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Landscapes respond to climate, tectonic motions and sea level, but this response is mediated by sediment transport. Understanding transmission of environmental signals is crucial for predicting landscape response to climate change, and interpreting paleo‐climate and tectonics from stratigraphy. Here we propose that sediment transport can act as a nonlinear filter that completely destroys (“shreds”) environmental signals. This results from ubiquitous thresholds in sediment transport systems; e.g., landsliding, bed load transport, and river avulsion. This “morphodynamic turbulence” is analogous to turbulence in fluid flows, where energy injected at one frequency is smeared across a range of scales. We show with a numerical model that external signals are shredded when their time and amplitude scales fall within the ranges of morphodynamic turbulence. As signal frequency increases, signal preservation becomes the exception rather than the rule, suggesting a critical re‐examination of purported sedimentary signals of external forcing.

Aim: In order to understand fire's impacts on vegetation dynamics, it is crucial that the distribution of fire sizes be known. We approached this distribution using a power-law distribution, which derives from self-organized criticality theory (SOC). We compute the global spatial variation in the power-law exponent and determine the main factors that explain its spatial distribution. Location: Global, at 2° grid resolution. Methods: We use satellite-derived MODIS burned-area data (MCD45) to obtain global individual fire size data for 2002-2010, grouped together for each 2° grid. A global map of fire size distribution was produced by plotting the exponent of the power law. The drivers of the spatial trends in fire size distribution, including vegetation productivity, precipitation, population density and net income, were analysed using a generalized additive model (GAM). Results: The power law gave a good fit for 93% of the global 2° grid cells with important fire activity. A global map of the fire size distribution, as approached by the power law shows strong spatial patterns. These are associated both with climatic variables (precipitation and evapotranspiration) and with anthropogenic variables (cropland cover and population density). Main conclusions: Our results indicate that the global fire size distribution changes over gradients of precipitation and aridity, and that it is strongly influenced by human activity. This information is essential for understanding potential changes in fire sizes as a result of climate change and socioeconomic dynamics. The ability to improve SOC fire models by including these human and climatic factors would benefit fire projections as well as fire management and policy.

Bedform evolution and preserved cross strata are known to respond to floods. However, it is unclear if autogenic dynamics mask the flood signal in bedform evolution and cross strata. To address this, we characterize the temporal structure of autogenic noise in steady‐state bedform evolution in a physical experiment. Results reveal the existence of bedform groups—quasi‐stable collections of bedforms—that migrate at a similar speed as bedforms. We find that bedform and bedform‐group turnover timescales are the key autogenic timescales of bed evolution that set the transition time‐periods between different noise regimes in bedform evolution. Results suggest that bedform‐group turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata. Our work provides a new framework for interrogating fluvial cross strata for reconstruction of past floods. Plain Language Summary Bedforms are wavy features found regularly on the beds of rivers. Bedform deposits are the building blocks of the rock record on Earth and Mars. Bedforms and their deposits respond to floods; however, it is unclear if all floods are similarly represented in bedforms and their deposits. To address this, we identified the timescales over which bed elevation and sediment discharge are variable in a steady‐state experiment of bedform evolution using high‐resolution data. We investigated the time series of bed elevation to document the existence of bedform groups, which represent a collection of bedforms that have deep scours at their upstream and downstream end. We find that the turnover timescales (time required to move an entire land feature) of bedforms and bedform groups are the key controls on noise in bedform evolution. Results suggest that the signal of floods with duration less than bedform turnover timescale will not be found in bedform data and their deposits. However, floods with duration greater than the bedform‐group turnover timescale are likely to be expressed in bedform data and their deposits. These results provide a new theory for how floods are represented in river deposits. Key Points We show the existence of bedform groups, which are quasi‐stable collections of bedforms, previously found in aeolian dune evolution models Bedform and bedform group turnover timescales are key autogenic timescales that describe the temporal structure of noise in bed elevation Floods of duration shorter than bedform turnover timescale are expected to be unrecognizable in bed elevation and preserved cross strata

We investigate similarities and differences between natural and simulated slow earthquakes using nonlinear dynamical system tools. We use spatio‐temporal slip potency rate data derived from Global Navigation Satellite System (GNSS) position time series in the Cascadia subduction zone and numerical simulations intended to reproduce their pulse‐like behavior and scaling laws. We provide metrics to evaluate the accuracy of simulations in mimicking slow earthquake dynamics. We investigate the influence of spatio‐temporal coarsening as well as observational noise. Despite the use of many degrees of freedom, numerical simulations display a surprisingly low average dimension, akin to natural slow earthquakes. Instantaneous dynamical indices can reach large values (>10) instead, and differences persist between numerical simulations and natural observations. We propose to use the suggested metrics as an additional tool to narrow the divergence between slow earthquake observations and dynamical simulations. Plain Language Summary Earthquakes are natural phenomena resulting from the Earth's crust cyclically loading and unloading. The unpredictability of seismic events, combined with the large energy they release during the co‐seismic phase, poses not only scientific challenges but also significant threats to numerous populated regions at risk. Numerical simulations of the seismic cycle are widely used to better understand the dynamics of this natural phenomenon. Nonetheless, a direct comparison of earthquake observations and numerical simulations of the seismic cycle is currently prevented by the lengthy recurrence time of large seismic events rupturing the same fault segment and the short observational record at our disposal. Slow earthquakes, exhibiting lower recurrence times, serve as a viable alternative for validating models against real‐world observations. We investigate similarities and differences between natural and simulated slow earthquakes through the lens of nonlinear dynamical system theory. We study the effects of observational noise and spatio‐temporal coarsening putting the simulations in conditions like real‐world observations. We find that observational noise does not suffice to explain the higher complexity retrieved for natural observations. By refining our understanding of these dynamical systems, this study contributes to advancements in seismic research, offering a picture of the complexities involved on active faults. Key Points Natural observations and numerical simulations of slow earthquakes share common average dynamical properties Natural observations show higher complexity than numerical simulations Matching instantaneous dynamical properties can help reducing the discrepancies between natural and simulated slow earthquakes

Wildfire ecosystems are thought to be self‐regulated through pattern–process interactions between ignition frequency and location, and patterns of burned and recovering vegetation. Yet, recent increases in the frequency of large wildfires call into question the application of self‐organization theory to landscape resilience. Topography represents a stable bottom‐up template upon which fire interacts as both a physical and an ecological process. However, it is unclear how topographic control changes geographically and across spatial scales. We analyzed fire perimeter and topography data from 16 Bailey ecoregions across the State of California to identify spatial correspondence between ecoregional fire event and topographic patch size distributions. We found both sets of distributions followed a power‐law form and were statistically similar across several orders of magnitude, for most ecoregions. As a direct test of topographic controls on fire event perimeters, we used a paired t‐test across ~11,000 fires to identify differences in topographic attributes at fire boundaries versus fire interiors. Statistical significance was determined using 500 iterations of a neutral landscape model. Level of topographic control varied significantly by ecoregion and across topographic features. For example, north–south aspect breaks, valley bottoms, and roads showed a consistently high degree of spatial control on wildfire perimeters. Topographic controls were most pronounced in mountainous ecoregions and were least influential in arid regions. Ridgetops provided a low‐level control across all ecoregions. Spatial control was strongest for small (100–102 ha) to medium (103–104 ha) fire sizes, suggesting that controls were scale‐dependent rather than scale‐invariant. Roads were the dominant control across all ecoregions; however, removing roads from the analyses had no significant effect on the overall role of topography on wildfire extinguishment in this analysis. This result suggested that certain topographic settings show strong spatial control on fire growth, despite the presence of roads. Our results support the observation that both bottom‐up and top‐down factors constrain fire sizes and that there are likely scaling regions within fire size distributions wherein the dominance of these spatial controls varies. Human influences on fire spread may either diminish or enhance the role of some bottom‐up and top‐down factors, adding further complexity.

Key Points Size‐frequency distributions of landslides are well described by a power function for events with areas larger than 10,000 m2 Landslide scaling has been modeled with self‐organized criticality, non‐parameterized cellular automata models, and mechanical models This paper is a review of landslide and rockfall studies of hilly and mountainous regions worldwide. Repositories of landslide inventories are available online (e.g., Tanyaş et al., 2017; https://doi:10.1002/2017JF004236). The landslide inventories predominantly record the surface area of deep‐seated, fast‐moving, landslides, generally triggered by an earthquake or rainfall event, and such landslides are the primary focus of this review. The size‐frequency distributions of landslides and rockfalls are well described by a power function for larger (generally for the largest 2 orders of magnitude) of event sizes (e.g., Malamud et al., 2004; https://doi:10.1002/esp.1064; Tanyaş et al., 2018; https://doi:10.1002/esp.4359). Smaller event sizes are under‐represented by the power function that describes the larger events (e.g., Stark & Hovius, 2001; https://doi.org/10.1029/2000GL008527). The deviation from a power function at smaller sizes is arguably not a simple detection issue and possible explanations include lack of temporal resolution in sampling, and amalgamation of smaller events into larger events when mapping (e.g., Tanyaş et al., 2019; https://doi:10.1002/esp.4543). Self‐organized criticality models and cellular automata models have been developed that replicate the power scaling behavior (e.g., Hergarten, 2013). The self‐organized criticality models are alluring in their simplicity but have shortcomings such as failing to recreate the same scaling exponent as observed in nature (e.g., Hergarten, 2002). Parameterized cellular automata models include one or more relevant variables that affect shear stress in the surface materials and come closer to replicating the scaling exponents observed for natural systems (e.g., D'Ambrosio et al., 2003; https://doi:10.5194/nhess‐3‐545‐2003). Mechanical models have also successfully replicated the observed power scaling (e.g., Jeandet et al., 2019; https://doi:10.1029/2019GL082351).

A statistical mechanical mean-field approach to the temporal development of biological regulation provides a phenomenological, but basic description of the dynamical behavior of genome expression in terms of autonomous self-organization with a critical transition (Self-Organized Criticality: SOC). This approach reveals the basis of self-regulation/organization of genome expression, where the extreme complexity of living matter precludes any strict mechanistic approach. The self-organization in SOC involves two critical behaviors: scaling-divergent behavior (genome avalanche) and sandpile-type critical behavior. Genome avalanche patterns—competition between order (scaling) and disorder (divergence) reflect the opposite sequence of events characterizing the self-organization process in embryo development and helper T17 terminal cell differentiation, respectively. On the other hand, the temporal development of sandpile-type criticality (the degree of SOC control) in mouse embryo suggests the existence of an SOC control landscape with a critical transition state (i.e., the erasure of zygote-state criticality). This indicates that a phase transition of the mouse genome before and after reprogramming (immediately after the late 2-cell state) occurs through a dynamical change in a control parameter. This result provides a quantitative open-thermodynamic appreciation of the still largely qualitative notion of the epigenetic landscape. Our results suggest: (i) the existence of coherent waves of condensation/de-condensation in chromatin, which are transmitted across regions of different gene-expression levels along the genome; and (ii) essentially the same critical dynamics we observed for cell-differentiation processes exist in overall RNA expression during embryo development, which is particularly relevant because it gives further proof of SOC control of overall expression as a universal feature.

In drylands, the underlying vegetation structure is associated with ecosystem functioning and ecosystem resilience. Although scale-dependent patterns are also predicted, empirical evidence often demonstrates that patch sizes are distributed according to a power-law probability distribution function or truncated power-law probability distribution function for a varied range of environmental conditions. Using satellite images and field measures, we assessed the spatial pattern of vegetation patches for a wide range of vegetation cover values in a large set of Mediterranean dryland (MDL) plots, focusing on the statistical distribution function that better fits the patch sizes. We found that power-law or truncated power-law probability distribution function does not always fit the observed patch size frequencies, while lognormal probability density function always fit well to them, implying that the vegetation structure is scale dependent for a large range of conditions. We show how the sampling approach, fit methods, and system dimensionality can affect the patch size distribution, which can explain some conflicting evidence obtained from the empirical data. Our findings question the robustness of criticality as the underlying mechanism driving vegetation patterns in MDLs. The better fit to patch size distribution provided by lognormal as compared with power-law indicates that multiplicative effects of multivariate local influences underlie pattern formation, and suggests that the role of plant–plant facilitation can be overestimated for a large range of conditions.

In this work we study the stability of the equilibria reached by ecosystems formed by a large number of species. The model we focus on are Lotka-Volterra equations with symmetric random interactions. Our theoretical analysis, confirmed by our numerical studies, shows that for strong and heterogeneous interactions the system displays multiple equilibria which are all marginally stable. This property allows us to obtain general identities between diversity and single species responses, which generalize and saturate May's stability bound. By connecting the model to systems studied in condensed matter physics, we show that the multiple equilibria regime is analogous to a critical spin-glass phase. This relation suggests new experimental ways to probe marginal stability.

Biological systems reach hierarchical complexity that has no counterpart outside the realm of biology. Undoubtedly, biological entities obey the fundamental physical laws. Can today’s physics provide an explanatory framework for understanding the evolution of biological complexity? We argue that the physical foundation for understanding the origin and evolution of complexity can be gleaned at the interface between the theory of frustrated states resulting in pattern formation in glass-like media and the theory of self-organized criticality (SOC). On the one hand, SOC has been shown to emerge in spin-glass systems of high dimensionality. On the other hand, SOC is often viewed as the most appropriate physical description of evolutionary transitions in biology. We unify these two faces of SOC by showing that emergence of complex features in biological evolution typically, if not always, is triggered by frustration that is caused by competing interactions at different organizational levels. Such competing interactions lead to SOC, which represents the optimal conditions for the emergence of complexity. Competing interactions and frustrated states permeate biology at all organizational levels and are tightly linked to the ubiquitous competition for limiting resources. This perspective extends from the comparatively simple phenomena occurring in glasses to large-scale events of biological evolution, such as major evolutionary transitions. Frustration caused by competing interactions in multidimensional systems could be the general driving force behind the emergence of complexity, within and beyond the domain of biology.