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90 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Disturbance and diversity: Lichen species richness decreases with increasing anthropogenic disturbance

Anthropogenic disturbance is rapidly increasing through habitat degradation, development, and deforestation. Gaps remain in understanding the effects of this disturbance on diverse and ecologically important organisms such as lichens. In North America, studies have focused on epiphytic macrolichens and catastrophic disturbance, largely ignoring microlichens and less severe disturbances. The Intermediate Disturbance Hypothesis predicts these moderate disturbances will lead to higher species richness. Here we examine the effects of anthropogenic disturbance in the form of land management on overall lichen richness, including microlichens, and on the species richness of eight lichen functional groups. The study draws on a comprehensive data set of 872 species, in 208 one-hectare plots throughout the Southern Appalachian Mountains, a global biodiversity hotspot in eastern North America. A habitat quality index based on an established forestry metric was used as a proxy for anthropogenic disturbance and was quantified using a 10-part score including categories such as percent native tree canopy cover and degree of fragmentation. Linear models were used to compare habitat quality scores to overall species richness and to species richness of functional groups. Rather than following the Intermediate Disturbance Hypothesis, all groups uniformly follow a negative linear relationship: as disturbance increases, species richness decreases. This pattern held even for widely variable functional groups such as morphotype. Effective conservation of lichen richness should prioritize the maintenance of existing older, less-disturbed stands within large, contiguously forested areas.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2018

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Habitat

The combined effects of fire history, climate, and landscape features (e.g., edges) on habitat specialists need greater focus in fire ecology studies, which usually only emphasize characteristics of the most recent fire. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights, which are dynamic because of frequent fires. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells (that represented potential territories) because fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities vary between states as functions of environmental covariates. Covariates included vegetative type, edges (e.g., roads, forests), precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presence/absence of fire covariate, but also fire history covariates: time since the previous fire, the longest fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Edges reduced the effectiveness of fires in setting degraded scrub and flatwoods into earlier successional states making mechanical cutting an important tool to compliment frequent prescribed fires.

Breininger, David R.↗
DOE OSTI2023

Dimensionally reduced machine learning model for predicting single component octanol–water partition coefficients

Abstract MF-LOGP, a new method for determining a single component octanol–water partition coefficients ( $$LogP$$ LogP ) is presented which uses molecular formula as the only input. Octanol–water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make $$LogP$$ LogP predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average $$RMSE$$ RMSE = 0.77 ± 0.007, $$MAE$$ MAE = 0.52 ± 0.003, and $${R}^{2}$$ R 2 = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ( $$RMSE$$ RMSE = 0.42–1.54, $$MAE$$ MAE = 0.09–1.07, and $${R}^{2}$$ R 2 = 0.32–0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures. Graphical Abstract

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory

The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R² from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

Zeng, Yelu↗
DOE OSTI2022

Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level

Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting from winter storms and wildfires. However, the differences of mortality events among various species responding to the abrupt environmental changes remain poorly understood. In this study, we focused on three species, Wilson’s Warbler, Barn Owl, and Common Murre, with the highest mortality events that had been recorded by citizen scientists. We leveraged the citizen science data and multiple remotely sensed earth observations and employed the ensemble random forest models to disentangle the species responses to winter storm and wildfire. We found that the mortality events of Wilson’s Warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average daily snow cover. The Barn Owl’s mortalities were more identified in places with severe wildfire-induced air pollution. Both winter storms and wildfire had relatively mild effects on the mortality of Common Murre, which might be more related to anomalously warm water. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations. Additionally, the study emphasized the efficiency and effectiveness of monitoring large-scale abrupt environmental changes and conservation using remotely sensed and citizen science data.

47 OTHER INSTRUMENTATION↗
NASA NTRS2019

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗
DOE OSTI2025

Seasonal investigation of ultrafine-particle organic composition in an eastern Amazonian rainforest

Abstract. Reports on the composition of ultrafine particles (<100 nm in diameter) in the Amazon are scarce, due in part to the fact that new-particle formation has rarely been observed near ground level. Ultrafine particles near the surface have nevertheless been observed, leaving open questions regarding the sources and chemistry of their formation and growth, particularly as these vary across seasons. Here, we present measurements of the composition of ultrafine particles collected in the Tapajós National Forest (2.857° S, 54.959° W) during three different seasonal periods: 10–30 September 2016 (SEP), 18 November–23 December 2016 (DEC), and 22 May–21 June 2017 (JUN). Size-selected (5–70 nm) particles were collected daily (for 22 h each day) using an offline sampler. Samples collected during the three time periods were compiled and analyzed using liquid chromatography coupled with Orbitrap high-resolution mass spectrometry. Our findings suggest a sustained influence of isoprene organosulfate chemistry on ultrafine particles from the different periods. We present chemical evidence that indicates that biological-spore fragmentation impacted ultrafine-particle composition during the late wet season (JUN), while chemical markers for biomass burning and secondary chemistry peaked during the dry season (SEP and DEC). Higher oxidation states and degrees of unsaturation were observed for organics in the dry season (SEP and DEC), suggesting greater extents of aerosol aging. Finally, applying a volatility parameterization to the observed compounds suggests that organic sulfur species are likely key drivers of new-particle growth in the region due to their low volatility compared to other species.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Clustering and voids in the Lyman-alpha forest

In a statistical study of quasar absorption lines, no evidence is found for large voids in the Ly-alpha forest, contrary to some recent suggestions. The 28 Mpc gap found by Crotts (1987) in the spectrum of Q0420-388 is not statistically significant, and it is possible, from a sample of 18 quasar spectra, to reject at the 2 sigma level the hypothesis that comoving voids of 30 Mpc fill as much as 15 percent of the universe. However, a significant (at the 99 percent level) excess of small line intervals of velocity width 200-600 km/s is found. This provides evidence either for the gravitationally induced correlations proposed in some models or for the fragmented shells proposed in others.

Ostriker, Jeremiah P.↗
DOE OSTI2023

Diverging climate response of corn yield and carbon use efficiency across the U.S.

Abstract In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone

The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK). It was previously shown that the presence of sterane molecular biomarkers in this sample can be detected via ToF-SIMS (Pasterski, M. J. et al., Astrobiology 2023, 23, 936). We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithms─k-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)─to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. In terms of predictive accuracy and balanced accuracy, KNN was the best performing model and RPART the worst. The feature importance, or the specific features of the ToF-SIM spectra used by the models to make classifications, cannot be determined for KNN, preventing posthoc model interpretation. Nevertheless, the feature importance extracted from the other models was useful for interpreting spectra. In conclusion, we determined that some of the organic ions used to classify biomarker containing spectra may be fragment ions derived from kerogen which is abundant in this mudstone sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Spatial Replication Is Important for Developing Landscape Genetic Inferences for a Wetland Salamander

Habitat fragmentation is a pressing threat to wildlife populations, and maintenance of gene flow between populations is an essential goal of conservation. Resistance surfaces have emerged as an important tool for modelling connectivity and developing management strategies to mitigate effects of habitat fragmentation. However, recent studies have noted inconsistencies in the factors most strongly associated with connectivity across different landscapes. Thus, replication of genetic-based resistance surface optimisation across landscapes may be necessary for making robust conclusions about the influence of environmental variables. Accordingly, replication represents a substantive challenge and opportunity in the field of landscape genetics. In this study, we conducted replicated landscape genetic analyses across five landscapes in Tennessee and Kentucky for a threatened wetland amphibian, the four-toed salamander (Hemidactylium scutatum). We tested multiple hypotheses of how different landscape features that could directly affect small, desiccation-intolerant amphibians (e.g., canopy cover) influenced gene flow and assessed the appropriate scale at which to model different features. We found some concordance in the landscape features that influenced gene flow (e.g., a common importance of forest cover and topography), but also some differences—potentially owing to the difference in variability of predictors across landscapes. We also found discordance in the scale of effect of different features across landscapes. In conclusion, our work emphasises that flat areas of moist forest not bisected by roads may be important for H. scutatum conservation, and our replicated design allows us to identify relationships that would have been missed if only using one study site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. Finally, a synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation

60 APPLIED LIFE SCIENCES↗
NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗
NASA NTRS2000

Ectomycorrhizal specificity patterns in a mixed Pinus contorta and Picea engelmannii forest in Yellowstone National Park

We used molecular genetic methods to test two hypotheses, (i) that host plant specificity among ectomycorrhizal fungi would be common in a closed-canopy, mixed Pinus contorta-Picea engelmannii forest in Yellowstone National Park and (ii) that specificity would be more common in the early successional tree species, P. contorta, than in the invader, P. engelmannii. We identified 28 ectomycorrhizal fungal species collected from 27 soil cores. The proportion of P. engelmannii to P. contorta ectomycorrhizae was nearly equal (52 and 48%, respectively). Of the 28 fungal species, 18 composed greater than 95% of the fungal community. No species was associated exclusively with P. contorta, but four species, each found in only one core, and one species found in two cores were associated exclusively with P. engelmannii. These fungi composed less than 5% of the total ectomycorrhizae. Thus, neither hypothesis was supported, and hypothesized benefits of ectomycorrhizal specificity to both trees and fungi probably do not exist in this system.

Trees/microbiology↗
DOE OSTI2024

Fragmentation in Collisions of Snow with Graupel/Hail: New Formulation from Field Observations

Abstract Secondary ice production (SIP) has been attributed to the generation of most ice particles observed in precipitating clouds with cloud tops warmer than −36°C, from various aircraft- and ground-based field observations across the globe. One of the known SIP mechanisms is fragmentation during collisions among ice particles. It has been studied with our theoretical formulation, which has been applied in microphysical schemes of atmospheric models in a few studies. These have predicted an extensive impact on cloud glaciation and radiative properties. However, there has been a lack of experimental field studies, especially involving naturally falling snowflakes, to better understand this particular mechanism of SIP. This study reports the first field measurements with modern technology for fragmentation during collisions between naturally falling snowflakes and graupel/hail particles. This was observed with an innovatively designed portable chamber that was deployed outdoors in northern Sweden. Applying the observations from this field-based study, we optimized the existing formulation for predicting numbers of fragments from collisions of snow with graupel/hail. The observations show the average numbers of fragments per collision for dendritic (3–12 mm) and nondendritic (1–3 mm) snow were about 12 and 1, respectively. This represents a boost of predicted fragment numbers relative to our original formulation published in 2017. The updated formulation for breakup in ice–ice collisions can be implemented in the microphysical schemes of atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Consequences of impacts of small asteroids and comets with Earth

The fragmentation of a small asteroid in the atmosphere greatly increases its cross sections for aerodynamic braking and energy dissipation. At a typical impact velocity of 22 km/s, the atmosphere absorbs more than half the kinetic energy of stony meteoroids with diameters, D(sub m), less than 220 m and iron meteoroids with D(sub m) less than 80 m. The corresponding diameter for comets with impact velocity 50 km/s is D(sub m) less than 1600 m. Most of the atmospheric energy dissipation occurs in a fraction of a scale height, so large meteors appear to 'explode' or 'flare' at the end of their visible paths. This dissipation of energy in the atmosphere protects the earth from direct impact damage (e.g., craters), but it produces a blast wave that can do considerable damage. The area of destruction around the impact point in which the over-pressure in the blast wave exceeds 4 lb/sq in = 2.8 x 10(exp 5) dynes/cu cm, which is enough to knock over trees and destroy buildings, increases rapidly from zero for chondritic meteoroids less than 56 m in diameter (15 megatons) to about 200 sq km for those 80 m in diameter (48 megatons); the probable diameter of the tunguska impactor of 1908 is about 80 m. Crater formation and earthquakes are not significant in land impacts by stony asteroids less than about 200 m in diameter because of the air protection. A tsunami is probably the most devastating type of damage for asteroids 200 m to 1 km in diameter. An impact by an asteroid this size anywhere in the Atlantic would devastate coastal areas on both sides of the ocean. An asteroid a few kilometers across would produce a tsunami that would reach the foothills of the Appalachian Mountains in the upper half of the East Coast of the United States. Most of Florida is protected from a tsunami by the gradual slope of the ocean off its coast, which causes most of the tsunami energy to be reflected back into the Atlantic. The atmosphere plume produced by asteroids with diameters exceeding about 120 m cannot be contained by the atmosphere, so this bubble of high-temperature gas forms a new layer on top of the atmosphere. The dust entrapped in this hot gas is likely to have optical depths exceeding tau = 10 for asteroids with diameters exceeding about 0.5 to 1 km. The optical flux from asteroids 60 m or more in diameter is enough to ignite pine forests. However, the blast wave from an impacting asteroid goes beyond the radius in which the fire starts. The blast wave tends to blow out the fire, so it is likely that the impact will char the forest, as at Tunguska, but the impact will not produce a sustained fire. Because comets dissipate their energy much higher in the atmosphere than asteroids, they illuminate a much larger region and their blast wave is weaker. So they are much more effective in producing large fires. This suggests that the KT impactor was a comet rather than an asteroid.

Hills, J. G.↗
DOE OSTI2025

Ecological acclimation: A framework to integrate fast and slow responses to climate change

Ecological responses to climate change occur across vastly different time-scales, from minutes for physiological plasticity to decades or centuries for community turnover and evolutionary adaptation. Accurately predicting the range of ecosystem trajectories will require models that incorporate both fast processes that may keep pace with climate change and slower ones likely to lag behind and generate disequilibrium dynamics. However, the knowledge necessary for this integration is currently fragmented across disciplines. We develop ‘ecological acclimation’ as a unifying framework to emphasize the similarity of dynamics driven by processes operating on dramatically different time-scales and levels of biological organization. The framework focuses on ecoclimate sensitivities, measured as the change in an ecological response variable per unit of climate change. Acclimation processes acting at different time-scales cause these sensitivities to shift in magnitude and even direction over time. We highlight shifting ecoclimate sensitivities in case studies from diverse ecosystems, including terrestrial plant communities, coral reefs and soil microbiomes. Models predicting future ecosystem states inevitably make assumptions about acclimation processes; these assumptions must be explicit for users to evaluate whether a model is appropriate for a given forecast horizon. Similarly, decision frameworks that clearly account for multiple acclimation processes and their distinct time-scales will help natural resource managers plan for ecological impacts of climate change from years to many decades into the future. We outline a synthetic research programme focused on the time-scales of ecological acclimation to reduce uncertainty in ecological forecasts.

climate adaptation↗