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In simulation models of populations or communities, individual plants have often been obfuscated in favor of aggregated vegetation. This simplification comes with a loss of biological detail and a smoothing out of the demographic noise engendered by stochastic individual-scale processes and heterogeneities, which is significant among others when studying the viability of small populations facing challenging fluctuating environmental conditions. This consideration has motivated the development of precise plant-centered models. The accuracy gained in the representation of plant biology has then, however, often been balanced by the disappearance in models of important plant-soil interactions (esp. water dynamics) due to the inability of most individual-based frameworks to simulate complex continuous processes. In this study, we used a hybrid modeling approach, namely integrated System Dynamics (SD)—Individual-based (IB), to illustrate the importance of individual plant dynamics to explain spatial self-organization of vegetation in arid environments. We analyzed the behavior of this model under different parameter sets either related to individual plant properties (such as seed dispersal distance and reproductive age) or the environment (such as intensity and yearly distribution of precipitation events). While the results of this work confirmed the prevailing theory on vegetation patterning, they also revealed the importance therein of plant-level processes that cannot be rendered by reaction-diffusion models. Initial spatial distribution of plants, reproductive age, and average seed dispersal distance, by impacting patch size and vegetation aggregation, affected pattern formation and population survival under climatic variations. Besides, changes in precipitation regime altered the demographic structure and spatial organization of vegetation patches by affecting plants differentially depending on their age and biomass. Water availability influenced non-linearly total biomass density. Remarkably, lower precipitation resulted in lower mean plant age yet higher mean individual biomass. Moreover, seasonal variations in rainfall greater than a threshold (here, ±0.45 mm from the 1.3 mm baseline) decreased mean total biomass and generated limit cycles, which, in the case of large variations, were preceded by chaotic demographic and spatial behavior. In some cases, peculiar spatial patterns (e.g., rings) were also engendered. On a technical note, the shortcomings of the present model and the benefit of hybrid modeling for virtual investigations in plant science are discussed.

The organic matter cycle is one of the most fundamental processes in ecosystems affecting the soil and controlling its functions. The soil complex microbiome is made up of thousands of bacterial and hundreds of fungal strains that coexist on the many different available organic carbon sources. In natural plant communities, freshly fallen leaf-litter and dead roots are subject to decomposition by a complex food-web composed of both microbial saprotrophs and invertebrate detritivores. The litter chemical composition varies dramatically among species in relation to plant life forms (conifer, broadleaf, nitrogen-fixing, graminoid) and, within species, with plant organs (leaf, root, woody tissues). This paper reviews the usefulness of advanced chemical technologies to study the composition of both plant litter and organic amendments, supporting the description of their mechanism of action and attention to their potential applications. First, a critical review is presented on the limitations of C/N and lignin/N ratios, still widely used as basic indicators of litter chemistry. Second, the potential of the solid state 13C-CPMAS NMR is reported as a powerful tool to assess the chemical composition of both litter and organic amendments. Finally, six different study cases are reported to provide evidence of the usefulness of such metabolomic approach for the description of organic matter chemistry aimed to an effective prediction of its impact on soil ecosystem functions.

The increasing availability of phylogenetic information facilitates the use of evolutionary methods in community ecology to reveal the importance of evolution in the species assembly process. However, while several methods have been applied to a wide range of communities across different spatial scales with the purpose of detecting non-random phylogenetic patterns, the spatial aspects of phylogenetic community structure have received far less attention. Accordingly, the question for this study is: can point pattern analysis be used for revealing the phylogenetic structure of multi-species assemblages? We introduce a new individual-centered procedure for analyzing the scale-dependent phylogenetic structure of multi-species point patterns based on digitized field data. The method uses nested circular plots with increasing radii drawn around each individual plant and calculates the mean phylogenetic distance between the focal individual and all individuals located in the circular ring delimited by two successive radii. This scale-dependent value is then averaged over all individuals of the same species and the observed mean is compared to a null expectation with permutation procedures. The method detects particular radius values at which the point pattern of a single species exhibits maximum deviation from the expectation towards either phylogenetic aggregation or segregation. Its performance is illustrated using data from a grassland community in Hungary and simulated point patterns. The proposed method can be extended to virtually any distance function for species pairs, such as functional distances.

Large scale wildfire events that occurred around the world involved a massive loss of animal lives, with a consequent economic impact on agricultural holdings and damages to ecosystems. Preparing animals for a wildfire evacuation requires an extra level of planning, preparedness and coordination, which is missing in the current practice. This paper describes a conceptual framework of an ICT system implemented to support the activities of the Regional Veterinary referral Center for non-epidemic emergencies (CeRVEnE) in the Campania Region for the twofold objectives. On the one hand, it realizes the monitoring of the wooded areas under risk of fire in the so-called &ldquo, Mount Vesuvius&rsquo, red zone&rdquo, On the other hand, it determines the OPtimal Evacuation Route for Animals (OPERA) in case of fire, for each of the reported animal species living in the mentioned red zone. The main innovation of the proposed system lies in its software architecture that aims at integrating a Distributed Sensor Network (DSN), an ad-hoc software to generate timely simulations for fire risk modeling, and a GIS (Geographic Information System) for both the activities of web mapping and OPERA definition. This paper shows some effective preliminary results of the system implementation. The importance of the system mainly lies in its accordance with the so-called &ldquo, Foresight approach&rdquo, perspective, that provides models and tools to guarantee the prevention of systematic failure in disaster risk management, and becomes moreover critical in the case of Mount Vesuvius, which hosts a unique combination of both animal and anthropic elements within a delicate natural ecosystem.

Wound closure in plant stems (after either fire or mechanical damage) is a complex, multi-scale process that involves the formation of a callous tissue (callus lips) responsible for cell proliferation and overgrowth at the injury edges, resulting in coverage of the scarred tissue. Investigating such phenomena, it is difficult to discriminate between cell-specific growth responses, associated with physiological adaptations, and cell proliferation reactions emerging from specific cambium dynamics due to changes in mechanical constrains. In particular, the effects of cell-cell mechanical interactions on the wound closure process have never been investigated. To understand to what extent callus lip formation depends on the intra-tissue mechanical balance of forces, we built a simplified individual-based model (IBM) of cell division and differentiation in a generic woody tissue. Despite its simplified physiological assumptions, the model was capable to simulate callus hyperproliferation and wound healing as an emergent property of the mechanical interactions between individual cells. The model output suggests that the existence of a scar alone does constrain the growth trajectories of the remaining proliferating cells around the injury, thus resulting in the wound closure, ultimately engulfing the damaged tissue in the growing stem.

We address and discuss the application of nonlinear Galerkin methods for the model reduction and numerical solution of partial differential equations (PDE) with Turing instabilities in comparison with standard (linear) Galerkin methods. The model considered is a system of PDEs modelling the pattern formation in vegetation dynamics. In particular, by constructing the approximate inertial manifold on the basis of the spectral decomposition of the solution, we implement the so-called Euler-Galerkin method and we compare its efficiency and accuracy versus the linear Galerkin methods. We compare the efficiency of the methods by (a) the accuracy of the computed bifurcation points, and, (b) by the computation of the Hausdorff distance between the limit sets obtained by the Galerkin methods and the ones obtained with a reference finite difference scheme. The efficiency with respect to the required CPU time is also accessed. For our illustrations we used three different ODE time integrators, from the Matlab ODE suite. Our results indicate that the performance of the Euler-Galerkin method is superior compared to the linear Galerkin method when either explicit or linearly implicit time integration scheme are adopted. For the particular problem considered, we found that the dimension of approximate inertial manifold is strongly affected by the lenght of the spatial domain. Indeeed, we show that the number of modes required to accurately describe the long time Turing pattern forming solutions increases as the domain increases.

We revisit the theorem stating that it is possible to approximate with any accuracy any real continuous function with a class of relational maps. In other words, relational maps are universal approximators. We review the key works that have proved this property, highlighting their limitations and providing yet another proof that it is not restricted by certain assumptions considered in early proofs. We also show how one can go inversely to approximate these systems with a series of polynomials. This provides us with analytical expressions of these maps which can facilitate a series of important analysis tasks such as modeling and numerical analysis of ill-defined-uncertain complex systems.

Plant invasions can have relevant impacts on biogeochemical cycles, whose extent, in Mediterranean ecosystems, have not yet been systematically assessed comparing litter carbon (C) and nitrogen (N) dynamics between invasive plants and native communities. We carried out a one-year litterbag experiment in 4 different plant communities (grassland, sand dune, riparian and mixed forests) on 8 invasives and 24 autochthonous plant species, used as control. Plant litter was characterized for mass loss, N release, proximate lignin and litter chemistry by 13C CPMAS NMR. Native and invasive species showed significant differences in litter chemical traits, with invaders generally showing higher N concentration and lower lignin/N ratio. Mass loss data revealed no consistent differences between native and invasive species, although some woody and vine invaders showed exceptionally high decomposition rate. In contrast, N release rate from litter was faster for invasive plants compared to native species. N concentration, lignin content and relative abundance of methoxyl and N-alkyl C region from 13C CPMAS NMR spectra were the parameters that better explained mass loss and N mineralization rates. Our findings demonstrate that during litter decomposition invasive species litter has no different decomposition rates but greater N release rate compared to natives. Accordingly, invasives are expected to affect N cycle in Mediterranean plant communities, possibly promoting a shift of plant assemblages.

Background and Aims Modelling organic matter decomposition is fundamental to understand biogeochemical cycling in terrestrial ecosystems. Current models use C/N or Lignin/N ratios to describe susceptibility to decomposition, or implement separate C pools decaying with different rates, disregarding biomolecular transformations and interactions and their effect on decomposition dynamics. We present a new process-based model of decomposition including a description of biomolecular dynamics obtained by 13C-CPMAS NMR spectroscopy. Methods Baseline decay rates for relevant molecular classes and intermolecular protection were calibrated by best fitting of experimental data from leaves of 20 plant species decomposing for 180 days in controlled optimal conditions. The model was validated against field data from leaves of 32 plant species decomposing for 1-year at four sites in Mediterranean ecosystems. Results Simulations correctly reproduced mass loss data and variations of selected molecular classes both in controlled conditions and in the field, for a wide range of plant molecular composition and environmental conditions. Conclusions Our innovative approach accurately predicted decomposition of a wide range of litters across different climates. Prediction accuracy emerged from the species-specific partitioning of molecular types and from the representation of intermolecular interactions. Further application should be planned in other ecosystems based on long-term decomposition datasets.

Spatial patterns and self-organization of plants has been a subject of fascination because the underlying mechanisms have been hard to determine, raising different explanatory hypotheses. Plant–soil negative feedback (PSNF) – defined as the induction of negative conditions for conspecific establishment – has been widely studied in both field and laboratory conditions, and conceptually demonstrated by some modelling works. We present a mechanistic model, integrating individual plants inside an agent-based framework, to explore the effects of PSNF on the spatial and temporal dynamics of virtual populations and communities of plants of diverse growth forms. This endeavour led to the reproduction of well-known vegetation patterns observed at various scales, demonstrating for the first time a unified mechanism behind the spatial patterns of Janzen–Connell seedlings’ distribution, ring formation, and the high species mobility in species-rich grasslands. These results support the ecological relevance of PSNF in the regulation of spatial organization and biodiversity dynamics in plant communities. More specifically, PSNF due to autoxicity seems most coherent with the spatio-temporal scale of dynamics displayed here.

DNA is usually known as the molecule that carries the instructions necessary for cell functioning and genetic inheritance. A recent discovery reported a new functional role for extracellular DNA. After fragmentation, either by natural or artificial decomposition, small DNA molecules (between ∼50 and ∼2000 bp) exert a species specific inhibitory effect on individuals of the same species. Evidence shows that such effect occurs for a wide range of organisms, suggesting a general biological process. In this paper we explore the possible molecular mechanisms behind those findings and discuss the ecological implications, specifically those related to plant species coexistence.