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The Caldor fire burned ~222,000 acres of the Eastern Sierra Nevada during summer–fall 2021. We evaluated the effects of this “megafire” on the physical properties of a sandy soil developed from glacial tills to document fire-induced soil modifications in this region. We measured soil water retention and hydraulic conductivity functions as well as the thermal properties of five core samples from control (unburned) areas and eight core samples from burned soil of the same soil unit. Soil water repellency was measured in terms of water drop penetration time (WDPT) in the field and apparent contact angle in the laboratory on control and burned soil as well as ash samples. Soil organic matter (SOM) and particle and aggregate size distributions were determined on control and burned soil samples. Additionally, scanning electron microscopy (SEM) was used to image microaggregates of control and burned soil samples. We found a significant difference in SOM content and sand and silt aggregate size distribution between control and burned samples, which we associated with the disintegration of microaggregates due to the fire. We found no significant difference between soil water retention and hydraulic conductivity functions of control and burned soil but observed greater variation in saturated hydraulic conductivity and systematic shifts in thermal conductivity functions of burned compared to control samples. WDPT and apparent contact angle values were significantly higher for burned soils, indicating the occurrence of fire-induced soil hydrophobicity (FISH). Interestingly, the average apparent contact angle of the control soil was >90°, indicating that even the unburned soil was hydrophobic. However, the ash on top of the burned soil was found to be hydrophilic, having apparent contact angles <10°. Our results indicate that SOM and microaggregates were readily affected by the Caldor fire, even for sandy soil with a weakly developed structure. The fire seemed to have moderated thermal properties, significantly and soil wettability but had only minimal effects on water retention and hydraulic conductivity functions. Our findings demonstrate the complex nature of fire-soil interactions in a natural environment and highlight the need for additional investigation into the causes and processes associated with FISH and structure alterations due to fire to improve our ability to rapidly determine potential problem areas in terms of hazards commonly associated with fire-affected soils.

This study aims to understand the effects of wildfires in sagebrush ecosystem on soil properties by examining connections between Soil Water Repellency (SWR), reflectance, and chemistry. Ash and burned soil samples were collected after performing laboratory burns of three common sagebrush plants: sagebrush, rabbitbrush, and bitterbrush. The collected samples were analyzed for their physical properties, including SWR measured by Water Drop Penetration Time (WDPT) and Apparent Contact Angle (ACA), and solar spectral reflectance in the wavelength range of 350 to 2500 nm. Chemical functional groups of the samples were analyzed using Fourier-Transform Infrared (FTIR) spectroscopy. WDPT and ACA values were in the range of 1 to 600 s and ~10° to 88°, respectively, for all three tested fuels. The FTIR analysis showed a decrease (~2 to 4 times) in the ratio of COO−/C=C signals for the burned soil samples compared to the unburned soil samples. Overall, increase in temperature and ACA levels for the samples of burned and burned soil from a 2 cm depth led to increased formation of non-polar compounds with C=C functional groups, and decarboxylation.

Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or ion of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species‐level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long‐term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long‐term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.

Global change drivers (GCDs) are expected to alter community structure and consequently, the services that ecosystems provide. Yet, few experimental investigations have examined effects of GCDs on plant community structure across multiple ecosystem types, and those that do exist present conflicting patterns. In an unprecedented global synthesis of over 100 experiments that manipulated factors linked to GCDs, we show that herbaceous plant community responses depend on experimental manipulation length and number of factors manipulated. We found that plant communities are fairly resistant to experimentally manipulated GCDs in the short term (10 y). In contrast, long-term (<10 y) experiments show increasing community divergence of treatments from control conditions. Surprisingly, these community responses occurred with similar frequency across the GCD types manipulated in our database. However, community responses were more common when 3 or more GCDs were simultaneously manipulated, suggesting the emergence of additive or synergistic effects of multiple drivers, particularly over long time periods. In half of the cases, GCD manipulations caused a difference in community composition without a corresponding species richness difference, indicating that species reordering or replacement is an important mechanism of community responses to GCDs and should be given greater consideration when examining consequences of GCDs for the biodiversity–ecosystem function relationship. Human activities are currently driving unparalleled global changes worldwide. Our analyses provide the most comprehensive evidence to date that these human activities may have widespread impacts on plant community composition globally, which will increase in frequency over time and be greater in areas where communities face multiple GCDs simultaneously.

The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal; properties of any single mode become inadequate uncertainty measures, and sampling methods become inefficient for irregular posteriors or high-dimensional parameter spaces. We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo sampling to assess uncertainty using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study. The inversion is performed using an updated version of the Obsidian distributed inversion software. We find that the posterior for this inversion has a complex local covariance structure, hindering the efficiency of adaptive sampling methods that adjust the proposal based on the chain history. Within the context of a parallel-tempered Markov chain Monte Carlo scheme for exploring high-dimensional multi-modal posteriors, a preconditioned Crank–Nicolson proposal outperforms more conventional forms of random walk. Aspects of the problem setup, such as priors on petrophysics and on 3-D geological structure, affect the shape and separation of posterior modes, influencing sampling performance as well as the inversion results. The use of uninformative priors on sensor noise enables optimal weighting among multiple sensors even if noise levels are uncertain.

A major challenge in global change ecology is to predict the trajectory and magnitude of community change in response to global change drivers (GCDs). Here, we present a new framework that not only increases the predictive power of individual studies, but also allows for synthesis across GCD studies and ecosystems. First, we suggest that by quantifying community dissimilarity of replicates both among and within treatments, we can infer both the magnitude and predictability of community change, respectively. Second, we demonstrate the utility of integrating rank abundance curves with measures of community dissimilarity to understand the species-level dynamics driving community changes and propose a series of testable hypotheses linking changes in rank abundance curves with shifts in community dissimilarity. Finally, we review six case studies that demonstrate how our new conceptual framework can be applied. Overall, we present a new framework for holistically predicting community responses to GCDs that has broad applicability in this era of unprecedented global change and novel environmental conditions.