109 research outputs found
A model of a perennial grass (Andropogon gayanus) for West Africa (PGWA): description and user's guide
Inferring Long-term Dynamics of Ecological Communities Using Combinatorics
In an increasingly changing world, predicting the fate of species across the
globe has become a major concern. Understanding how the population dynamics of
various species and communities will unfold requires predictive tools that
experimental data alone can not capture. Here, we introduce our combinatorial
framework, Widespread Ecological Networks and their Dynamical Signatures
(WENDyS) which, using data on the relative strengths of interactions and growth
rates within a community of species predicts all possible long-term outcomes of
the community. To this end, WENDyS partitions the multidimensional parameter
space (formed by the strengths of interactions and growth rates) into a finite
number of regions, each corresponding to a unique set of coarse population
dynamics. Thus, WENDyS ultimately creates a library of all possible outcomes
for the community. On the one hand, our framework avoids the typical
``parameter sweeps'' that have become ubiquitous across other forms of
mathematical modeling, which can be computationally expensive for ecologically
realistic models and examples. On the other hand, WENDyS opens the opportunity
for interdisciplinary teams to use standard experimental data (i.e., strengths
of interactions and growth rates) to filter down the possible end states of a
community. To demonstrate the latter, here we present a case study from the
Indonesian Coral Reef. We analyze how different interactions between anemone
and anemonefish species lead to alternative stable states for the coral reef
community, and how competition can increase the chance of exclusion for one or
more species. WENDyS, thus, can be used to anticipate ecological outcomes and
test the effectiveness of management (e.g., conservation) strategies.Comment: 25 pages, 9 figure
Resource allocation by the marine cyanobacterium Synechococcus WH8102 in response to different nutrient supply ratios
Differences in relative availability of nitrate vs. phosphate may contribute to regional variations in plankton elemental stoichiometry. As a representative of the globally abundant marine Synechococcus, strain WH8102 was grown in 16 chemostats up to 52 d at a fixed growth rate with nitrogen–phosphorus ratios (N : Psupply) of 1–50. Initially, the phosphate and nitrate concentrations in the vessel decreased when the respective nutrient was limiting. Cell growth generally stabilized, although several chemostats had apparent oscillations in biomass. We observed extensive plasticity in the elemental content and ratios. N : Pcell matched the supply values between N : Psupply 5 and 20. The C : Pcell followed a similar trend. In contrast, the mean C : Ncell was 6.8 and did not vary as a function of supply ratios. We also observed that induction of alkaline phosphatase, the fraction of P allocated to nucleic acids, and the lipid sulfoquinovosyldiacylglycerol : phosphatidyglycerol ratio inversely correlated with P availability. Our results suggest that this extensive plasticity in the elemental content and ratios depends both on the external nutrient availability as well as past growth history. Thus, our study provides a quantitative understanding of the regulation of the elemental stoichiometry of an abundant ocean phytoplankton lineage
Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions
Large-scale data resulting from users online interactions provide the
ultimate source of information to study emergent social phenomena on the Web.
From individual actions of users to observable collective behaviors, different
mechanisms involving emotions expressed in the posted text play a role. Here we
combine approaches of statistical physics with machine-learning methods of text
analysis to study emergence of the emotional behavior among Web users. Mapping
the high-resolution data from digg.com onto bipartite network of users and
their comments onto posted stories, we identify user communities centered
around certain popular posts and determine emotional contents of the related
comments by the emotion-classifier developed for this type of texts. Applied
over different time periods, this framework reveals strong correlations between
the excess of negative emotions and the evolution of communities. We observe
avalanches of emotional comments exhibiting significant self-organized critical
behavior and temporal correlations. To explore robustness of these critical
states, we design a network automaton model on realistic network connections
and several control parameters, which can be inferred from the dataset.
Dissemination of emotions by a small fraction of very active users appears to
critically tune the collective states
Self-organization without conservation: true or just apparent scale-invariance?
The existence of true scale-invariance in slowly driven models of
self-organized criticality without a conservation law, as forest-fires or
earthquake automata, is scrutinized in this paper. By using three different
levels of description - (i) a simple mean field, (ii) a more detailed
mean-field description in terms of a (self-organized) branching processes, and
(iii) a full stochastic representation in terms of a Langevin equation-, it is
shown on general grounds that non-conserving dynamics does not lead to bona
fide criticality. Contrarily to conserving systems, a parameter, which we term
"re-charging" rate (e.g. the tree-growth rate in forest-fire models), needs to
be fine-tuned in non-conserving systems to obtain criticality. In the infinite
size limit, such a fine-tuning of the loading rate is easy to achieve, as it
emerges by imposing a second separation of time-scales but, for any finite
size, a precise tuning is required to achieve criticality and a coherent
finite-size scaling picture. Using the approaches above, we shed light on the
common mechanisms by which "apparent criticality" is observed in non-conserving
systems, and explain in detail (both qualitatively and quantitatively) the
difference with respect to true criticality obtained in conserving systems. We
propose to call this self-organized quasi-criticality (SOqC). Some of the
reported results are already known and some of them are new. We hope the
unified framework presented here helps to elucidate the confusing and
contradictory literature in this field. In a second accompanying paper, we
shall discuss the implications of the general results obtained here for models
of neural avalanches in Neuroscience for which self-organized scale-invariance
in the absence of conservation has been claimed.Comment: 40 pages, 7 figures
Simplest nonequilibrium phase transition into an absorbing state
We study in further detail particle models displaying a boundary-induced
absorbing state phase transition [Phys. Rev. E. {\bf 65}, 046104 (2002) and
Phys. Rev. Lett. {\bf 100}, 165701 (2008)] . These are one-dimensional systems
consisting of a single site (the boundary) where creation and annihilation of
particles occur and a bulk where particles move diffusively. We study different
versions of these models, and confirm that, except for one exactly solvable
bosonic variant exhibiting a discontinuous transition and trivial exponents,
all the others display non-trivial behavior, with critical exponents differing
from their mean-field values, representing a universality class. Finally, the
relation of these systems with a -dimensional non-Markovian process is
discussed.Comment: 9 pages, 7 figures, minor change
Quasi-Neutral theory of epidemic outbreaks
Some epidemics have been empirically observed to exhibit outbreaks of all
possible sizes, i.e., to be scalefree or scale-invariant. Different
explanations for this finding have been put forward; among them there is a
model for "accidental pathogens" which leads to power-law distributed outbreaks
without apparent need of parameter fine tuning. This model has been claimed to
be related to self-organized criticality, and its critical properties have been
conjectured to be related to directed percolation. Instead, we show that this
is a (quasi) neutral model, analogous to those used in Population Genetics and
Ecology, with the same critical behavior as the voter-model, i.e. the theory of
accidental pathogens is a (quasi)-neutral theory. This analogy allows us to
explain all the system phenomenology, including generic scale invariance and
the associated scaling exponents, in a parsimonious and simple way.Comment: 13 pages, 6 figures Accepted for publication in PLoS ONE the text
have been modified in orden to improve the figure's resolutio
Patchiness and Demographic Noise in Three Ecological Examples
Understanding the causes and effects of spatial aggregation is one of the
most fundamental problems in ecology. Aggregation is an emergent phenomenon
arising from the interactions between the individuals of the population, able
to sense only -at most- local densities of their cohorts. Thus, taking into
account the individual-level interactions and fluctuations is essential to
reach a correct description of the population. Classic deterministic equations
are suitable to describe some aspects of the population, but leave out features
related to the stochasticity inherent to the discreteness of the individuals.
Stochastic equations for the population do account for these
fluctuation-generated effects by means of demographic noise terms but, owing to
their complexity, they can be difficult (or, at times, impossible) to deal
with. Even when they can be written in a simple form, they are still difficult
to numerically integrate due to the presence of the "square-root" intrinsic
noise. In this paper, we discuss a simple way to add the effect of demographic
stochasticity to three classic, deterministic ecological examples where
aggregation plays an important role. We study the resulting equations using a
recently-introduced integration scheme especially devised to integrate
numerically stochastic equations with demographic noise. Aimed at scrutinizing
the ability of these stochastic examples to show aggregation, we find that the
three systems not only show patchy configurations, but also undergo a phase
transition belonging to the directed percolation universality class.Comment: 20 pages, 5 figures. To appear in J. Stat. Phy
Diffusion in stochastic sandpiles
We study diffusion of particles in large-scale simulations of one-dimensional
stochastic sandpiles, in both the restricted and unrestricted versions. The
results indicate that the diffusion constant scales in the same manner as the
activity density, so that it represents an alternative definition of an order
parameter. The critical behavior of the unrestricted sandpile is very similar
to that of its restricted counterpart, including the fact that a data collapse
of the order parameter as a function of the particle density is only possible
over a very narrow interval near the critical point. We also develop a series
expansion, in inverse powers of the density. for the collective diffusion
coefficient in a variant of the stochastic sandpile in which the toppling rate
at a site with particles is , and compare the theoretical
prediction with simulation results.Comment: 21 page
A theoretical foundation for multi-scale regular vegetation patterns
Self-organized regular vegetation patterns are widespread and thought to mediate ecosystem functions such as productivity and robustness, but the mechanisms underlying their origin and maintenance remain disputed. Particularly controversial are landscapes of overdispersed (evenly spaced) elements, such as North American Mima mounds, Brazilian murundus, South African heuweltjies, and, famously, Namibian fairy circles. Two competing hypotheses are currently debated. On the one hand, models of scale-dependent feedbacks, whereby plants facilitate neighbours while competing with distant individuals, can reproduce various regular patterns identified in satellite imagery. Owing to deep theoretical roots and apparent generality, scale-dependent feedbacks are widely viewed as a unifying and near-universal principle of regular-pattern formation despite scant empirical evidence. On the other hand, many overdispersed vegetation patterns worldwide have been attributed to subterranean ecosystem engineers such as termites, ants, and rodents. Although potentially consistent with territorial competition, this interpretation has been challenged theoretically and empirically and (unlike scale-dependent feedbacks) lacks a unifying dynamical theory, fuelling scepticism about its plausibility and generality. Here we provide a general theoretical foundation for self-organization of social-insect colonies, validated using data from four continents, which demonstrates that intraspecific competition between territorial animals can generate the large-scale hexagonal regularity of these patterns. However, this mechanism is not mutually exclusive with scale-dependent feedbacks. Using Namib Desert fairy circles as a case study, we present field data showing that these landscapes exhibit multi-scale patterning-previously undocumented in this system-that cannot be explained by either mechanism in isolation. These multi-scale patterns and other emergent properties, such as enhanced resistance to and recovery from drought, instead arise from dynamic interactions in our theoretical framework, which couples both mechanisms. The potentially global extent of animal-induced regularity in vegetation-which can modulate other patterning processes in functionally important ways-emphasizes the need to integrate multiple mechanisms of ecological self-organization
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