77 research outputs found
Growth or Reproduction: Emergence of an Evolutionary Optimal Strategy
Modern ecology has re-emphasized the need for a quantitative understanding of
the original 'survival of the fittest theme' based on analyzis of the intricate
trade-offs between competing evolutionary strategies that characterize the
evolution of life. This is key to the understanding of species coexistence and
ecosystem diversity under the omnipresent constraint of limited resources. In
this work we propose an agent based model replicating a community of
interacting individuals, e.g. plants in a forest, where all are competing for
the same finite amount of resources and each competitor is characterized by a
specific growth-reproduction strategy. We show that such an evolution dynamics
drives the system towards a stationary state characterized by an emergent
optimal strategy, which in turn depends on the amount of available resources
the ecosystem can rely on. We find that the share of resources used by
individuals is power-law distributed with an exponent directly related to the
optimal strategy. The model can be further generalized to devise optimal
strategies in social and economical interacting systems dynamics.Comment: 10 pages, 5 figure
Family-specific scaling laws in bacterial genomes
Among several quantitative invariants found in evolutionary genomics, one of
the most striking is the scaling of the overall abundance of proteins, or
protein domains, sharing a specific functional annotation across genomes of
given size. The size of these functional categories change, on average, as
power-laws in the total number of protein-coding genes. Here, we show that such
regularities are not restricted to the overall behavior of high-level
functional categories, but also exist systematically at the level of single
evolutionary families of protein domains. Specifically, the number of proteins
within each family follows family-specific scaling laws with genome size.
Functionally similar sets of families tend to follow similar scaling laws, but
this is not always the case. To understand this systematically, we provide a
comprehensive classification of families based on their scaling properties.
Additionally, we develop a quantitative score for the heterogeneity of the
scaling of families belonging to a given category or predefined group. Under
the common reasonable assumption that selection is driven solely or mainly by
biological function, these findings point to fine-tuned and interdependent
functional roles of specific protein domains, beyond our current functional
annotations. This analysis provides a deeper view on the links between
evolutionary expansion of protein families and the functional constraints
shaping the gene repertoire of bacterial genomes.Comment: 41 pages, 16 figure
Reconciling cooperation, biodiversity and stability in complex ecological communities
Empirical observations show that ecological communities can have a huge
number of coexisting species, also with few or limited number of resources.
These ecosystems are characterized by multiple type of interactions, in
particular displaying cooperative behaviors. However, standard modeling of
population dynamics based on Lotka-Volterra type of equations predicts that
ecosystem stability should decrease as the number of species in the community
increases and that cooperative systems are less stable than communities with
only competitive and/or exploitative interactions. Here we propose a stochastic
model of population dynamics, which includes exploitative interactions as well
as cooperative interactions induced by cross-feeding. The model is exactly
solved and we obtain results for relevant macro-ecological patterns, such as
species abundance distributions and correlation functions. In the large system
size limit, any number of species can coexist for a very general class of
interaction networks and stability increases as the number of species grows.
For pure mutualistic/commensalistic interactions we determine the topological
properties of the network that guarantee species coexistence. We also show that
the stationary state is globally stable and that inferring species interactions
through species abundance correlation analysis may be misleading. Our
theoretical approach thus show that appropriate models of cooperation naturally
leads to a solution of the long-standing question about complexity-stability
paradox and on how highly biodiverse communities can coexist.Comment: 25 pages, 10 figure
Randomness and Criticality in Biological Interactions
In this thesis we study from a physics perspective two problems related to biological interactions. In the first part of this thesis we consider ecological interactions, that shape ecosystems and determine their fate, and their relation with stability of ecosystems. Using random matrix theory we are able to identify the key aspect, the order parameters, determining the stability of large ecosystems. We then consider the problem of determining the persistence of a population living in a randomly fragmented landscape. Using some techniques borrowed from random matrix theory applied to disordered systems, we are able to identify what are the key drivers of persistence. The second part of the thesis is devoted to the observation that many living systems seem to tune their interaction close to a critical point. We introduce a stochastic model, based on information theory, that predict the critical point as a natural outcome of a process of evolution or adaptation, without fine-tuning of parameters
Cooperation, competition and the emergence of criticality in communities of adaptive systems
The hypothesis that living systems can benefit from operating at the vicinity
of critical points has gained momentum in recent years. Criticality may confer
an optimal balance between exceedingly ordered and too noisy states. We here
present a model, based on information theory and statistical mechanics,
illustrating how and why a community of agents aimed at understanding and
communicating with each other converges to a globally coherent state in which
all individuals are close to an internal critical state, i.e. at the borderline
between order and disorder. We study --both analytically and computationally--
the circumstances under which criticality is the best possible outcome of the
dynamical process, confirming the convergence to critical points under very
generic conditions. Finally, we analyze the effect of cooperation (agents try
to enhance not only their fitness, but also that of other individuals) and
competition (agents try to improve their own fitness and to diminish those of
competitors) within our setting. The conclusion is that, while competition
fosters criticality, cooperation hinders it and can lead to more ordered or
more disordered consensual solutions.Comment: 20 pages, 5 figures. Supplementary Material: 8 page
The effect of demographic stochasticity on predatory-prey oscillations
The ecological dynamics of interacting predator and prey populations can
display sustained oscillations, as for instance predicted by the
Rosenzweig-MacArthur predator-prey model. The presence of demographic
stochasticity, due to the finiteness of population sizes, alters the amplitude
and frequency of these oscillations. Here we present a method for
characterizing the effects of demographic stochasticity on the limit cycle
attractor of the Rosenzweig-MacArthur. We show that an angular Brownian motion
well describes the frequency oscillations. In the vicinity of the bifurcation
point, we obtain an analytical approximation for the angular diffusion
constant. This approximation accurately captures the effect of demographic
stochasticity across parameter values
Disentangling the effect of hybrid interactions and of the constant effort hypothesis on ecological community stability
In the last years, a remarkable theoretical effort has been made in order to understand the relation between stability and complexity in ecological communities. Yet, what maintains species diversity in real ecological communities is still an open question. The non-random structures of ecological interaction networks have been recognized as one key ingredient impacting the maximum number of coexisting species within the ecological community. However most of the earlier theoretical studies have considered communities with only one interaction type (either antagonistic, competitive or mutualistic). Recently, it has been proposed that multiple interaction types might stabilize ecosystems and that, in this hybrid case, increasing complexity increases stability. Here we show that these results depend on ad hoc hypothesis that the authors used in their model and we highlight the need to disentangle the role of multiple interaction types and constant interaction effort allocation on community stability. Indeed, we find that mixing of mutualistic and predator–prey interaction types does not stabilize the community dynamics and we demonstrate that a positive correlation between complexity and stability is observed only if a constant effort allocation is imposed in the ecological interactions. Synthesis In recent years a sparkling research has been devoted to the search of new theoretical mechanisms to explain way ecosystems may persist despite their complexity. Here we show that, contrary to what recently suggested (Mougi et al. 2012), the mismatch between theoretical results and empirical evidences on the stability of ecological community is still there also for communities with both mutualistic and antagonistic interactions, and the 'complexity-stability' paradox is still alive. Indeed, we demonstrate that their results arise as an artifact of the peculiar rescaling of the interaction strengths they imposed. Our study suggests that further theoretical studies and experimental evidences are still needed to better understand the role of interaction strengths in real ecological communities
Zipf and Heaps laws from dependency structures in component systems
Complex natural and technological systems can be considered, on a
coarse-grained level, as assemblies of elementary components: for example,
genomes as sets of genes, or texts as sets of words. On one hand, the joint
occurrence of components emerges from architectural and specific constraints in
such systems. On the other hand, general regularities may unify different
systems, such as the broadly studied Zipf and Heaps laws, respectively
concerning the distribution of component frequencies and their number as a
function of system size. Dependency structures (i.e., directed networks
encoding the dependency relations between the components in a system) were
proposed recently as a possible organizing principles underlying some of the
regularities observed. However, the consequences of this assumption were
explored only in binary component systems, where solely the presence or absence
of components is considered, and multiple copies of the same component are not
allowed. Here, we consider a simple model that generates, from a given ensemble
of dependency structures, a statistical ensemble of sets of components,
allowing for components to appear with any multiplicity. Our model is a minimal
extension that is memoryless, and therefore accessible to analytical
calculations. A mean-field analytical approach (analogous to the "Zipfian
ensemble" in the linguistics literature) captures the relevant laws describing
the component statistics as we show by comparison with numerical computations.
In particular, we recover a power-law Zipf rank plot, with a set of core
components, and a Heaps law displaying three consecutive regimes (linear,
sub-linear and saturating) that we characterize quantitatively
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