285 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
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
Niche emergence as an autocatalytic process in the evolution of ecosystems
The utilisation of the ecospace and the change in diversity through time has been suggested to be due to the effect of niche partitioning, as a global long-term pattern in the fossil record. However, niche partitioning, as a way to coexist, could be a limited means to share the environmental resources and condition during evolutionary time. In fact, a physical limit impedes a high partitioning without a high restriction of the niche's variables. Here, we propose that niche emergence, rather than niche partitioning, is what mostly drives ecological diversity. In particular, we view ecosystems in terms of autocatalytic sets: catalytically closed and self-sustaining reaction (or interaction) networks. We provide some examples of such ecological autocatalytic networks, how this can give rise to an expanding process of niche emergence (both in time and space), and how these networks have evolved over time (so-called evoRAFs). Furthermore, we use the autocatalytic set formalism to show that it can be expected to observe a power-law in the size distribution of extinction events in ecosystems. In short, we elaborate on our earlier argument that new species create new niches, and that biodiversity is therefore an autocatalytic process
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
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
Throughflow centrality is a global indicator of the functional importance of species in ecosystems
To better understand and manage complex systems like ecosystems it is
critical to know the relative contribution of system components to system
functioning. Ecologists and social scientists have described many ways that
individuals can be important; This paper makes two key contributions to this
research area. First, it shows that throughflow, the total energy-matter
entering or exiting a system component, is a global indicator of the relative
contribution of the component to the whole system activity. It is global
because it includes the direct and indirect exchanges among community members.
Further, throughflow is a special case of Hubbell status as defined in social
science. This recognition effectively joins the concepts, enabling ecologists
to use and build on the broader centrality research in network science. Second,
I characterize the distribution of throughflow in 45 empirically-based trophic
ecosystem models. Consistent with expectations, this analysis shows that a
small fraction of the system components are responsible for the majority of the
system activity. In 73% of the ecosystem models, 20% or less of the nodes
generate 80% or more of the total system throughflow. Four or fewer dominant
nodes are required to account for 50% of the total system activity. 121 of the
130 dominant nodes in the 45 ecosystem models could be classified as primary
producers, dead organic matter, or bacteria. Thus, throughflow centrality
indicates the rank power of the ecosystems components and shows the power
concentration in the primary production and decomposition cycle. Although these
results are specific to ecosystems, these techniques build on flow analysis
based on economic input-output analysis. Therefore these results should be
useful for ecosystem ecology, industrial ecology, the study of urban
metabolism, as well as other domains using input-output analysis.Comment: 7 figures, 2 table
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)
Functional Integration of Ecological Networks through Pathway Proliferation
Large-scale structural patterns commonly occur in network models of complex
systems including a skewed node degree distribution and small-world topology.
These patterns suggest common organizational constraints and similar functional
consequences. Here, we investigate a structural pattern termed pathway
proliferation. Previous research enumerating pathways that link species
determined that as pathway length increases, the number of pathways tends to
increase without bound. We hypothesize that this pathway proliferation
influences the flow of energy, matter, and information in ecosystems. In this
paper, we clarify the pathway proliferation concept, introduce a measure of the
node--node proliferation rate, describe factors influencing the rate, and
characterize it in 17 large empirical food-webs. During this investigation, we
uncovered a modular organization within these systems. Over half of the
food-webs were composed of one or more subgroups that were strongly connected
internally, but weakly connected to the rest of the system. Further, these
modules had distinct proliferation rates. We conclude that pathway
proliferation in ecological networks reveals subgroups of species that will be
functionally integrated through cyclic indirect effects.Comment: 29 pages, 2 figures, 3 tables, Submitted to Journal of Theoretical
Biolog
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