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The methods of network environ analysis (NEA) currently apply to steady-state models. Networks of real ecosystems are near steady-state in long-term mean characteristics, but are dynamic in short-term responses. A formal mathematical approach to dynamic NEA analysis has never been fully developed, though Hippe [Hippe, P.W., 1983. Environ analysis of linear compartmental systems: the dynamic, time-invariant case. Ecol. Model. 19, 1–26] offers one approach. Another potential approach to addressing this limitation is to analyze a discrete-time series of steady-state models, each a snapshot for the time period it represents. Using concepts from open-loop control theory, four throughflow-based ecological control terms (control ratio, CR; control difference, CD; system control, scj; and total system control, TC) as developed using an environ framework are evaluated for 16 consecutive seasons of nitrogen cycling in the Neuse River Estuary, North Carolina, USA. Results of this assessment offer a quantitative measure of the quasi-dynamic distributed control in this network. The NOx and Sediment components assume opposing but dominant roles (high scj magnitudes) in all 16 seasons. Low total compartmental throughflow (Ti) to respective boundary inflow (zi) or outflow (yj) ratios are shown to be indicators of component control dominance, suggesting a role for boundary flows in the consideration of a system component's dominance. This conclusion may also be a property of the high cycling nature of this nitrogen model (average Finn cycling index of 89% for all 16 seasons). TC appears to be correlated with total system throughflow (TST), suggesting that TST may indicate a system's distributed control patterns. This correlation may likewise be fruitful if TST proves to be an indicator for system stability, an important and logical consideration in the notion of control.

Using an ecological food web model, we examine the change in exergy storage and energy throughflow (power) as a consequence of eight network changes including (1) increased input, (2) removal of food chain links, (3) addition of new pathways (links) from producers to herbivores, (4) addition of new pathways (links) from herbivores to carnivores, (5) food chain prolongation, (6) increased flow rates in the food chains, (7) transfer of energy from one food chain to another, and, (8) reduced loss as detritus. It has been shown previously that exergy storage and energy throughflow increase during stages of ecosystem growth and development. We hypothesis that network changes yielding the highest exergy storage and throughflow will have a selective advantage and would be more favorable to the overall system organization. This hypothesis could be used to explain the selective criterion for changes that occur in an ecological network. Here, we investigate which changes, in general, lead to these more favorable conditions. Model results demonstrate the following six rules regarding network effects on exergy and power: I. Increased input gives proportional increase of exergy and power; II. Additional links only affect power and exergy when they increase the overall network throughflow, thus the connection placement is important; III. Food chain prolongation has a positive effect on the power and exergy of the network; IV. Reduction of loss of exergy to the environment or as detritus (with a β value of 1 only) yields a higher power and exergy of the network; V. Faster cycling—detritus is decomposed faster or the transfer rates between two tropic levels are increased—implies higher power and exergy; and, VI. Input of additional exergy or energy recycling flows has bigger effect the earlier in the food chain the addition takes place.

In this paper, network utility analysis is used to investigate the efficiency of flow between two ecological components. Empirical studies have shown that the efficiency of resource transfer between trophic levels in an ecosystem tends to be relatively low in spite of the considerable amount of time organisms have had to evolve efficient uptake and assimilation processes. In fact, a general rule of thumb in ecology is that only about 10% of the total energy intake into an organism is transferred to the next trophic level. In addition to the possible thermodynamic, physical, chemical, and biological limitations, this research indicates another possible limitation to trophic transfer efficiency. Utility analysis, a resource-based input–output measure of the overall usefulness a compartmental flow has on the entire system, is used to identify the qualitative and quantitative relations in a model. However, when the transfer efficiencies are too large the utility metric is not calculable. This is interpreted to mean that utility, which is inherently positive, is not conveyed within these systems. This research shows for simple food chain models where the breakdown in utility analysis occurs and what patterns exist as systems approach this threshold. Although computable utility may not be a necessary condition for low trophic efficiency, it may be sufficient to explain low transfer efficiencies in ecosystems.

An iterative approach to working with a community's environmental concerns, at the interface between science and society, is presented. We call this adaptive community learning, within which the principles of adaptive environmental assessment and management are embedded. Handling uncertainty - indeed, gross uncertainty - is integral to our approach. We place our discussion in the context of the evolving foundations of the kind of science needed to address contemporary issues of the environment and sustainability. We then illustrate development and application of our approach in a prototypical case study of managing the quality of Lake Lanier, Georgia (USA), in the face of potentially significant suburbanisation of its watershed. Our primary purpose is to communicate the entirety of the approach as succinctly as possible, without the clutter of the specific results emerging in the several disciplines keyed into the project as a whole.

Environ analysis, based on network theory, is a methodology to quantify how objects interact with and depend on other objects in a system. The primary result from the method provides input and output ‘environs’. In addition, application of network environ analysis on empirical data sets and ecosystem models has revealed several important and unexpected results that have been identified and summarized in the literature as network environ properties. Data requirements for the analysis include the intercompartmental flows, compartmental storages, and boundary input and output flows. This article reviews the theoretical underpinning of the analysis and briefly introduces some the main properties such as indirect effects ratio, network homogenization, and network mutualism.

Publisher Summary This chapter focuses on an emerging coherent theory of CAHSystems. They have both intrinsic and instrumental values. The orientation of First-World societies is pragmatic, which must be tempered by the awareness of the critical importance of a new-knowledge generation, which cannot be judged practically. Old knowledge can be applied with inevitable results when human adaptability becomes increasingly impoverished. The area of CAHSystems represents new ground in science as 21st Century begins to unfold. Pragmatically, a turn to CAHSystems is motivated by the needs to integrate and make sense of the information overload to grapple with complexly interwoven environmental, political, economic, social, and ethical issues that span huge scales of space and time. The development and focus of this theory is still in its infancy and must be nurtured, but the pressing need to discover and integrate complex systems knowledge argues for its continuing promotion and coordination. Few tools are available to grapple with systems, and one of these is interdisciplinary modeling, which represents a potentially powerful means for integrating observation, theory, and practice. Development of CAHSystems Theory and modeling are essential if humanity is realized as a balanced and sustainable condition within the frames of healthy, diverse, and viable ecosystems across the globe.

6 pages; Parasitism is a pervasive phenomenon in nature involving, either as hosts or as parasites, virtually all species on Earth. By definition, parasites are costly for their hosts as they divert resources for their growth, reproduction, and survival with no rewards for the hosts. Parasitism is not restricted to a few taxonomic groups as a parasitic lifestyle has evolved in groups as diverse as viruses, bacteria, protozoa, invertebrate and vertebrate metazoan. This taxonomic diversity is, of course, also associated with a tremendous diversity of life cycles, host exploitation strategies, transmission modes, and virulence levels. Given the cost of parasitism, hosts are expected to evolve defense mechanisms aiming at limiting the negative effect of parasitism on their fitness. In agreement with this view, hosts have evolved a series of morphological, physiological, and behavioral adaptations to fight off parasitic attacks. On their side, parasites have responded to the selection pressures exerted by their hosts by evolving counter-adaptations to overcome host defenses. These cycles of host–parasite adaptations/counter-adaptations define the so-called coevolutionary process, one of the most prominent characteristics of host–parasite interactions.