Temporal Scales Research Articles

Spatio-temporal variations in activity of aerobic methane oxidation and community structure of methanotrophs in sediment of Wuxijiang River

Rivers are hotspots for methane (CH4) emissions, and aerobic methane oxidation is a crucial process in controlling emissions. The spatio-temporal heterogeneity of river environment can greatly affect the methane oxidation process. However, currently, few studies have focused on the spatio-temporal changes in activity of methane oxidation and the associated microbiome in riverine ecosystems, which hinders a comprehensive understanding the role of this process in reducing emissions of CH4. Here, we investigated the variations in methane oxidation activity and community of methanotrophs in sediment of a mountain river across different reaches and seasons. The potential methane oxidation rate ranged from 24.11 to 493.03 nmol CH4 g-1 (sediment) d-1, which was significantly greater in sediment obtained during the winter than in that obtained during the summer. Moreover, the rate in middle reaches was significantly greater than that in upper and lower reaches in summer. The abundance of pmoA gene of methanotrophs ranged from 2.45×10⁶ to 2.98×10⁷ copies g-1 (sediment), which was also significantly greater in winter than in summer and showed significant variations among reaches. Additionally, methanotrophic diversity and community composition exhibited significant variations across both reaches and seasons, and the relative abundance of Methylococcus and Methylocystis was closely associated with methane oxidation activity. Sediment NH4+ content, pH and temperature were potentially crucial factors affecting the activity or methanotrophic community. In conclusion, it is necessary to consider both temporal and spatial scales to improve our understanding of the significance and driving mechanisms of methane oxidation in controlling CH4 emissions from rivers.

Monitoring water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

Monitoring water quality is crucial for water exchange, precise feeding, and quality control of water products in freshwater aquaculture. In light of the issue of spatial heterogeneity in freshwater aquaculture pond waters and the constraints of conventional sensor detection techniques and traditional machine learning models. In this study, UAV multispectral images were combined with four machine learning algorithms (Ridge, XGBoost, CatBoost, RF) and the Stacking model to model the estimation of Chlorophyll a (Chl-a) and Turbidity and map their spatial distribution. The findings indicate that, in contrast to machine learning models, the Stacking model of water quality parameter performs better with higher accuracy. Meanwhile,for Chl-a and Turbidity the optimal sub-model combination in the Stacking model varies, with the most effective estimation model for Chl-a concentration identified as RF-XGB-Ridge (R2 = 0.84, RMSE=1.882 µg/L, MAE=3.433 µg/L and Slope = 0.791). As to Turbidity, the RF-CAB-Ridge model demonstrates superior performance, with macro-averaged precision (macro-p) of 93.3 %, macro-averaged recall (macro-R) of 88.8 %, macro-averaged F1-score (macro-F1) of 0.895, and Kappa coefficient of 0.813. Furthermore, the results of the joint analyses, which included measured samples and management measures at the test site, demonstrated that the spatial distribution maps of Chl-a and Turbidity were in alignment with the current status of water quality at the test site. This consistency was observed across both temporal and spatial scales. The results demonstrate that the integration of UAV multispectral images with the Stacking model can enhance the precision of water quality parameter models, facilitates the examination of the spatial and temporal distribution of water quality parameters and the underlying influencing factors, and advances the capability for dynamic monitoring of water quality parameters in freshwater aquaculture regions. Concurrently, it offers fundamental theoretical and methodological assistance for the precise regulation of water quality in freshwater aquaculture ponds and the formulation of optimal production management strategies.

Unraveling the Complexity of the N e/N c Ratio for Conservation of Large and Widespread Pelagic Fish Species: Current Status and Challenges.

Estimating and understanding the ratio between effective population size (N e) and census population size (N c) are pivotal in the conservation of large marine pelagic fish species, including bony fish such as tunas and cartilaginous fish such as sharks, given the challenges associated with obtaining accurate estimates of their abundance. The difficulties inherent in capturing and monitoring these species in vast and dynamic marine environments often make direct estimation of their population size challenging. By focusing on N e, it is conceivable in certain cases to approximate census size once the N e/N c ratio is known, although this ratio can vary and does not always increase linearly, as it is influenced by various ecological and evolutionary factors. Thus, this ratio presents challenges and complexities in the context of pelagic species conservation. To delve deeper into these challenges, firstly, we recall the diverse types of effective population sizes, including contemporary and historical sizes, and their implications in conservation biology. Secondly, we outline current knowledge about the influence of life history traits on the N e/N c ratio in the light of examples drawn from large and abundant pelagic fish species. Despite efforts to document an increasing number of marine species using recent technologies and statistical methods, establishing general rules to predict N e/N c remains elusive, necessitating further research and investment. Finally, we recall statistical challenges in relating N e and N c emphasizing the necessity of aligning temporal and spatial scales. This last part discusses the roles of generation and reproductive cycle effective population sizes to predict genetic erosion and guiding management strategies. Collectively, these sections underscore the multifaceted nature of effective population size estimation, crucial for preserving genetic diversity and ensuring the long-term viability of populations. By navigating statistical and theoretical complexities, and addressing methodological challenges, scientists should be able to advance our understanding of the N e/N c ratio.

Coarse-grained molecular dynamic model and wettability simulation of graphite materials

Although progress has been made in high-performance computing, there are still limitations on temporal and spatial scales of molecular dynamic calculations. A major issue in molecular dynamic (MD) simulations is the computational cost, and coarse-grained methods can save computational costs and accelerate calculations by reducing the degrees of freedom in the system. This method takes a selected group of representative atoms in the atomic microstructure as a bead and uses the proportional relationship of atomic scale atomic potentials in MD simulation to define the interaction between beads and solve the motion equation of beads. This article proposed a coarse-grained potential for graphite based on the modification of Airebo potential, with an n3: 1 mapping, and established a corresponding n3: 1 coarse-grained model, where one bead represents the number of n3 atoms. The results indicated that the coarse-grained model well reflected the basic thermal and mechanical properties of graphite. In addition, this article also proposed a coarsening method for the Lennard–Jones (L–J) potential function parameters and established a coarse-grained wetting model with n = 2. The results indicate that the coarse-grained wetting model can effectively predict the wetting performance of copper droplets on graphite in only 60% of the time using the all-atom model. The coarsening potential function and model proposed in this article are also applicable to graphite with rough surfaces. It can be anticipated that coarse-grained molecular dynamics methods will have more applications in the future, as they can handle large-scale calculations more quickly.

A multi-perspective assessment of satellite surface Ozone products in China: Spatiotemporal variability, land cover impacts and pollution monitoring capability

Accurate Ozone concentration datasets are essential for comprehensively understanding air quality, climate change, and the health and societal impacts related to Ozone exposure. Currently, there is limited research comparing near-surface Ozone concentration products. In this study, we employed continuous evaluation metrics (R2, RMSE, RB) and classified statistical indicators (POD, FAR, CSI) to comprehensively compare and assess three near-surface Ozone concentration products (CHAP O3, LGAP O3, TAP O3) in China from 2015 to 2020. Our findings indicate that CHAP outperforms other products in estimating near-surface Ozone concentration and monitoring Ozone pollution in China. In terms of temporal scale, the three Ozone products exhibited a strong correlation in specific regions across multiple time scales (R2> 0.6). The RMSE values of the products were lower in CHAP, followed by TAP, and higher in LGAP. Notably, LGAP significantly underestimated Ozone concentrations over time. Regarding spatial scale, CHAP and TAP align closely with ground observations, whereas LGAP shows significant underestimation in latitude and longitude, with the discrepancy increasing gradually along the longitude. All three products exhibited strong correlations with measured data at various sites (R2> 0.6). The RMSE and | RB| distributions of CHAP and TAP were similar and superior to LGAP. CHAP and TAP perform better than LGAP under specific land cover types, especially in urban and vegetation-covered areas, while their performance is relatively poorer in farmland-covered areas. Concerning pollution events, CHAP and TAP exhibit strong detection capabilities for Ozone pollution events in the Beijing–Tianjin–Hebei (BTH) region and other majority areas, with POD values exceeding 0.6, and FAR values below 0.1, showcasing excellent CSI performance. In contrast, LGAP has weaker detection capabilities, with POD values primarily in the 0–0.2 range and lower CSI values. This comprehensive evaluation sheds light on the strengths and weaknesses of different near-surface Ozone concentration products and their implications for assessing Ozone pollution in China.

Vertical Distribution of Rocky Intertidal Organisms Shifts With Sea-Level Variability on the Northeast Pacific Coast.

Disentangling the effects of cyclical variability in environmental forcing and long-term climate change on natural communities is a major challenge for ecologists, managers, and policy makers across ecosystems. Here we examined whether the vertical distribution of rocky intertidal taxa has shifted with sea-level variability occurring at multiple temporal scales and/or long-term anthropogenic sea-level rise (SLR). Because of the distinct zonation characteristic of intertidal communities, any shift in tidal dynamics or average sea level is expected to have large impacts on community structure and function. We found that across the Northeast Pacific Coast (NPC), sea level exhibits cyclical seasonal variability, tidal amplitude exhibits ecologically significant variability coherent with the 18.6-year periodicity of lunar declination, and long-term sea-level rise is occurring. Intertidal taxa largely do not exhibit significant vertical distribution shifts coherent with short-term (monthly to annual) sea-level variability but do exhibit taxa-specific vertical distribution shifts coherent with cyclical changes in lunar declination and long-term SLR at decadal timescales. Finally, our results show that responses to cyclical celestial mechanics and SLR vary among taxa, primarily according to their vertical distribution. Long-term SLR is occurring on ecologically relevant scales, but the confounding effects of cyclical celestial mechanics make interpreting shifts in zonation or community structure challenging. Such cyclical dynamics alternatingly amplify and dampen long-term SLR impacts and may modify the impacts of other global change related stressors, such as extreme heat waves and swell events, on intertidal organisms living at the edge of their physiological tolerances. As a result, intertidal communities will likely experience cyclical periods of environmental stress and concomitant nonlinear shifts in structure and function as long-term climate change continues. Our results demonstrate that consistent, large-scale monitoring of marine ecosystems is critical for understanding natural variability in communities and documenting long-term change.

Evaluation of nature-based climate solutions for agricultural landscapes in the Galápagos Islands

The Galápagos Islands are highly vulnerable to climate and environmental change, and nature-based solutions can help local communities adapt local agricultural systems. Through a comparative analysis, we evaluated the effects of three land management strategies on soil temperature, soil water availability and storage, and carbon stocks in Santa Cruz Island (Galápagos Archipelago). We installed six monitoring sites that consisted of two replicates per pathway: (i) the avoided loss of tropical forest, (ii) the conservation of scattered trees and living fences in at-risk agroforestry system, and (iii) the increase in biomass after a reduction of the grazing intensity. The monitoring sites were equipped with a dense network of rain gauges, air temperature and relative humidity sensors, and capacitance/frequency probes that registered volumetric water content and soil temperature. After pedological characterization of the soil profiles, the soil physico-chemical and hydrophysical properties were determined in laboratory. Over a period of 30 months (July 2019 to December 2021), hydrometeorological and soil environmental data were collected.We assessed differences in soil temperature, moisture availability and soil organic carbon content between native forests, sites under traditional agroforestry and under passive restoration. Forest soils were 12 % cooler;, and soil moisture under forest was 20 % higher than in parcels with silvopastural management. Forest soils had a lower dry bulk density, lower saturated hydraulic conductivity and higher water retention capacity in comparison with the other two management types. In silvopastural systems, a decrease of grazing intensity had a positive effect on soil carbon stocks, that were about 50 % higher than in soils under traditional management. This study shows that avoided loss of tropical forest within an agricultural landscape is a promising strategy to mitigate increasing soil temperatures, agricultural drought, and decline in soil organic carbon content. Continued monitoring of the experimental sites is necessary to corroborate the findings of this investigation at longer temporal scales.

CO2 and CH4 Concentrations in Headwater Wetlands Influenced by Morphology and Changing Hydro-Biogeochemical Conditions

AbstractHeadwater wetlands are important sites for carbon storage and emissions. While local- and landscape-scale factors are known to influence wetland carbon biogeochemistry, the spatial and temporal heterogeneity of these factors limits our predictive understanding of wetland carbon dynamics. To address this issue, we examined relationships between carbon dioxide (CO2) and methane (CH4) concentrations with wetland hydrogeomorphology, water level, and biogeochemical conditions. We sampled water chemistry and dissolved gases (CO2 and CH4) and monitored continuous water level at 20 wetlands and co-located upland wells in the Delmarva Peninsula, Maryland, every 1–3 months for 2 years. We also obtained wetland hydrogeomorphologic metrics at maximum inundation (area, perimeter, and volume). Wetlands in our study were supersaturated with CO2 (mean = 315 μM) and CH4 (mean = 15 μM), highlighting their potential role as carbon sources to the atmosphere. Spatial and temporal variability in CO2 and CH4 concentrations was high, particularly for CH4, and both gases were more spatially variable than temporally. We found that groundwater is a potential source of CO2 in wetlands and CO2 decreases with increased water level. In contrast, CH4 concentrations appear to be related to substrate and nutrient availability and to drying patterns over a longer temporal scale. At the landscape scale, wetlands with higher perimeter:area ratios and wetlands with higher height above the nearest drainage had higher CO2 and CH4 concentrations. Understanding the variability of CO2 and CH4 in wetlands, and how these might change with changing environmental conditions and across different wetland types, is critical to understanding the current and future role of wetlands in the global carbon cycle.

Spatial and temporal distribution patterns of ants (Hymenoptera: Formicidae) in Gobi Desert ecosystems, Northwest China

Ants (Hymenoptera: Formicidae) represent the most critical arthropod community in desert ecosystems, where interactions among vegetation, soil, and climate dictate ant assemblages. Nonetheless, our understanding of how various factors influence desert ant assemblages across different spatial and temporal scales remains limited. Therefore, this study aims to analyse the temporal and spatial distribution patterns of desert ants and to determine the effects of precipitation and temperature variations on ant assemblages. To achieve this, we continuously monitored the monthly dynamics of ants at 72 uniform 8m × 8m grid points in the Gobi Desert ecosystem of Northwest China from 2015 to 2020 using pitfall traps. The results indicated that Messor desertus and Cataglyphis aenescens were the dominant ant species, with significant annual and monthly variations in the number of individuals captured from 2015 to 2020. In 2020, monthly captures of M. desertus exhibited a bimodal pattern, peaking in November, whereas those of C. aenescens exhibited a unimodal pattern, peaking in June. Annual data revealed that population size was significantly positively correlated spatially at a distance of 24 meters. Semi-variance analysis and Moran’s I indicated that structural factors predominantly controlled the ant assemblages at a small scale from 2015 to 2020. Annual catches of desert ants tended to decrease with increasing annual precipitation, while an opposite trend was rising average annual temperatures. In conclusion, variations in annual and monthly precipitation and temperature influenced the temporal response patterns of desert ants, thereby altering their spatial assemblages.

Temporal scaling of carbon emission accumulations and rates of the Meso-Cenozoic hyperthermal events: implication to the Anthropocene global warming

Anthropocene global warming is largely associated with fossil fuel carbon emissions. Temporal scaling provides a way to place current carbon emissions on a geological scale. The scaling of carbon emissions at the onset of hyperthermal events suggests that we might anticipate higher carbon emission rates over longer time scales than what we currently observe in the Anthropocene. However, this inference is uncertain due to limited data concerning the accumulations and time intervals of carbon emissions of Meso-Cenozoic hyperthermal events. While on the long-time hyperthermal-event scales of several to hundreds of kiloyears, modern carbon accumulations and emission rates are 9 times greater than those of the hyperthermal-event emissions. The present-day carbon release can be effectively compared to the onset of hyperthermal events through temporal scaling. If current carbon emission trends persist, we may reach the carbon emission thresholds for hyperthermal events in one to three hundred years, getting an intensified hydrological cycle, enhanced continental weathering and ocean acidification. And if the situation gets worse, we may reach the upper limit of the carbon emission threshold for hyperthermal events (e.g., Permian-Triassic Boundary event, PTB) with a biotic mass extinction over four to thirteen hundred years. This study offers new insights into current carbon emissions from a temporal scale perspective, enhancing our understanding of contemporary climate change.

The impact of plant-derived fire management prescriptions on fire-responsive bird species.

In fire-prone regions, the occurrence of some faunal species is contingent on the presence of resources that arise through post-fire plant succession. Through planned burning, managers can alter resource availability and aim to provide the conditions required to promote biodiversity. Understanding how species occurrence changes at different spatial and temporal scales after fire is essential to achieve this goal. However, many fire prescriptions are guided primarily by the responses of fire-sensitive plants when setting tolerable fire intervals. This approach assumes that maintaining floristic diversity will satisfy the requirements of fauna. We surveyed bird species in two semi-arid vegetation types across an environmental gradient in south-eastern Australia. We conducted four surveys at each of 253 sites across a 75-year chronosequence of time since fire and used generalized additive mixed models to examine changes in the occurrence of birds in response to time since fire. Model predictions were compared to plant-derived fire prescriptions currently guiding fire management in the region. Time since fire was a significant predictor for 18 of 28 species modeled, in at least one vegetation type, over a gradient of 1.3° of latitude. We detected considerable variation in the responses of some species, both between vegetation types and geographically within a vegetation type. Our evaluation of plant-derived fire prescriptions suggests that the intervals considered acceptable for maintaining floristic diversity may not be sustainable for populations of birds requiring longer unburnt vegetation, with 6 of the 12 species assessed attaining a mean occurrence probability of 20.3% by the minimum tolerable fire interval, and 57.3% by the maximum tolerable fire interval, in their respective vegetation types. Our findings highlight the potential vulnerability of fire-responsive bird species if fire prescriptions are applied in a manner that fails to account for the slow development of habitat resources needed by some species, and the variation detected within the responses of species. This highlights the need for species-specific data collected at an appropriate spatial scale to inform management plans.

Lack of Small-Scale Changes in Breeding Birds after a Fire: Does the Resilience of Cork Oaks Favor Rapid Recolonization in Suburban Wood Patches?

Forest fires are disturbance events that can impact biological assemblages at multiple scales. In this study, the structures of breeding bird communities in cork oak patches located in an agro-mosaic suburban landscape of central Italy (Rome) were compared at the local scale with a fine-grained mapping method before (2018) and after (2023) a fire event occurred in July 2022. The analyses did not reveal any significant changes in the density of territorial pairs or in the diversity metrics, both univariate (Shannon–Wiener index, evenness, Margalef normalized richness) and bivariate (Whittaker and k-dominance plots, abundance/biomass curves) of diversity. Even when the guilds of strictly forest-related species were compared, no differences emerged before and after the fire. This counterintuitive phenomenon may be due to the characteristics of the dominant tree, the cork oak (Quercus suber), a sclerophilous tree that is very resilient to fires and able to recover foliage in the following spring season, thus allowing rapid bird recolonization. However, other small-scale phenomena (e.g., the ‘crowding effect’ and local dispersal of territorial pairs from remnant wood patches not affected by fire) may explain this lack of change in breeding bird density and diversity. Further studies should be carried out at larger spatial and temporal scales and at different levels of fire frequency and intensity to confirm these responses at the guild/community level in suburban cork oak wood patches.

ModelBark: a toy model to study bark formation in woody species

Abstract The study of growth of woody species is a challenging issue, primarily due to the complexity of the involved processes, which span broad spatial and temporal scales. Very often, this latter aspect almost precludes complete experimentation, thereby hindering the comparison between theoretical predictions and real-world observations. Computer simulations offer an alternative approach, allowing for data collection based on theoretical assumptions, and has been applied to the analysis of different features in plant development, as the production of secondary vascular tissues, xylem and phloem, in woody plants. However, the simulation of bark development faces added difficulties, due to the scarcity of experimental observations to base the model on. In this paper, we introduce a computer model designed to simulate bark formation based on mechanical stimuli acting on the various types of cells comprising this tissue. Our model can be conceptualized as a cellular automaton of variable size with non-local updating rules. By adjusting the parameters defining the model, we investigate the most influential factors in bark development, obtaining the most common bark types observed in trees. Furthermore, we provide an intuitive interface, making it suitable for educational purposes as well.

A dual mechanism drives the enrichment of pedogenic magnetic particles derived from red beds

Iron oxides and associated magnetic properties are considered good indicators of the pedogenic environment and regional climate. However, these indicators are under debate for soils developed from sediments. The accumulation of magnetic particles derived from sediments depends not only on the formation of iron oxides but also on the existence and transformation of detrital iron oxides. To explore the formation mechanism of magnetic particles accompanied by pedogenetic processes, a toposequence consisting of six profiles derived from red beds with contrasting drainage conditions under a subtropical monsoon climate is examined. We generally observed upward magnetic enhancement but inconsistent correlations between magnetism and iron oxide phases. The magnetic enhancement in the well drained profiles was dominated by the reverse transformation of detrital hematite to fine maghemite particles, whereas in the poorly drained profiles, coarse magnetite particles formed with soil organic matter enrichment in an anaerobic way. The lower magnetism in the transition profiles is attributed to the hydration of iron oxides into goethite before the pedogenic environment became anaerobic. The different magnetic enrichment mechanisms explain the nonlinear responses of magnetism and color to climate across temporal and spatial scales. These findings also suggest that soil magnetism is promising for tracing soil moisture and ecological evolution, especially in red soils and sediments across the surfaces of the Earth.

Mixed effectiveness of global protected areas in resisting habitat loss

Protected areas are the cornerstones of conservation efforts to mitigate the anthropogenic pressures driving biodiversity loss. Nations aim to protect 30% of Earth’s land and water by 2030, yet the effectiveness of protected areas remains unclear. Here we analyze the performance of over 160,000 protected areas in resisting habitat loss at different spatial and temporal scales, using high-resolution data. We find that 1.14 million km2 of habitat, equivalent to three times the size of Japan, across 73% of protected areas, had been altered between 2003 and 2019. These protected areas experienced habitat loss due to the expansion of built-up land, cropland, pastureland, or deforestation. Larger and stricter protected areas generally had lower rates of habitat loss. While most protected areas effectively halted the expansion of built-up areas, they were less successful in preventing deforestation and agricultural conversion. Protected areas were 33% more effective in reducing habitat loss compared to unprotected areas, though their ability to mitigate nearby human pressures was limited and varied spatially. Our findings indicate that, beyond establishing new protected areas, there is an urgent need to enhance the effectiveness of existing ones to better prevent habitat loss and achieve the post-2020 global biodiversity goals.

Transdisciplinary Research Supports the Sustainability of Barrier Island Systems Threatened by Climate Change

AbstractThe management of developed barrier islands is often piece‐meal and reactionary despite the complex, dynamic nature of these systems, and sustainable practices will become increasingly difficult due to heightened pressures of climate change. Adaptation actions, including nature‐based solutions, need to be thoroughly evaluated prior to implementation to understand system‐wide impacts and avoid maladaptation. Anarde et al. (2024a), (https://doi.org/10.1029/2023ef003672), Anarde et al. (2024b), (https://doi.org/10.1029/2023ef004200) is the latest important contribution in a growing body of transdisciplinary research that more robustly evaluates the complex physical process‐and‐response relationship of barrier systems via sophisticated numerical modeling approaches that also interface with socioeconomic models to inform coastal management actions in response to mitigating coastal risk. This new research indicates the importance of coordinated system‐scale barrier island management, as strategies to reduce coastal hazard risk in one location will directly affect adjacent communities. Further, this work demonstrates that reducing barrier management interventions may actually promote barrier recovery and sustainability in the face of sea level rise. In addition, recent advances in the analysis and application of remotely sensed data from satellites and oblique aerial photography provide scientists an unprecedented opportunity to track coastal evolution over a wide range of spatial and temporal scales at minimal cost. As sea level rise and changing storm patterns challenge the sustainable management of barrier island systems, integrating these advanced, transdisciplinary tools will enable scientists and coastal practitioners to more thoroughly evaluate coastal adaptation options, efficiently invest limited resources to mitigate coastal hazard risk for communities, support healthy ecosystems, and reduce system‐wide impacts.

PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement

In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. To broaden upon it, the choice of indicators and their subsequent ranking for the aggregation into an index is subjective in nature. This aggregation is not empirically validated and is prone to omit the nuances of localized resilience changes and causal factors affecting it, while leading to oversimplified conclusions. Meanwhile, there is a lack of scientifically and computationally rigorous, user-friendly tools that can support customized resilience assessment with consideration of local conditions. This study addresses these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customizable Resilience Inference Measurement (RIM), designed for multi-scale community resilience assessment and influential socioeconomic factors identification; 2) To implement a Platform for Resilience Inference Measurement and Enhancement (PRIME) module in the CyberGISX platform backed by high-performance computing, enabling users to apply and customize RIM to compute and visualize disaster resilience; 3) To demonstrate the utility of PRIME through a representative study to understand the geographical disparities of county-level community resilience to natural hazards in the United States and identifying the driving factors of resilience in the social domain. Customizable RIM generates vulnerability, adaptability, and overall resilience scores derived from empirical parameters—hazard threat, damage, and recovery. Computationally intensive Machine Learning (ML) methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. This setup provides a foundation for assessing resilience and strategizing enhancement interventions.

Identification of driving mechanisms of actual evapotranspiration in the Yiluo River Basin based on structural equation modeling

BackgroundActual evapotranspiration (ETa) is a crucial aspect of the hydrological cycle. It serves as a vital link between the soil–vegetation–atmosphere continuum. Quantifying the leading factors of regional ETa change and revealing the multi-factor compound driving mechanism of ETa evolution is necessary. Structural equation modeling (SEM) has been widely used to study the structural relationships between variables in large-scale areas. However, there is an urgent need for more in-depth exploration of these complex relationships at the grid scale. Therefore, the Yiluo River Basin, a representative area of soil and water conservation engineering demonstration in the Loess Plateau, was selected as the study area, and the SEM at the basin scale and grid-scale were constructed to carry out the research.ResultsThe data indicate that ETa decreased at 1.97 mm per year at the watershed scale from 1982 to 2020. Climate change had the greatest impact on the change of ETa in the watershed, with a total impact coefficient of over 0.9. The direct impact of climate change on ETa increased by 0.571 from 1982–1992 to 1993–2020. The direct impact coefficients of vegetation cover and soil moisture decreased by 0.402 and 0.102, respectively, while the impact coefficient of the water body factors increased by 0.096. At the scale of individual grid cells, the ETa in the watershed was affected by changes in watershed climate, vegetation, and soil moisture, with contributions ranging from − 0.31 to 0.22, − 1.09 to − 0.08, and 0.61 to 0.90, respectively. Spatially, vegetation and soil moisture had a stronger impact on ETa in the upstream area, while climate change had a negative effect, and the downstream region had the opposite effect. Furthermore, the regulatory impact of large reservoirs mitigated the response of water surface evaporation to climate change in the upstream region.ConclusionsThe application of SEM at different spatial and temporal scales has effectively quantified the driving mechanisms behind actual evapotranspiration in the Yiluo River Basin, while visually representing the spatial distribution characteristics of various influencing factors on ETa. This research provides a theoretical foundation for studying slope water consumption processes and circulation mechanisms.

Grass leaf structural and stomatal trait responses to climate gradients assessed over the 20th century and across the Great Plains, USA.

Abstract. Using herbarium specimens spanning 133 years and field-collected measurements, we assessed intraspecific trait (leaf structural and stomatal) variability from grass species in the Great Plains of North America. We focused on two widespread, closely related grasses from the tribe Paniceae: Dichanthelium oligosanthes subsp. scribnerianum (C3) and Panicum virgatum (C4). Thirty-one specimens per taxon were sampled from local herbaria from the years 1887 to 2013 to assess trait responses across time to changes in atmospheric [CO2] and growing season precipitation and temperature. In 2021 and 2022, the species were measured from eight grasslands sites to explore how traits vary spatially across natural continental precipitation and temperature gradients. Δ13C increased with atmospheric [CO2] for D. oligosanthes but decreased for P. virgatum, likely linked to increases in precipitation in the study region over the past century. Notably, this is the first record of decreasing Δ13C over time for a C4 species illustrating 13C linkages to climate. As atmospheric [CO2] increased, C:N increased and δ15N decreased for both species and %N decreased for D. oligosanthes. Across a large precipitation gradient, D. oligosanthes leaf traits were more responsive to changes in precipitation than those of P. virgatum. In contrast, only two traits of P. virgatum responded to increases in temperature across a gradient: specific leaf area (increase) and leaf dry matter content (decrease). The only shared significant trend between species was increased C:N with precipitation. Our work demonstrates that these closely related grass species with different photosynthetic pathways exhibited various trait responses across temporal and spatial scales, illustrating the key role of scale of inquiry for forecasting leaf trait responses to future environmental change.

Historical pyrodiversity in Douglas-fir forests of the southern Cascades of Oregon, USA

Our understanding of forest dynamics and successional pathways in coastal Douglas-fir (Pseudotsuga menziesii var menziesii) forests with relatively frequent mixed-severity fires is limited by a lack of annually precise dendroecological reconstructions that combine records of historical fires and tree establishment. The processes by which old-forest heterogeneity developed under historical fire regimes with recurrent low- and moderate-severity fires has not been well studied at fine temporal scales and across spatial scales. We developed crossdated multi-century records of fire and tree establishment histories in old forests (170 – 550 years) at 34 plots distributed across six sites. Study sites include warm-dry to cool-moist Douglas-fir forest types found in the southern west Cascades of Oregon, USA. Spatial variability in historical fire frequency and fire effects resulted in tremendous diversity in forest developmental histories, age structure, and forest conditions. Most historical fire intervals were very frequent (<10 years) to frequent (<25 years) in dry Douglas-fir forests. Exceptionally high fire frequency and an abrupt decrease in fire frequency after European colonization in dry Douglas-fir forests adds to growing evidence and recognition of Indigenous fire stewardship in montane Douglas-fir forests. In moist forests where Douglas-fir is seral to western hemlock, fire intervals were frequent to moderately frequent (<50 years), but intervals varied substantially over time. Relatively young moist forests burned frequently while mature moist forests had long fire intervals (50–160 years). Nearly all tree establishment cohorts were preceded by either stand-replacing (28 %) or non-stand-replacing fires (64 %). However, tree cohorts only provided evidence of 16 % of historical fire events that we reconstructed from cambial fire scars. This study demonstrates that frequent fire can be an important driver of forest development and in some contexts shapes the structure of coastal old-growth Douglas-fir forests, which are often characterized as developing from endogenous disturbances during long fire-free periods. The high level of pyrodiversity we observed was associated with variation in and interactions of micro-climate, topography, fuels, and Indigenous fire stewardship. We recommend rigorous dendroecological reconstructions across the coastal Douglas-fir region to refine our understanding of the geography of fire-mediated forest developmental dynamics in this important forest type, to inform forest management, conservation, and ecocultural restoration.