Explorations into a Dynamic Process-Oriented Soil Science (Elsevier Insights)

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Recognition and Redistribution in Contemporary Democracies, K. Practice, London: download Press. They may reach operations to ambiguities in sons that offer and necessarily deliver corresponding numbers of perspectives and meristem claims Kellner, The second step involved the study of associations of biological traits with environmental variables and with each other, seeking support for ecological theories that would allow the definition of functional relationships in the marine benthos Alexandridis et al. Each of the community assembly mechanisms that are represented by these relationships encompasses a variety of processes that could potentially be modeled in more detail.

The level of representation was dictated by the available trait, environmental and theoretical knowledge. Hence, biological traits were used as proxies for the role of functional groups in a set of theoretically anticipated community assembly mechanisms. Ideally, the rules of interaction in IBMs are formulated in terms of fitness maximization, providing a representation that is more general and reliable than empirical formulations Stillman et al. In cases where entire communities and their complex webs of interactions need to be represented, IBMs have had to settle for more implicit representations of this first principle.

Our rules of interaction are partly phenomenological, but they represent well-established ecological theories that use fitness maximization as their basis. Furthermore, the algorithmic representation of interactions allowed the incorporation of expert knowledge, which is widely available but often difficult to formulate mathematically. The nonparametric up-scaling approach of Cipriotti et al. The important state variables of the fine-scale model define a state space, which is divided into a finite number of discrete states.

Simulation runs of this model, covering a range of pertinent initial conditions, states and drivers, define transition matrices that are used by the coarse-scale model. The resulting up-scaling is not dynamic and it is restricted to the range of the simulations. Computational limitations imposed a mismatch between the two scales, which was bridged by using a toroidal lattice with no boundaries and a mean representation of some processes. This study identified transfer processes and spatial heterogeneity at intermediate scales as important components of the link between fine- and coarse-scale patterns and processes.

Similarly to this conclusion, the model presented here consists of two spatially distinct representations that are linked by larval dispersal and cell variability. The cells correspond to large ecosystem areas and each of them is represented by a community of much smaller surface. The link between community and ecosystem is implemented through aggregate community measures fed into the ecosystem model, whose output in turn influences community assembly parameters.

Diversity at this scale is maintained as a result of inter-group competitive trade-offs and intra-group inhibition. Pre-emptive competition for space is represented explicitly, while exploitative competition for food is implied in the overgrowth competition. The role of epibiosis and sediment engineering is represented through the modification of settlement probabilities, which are initially defined by a combination of reproduction-related traits.

The generated abundance patterns were characterized by the gradual dominance of a few groups, through a process that represents competitive exclusion. Abundance patterns at this level were shaped by diversity among the cells and the process of local dispersal. Inter-cell diversity was driven by the distinction between the subtidal and the intertidal zone and the random initial dominance of stabilizers or destabilizers in each cell.

The few areas of destabilizer-dominated cells that were left after 10 years of simulation did not seem able to resist the general trend of cells becoming dominated by sediment stabilizers. Exploratory longer simulations of the model indicate that it would probably fail at sustaining high diversity levels, mostly due to the eventual dominance of sediment stabilizers.

They played this role by facilitating the creation of clusters of cells that were dominated by sediment stabilizers or destabilizers.

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  7. It prevented small functional groups from dominating and restricted total group abundances to realistic levels. The gradual elimination of the three epibiotic groups could indicate that epibiosis cannot by itself sustain observed levels of epifaunal diversity, calling for the inclusion of hard substrate types. However, the resulting rareness of groups is also a feature of the observations, which disappears from the model output only when these groups are eliminated. This discrepancy between observations and the final years of the simulation could be due to the concurrent development of cells in the model.

    Assemblages that are at different successional stages should sustain group rareness and be a more realistic representation of a system that is subject to frequent perturbations, such as wave action or extreme cold Desroy, Some associations of group distribution in the observations could also be seen in the model output, indicating the adequate representation of certain community assembly mechanisms, such as environmental filtering caused by tidal zonation and trophic interactions.

    It is not clear whether the similarity of observations with patterns produced by the model due to biogenic habitat modification reflects a good depiction of this phenomenon or just mimicry of environmental filtering caused by substrate type. The same holds true for patterns of spatial correlation. Compositional similarity at small distances and dissimilarity at intermediate distances emerged early in the simulation, due to tidal zonation.

    Compositional dissimilarity at large distances, on the other hand, emerged only toward the end of the simulation, probably due to the formation of clusters of synchronized cells. However, the role of substrate, especially in interaction with biogenic habitat modification, is not clear. It appears that the potential of the model to explore the assembly mechanisms of benthic communities could be drastically increased through a few extensions.

    The combination of sediment engineering with a distinction between different substrate types could allow the model to sustain high diversity levels. This task would be facilitated by the addition of a realistic disturbance regime, which would keep the model away from equilibrium and promote the persistence of rare groups. The explicit representation of these processes would help disentangle the role of substrate type, habitat modification and physical disturbance in shaping marine benthos.

    The model might also benefit from an improved formulation of trophic interactions.

    Download Explorations Into A Dynamic Process Oriented Soil Science (Elsevier Insights)

    A more detailed description of trophic strategies could be combined with known links between community structure and organic fluxes Herman et al. Any understanding of community assembly mechanisms that is gained by this and future versions of the model should eventually allow its transition to a predictive modeling tool, which could improve projections of benthic biodiversity responses to environmental change.

    The same holds true for the response of benthic communities to expected additions of invasive species with certain combinations of the available traits. Ultimately, the use of the model for predictive purposes would require its precise parameterization and validation through context-oriented approaches Kubicek et al. The study of this link requires biodiversity patterns to be explicitly associated with ecosystem functions, such as energy and elemental cycling, the provision of habitat or the modification of physical properties of the system Frid et al.

    Explorations into a Dynamic Process-Oriented Soil Science

    The role of these traits can be extended to that of indicators of ecosystem functioning, in order to associate the latter with modeled biodiversity. This task can be assisted by theoretical Brown et al.

    Our mechanistic model of the processes that shape estuarine benthos at different spatial scales incorporates novel approaches to the ecological aggregation of communities and the formulation of functional relationships, filling existing gaps in benthic biodiversity modeling. The former allowed the separation of areas with synchronous community composition and the latter kept the abundances of benthic macroinvertebrates well below the levels expected on the basis of resources availability.

    Environmental filtering due to tidal zonation and prey—predator interactions were represented sufficiently well, while biogenic habitat modification and its interaction with substrate type would benefit from additional research and a more realistic representation. Ultimately, the main challenges of this modeling framework will consist in improving projections of biodiversity responses to environmental change and investigating the links between benthic biodiversity and ecosystem functioning.

    List of species and their classification. ODD description of the fine-scale model. Observed functional group abundances. Source code of the NetLogo models. Model output of the benchmark simulation. Models subtidal and intertidal are the fine-scale models of the subtidal and the intertidal zone, respectively.

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    View 6 tweets. Share Twitter Facebook Email. Individual-based simulation of the spatial and temporal dynamics of macroinvertebrate functional groups provides insights into benthic community assembly mechanisms. View article. PeerJ - the journal thePeerJ days ago. David Bapst dwbapst days ago. RT djbirddanerd: Individual-based simulation of the spatial and temporal dynamics of macroinvertebrate functional groups provides insights….

    Kenneth De Baets djbirddanerd days ago. Introduction Environmental change appears to adversely affect ecosystem functioning, both directly and through its impact on biodiversity Hooper et al. Materials and Methods The model consists of two spatially structured cellular automaton sub-models Fig. Download full-size image. DOI: Table 1: Functional groups of species with their assigned representative species and biological trait values. Table 2: Parameterization of the subtidal and the intertidal version of the fine-scale model.

    Data and code. Code and data used to run the models. I published in PeerJ and it is very fast, has good editors, has consistently given good quality and rigorous reviews of my work, and produces visually appealing manuscripts. Matthew Jackson PeerJ author. Publish Free in Content Alert NEW.

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