184 research outputs found
A survey for the use of remote sensing in the Chesapeake Bay region
Environmental problem areas concerning the Chesapeake Bay region are reviewed along with ongoing remote sensing programs pertaining to these problems, and recommendations are presented to help fill lacunae in present research and to utilize the remote sensing capabilities of NASA to their fullest. A list of interested organizations and individuals is presented for each category. The development of technologies to monitor dissolved nutrients in bay waters, the initiation of a census of the disappearing rooted acquatic plants in the littoral zones, and the mapping of natural building constraints in the growth regions of the states of Maryland and Virginia are among the recommendations presented
Beyond connectedness: why pairwise metrics cannot capture community stability
The connectedness of species in a trophic web has long been a key structural characteristic for both theoreticians and empiricists in their understanding of community stability. In the past decades, there has been a shift from focussing on determining the number of interactions to taking into account their relative strengths. The question is: How do the strengths of the interactions determine the stability of a community? Recently, a metric has been proposed which compares the stability of observed communities in terms of the strength of three- and two-link feedback loops (cycles of interaction strengths). However, it has also been suggested that we do not need to go beyond the pairwise structure of interactions to capture stability. Here, we directly compare the performance of the feedback and pairwise metrics. Using observed food-web structures, we show that the pairwise metric does not work as a comparator of stability and is many orders of magnitude away from the actual stability values. We argue that metrics based on pairwise-strength information cannot capture the complex organization of strong and weak links in a community, which is essential for system stability
Spatial effects in real networks: measures, null models, and applications
Spatially embedded networks are shaped by a combination of purely topological
(space-independent) and space-dependent formation rules. While it is quite easy
to artificially generate networks where the relative importance of these two
factors can be varied arbitrarily, it is much more difficult to disentangle
these two architectural effects in real networks. Here we propose a solution to
the problem by introducing global and local measures of spatial effects that,
through a comparison with adequate null models, effectively filter out the
spurious contribution of non-spatial constraints. Our filtering allows us to
consistently compare different embedded networks or different historical
snapshots of the same network. As a challenging application we analyse the
World Trade Web, whose topology is expected to depend on geographic distances
but is also strongly determined by non-spatial constraints (degree sequence or
GDP). Remarkably, we are able to detect weak but significant spatial effects
both locally and globally in the network, showing that our method succeeds in
retrieving spatial information even when non-spatial factors dominate. We
finally relate our results to the economic literature on gravity models and
trade globalization
The Empirical Modeling of an Ecosystem
The authors have endeavored to create a verified a-posteriori model of a planktonic ecosystem. Verification of an empirically derived set of first-order, quadratic differential equations proved elusive due to the sensitivity of the model system to changes in initial conditions. Efforts to verify a similarly derived set of linear differential equations were more encouraging, yielding reasonable behavior for half of the ten ecosystem compartments modeled. The well-behaved species models gave indications as to the rate-controlling processes in the ecosystem
Maximum Power Efficiency and Criticality in Random Boolean Networks
Random Boolean networks are models of disordered causal systems that can
occur in cells and the biosphere. These are open thermodynamic systems
exhibiting a flow of energy that is dissipated at a finite rate. Life does work
to acquire more energy, then uses the available energy it has gained to perform
more work. It is plausible that natural selection has optimized many biological
systems for power efficiency: useful power generated per unit fuel. In this
letter we begin to investigate these questions for random Boolean networks
using Landauer's erasure principle, which defines a minimum entropy cost for
bit erasure. We show that critical Boolean networks maximize available power
efficiency, which requires that the system have a finite displacement from
equilibrium. Our initial results may extend to more realistic models for cells
and ecosystems.Comment: 4 pages RevTeX, 1 figure in .eps format. Comments welcome, v2: minor
clarifications added, conclusions unchanged. v3: paper rewritten to clarify
it; conclusions unchange
Information theory explanation of the fluctuation theorem, maximum entropy production and self-organized criticality in non-equilibrium stationary states
Jaynes' information theory formalism of statistical mechanics is applied to
the stationary states of open, non-equilibrium systems. The key result is the
construction of the probability distribution for the underlying microscopic
phase space trajectories. Three consequences of this result are then derived :
the fluctuation theorem, the principle of maximum entropy production, and the
emergence of self-organized criticality for flux-driven systems in the
slowly-driven limit. The accumulating empirical evidence for these results
lends support to Jaynes' formalism as a common predictive framework for
equilibrium and non-equilibrium statistical mechanics.Comment: 21 pages, 0 figures, minor modifications, version to appear in J.
Phys. A. (2003
Ecosystem biogeochemistry considered as a distributed metabolic network ordered by maximum entropy production
Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of The Royal Society for personal use, not for redistribution. The definitive version was published in Philosophical Transactions of the Royal Society B 365 (2010): 1417-1427, doi:10.1098/rstb.2009.0272.We examine the application of the maximum entropy production principle for describing ecosystem biogeochemistry. Since ecosystems can be functionally stable despite changes in species composition, we utilize a distributed metabolic network for describing biogeochemistry, which synthesizes generic biological structures that catalyze reaction pathways, but is otherwise organism independent. Allocation of biological structure and regulation of biogeochemical reactions is determined via solution of an optimal control problem in which entropy production is maximized. However, because synthesis of biological structures cannot occur if entropy production is maximized instantaneously, we propose that information stored within the metagenome allows biological systems to maximize entropy production when averaged over time. This differs from abiotic systems that maximize entropy production at a point in space-time, which we refer to as the steepest descent pathway. It is the spatiotemporal averaging that allows biological systems to outperform abiotic processes in entropy production, at least in many situations. A simulation of a methanotrophic system is used to demonstrate the approach. We conclude with a brief discussion on the implications of viewing ecosystems as self organizing molecular machines that function to maximize entropy production at the ecosystem level of organization.The work presented here was funded by the PIE-LTER program (NSF OCE-0423565), as well as from NSF CBET-0756562, NSF EF-0928742 and NASA Exobiology and Evolutionary Biology (NNG05GN61G)
The meaning of life in a developing universe
The evolution of life on Earth has produced an organism that is beginning to model and understand its own evolution and the possible future evolution of life in the universe. These models and associated evidence show that evolution on Earth has a trajectory. The scale over which living processes are organized cooperatively has increased progressively, as has its evolvability. Recent theoretical advances raise the possibility that this trajectory is itself part of a wider developmental process. According to these theories, the developmental process has been shaped by a larger evolutionary process that involves the reproduction of universes. This evolutionary process has tuned the key parameters of the universe to increase the likelihood that life will emerge and develop to produce outcomes that are successful in the larger process (e.g. a key outcome may be to produce life and intelligence that intentionally reproduces the universe and tunes the parameters of ‘offspring’ universes). Theory suggests that when life emerges on a planet, it moves along this trajectory of its own accord. However, at a particular point evolution will continue to advance only if organisms emerge that decide to advance the evolutionary process intentionally. The organisms must be prepared to make this commitment even though the ultimate nature and destination of the process is uncertain, and may forever remain unknown. Organisms that complete this transition to intentional evolution will drive the further development of life and intelligence in the universe. Humanity’s increasing understanding of the evolution of life in the universe is rapidly bringing it to the threshold of this major evolutionary transition
Topological reversibility and causality in feed-forward networks
Systems whose organization displays causal asymmetry constraints, from
evolutionary trees to river basins or transport networks, can be often
described in terms of directed paths (causal flows) on a discrete state space.
Such a set of paths defines a feed-forward, acyclic network. A key problem
associated with these systems involves characterizing their intrinsic degree of
path reversibility: given an end node in the graph, what is the uncertainty of
recovering the process backwards until the origin? Here we propose a novel
concept, \textit{topological reversibility}, which rigorously weigths such
uncertainty in path dependency quantified as the minimum amount of information
required to successfully revert a causal path. Within the proposed framework we
also analytically characterize limit cases for both topologically reversible
and maximally entropic structures. The relevance of these measures within the
context of evolutionary dynamics is highlighted.Comment: 9 pages, 3 figure
Tangled Nature: A model of emergent structure and temporal mode among co-evolving agents
Understanding systems level behaviour of many interacting agents is
challenging in various ways, here we'll focus on the how the interaction
between components can lead to hierarchical structures with different types of
dynamics, or causations, at different levels. We use the Tangled Nature model
to discuss the co-evolutionary aspects connecting the microscopic level of the
individual to the macroscopic systems level. At the microscopic level the
individual agent may undergo evolutionary changes due to mutations of
strategies. The micro-dynamics always run at a constant rate. Nevertheless, the
system's level dynamics exhibit a completely different type of intermittent
abrupt dynamics where major upheavals keep throwing the system between
meta-stable configurations. These dramatic transitions are described by a
log-Poisson time statistics. The long time effect is a collectively adapted of
the ecological network. We discuss the ecological and macroevolutionary
consequences of the adaptive dynamics and briefly describe work using the
Tangled Nature framework to analyse problems in economics, sociology,
innovation and sustainabilityComment: Invited contribution to Focus on Complexity in European Journal of
Physics. 25 page, 1 figur
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