Cycling in ecosystems: An individual based approach

Dynamic ecological system analysis

“…However, the authors' approach was approximate, computationally resource-intensive, and offered no guarantees of series convergence. While a guarantee of convergence is added by [29] , the computation remained resource-intensive due to the individual-based simulation technique [30] , [43] .…”

“…There are earlier approaches in the literature for the analysis of dynamic ecosystems, but these are either essentially closed-form abstract formulations [19] , or designed for special cases, such as linear systems with time-dependent inputs [23] . In addition, there are also agent-based techniques for dynamic compartmental system analysis [29] , [30] , [41] . These are, however, computational methods that rely on network particle tracking simulations.…”

Thermostatistical distribution of a trophic energy proxy with analytical consequences for evolutionary ecology, species coexistence and the maximum entropy formalism

“…This exchange guarantees that any stationary ecosystem behaves just as a lab balloon that contains a gas at a constant temperature, in spite of the absence of any kind of tangible walls in the ecological context: m e  I e 2  H p = k e "pushes" the set of plots toward a "thermo-statistical accretion center" (a sort of dynamic cage with invisible but effective limits) that combines maximum eco-kinetic energy values per plot with intermediate species diversity values and it can only be avoided either towards a regressive successional position (due to a net leakage of energy from the system) or towards a progressive successional position (due to a net input of energy into the system). Therefore this proposal, despite some differences in comparison with some proposals within Network Environ Analysis (e.g., in regard to theoretical foundations; methodological approach; depth of mathematical abstractions; analytical importance granted to non-stationary states as a priority research goal Shevtsov et al, 2009 necessity of a detailed tracking of real or hypothetical particles Kazanci et al, 2009 due to time dependency or non-assumption of ergodicity Schramski et al, 2011, pp. 420-427 and nature of the interdisciplinary links between conventional physics and orthodox ecology), coincides with one of the most general conclusion from NEA: that "real ecosystems are near steady-state in long-term mean characteristics, but are dynamic in short-term responses" (Schramski et al, 2007).…”

Hierarchical networks

“…The four cases are: I: transfer of D3 to B1 and from C2 to D4 II: additionally to 1 transfer from D2 to D4 III: transfer from B4 to C2 IV: transfer from B4 to C2 by a relatively high respiration coefficient (0.2 1/24 h) Case 0 is the results from Table 2 the system resulting from a given input are operating among the components in the ecosystem. Aggradation indices calculated for ecosystems as a result of the network are based on the network giving the interaction among the components (see Kazanci, 2007;Kazanci et al, 2009;Jørgensen, 2012). Similarly we could also develop a network of a cell and its molecular processes (interactions), a network of organisms and the corresponding interactions among organs and a network of the ecosphere and the exchanges of work energy, matter and information among the landscapes.…”