at 18 monitoring well sites available in the area. Furthermore, time-series synthetic-aperture radar (SAR) interferometry processing was applied to estimate the temporal change in land displacement using the Phased Array type L-band SAR (PALSAR) data from 2007 to 2010. The result of downward displacement, signifying subsidence, corresponded with the Global Navigation Satellite System (GNSS) data measurements at stations distributed in the observed subsided areas, i.e., CDNP and CPBI. The displacement varied consistently with changes in groundwater level. In regard to maintaining groundwater utilization, the hazard–risk relation of the groundwater depletion, i.e., low (0–25%), moderate (25–50%), and high (>50%), and the presence/absence of subsidence were utilized to classify groundwater conservation into safe, vulnerable, critical, and damaged zones. This application can be considered effective in providing spatial information for sustainable groundwater management.

Abstract: As Akasofu noted, no two geomagnetic storms are identical, yet the storm that occurred between November 12 and 14, 2025, stands out as an exceptional phenomenon. Its impact was evident across multiple layers of the ionosphere and numerous parameters, making it essential to conduct a comprehensive multi-parameter analysis of this event. Such an analysis relied upon data from the four LAERT topside sounders mounted aboard the recently-launched Ionosfera-M satellites. Global ionospheric dynamics was thoroughly examined during the storm period, particularly focusing on the polar and auroral zones, along with the equatorial anomaly region. Notable features included sharp electron density gradients, widespread F-layer disturbances, and the formation of giant plasma bubbles. These elements collectively contributed to the dynamic picture of the ionospheric storm captured through multi-parameter measurements by the LAERT sounders.

Abstract:

The ecological status of lakes based on ichthyofauna, as defined by the Water Framework Directive, is assessed using intercalibrated methods. However, the methods adopted (in Poland, the Lake Fish Index LFI-EN method, based on results of one-off fishing with multi-mesh gillnets) are labor-intensive and do not allow for frequent repeat testing. Therefore, the concept of a simple model describing changes in the relative number of single traces in the vertical profile (according to the TS target strength distribution) in a lake is presented, as well as an index (the sum of deviations from such a model), enabling quantification of the similarity of TS distributions in lakes with this model. Preliminary analyses were conducted on acoustic data collected in Lake Dejguny. This lake—the condition of which could be estimated based on historical data using the relationships between LFI and the degree of lake eutrophication (expressed by Carlson’s TSI)—was assessed as having a good status in 2006, whereas in 2021, (based on LFI-EN) it had a moderate status. The study tested the TS distribution model, calculated as the arithmetic mean of the relative number of single traces in 2 m-thick layers. It was also shown that the proposed indicator can effectively signal deterioration of ecological status—the sum of the absolute values of the TS distribution deviations in 2021 (moderate status) from the model was more than seven times greater than the sum of the deviations of the distributions from which the model was built (good status). The obtained results confirmed the hypothesis about the possibility of determining a characteristic distribution of single traces in the vertical profile when the lake was classified as being in good condition.

Abstract: Video synthetic aperture radar (SAR) enables observation of moving targets by leveraging temporal information across successive frames. In particular, dynamic shadows in video SAR image sequences provide critical cues for detecting moving objects whose energy is smeared or Doppler-shifted. To achieve high-resolution imaging at a high frame rate for effective dynamic scene monitoring, video SAR systems typically operate at extremely high frequencies or even in the terahertz band, rather than the microwave band. However, terahertz video SAR suffers from significant signal attenuation due to atmospheric absorption. We present a deep learning framework for high-frame-rate and high-resolution imaging with microwave video SAR system. In this framework, the problem of microwave video SAR imaging is formulated as an image super-resolution reconstruction task for low-resolution yet high-frame-rate image sequences from microwave video SAR. We develop a simple yet effective image super-resolution reconstruction network that is completely built upon convolutional neural networks. The designed network takes a low-resolution image sequence and the corresponding high-resolution image with blurred shadows as input, and then produces a high-resolution image sequence where shadows are clearly visible. Furthermore, the network is trained in a self-supervised manner and thus does not require desired high-resolution image sequences as ground truth, which is appealing to practical applications. Processing results of real data from two different video SAR systems have shown good performance of the proposed approach with convincing generalization ability.

Abstract: The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—for improving Satellite Rainfall Monitor (SRM) estimates in the Cagayan de Oro River Basin, Northern Mindanao. When trained on comprehensive 2019-2020 data, Random Forest and Hybrid Ensemble substantially outperformed Quantile Map-ping, achieving excellent calibration accuracy (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested on an independent year with substantially different rain-fall patterns (2021: 120% higher mean rainfall, 33% increase in rainy-day frequency), performance rankings reversed completely. Quantile Mapping maintained satisfactory operational performance (R² = 0.53, RMSE = 5.23 mm), showing improvement over training conditions, while Random Forest and Hybrid Ensemble both failed dramati-cally, with R² dropping to 0.46 and 0.41 respectively despite their excellent training performance. This highlights that training accuracy alone poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping's percentile-based cor-rection naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in basins with limited data, equipment failures, and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability.

Abstract: Africa is a land of paradox. It is home to the world’s largest uncultivated arable land. Yet, food insecurity remains a pervasive in urban centers. With increasing rural-urban migration, hunger and obesity will continue to pose considerable threats to urban residents. It is in that respect that the paper rehabilitates the notion of urban agriculture (UA) as a buffer against uncertainties in food access, both in terms of quality and quantity. Despite the numerous potentials of this practice, few urban residents in Africa engage in producing their own food. While a plethora of contemporary literature has explored the challenges undercutting UA, there are still unanswered questions. Lingering questions concern what the underlying cause of limited participation in participation in the practice is. The paper discovered that a substantial percentage of African cities lack a comprehensive urban policy. To that end, there is no comprehensive guideline in the allocation of land and logistical support for potential farmers. The paper argues that, since UA has the potential to enhance the resilience of urban residents to climate change and ultimately, food insecurity, it is imperative for states to frame urban policies that recognize UA as an essential component of urban development.

Abstract: Methanogens, also known as methanogenic archaea, are among the most ancient and widespread microorganisms, despite their particular requirements for growth. These oxygen-sensitive microorganisms have impacted climate and biogeochemical cycles throughout Earth’s history, although their specific roles in the long-term carbon cycle remain little explored. Methanogens evolved early during Earth’s history, likely during the Archaean Eon, in layered benthic microbial communities called microbial mats. These ancient mats, when lithified, form microbialites that represent some of the earliest evidence of life in the fossil record dating back > 3.5 Gy. Contemporary microbial mats experience a wide range of fluctuating conditions, including dramatic diel shifts in oxygen, sulfide, redox, temperature, salinity and pH. Methanogens are an integral part of marine and freshwater microbial mats and have been identified in the oxic zone of these sedimentary ecosystems; however, their adaptations to apparently unfavorable conditions and their role in long-term CO2 sequestration through precipitation of carbonate are unclear. Furthermore, the importance and coevolution of methanogens and microbial mats may explain the global role these organisms had on Earth’s major climate events during the Archean and Proterozoic eons, notably in the ending of icehouse periods and recovery of mats following mass extinctions – often in conditions with low or no oxygen. In addition to an important role in the evolution of our planet, methanogens may also produce biosignatures that are relevant for astrobiology research [and space exploration]. This review will discuss the diversity, physiology, and ecology of methanogens in order to clarify their role in biogeochemical processes through geologic time.