The two faces of Janus: Processes can be both exogenous forcings and endogenous feedbacks with wind as a case study

Janus is the Roman god of transitions. In many environments, state transitions are an important part of our understanding of ecological change. These transitions are controlled by the interactions between exogenous forcing factors and stabilizing endogenous feedbacks. Forcing factors and feedbacks are typically considered to consist of different processes. We argue that during extreme events, a process that usually forms part of a stabilizing feedback can behave as a forcing factor. And thus, like Janus, a single process can have two faces. The case explored here pertains to state change in drylands where interactions between wind erosion and vegetation form an important feedback that encourages grass-to-shrub state transitions. Wind concentrates soil resources in shrub-centered fertile islands, removes resources through loss of fines to favor deep-rooted shrubs, and abrades grasses' photosynthetic tissue, thus further favoring the shrub state that, in turn, experiences greater aeolian transport. This feedback is well documented but the potential of wind to act also as a forcing has yet to be examined. Extreme wind events have the potential to act like other drivers of state change, such as drought and grazing, to directly reduce grass cover. This study examines the responses of a grass-shrub community after two extreme wind events in 2019 caused severe deflation. We measured grass cover and root exposure due to deflation, in addition to shrub height, grass patch size, and grass greenness along 50-m transects across a wide range of grass cover. Root exposure was concentrated in the direction of erosive winds during the storms and sites with low grass cover were associated with increased root exposure and reduced greenness. We argue that differences between extreme, rare wind events and frequent, small wind events are significant enough to be differences in kind rather than differences in degree allowing extreme winds to behave as endogenous forcings and common winds to participate in an endogenous stabilizing feedback. Several types of state change in other ecological systems in are contextualized within this framework.

Spatial prediction of soil depth using environmental covariates by quantile regression forest model

Prediction of soil depth for larger areas provides primary information on soil depth and its spatial distribution that becomes vital for land resource management, crop, nutrient, and ecosystem modeling. The present study assessed the spatial distribution of soil depth over 160,205 km² of Andhra Pradesh, India, using 20 covariables by quantile regression forest (QRF). An aggregate of 2854 soil datasets compiled from various physiographic units were randomly partitioned into 80:20 ratio for calibration (2283 samples) and validation (571 samples). Landsat imagery, terrain datasets (8), and bioclimatic factors (11) were utilized as covariates. The QRF model outputs signified that precipitation, multi-resolution index of valley bottom flatness (MrVBF), mean diurnal range, isothermality, and elevation were the most important variables influencing soil depth variability across the landscape. Spatial prediction of soil depth by QRF model yielded a ME of - 1.81 cm, RMSE of 34 cm, PICP of 90.2, and a R² value of 42% as compared to ordinary kriging which results in a ME of - 0.14 cm, a RMSE of 37 cm, and a R² value of 32%. As soil depth is spatially dynamic and has significant correlation with terrain and environmental covariates, better prediction was possible by the QRF model. However, high-density bioclimatic variables could be utilized along with high-resolution terrain variables to improve the predictive accuracy.

Age distribution dynamics with stochastic jumps in mortality

While deterministic age distribution models have been extensively studied and applied in various disciplines, little work has been devoted to understanding the role of stochasticity in birth and mortality terms. In this paper, we analyse a stochastic M'Kendrick-von Foerster equation in which jumps in mortality represent intense losses of population due to external events. We present explicit solutions for the probability density functions of the age distribution and the total population and for the temporal dynamics of their moments. We also derive the dynamics of the mean age of the population and its harmonic mean. The framework is then used to calculate the age distribution of salt in the soil root zone, where the accumulation of salt by atmospheric deposition is counteracted by plant uptake and by jump losses due to percolation events.

Phase transitions driven by state-dependent poisson noise

Nonlinear systems driven by state-dependent Poisson noise are introduced to model the persistence of climatic anomalies in land-atmosphere interaction caused by the soil-moisture dependence of the frequency of rainfall events. It is found that these systems may give rise to bimodal probability distributions, while the state variable randomly persists around the preferential states because of transient dynamics that are opposite to the long-term behavior. Mean-field analysis and numerical simulations of the spatially distributed systems reveal a symmetry-breaking bifurcation for sufficiently strong spatial diffusive couplings and intermediate noise intensities. In such conditions, the initial development of spatial patterns is followed by a stable configuration, selected on the bases of the initial conditions in correspondence of the remnants of the modes of the uncoupled system.